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All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to [https://www.mdpi.com/openaccess](https://www.mdpi.com/openaccess).

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Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

*   [

    **_Molecules_**  

    Nuclear Quantum Effects in the Ionic Dissociation Dynamics of HCl on the Water Ice Cluster

    ](/1420-3049/30/3/442)

*   [

    **_Agriculture_**  

    From Reality to Virtuality: Revolutionizing Livestock Farming Through Digital Twins

    ](/2077-0472/14/12/2231)

*   [

    **_Catalysts_**  

    Last 20 Years Update of DPEDA-Based Organocatalysts for Asymmetric Reactions

    ](/2073-4344/14/12/915)

*   [

    **_Coatings_**  

    Properties of Nanoparticles Incorporated into Black PEO Coating on TC4 Alloy

    ](/2079-6412/15/1/21)

*   [

    **_Current Oncology_**  

    Malignant Pleural Effusion: Diagnosis and Treatment—Up-to-Date Perspective

    ](/1718-7729/31/11/507)

[](#)[](#)

2 of 5

Recent Articles  [](/latest_articles)

-------------------------------------

17 pages, 1054 KiB  [](/1996-1073/18/5/1284/pdf?version=1741225462 "Article PDF")

Open AccessArticle

[A Method for Restoring Power Supply to Distribution Networks Considering the Coordination of Multiple Resources Under Typhoon-Induced Waterlogging Disasters](/1996-1073/18/5/1284)

by **Hao Dai**, **Dafu Liu**, **Guowei Liu**, **Hao Deng**, **Lisheng Xin**, **Longlong Shang**, **Ziyu Liu**, **Ziwen Xu**, **Jiaju Shi** and **Chen Chen**

_Energies_ **2025**, _18_(5), 1284; https://doi.org/10.3390/en18051284 (registering DOI) - 6 Mar 2025

[**Abstract**](#)

Recently, frequent typhoons and waterlogging disasters have caused severe damage to the power distribution networks in coastal cities. In response to this issue, how to efficiently develop recovery plans and achieve flexible resource coordination has become key for urban power grids in regard [\[...\] Read more.](#)

Recently, frequent typhoons and waterlogging disasters have caused severe damage to the power distribution networks in coastal cities. In response to this issue, how to efficiently develop recovery plans and achieve flexible resource coordination has become key for urban power grids in regard to coping with extreme natural disasters. Therefore, this article proposes a multi type flexible resource collaborative scheduling method for power supply restoration in distribution networks which realizes cooperation between maintenance teams and mobile energy storage in the scenario of wind and flood composite disasters, simultaneously completing the transfer of important loads through topology reconstruction. Firstly, a damage model for distribution network nodes and lines under wind–flood composite disasters was established to address the impact of typhoons and waterlogging disasters on the distribution network. Then, based on the inherent characteristics of multiple types of flexible resources, various collaborative recovery models for flexible resources after disasters were established. Finally, the effectiveness of the proposed method was verified through the coupling example of a 33-node distribution network and a 30-node transportation network. [Full article](/1996-1073/18/5/1284)

(This article belongs to the Special Issue [Advanced Design and Optimization for Integrated Power and Energy Systems](        

    /journal/energies/special_issues/DJ1T82119H

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/energies/energies-18-01284/article_deploy/html/images/energies-18-01284-g001-550.jpg?1741225559 "

<strong>Figure 1</strong><br/>

<p>Wind fragility curves of lines and towers.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1284'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01284/article_deploy/html/images/energies-18-01284-g002-550.jpg?1741225560 "

<strong>Figure 2</strong><br/>

<p>Waterlogging fragility curve of an electrical substation.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1284'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01284/article_deploy/html/images/energies-18-01284-g003-550.jpg?1741225562 "

<strong>Figure 3</strong><br/>

<p>Example testing system.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1284'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01284/article_deploy/html/images/energies-18-01284-g004-550.jpg?1741225563 "

<strong>Figure 4</strong><br/>

<p>Variation in load recovery ratios over time under three different solutions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1284'>Full article</a></strong>

")

15 pages, 246 KiB  [](/2076-328X/15/3/316/pdf?version=1741225849 "Article PDF")

Open AccessArticle

[An Investigation into Academic Stress and Coping Strategies of South Korean Third Culture Kid (TCK) College Students](/2076-328X/15/3/316)

by **Young-An Ra** and **Kahyen Shin**

_Behav. Sci._ **2025**, _15_(3), 316; https://doi.org/10.3390/bs15030316 (registering DOI) - 6 Mar 2025

[**Abstract**](#)

This study aimed to increase the understanding of academic stress and coping strategies of third culture kids (TCKs) in South Korean colleges. For this aim, six Korean college students who are TCKs were interviewed. For analyzing the interview data, consensual qualitative research was [\[...\] Read more.](#)

This study aimed to increase the understanding of academic stress and coping strategies of third culture kids (TCKs) in South Korean colleges. For this aim, six Korean college students who are TCKs were interviewed. For analyzing the interview data, consensual qualitative research was used. As a result, participants’ academic stressors were related to language, interpersonal relationships, learning strategies, career issues, and financial difficulties. As their coping strategies, they reported preparation and review, help-seeking, group study, goal orientation, self-efficacy, and control belief. The results of this study can help South Korean TCK college students with academic stressors, reducing their related stress and allowing them to adjust well in college. We also discussed how educational institutions can help those students overcome academic stress and find their coping strategies. [Full article](/2076-328X/15/3/316)

(This article belongs to the Section [Developmental Psychology](/journal/behavsci/sections/developmental_psychology))  

42 pages, 31756 KiB  [](/2076-3417/15/5/2830/pdf?version=1741225341 "Article PDF")

Open AccessArticle

[Models to Identify Small Brain White Matter Hyperintensity Lesions](/2076-3417/15/5/2830)

by **Darwin Castillo**, **María José Rodríguez-Álvarez**, **René Samaniego** and **Vasudevan Lakshminarayanan**

_Appl. Sci._ **2025**, _15_(5), 2830; https://doi.org/10.3390/app15052830 (registering DOI) - 6 Mar 2025

[**Abstract**](#)

According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke and Alzheimer’s Disease and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain [\[...\] Read more.](#)

According to the World Health Organization (WHO), peripheral and central neurological disorders affect approximately one billion people worldwide. Ischemic stroke and Alzheimer’s Disease and other dementias are the second and fifth leading causes of death, respectively. In this context, detecting and classifying brain lesions constitute a critical area of research in medical image processing, significantly impacting clinical practice. Traditional lesion detection, segmentation, and feature extraction methods are time-consuming and observer-dependent. In this sense, research in the machine and deep learning methods applied to medical image processing constitute one of the crucial tools for automatically learning hierarchical features to get better accuracy, quick diagnosis, treatment, and prognosis of diseases. This project aims to develop and implement deep learning models for detecting and classifying small brain White Matter hyperintensities (WMH) lesions in magnetic resonance images (MRI), specifically lesions concerning ischemic and demyelination diseases. The methods applied were the UNet and Segmenting Anything model (SAM) for segmentation, while YOLOV8 and Detectron2 (based on MaskRCNN) were also applied to detect and classify the lesions. Experimental results show a Dice coefficient (DSC) of 0.94, 0.50, 0.241, and 0.88 for segmentation of WMH lesions using the UNet, SAM, YOLOv8, and Detectron2, respectively. The Detectron2 model demonstrated an accuracy of 0.94 in detecting and 0.98 in classifying lesions, including small lesions where other models often fail. The methods developed give an outline for the detection, segmentation, and classification of small and irregular morphology brain lesions and could significantly aid clinical diagnostics, providing reliable support for physicians and improving patient outcomes. [Full article](/2076-3417/15/5/2830)

(This article belongs to the Special Issue [MR-Based Neuroimaging](        

    /journal/applsci/special_issues/048ALE3TC6

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g001-550.jpg?1741225421 "

<strong>Figure 1</strong><br/>

<p>The flowchart of the proposed methodology: (<b>a</b>) the dataset, acquisition, number of images, preprocessing steps, and data augmentation methods applied to the dataset; (<b>b</b>) the DL methods, feature extraction, segmentation using UNET and SAM, and classification and detection using YOLOv8 and Detectron2.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g002-550.jpg?1741225423 "

<strong>Figure 2</strong><br/>

<p>Some examples of images used in the dataset for training.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g003-550.jpg?1741225425 "

<strong>Figure 3</strong><br/>

<p>Slices from the volume of a real patient with two lesions: ischemia (red circles) and demyelination (yellow circles). It is seen that the lesions are not continuous between slices. The lesions change in each slice, as well as their size, shape, and location. The different colors refer to the two types of lesions in the slice.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g004-550.jpg?1741225427 "

<strong>Figure 4</strong><br/>

<p>Examples of images before and after the noise and artifact reduction.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g005-550.jpg?1741225429 "

<strong>Figure 5</strong><br/>

<p>Examples of data augmentation. Some artifacts and noise are also generated.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g006-550.jpg?1741225432 "

<strong>Figure 6</strong><br/>

<p>Examples of data augmentation using SNGAN at 275 epochs. The synthetic images do not show good confidence in the lesions of ischemia and demyelination diseases.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g007-550.jpg?1741225433 "

<strong>Figure 7</strong><br/>

<p>The UNet model for segmenting brain lesions related to ischemia and demyelination.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g008-550.jpg?1741225434 "

<strong>Figure 8</strong><br/>

<p>Segment Anything Model (SAM) architecture with the data and trained models used in this project. The model consists of an image encoder to extract image embeddings, a prompt encoder, and a mask decoder to predict segmentation masks using the image and prompt embeddings. This figure was adapted from [<a href=\"#B57-applsci-15-02830\" class=\"html-bibr\">57</a>].</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g009-550.jpg?1741225435 "

<strong>Figure 9</strong><br/>

<p>Architecture overview YOLOv8 applied in this project. The input image passes to the training process between the Bounding Boxes and Class Probability to give the input image with the bounding boxes and the probability of detection. This figure was adapted from [<a href=\"#B60-applsci-15-02830\" class=\"html-bibr\">60</a>].</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g010-550.jpg?1741225437 "

<strong>Figure 10</strong><br/>

<p>Architecture overview of Detectron2—R50-FPN applied in this project. The input image passes to the Backbone Network, Region Proposal Network, and Box Head with Fast R-CNN for object identification.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g011-550.jpg?1741225439 "

<strong>Figure 11</strong><br/>

<p>Correlogram of lesions distribution and characteristics. (<b>a</b>) Spatial distribution of lesions, showing their tendency to occur in specific brain regions. In (<b>b</b>), width and (<b>c</b>) height are shown an analysis of lesion dimensions, indicating that the majority are small, with sizes below 0.2 pixels. It is observed that despite the localization trends, there is no strong correlation pattern between lesion occurrence and size.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g012-550.jpg?1741225439 "

<strong>Figure 12</strong><br/>

<p>Training and validation loss curves from UNet model. The training and validation loss curves converge after 100 epochs and stabilize around 0.35, indicating that the model has learned most of the features and is refining its predictions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g013a-550.jpg?1741225441 "

<strong>Figure 13</strong><br/>

<p>Examples of the lesion prediction using the UNet model. (<b>a</b>) Original images with their corresponding (<b>b</b>) masks and (<b>c</b>) prediction results.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g013b-550.jpg?1741225442 "

<strong>Figure 13 Cont.</strong><br/>

<p>Examples of the lesion prediction using the UNet model. (<b>a</b>) Original images with their corresponding (<b>b</b>) masks and (<b>c</b>) prediction results.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g014-550.jpg?1741225443 "

<strong>Figure 14</strong><br/>

<p>The training and validation loss of the SAM model with each of the trained models: (<b>a</b>) vit-base, (<b>b</b>) vit-large, and (<b>c</b>) vit-huge.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g015a-550.jpg?1741225445 "

<strong>Figure 15</strong><br/>

<p>Some examples of segmentation lesion predictions using the SAM model with 25 epochs of training and different processors. (<b>a</b>) MR Image. (<b>b</b>) Ground truth mask. (<b>c</b>) Predicted mask. The orange areas correspond to the WMH lesions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g015b-550.jpg?1741225446 "

<strong>Figure 15 Cont.</strong><br/>

<p>Some examples of segmentation lesion predictions using the SAM model with 25 epochs of training and different processors. (<b>a</b>) MR Image. (<b>b</b>) Ground truth mask. (<b>c</b>) Predicted mask. The orange areas correspond to the WMH lesions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g016-550.jpg?1741225448 "

<strong>Figure 16</strong><br/>

<p>Confusion matrix of the YOLO detection model using the pre-trained “yolov8n-seg.pt” model. On the right side, and below it, are some examples of the classification of the lesions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g017-550.jpg?1741225449 "

<strong>Figure 17</strong><br/>

<p>The experimental results with the pre-trained “yolov8n-seg.pt” model. The graphs are concerned with the trend of training and validation loss scores over the epochs and their corresponding precision and recall metrics related to bounding box prediction, segmentation, and classification of lesions. The values of the loss or metric are plotted on the <span class=\"html-italic\">y</span>-axis, and the epochs are represented on the <span class=\"html-italic\">x</span>-axis.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g018-550.jpg?1741225452 "

<strong>Figure 18</strong><br/>

<p>Examples of detection of the lesions. The original image without detecting a lesion is shown on the left side, and the lesion prediction with model Detectron2 is shown on the right. In the center column, the classification done by the radiologist expert is shown; the “yellow” color refers to demyelination lesions and the violet color refers to ischemia lesions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g019-550.jpg?1741225454 "

<strong>Figure 19</strong><br/>

<p>Graphs of loss and accuracy metrics for the lesion detection model. (<b>a</b>) Loss curves over training iterations illustrate the optimization of different loss components, including classification, bounding box regression, mask loss, and total loss. (<b>b</b>) Accuracy trends over training iterations, depicting the performance of the Fast R-CNN classifier and Mask R-CNN segmentation accuracy. The stability and convergence of these metrics indicate the model’s learning progress and effectiveness.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g020-550.jpg?1741225455 "

<strong>Figure 20</strong><br/>

<p>Graphs of false positive and false negative rates over training iterations. The false negative rate (orange) decreases steadily, indicating improved sensitivity. The false positive rate (blue) remains relatively stable, suggesting consistent precision in lesion detection. These trends highlight the model’s learning process and ability to refine segmentation accuracy over time.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g021-550.jpg?1741225456 "

<strong>Figure 21</strong><br/>

<p>Correlogram of the balanced number of instances (lesions) used for the YOLO model to detect and classify ischemia and demyelination. The bar plots at the top represent the distribution of lesion instances across classes. The scatter plots (<span class=\"html-italic\">x</span>-<span class=\"html-italic\">y</span> plane) illustrate the location and distribution of the lesions (scattered points) within the image, as well as the correlation between lesion height and width, providing insights into lesion size variability. This visualization includes information on the lesion characteristics.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g022-550.jpg?1741225457 "

<strong>Figure 22</strong><br/>

<p>Experimental analysis results, including training and validation metrics, precision–recall curves, and mean average precision (mAP) metrics. These graphs illustrate the model’s learning progression, performance across evaluation metrics, and effectiveness in detecting and segmenting lesions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g023-550.jpg?1741225459 "

<strong>Figure 23</strong><br/>

<p>Confusion matrix of the YOLO classification to distinguish between ischemic and demyelination lesions using the pre-trained “yolov8n-seg.pt” model. The images on the top right side and lower sections are examples of the classification of the lesions with their corresponding confidence scores in predicting each lesion type. These results highlight the model’s effectiveness in lesion classification while revealing potential challenges in differentiating lesions with similar radiological characteristics.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g024-550.jpg?1741225463 "

<strong>Figure 24</strong><br/>

<p>Examples of detection and classification of the lesions. The image without a classification is shown on the left side, and the classification prediction with model Detectron2 is shown on the right. The labels “ische” refer to ischemia, and “demy” refer to demyelination diseases, respectively. In the center column, the classification performed by the radiologist expert is shown; the “yellow” color refers to demyelination lesions and the violet color refers to ischemia lesions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g025-550.jpg?1741225464 "

<strong>Figure 25</strong><br/>

<p>Training performance metrics for classifying and detecting lesions using the Detectron2 model. (<b>a</b>) Loss curves over iterations, including classification loss (orange), bounding box regression loss (blue), mask loss (green), and total loss (brown), indicate the behavior convergence of the model. (<b>b</b>) Accuracy metrics over iterations, illustrating the classification performance of the Fast R-CNN and Mask R-CNN models. The increasing accuracy trends suggest improved learning stability and effectiveness in lesion detection and classification between ischemia and demyelination.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g026-550.jpg?1741225465 "

<strong>Figure 26</strong><br/>

<p>Graphs of the metrics concerning the model’s false positive and false negative rates. The graph illustrates how the model refines its predictions over time, with the false negative rate (red) decreasing as the model improves sensitivity and correctly identifies more lesions. Similarly, the false positive rate (yellow) stabilizes, indicating enhanced precision in lesion classification.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g027-550.jpg?1741225466 "

<strong>Figure 27</strong><br/>

<p>Graphs of performance metrics of the classification model over training iterations. (<b>a</b>) Accuracy, (<b>b</b>) recall, (<b>c</b>) precision, and (<b>d</b>) F1_score values of the classification model. All values maintain an average value above 0.9, indicating high classification reliability.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g028a-550.jpg?1741225469 "

<strong>Figure 28</strong><br/>

<p>Visual comparison of the detection and classification of the lesions using the Detectron2 and YOLOv8 models against the radiologist expert. In the Detectron2 Model, a threshold for lesion detection of 0.8 and 0.5 is used. In the YOLOv8 model, the threshold used for lesion detection is 0.2.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g028b-550.jpg?1741225471 "

<strong>Figure 28 Cont.</strong><br/>

<p>Visual comparison of the detection and classification of the lesions using the Detectron2 and YOLOv8 models against the radiologist expert. In the Detectron2 Model, a threshold for lesion detection of 0.8 and 0.5 is used. In the YOLOv8 model, the threshold used for lesion detection is 0.2.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g029-550.jpg?1741225473 "

<strong>Figure 29</strong><br/>

<p>Visual comparison of the detection and classification of the lesions using the Detectron2 and threshold for lesion detection of 0.8 and 0.5. The threshold level allows for a change in the sensitivity of the detection of lesions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g030-550.jpg?1741225474 "

<strong>Figure 30</strong><br/>

<p>Comparative analysis of detection and classification performance metrics of accuracy, precision, recall, F1-score, sensitivity, and specificity for detection and classification of the lesions across three evaluations: criteria by Experts (blue bars), Detectron2 (orange bars), and YOLOv8 (green bars). The graph highlights the strong performance of the Detectron2 model comparable to the reliability of the expert’s criteria and the lower performance of YOLOv8, particularly for recall and sensitivity.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")[](https://pub.mdpi-res.com/applsci/applsci-15-02830/article_deploy/html/images/applsci-15-02830-g031-550.jpg?1741225474 "

<strong>Figure 31</strong><br/>

<p>ROC curve comparison for detection and classification of the lesions using the criteria by Experts (AUC = 0.976), Detectron2 (AUC = 0.929), and YOLOv8 (AUC = 0.524). The curve illustrates the trade-off between true positive rate (sensitivity) and false positive rate, highlighting the good performance of Detectron2, comparable to experts, while YOLOv8 shows limited discriminatory power for lesion classification. The dashed diagonal line represents a random classifier (AUC = 0.5).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/15/5/2830'>Full article</a></strong>

")

13 pages, 264 KiB  [](/2075-1680/14/3/195/pdf?version=1741225329 "Article PDF")

Open AccessArticle

[On Inverse and Implicit Function Theorem for Sobolev Mappings](/2075-1680/14/3/195)

by **Mihai Cristea**

_Axioms_ **2025**, _14_(3), 195; https://doi.org/10.3390/axioms14030195 (registering DOI) - 6 Mar 2025

[**Abstract**](#)

We extend Clarke’s local inversion theorem for Sobolev mappings. We use this result to find a general implicit function theorem for continuous locally Lipschitz mapping in the first variable and satisfying just a topological condition in the second variable. An application to control [\[...\] Read more.](#)

We extend Clarke’s local inversion theorem for Sobolev mappings. We use this result to find a general implicit function theorem for continuous locally Lipschitz mapping in the first variable and satisfying just a topological condition in the second variable. An application to control systems is given. [Full article](/2075-1680/14/3/195)

(This article belongs to the Section [Mathematical Analysis](/journal/axioms/sections/mathematical_analysis))  

_attachment_

Supplementary material:  

[Supplementary File 1 (ZIP, 47258 KiB)](/1420-3049/30/5/1173/s1?version=1741224801)  

25 pages, 11548 KiB  [](/1420-3049/30/5/1173/pdf?version=1741224800 "Article PDF")

Open AccessArticle

[The Effects of Sika Deer Antler Peptides on 3T3-L1 Preadipocytes and C57BL/6 Mice via Activating AMPK Signaling and Gut Microbiota](/1420-3049/30/5/1173)

by **Tong Sun**, **Zezhuang Hao**, **Fanying Meng**, **Xue Li**, **Yihua Wang**, **Haowen Zhu**, **Yong Li** and **Yuling Ding**

_Molecules_ **2025**, _30_(5), 1173; https://doi.org/10.3390/molecules30051173 (registering DOI) - 6 Mar 2025

[**Abstract**](#)

(1) Background: To explore the anti-obesity effects and mechanisms of sika deer velvet antler peptides (sVAP) on 3T3-L1 preadipocytes and in high-fat diet (HFD)-induced obese mice. (2) Methods: sVAP fractions of different molecular weights were obtained via enzymatic hydrolysis and ultrafiltration. Their anti-lipid [\[...\] Read more.](#)

(1) Background: To explore the anti-obesity effects and mechanisms of sika deer velvet antler peptides (sVAP) on 3T3-L1 preadipocytes and in high-fat diet (HFD)-induced obese mice. (2) Methods: sVAP fractions of different molecular weights were obtained via enzymatic hydrolysis and ultrafiltration. Their anti-lipid effects on 3T3-L1 cells were assessed with Oil Red O staining. The optimal fraction was tested in HFD-induced obese C57BL/6 mice to explore anti-obesity mechanisms. Peptide purification used LC-MS/MS, followed by sequence analysis and molecular docking for activity prediction. (3) Results: The peptide with the best anti-obesity activity was identified as sVAP-3K (≤3 kDa). sVAP-3K reduced lipid content and proliferation in 3T3-L1 cells, improved lipid profiles and ameliorated adipocyte degeneration in HFD mice, promoted the growth of beneficial gut microbiota, and maintained lipid metabolism. Additionally, sVAP-3K activated the AMP-activated protein kinase (AMPK) signaling pathway, regulating adipogenic transcription factors. sVAP-3K exhibited ten major components (peak area ≥ 1.03 × 108), with four of the most active components being newly discovered natural oligopeptides: RVDPVNFKL (_m/z_ 363.21371), GGEFTPVLQ (_m/z_ 474.24643), VDPENFRL (_m/z_ 495.25735), and VDPVNFK (_m/z_ 818.44043). (4) Conclusion: This study identifies four novel oligopeptides in sVAP-3K as key components for anti-obesity effects, offering new evidence for developing natural weight-loss drugs from sika deer velvet. [Full article](/1420-3049/30/5/1173)

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g001-550.jpg?1741224888 "

<strong>Figure 1</strong><br/>

<p>Screening the optimal deer antler hydrolysate using 3T3-L1 preadipocytes. (<b>A</b>) Cytotoxicity assay of different hydrolysate at 50 μg/mL. (<b>B</b>) Quantification of the lipid accumulation in 50 μg/mL sVAP-treated and non-treated (control) adipocytes after Oil Red O elution. (<b>C</b>) Intracellular lipid accumulation in 3T3-L1 adipocytes after completion of the differentiation process (50 μm = 20×). Microscopic images of adipocytes stained with Oil Red O. Compared to the CON group: * <span class=\"html-italic\">p</span> &lt; 0.05, ** <span class=\"html-italic\">p</span> &lt; 0.01, *** <span class=\"html-italic\">p</span> &lt; 0.001, and **** <span class=\"html-italic\">p</span> &lt; 0.0001. Pep.—pepsin; Try.—trypsin; Chy.—chymotrypsin; Mul.—multi-enzyme; Dis.—dispase; Alc.—alcalase; Pro.—protamex.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g002-550.jpg?1741224889 "

<strong>Figure 2</strong><br/>

<p>Cytotoxicity assay of sVAP with different molecular weights on 3T3-L1 cells.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g003-550.jpg?1741224890 "

<strong>Figure 3</strong><br/>

<p>(<b>A</b>) Fix cells and stain with ORO (50 μm). (<b>B</b>) Dissolve the stained lipid droplets in isopropanol and quantify intracellular Lip–acc. Compared to the CON group: ** <span class=\"html-italic\">p</span> &lt; 0.01, and *** <span class=\"html-italic\">p</span> &lt; 0.001.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g004-550.jpg?1741224892 "

<strong>Figure 4</strong><br/>

<p>The effect of oral administration of sVAP-3K on HFD-induced weight gain and dietary intake in C57BL/6 mice. Administer sVAP (150 or 300 mg/kg) to HFD-induced C57BL/6 mice five times a week for 11 weeks. (<b>A</b>) Display of representative mice from each group at the end of week 11. (<b>B</b>) Image confirming the body adipose using CT method. (<b>C</b>) Mouse abdominal circumference data. (<b>D</b>) Mouse body length data. (<b>E</b>) The weight of mice was measured every week. (<b>F</b>) The average weekly food intake of each group. (<b>G</b>,<b>H</b>) Measurements of abdominal fat and liver weight. Compared to the CON group: ** <span class=\"html-italic\">p</span> &lt; 0.01, *** <span class=\"html-italic\">p</span> &lt; 0.001, and **** <span class=\"html-italic\">p</span> &lt; 0.0001. Compared to the HFD group: # <span class=\"html-italic\">p</span> &lt; 0.05 and #### <span class=\"html-italic\">p</span> &lt; 0.0001.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g005-550.jpg?1741224893 "

<strong>Figure 5</strong><br/>

<p>The blood glucose levels and (<b>A</b>) offline curve area AUC of (<b>B</b>) different groups of C57BL/6 mice at 0, 15, 30, 60, and 120 min after ingestion of 20% glucose. Compared to the CON group: **** <span class=\"html-italic\">p</span> &lt; 0.0001. Compared to the HFD group: ## <span class=\"html-italic\">p</span> &lt; 0.01 and ### <span class=\"html-italic\">p</span> &lt; 0.001.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g006-550.jpg?1741224894 "

<strong>Figure 6</strong><br/>

<p>(<b>A</b>) High-density lipoprotein cholesterol content. (<b>B</b>) Low-density lipoprotein cholesterol. (<b>C</b>) Total cholesterol content. (<b>D</b>) Triglyceride levels. (<b>E</b>) Aspartate transaminase. (<b>F</b>) Alanine transaminase. Compared to the CON group: *** <span class=\"html-italic\">p</span> &lt; 0.001 and **** <span class=\"html-italic\">p</span> &lt; 0.0001. Compared to the HFD group: # <span class=\"html-italic\">p</span> &lt; 0.05, ## <span class=\"html-italic\">p</span> &lt; 0.01, ### <span class=\"html-italic\">p</span> &lt; 0.001, and #### <span class=\"html-italic\">p</span> &lt; 0.0001.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g007-550.jpg?1741224896 "

<strong>Figure 7</strong><br/>

<p>(<b>A</b>) The effect of oral sVAP−3K on protein expression in liver tissue of HFD mice. (<b>B</b>) The effect of oral sVAP−3K on protein expression in abdominal fat tissue of HFD mice. P−AMPK and lipogenesis-related proteins were evaluated by Western blotting using specific protein antibodies. GADPH protein is used as an internal control. Compared to the CON group: **** <span class=\"html-italic\">p</span> &lt; 0.0001. Compared to the HFD group: ## <span class=\"html-italic\">p</span> &lt; 0.01, ### <span class=\"html-italic\">p</span> &lt; 0.001, and #### <span class=\"html-italic\">p</span> &lt; 0.0001.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g008-550.jpg?1741224900 "

<strong>Figure 8</strong><br/>

<p>Pathological sections of C57BL/6 mouse liver and adipose tissue. (<b>A</b>) HE staining results of mice liver tissue. (<b>B</b>) ORO staining results of mice liver tissue. (<b>C</b>) HE staining results of abdominal fat tissue in mice. (<b>D</b>) The results of abdominal fat ORO staining in mice. (<b>E</b>,<b>F</b>) The effects of sVAP-3K on adipocytes in the liver and abdominal fat tissue of mice. (<b>E</b>) Liver ORO value. (<b>F</b>) Fat ORO value. (<b>G</b>) The number of adipocytes in each group per equal area. Compared to the CON group: **** <span class=\"html-italic\">p</span> &lt; 0.0001. Compared to the HFD group: ### <span class=\"html-italic\">p</span> &lt; 0.001, and #### <span class=\"html-italic\">p</span> &lt; 0.0001. a/b/c/d1—CON group; a/b/c/d2—HFD group; a/b/c/d3—HFD-P group; a/b/c/d4—HFD-L group; a/b/c/d5—HFD-H group.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g009-550.jpg?1741224903 "

<strong>Figure 9</strong><br/>

<p>Analysis of gut flora diversity of sVAP−3K on HFD mice. (<b>A</b>) Dilution curve. (<b>B</b>) Chao index. (<b>C</b>) Ace index. (<b>D</b>) Sobs index. (<b>E</b>) Shannon index. (<b>F</b>) Simpson index. (<b>G</b>) PCA diagram. (<b>H</b>) PCoA diagram. (<b>I</b>) PC1 diagram. Compared to the CON group: * <span class=\"html-italic\">p</span> &lt; 0.05, ** <span class=\"html-italic\">p</span> &lt; 0.01.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g010-550.jpg?1741224906 "

<strong>Figure 10</strong><br/>

<p>Analysis of community composition in each group. (<b>A</b>) Venn diagram. (<b>B</b>) Circos diagram. (<b>C</b>) Distribution at the level of the phylum. (<b>D</b>) The ratio of F/B in each group. (<b>E</b>) Distribution at the level of the genus. (<b>F</b>) Community heatmap analysis on genus level. Compared to the CON group: **** <span class=\"html-italic\">p</span> &lt; 0.0001. Compared to the HFD group: #### <span class=\"html-italic\">p</span> &lt; 0.0001.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g011-550.jpg?1741224909 "

<strong>Figure 11</strong><br/>

<p>LEfSe analysis: (<b>A</b>) LDA score plot and (<b>B</b>) LEfSe clade evolutionary diagram.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g012-550.jpg?1741224910 "

<strong>Figure 12</strong><br/>

<p>sVAP-3K component total ion chromatogram.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g013-550.jpg?1741224913 "

<strong>Figure 13</strong><br/>

<p>sVAP −3K secondary mass spectrometry image. (<b>A</b>) RVDPVNFKL; (<b>B</b>) GGEFTPVLQ; (<b>C</b>) VDPENFRL; (<b>D</b>) VDPVNFK.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g014-550.jpg?1741224915 "

<strong>Figure 14</strong><br/>

<p>Docking results with 8K8C molecules. (<b>A</b>) RVDPVNFKL; (<b>B</b>) GGEFTPVLQ; (<b>C</b>) VDPENFRL; (<b>D</b>) VDPVNFK.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")[](https://pub.mdpi-res.com/molecules/molecules-30-01173/article_deploy/html/images/molecules-30-01173-g015-550.jpg?1741224917 "

<strong>Figure 15</strong><br/>

<p>Docking results with 3LMF molecules. (<b>A</b>) RVDPVNFKL; (<b>B</b>) GGEFTPVLQ; (<b>C</b>) VDPENFRL; (<b>D</b>) VDPVNFK.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1420-3049/30/5/1173'>Full article</a></strong>

")

_attachment_

Supplementary material:  

[Supplementary File 1 (ZIP, 1550 KiB)](/1996-1073/18/5/1283/s1?version=1741224093)  

25 pages, 3834 KiB  [](/1996-1073/18/5/1283/pdf?version=1741224092 "Article PDF")

Open AccessArticle

[Stochastic Capacity Expansion Model Accounting for Uncertainties in Fuel Prices, Renewable Generation, and Demand](/1996-1073/18/5/1283)

by **Naga Srujana Goteti**, **Eric Hittinger** and **Eric Williams**

_Energies_ **2025**, _18_(5), 1283; https://doi.org/10.3390/en18051283 (registering DOI) - 6 Mar 2025

[**Abstract**](#)

Capacity expansion models for electricity grids typically use deterministic optimization, addressing uncertainty through ex-post analysis by varying input parameters. This paper presents a stochastic capacity expansion model that integrates uncertainty directly into optimization, enabling the selection of a single strategy robust across a [\[...\] Read more.](#)

Capacity expansion models for electricity grids typically use deterministic optimization, addressing uncertainty through ex-post analysis by varying input parameters. This paper presents a stochastic capacity expansion model that integrates uncertainty directly into optimization, enabling the selection of a single strategy robust across a defined range of uncertainties. Two cost-based risk objectives are explored: “risk-neutral” minimizes expected total system cost, and “risk-averse” minimizes the most expensive 5% of the cost distribution. The model is applied to the U.S. Midwest grid, accounting for uncertainties in electricity demand, natural gas prices, and wind generation patterns. While uncertain gas prices lead to wind additions, wind variability leads to reduced adoption when explicitly accounted for. The risk-averse objective produces a more diverse generation portfolio, including six GW more solar, three GW more biomass, along with lower current fleet retirements. Stochastic objectives reduce mean system costs by 4.5% (risk-neutral) and 4.3% (risk-averse) compared to the deterministic case. Carbon emissions decrease by 1.5% under the risk-neutral objective, but increase by 3.0% under the risk-averse objective due to portfolio differences. [Full article](/1996-1073/18/5/1283)

(This article belongs to the Special Issue [Renewable Energy Power Generation and Power Demand Side Management](        

    /journal/energies/special_issues/P2K4WHA0IJ

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g001-550.jpg?1741224251 "

<strong>Figure 1</strong><br/>

<p>Framework for determining the optimized investment plan under uncertainty of inputs from 2020 to 2050 across the Midcontinent Independent System Operation (MISO) region. The sampling model (C) runs the long-term assessment model (B) over a random sample of input variables to calculate the distribution of the expected total cost of electricity for different stochastic inputs using fixed costs and the dispatch model (A) for variable costs. The optimization–objective evaluation model (D) produces a single cost value from the distribution of system costs for the optimization, based on the user-defined objective. This study uses conditional value-at-risk (CVaR) to assign a risk-adjusted value from the distribution of outputs from the cost model. The decision model (E) first uses genetic algorithm, then pattern search once the genetic algorithm finds an optimal neighborhood, to identify investment plans that minimize CVaR, given the uncertainty-driven distribution from C and the objective defined in A. An existing dispatch model can be used as is with this framework by calling on the relevant subfunctions in the long-term assessment model (B).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g002-550.jpg?1741224252 "

<strong>Figure 2</strong><br/>

<p>Fuel prices of coal, natural gas, uranium, and oil used in the deterministic scenario. The <span class=\"html-italic\">x</span>-axis represents the year, and the <span class=\"html-italic\">y</span>-axis represents the fuel price in USD per million British thermal units. All fuel prices except natural gas are based on U.S. Energy Information Administration data [<a href=\"#B49-energies-18-01283\" class=\"html-bibr\">49</a>]. For natural gas, prices for the deterministic scenario are set to the mean of the distribution in stochastic scenario (see the Distribution of Natural Gas Prices section).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g003-550.jpg?1741224253 "

<strong>Figure 3</strong><br/>

<p>Natural gas Henry Hub spot prices from 1990 to 2018 and sample simulated price scenarios with uncertainty cone from 2018 to 2050. The <span class=\"html-italic\">x</span>-axis represents the year, and the <span class=\"html-italic\">y</span>-axis represents the natural gas price in USD per metric million British thermal units. Ornstein–Uhlenbeck mean-reversion process is used to create stochastic natural gas prices as an input to the long-term assessment model. Each colored dotted line indicates one possible sample price trajectory until 2050. Three out of a thousand samples are illustrated in the figure.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g004-550.jpg?1741224255 "

<strong>Figure 4</strong><br/>

<p>Uncertainty cone of average annual load growth from 2015 to 2050. The <span class=\"html-italic\">x</span>-axis represents the year, and the <span class=\"html-italic\">y</span>-axis represents the annual average hourly demand in gigawatt hours. Each colored dotted line indicates one possible average growth trajectory until 2050. Three out of a thousand samples are illustrated in the figure.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g005-550.jpg?1741224256 "

<strong>Figure 5</strong><br/>

<p>Four random samples of hourly load patterns for two summer days in the year 2035. The <span class=\"html-italic\">x</span>-axis represents the hour, and the <span class=\"html-italic\">y</span>-axis represents the hourly demand in gigawatt hours. The samples are created from multiplying a random point of annual demand growth distribution from the year 2035 with a random normalized hourly load pattern over a year from the historical data. Similar profiles are created at 5-year steps from 2020 to 2050 for 8760 h for every Monte Carlo simulation.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g006-550.jpg?1741224257 "

<strong>Figure 6</strong><br/>

<p>Four random samples of hourly wind output for two example days during the summer. The <span class=\"html-italic\">x</span>-axis represents the sampled hours, and the <span class=\"html-italic\">y</span>-axis represents the normalized wind output per unit megawatt of capacity. Each color represents one sample year of data describing the historical wind generation observed in the Midcontinent Independent System Operator (MISO) region.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g007-550.jpg?1741224259 "

<strong>Figure 7</strong><br/>

<p>2020–2050 generator capacity mix (gigawatts) (<b>top</b> figure) and generation (terawatt hours) (<b>bottom</b> figure) in the stochastic risk-neutral scenario (objective = minimize mean of cost distribution), using the mean natural gas prices and demand values from the input distributions. Colors of the bars indicate the generator type. (Gas CT = gas combustion turbine, Gas CC = gas combined cycle).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g008-550.jpg?1741224260 "

<strong>Figure 8</strong><br/>

<p>Probability distribution of discounted total system cost of electricity for risk-neutral and deterministic scenarios, generated when the resultant investment plans are run through a sample of 1000 random natural gas prices, demand, and historical wind variations. The <span class=\"html-italic\">x</span>-axis represents the total discounted system cost of electricity in billions of USD, and the <span class=\"html-italic\">y</span>-axis represents the probability. Colors represent the scenarios. The risk-neutral scenario is optimized for conditional value-at-risk (CVaR) at 0% (mean of the input distribution) and the deterministic scenario is optimized for average input values (not distributions).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g009-550.jpg?1741224261 "

<strong>Figure 9</strong><br/>

<p>Probability distribution of the discounted total cost of the electricity system for risk-neutral and risk-averse scenarios. The <span class=\"html-italic\">x</span>-axis represents the total discounted system cost in billions of USD, and the <span class=\"html-italic\">y</span>-axis represents the probability. The risk neutral scenario is optimized for conditional value-at-risk (CVaR) at 0%, which is the mean of the distribution. The risk averse scenario is optimized for CVaR at 95%.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g010-550.jpg?1741224262 "

<strong>Figure 10</strong><br/>

<p>Boxplot of unserved energy (not produced by MISO generators, purchased at cost of USD 10,000/megawatt hours) comparing the deterministic, risk-neutral, and risk-averse scenarios. Deterministic scenarios have higher unserved energy values due to not accounting for high-demand/low wind generation scenarios. The <span class=\"html-italic\">x</span>-axis represents the year, and the <span class=\"html-italic\">y</span>-axis represents the unserved energy in terawatt hours. Colors represent different scenarios. (MISO = Midcontinent Independent System Operator).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g011-550.jpg?1741224263 "

<strong>Figure 11</strong><br/>

<p>Scatter plot of cumulative additions of different generation technologies by 2050, comparing the deterministic, risk-neutral, and risk-averse scenarios. The risk-averse scenario is optimized for conditional value-at-risk (CVaR) at 95% and risk-neutral scenario is optimized for CVaR at 0% of the system cost distribution. The <span class=\"html-italic\">x</span>-axis represents the generation technologies, and the <span class=\"html-italic\">y</span>-axis represents the cumulative capacity additions (or retirements as negative values) in gigawatts. Colors represent different scenarios.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")[](https://pub.mdpi-res.com/energies/energies-18-01283/article_deploy/html/images/energies-18-01283-g012-550.jpg?1741224264 "

<strong>Figure 12</strong><br/>

<p>Cumulative probability distributions of the output emissions for deterministic, risk-neutral and risk-averse scenarios by the year 2050. The <span class=\"html-italic\">x</span>-axis represents the emissions in kg/megawatt hours and the <span class=\"html-italic\">y</span>-axis represents the cumulative probability. Colors represent the scenarios. The risk-neutral scenario is optimized for conditional value-at-risk (CVaR) at 0% and risk-averse scenario is optimized for CVaR at 95%.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1996-1073/18/5/1283'>Full article</a></strong>

")

12 pages, 215 KiB  [](/2039-4403/15/3/91/pdf?version=1741223254 "Article PDF")

Open AccessPerspective

[Creating a Supportive Work Environment: A Cognitive Behavioral Approach for Nurse Leaders](/2039-4403/15/3/91)

by **Nurit Zusman**, **Caryn Scheinberg Andrews**, **Vladislav Kaslin** and **Anna C. Kienski Woloski Wruble**

_Nurs. Rep._ **2025**, _15_(3), 91; https://doi.org/10.3390/nursrep15030091 (registering DOI) - 6 Mar 2025

[**Abstract**](#)

Purpose: This article focuses on identifying cognitive behavioral (CB) techniques that can help nurse supervisors more effectively navigate interpersonal challenges, reduce workplace stress, improve team cohesion, and, ultimately, enhance overall organizational performance and staff well-being. Approach: Through a comprehensive review of leadership literature [\[...\] Read more.](#)

Purpose: This article focuses on identifying cognitive behavioral (CB) techniques that can help nurse supervisors more effectively navigate interpersonal challenges, reduce workplace stress, improve team cohesion, and, ultimately, enhance overall organizational performance and staff well-being. Approach: Through a comprehensive review of leadership literature and clinical management practices, we determined that CB techniques could be integrated into nursing management. Two hypothetical scenarios within this context are offered, where CB techniques can enhance nursing leadership effectiveness. Conclusions and Recommendations: CB techniques offer a humanistic approach to nursing leadership through: (1) providing tools for leaders to reframe challenges and frustrations, particularly in resource-limited settings; (2) offering stress-management strategies for nursing leaders; and (3) enhancing communication skills, self-awareness, and team motivation. These applications can potentially improve both staff and management satisfaction, ultimately improving patient care quality. Healthcare organizations should consider incorporating CB techniques into their leadership development programs. We suggest practical ways to implement these techniques in daily nursing management, emphasizing the importance of creating supportive and safe work environments and provide recommendations for future research. This perspective extends the cognitive behavioral approach beyond its traditional therapeutic context into nursing leadership, providing a novel theoretical framework for understanding and enhancing leadership development in healthcare settings. [Full article](/2039-4403/15/3/91)

11 pages, 233 KiB  [](/2813-9844/7/1/22/pdf?version=1741222267 "Article PDF")

Open AccessArticle

[Parent–Child Dyadic Synchrony, Prosocial and Aggressive Behavior with Peers, and Friendship Quality in Early Adolescence](/2813-9844/7/1/22)

by **Eric W. Lindsey**

_Psychol. Int._ **2025**, _7_(1), 22; https://doi.org/10.3390/psycholint7010022 (registering DOI) - 6 Mar 2025

[**Abstract**](#)

The present study examined the contribution of mother–child and father–child synchrony to early adolescents’ prosocial and aggressive behavior with peers and friendship quality. Data were collected as part of a cross-sectional study from 185 early adolescents (_M_ age = 12.48, _SD_ = [\[...\] Read more.](#)

The present study examined the contribution of mother–child and father–child synchrony to early adolescents’ prosocial and aggressive behavior with peers and friendship quality. Data were collected as part of a cross-sectional study from 185 early adolescents (_M_ age = 12.48, _SD_ = 1.03) and their parents. Separate father–adolescent and mother–adolescent interaction sessions were used to assess three dimensions of synchrony: (a) dyadic synchrony, (b) shared positive affect, and (c) conversational equality. Parents rated adolescents’ prosocial and aggressive behavior toward peers. Adolescents reported the quality of their relationships with their best friends. Regression analyses revealed that both mother–adolescent and father–adolescent shared positive affect were associated with higher parent-rated prosocial behavior, lower parent-rated peer aggression, and higher adolescent self-reported friendship intimacy. Likewise, mother–adolescent and father–adolescent conversational equality were each associated with higher parent-rated prosocial behavior. Only father–adolescent conversational equality was associated with adolescent-reported friendship intimacy. No measure of parent–adolescent dyadic synchrony was associated with adolescent-reported friendship conflict. [Full article](/2813-9844/7/1/22)

23 pages, 1053 KiB  [](/1424-8220/25/5/1605/pdf?version=1741226024 "Article PDF")

Open AccessArticle

[Task Planning of Multiple Unmanned Aerial Vehicles Based on Minimum Cost and Maximum Flow](/1424-8220/25/5/1605)

by **Xiaodong Shi**, **Xiangping Zhai**, **Rui Wang**, **Yi Le**, **Shuang Fu** and **Ningzhong Liu**

_Sensors_ **2025**, _25_(5), 1605; [https://doi.org/10.3390/s25051605](https://doi.org/10.3390/s25051605) - 5 Mar 2025

[**Abstract**](#)

With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs’ distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to [\[...\] Read more.](#)

With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs’ distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to extend UAV coverage and improve delivery completion rates. For densely distributed depots in wide-area regions, we develop algorithms for task allocation and path planning in a truck-independent UAV system. Specifically, a minimum-cost, maximum-flow model is constructed to obtain sub-paths covering all delivery tasks, and resource tree-based algorithms are used to construct global paths for UAVs and trucks. Simulation results show that our algorithms reduce total energy consumption by 11.53% and 9.15% under different task points, which suggests that our proposed method can significantly enhance delivery efficiency, offering a promising solution for future logistics operations. [Full article](/1424-8220/25/5/1605)

(This article belongs to the Special Issue [AI-IoT for New Challenges in Smart Cities](        

    /journal/sensors/special_issues/AI_IoTNCSC

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/sensors/sensors-25-01605/article_deploy/html/images/sensors-25-01605-g001-550.jpg?1741226122 "

<strong>Figure 1</strong><br/>

<p>Illustration of the truck-independent UAV system.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/25/5/1605'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sensors/sensors-25-01605/article_deploy/html/images/sensors-25-01605-g002-550.jpg?1741226123 "

<strong>Figure 2</strong><br/>

<p>The MCMF model for the task-planning problem of the truck-independent UAV system.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/25/5/1605'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sensors/sensors-25-01605/article_deploy/html/images/sensors-25-01605-g003-550.jpg?1741226125 "

<strong>Figure 3</strong><br/>

<p>Constructing multi-UAV paths using the matching method.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/25/5/1605'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sensors/sensors-25-01605/article_deploy/html/images/sensors-25-01605-g004-550.jpg?1741226126 "

<strong>Figure 4</strong><br/>

<p>Finding the initial route of the UAV based on the resource tree.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/25/5/1605'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sensors/sensors-25-01605/article_deploy/html/images/sensors-25-01605-g005-550.jpg?1741226128 "

<strong>Figure 5</strong><br/>

<p>Three insertion point positions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/25/5/1605'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sensors/sensors-25-01605/article_deploy/html/images/sensors-25-01605-g006-550.jpg?1741226130 "

<strong>Figure 6</strong><br/>

<p>Diagrams of initial environments.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/25/5/1605'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sensors/sensors-25-01605/article_deploy/html/images/sensors-25-01605-g007-550.jpg?1741226132 "

<strong>Figure 7</strong><br/>

<p>Task allocation results of MRTP and GTP with 30 task points.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/25/5/1605'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sensors/sensors-25-01605/article_deploy/html/images/sensors-25-01605-g008-550.jpg?1741226133 "

<strong>Figure 8</strong><br/>

<p>Task allocation results of MRTP and GTP with 15 task points.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/25/5/1605'>Full article</a></strong>

")

20 pages, 1338 KiB  [](/1660-4601/22/3/380/pdf?version=1741225812 "Article PDF")

Open AccessReview

[The Impact of Microplastics in Food and Drugs on Human Health: A Review of the MENA Region](/1660-4601/22/3/380)

by **Noha Alziny**, **Fadya M. Elgarhy**, **Ayan Musa Mohamed**, **Hager Yehia Shalaby**, **Noran Nounou**, **Omnia Soliman** and **Anwar Abdelnaser**

_Int. J. Environ. Res. Public Health_ **2025**, _22_(3), 380; [https://doi.org/10.3390/ijerph22030380](https://doi.org/10.3390/ijerph22030380) - 5 Mar 2025

[**Abstract**](#)

Microplastics (MPs), defined as plastic particles smaller than 5 mm, have emerged as a global environmental and public health crisis, infiltrating air, water, soil, and food systems worldwide. MPs originate from the breakdown of larger plastic debris, single-use plastics, and industrial processes, entering [\[...\] Read more.](#)

Microplastics (MPs), defined as plastic particles smaller than 5 mm, have emerged as a global environmental and public health crisis, infiltrating air, water, soil, and food systems worldwide. MPs originate from the breakdown of larger plastic debris, single-use plastics, and industrial processes, entering food. Emerging evidence underscores the ability of MPs to cross biological barriers, including the blood–brain barrier, triggering neuroinflammatory responses and contributing to neurodegenerative diseases such as Alzheimer’s and Parkinson’s. Polystyrene (PS), a common type of MP, activates microglial cells, releasing pro-inflammatory cytokines like tumor necrosis factor (TNF-α) and interleukins, which increase neuronal damage. MPs have also been linked to cardiovascular diseases, with studies detecting polyethylene (PE) and polyvinyl chloride (PVC) in carotid artery plaques, increasing the risk of myocardial infarction and stroke. Furthermore, MPs disrupt endocrine function, alter lipid metabolism, and induce gut microbiome imbalances, posing multifaceted health risks. In the MENA region, MP pollution is particularly severe, with the Mediterranean Sea receiving an estimated 570,000 tons of plastic annually, equivalent to 33,800 plastic bottles per minute. Studies in Egypt, Lebanon, and Tunisia document high MP concentrations in marine ecosystems, with herbivorous fish like _Siganus rivulatus_ containing over 1000 MPs per individual due to the ingestion of contaminated seaweed. Despite these findings, public awareness and regulatory frameworks remain inadequate, with only 24% of Egyptians demonstrating sufficient knowledge of safe plastic use. This review emphasizes the urgent need for region-specific research, policy interventions, and public awareness campaigns to address MP pollution. Recommendations include sustainable waste management practices, the promotion of biodegradable alternatives, and enhanced monitoring systems to mitigate the health and environmental impacts of MPs in the MENA region. [Full article](/1660-4601/22/3/380)

(This article belongs to the Section [Environmental Health](/journal/ijerph/sections/environment_health))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/ijerph/ijerph-22-00380/article_deploy/html/images/ijerph-22-00380-g001-550.jpg?1741225927 "

<strong>Figure 1</strong><br/>

<p>Illustrates sources of MP contamination.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1660-4601/22/3/380'>Full article</a></strong>

")[](https://pub.mdpi-res.com/ijerph/ijerph-22-00380/article_deploy/html/images/ijerph-22-00380-g002-550.jpg?1741225928 "

<strong>Figure 2</strong><br/>

<p>The toxic impact of MPs on human health.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1660-4601/22/3/380'>Full article</a></strong>

")[](https://pub.mdpi-res.com/ijerph/ijerph-22-00380/article_deploy/html/images/ijerph-22-00380-g003-550.jpg?1741225929 "

<strong>Figure 3</strong><br/>

<p>Toxic effects of MPs on nervous system.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1660-4601/22/3/380'>Full article</a></strong>

")

61 pages, 8313 KiB  [](/2227-9040/13/3/92/pdf?version=1741225889 "Article PDF")

Open AccessReview

[Promising Solutions to Address the Non-Specific Adsorption in Biosensors Based on Coupled Electrochemical-Surface Plasmon Resonance Detection](/2227-9040/13/3/92)

by **Alina Vasilescu**, **Szilveszter Gáspár**, **Mihaela Gheorghiu**, **Cristina Polonschii**, **Roberta Maria Banciu**, **Sorin David**, **Eugen Gheorghiu** and **Jean-Louis Marty**

_Chemosensors_ **2025**, _13_(3), 92; [https://doi.org/10.3390/chemosensors13030092](https://doi.org/10.3390/chemosensors13030092) - 5 Mar 2025

[**Abstract**](#)

Nonspecific adsorption (NSA) impacts the performance of biosensors in complex samples. Coupled electrochemical–surface plasmon resonance biosensors (EC-SPR) offer interesting opportunities to evaluate NSA. This review details the main solutions to minimize fouling in electrochemical (EC), surface plasmon resonance (SPR) and EC-SPR biosensors. The [\[...\] Read more.](#)

Nonspecific adsorption (NSA) impacts the performance of biosensors in complex samples. Coupled electrochemical–surface plasmon resonance biosensors (EC-SPR) offer interesting opportunities to evaluate NSA. This review details the main solutions to minimize fouling in electrochemical (EC), surface plasmon resonance (SPR) and EC-SPR biosensors. The discussion was centered on blood, serum and milk as examples of complex matrices. Emphasis was placed on antifouling coatings, NSA evaluation protocols and universal functionalization strategies to obtain antifouling biosensors. In the last 5 years, various antifouling coatings were developed for EC biosensors, including new peptides, cross-linked protein films and hybrid materials. Due to the comparatively much more scarce literature, for SPR and EC-SPR biosensors the discussion was extended to the early 2010s. The analysis revealed a wide range of antifouling materials with tunable conductivity, thickness and functional groups that can be tested in the future with EC-SPR. The high-throughput screening of new materials, molecular simulations and machine learning-assisted evaluations will even further widen the range of antifouling materials available for biosensors. The minimization of NSA’s impact on the analytical signal is moreover facilitated by unique sensing mechanisms associated with the bioreceptor or the particularities of the detection method. It is hoped that this review will encourage research in the field of EC-SPR biosensors. [Full article](/2227-9040/13/3/92)

(This article belongs to the Special Issue [Sensors for Food Testing, Environmental Analysis, and Medical Diagnostics](        

    /journal/chemosensors/special_issues/G8A0J890B5

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g001-550.jpg?1741226015 "

<strong>Figure 1</strong><br/>

<p>Impact of fouling on the analytical signal of biosensors, illustrated for (<b>A</b>) an E-AB biosensor, showing the signal degradation in time, manifested as sensor drift, due to fouling and the dissolution of the coating layer, (<b>B</b>) an immunosensor with detection by SPR and (<b>C</b>) an EC enzyme biosensor. Redrawn in part from [<a href=\"#B21-chemosensors-13-00092\" class=\"html-bibr\">21</a>,<a href=\"#B24-chemosensors-13-00092\" class=\"html-bibr\">24</a>] (<b>A</b>). Details are given in the text.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g002-550.jpg?1741226016 "

<strong>Figure 2</strong><br/>

<p>Mechanisms of NSA in biosensors and the main strategies used to minimize fouling by addressing (1) sample preparation, (2) the interaction between the sample components and the biosensor interface and (3) the properties of the (coated) sensing interface. Shown are the reduction in NSA by centrifugation or filtration of the investigated sample (1, bottom left), supplementing the sample with salts, detergents and/or proteins (2a, bottom center), using reference sensors lacking biorecognition elements (2b, bottom center), using a sacrificial layer that is removed together with fouling species (2c, bottom center) and appropriate modification of the surface of the sensor with species able to repel fouling species (3, bottom right).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g003-550.jpg?1741226017 "

<strong>Figure 3</strong><br/>

<p>The main mechanisms of counteracting NSA with antifouling coatings, illustrated for (<b>a</b>) a hydrophilic surface, (<b>b</b>) a polyethylene glycol (PEG)-coated surface, (<b>c</b>) a surface coated with a zwitterionic polymer, (<b>d</b>), a surface coated with a dense alkanethiol self-assembled monolayer (SAM) and (<b>e</b>) a negatively charged surface, e.g., coated with a layer of cross-linked bovine serum albumin (cBSA). Details are given in the text.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g004-550.jpg?1741226018 "

<strong>Figure 4</strong><br/>

<p>(<b>A</b>) Schematic illustration of the fouling testing process with biosensor and control coated interface. (<b>B</b>) Typical SPR experimental setup based on the Kretschmann configuration and sensorgrams showing the signal change in SPR due to NSA from sample solution. (<b>C</b>) Typical setup used with EC biosensors and a frequently used way to measure the NSA by differential pulse voltammetry (DPV) using ferrocyanide/ferricyanide. Representation of the potential pulses applied in DPV and of the measured change in the EC signal (“signal suppression”) resulting from the electrode fouling.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g005-550.jpg?1741226020 "

<strong>Figure 5</strong><br/>

<p>Examples of molecules often used in passive antifouling coatings for biosensors. (<b>A</b>) PEG-based thiols. (<b>B</b>) Alkanethiols: 6-mercapto-1-hexanol (MCH), 3-mercapto-1-propionic acid (MPA) and 11-mercapto-1-undecanoic acid (MUA). (<b>C</b>) Zwitterionic polymers and thiols. PC: phosphoryl choline. SB: sulfobetaine. CB: carboxybetaine. MPC: 2-methacryloyl phosphorylcholine. SBMA: sulfobetaine methacrylate. CBMA: carboxybetaine methacrylate. PPC: phenyl phosphoryl choline. APDMAO: 3-aminopropyldimethylamine oxide. (<b>D</b>) Antifouling peptide EKEKEKE. (<b>E</b>) Polysaccharides: chitosan and hyaluronic acid (HA). (<b>F</b>) Cross-linked BSA (cBSA). (<b>G</b>) Polyadenine.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g006-550.jpg?1741226021 "

<strong>Figure 6</strong><br/>

<p>Strategies to minimize NSA in EC biosensors using (<b>A</b>) nanoporous electrodes; (<b>B</b>) porous membranes; (<b>C</b>) liquid filters such as the continuous diffusion filter (CDF); (<b>D</b>) transient coatings; (<b>E</b>) antifouling coatings; (<b>F</b>) Minimizing the impact of fouling on E-AB biosensors by (i) using antifouling layers based on the SAM of phosphatidyl choline (PC)-ended-thiol or MCH and hydrogel overcoats and by (ii) using drift correction algorithms such as KDM. Lower right: variation in time of the square wave voltammetry (SWV) signal gain for the aptamer-bound state (blue) and for the unbound state (grey), when the sensor is exposed to a pulse of analyte. The signal drifts are synchronized, which enables to obtain a drift-corrected signal (green) by applying KDM. Redrawn in part from [<a href=\"#B96-chemosensors-13-00092\" class=\"html-bibr\">96</a>] (<b>A</b>) and [<a href=\"#B97-chemosensors-13-00092\" class=\"html-bibr\">97</a>] (<b>C</b>,<b>F</b>).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g007-550.jpg?1741226024 "

<strong>Figure 7</strong><br/>

<p>Electrochemical sensing platform for the detection of SARS-CoV-2 virus, integrating four working electrodes coated with porous antifouling nanocomposite. (<b>A</b>) Illustration of the coating process leading to a thick, nanoporous film of cBSA with embedded AuNW (AuNW-cBSA). (<b>B</b>) Plot of the performance of the AuNW-cBSA sensor for the multiplexed detection of viral RNA (ORF1a), antigen (nucleocapsid protein, NP), and IgG antibody associated with SARS-CoV-2 infection, for four sets of combinations of COVID-19-positive and -negative NPS. NC represents the negative control. Data represented as mean values ± SD (n = 3 independent EC chips). Statistical significance was tested (*** <span class=\"html-italic\">p</span>&lt; 0.0001; **** <span class=\"html-italic\">p</span> &lt; 0.0001; two-tailed Student’s <span class=\"html-italic\">t</span>-test; n.s.: not statistically significant). (<b>C</b>) Cyclic voltammograms recorded with the multiplexed sensors for SARS-CoV-2ORF1a, NP, IgG antibody, and NC. Reprinted from [<a href=\"#B82-chemosensors-13-00092\" class=\"html-bibr\">82</a>]. Creative Commons Attribution 4.0 International License.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g008-550.jpg?1741226025 "

<strong>Figure 8</strong><br/>

<p>(<b>A</b>) The structure of PC-SH (i.e., MPC conjugated to 1,6 hexanedithiol linker) including both surface anchoring and antifouling capabilities. (<b>B</b>) Chemical structure of PSS-doped PEDOT. (<b>C</b>) Graphical illustration of biosensor development steps. (<b>D</b>) Variation of the antifouling capacity of bare GCE, Au NPs/PEDOT/GCE, and PC/Au NPs/PEDOT/GCE exposed to 1% milk. (<b>E</b>) The resistance to NSA of bare GCE, Au NPs/PEDOT/GCE, and PC/Au NPs/PEDOT/GCE exposed for 30 min to 0.1%, 1%, 10%, and 20% milk. Reprinted from [<a href=\"#B42-chemosensors-13-00092\" class=\"html-bibr\">42</a>], with permission from Elsevier.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g009-550.jpg?1741226028 "

<strong>Figure 9</strong><br/>

<p>(<b>A</b>) Graphical representation of the steps of development of the biosensor for the detection of IgG. (<b>B</b>) The synthesis of DNA–peptide conjugates used in the IgG biosensor by click chemistry. (<b>C</b>) Schematic representation of the fabrication process of the genosensor for the detection of COVID-19 infection, based on DNA–peptide conjugates obtained by avidin–neutravidin affinity. (<b>D</b>) Graphical illustration of the development of the biosensor for the detection of CA125 based on DNA aptamer–peptide conjugates obtained by click chemistry and attached to peptide-coated electrodes by streptavidin–biotin affinity. Reprinted from [<a href=\"#B43-chemosensors-13-00092\" class=\"html-bibr\">43</a>] (<b>A</b>,<b>B</b>), [<a href=\"#B74-chemosensors-13-00092\" class=\"html-bibr\">74</a>] (<b>C</b>) and [<a href=\"#B44-chemosensors-13-00092\" class=\"html-bibr\">44</a>] (<b>D</b>), with permission.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g010-550.jpg?1741226029 "

<strong>Figure 10</strong><br/>

<p>(<b>A</b>) Graphical representation of the development/evaluation steps of the SPR biosensor with smart layer design and Kretschmann configuration. (<b>B</b>) Shifts in the resonance angle versus full dip assessment using Transfer Matrix approaches for quantitative SPR assays and NSA control. Numbers associated to the curves with different colors correspond to steps indicated in (<b>A</b>).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g011-550.jpg?1741226030 "

<strong>Figure 11</strong><br/>

<p>A schematic representation of an experimental setup used for coupled EC-SPR measurements, exemplified for the case of P-EIS testing. Details are given in the text. Reprinted from [<a href=\"#B25-chemosensors-13-00092\" class=\"html-bibr\">25</a>] with permission.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g012-550.jpg?1741226031 "

<strong>Figure 12</strong><br/>

<p>(<b>A</b>) SPR and (<b>B</b>) admittance density responses recorded with the P-EIS and IgG-based biosensor for solutions of NaCl, glucose, BSA and anti-IgG prepared in PBS buffer. The flow rate was 60 μL/min. A 100 Hz, 200 mVpp potential modulation with 120 mV DC bias was applied. Reproduced from [<a href=\"#B186-chemosensors-13-00092\" class=\"html-bibr\">186</a>] with permission.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g013-550.jpg?1741226032 "

<strong>Figure 13</strong><br/>

<p>(<b>Left</b>) Schematic illustration of P-EIM experimental setup. An AC potential modulation is applied to the gold-coated “sensor chip” (WE) via a potentiostat. A p-polarized collimated point source red LED light is directed to the sensor chip via a triangle prism at the SPR resonance angle, and the reflected light is recorded via a CCD camera through a zoom lens. The DC component of the image is the conventional SPR image, and the AC component is converted to an admittance image via real-time data processing. (<b>Center</b>) The admittance amplitude image showing an area on a gold sensor chip printed with proteins. The image size is 3.63 × 2.27 mm. The responses for each protein spot are reference-corrected. (<b>Right</b>) Admittance responses for the interaction of imatinib (0.2 μM) with the proteins in the sensor array. Solid lines are kinetic fitting curves. Reproduced from [<a href=\"#B183-chemosensors-13-00092\" class=\"html-bibr\">183</a>] with permission.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g014-550.jpg?1741226033 "

<strong>Figure 14</strong><br/>

<p>(<b>A</b>) The dependence of the relative variations in the EIS impedance magnitude at 3.5 Hz (<b>A</b>) and the SPR response (<b>B</b>) on anti-human-IgG concentration upon successive injections of increasing concentrations (20 nM, 40 nM, and 80 nM) on the cBSA surface and cBSA-HIgG-modified surface. The dependence of the relative variations in the EIS impedance magnitude at 3.5 Hz (<b>C</b>) and the SPR response (<b>D</b>) on biotin concentration upon successive injections of increasing concentrations (40 μM, 400 μM, and 4 mM). Reproduced from [<a href=\"#B25-chemosensors-13-00092\" class=\"html-bibr\">25</a>], with permission.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g015-550.jpg?1741226034 "

<strong>Figure 15</strong><br/>

<p>(<b>A</b>) Graphical illustration of the cloaking membrane approach for detection of proteins in serum samples. (<b>B</b>) SPR sensorgram showing the membrane cloaking process with nanoparticle-conjugated anti-rabbit IgG spiked in serum. (<b>C</b>) Flow injection response of TMB for in situ EC-SPR analysis with no HRP (curve a) and immobilized HRP (curve b). Reproduced from [<a href=\"#B27-chemosensors-13-00092\" class=\"html-bibr\">27</a>] with permission.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g016-550.jpg?1741226036 "

<strong>Figure 16</strong><br/>

<p>(<b>A</b>) Representation of the nanoswitches signaling mechanism. A potentiostat is used to measure electrochemical voltammograms (i) and apply the voltage on the gold surface. The light containing plasmonic information is collected in two ways. First, a collimated beam is detected on a CCD camera to obtain the full angular/intensity dependency used in conventional angular SPR sensing (ii). Second, light from a fixed angle is sent on a photodetector. The average intensity measured by this Si detector is the SPR curve intensity at fixed angle (DC component). The detector is also coupled to a lock-in amplifier to obtain the EC-SPR signal (AC component) (iii). The inset graph shows the relation between the full SPR curve and the EC-SPR signal as the refractive index is modulated. (<b>B</b>) Schematic representation of the EC-SPR AC voltammetry method showing the variation in time of the applied potential and the resulting EC-SPR signal. (<b>C</b>) EC-SPR AC voltammograms measured for a Nanoswitch_1-modified surface and a MCH-modified surface. Inset: the EC (only) voltammogram with the Nanoswitch_1 modified surface. Reproduced from [<a href=\"#B185-chemosensors-13-00092\" class=\"html-bibr\">185</a>] with permission.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9040/13/3/92'>Full article</a></strong>

")[](https://pub.mdpi-res.com/chemosensors/chemosensors-13-00092/article_deploy/html/images/chemosensors-13-00092-g017-550.jpg?1741226038 "

<strong>Figure 17</strong><br/>

<p>(<b>A</b>) Principle of the EC-SPR detection of miRNA-145. (<b>B</b>) a: Real-time monitoring of the interaction between immobilized RNA strands on SPR gold substrates and miRNA-145 in the range 1.0 fM–150 nM by SPR (left), followed by EC-SPR measurement in the presence of ferro/ferricyanide redox probe (right). Line 1: baseline in buffer. Line 2: monitoring the interaction between surface-immobilized RNA and miRNA-145. Line 3: washing with buffer. Dashed line: injection of buffer without miRNA-145. Line 4: combined EC-SPR after injection of 5 mM ferro/ferricyanide. Line 5: washing with buffer. (<b>b,c</b>) Graphical representations of the total angle variation as a function of the logarithm of the miRNA-145 concentration obtained under conditions of hybridization equilibrium by SPR (<b>b</b>) and combined EC-SPR (<b>c</b>) Reproduced from [<a href=\"#B182-chemosensors-13-00092\" class=\"html-bibr\">182</a>] with permission.</p>

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Open AccessArticle

[The Environmental Analysis of the Post-Use Management Scenarios of the Heat-Shrinkable Film](/2073-4360/17/5/690)

by **Patrycja Walichnowska**, **Józef Flizikowski**, **Andrzej Tomporowski**, **Marek Opielak** and **Wojciech Cieślik**

_Polymers_ **2025**, _17_(5), 690; https://doi.org/10.3390/polym17050690 (registering DOI) - 5 Mar 2025

[**Abstract**](#)

The post-use management of plastic films, including shrink films, poses a significant environmental and technological challenge for the industry. Due to their durability and difficulty in degradation, these wastes contribute to environmental pollution, generating microplastics and greenhouse gas emissions during improper disposal. This [\[...\] Read more.](#)

The post-use management of plastic films, including shrink films, poses a significant environmental and technological challenge for the industry. Due to their durability and difficulty in degradation, these wastes contribute to environmental pollution, generating microplastics and greenhouse gas emissions during improper disposal. This paper examines different post-use management methods for shrink wrap, such as recycling, landfilling, and incineration, and assesses their impact on the environmental impact of the bottle packaging process using a life-cycle analysis (LCA). This study shows that the recycling option has the lowest potential environmental impact. Compared to other post-use management options, recycling reduces the potential environmental impact by more than 50%. The analysis also shows that the tested scenario using recycled film and photovoltaic energy has the lowest potential environmental impact. Using recycled film and powering the process with renewable energy reduces the potential environmental impact by about 95% compared to Scenario 1 and by about 85% in Scenario 3. [Full article](/2073-4360/17/5/690)

(This article belongs to the Section [Circular and Green Polymer Science](/journal/polymers/sections/Circ_Green))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/polymers/polymers-17-00690/article_deploy/html/images/polymers-17-00690-g001-550.jpg?1741225861 "

<strong>Figure 1</strong><br/>

<p>Analyzed technological process including the post-use management scenarios of the heat-shrinkable film (own elaboration).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2073-4360/17/5/690'>Full article</a></strong>

")[](https://pub.mdpi-res.com/polymers/polymers-17-00690/article_deploy/html/images/polymers-17-00690-g002-550.jpg?1741225862 "

<strong>Figure 2</strong><br/>

<p>Impact of the analyzed variants on the human health, DALY (own elaboration).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2073-4360/17/5/690'>Full article</a></strong>

")[](https://pub.mdpi-res.com/polymers/polymers-17-00690/article_deploy/html/images/polymers-17-00690-g003-550.jpg?1741225863 "

<strong>Figure 3</strong><br/>

<p>Impact of the analyzed variants on the ecosystem quality, PDF × m<sup>2</sup> × year (own elaboration).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2073-4360/17/5/690'>Full article</a></strong>

")[](https://pub.mdpi-res.com/polymers/polymers-17-00690/article_deploy/html/images/polymers-17-00690-g004-550.jpg?1741225864 "

<strong>Figure 4</strong><br/>

<p>Impact of the analyzed scenarios on the human health, DALY (own elaboration).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2073-4360/17/5/690'>Full article</a></strong>

")[](https://pub.mdpi-res.com/polymers/polymers-17-00690/article_deploy/html/images/polymers-17-00690-g005-550.jpg?1741225864 "

<strong>Figure 5</strong><br/>

<p>Impact of the analyzed scenarios on the ecosystem quality, PDF × m<sup>2</sup> × year (own elaboration).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2073-4360/17/5/690'>Full article</a></strong>

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_attachment_

Supplementary material:  

[Supplementary File 1 (ZIP, 4284 KiB)](/2073-4409/14/5/380/s1?version=1741188363)  

12 pages, 1145 KiB  [](/2073-4409/14/5/380/pdf?version=1741225597 "Article PDF")

Open AccessArticle

[Decreased Expression of Aquaporins as a Feature of Tubular Damage in Lupus Nephritis](/2073-4409/14/5/380)

by **Melchior Maxime**, **Van Eycken Marie**, **Nicaise Charles**, **Duquesne Thomas**, **Longueville Léa**, **Collin Amandine**, **Decaestecker Christine**, **Salmon Isabelle**, **Delporte Christine** and **Soyfoo Muhammad**

_Cells_ **2025**, _14_(5), 380; [https://doi.org/10.3390/cells14050380](https://doi.org/10.3390/cells14050380) - 5 Mar 2025

[**Abstract**](#)

**Background:** Tubulointerstitial hypoxia is a key factor for lupus nephritis progression to end-stage renal disease. Numerous aquaporins (AQPs) are expressed by renal tubules and are essential for their proper functioning. The aim of this study is to characterize the tubular expression of AQP1, [\[...\] Read more.](#)

**Background:** Tubulointerstitial hypoxia is a key factor for lupus nephritis progression to end-stage renal disease. Numerous aquaporins (AQPs) are expressed by renal tubules and are essential for their proper functioning. The aim of this study is to characterize the tubular expression of AQP1, AQP2 and AQP3, which could provide a better understanding of tubulointerstitial stress during lupus nephritis. **Methods:** This retrospective monocentric study was conducted at Erasme-HUB Hospital. We included 37 lupus nephritis samples and 9 healthy samples collected between 2000 and 2020, obtained from the pathology department. Immunohistochemistry was performed to target AQP1, AQP2 and AQP3 and followed by digital analysis. **Results:** No difference in AQP1, AQP2 and AQP3 staining location was found between healthy and lupus nephritis samples. However, we observed significant differences between these two groups, with a decrease in AQP1 expression in the renal cortex and in AQP3 expression in the cortex and medulla. In the subgroup of proliferative glomerulonephritis (class III/IV), this decrease in AQPs expression was more pronounced, particularly for AQP3. In addition, within this subgroup, we detected lower AQP2 expression in patients with higher interstitial inflammation score and lower AQP3 expression when higher interstitial fibrosis and tubular atrophy were present. **Conclusions:** We identified significant differences in the expression of aquaporins 1, 2, and 3 in patients with lupus nephritis. These findings strongly suggest that decreased AQP expression could serve as an indicator of tubular injury. Further research is warranted to evaluate AQP1, AQP2, and AQP3 as prognostic markers in both urinary and histological assessments of lupus nephritis. [Full article](/2073-4409/14/5/380)

(This article belongs to the Special Issue [Novel Autoantibodies in Systemic Autoimmune Diseases: Challenges and Perspectives](        

    /journal/cells/special_issues/ACCS698FVH

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/cells/cells-14-00380/article_deploy/html/images/cells-14-00380-g001-550.jpg?1741225676 "

<strong>Figure 1</strong><br/>

<p>Representative staining of AQP1, AQP2 and AQP3 in lupus nephritis (LN) and healthy (CTRL) kidney tissue with a focus on glomerular (G), cortical (Cx), or medullary (Med) structures (field magnification 200×).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2073-4409/14/5/380'>Full article</a></strong>

")[](https://pub.mdpi-res.com/cells/cells-14-00380/article_deploy/html/images/cells-14-00380-g002-550.jpg?1741225677 "

<strong>Figure 2</strong><br/>

<p>Semiquantitative analysis of AQP1, AQP2 and AQP3 staining in lupus nephritis (LN) and healthy (CTRL) kidney tissue. Results are displayed with box-and-whisker plots (min-max-median–Q1–Q3) and overlaying dots for individual data points (male patient in black). Data are expressed as percentage of labeled area within the region-of-interest (%ROI) (AQP1 Cortex: n = 8 for CTRL, n = 32 for LN; AQP1 Medulla: n = 8 for CTRL, n = 15 for LN; AQP2 cortex: n = 9 for CTRL, n = 31; AQP2 medulla: n = 9 for CTRL, n = 15; AQP3 cortex: n = 9 for CTRL, n = 33 for LN; AQP3 medulla: n = 9 for CTRL, n = 15 for LN; tested by Mann–Whitney).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2073-4409/14/5/380'>Full article</a></strong>

")[](https://pub.mdpi-res.com/cells/cells-14-00380/article_deploy/html/images/cells-14-00380-g003-550.jpg?1741225678 "

<strong>Figure 3</strong><br/>

<p>Subgroup analysis of AQPs expression in lupus nephritis. (<b>A</b>) Proportion of staining in renal cortex (%ROI) according to proliferative LN (P-LN; Class III and IV considered as proliferative) or non-proliferative LN (NP-LN; Class I, II or V considered as non-proliferative) (<b>B</b>) Proportion of staining in renal cortex of proliferative LN (%ROI) according to absence (0) or presence (score of 1 or more) of tubular or interstitial damage according to the NIH LN activity and chronicity scoring system. Only significant results are shown. Results are displayed with box-and-whisker plots (min-max-median–Q1–Q3) and overlaying dots for individual data points (male patient in black). Data are expressed as percentage of labeled area within the region-of-interest (%ROI) ((<b>A</b>) AQP1 n = 8 for ctrl, n = 8 for NP-LN, n = 24 for P-LN, tested by Kruskal–Wallis with Dunn’s multiple comparison test; AQP3 n = 8 for ctrl, n = 8 for NP-LN, n = 24 for P-LN, tested by Kruskal–Wallis with Dunn’s multiple comparison test. (<b>B</b>) AQP2 interstitial leukocytes score n = 24, n = 10 for score 0, n = 15 for score 1 or more; AQP3 interstitial leukocytes score n = 24, n = 11 for score 0, n = 13 for score 1+; AQP3 tubular atrophy score n = 24, n = 11 for score 0, n = 13 for score 1; tested by Mann–Whitney).</p>

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Open AccessCommunication

[Molecular Characterization of Presumptive _Klebsiella pneumoniae_ Isolates from Companion and Farm Animals in Germany Reveals Novel Sequence Types](/2076-0817/14/3/259)

by **Marwa Bassiouny**, **Peter A. Kopp**, **Ivonne Stamm**, **Hanka Brangsch**, **Heinrich Neubauer** and **Lisa D. Sprague**

_Pathogens_ **2025**, _14_(3), 259; https://doi.org/10.3390/pathogens14030259 (registering DOI) - 5 Mar 2025

[**Abstract**](#)

_Klebsiella_ (_K_.) _pneumoniae_ is a One Health pathogen that has been isolated from humans, animals, and environmental sources and is responsible for a diverse range of potentially life-threatening infections. In the present study, we analyzed the genomes of 64 presumptive _K._ [\[...\] Read more.](#)

_Klebsiella_ (_K_.) _pneumoniae_ is a One Health pathogen that has been isolated from humans, animals, and environmental sources and is responsible for a diverse range of potentially life-threatening infections. In the present study, we analyzed the genomes of 64 presumptive _K. pneumoniae_ strains isolated in 2023 from different companion and farm animals in Germany. Using whole-genome sequencing (WGS) data, 59 isolates (92.2%) were identified as _K. pneumoniae_ and five (7.8%) as _K. quasipneumoniae_. Multilocus sequence typing (MLST) assigned 53 isolates to 46 distinct sequence types (STs). Eleven isolates could not be assigned to existing STs of the Pasteur classification scheme because they contained novel alleles not previously documented. Thus, these were considered novel and designated as ST7681-ST7689 and ST7697-ST7698. Almost all isolates in this study were assigned unique STs, and only five STs were shared among multiple isolates. This research highlights the genetic diversity among _K. pneumoniae_ strains isolated from different companion and farm animals in Germany, provides information to help in surveillance strategies to mitigate zoonotic transmission risks, and demonstrates the value of WGS and MLST in identifying novel STs of _K. pneumoniae_. [Full article](/2076-0817/14/3/259)

[►▼ Show Figures](#)

Figure 1

[](https://pub.mdpi-res.com/pathogens/pathogens-14-00259/article_deploy/html/images/pathogens-14-00259-g001-550.jpg?1741225642 "

<strong>Figure 1</strong><br/>

<p>The phylogenetic tree of 64 <span class=\"html-italic\">K. pneumoniae</span>/<span class=\"html-italic\">quasipneumoniae</span> isolates from companion and farm animals in Germany was constructed using the Neighbor-Joining (NJ) method based on MLST data. The tree includes STs and corresponding geographical locations. Novel STs are indicated in red.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-0817/14/3/259'>Full article</a></strong>

")

35 pages, 2194 KiB  [](/2071-1050/17/5/2245/pdf?version=1741225467 "Article PDF")

Open AccessSystematic Review

[Leveraging Advanced Technologies for (Smart) Transportation Planning: A Systematic Review](/2071-1050/17/5/2245)

by **Heejoo Son**, **Jinhyeok Jang**, **Jihan Park**, **Akos Balog**, **Patrick Ballantyne**, **Heeseo Rain Kwon**, **Alex Singleton** and **Jinuk Hwang**

_Sustainability_ **2025**, _17_(5), 2245; https://doi.org/10.3390/su17052245 (registering DOI) - 5 Mar 2025

[**Abstract**](#)

Transportation systems worldwide are facing numerous challenges, including congestion, environmental impacts, and safety concerns. This study used a systematic literature review to investigate how advanced technologies (e.g., IoT, AI, digital twins, and optimization methods) support smart transportation planning. Specifically, this study examines the [\[...\] Read more.](#)

Transportation systems worldwide are facing numerous challenges, including congestion, environmental impacts, and safety concerns. This study used a systematic literature review to investigate how advanced technologies (e.g., IoT, AI, digital twins, and optimization methods) support smart transportation planning. Specifically, this study examines the interrelationships between transportation challenges, proposed solutions, and enabling technologies, providing insights into how these innovations support smart mobility initiatives. A systematic literature review, following PRISMA guidelines, identified 26 peer-reviewed articles published between 2013 and 2024, including studies that examined smart transportation technologies. To quantitatively assess relationships among key concepts, a Sentence BERT-based natural language processing approach was employed to compute alignment scores between transportation challenges, technological solutions, and implementation strategies. The findings highlight the fact that real-time data collection, predictive analytics, and digital twin simulations significantly enhance traffic flow, safety, and operational efficiency while mitigating environmental impacts. The analysis further reveals strong correlations between traffic congestion and public transit optimization, reinforcing the effectiveness of integrated, data-driven strategies. Additionally, IoT-based sensor networks and AI-driven decision-support systems are shown to play a critical role in sustainable urban mobility by enabling proactive congestion management, multimodal transportation planning, and emission reduction strategies. From a policy perspective, this study underscores the need for investment in urban-scale data infrastructures, the integration of digital twin modeling into long-term planning frameworks, and the alignment of optimization tools with public transit improvements to foster equitable and efficient mobility. These findings offer actionable recommendations for policymakers, engineers, and planners, guiding data-driven resource allocation and legislative strategies that support sustainable, adaptive, and technologically advanced transportation ecosystems. [Full article](/2071-1050/17/5/2245)

(This article belongs to the Collection [Advances in Transportation Planning and Management](    /journal/sustainability/topical_collections/3U8V9U114R

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/sustainability/sustainability-17-02245/article_deploy/html/images/sustainability-17-02245-g001-550.jpg?1741225560 "

<strong>Figure 1</strong><br/>

<p>Overview of the study selection process.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2245'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02245/article_deploy/html/images/sustainability-17-02245-g002-550.jpg?1741225563 "

<strong>Figure 2</strong><br/>

<p>Integrative mapping of transportation issues, proposed solutions, and enabling technologies and approaches based on the results of BERT analysis.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2245'>Full article</a></strong>

")

_attachment_

Supplementary material:  

[Supplementary File 1 (ZIP, 116 KiB)](/2071-1050/17/5/2265/s1?version=1741174901)  

20 pages, 5511 KiB  [](/2071-1050/17/5/2265/pdf?version=1741225304 "Article PDF")

Open AccessArticle

[Assessment of Circular Economy Implementation in Municipal Waste Management Through Performance Indicators and Citizens’ Opinion in a City in Western Greece](/2071-1050/17/5/2265)

by **Christina Emmanouil**, **Dimitrios Roumeliotis**, **Alexandros Kostas** and **Dimitra G. Vagiona**

_Sustainability_ **2025**, _17_(5), 2265; [https://doi.org/10.3390/su17052265](https://doi.org/10.3390/su17052265) - 5 Mar 2025

[**Abstract**](#)

Municipal solid waste management (MSWM) is an advantageous subject for implementing circular economy (CE) strategies. In this context, the waste generation and waste collection steps of MSWM in the third largest Greek city (Patras), in western Greece, were evaluated according to the proposed [\[...\] Read more.](#)

Municipal solid waste management (MSWM) is an advantageous subject for implementing circular economy (CE) strategies. In this context, the waste generation and waste collection steps of MSWM in the third largest Greek city (Patras), in western Greece, were evaluated according to the proposed CE indicators. Public opinion and knowledge on CE in MSWM were also evaluated in a small sample of citizens from the Municipality of Patras (207 individuals) through a questionnaire survey. Results showed that (a) the CE performance indicators objectively assessed circularity in MSWM; (b) Patras fared better than Greece and EU in some indicators \[waste generation (kg per capita × year), food waste generation (kg per capita × year)\] and worse in others \[food waste composting (% _w_/_w_), WEEE recycling (kg per capita × year)\]; (c) citizens have not adopted CE practices in their waste management; and (d) there is a clear reluctance to change practices in older individuals. Based on these results, some recommendations for improvement were made. These results may aid in delineating existing conditions in MSWM in large eastern Mediterranean cities and contribute to the transition toward a reduction in waste disposal and an increase in material reuse. [Full article](/2071-1050/17/5/2265)

(This article belongs to the Section [Waste and Recycling](/journal/sustainability/sections/waste_recycling_sus))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/sustainability/sustainability-17-02265/article_deploy/html/images/sustainability-17-02265-g001-550.jpg?1741225386 "

<strong>Figure 1</strong><br/>

<p>The CE performance indicators selected to be calculated for the Municipality of Patras.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2265'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02265/article_deploy/html/images/sustainability-17-02265-g002-550.jpg?1741225387 "

<strong>Figure 2</strong><br/>

<p>Relative distribution of the respondents’ place of residence.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2265'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02265/article_deploy/html/images/sustainability-17-02265-g003a-550.jpg?1741225390 "

<strong>Figure 3</strong><br/>

<p>Number of answers in each category. (<b>A</b>) Question II1, (<b>B</b>) Question II2, (<b>C</b>) Question II3, (<b>D</b>) Question II4, (<b>E</b>) Question II5, (<b>F</b>) Question II6, (<b>G</b>) Question II7, (<b>H</b>) Question II8, (<b>I</b>) Question II9, and (<b>J</b>) Question II10.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2265'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02265/article_deploy/html/images/sustainability-17-02265-g003b-550.jpg?1741225395 "

<strong>Figure 3 Cont.</strong><br/>

<p>Number of answers in each category. (<b>A</b>) Question II1, (<b>B</b>) Question II2, (<b>C</b>) Question II3, (<b>D</b>) Question II4, (<b>E</b>) Question II5, (<b>F</b>) Question II6, (<b>G</b>) Question II7, (<b>H</b>) Question II8, (<b>I</b>) Question II9, and (<b>J</b>) Question II10.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2265'>Full article</a></strong>

")

17 pages, 7189 KiB  [](/1422-0067/26/5/2294/pdf?version=1741225227 "Article PDF")

Open AccessArticle

[Circ\_0011446 Regulates Intramuscular Adipocyte Differentiation in Goats via the miR-27a-5p/FAM49B Axis](/1422-0067/26/5/2294)

by **Jian-Mei Wang**, **Jin-Shi Lv**, **Ke-Han Liu**, **Yan-Yan Li**, **Jiang-Jiang Zhu**, **Yan Xiong**, **Yong Wang** and **Ya-Qiu Lin**

_Int. J. Mol. Sci._ **2025**, _26_(5), 2294; [https://doi.org/10.3390/ijms26052294](https://doi.org/10.3390/ijms26052294) - 5 Mar 2025

[**Abstract**](#)

Intramuscular fat (IMF), or marbling, is a critical indicator of goat meat quality. Non-coding RNAs play a key role in the formation and deposition of IMF in vertebrates by regulating genes involved in its synthesis, degradation, and transport. The competing endogenous RNA (ceRNA) [\[...\] Read more.](#)

Intramuscular fat (IMF), or marbling, is a critical indicator of goat meat quality. Non-coding RNAs play a key role in the formation and deposition of IMF in vertebrates by regulating genes involved in its synthesis, degradation, and transport. The competing endogenous RNA (ceRNA) hypothesis identifies circular RNAs (circRNAs) as natural “sponges” for microRNAs (miRNAs). However, the precise mechanisms of circRNAs in goat IMF remain poorly understood. In the current study, we utilized existing sequencing data to construct a ceRNA regulatory network associated with intramuscular adipogenesis and fat deposition in goats. Our goal was to elucidate the post-transcriptional regulatory mechanism of family with sequence similarity 49 member B (FAM49B). Functionally, FAM49B was found to inhibit the differentiation of intramuscular preadipocytes and to directly interact with miR-27a-5p. Mechanistically, dual-luciferase reporter assays and quantitative real-time PCR (qRT-PCR) confirmed the interaction between circ0011446 and miR-27a-5p. Circ0011446 enhanced the expression of FAM49B mRNA and protein through post-transcriptional regulation. As a ceRNA, circ0011446 competitively binds miR-27a-5p, preventing miR-27a-5p from degrading FAM49B. In conclusion, our findings demonstrate that circ0011446 suppresses goat adipogenic differentiation of intramuscular preadipocytes by regulating the expression of the downstream target gene FAM49B through miR-27a-5p sequestration. This study provides a reference for goat meat quality or livestock breeding. [Full article](/1422-0067/26/5/2294)

(This article belongs to the Section [Molecular Biology](/journal/ijms/sections/Molecular_Biology))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/ijms/ijms-26-02294/article_deploy/html/images/ijms-26-02294-g001-550.jpg?1741147121 "

<strong>Figure 1</strong><br/>

<p>Identification of circ_0011446. (<b>A</b>). Cyclization diagram of circ_0011446 and sequence diagram of the back-splicing site analysis. (<b>B</b>). circ_0011446 ring-forming identification. M is the marker; RNase R (+) and RNase R (−) represent whether there is RNase R digestion; <span class=\"html-fig-inline\" id=\"ijms-26-02294-i001\"><img alt=\"Ijms 26 02294 i001\" src=\"/ijms/ijms-26-02294/article_deploy/html/images/ijms-26-02294-i001.png\"/></span> and  <span class=\"html-fig-inline\" id=\"ijms-26-02294-i002\"><img alt=\"Ijms 26 02294 i002\" src=\"/ijms/ijms-26-02294/article_deploy/html/images/ijms-26-02294-i002.png\"/></span> represent the convergent prime and divergent prime, respectively. (<b>C</b>). Relative circ_0011446 expression during goat intramuscular adipocyte differentiation. Different lowercase letters indicate significant differences between groups according to one-way ANOVA (<span class=\"html-italic\">p</span> &lt; 0.01).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1422-0067/26/5/2294'>Full article</a></strong>

")[](https://pub.mdpi-res.com/ijms/ijms-26-02294/article_deploy/html/images/ijms-26-02294-g002-550.jpg?1741147125 "

<strong>Figure 2</strong><br/>

<p>circ_0011446 inhibits adipogenic differentiation in goat. (<b>A</b>). Overexpression efficiency of circ_0011446. (<b>B</b>,<b>F</b>). Images of mature intramuscular adipocytes stained with Oil Red O (<b>left panel</b>) and OD value determination (<b>right panel</b>) following circ_0011446 overexpression (<b>B</b>) and knockdown (<b>F</b>), respectively. Oil Red O staining signal was quantified by absorbance at 490 nm. (<b>C</b>,<b>G</b>). Staining of mature intramuscular adipocytes with Bodipy (<b>left panel</b>) and fluorescence area quantification (<b>right panel</b>) after overexpression (<b>C</b>) and knockdown (<b>G</b>) of circ_0011446, respectively. (<b>D</b>,<b>H</b>). mRNA expression levels of adipogenic marker genes following circ_0011446 overexpression (<b>D</b>) and knockdown (<b>H</b>), respectively. (<b>E</b>). Interference efficiency of circ_0011446. All data are presented as mean ± SEM, * <span class=\"html-italic\">p</span> &lt; 0.05, ** <span class=\"html-italic\">p</span> &lt; 0.01.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1422-0067/26/5/2294'>Full article</a></strong>

")[](https://pub.mdpi-res.com/ijms/ijms-26-02294/article_deploy/html/images/ijms-26-02294-g003-550.jpg?1741147125 "

<strong>Figure 3</strong><br/>

<p>circ_0011446 acts as a miR-27a-5p sponge. (<b>A</b>). Nucleocytoplasmic separation demonstrated the predominant cytoplasmic localization of circ_0011446. (<b>B</b>). Subcellular localization of circ_0011446 shown by FISH. (<b>C</b>). Potential miRNA binding sites of circ_0009659 predicted by RNAHybrid. (<b>D</b>,<b>E</b>). Expression changes of miR-27a-5p after overexpression (<b>D</b>) or knockdown (<b>E</b>) of circ_0011446. (<b>F</b>). Dual-luciferase reporter assay results showed that miR-27a-5p targets circ_0011446. All data are presented as mean ± SEM, ** <span class=\"html-italic\">p</span> &lt; 0.01.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1422-0067/26/5/2294'>Full article</a></strong>

")[](https://pub.mdpi-res.com/ijms/ijms-26-02294/article_deploy/html/images/ijms-26-02294-g004-550.jpg?1741147126 "

<strong>Figure 4</strong><br/>

<p>Effect of miR-27a-5p on differentiation of goat intramuscular preadipocytes. (<b>A</b>,<b>E</b>). Overexpression (<b>A</b>) or knockdown (<b>E</b>) efficiency of miR-27a-5p detected by qRT-PCR. (<b>B</b>,<b>F</b>). The effect of miR-27a-5p overexpression (<b>B</b>) or knockdown (<b>F</b>) on adipogenic differentiation was quantitatively assessed by Oil Red O staining, with the OD value of Oil Red O dye at 490 nm measured. (<b>C</b>,<b>G</b>). Bodipy staining analysis of miR-27a-5p overexpression (<b>C</b>) or knockdown (<b>G</b>) in intramuscular preadipocytes. (<b>D</b>,<b>H</b>). The effect of miR-27a-5p overexpression (<b>D</b>) or knockdown (<b>H</b>) on adipogenic marker gene expression was detected by qRT-PCR. * <span class=\"html-italic\">p</span> &lt; 0.05, ** <span class=\"html-italic\">p</span> &lt; 0.01, compared to that of NC.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1422-0067/26/5/2294'>Full article</a></strong>

")[](https://pub.mdpi-res.com/ijms/ijms-26-02294/article_deploy/html/images/ijms-26-02294-g005-550.jpg?1741147126 "

<strong>Figure 5</strong><br/>

<p>Prediction and validation of the targeting relationship between miR-27a-5p and FAM49B. (<b>A</b>). TargetScan, MIRDB, and Starbase predicted miR-27a-5p targeting FAM49B. (<b>B</b>,<b>C</b>). The effect of miR-27a-5p overexpression (<b>B</b>) and knockdown (<b>C</b>) on FAM49B expression was detected by qRT-PCR. (<b>D</b>). A dual-luciferase reporter assay verified the targeting relationship between miR-27a-5p and FAM49B (<b>Right panel</b>), with construction of wild-type and mutant vectors (<b>Left panel</b>). The data were shown as the mean ± SEM (<span class=\"html-italic\">n</span> = 3) (** <span class=\"html-italic\">p</span> &lt; 0.01).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1422-0067/26/5/2294'>Full article</a></strong>

")[](https://pub.mdpi-res.com/ijms/ijms-26-02294/article_deploy/html/images/ijms-26-02294-g006-550.jpg?1741147126 "

<strong>Figure 6</strong><br/>

<p>Role of FAM49B in goat intramuscular preadipocyte differentiation. (<b>A</b>,<b>E</b>). FAM49B overexpression (<b>A</b>) or knockdown (<b>E</b>) efficiency. (<b>B</b>,<b>F</b>). Oil Red O staining of mature adipocytes (<b>left</b>) and OD value at 490 nm (<b>right</b>) after FAM49B overexpression (<b>B</b>) or knockdown (<b>F</b>). (<b>C</b>,<b>G</b>). Bodipy staining of mature adipocytes (left) and fluorescence area quantification (right) after FAM49B overexpression (<b>C</b>) or knockdown (<b>G</b>). (<b>D</b>,<b>H</b>). mRNA levels of adipogenic markers after FAM49B overexpression (<b>D</b>) or knockdown (<b>H</b>). Data are presented as mean ± SEM, * <span class=\"html-italic\">p</span> &lt; 0.05, ** <span class=\"html-italic\">p</span> &lt; 0.01.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1422-0067/26/5/2294'>Full article</a></strong>

")[](https://pub.mdpi-res.com/ijms/ijms-26-02294/article_deploy/html/images/ijms-26-02294-g007-550.jpg?1741147127 "

<strong>Figure 7</strong><br/>

<p>Model of circ_0011446 functioning as a ceRNA to regulate FAM49B expression by sponging miR-27a-5p, thus inhibiting the differentiation of goat intramuscular adipocyte.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1422-0067/26/5/2294'>Full article</a></strong>

")

19 pages, 3537 KiB  [](/2071-1050/17/5/2264/pdf?version=1741225046 "Article PDF")

Open AccessArticle

[To the Issue of Assessment of the Technical Condition of Underground Structures of Buildings](/2071-1050/17/5/2264)

by **Oleksandr Semko**, **Yuriy Vynnykov**, **Olena Filonenko**, **Oleg Yurin**, **Tetiana Ilchenko**, **Olena Hranko**, **Volodymyr Semko**, **Adriana Salles**, **Ricardo Mateus**, **Luís Bragança**, **Roman Rabenseifer** and **Nataliia Mahas**

_Sustainability_ **2025**, _17_(5), 2264; [https://doi.org/10.3390/su17052264](https://doi.org/10.3390/su17052264) - 5 Mar 2025

[**Abstract**](#)

A survey and assessment of the technical condition of basement and semi-basement structures in public buildings aged 60 to 130 years were conducted to evaluate their suitability for use as basic shelters. Based on the survey results, the most adverse impacts were identified, [\[...\] Read more.](#)

A survey and assessment of the technical condition of basement and semi-basement structures in public buildings aged 60 to 130 years were conducted to evaluate their suitability for use as basic shelters. Based on the survey results, the most adverse impacts were identified, including changes in groundwater levels, improper building operation, and the characteristic damages to underground structural elements. Structural solutions were proposed to eliminate the consequences of these damages. The reviewed cases indicate that the vertical and horizontal waterproofing systems used during construction cannot perform their function throughout the building’s entire life cycle. When designing new buildings, waterproof materials should be used for the enclosing structures of underground premises. While this may have a higher initial cost than membrane or coating waterproofing, considering life-cycle costs, it can provide a positive economic effect and improve the quality and comfort of the indoor environment. [Full article](/2071-1050/17/5/2264)

(This article belongs to the Special Issue [Advances in Sustainable Geotechnical Engineering and Civil Engineering](        

    /journal/sustainability/special_issues/X18VL1CJFA

))  

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Figure 1

Figure 1

[](https://pub.mdpi-res.com/sustainability/sustainability-17-02264/article_deploy/html/images/sustainability-17-02264-g001-550.jpg?1741225158 "

<strong>Figure 1</strong><br/>

<p>Primary defects of the inspected basement shelter that has been out of operation: (<b>a</b>) damage to the finishing layer and erosion of the shelter’s wall and floor structures due to prolonged water exposure and saturation; (<b>b</b>) corrosion damage to the reinforcement of precast reinforced concrete roof panels, formation of corrosion-induced crack, deterioration of protective plaster, and efflorescence.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2264'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02264/article_deploy/html/images/sustainability-17-02264-g002-550.jpg?1741225162 "

<strong>Figure 2</strong><br/>

<p>Relevant defects of the inspected historical gymnasium building in the Poltava region: (<b>a</b>) the pavement around the building is in an unfit condition, and the site grading promotes the accumulation of atmospheric water, leading to its localized infiltration into the foundation base; (<b>b</b>) deterioration of the interior wall surfaces in the basement due to capillary moisture rise, resulting in plaster damage and efflorescence formation.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2264'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02264/article_deploy/html/images/sustainability-17-02264-g003-550.jpg?1741225168 "

<strong>Figure 3</strong><br/>

<p>Primary defects of the basement in the administrative building in Poltava: (<b>a</b>) deterioration of the interior wall surfaces in the basement, with efflorescence appearing on the walls; (<b>b</b>) corrosion damage to the basement ceiling structures, along with efflorescence on the wall surfaces; (<b>c</b>) destruction of the brick masonry of the basement walls; and (<b>d</b>) disruption of the air–moisture balance within the premises.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2264'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02264/article_deploy/html/images/sustainability-17-02264-g004-550.jpg?1741225170 "

<strong>Figure 4</strong><br/>

<p>Flooding of the school basement rooms: destruction of the lime matrix in the joints and moisture saturation of the brick structures in the basement.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2264'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02264/article_deploy/html/images/sustainability-17-02264-g005-550.jpg?1741225174 "

<strong>Figure 5</strong><br/>

<p>General view of the sub-basement slab: corrosion damage to the reinforcement bars of the floor slabs and destruction of the concrete protective layer.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2264'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02264/article_deploy/html/images/sustainability-17-02264-g006-550.jpg?1741225176 "

<strong>Figure 6</strong><br/>

<p>Specific defects of the academic building: (<b>a</b>) damaged pavement around the building, allowing water to flow through the wall and flood the basement; (<b>b</b>) deterioration of the basement wall surfaces due to plaster damage and efflorescence formation.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2264'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02264/article_deploy/html/images/sustainability-17-02264-g007-550.jpg?1741225179 "

<strong>Figure 7</strong><br/>

<p>Analysis of damage recurrence in the inspected basement premises: (<b>a</b>) distribution of corrosion wear of reinforcement bars in the structures of the non-operational shelter; (<b>b</b>) distribution of efflorescence area on the wall surfaces of the non-operational shelter; (<b>c</b>) distribution of frost-induced brick deterioration depth relative to the defect area on the walls of the gymnasium building in the Poltava region; (<b>d</b>) distribution of efflorescence area on the wall surfaces of the basement premises in the gymnasium building in the Poltava region; (<b>e</b>) distribution of corrosion wear of the reinforcement bars in the basement structures of the administrative building; and (<b>f</b>) distribution of efflorescence area on the wall surfaces of the basement premises of the administrative building.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2264'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02264/article_deploy/html/images/sustainability-17-02264-g008-550.jpg?1741225181 "

<strong>Figure 8</strong><br/>

<p>Analysis of damage recurrence in the inspected basement premises: (<b>a</b>) distribution of corrosion wear of reinforcement bars in the floor slabs of the two-story school building; (<b>b</b>) distribution of the depth of mortar joint leaching in the brick walls of the basement part of the two-story school building; (<b>c</b>) distribution of the area of efflorescence on the wall surfaces of the basement premises of the university building; and (<b>d</b>) distribution of damage to the floor slabs of the basement premises of the university building.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2264'>Full article</a></strong>

")

19 pages, 2855 KiB  [](/2071-1050/17/5/2275/pdf?version=1741224628 "Article PDF")

Open AccessArticle

[Cooling Efficiency of Urban Green Spaces Across Functional Zones: Mitigating Heat Island Effects Through Spatial Configuration](/2071-1050/17/5/2275)

by **Ying Wang** and **Yin Ren**

_Sustainability_ **2025**, _17_(5), 2275; [https://doi.org/10.3390/su17052275](https://doi.org/10.3390/su17052275) - 5 Mar 2025

[**Abstract**](#)

With the acceleration of urbanization, the urban heat island effect has garnered increasing attention. However, few studies have explored the differential impacts of urban green spaces on the UHI across various urban functional zones (UFZs). This study takes Xiamen Island as the research [\[...\] Read more.](#)

With the acceleration of urbanization, the urban heat island effect has garnered increasing attention. However, few studies have explored the differential impacts of urban green spaces on the UHI across various urban functional zones (UFZs). This study takes Xiamen Island as the research object and selects nine representative landscape pattern indices to characterize the spatial patterns of UGS in each urban functional zone. Through Pearson correlation analysis, four landscape indices—largest patch index (LPI), mean patch area (AREA\_MN), area-weighted average shape index (SHAPE\_AM), and aggregation index (AI)—were chosen to reveal the varying influences of UGS spatial patterns on the UHI in different urban functional zones. These four landscape indices reflect aspects such as area, shape complexity, density size, and variation, as well as the aggregation of UGS. To address the spatial autocorrelation of variables, a spatial regression model was established. Given that the parameters of the spatial lag model outperformed those of the spatial error model, the spatial lag model was selected. Key findings reveal that the cooling efficiency of UGS varies across UFZs. In urban residential zones (URZs), UGS with complex shapes significantly enhances cooling, as indicated by a negative correlation between SHAPE\_AM and LST (β = −0.446, _p_ < 0.05). In urban village zones (UVZs), larger green patches have a stronger cooling effect, with AREA\_MN showing a significant negative correlation with LST (β = −1.772, _p_ < 0.05). The results indicate that UGS in different urban functional zones plays distinct roles in mitigating the UHI, with its cooling effects being associated with the spatial patterns of UGS. Therefore, it is recommended to adopt differentiated planning strategies for UGS in various urban functional zones to contribute to a more sustainable and thermally comfortable urban environment. [Full article](/2071-1050/17/5/2275)

(This article belongs to the Special Issue [Urbanization and Environmental Sustainability—2nd Edition](        

    /journal/sustainability/special_issues/MQV4K8PEY8

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/sustainability/sustainability-17-02275/article_deploy/html/images/sustainability-17-02275-g001-550.jpg?1741224724 "

<strong>Figure 1</strong><br/>

<p>Map of the study area.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2275'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02275/article_deploy/html/images/sustainability-17-02275-g002-550.jpg?1741224726 "

<strong>Figure 2</strong><br/>

<p>Urban functional zones and urban green space in the study area.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2275'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02275/article_deploy/html/images/sustainability-17-02275-g003-550.jpg?1741224727 "

<strong>Figure 3</strong><br/>

<p>Spatial distribution of LST in the study area.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2275'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02275/article_deploy/html/images/sustainability-17-02275-g004-550.jpg?1741224728 "

<strong>Figure 4</strong><br/>

<p>The proportion of LST categories in each urban functional zone.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2275'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02275/article_deploy/html/images/sustainability-17-02275-g005-550.jpg?1741224729 "

<strong>Figure 5</strong><br/>

<p>Aggregation map of LST in urban functional zones.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2275'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02275/article_deploy/html/images/sustainability-17-02275-g006-550.jpg?1741224730 "

<strong>Figure 6</strong><br/>

<p>Pearson correlation coefficients for LST and landscape metrics.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2275'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02275/article_deploy/html/images/sustainability-17-02275-g007-550.jpg?1741224731 "

<strong>Figure 7</strong><br/>

<p>Residual plot for spatial lag model analysis of urban heat island effect.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2275'>Full article</a></strong>

")

_attachment_

Supplementary material:  

[Supplementary File 1 (ZIP, 22071 KiB)](/2223-7747/14/5/811/s1?version=1741179621)  

14 pages, 49507 KiB  [](/2223-7747/14/5/811/pdf?version=1741224662 "Article PDF")

Open AccessArticle

[Elevated Atmospheric CO2 Concentrations Reduce Tomato Mosaic Virus Severity in Tomato Plants](/2223-7747/14/5/811)

by **Giovanni Marino**, **Andrea Carli**, **Antonio Raschi**, **Mauro Centritto**, **Emanuela Noris**, **Chiara D’Errico** and **Slavica Matić**

_Plants_ **2025**, _14_(5), 811; [https://doi.org/10.3390/plants14050811](https://doi.org/10.3390/plants14050811) - 5 Mar 2025

[**Abstract**](#)

Tomato mosaic disease, caused by tomato mosaic virus (ToMV), was studied under naturally elevated \[CO2\] concentrations to simulate the potential impacts of future climate scenarios on the ToMV–tomato pathosystem. Tomato plants infected with ToMV were cultivated under two distinct \[CO2 [\[...\] Read more.](#)

Tomato mosaic disease, caused by tomato mosaic virus (ToMV), was studied under naturally elevated \[CO2\] concentrations to simulate the potential impacts of future climate scenarios on the ToMV–tomato pathosystem. Tomato plants infected with ToMV were cultivated under two distinct \[CO2\] environments: elevated \[CO2\] (naturally enriched to approximately 1000 μmol mol−1) and ambient \[CO2\] (ambient atmospheric \[CO2\] of 420 μmol mol−1). Key parameters, including phytopathological (disease index, ToMV gene expression), growth-related (plant height, leaf area), and physiological traits (chlorophyll content, flavonoid levels, nitrogen balance index), were monitored to assess the effects of elevated \[CO2\]. Elevated \[CO2\] significantly reduced the disease index from 2.4 under ambient \[CO2\] to 1.7 under elevated \[CO2\]. Additionally, viral RNA expression was notably lower in plants grown at elevated \[CO2\] compared to those under ambient \[CO2\]. While ToMV infection led to reductions in the chlorophyll content and nitrogen balance index and an increase in the flavonoid levels under ambient \[CO2\], these physiological effects were largely mitigated under elevated \[CO2\]. Infected plants grown at elevated \[CO2\] showed values for these parameters that approached those of healthy plants grown under ambient \[CO2\]. These findings demonstrate that elevated \[CO2\] helps to mitigate the effects of tomato mosaic disease and contribute to understanding how future climate scenarios may influence the tomato–ToMV interaction and other plant–pathogen interactions. [Full article](/2223-7747/14/5/811)

(This article belongs to the Special Issue [Physiological, Biochemical and Ecological Effects of Diseases on Plants](        

    /journal/plants/special_issues/W83Q2U8TF1

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/plants/plants-14-00811/article_deploy/html/images/plants-14-00811-g001-550.jpg?1741224823 "

<strong>Figure 1</strong><br/>

<p>Effects of tomato mosaic virus (ToMV) infection on tomato plants at ambient and elevated [CO<sub>2</sub>]. (<b>A</b>) Representative images of the fourth leaf from the apexes of ToMV-inoculated tomato plants under ambient and elevated [CO<sub>2</sub>] conditions, photographed 40 days post-inoculation (dpi); scale bar, 1 cm. (<b>B</b>) Frequency distributions of disease index (DI) from the fourth leaf, obtained by combining values from six biological replicates, using the scoring method described in Ref. [<a href=\"#B13-plants-14-00811\" class=\"html-bibr\">13</a>]. (<b>C</b>) Mean DI values under ambient and elevated [CO<sub>2</sub>] conditions. Error bars represent the standard error of the mean (SE). Different lowercase letters indicate statistically significant differences between groups, determined using the Kruskal–Wallis test at the 1% probability level.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2223-7747/14/5/811'>Full article</a></strong>

")[](https://pub.mdpi-res.com/plants/plants-14-00811/article_deploy/html/images/plants-14-00811-g002-550.jpg?1741224826 "

<strong>Figure 2</strong><br/>

<p>Effects of tomato mosaic virus (ToMV) infection and [CO<sub>2</sub>] levels on leaf size and plant height. (<b>A</b>) Representative photographs of leaves from healthy and ToMV-inoculated tomato plants grown under ambient and elevated [CO<sub>2</sub>] conditions, taken 40 days post-inoculation (dpi). Scale bar: 1 cm. (<b>B</b>) Mean leaflet area for healthy and ToMV-inoculated plants under ambient and elevated [CO<sub>2</sub>] conditions. Error bars represent the standard error of the mean (SE). Statistical significance of [CO<sub>2</sub>] level (CO<sub>2</sub>), infection (I), and their interaction (CO<sub>2</sub> × I) was analyzed by two-way ANOVA. Groups with the same lowercase letters are not significantly different based on Tukey’s test at the 5% probability level. (<b>C</b>) Representative images of ToMV-inoculated tomato plants grown under ambient and elevated [CO<sub>2</sub>] conditions, photographed at 40 dpi. Scale bar: 10 cm. (<b>D</b>) Mean plant height for healthy and ToMV-inoculated plants under different [CO<sub>2</sub>] levels. Error bars represent SE. Statistical significance of [CO<sub>2</sub>] level (CO<sub>2</sub>), infection (I), and their interaction (CO<sub>2</sub> × I) was determined by two-way ANOVA. Groups with the same lowercase letters are not significantly different according to Tukey’s test at the 5% probability level.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2223-7747/14/5/811'>Full article</a></strong>

")[](https://pub.mdpi-res.com/plants/plants-14-00811/article_deploy/html/images/plants-14-00811-g003-550.jpg?1741224827 "

<strong>Figure 3</strong><br/>

<p>Measurements of (<b>A</b>) chlorophyll (Chl), (<b>B</b>) flavonoids (Flav) and (<b>C</b>) nitrogen balance index (NBI). Data are means of 6 plants per treatment. Error bars represent the standard error of the mean (SE). Statistical significance of [CO<sub>2</sub>] level (CO<sub>2</sub>), infection (I), and their interaction (CO<sub>2</sub> × I) was analyzed by two-way ANOVA. Groups with the same lowercase letters are not significantly different according to Tukey’s test at the 5% probability level.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2223-7747/14/5/811'>Full article</a></strong>

")[](https://pub.mdpi-res.com/plants/plants-14-00811/article_deploy/html/images/plants-14-00811-g004-550.jpg?1741224828 "

<strong>Figure 4</strong><br/>

<p>Measurements of leaf gas exchange (<b>A</b>) photosynthesis (<span class=\"html-italic\">A</span>), (<b>B</b>) stomatal conductance (<math display=\"inline\"><semantics>

<msub>

<mi>g</mi>

<mi>s</mi>

</msub>

</semantics></math>), (<b>C</b>) fluorescence (ΦPSII), and (<b>D</b>) electron transport (ETR). Data are means of 4 plants per treatment. Error bars represent the standard error of the mean (SE). Groups with the same lowercase letters are not significantly different according to the Kruskal–Wallis test at the 5% probability level. Different uppercase letters indicate statistically significant differences in the treatments (ambient or elevated [CO<sub>2</sub>] levels) according to the Mann–Whitney test at the 5% probability level.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2223-7747/14/5/811'>Full article</a></strong>

")[](https://pub.mdpi-res.com/plants/plants-14-00811/article_deploy/html/images/plants-14-00811-g005-550.jpg?1741224830 "

<strong>Figure 5</strong><br/>

<p>(<b>A</b>) Tomato mosaic virus expression levels of <math display=\"inline\"><semantics>

<mrow>

<mi>C</mi>

<mi>P</mi>

</mrow>

</semantics></math> and <math display=\"inline\"><semantics>

<mrow>

<mi>M</mi>

<mi>P</mi>

</mrow>

</semantics></math> genes at 40 days post-inoculation (dpi) under ambient and elevated [CO<sub>2</sub>] levels. Mean values of expression levels vs. different [CO<sub>2</sub>] levels; error bars represent the standard error of the mean (SE); lowercase letters indicate the statistical significance between groups, calculated using the Mann–Whitney test at a 5% probability level. (<b>B</b>) Expression levels of pathogenesis-related genes (<math display=\"inline\"><semantics>

<mrow>

<mi>P</mi>

<mi>R</mi>

<mn>1</mn>

</mrow>

</semantics></math>, <math display=\"inline\"><semantics>

<mrow>

<mi>P</mi>

<mi>R</mi>

<mn>2</mn>

</mrow>

</semantics></math>, <math display=\"inline\"><semantics>

<mrow>

<mi>P</mi>

<mi>R</mi>

<mn>5</mn>

</mrow>

</semantics></math>) involved in biotic stress signaling pathways in leaves from tomato plants infected with tomato mosaic virus at 40 dpi under normal and elevated [CO<sub>2</sub>] levels; lowercase letters indicate statistical significance between groups, calculated using Student’s t test at a 1% probability level.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2223-7747/14/5/811'>Full article</a></strong>

")

27 pages, 8642 KiB  [](/2072-4292/17/5/924/pdf?version=1741224121 "Article PDF")

Open AccessArticle

[A Safe and Efficient Global Path-Planning Method Considering Multiple Environmental Factors of the Moon Using a Distributed Computation Strategy](/2072-4292/17/5/924)

by **Ruyan Zhou**, **Yuchuan Liu**, **Zhonghua Hong**, **Haiyan Pan**, **Yun Zhang**, **Yanling Han** and **Jiang Tao**

_Remote Sens._ **2025**, _17_(5), 924; [https://doi.org/10.3390/rs17050924](https://doi.org/10.3390/rs17050924) - 5 Mar 2025

[**Abstract**](#)

Lunar-rover path planning is a key topic in lunar exploration research, with safety and computational efficiency critical for achieving long-distance planning. This paper proposes a distributed path-planning method that considers multiple lunar environmental factors, addressing the issues of inadequate safety considerations and low [\[...\] Read more.](#)

Lunar-rover path planning is a key topic in lunar exploration research, with safety and computational efficiency critical for achieving long-distance planning. This paper proposes a distributed path-planning method that considers multiple lunar environmental factors, addressing the issues of inadequate safety considerations and low computational efficiency in current research. First, a set of safety evaluation rules is constructed by considering factors such as terrain slope, roughness, illumination, and rock abundance. Second, a distributed path-planning strategy based on a safety-map tile pyramid (DPPS-STP) is proposed, using a weighted A\* algorithm with hash table-based open and closed lists (OC-WHT-A\*) on a Spark cluster for efficient and safer path planning. Additionally, high-resolution digital orthophoto maps (DOM) are utilized for small crater detection, enabling more refined path planning built upon the overall mission-planning result. The method was validated in four lunar regions with distinct characteristics. The results show that DPPS-STP, which considers multiple environmental factors, effectively reduces the number of hazardous nodes and avoids crater obstacles. For long-distance tasks, it achieves an average speedup of up to 11.5 times compared to the single-machine OC-WHT-A\*, significantly improving computational efficiency. [Full article](/2072-4292/17/5/924)

(This article belongs to the Section [Satellite Missions for Earth and Planetary Exploration](/journal/remotesensing/sections/satellite_missions))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g001-550.jpg?1741224253 "

<strong>Figure 1</strong><br/>

<p>The overall framework of the proposed method.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g002-550.jpg?1741224254 "

<strong>Figure 2</strong><br/>

<p>The cutting and storage process of the safety-map tile pyramid.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g003-550.jpg?1741224255 "

<strong>Figure 3</strong><br/>

<p>The iterative process of DPPS-STP.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g004-550.jpg?1741224256 "

<strong>Figure 4</strong><br/>

<p>The data structure design of the A* algorithm with hash table-based open and closed lists, and the weighted A* algorithm with hash table-based open and closed lists.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g005-550.jpg?1741224260 "

<strong>Figure 5</strong><br/>

<p>Research regions and corresponding locations.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g006-550.jpg?1741224262 "

<strong>Figure 6</strong><br/>

<p>Path comparison of different A* algorithms in single-machine environment: (<b>a</b>) short-distance path-planning results. (<b>b</b>) Medium-distance path-planning results. (<b>c</b>) Long-distance path-planning results.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g007-550.jpg?1741224264 "

<strong>Figure 7</strong><br/>

<p>Time cost comparison of path planning for single-machine and distributed A* algorithms across three regions.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g008-550.jpg?1741224266 "

<strong>Figure 8</strong><br/>

<p>Comparison of single-machine and distributed path planning in three regions. (<b>a</b>) Path result in the Oceanus Procellarum region. (<b>b</b>) Path result in the CE-4 landing region. (<b>c</b>) Path result in the south-pole region. (<b>d</b>) Local size enlargement of (<b>a</b>). (<b>e</b>) Local size enlargement of (<b>b</b>). (<b>f</b>) Local size enlargement of (<b>c</b>).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g009-550.jpg?1741224268 "

<strong>Figure 9</strong><br/>

<p>Comparison of single-machine and distributed path-planning algorithms in Endurance landing region. (<b>a</b>) Local size enlargement of (<b>c</b>). (<b>b</b>) Local size enlargement of (<b>c</b>). (<b>c</b>) Path result in the Endurance mission landing region.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g010-550.jpg?1741224270 "

<strong>Figure 10</strong><br/>

<p>(<b>a</b>) Long-distance path optimization comparison in the south-pole region based on the Bresenham algorithm. (<b>b</b>) Local size enlargement of (<b>a</b>).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g011-550.jpg?1741224272 "

<strong>Figure 11</strong><br/>

<p>Comparison of path-planning results with and without crater obstacles.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g012-550.jpg?1741224274 "

<strong>Figure 12</strong><br/>

<p>Comparison of path-planning results in the south-pole region with and without average-illumination-rate constraints.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g013-550.jpg?1741224276 "

<strong>Figure 13</strong><br/>

<p>Comparison of path-planning results in the CE-4 landing region with and without roughness factor constraints.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")[](https://pub.mdpi-res.com/remotesensing/remotesensing-17-00924/article_deploy/html/images/remotesensing-17-00924-g014-550.jpg?1741224278 "

<strong>Figure 14</strong><br/>

<p>Comparison of path-planning results in the Oceanus Procellarum region with and without rock abundance factor constraint.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2072-4292/17/5/924'>Full article</a></strong>

")

19 pages, 691 KiB  [](/2673-7590/5/1/32/pdf?version=1741223540 "Article PDF")

Open AccessReview

[Novice and Young Drivers and Advanced Driver Assistant Systems: A Review](/2673-7590/5/1/32)

by **Fariborz Mansourifar**, **Navid Nadimi** and **Fahimeh Golbabaei**

_Future Transp._ **2025**, _5_(1), 32; [https://doi.org/10.3390/futuretransp5010032](https://doi.org/10.3390/futuretransp5010032) - 5 Mar 2025

[**Abstract**](#)

The risk of serious crashes is notably higher among young and novice drivers. This increased risk is due to several factors, including a lack of recognition of dangerous situations, an overestimation of driving skills, and vulnerability to peer pressure. Recently, advanced driver assistance [\[...\] Read more.](#)

The risk of serious crashes is notably higher among young and novice drivers. This increased risk is due to several factors, including a lack of recognition of dangerous situations, an overestimation of driving skills, and vulnerability to peer pressure. Recently, advanced driver assistance systems (ADAS) have been integrated into vehicles to help mitigate crashes linked to these factors. While numerous studies have examined ADAS broadly, few have specifically investigated its effects on young and novice drivers. This study aimed to address that gap by exploring ADAS’s impact on these drivers. Most studies in this review conclude that ADAS is beneficial for young and novice drivers, though some research suggests its impact may be limited or even negligible. Tailoring ADAS to address the unique needs of young drivers could enhance both the system’s acceptance and reliability. The review also found that unimodal warnings (e.g., auditory or visual) are as effective as multimodal warnings. Of the different types of warnings, auditory and visual signals proved the most effective. Additionally, ADAS can influence young drivers’ car-following behavior; for instance, drivers may maintain greater safety buffers or drive closely to avoid alarm triggers, likely due to perceived system unreliability. Aggressive drivers tend to benefit most from active ADAS, which actively intervenes to assist the driver. Future research could explore the combined effects of multiple ADAS functions within a single vehicle on young and novice drivers to better understand how these systems interact and impact driver behavior. [Full article](/2673-7590/5/1/32)

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Figure 1

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<strong>Figure 1</strong><br/>

<p>Protocol used for database search.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-7590/5/1/32'>Full article</a></strong>

")[](https://pub.mdpi-res.com/futuretransp/futuretransp-05-00032/article_deploy/html/images/futuretransp-05-00032-g002-550.jpg?1741223617 "

<strong>Figure 2</strong><br/>

<p>The trend of relevant publications.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-7590/5/1/32'>Full article</a></strong>

")

21 pages, 2798 KiB  [](/2071-1050/17/5/2273/pdf?version=1741223513 "Article PDF")

Open AccessArticle

[A Cloud Model-Based Optimal Combined Weighting Framework for the Comprehensive Reliability Evaluation of Power Systems with High Penetration of Renewable Energies](/2071-1050/17/5/2273)

by **Bin Zhang**, **Longxun Xu**, **Hongchun Shu**, **Shanxue Gao**, **Mengdie Li**, **Zun Ma**, **Junkai Liang** and **Kewei Xu**

_Sustainability_ **2025**, _17_(5), 2273; [https://doi.org/10.3390/su17052273](https://doi.org/10.3390/su17052273) - 5 Mar 2025

[**Abstract**](#)

Reliability has long been a critical attribute of power systems that cannot be ignored. Numerous blackout events have highlighted the increasing risk of outages in power systems due to the prominence of high-proportion power electronics and renewable energy utilization. Traditional reliability assessment methods, [\[...\] Read more.](#)

Reliability has long been a critical attribute of power systems that cannot be ignored. Numerous blackout events have highlighted the increasing risk of outages in power systems due to the prominence of high-proportion power electronics and renewable energy utilization. Traditional reliability assessment methods, which typically take dozens of hours to assess the adequacy of steady-state conditions, cannot reflect the real-time reliability performance of the system. Moreover, the weakness identification methods can only quantify the impact of component outages while ignoring other important operational factors. To address these issues, this paper constructs a three-hierarchy reliability evaluation index system (REIS) for power systems, consisting of the comprehensive reliability evaluation index (CREI) as the top hierarchy, four primary indices in the middle, and lots of subjective and objective indices on the bottom. To quantify the performance of different calculation methods for these indices, a combined weighting framework is proposed. Finally, the REIS level is evaluated according to the Wasserstein distances between the CREI cloud model and standard cloud models. In the case study, the proposed method is verified through its application to the power grids of two cities in a province in southern China, demonstrating its practicality and effectiveness. [Full article](/2071-1050/17/5/2273)

(This article belongs to the Special Issue [Renewable Energy and Power System Transformation: Striving Towards Carbon Neutrality](        

    /journal/sustainability/special_issues/AY4OJ5TMN3

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/sustainability/sustainability-17-02273/article_deploy/html/images/sustainability-17-02273-g001-550.jpg?1741223615 "

<strong>Figure 1</strong><br/>

<p>Combined weighting framework based on minimum hyperentropy of cloud models.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2273'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02273/article_deploy/html/images/sustainability-17-02273-g002-550.jpg?1741223618 "

<strong>Figure 2</strong><br/>

<p>The flowchart of the comprehensive reliability evaluation method based on the cloud model.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2273'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02273/article_deploy/html/images/sustainability-17-02273-g003-550.jpg?1741223619 "

<strong>Figure 3</strong><br/>

<p>Evaluation results of cloud models for primary indices: (<b>a</b>) City A; (<b>b</b>) City B.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2273'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02273/article_deploy/html/images/sustainability-17-02273-g004-550.jpg?1741223620 "

<strong>Figure 4</strong><br/>

<p>Evaluation results of the CREI cloud model.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2273'>Full article</a></strong>

")

11 pages, 452 KiB  [](/2227-9067/12/3/329/pdf?version=1741223328 "Article PDF")

Open AccessArticle

[Optimising Psychological Well-Being in Chinese-Australian Adolescents: A 24-Hour Movement Guidelines Approach](/2227-9067/12/3/329)

by **Wei-Cheng Chao**, **Asaduzzaman Khan**, **Jui-Chi Shih**, **Wen Li**, **Ching-Lin Wu**, **Kuan-Chung Chen** and **Bill Cheng**

_Children_ **2025**, _12_(3), 329; https://doi.org/10.3390/children12030329 (registering DOI) - 5 Mar 2025

[**Abstract**](#)

Background: Chinese-Australian adolescents face unique academic and cultural challenges that may impact their lifestyle and psychological well-being. Physical activity, screen time, and sleep are known to influence well-being. However, research on the adherence to the 24-Hour Movement Guidelines among Chinese-Australian adolescents remains limited [\[...\] Read more.](#)

Background: Chinese-Australian adolescents face unique academic and cultural challenges that may impact their lifestyle and psychological well-being. Physical activity, screen time, and sleep are known to influence well-being. However, research on the adherence to the 24-Hour Movement Guidelines among Chinese-Australian adolescents remains limited and awaits further investigation. Objective: This study hypothesized a significant positive association between adherence to the 24-Hour Movement Guidelines for physical activity, screen time, and sleep, and the psychological well-being of Chinese-Australian adolescents. Methods: A self-reported questionnaire was distributed to two language schools in Brisbane, Australia, targeting high school students from grades 7 to 12 with Chinese-Australian backgrounds. This study used multiple linear regression modelling to examine the associations between meeting or not meeting recommendations. Meeting the 24-Hour Movement Guidelines was defined as ≥60 min/day of moderate to vigorous physical activity (MVPA), ≤2 h/day of recreational screen time, and 9–11 h/night of sleep. Results: Out of 251 participants (average age: 13.31 years; 58% female), only 20.3% met two or three recommendations, while 43.3% met one, and 36.2% met none. The most common compliance was meeting only the screen time guideline alone (48%), while 9.6% met either MVPA + screen time or screen time + sleep. The regression analysis showed that meeting at least MVPA (β = 1.41, 95% CI: 0.07 to 2.74) or at least sleep (β = 1.40, 95% CI: 0.19 to 2.60) was associated with better psychological well-being. Notably, meeting MVPA and sleep guidelines was significantly associated with higher well-being (β = 3.83, 95% CI: 1.06–6.60). From the results, adherence to additional 24-Hour Movement Guidelines was associated with improved psychosocial well-being. However, a small proportion of adolescents met all the guidelines. Conclusions: Greater adherence to physical activity and sleep guidelines is linked to better psychological well-being among Chinese-Australian adolescents. These results highlight the importance of promoting healthy behaviours and implementing public health strategies to enhance education on exercise and sleep, particularly at the school and family levels, to support adolescents’ psychological well-being. [Full article](/2227-9067/12/3/329)

(This article belongs to the Section [Pediatric Mental Health](/journal/children/sections/Child_Adolescent_Psychiatry))  

[►▼ Show Figures](#)

Figure 1

[](https://pub.mdpi-res.com/children/children-12-00329/article_deploy/html/images/children-12-00329-g001-550.jpg?1741223395 "

<strong>Figure 1</strong><br/>

<p>Venn diagram showing the proportions (%) of adolescents meeting the Australian 24-Hour Movement Guideline recommendations and no recommendation (<span class=\"html-italic\">n</span> = 251).</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9067/12/3/329'>Full article</a></strong>

")

46 pages, 3165 KiB  [](/2227-9059/13/3/634/pdf?version=1741223050 "Article PDF")

Open AccessReview

[Unveiling the Miracle Tree: Therapeutic Potential of _Moringa oleifera_ in Chronic Disease Management and Beyond](/2227-9059/13/3/634)

by **Edgar Yebran Villegas-Vazquez**, **Rocio Gómez-Cansino**, **Gabriel Marcelino-Pérez**, **Domingo Jiménez-López** and **Laura Itzel Quintas-Granados**

_Biomedicines_ **2025**, _13_(3), 634; https://doi.org/10.3390/biomedicines13030634 (registering DOI) - 5 Mar 2025

[**Abstract**](#)

_Moringa oleifera_ (MO) has gained recognition as a potent natural intervention for preventing and managing chronic diseases (CDs) due to its diverse phytochemical composition and pharmacological properties. Rich in antioxidants, polyphenols, flavonoids, and glucosinolates, MO exerts anti-inflammatory, anti-hyperglycemic, cardioprotective, and anti-obesity effects. These [\[...\] Read more.](#)

_Moringa oleifera_ (MO) has gained recognition as a potent natural intervention for preventing and managing chronic diseases (CDs) due to its diverse phytochemical composition and pharmacological properties. Rich in antioxidants, polyphenols, flavonoids, and glucosinolates, MO exerts anti-inflammatory, anti-hyperglycemic, cardioprotective, and anti-obesity effects. These properties make it a valuable therapeutic agent for CDs, including diabetes, cardiovascular diseases, obesity, neurodegenerative disorders, and cancer. MO’s ability to modulate oxidative stress and inflammation—key drivers of CDs—highlights its significant role in disease prevention and treatment. MO enhances insulin sensitivity, regulates lipid profiles and blood pressure, reduces inflammation, and protects against oxidative damage. MO also modulates key signaling pathways involved in cancer and liver disease prevention. Studies suggest that MO extracts possess anticancer activity by modulating apoptosis, inhibiting tumor cell proliferation, and interacting with key signaling pathways, including YAP/TAZ, Nrf2-Keap1, TLR4/NF-κB, and Wnt/β-catenin. However, challenges such as variability in bioactive compounds, taste acceptability, and inconsistent clinical outcomes limit their widespread application. While preclinical studies support its efficacy, large-scale clinical trials, standardized formulations, and advanced delivery methods are needed to optimize its therapeutic potential. MO’s multifunctional applications make it a promising and sustainable solution for combating chronic diseases, especially in resource-limited settings. [Full article](/2227-9059/13/3/634)

(This article belongs to the Special Issue [Natural Product for the Interventions of Chronic Diseases: From Source to Therapy](        

    /journal/biomedicines/special_issues/9N07T07TOM

))  

[►▼ Show Figures](#)

Graphical abstract

Graphical abstract

[](https://pub.mdpi-res.com/biomedicines/biomedicines-13-00634/article_deploy/html/images/biomedicines-13-00634-ag-550.jpg?1741223119 "

<strong>Graphical abstract</strong><br/><strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9059/13/3/634'>Full article</a></strong>

")[](https://pub.mdpi-res.com/biomedicines/biomedicines-13-00634/article_deploy/html/images/biomedicines-13-00634-g001-550.jpg?1741223117 "

<strong>Figure 1</strong><br/>

<p>Mechanisms of Moringa’s Anti-Cancer Bioactivity. The anti-cancer potential of several MO extracts triggers multiple molecular pathways, modulating the expression of markers such as TNF-α; altering mitochondrial membrane potential; promoting ROS generation; downregulating COX-2, Wnt/β-catenin, NF-κB, and BCL-2 expression; and influencing antioxidant enzyme activities such as CAT, SOD, glutathione reductase, and GSH-Px. Additionally, the extracts enhance cell cycle arrest and upregulate markers such as BAX, 8-oxo-dG, apurinic sites, p53, GST (glutathione S-transferase), p-ERK1/2, AKT, and QR, contributing to their anticancer effects. Created in <a href=\"https://BioRender.com/c24l810\" target=\"_blank\">https://BioRender.com/c24l810</a>, accessed on 28 February 2025.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2227-9059/13/3/634'>Full article</a></strong>

")

_attachment_

Supplementary material:  

[Supplementary File 1 (ZIP, 1425 KiB)](/2071-1050/17/5/2257/s1?version=1741172491)  

25 pages, 2879 KiB  [](/2071-1050/17/5/2257/pdf?version=1741222916 "Article PDF")

Open AccessArticle

[Unlocking Green Export Opportunities: Empirical Insights from Southern Cone Economies](/2071-1050/17/5/2257)

by **Carla Carolina Pérez-Hernández**, **María Guadalupe Montiel-Hernández** and **Blanca Cecilia Salazar-Hernández**

_Sustainability_ **2025**, _17_(5), 2257; [https://doi.org/10.3390/su17052257](https://doi.org/10.3390/su17052257) - 5 Mar 2025

[**Abstract**](#)

This paper develops a strategic framework that integrates the theoretical perspectives of evolutionary economic geography and economic complexity to identify green export opportunities. By combining feasibility factors—such as green specialization, relatedness, and trade inertia—with desirability criteria like income, equity, and low emissions, the [\[...\] Read more.](#)

This paper develops a strategic framework that integrates the theoretical perspectives of evolutionary economic geography and economic complexity to identify green export opportunities. By combining feasibility factors—such as green specialization, relatedness, and trade inertia—with desirability criteria like income, equity, and low emissions, the framework offers a comprehensive approach to identify green export diversification. The empirical application, focused on the Southern Cone (Argentina, Brazil, Chile, Paraguay, and Uruguay), suggests that economies should prioritize green opportunities aligned with their existing capabilities, gradually expanding into higher-risk, higher-return options. The study provides tailored green export diversification portfolios for each country, identifying key opportunities in renewable energy products for Argentina and Brazil, lithium-related inputs for Chile, biofuels for Paraguay, and green hydrogen for Uruguay. These findings offer valuable insights for the design of public policies aimed at fostering green export diversification. [Full article](/2071-1050/17/5/2257)

(This article belongs to the Special Issue [Ecological Transition in Economics](        

    /journal/sustainability/special_issues/F9077QAD57

))  

[►▼ Show Figures](#)

Figure 1

Figure 1

[](https://pub.mdpi-res.com/sustainability/sustainability-17-02257/article_deploy/html/images/sustainability-17-02257-g001-550.jpg?1741223036 "

<strong>Figure 1</strong><br/>

<p>Number of green products by type of strategy and country. Note: feasible*: S1 + S2 + S3; desirable*: S4 + S5 + S6; desirable within reach*: products that fit in at least one feasible and one desirable condition. Source: own elaboration. (*) means that the product only counts in one kind of strategy. Also, notice that blue color shows a high concentration of products.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2257'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02257/article_deploy/html/images/sustainability-17-02257-g002-550.jpg?1741223038 "

<strong>Figure 2</strong><br/>

<p>Green product space. Note: Product Complexity Index (PCI): degree of sophistication of products [<a href=\"#B66-sustainability-17-02257\" class=\"html-bibr\">66</a>]; PRODY: income level associated with a specific product [<a href=\"#B62-sustainability-17-02257\" class=\"html-bibr\">62</a>]; Product Gini Index (PGI): links exported products to the average level of income inequality in exporting countries [<a href=\"#B59-sustainability-17-02257\" class=\"html-bibr\">59</a>]; Product Emissions Intensity Index (PEII): weighted measure of emissions at the product level in exporting countries [<a href=\"#B42-sustainability-17-02257\" class=\"html-bibr\">42</a>]. Network visualization created from trade data (exports averaged) over the period 2018–2022. Source: own elaboration. Additional information about how to build the green product space is available on <a href=\"#app1-sustainability-17-02257\" class=\"html-app\">Supplementary Material S.M-8</a>.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2257'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02257/article_deploy/html/images/sustainability-17-02257-g003-550.jpg?1741223040 "

<strong>Figure 3</strong><br/>

<p>Argentina’s green product space. Note: nodes are colored by their type of green -strategy. Blue nodes are desirable green products, orange nodes are feasible green products, and the red ones are products desirable within reach. Source: own elaboration.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2257'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02257/article_deploy/html/images/sustainability-17-02257-g004-550.jpg?1741223042 "

<strong>Figure 4</strong><br/>

<p>Brazil’s green product space. Note: nodes are colored by their type of green strategy. Blue nodes are desirable green products, orange nodes are feasible green products, and the red ones are products desirable within reach. Source: own elaboration.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2257'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02257/article_deploy/html/images/sustainability-17-02257-g005-550.jpg?1741223043 "

<strong>Figure 5</strong><br/>

<p>Chile’s green product space. Note: nodes are colored by their type of green strategy. Blue nodes are desirable green products and orange nodes are feasible green products. Source: own elaboration.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2257'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02257/article_deploy/html/images/sustainability-17-02257-g006-550.jpg?1741223045 "

<strong>Figure 6</strong><br/>

<p>Paraguay’s green product space. Note: nodes are colored by their type of green strategy. Blue nodes are desirable green products, orange nodes are feasible green products. Source: Own elaboration.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2257'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02257/article_deploy/html/images/sustainability-17-02257-g007-550.jpg?1741223046 "

<strong>Figure 7</strong><br/>

<p>Uruguay’s green product space. Note: nodes are colored by their type of green strategy. Blue nodes are desirable green products and orange nodes are feasible green products. Source: own elaboration.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2257'>Full article</a></strong>

")[](https://pub.mdpi-res.com/sustainability/sustainability-17-02257/article_deploy/html/images/sustainability-17-02257-g008-550.jpg?1741223048 "

<strong>Figure 8</strong><br/>

<p>Boxplots of economic complexity, income, inequality, and emissions for green products under diversification strategies. Note: feasible strategies: S1: maintenance; S2: related capabilities; S3: trade inertia; desirable strategies: S4: high income; S5: just transition; S6; high complexity. Product Complexity Index (PCI): degree of sophistication of products [<a href=\"#B66-sustainability-17-02257\" class=\"html-bibr\">66</a>]; PRODY: income level associated with a specific product [<a href=\"#B62-sustainability-17-02257\" class=\"html-bibr\">62</a>]; Product Gini Index (PGI): links exported products to the average level of income inequality in exporting countries [<a href=\"#B59-sustainability-17-02257\" class=\"html-bibr\">59</a>]; Product Emissions Intensity Index (PEII): weighted measure of emissions at the product level in exporting countries [<a href=\"#B42-sustainability-17-02257\" class=\"html-bibr\">42</a>]. Source: own elaboration.</p>

<strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2071-1050/17/5/2257'>Full article</a></strong>

")

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[Explore All Journals...](/about/journals)

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News

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27 February 2025  

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17 February 2025  

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5 Mar 2025

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4 Mar 2025

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Topics

------

Topic in BioMedInformatics, Cancers, Cells, Diagnostics, Immuno, IJMS

[Inflammatory Tumor Immune Microenvironment](/topics/ITIM) Topic Editors: William Cho, Anquan Shang  

Deadline: 15 March 2025

Topic in Agriculture, Animals, Dairy, Genes, IJMS, Veterinary Sciences

[Application of Reproductive and Genomic Biotechnologies for Livestock Breeding and Selection](/topics/E2H58E0298) Topic Editors: Manuel García-Herreros, Pedro M. Aponte  

Deadline: 20 March 2025

Topic in IJMS, Molecules, Antioxidants, CIMB, BioChem, Foods

[Biological Activities and Chemical Composition of Bee Products and Derivatives](/topics/BACCBPD) Topic Editors: Maria da Graça Costa G. Miguel, Ofélia Anjos  

Deadline: 31 March 2025

Topic in Energies, Processes, Electronics, Applied Sciences, WEVJ

[Energy Management and Efficiency in Electric Motors, Drives, Power Converters and Related Systems](/topics/energy_management) Topic Editors: Mario Marchesoni, Alfonso Damiano  

Deadline: 15 April 2025

Topic in Energies, Processes, Water, Sustainability, Buildings

[Towards a Greener Tomorrow—Modern Trends in Cooling and Water Solutions](/topics/cooling_water) Topic Editors: Jaroslaw Krzywanski, Marcin Sosnowski, Karol Sztekler, Anna Pajdak, Anna Zylka, Anna Kulakowska, Karolina Grabowska, Dorian Skrobek  

Deadline: 30 April 2025

Topic in Applied Sciences, Inventions, JMSE, Oceans, Remote Sensing, Sensors

[Ship Dynamics, Stability and Safety](/topics/TWX8A62OOH) Topic Editors: Zaojian Zou, Weilin Luo  

Deadline: 20 May 2025

Topic in Remote Sensing, Sensors, Smart Cities, Vehicles, Geomatics

[Information Sensing Technology for Intelligent/Driverless Vehicle, 2nd Edition](/topics/sensing_ii) Topic Editors: Yan Huang, Yi Ren, Penghui Huang, Jun Wan, Zhanye Chen, Shiyang Tang  

Deadline: 31 May 2025

Topic in Biomedicines, Current Oncology, Diagnostics, Gastrointestinal Disorders, JCM, Livers, Transplantology

[Advances in Gastrointestinal and Liver Disease: From Physiological Mechanisms to Clinical Practice](/topics/P72DQO0735) Topic Editors: Davide Giuseppe Ribaldone, Gian Paolo Caviglia  

Deadline: 20 June 2025

Topic in Buildings, Energies, Entropy, Resources, Sustainability, Processes

[Advances in Solar Heating and Cooling](/topics/Solar_Heating_Cooling) Topic Editors: Salvatore Vasta, Sotirios Karellas, Marina Bonomolo, Alessio Sapienza, Uli Jakob  

Deadline: 30 June 2025

Topic in Applied Sciences, Energies, Buildings, Smart Cities, AI

[Smart Electric Energy in Buildings](/topics/smart) Topic Editors: Daniel Villanueva Torres, Ali Hainoun, Sergio Gómez Melgar  

Deadline: 15 July 2025

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Selected Special Issues

-----------------------

Special Issue in Sustainability

[Artificial Intelligence in Sustainable Transportation](/journal/sustainability/special_issues/0FLED632DP) Guest Editors: Ye Li, Dominique Gruyer, Meiting Tu  

Deadline: 6 March 2025

Special Issue in Remote Sensing

[Time-Lapse Geophysical and Remote Sensing-Based Imaging and Diagnosis on Urban and Natural Hazards](/journal/remotesensing/special_issues/Geophysical_RS_Urban_Natural_Hazards) Guest Editors: Wallace W.L. Lai, Xiaolin Zhu, Xuehong Chen  

Deadline: 6 March 2025

Special Issue in Land

[Influence of Urbanization-Related Radical Land Modification on Urban Extreme Climate: 2nd Edition](/journal/land/special_issues/116A81E55S) Guest Editors: Guoyu Ren, Yali Luo  

Deadline: 6 March 2025

Special Issue in Viruses

[Epidemiology, Pathogenesis and Immunity of Adenovirus](/journal/viruses/special_issues/F3E30EXS64) Guest Editors: Adriana Kajon, Jay R. Radke  

Deadline: 6 March 2025

Special Issue in Minerals

[Recent Advances in Bone Diagenesis](/journal/minerals/special_issues/G98047PZ42) Guest Editors: Paul V. Ullmann, Jennifer Anné  

Deadline: 7 March 2025

Special Issue in JCM

[Advances in Clinical Rheumatology](/journal/jcm/special_issues/I87L4H1081) Guest Editor: Jacopo Ciaffi  

Deadline: 7 March 2025

Special Issue in Nanomaterials

[Nano-Based Advanced Thermoelectric Design](/journal/nanomaterials/special_issues/0A03BP244N) Guest Editors: Jin Wang, Zhen Cao  

Deadline: 7 March 2025

Special Issue in Humanities

[Law and Literature: Graffiti](/journal/humanities/special_issues/ODELATD4X0) Guest Editor: Lindsay Stern  

Deadline: 8 March 2025

Special Issue in Pathogens

[Women’s Special Issue Series: Pathogens](/journal/pathogens/special_issues/04W52WF0L5) Guest Editors: Anna Rosa Garbuglia, Nicola Carter  

Deadline: 8 March 2025

Special Issue in Sustainability

[Renewable Energy Communities: Smart Solutions and the Management of Distributed Energy Sources for the Optimization of Shared Energy](/journal/sustainability/special_issues/192LO5QZ1L) Guest Editor: Elisa Belloni  

Deadline: 8 March 2025

Selected Collections

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Topical Collection in Plants

[Feature paper in Plant Response to Abiotic Stress and Climate Change](/journal/plants/topical_collections/Feature_paper_Abiotic_Stress_Climate_Change) Collection Editors: Veronica De Micco, Luigi Sanita' di Toppi  

Topical Collection in Sensors

[Electronic Noses](/journal/sensors/topical_collections/E_N) Collection Editor: Jose V. Ros-Lis  

Topical Collection in Sustainability

[Metacognition, Learning Strategies, and Self-Regulated Learning to Promote Sustained Learning](/journal/sustainability/topical_collections/sustainability_education_learning) Collection Editor: Antonio P. Gutierrez de Blume  

Topical Collection in IJERPH

[Emotional Regulation and Mental Health](/journal/ijerph/topical_collections/ERMH) Collection Editors: María Jesús Irurtia, Jairo Rodríguez-Medina, Arthur L. Cantos  

Topical Collection in Cancers

[Imaging Biomarker in Oncology](/journal/cancers/topical_collections/Imaging_Biomarker) Collection Editor: Damiano Caruso  

Topical Collection in Healthcare

[Osteopathic and Manual Therapy Healthcare Reconceptualization: Health Needs and New Evidence](/journal/healthcare/topical_collections/Osteopathic_Manipulative_Treatment) Collection Editor: Marco Tramontano  

Topical Collection in Applied Sciences

[The Development and Application of Fuzzy Logic](/journal/applsci/topical_collections/Fuzzy_Logic_Software_Patern_Recognition) Collection Editors: Seongsoo Cho, Bhanu Shrestha  

Topical Collection in Cells

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