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16 pages, 3629 KB  
Article
Household Food Insecurity Alters Gut Microbiome Composition and Enriches Sutterella in Ethiopian Schoolchildren
by Angie Zhu, Fisseha Bonja Geleto, Musa Mohammed Ali, Hagos Ashenafi, Berhanu Erko and Bineyam Taye
Nutrients 2026, 18(4), 680; https://doi.org/10.3390/nu18040680 (registering DOI) - 20 Feb 2026
Abstract
Background: Household food insecurity (HFI) adversely affects child development by restricting caloric intake, dietary diversity, and food quality. Since diet is a key factor influencing the gut microbiome, HFI may negatively impact health by altering microbial communities. However, direct evidence linking HFI to [...] Read more.
Background: Household food insecurity (HFI) adversely affects child development by restricting caloric intake, dietary diversity, and food quality. Since diet is a key factor influencing the gut microbiome, HFI may negatively impact health by altering microbial communities. However, direct evidence linking HFI to changes in the gut microbiome is limited. Therefore, we investigated the effects of HFI as a composite variable and used individual HFI assessment questions as specific proxies for dietary deprivation on the gut microbiome in a group of Ethiopian schoolchildren. Methods: Fecal samples were collected from 57 school-aged children in Ethiopia, and microbial profiles were established using 16S rRNA amplicon paired-end sequencing. Food insecurity was assessed using the Household Food Insecurity Access Scale (HFIAS). Results: We observed no significant differences in alpha diversity across food security status (Wilcoxon p > 0.05). However, beta diversity analysis revealed a significant shift in microbiome composition between food-secure and food-insecure individuals (Bray–Curtis dissimilarity; PERMANOVA, p < 0.05). Further analyses of individual HFIAS questions as specific proxies for dietary deprivation showed that limited dietary variety, consumption of disliked foods, and reduced meal size were each associated with significant changes in microbial compositions (PERMANOVA; all q < 0.05). Differential abundance analyses consistently identified Sutterella as significantly more abundant among food-insecure participants (composite model q = 0.11; component-specific models q < 0.05). Additionally, a microbial feature-based machine learning model accurately predicted food security status (AUC = 0.81), with Sutterella emerging as the top predictive feature. Conclusions: Our findings suggest that food insecurity metrics are associated with alterations in gut microbial composition. The consistent enrichment of Sutterella in food-insecure children in this study suggests the need for future mechanistic studies to explore its role in mediating the effects of food insecurity. Full article
(This article belongs to the Section Prebiotics, Probiotics and Postbiotics)
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66 pages, 8585 KB  
Review
Polyurethane Recycling: Sustainable Development Perspectives and Innovative Approaches
by Konrad Polecki, Joanna Paciorek-Sadowska, Marcin Borowicz, Marek Isbrandt and Iwona Zarzyka
Materials 2026, 19(4), 805; https://doi.org/10.3390/ma19040805 (registering DOI) - 19 Feb 2026
Abstract
Polyurethanes are widely used polymeric materials; their crosslinked structure and compositional diversity significantly hinder effective end-of-life management. The review emphasizes polyurethane recycling technologies, with chemical aspects discussed only insofar as they directly affect recyclability. The influence of polyol and isocyanate structure on phase [...] Read more.
Polyurethanes are widely used polymeric materials; their crosslinked structure and compositional diversity significantly hinder effective end-of-life management. The review emphasizes polyurethane recycling technologies, with chemical aspects discussed only insofar as they directly affect recyclability. The influence of polyol and isocyanate structure on phase separation, network architecture and thermal stability is discussed in the context of degradation and depolymerization mechanisms. Mechanical, chemical, thermochemical and emerging biological recycling routes are compared, with emphasis on their respective advantages, limitations and technological maturity. Mechanical recycling remains the most accessible option on an industrial scale but typically leads to reduced mechanical and thermal-insulation performance. Chemical recycling—particularly glycolysis, hydrolysis and aminolysis—enables partial recovery of polyols suitable for reuse in new polyurethane formulations, albeit at the cost of higher energy demand and increased process complexity. The environmental impact of polyurethane recycling is considered in terms of energy consumption, greenhouse-gas emissions, waste-reduction potential and alignment with circular-economy principles. Emerging biological and hybrid recycling strategies are highlighted as promising low-temperature alternatives with potential environmental benefits, despite their current low technological readiness. Key structural and technological barriers to efficient polyurethane recycling are identified, and future research directions toward improved sustainability and resource efficiency are outlined. Full article
(This article belongs to the Section Polymeric Materials)
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30 pages, 11808 KB  
Article
Evolutionary Characteristics and Dynamic Mechanism of the Global Transportation Carbon Emission Spatial Correlation Network
by Yi Liang, Han Liu, Zhaoge Wu, Xiaoduo Wang and Zhaoxu Yuan
ISPRS Int. J. Geo-Inf. 2026, 15(2), 89; https://doi.org/10.3390/ijgi15020089 (registering DOI) - 19 Feb 2026
Abstract
This study constructs a global transportation carbon emission spatial correlation network via a modified gravity model and explores its evolutionary characteristics and dynamic mechanisms by integrating three-dimensional evolutionary analysis (node, overall, structural) and temporal exponential random graph model (TERGM). The main findings are [...] Read more.
This study constructs a global transportation carbon emission spatial correlation network via a modified gravity model and explores its evolutionary characteristics and dynamic mechanisms by integrating three-dimensional evolutionary analysis (node, overall, structural) and temporal exponential random graph model (TERGM). The main findings are as follows: (1) Global transportation carbon emission spatial correlation intensity keeps rising, with improved connectivity and integration, forming three regionally agglomerated correlation poles centered on the United States (America), China (Asia) and major European countries (Europe). (2) Network centrality distributes asymmetrically: Switzerland, Norway and the United States remain core nodes, while China, Japan and other Asian economies with strong direct correlation radiation are not in the core tier. (3) Third, evolutionary dynamics stem from the synergistic interaction of multidimensional attributes. ① Economic level positively drives bidirectional connection emission and attraction; economic scale and openness curb emission but boost attraction, while tertiary industry structure inhibits both. ② Only economic level and government efficiency exert significant positive effects on absdiff, fostering network heterophilic attraction. ③ Spatial and institutional proximity in edgecov effectively facilitate connection formation. ④ Endogenous network variables present a collaborative mechanism of reciprocity and transmission, constrained by network density. ⑤ Temporal effects show early connection structure forms path dependence, resulting in low dynamic variability and overall network stability. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
24 pages, 4248 KB  
Article
Multi-Scale Feature Learning for Farmland Segmentation Under Complex Spatial Structures
by Yongqi Han, Yuqing Wang, Yun Zhang, Hongfu Ai, Chuan Qin and Xinle Zhang
Entropy 2026, 28(2), 242; https://doi.org/10.3390/e28020242 (registering DOI) - 19 Feb 2026
Abstract
Fragmented, irregular, and scale-heterogeneous farmland parcels introduce high spatial complexity into high-resolution remote sensing imagery, leading to boundary ambiguity and inter-class spectral confusion that hinder effective feature discrimination in semantic segmentation. To address these challenges, we propose CSMNet, which adopts a ConvNeXt V2 [...] Read more.
Fragmented, irregular, and scale-heterogeneous farmland parcels introduce high spatial complexity into high-resolution remote sensing imagery, leading to boundary ambiguity and inter-class spectral confusion that hinder effective feature discrimination in semantic segmentation. To address these challenges, we propose CSMNet, which adopts a ConvNeXt V2 encoder for hierarchical representation learning and a multi-scale fusion architecture with redesigned skip connections and lateral outputs to reduce semantic gaps and preserve cross-scale information. An adaptive multi-head attention module dynamically integrates channel-wise, spatial, and global contextual cues through a lightweight gating mechanism, enhancing boundary awareness in structurally complex regions. To further improve robustness, a hybrid loss combining Binary Cross-Entropy and Dice loss is employed to alleviate class imbalance and ensure reliable extraction of small and fragmented parcels. Experimental results from Nong’an County demonstrate that the proposed model achieves superior performance compared with several state-of-the-art segmentation methods, attaining a Precision of 95.91%, a Recall of 93.95%, an F1-score of 94.92%, and an IoU of 90.85%. The IoU exceeds that of Unet++ by 8.92% and surpasses PSPNet, SegNet, DeepLabv3+, TransUNet, SeaFormer and SegMAN by more than 15%, 10%, 7%, 6%, 5% and 2%, respectively. These results indicate that CSMNet effectively improves information utilization and boundary delineation in complex agricultural landscapes. Full article
(This article belongs to the Section Multidisciplinary Applications)
28 pages, 2552 KB  
Article
Exploring the Nonlinear Effects of the Built Environment on Ecological Resilience in a High-Density City: A Case Study of Wuhan
by Kejia Fu, Jianping Wu and Yong Huang
Buildings 2026, 16(4), 844; https://doi.org/10.3390/buildings16040844 - 19 Feb 2026
Abstract
Understanding how the built environment relates to urban ecological resilience is essential for resilience-oriented planning in high-density cities. Using Wuhan, China, as a case study, we constructed a 1 km grid-based Ecological Resilience Index (ERI) by integrating ecosystem resistance, adaptability, and recovery, and [...] Read more.
Understanding how the built environment relates to urban ecological resilience is essential for resilience-oriented planning in high-density cities. Using Wuhan, China, as a case study, we constructed a 1 km grid-based Ecological Resilience Index (ERI) by integrating ecosystem resistance, adaptability, and recovery, and we confirmed significant spatial autocorrelation in ERI. We then applied a Bayesian-optimized XGBoost model with block-based spatial cross-validation to improve robustness under spatial dependence, and used SHAP to interpret nonlinear, threshold-like patterns and interactions among predictors. The results indicate that building coverage ratio (BCR), nighttime light intensity (NTL), elevation (ELE), mean building height (MBH), and precipitation (PRE) were the most influential predictors of ERI. SHAP main effects indicate clear non-monotonic and threshold-like response patterns across key predictors. SHAP interaction analysis further suggests that, under high BCR, the SHAP interaction term tends to be positive when MBH is below approximately 10 m, whereas the interaction between high NTL and low MBV is predominantly negative. This study provides fine-scale empirical evidence to inform the optimization of three-dimensional urban morphology to support urban ecological resilience. Full article
18 pages, 348 KB  
Article
FDC-LGL: Fast Discrete Clustering with Local Graph Learning for Large-Scale Datasets
by Shenfei Pei, Ruiyu Huang and Zengwei Zheng
Mathematics 2026, 14(4), 725; https://doi.org/10.3390/math14040725 - 19 Feb 2026
Abstract
Graph-based clustering is a fundamental task in unsupervised machine learning and has been extensively applied to complex data mining scenarios, such as pattern recognition and data classification. However, most existing graph clustering algorithms still face significant challenges, including low graph learning efficiency, poor [...] Read more.
Graph-based clustering is a fundamental task in unsupervised machine learning and has been extensively applied to complex data mining scenarios, such as pattern recognition and data classification. However, most existing graph clustering algorithms still face significant challenges, including low graph learning efficiency, poor adaptability to datasets with large numbers of samples and clusters, and inevitable accuracy loss caused by post-processing steps. To effectively tackle these critical challenges and enhance clustering performance, we propose a novel Fast Discrete Clustering algorithm integrated with Local Graph Learning, namely FDC-LGL. Based on the classical normalized cut criterion, the proposed algorithm innovatively integrates a Local Graph Learning module into the clustering objective function, efficiently and reliably learning graph structures by introducing second-order neighbor constraints. It directly outputs accurate clustering results through a discrete indicator matrix, thereby eliminating the need for additional post-processing. Extensive comparative experiments conducted on synthetic datasets, medium-scale real-world datasets, and large-scale real-world datasets demonstrate that FDC-LGL is significantly superior to other state-of-the-art graph clustering algorithms in terms of key evaluation metrics, including clustering accuracy (ACC), normalized mutual information (NMI), and the adjusted rand index (ARI), as well as computational efficiency. Full article
21 pages, 6127 KB  
Article
A Sensor-Based Magnetite Ore Sorting System Integrating Empirical Mode Decomposition and Convolutional Neural Network
by Yankui Ren, Yan Yang, Jipeng Wang, Chunrong Pan, Fenglian Yuan, Weiqian Chen and Jianzhao Wang
Minerals 2026, 16(2), 210; https://doi.org/10.3390/min16020210 - 19 Feb 2026
Abstract
To address the challenge of poor separation performance exhibited by conventional magnetic separation equipment when processing coarse-grained, low-grade magnetite ore, this paper proposes a novel ore recognition method that integrates empirical mode decomposition (EMD) with a convolutional neural network (CNN). First, the original [...] Read more.
To address the challenge of poor separation performance exhibited by conventional magnetic separation equipment when processing coarse-grained, low-grade magnetite ore, this paper proposes a novel ore recognition method that integrates empirical mode decomposition (EMD) with a convolutional neural network (CNN). First, the original signal undergoes standardization to suppress sensor baseline drift. Then, it is decomposed by using EMD to obtain a series of intrinsic mode functions (IMFs). Subsequently, based on scaling exponents and kurtosis values, IMFs containing significant feature information are selected and fused, resulting in a reconstructed signal with substantially reduced noise. To preserve effective features, the absolute values of the reconstructed signal are taken, followed by normalization and dimensional transformation to convert it into a two-dimensional matrix format, thereby constructing training, validation, and test sets. Finally, a CNN is designed and optimized to automatically extract discriminative features from the preprocessed samples, enabling accurate classification of magnetite ore grades. Experimental results demonstrate that the proposed comprehensive identification method achieves effective and stable classification performance across different ore grades. Specifically, the implementation of standardization and EMD-based denoising has been demonstrated to enhance the accuracy of CNNs in recognizing diverse ores. Full article
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23 pages, 556 KB  
Article
Does Rural Labor Aging Hinder Green Agricultural Transformation: Evidence from China with a Digitalization Perspective
by Baoji Zhou, Xinfeng Zuo and Chen Lu
Sustainability 2026, 18(4), 2094; https://doi.org/10.3390/su18042094 - 19 Feb 2026
Abstract
The rapid aging of China’s rural labor force is reshaping agricultural production and may complicate the transition toward greener development. Yet whether—and through what channels—labor aging hampers green agricultural transformation (GAT) remains insufficiently understood. Drawing on provincial panel data from 2012 to 2023, [...] Read more.
The rapid aging of China’s rural labor force is reshaping agricultural production and may complicate the transition toward greener development. Yet whether—and through what channels—labor aging hampers green agricultural transformation (GAT) remains insufficiently understood. Drawing on provincial panel data from 2012 to 2023, this study employs fixed-effects models to empirically investigate the relationship between rural labor aging and GAT, while also examining the moderating role of digitalization. The empirical findings reveal a robust negative association between rural labor aging and GAT, and this finding remains robust to alternative specifications as well as endogeneity treatments. Mechanism tests indicate that aging may constrain GAT by weakening regional innovation capacity, slowing fertilizer reduction, and limiting the expansion of scale operations. Moreover, digital village construction significantly mitigates the detrimental impact of aging on GAT. This buffering role is heterogeneous, with stronger mitigation observed in provinces characterized by more severe aging and higher levels of economic development. Overall, the evidence highlights the potential of digital tools and rural digital infrastructure to sustain GAT in an aging society. Full article
(This article belongs to the Special Issue Agriculture, Food, and Resources for Sustainable Economic Development)
21 pages, 1679 KB  
Article
Optimization of UWB Base Station Deployment for Formwork Scaffolds in Underground Construction with Sub-Meter Positioning Accuracy by Semi-Controlled Field Experiments
by Gang Yao, Lang Liu, Yang Yang, Xiaodong Cai, Xin Yang, Huiwen Hou, Mingpu Wang and Pengcheng Li
Sensors 2026, 26(4), 1340; https://doi.org/10.3390/s26041340 - 19 Feb 2026
Abstract
Fall-from-height fatalities in underground construction are closely associated with formwork scaffold operations, where dense steel members cause severe non-line-of-sight (NLOS) and multipath effects that degrade positioning performance. Although ultra-wideband (UWB) technology offers high theoretical ranging accuracy, its deployment-dependent performance in metal-rich scaffold environments [...] Read more.
Fall-from-height fatalities in underground construction are closely associated with formwork scaffold operations, where dense steel members cause severe non-line-of-sight (NLOS) and multipath effects that degrade positioning performance. Although ultra-wideband (UWB) technology offers high theoretical ranging accuracy, its deployment-dependent performance in metal-rich scaffold environments remains insufficiently quantified. This study focuses on physical deployment optimization rather than algorithmic compensation. A full-scale formwork scaffold was constructed, and a stepwise one-factor controlled experimental design was employed to quantify the effects of anchor height (H) and horizontal spacing (S) on 3D positioning accuracy. The results show that sub-meter accuracy can be achieved through appropriate deployment, with a minimum 3D RMSE of 0.317 m and over 80% of single-axis errors confined within a 0.2 m engineering-valid region. For this specific setup, the optimal S = 1.5 m correlates with the scaffold grid size (approximately 0.8 times the 1.8 m bay width). While we hypothesize this ratio dependency applies to other geometries, this remains a site-specific observation requiring future cross-validation. Further analysis indicates that this deployment balances vertical signal visibility and multipath suppression. In addition, while the Position Dilution of Precision (PDOP) metric reflects geometric sensitivity, it does not linearly correlate with actual positioning errors under coplanar UWB deployments. These findings provide a rigorous static error model, serving as a critical prerequisite for developing robust real-time safety monitoring systems in scaffold-intensive construction environments. Full article
(This article belongs to the Section Navigation and Positioning)
20 pages, 7522 KB  
Article
Vibration-Based Wear State Assessment of Hopper Scales: A Coupled DEM–FEM Approach
by Yichen Zhang, Xingdong Wang, Xu She and Zongwu Wu
Machines 2026, 14(2), 238; https://doi.org/10.3390/machines14020238 - 19 Feb 2026
Abstract
Hopper scales are critical dynamic metering equipment in industrial production, yet their metrological performance is often compromised by wear on weighing units over long-term service. This study proposes a wear state assessment method based on the evolution of vibration features. Focusing on the [...] Read more.
Hopper scales are critical dynamic metering equipment in industrial production, yet their metrological performance is often compromised by wear on weighing units over long-term service. This study proposes a wear state assessment method based on the evolution of vibration features. Focusing on the rocker-column weighing unit, we analyzed the mechanism by which geometric changes in the spherical indenter—caused by fretting wear—alter the system’s constraint state. A global-to-local coupled Discrete Element Method and Finite Element Method (DEM–FEM) model was constructed to account for material-structure interactions, alongside a dynamic simulation model considering wear evolution. The simulation accuracy was validated through a dedicated experimental platform. The results indicate that as spherical wear intensifies, the low-frequency swaying of the indenter is suppressed, causing the system’s vibration mode to transition from a flexible, swaying-dominated state to a high-frequency, rigid-impact-dominated state. In the frequency domain, this manifests as energy migration, characterized by attenuation of the low-frequency main peak and an elevation of the high-frequency broadband noise floor. Crucially, as a key innovation for wear diagnosis, this study reveals the directional sensitivity of statistical indicators. While the Root Mean Square (RMS) exhibits a non-monotonic V-shaped trend, the Kurtosis and Margin factors of the tangential vibration demonstrate superior monotonic sensitivity. Under severe wear conditions, these two indicators increase by 14 and 11 times, respectively. These findings provide highly effective diagnostic criteria and hold significant engineering application value for the predictive maintenance of industrial dynamic weighing systems. Full article
(This article belongs to the Section Friction and Tribology)
25 pages, 3654 KB  
Article
MDF2Former: Multi-Scale Dual-Domain Feature Fusion Transformer for Hyperspectral Image Classification of Bacteria in Murine Wounds
by Decheng Wu, Wendan Liu, Rui Li, Xudong Fu, Lin Tao, Yinli Tian, Anqiang Zhang, Zhen Wang and Hao Tang
J. Imaging 2026, 12(2), 90; https://doi.org/10.3390/jimaging12020090 - 19 Feb 2026
Abstract
Bacterial wound infection poses a major challenge in trauma care and can lead to severe complications such as sepsis and organ failure. Therefore, rapid and accurate identification of the pathogen, along with targeted intervention, is of vital importance for improving treatment outcomes and [...] Read more.
Bacterial wound infection poses a major challenge in trauma care and can lead to severe complications such as sepsis and organ failure. Therefore, rapid and accurate identification of the pathogen, along with targeted intervention, is of vital importance for improving treatment outcomes and reducing risks. However, current detection methods are still constrained by procedural complexity and long processing times. In this study, a hyperspectral imaging (HSI) acquisition system for bacterial analysis and a multi-scale dual-domain feature fusion transformer (MDF2Former) were developed for classifying wound bacteria. MDF2Former integrates three modules: a multi-scale feature enhancement and fusion module that generates tokens with multi-scale discriminative representations, a spatial–spectral dual-branch attention module that strengthens joint feature modeling, and a frequency and spatial–spectral domain encoding module that captures global and local interactions among tokens through a hierarchical stacking structure, thereby enabling more efficient feature learning. Extensive experiments on our self-constructed HSI dataset of typical wound bacteria demonstrate that MDF2Former achieved outstanding performance across five metrics: Accuracy (91.94%), Precision (92.26%), Recall (91.94%), F1-score (92.01%), and Kappa coefficient (90.73%), surpassing all comparative models. These results have verified the effectiveness of combining HSI with deep learning for bacterial identification, and have highlighted its potential in assisting in the identification of bacterial species and making personalized treatment decisions for wound infections. Full article
(This article belongs to the Section Color, Multi-spectral, and Hyperspectral Imaging)
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14 pages, 1181 KB  
Article
The Antenatal Origins of Postpartum Distress: A Retrospective Longitudinal Analysis of Depression and Anxiety Trajectories
by Larisa-Mihaela Holbanel, Adina Turcu-Stiolica, Sebastian Constantin Toma, Mihail-Cristian Pirlog and Victor Gheorman
Med. Sci. 2026, 14(1), 102; https://doi.org/10.3390/medsci14010102 - 19 Feb 2026
Abstract
Background/Objectives: While postpartum depression is widely screened, the predictive value of antenatal symptoms remains underutilized. This study aimed to retrospectively analyze the longitudinal trajectory of depressive and anxiety symptoms from mid-pregnancy to the late postpartum period to identify critical windows for intervention [...] Read more.
Background/Objectives: While postpartum depression is widely screened, the predictive value of antenatal symptoms remains underutilized. This study aimed to retrospectively analyze the longitudinal trajectory of depressive and anxiety symptoms from mid-pregnancy to the late postpartum period to identify critical windows for intervention and assess the impact of mental health service utilization. Methods: A retrospective longitudinal cohort study was conducted on 125 pregnant women monitored at the Emergency County Clinical Hospital of Craiova, Romania. Depression was assessed using the Edinburgh Postnatal Depression Scale (EPDS) and Patient Health Questionnaire-9 (PHQ-9), while anxiety was evaluated using the Generalized Anxiety Disorder-7 (GAD-7) scale. Data were collected at four intervals: 20–24 weeks (T1), 32–36 weeks (T2), 6 weeks postpartum (T3), and 12 weeks postpartum (T4). Results: The highest burden of depressive symptoms occurred in the antenatal period (mean EPDS: 15.6 ± 9.41) rather than postpartum. Antenatal depression scores were strongly correlated with postpartum scores (rho = 0.98, p < 0.001), indicating a stable continuum of distress, though this high correlation may also reflect measurement inertia. Anxiety scores demonstrated a “plateau” effect during pregnancy (mean GAD-7 ≈ 8.0) before declining postpartum. A stratified analysis revealed a “treatment paradox”: women receiving mental health services had higher baseline morbidity and a slower rate of recovery compared to those who did not, remaining symptomatic at 12 weeks (mean EPDS: 14.2 vs. 11.0, p = 0.049). Conclusions: Perinatal distress in this cohort was primarily an antenatal phenomenon that persisted in the postpartum period. The “antenatal peak” suggests the hypothesis that screening should commence in the second trimester. Current interventions appear to stabilize but not fully resolve symptoms in high-risk women, suggesting a need for more intensive management strategies. Full article
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21 pages, 3350 KB  
Article
GIS Partial Discharge Fault Diagnosis Based on Multi-Source Feature Fusion and ResNet-MLP
by Bingjian Jia, Qing Sun, Weiwei Guo, Mingzheng Wang, Qian Wang and Hongfeng Zhao
Energies 2026, 19(4), 1073; https://doi.org/10.3390/en19041073 - 19 Feb 2026
Abstract
Partial discharge (PD) signals in gas-insulated switchgear (GIS) exhibit complex characteristics, and single-modal feature recognition methods face limitations in achieving satisfactory diagnostic accuracy due to incomplete fault information representation. This paper proposes a multi-modal fault diagnosis framework that effectively integrates complementary information from [...] Read more.
Partial discharge (PD) signals in gas-insulated switchgear (GIS) exhibit complex characteristics, and single-modal feature recognition methods face limitations in achieving satisfactory diagnostic accuracy due to incomplete fault information representation. This paper proposes a multi-modal fault diagnosis framework that effectively integrates complementary information from different sensing modalities to improve defect identification performance. First, PRPD time-domain statistical features from HFCT measurements and frequency-domain features from UHF signals are extracted to construct a comprehensive hybrid feature set. Z-score normalization is applied to eliminate scale differences between heterogeneous features. Principal component analysis (PCA) is then employed for dimensionality reduction, preserving essential discriminative information while removing redundancy. Finally, a ResNet-MLP classifier with skip connections is designed to enhance nonlinear feature extraction and alleviate gradient vanishing problems in deep network training. Experimental validation on four typical defect types—protrusion defect, floating discharge, metal particle discharge, and surface discharge on insulator—demonstrates that the proposed method achieves 99.38% classification accuracy on the test set, with consistently high precision, recall, and F1-score across all categories. The proposed approach significantly outperforms standard MLP without residual connections, achieving 98.94% ± 0.49% accuracy compared to 95.47% ± 3.72% over 20 independent runs, demonstrating superior diagnostic accuracy and generalization capability for GIS insulation fault diagnosis. Full article
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16 pages, 1673 KB  
Article
Differential Evolution-Based Optimization of Hybrid PV–Wind Energy Using Reanalysis Data
by Tecil Jinu Puzhimel and George Pappas
Appl. Sci. 2026, 16(4), 2054; https://doi.org/10.3390/app16042054 - 19 Feb 2026
Abstract
Hybrid photovoltaic (PV) systems augmented by wind-induced energy contributions can improve energy reliability under variable atmospheric conditions. However, their performance remains highly sensitive to site-specific weather patterns, panel orientation, and system parameter selection. This study presents a computational optimization framework based on Differential [...] Read more.
Hybrid photovoltaic (PV) systems augmented by wind-induced energy contributions can improve energy reliability under variable atmospheric conditions. However, their performance remains highly sensitive to site-specific weather patterns, panel orientation, and system parameter selection. This study presents a computational optimization framework based on Differential Evolution (DE) to enhance the combined energy output of a hybrid PV–wind system using high-resolution reanalysis data. Hourly solar irradiance from NASA POWER and near-surface wind components from ERA5 were processed through a unified data ingestion and preprocessing pipeline supporting GRIB and NetCDF formats to evaluate seasonal and annual energy production. The optimization jointly adjusted PV tilt angle, effective PV area scaling, and a wind energy scaling parameter to maximize total energy yield. Case studies for San Antonio (TX), Denver (CO), and Albuquerque (NM) demonstrate seasonal energy gains of 36–57% and annual improvements of 36.9–56.2% relative to baseline fixed-parameter configurations. The results indicate that evolutionary optimization combined with reanalysis-driven energy modeling provides a robust and scalable approach for improving hybrid renewable energy performance across diverse climatic regions. Full article
(This article belongs to the Special Issue AI Technologies Applied to Energy Systems and Smart Grids)
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15 pages, 1669 KB  
Article
Impact of Large-Scale Wildfires and Meteorological Factors on PM Concentrations in Agricultural Regions: Non-Linear Relationship Analysis Using GAM
by Hee-Jin Kim, Ki-Youn Kim and Jin-Ho Kim
Atmosphere 2026, 17(2), 216; https://doi.org/10.3390/atmos17020216 - 19 Feb 2026
Abstract
The intensification of large-scale wildfires, driven by climate change, presents a critical threat to agricultural ecosystems, specifically during the vulnerable sowing season in March. Departing from the prevailing focus on urban air quality, this study elucidates the spatiotemporal dynamics of particulate matter (PM) [...] Read more.
The intensification of large-scale wildfires, driven by climate change, presents a critical threat to agricultural ecosystems, specifically during the vulnerable sowing season in March. Departing from the prevailing focus on urban air quality, this study elucidates the spatiotemporal dynamics of particulate matter (PM) in eight major Korean agricultural regions during the March 2025 wildfires. By employing a Generalized Additive Model (GAM), we characterized the complex non-linear interactions between PM concentrations and meteorological variables. The analysis reveals a substantial elevation in PM levels during the wildfire event relative to the pre-fire baseline. Most notably, the Sangju region experienced the most acute accumulation, with PM-10 and PM-2.5 concentrations surging by 74% and 46%, respectively; this intensification was significantly compounded by topographic trapping and surface inversion phenomena. Furthermore, GAM results identified temperature and relative humidity as the primary determinants of PM retention, whereas wind speed demonstrated a distinct non-linear, U-shaped effect, facilitating particulate resuspension at higher velocities. These findings quantitatively underscore the susceptibility of agricultural environments to wildfire-induced aerosols and highlight the imperative for establishing agriculture-specific monitoring networks and early warning protocols to safeguard crop productivity. Full article
(This article belongs to the Section Air Quality)
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