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17 pages, 3498 KB  
Article
Self-Supervised Learning and Multi-Sensor Fusion for Alpine Wetland Vegetation Mapping: Bayinbuluke, China
by Muhammad Murtaza Zaka, Alim Samat, Jilili Abuduwaili, Enzhao Zhu, Arslan Akhtar and Wenbo Li
Plants 2025, 14(20), 3153; https://doi.org/10.3390/plants14203153 (registering DOI) - 13 Oct 2025
Abstract
Accurate mapping of wetland vegetation is essential for ecological monitoring and conservation, yet it remains challenging due to the spatial heterogeneity of wetlands, the scarcity of ground-truth data, and the spread of invasive species. Invasive plants alter native vegetation patterns, making their early [...] Read more.
Accurate mapping of wetland vegetation is essential for ecological monitoring and conservation, yet it remains challenging due to the spatial heterogeneity of wetlands, the scarcity of ground-truth data, and the spread of invasive species. Invasive plants alter native vegetation patterns, making their early detection critical for preserving ecosystem integrity. This study proposes a novel framework that integrates self-supervised learning (SSL), supervised segmentation, and multi-sensor data fusion to enhance vegetation classification in the Bayinbuluke Alpine Wetland, China. High-resolution satellite imagery from PlanetScope-3 and Jilin-1 was fused, and SSL methods—including BYOL, DINO, and MoCo v3—were employed to learn transferable feature representations without extensive labeled data. The results show that SSL methods exhibit consistent variations in classification performance, while multi-sensor fusion significantly improves the detection of rare and fragmented vegetation patches and enables the early identification of invasive species. Overall, the proposed SSL–fusion strategy reduces reliance on labor-intensive field data collection and provides a scalable, high-precision solution for wetland monitoring and invasive species management. Full article
(This article belongs to the Special Issue Computer Vision Techniques for Plant Phenomics Applications)
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15 pages, 1167 KB  
Article
Optimal Configuration of Transformer–Energy Storage Deeply Integrated System Based on Enhanced Q-Learning with Hybrid Guidance
by Zhe Li, Li You, Yiqun Kang, Daojun Tan, Xuan Cai, Haozhe Xiong and Yonghui Liu
Processes 2025, 13(10), 3267; https://doi.org/10.3390/pr13103267 (registering DOI) - 13 Oct 2025
Abstract
This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource allocation and the [...] Read more.
This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource allocation and the superior capability of artificial intelligence algorithms in addressing multi-dimensional, multi-constrained optimization challenges. A multi-objective optimization model is first formulated with dual objectives: minimizing voltage deviation levels and comprehensive economic costs. To overcome the limitations of conventional methods in complex power systems—particularly regarding solution quality and convergence speed—an enhanced Q-learning with hybrid guidance algorithm is proposed. The improved algorithm demonstrates strengthened local search capability and accelerated late-stage convergence performance. Validation using a real-world urban power grid in China confirms the method’s effectiveness. Compared to traditional approaches, the proposed solution achieves optimal TES-DIS planning through autonomous learning, demonstrating (1) 70.73% cost reduction and (2) 89.85% faster computational efficiency. These results verify the method’s capability for intelligent, simplified power system planning with superior optimization performance. Full article
(This article belongs to the Special Issue Applications of Smart Microgrids in Renewable Energy Development)
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22 pages, 6581 KB  
Article
Near-Field Aerodynamic Noise of Subway Trains: Comparative Mechanisms in Open Tracks vs. Confined Tunnels
by Xiao-Ming Tan, Zi-Xi Long, Cun-Rui Xiang, Xiao-Hong Zhang, Bao-Jun Fu, Xu-Long He and Yuan-Sheng Chen
Symmetry 2025, 17(10), 1724; https://doi.org/10.3390/sym17101724 (registering DOI) - 13 Oct 2025
Abstract
As the operational speeds of subway trains in China incrementally increase to 160 km/h, the enclosed nature of tunnel environments poses significant challenges by restricting free airflow. This limitation leads to intense airflow disturbances and turbulence phenomena within tunnels, consequently exacerbating aerodynamic noise [...] Read more.
As the operational speeds of subway trains in China incrementally increase to 160 km/h, the enclosed nature of tunnel environments poses significant challenges by restricting free airflow. This limitation leads to intense airflow disturbances and turbulence phenomena within tunnels, consequently exacerbating aerodynamic noise issues. This study utilizes compressible Large Eddy Simulation (LES) and acoustic finite element methods to construct a computational model of aerodynamic noise for subway trains within tunnels. It employs this model to compare and analyze the near-field noise characteristics of subway trains traveling at 120 km/h on open tracks versus in infinitely long tunnels. The findings indicate that the distribution of sound pressure levels on the surfaces of trains within tunnels is comparatively uniform, overall being 15 dB higher than those on open tracks. The presence of a high blockage ratio in tunnels intensifies the cavity flow between two air conditioning units, making it the region with the highest sound pressure level. The surface sound pressure spectrum within the tunnel shows greater similarity across different segments, with low-frequency sound pressure levels notably enhanced and high-frequency levels attenuating more rapidly compared to open tracks. It is recommended that in tunnels with high blockage ratios, the positioning of subway train air conditioning should not be too high, overly concentrated, submerged, or without the use of sound-absorbing materials. Such adjustments can effectively reduce the sound pressure levels in these areas, thereby enhancing the acoustic performance of the train within the tunnel. Full article
(This article belongs to the Section Engineering and Materials)
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25 pages, 3342 KB  
Article
Modelling Urban Plant Diversity Along Environmental, Edaphic, and Climatic Gradients
by Tuba Gül Doğan, Engin Eroğlu, Ecir Uğur Küçüksille, Mustafa İsa Doğan and Tarık Gedik
Diversity 2025, 17(10), 706; https://doi.org/10.3390/d17100706 (registering DOI) - 13 Oct 2025
Abstract
Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants [...] Read more.
Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R2 ≈ 0.937474; Richness R2 ≈ 0.855305; Evenness R2 ≈ 0.631796). Full article
(This article belongs to the Section Plant Diversity)
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29 pages, 735 KB  
Article
SME Strategic Leadership and Grouping as Core Levers for Sustainable Transition—New Wine Typology
by Marc Dressler
Sustainability 2025, 17(20), 9073; https://doi.org/10.3390/su17209073 (registering DOI) - 13 Oct 2025
Abstract
Consumer choices are largely influenced by sustainability, necessitating SMEs from the agri-food sector to strategically address sustainability and innovate their business models. Nonetheless, the challenge for such sustainable leadership lies in maintaining an equilibrium between innovation, sustainability, and financial performance. This study examined [...] Read more.
Consumer choices are largely influenced by sustainability, necessitating SMEs from the agri-food sector to strategically address sustainability and innovate their business models. Nonetheless, the challenge for such sustainable leadership lies in maintaining an equilibrium between innovation, sustainability, and financial performance. This study examined how strategic leadership fosters sustainability-oriented innovation within SMEs exemplified by the wine industry. A survey involving 354 German wineries served to analyze a multi-dimensional concept of innovation clusters (early adopters, pragmatists, pioneers, skeptics, conservatives), type of innovation, sustainability orientation, strategic ambitions, and business performance. Exploring the adoption of fungus-resistant grape varieties (FRV) allowed investigating how sustainability transitions to meet EU Green Deal targets are shaped by strategic groups involving strategic positioning and innovation clusters. There was a correlation between stronger sustainability orientation with greater innovation (Means up to 4.39). As per the findings, it was observed that high scores (p < 0.001, η2 = 0.144–0.160) in market and process innovation were obtained by early adopters and pioneers. These innovation champions excel in economic and social sustainability (p < 0.001) but nonetheless were found to be financially underperforming (Means 1.97–2.18). Innovations that were applied enhanced innovation scores (η2 = 0.128) but did not improve immediate performance. The strongest performance (Mean 2.60) was reported by skeptics though they fared poor in terms of sustainability and innovation. It was also noted that early adopters and pioneers (44–45%) were leading in FRV adoption, while a lag was observed within premium-oriented organizations. These insights may motivate SMEs in their quest for strategic sustainability and allow fine-tuning political and societal measures to achieve a sustainable transition and quantified Green Deal ambitions. It was concluded that long-term positioning was improved by sustainability-driven innovation, however, it would involve short-term performance trade-offs for SMEs. Political support should motivate the sustainable leadership champions to also safeguard profitability. Full article
(This article belongs to the Special Issue Sustainable Leadership and Strategic Management in SMEs)
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16 pages, 2558 KB  
Article
Rapid Prediction of Maximum Remaining Capacity in Lithium-Ion Batteries Based on Charging Segment Features and GA_DBO_BPNN
by Yifei Cao, Rui Wang, Qizhi Li, Peng Zhou, Aqing Li, Penghao Cui, Quanhong Tao and Zhendong Shao
Batteries 2025, 11(10), 375; https://doi.org/10.3390/batteries11100375 (registering DOI) - 13 Oct 2025
Abstract
Rapid and accurate prediction of the maximum remaining life of lithium-ion batteries is a critical technical challenge for enhancing battery management system reliability and enabling the efficient secondary utilization of retired batteries. Traditional approaches that rely on full charge–discharge cycles or complex electrochemical [...] Read more.
Rapid and accurate prediction of the maximum remaining life of lithium-ion batteries is a critical technical challenge for enhancing battery management system reliability and enabling the efficient secondary utilization of retired batteries. Traditional approaches that rely on full charge–discharge cycles or complex electrochemical models often suffer from long detection time and limited adaptability, making them unsuitable for fast testing scenarios. To address these limitations, this study proposes a novel capacity prediction method that integrates charging segment feature extraction with a back-propagation neural network (BPNN) co-optimized using the genetic algorithm (GA) and dung beetle optimizer (DBO). Leveraging the public CALCE datasets, key degradation-related features were extracted from partial charging segments to serve as inputs to the prediction framework. The hybrid GA_DBO algorithm is employed to jointly optimize the BPNN’s weights, learning rate, and activation thresholds. A comparative analysis is conducted across various charging durations (900 s, 1800 s, and 2700 s) to evaluate performance under different input lengths. Results reveal that the model using 1800 s charging segment features achieves the best overall accuracy, with a test set mean squared error (MSE) of 0.0001 Ah2, mean absolute error (MAE) of 0.0092 Ah, root mean square error (RMSE) of 0.0122 Ah, and a coefficient of determination (R2) of 99.66%, demonstrating strong robustness and predictive capability. This research overcomes the traditional reliance on full cycles, demonstrating the effectiveness of short charging segments combined with intelligent optimization algorithms. The proposed method offers a high-precision, low-cost solution for online battery health monitoring and rapid sorting of retired batteries, highlighting its significant engineering application potential. Full article
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15 pages, 2356 KB  
Article
A Fenton Oxidation-Based Integrated Strategy for the Treatment of Raw Gasoline Alkali Residue in Kashi
by Yucai Zhang, Xianghao Zha, Zhuo Zhang, Yangyang Guo, Shuying Yang, Haonan Qiu and Zhiwei Li
Toxics 2025, 13(10), 871; https://doi.org/10.3390/toxics13100871 (registering DOI) - 13 Oct 2025
Abstract
Gasoline alkali residue raw liquid, a kind of highly toxicity containing organic waste generated during petroleum refining, is characterized by its complex composition, high pollutant levels, and significant emission volume. The effective treatment of this wastewater remains a considerable challenge in environmental engineering. [...] Read more.
Gasoline alkali residue raw liquid, a kind of highly toxicity containing organic waste generated during petroleum refining, is characterized by its complex composition, high pollutant levels, and significant emission volume. The effective treatment of this wastewater remains a considerable challenge in environmental engineering. This study systematically investigates the degradation efficiency and mechanism of Fenton oxidation in reducing the chemical oxygen demand (COD) of raw gasoline alkali residue sourced from Kashi. The effects of H2O2 concentration and the H2O2/Fe2+ molar ratio on COD and TOC removal were examined. Results demonstrated that the COD and TOC removal efficiency exhibited an initial decrease followed by an increase with rising concentrations of Fe2+ and H2O2. Comparative assessment of different combined Fenton processes revealed distinct mechanistic differences among the composite oxidation systems. The integration of pretreatment with UV-Fenton oxidation was identified as the optimal strategy. Under optimal conditions (pH = 3.0, H2O2 concentration = 1.0 mol/L, H2O2/Fe2+ molar ratio = 5:0.10), the COD was reduced from 25,041 mg/L to 543 mg/L, achieving a COD removal rate of 97.8%. This study elucidates the reaction mechanism of the Fenton system in treating alkali residue and provides a theoretical foundation for the advanced treatment of high-concentration organic wastewater. Full article
(This article belongs to the Special Issue Technology and Principle of Removing Pollutants in Water)
42 pages, 6872 KB  
Article
Sustainable Water and Energy Management Through a Solar-Hydrodynamic System in a Lake Velence Settlement, Hungary
by Attila Kálmán, Antal Bakonyi, Katalin Bene and Richard Ray
Infrastructures 2025, 10(10), 275; https://doi.org/10.3390/infrastructures10100275 (registering DOI) - 13 Oct 2025
Abstract
The Lake Velence watershed faces increasing challenges driven by local and global factors, including the impacts of climate change, energy resource limitations, and greenhouse gas emissions. These issues, particularly acute in water management, are exacerbated by prolonged droughts, growing population pressures, and shifting [...] Read more.
The Lake Velence watershed faces increasing challenges driven by local and global factors, including the impacts of climate change, energy resource limitations, and greenhouse gas emissions. These issues, particularly acute in water management, are exacerbated by prolonged droughts, growing population pressures, and shifting land use patterns. Such dynamics strain the region’s scarce water resources, negatively affecting the environment, tourism, recreation, agriculture, and economic prospects. Nadap, a hilly settlement within the watershed, experiences frequent flooding and poor water retention, yet it also boasts the highest solar panel capacity per property in Hungary. This research addresses these interconnected challenges by designing a solar-hydrodynamic network comprising four multi-purpose water reservoirs. By leveraging the settlement’s solar capacity and geographical features, the reservoirs provide numerous benefits to local stakeholders and extend their impact far beyond their borders. These include stormwater management with flash flood mitigation, seasonal green energy storage, water security for agriculture and irrigation, wildlife conservation, recreational opportunities, carbon-smart winery developments, and the creation of sustainable blue-green settlements. Reservoir locations and dimensions were determined by analyzing geographical characteristics, stormwater volume, energy demand, solar panel performance, and rainfall data. The hydrodynamic system, modeled in Matlab, was optimized to ensure efficient water usage for irrigation, animal hydration, and other needs while minimizing evaporation losses and carbon emissions. This research presents a design framework for low-carbon and cost-effective solutions that address water management and energy storage, promoting environmental, social, and economic sustainability. The multi-purpose use of retained rainwater solves various existing problems/challenges, strengthens a community’s self-sustainability, and fosters regional growth. This integrated approach can serve as a model for other municipalities and for developing cost-effective inter-settlement and cross-catchment solutions, with a short payback period, facing similar challenges. Full article
(This article belongs to the Section Sustainable Infrastructures)
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22 pages, 3358 KB  
Article
MultiScaleSleepNet: A Hybrid CNN–BiLSTM–Transformer Architecture with Multi-Scale Feature Representation for Single-Channel EEG Sleep Stage Classification
by Cenyu Liu, Qinglin Guan, Wei Zhang, Liyang Sun, Mengyi Wang, Xue Dong and Shuogui Xu
Sensors 2025, 25(20), 6328; https://doi.org/10.3390/s25206328 (registering DOI) - 13 Oct 2025
Abstract
Accurate automatic sleep stage classification from single-channel EEG remains challenging due to the need for effective extraction of multiscale neurophysiological features and modeling of long-range temporal dependencies. This study aims to address these limitations by developing an efficient and compact deep learning architecture [...] Read more.
Accurate automatic sleep stage classification from single-channel EEG remains challenging due to the need for effective extraction of multiscale neurophysiological features and modeling of long-range temporal dependencies. This study aims to address these limitations by developing an efficient and compact deep learning architecture tailored for wearable and edge device applications. We propose MultiScaleSleepNet, a hybrid convolutional neural network–bidirectional long short-term memory–transformer architecture that extracts multiscale temporal and spectral features through parallel convolutional branches, followed by sequential modeling using a BiLSTM memory network and transformer-based attention mechanisms. The model obtained an accuracy, macro-averaged F1 score, and kappa coefficient of 88.6%, 0.833, and 0.84 on the Sleep-EDF dataset; 85.6%, 0.811, and 0.80 on the Sleep-EDF Expanded dataset; and 84.6%, 0.745, and 0.79 on the SHHS dataset. Ablation studies indicate that attention mechanisms and spectral fusion consistently improve performance, with the most notable gains observed for stages N1, N3, and rapid eye movement. MultiScaleSleepNet demonstrates competitive performance across multiple benchmark datasets while maintaining a compact size of 1.9 million parameters, suggesting robustness to variations in dataset size and class distribution. The study supports the feasibility of real-time, accurate sleep staging from single-channel EEG using parameter-efficient deep models suitable for portable systems. Full article
(This article belongs to the Special Issue AI on Biomedical Signal Sensing and Processing for Health Monitoring)
10 pages, 945 KB  
Communication
Development of New Amide Derivatives of Betulinic Acid: Synthetic Approaches and Structural Characterization
by Qinwei Xu, Yuhan Xie, Jin Qi, Zimo Ren, Carmine Coluccini and Paolo Coghi
Molbank 2025, 2025(4), M2072; https://doi.org/10.3390/M2072 (registering DOI) - 13 Oct 2025
Abstract
In this study, we report the synthesis of three new derivatives of betulinic acid, a pentacyclic triterpenoid known for its antitumor activity. These derivatives were synthesized via amide bond formation at the C-28 position using 3-[(Ethylimino)methylidene]amino-N,N-dimethylpropan-1-amine (EDC)/Hydroxybenzotriazole (HOBt) activation [...] Read more.
In this study, we report the synthesis of three new derivatives of betulinic acid, a pentacyclic triterpenoid known for its antitumor activity. These derivatives were synthesized via amide bond formation at the C-28 position using 3-[(Ethylimino)methylidene]amino-N,N-dimethylpropan-1-amine (EDC)/Hydroxybenzotriazole (HOBt) activation and various amines as nucleophiles. The synthesized compounds were characterized by nuclear magnetic resonance (NMR) techniques, including proton (1H), carbon-13 (13C), COSY, HSQC, and DEPT, as well as ultraviolet–visible (UV-VIS) spectroscopy, Fourier-transform infrared (IR) and elemental analysis. This work highlights the potential of semi-synthetic modification of betulinic acid to enhance anticancer properties while addressing challenges in solubility and bioavailability. Further structural optimization and formulation studies are warranted to improve drug-like properties and therapeutic applicability. Full article
(This article belongs to the Section Organic Synthesis and Biosynthesis)
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22 pages, 3254 KB  
Article
Optimizing Steel Industry and Air Conditioning Clusters Using Coordination-Based Time-Series Fusion Transformer
by Xinyu Luo, Zhaofan Zhou, Bin Li, Yumeng Zhang, Chenle Yi, Kun Shi and Songsong Chen
Processes 2025, 13(10), 3265; https://doi.org/10.3390/pr13103265 (registering DOI) - 13 Oct 2025
Abstract
The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible [...] Read more.
The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible load, offers stable regulation with an aggregation effect. This study explores the potential for coordinated load dispatch between the steel industry and air conditioning clusters to enhance power system flexibility. A power characteristic model for steel loads was developed based on energy consumption patterns, while a physical ETP model aggregated air conditioning loads. To improve forecasting accuracy, a parallel LSTM-Transformer model predicts both steel and air conditioning loads. CEEMDAN-VMD decomposition reduces noise in steel-load data, and the QR algorithm computes confidence intervals for load responses. The study further examines interactions between electric-arc furnace control strategies and air conditioning demand response. Case studies using real-world data demonstrate that the proposed model enhances prediction accuracy, peak suppression, and variance reduction. These findings provide insights into steel industry power fluctuations and large-scale air conditioning load adjustments. Full article
17 pages, 546 KB  
Article
AnomalyNLP: Noisy-Label Prompt Learning for Few-Shot Industrial Anomaly Detection
by Li Hua and Jin Qian
Electronics 2025, 14(20), 4016; https://doi.org/10.3390/electronics14204016 (registering DOI) - 13 Oct 2025
Abstract
Few-Shot Industrial Anomaly Detection (FSIAD) is an essential yet challenging problem in practical scenarios such as industrial quality inspection. Its objective is to identify previously unseen anomalous regions using only a limited number of normal support images from the same category. Recently, large [...] Read more.
Few-Shot Industrial Anomaly Detection (FSIAD) is an essential yet challenging problem in practical scenarios such as industrial quality inspection. Its objective is to identify previously unseen anomalous regions using only a limited number of normal support images from the same category. Recently, large pre-trained vision-language models (VLMs), such as CLIP, have exhibited remarkable few-shot image-text representation abilities across a range of visual tasks, including anomaly detection. Despite their promise, real-world industrial anomaly datasets often contain noisy labels, which can degrade prompt learning and detection performance. In this paper, we propose AnomalyNLP, a new Noisy-Label Prompt Learning approach designed to tackle the challenge of few-shot anomaly detection. This framework offers a simple and efficient approach that leverages the expressive representations and precise alignment capabilities of VLMs for industrial anomaly detection. First, we design a Noisy-Label Prompt Learning (NLPL) strategy. This strategy utilizes feature learning principles to suppress the influence of noisy samples via Mean Absolute Error (MAE) loss, thereby improving the signal-to-noise ratio and enhancing overall model robustness. Furthermore, we introduce a prompt-driven optimal transport feature purification method to accurately partition datasets into clean and noisy subsets. For both image-level and pixel-level anomaly detection, AnomalyNLP achieves state-of-the-art performance across various few-shot settings on the MVTecAD and VisA public datasets. Qualitative and quantitative results on two datasets demonstrate that our method achieves the largest average AUC improvement over baseline methods across 1-, 2-, and 4-shot settings, with gains of up to 10.60%, 10.11%, and 9.55% in practical anomaly detection scenarios. Full article
28 pages, 10614 KB  
Article
Assessment of Ecological Quality Dynamics and Driving Factors in the Ningdong Mining Area, China, Using the Coupled Remote Sensing Ecological Index and Ecological Grade Index
by Chengting Han, Peixian Li, He’ao Xie, Yupeng Pi, Yongliang Zhang, Xiaoqing Han, Jingjing Jin and Yuling Zhao
Sustainability 2025, 17(20), 9075; https://doi.org/10.3390/su17209075 (registering DOI) - 13 Oct 2025
Abstract
In response to the sustainability challenges of mining, restrictive policies aimed at improving ecological quality have been enacted in various countries and regions. The purpose of this study is to examine the environmental changes in the Ningdong mining area, located on the Loess [...] Read more.
In response to the sustainability challenges of mining, restrictive policies aimed at improving ecological quality have been enacted in various countries and regions. The purpose of this study is to examine the environmental changes in the Ningdong mining area, located on the Loess Plateau, over the past 25 years, due to many factors, such as coal mining, using the area as a case study. In this study, Landsat satellite images from 2000 to 2024 were used to derive the remote sensing ecological index (RSEI), while the RSEI results were comprehensively analyzed using the Sen+Mann-Kendall method with Geodetector, respectively. Simultaneously, this study utilized land use datasets to calculate the ecological grade (EG) index. The EG index was then analyzed in conjunction with the RSEI. The results show that in the time dimension, the ecological quality of the Ningdong mining area shows a non-monotonic trend of decreasing and then increasing during the 25-year period; The RSEI average reached its lowest value of 0.279 in 2011 and its highest value of 0.511 in 2022. In 2024, the RSEI was 0.428; The coupling matrix between the EG and RSEI indicates that the ecological environment within the mining area has improved. Through ecological factor-driven analysis, we found that the ecological environment quality in the study area is stably controlled by natural topography (slope) and climate (precipitation) factors, while also being disturbed by human activities. This experimental section demonstrates that ecological and environmental evolution is a complex process driven by the nonlinear synergistic interaction of natural and anthropogenic factors. The results of the study are of practical significance and provide scientific guidance for the development of coal mining and ecological environmental protection policies in other mining regions around the world. Full article
(This article belongs to the Special Issue Design for Sustainability in the Minerals Sector)
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21 pages, 5240 KB  
Article
Intelligent Settlement Forecasting of Surrounding Buildings During Deep Foundation Pit Excavation Using GWO-VMD-LSTM
by Huan Yin, Chuang He and Huafeng Shan
Buildings 2025, 15(20), 3688; https://doi.org/10.3390/buildings15203688 (registering DOI) - 13 Oct 2025
Abstract
In the context of deep foundation pit excavation, the settlement of surrounding buildings is a critical indicator for safety assessment and early warning. Due to the non-stationary and nonlinear characteristics of settlement data, traditional prediction approaches often fail to achieve satisfactory accuracy. To [...] Read more.
In the context of deep foundation pit excavation, the settlement of surrounding buildings is a critical indicator for safety assessment and early warning. Due to the non-stationary and nonlinear characteristics of settlement data, traditional prediction approaches often fail to achieve satisfactory accuracy. To address this challenge, this study proposes a hybrid prediction model integrating the Grey Wolf Optimizer (GWO), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) networks, referred to as the GWO-VMD-LSTM model. In the proposed framework, GWO is employed to optimize the key hyperparameters of VMD as well as LSTM, thereby ensuring robust decomposition and prediction performance. Experimental results based on settlement monitoring data from four typical points around the Yongning Hospital foundation pit in Taizhou, China, demonstrate that the proposed model achieves superior predictive accuracy compared with five benchmark models. Specifically, the GWO-VMD-LSTM model attained an average coefficient of determination (R2) of 0.951, mean squared error (MSE) of 0.002, root mean square error (RMSE) of 0.033 mm, mean absolute error (MAE) of 0.031 mm, and mean absolute percentage error (MAPE) of 1.324%, outperforming all alternatives. For instance, compared with the VMD-LSTM model, the proposed method improved R2 by 26.56% and reduced MAPE by 45.87%. These findings confirm that the GWO-VMD-LSTM model not only enhances the accuracy and generalization of settlement prediction but also provides a reliable and practical tool for real-time monitoring and risk assessment of buildings adjacent to deep foundation pits in soft soil regions. Full article
(This article belongs to the Section Building Structures)
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19 pages, 4130 KB  
Article
Deep Learning Application of Fruit Planting Classification Based on Multi-Source Remote Sensing Images
by Jiamei Miao, Jian Gao, Lei Wang, Lei Luo and Zhi Pu
Appl. Sci. 2025, 15(20), 10995; https://doi.org/10.3390/app152010995 (registering DOI) - 13 Oct 2025
Abstract
With global climate change, urbanization, and agricultural resource limitations, precision agriculture and crop monitoring are crucial worldwide. Integrating multi-source remote sensing data with deep learning enables accurate crop mapping, but selecting optimal network architectures remains challenging. To improve remote sensing-based fruit planting classification [...] Read more.
With global climate change, urbanization, and agricultural resource limitations, precision agriculture and crop monitoring are crucial worldwide. Integrating multi-source remote sensing data with deep learning enables accurate crop mapping, but selecting optimal network architectures remains challenging. To improve remote sensing-based fruit planting classification and support orchard management and rural revitalization, this study explored feature selection and network optimization. We proposed an improved CF-EfficientNet model (incorporating FGMF and CGAR modules) for fruit planting classification. Multi-source remote sensing data (Sentinel-1, Sentinel-2, and SRTM) were used to extract spectral, vegetation, polarization, terrain, and texture features, thereby constructing a high-dimensional feature space. Feature selection identified 13 highly discriminative bands, forming an optimal dataset, namely the preferred bands (PBs). At the same time, two classification datasets—multi-spectral bands (MS) and preferred bands (PBs)—were constructed, and five typical deep learning models were introduced to compare performance: (1) EfficientNetB0, (2) AlexNet, (3) VGG16, (4) ResNet18, (5) RepVGG. The experimental results showed that the EfficientNetB0 model based on the preferred band performed best in terms of overall accuracy (87.1%) and Kappa coefficient (0.677). Furthermore, a Fine-Grained Multi-scale Fusion (FGMF) and a Condition-Guided Attention Refinement (CGAR) were incorporated into EfficientNetB0, and the traditional SGD optimizer was replaced with Adam to construct the CF-EfficientNet architecture. The results indicated that the improved CF-EfficientNet model achieved high performance in crop classification, with an overall accuracy of 92.6% and a Kappa coefficient of 0.830. These represent improvements of 5.5 percentage points and 0.153, compared with the baseline model, demonstrating superiority in both classification accuracy and stability. Full article
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