Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (7,773)

Search Parameters:
Keywords = temporal distributions

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
28 pages, 2724 KiB  
Article
Data-Driven Dynamic Optimization for Hosting Capacity Forecasting in Low-Voltage Grids
by Md Tariqul Islam, M. J. Hossain and Md Ahasan Habib
Energies 2025, 18(15), 3955; https://doi.org/10.3390/en18153955 (registering DOI) - 24 Jul 2025
Abstract
The sustainable integration of Distributed Energy Resources (DER) with the next-generation distribution networks requires robust, adaptive, and accurate hosting capacity (HC) forecasting. Dynamic Operating Envelopes (DOE) provide real-time constraints for power import/export to the grid, ensuring dynamic DER integration and efficient network operation. [...] Read more.
The sustainable integration of Distributed Energy Resources (DER) with the next-generation distribution networks requires robust, adaptive, and accurate hosting capacity (HC) forecasting. Dynamic Operating Envelopes (DOE) provide real-time constraints for power import/export to the grid, ensuring dynamic DER integration and efficient network operation. However, conventional HC analysis and forecasting approaches struggle to capture temporal dependencies, the impact of DOE constraints on network operation, and uncertainty in DER output. This study introduces a dynamic optimization framework that leverages the benefits of the sensitivity gate of the Sensitivity-Enhanced Recurrent Neural Network (SERNN) forecasting model, Particle Swarm Optimization (PSO), and Bayesian Optimization (BO) for HC forecasting. The PSO determines the optimal weights and biases, and BO fine-tunes hyperparameters of the SERNN forecasting model to minimize the prediction error. This approach dynamically adjusts the import/export of the DER output to the grid by integrating the DOE constraints into the SG-PSO-BO architecture. Performance evaluation on the IEEE-123 test network and a real Australian distribution network demonstrates superior HC forecasting accuracy, with an R2 score of 0.97 and 0.98, Mean Absolute Error (MAE) of 0.21 and 0.16, and Root Mean Square Error (RMSE) of 0.38 and 0.31, respectively. The study shows that the model effectively captures the non-linear and time-sensitive interactions between network parameters, DER variables, and weather information. This study offers valuable insights into advancing dynamic HC forecasting under real-time DOE constraints in sustainable DER integration, contributing to the global transition towards net-zero emissions. Full article
Show Figures

Figure 1

30 pages, 3932 KiB  
Article
Banking on the Metaverse: Systemic Disruption or Techno-Financial Mirage?
by Alina Georgiana Manta and Claudia Gherțescu
Systems 2025, 13(8), 624; https://doi.org/10.3390/systems13080624 - 24 Jul 2025
Abstract
This study delivers a rigorous and in-depth bibliometric examination of 693 scholarly publications addressing the intersection of metaverse technologies and banking, retrieved from the Web of Science Core Collection. Through advanced scientometric tools, including VOSviewer and Bibliometrix, the research systematically unpacks the evolving [...] Read more.
This study delivers a rigorous and in-depth bibliometric examination of 693 scholarly publications addressing the intersection of metaverse technologies and banking, retrieved from the Web of Science Core Collection. Through advanced scientometric tools, including VOSviewer and Bibliometrix, the research systematically unpacks the evolving intellectual and thematic contours of this interdisciplinary frontier. The co-occurrence analysis of keywords reveals a landscape shaped by seven core thematic clusters, encompassing immersive user environments, digital infrastructure, experiential design, and ethical considerations. Factorial analysis uncovers a marked bifurcation between experience-driven narratives and technology-centric frameworks, with integrative concepts such as technology, information, and consumption serving as conceptual bridges. Network visualizations of authorship patterns point to the emergence of high-density collaboration clusters, particularly centered around influential contributors such as Dwivedi and Ooi, while regional distribution patterns indicate a tri-continental dominance led by Asia, North America, and Western Europe. Temporal analysis identifies a significant surge in academic interest beginning in 2022, aligning with increased institutional and commercial experimentation in virtual financial platforms. Our findings argue that the incorporation of metaverse paradigms into banking is not merely a technological shift but a systemic transformation in progress—one that blurs the boundaries between speculative innovation and tangible implementation. This work contributes foundational insights for future inquiry into digital finance systems, algorithmic governance, trust architecture, and the wider socio-economic consequences of banking in virtualized environments. Whether a genuine leap toward financial evolution or a sophisticated illusion, the metaverse in banking must now be treated as a systemic phenomenon worthy of serious scrutiny. Full article
Show Figures

Figure 1

26 pages, 3580 KiB  
Article
Delineating Urban High–Risk Zones of Disease Transmission: Applying Tensor Decomposition to Trajectory Big Data
by Tianhua Lu and Wenjia Zhang
ISPRS Int. J. Geo-Inf. 2025, 14(8), 285; https://doi.org/10.3390/ijgi14080285 - 23 Jul 2025
Abstract
Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of [...] Read more.
Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of populations in both space and time, which results in many studies only being able to employ static geostatistical analytical methods, neglecting the transmission risks associated with human mobility. This study utilized the mobile phone signaling data of Shenzhen residents from 2019 to 2020 and developed a CP tensor decomposition algorithm to decompose the long-sequence spatiotemporal trajectory data to detect high risk zones in terms of detecting overlapped community structures. Tensor decomposition algorithms revealed community structures in 2020 and the overlapping regions among these communities. Based on the overlap in spatial distribution and the similarity in temporal rhythms of these communities, we identified regions with spatiotemporal co-location as high–risk zones. Furthermore, we calculated the degree of population mixing in these areas to indicate the level of risk. These areas could potentially lead to rapid virus spread across communities. The research findings address the shortcomings of currently used static geographic statistical methods in delineating risk zones, and emphasize the critical importance of integrating spatial and temporal dimensions within behavioral big data analytics. Future research should consider utilizing non-aggregated individual trajectories to construct tensors, enabling the inclusion of individual and environmental attributes. Full article
Show Figures

Figure 1

20 pages, 7143 KiB  
Article
Predicting Potentially Suitable Habitats and Analyzing the Distribution Patterns of the Rare and Endangered Genus Syndiclis Hook. f. (Lauraceae) in China
by Lang Huang, Weihao Yao, Xu Xiao, Yang Zhang, Rui Chen, Yanbing Yang and Zhi Li
Plants 2025, 14(15), 2268; https://doi.org/10.3390/plants14152268 - 23 Jul 2025
Abstract
Changes in habitat suitability are critical indicators of the ecological impacts of climate change. Syndiclis Hook. f., a rare and endangered genus endemic to montane limestone and cloud forest ecosystems in China, holds considerable ecological and economic value. However, knowledge of its current [...] Read more.
Changes in habitat suitability are critical indicators of the ecological impacts of climate change. Syndiclis Hook. f., a rare and endangered genus endemic to montane limestone and cloud forest ecosystems in China, holds considerable ecological and economic value. However, knowledge of its current distribution and the key environmental factors influencing its habitat suitability remains limited. In this study, we employed the MaxEnt model, integrated with geographic information systems (ArcGIS), to predict the potential distribution of Syndiclis under current and future climate scenarios, identify dominant bioclimatic drivers, and assess temporal and spatial shifts in habitat patterns. We also analyzed spatial displacement of habitat centroids to explore potential migration pathways. The model demonstrated excellent performance (AUC = 0.988), with current suitable habitats primarily located in Hainan, Taiwan, Southeastern Yunnan, and along the Yunnan–Guangxi border. Temperature seasonality (bio7) emerged as the most important predictor (67.00%), followed by precipitation of the driest quarter (bio17, 14.90%), while soil factors played a relatively minor role. Under future climate projections, Hainan and Taiwan are expected to serve as stable climatic refugia, whereas the overall suitable habitat area is projected to decline significantly. Combined with topographic constraints, population decline, and limited dispersal ability, these changes elevate the risk of extinction for Syndiclis in the wild. Landscape pattern analysis revealed increased habitat fragmentation under warming conditions, with only 4.08% of suitable areas currently under effective protection. We recommend prioritizing conservation efforts in regions with habitat contraction (e.g., Guangxi and Yunnan) and stable refugia (e.g., Hainan and Taiwan). Conservation strategies should integrate targeted in situ and ex situ actions, guided by dominant environmental variables and projected migration routes, to ensure the long-term persistence of Syndiclis populations and support evidence-based conservation planning. Full article
(This article belongs to the Section Plant Ecology)
Show Figures

Figure 1

22 pages, 2538 KiB  
Article
Enhancing Supervisory Control with GPenSIM
by Reggie Davidrajuh, Shuanglin Tang and Yuming Feng
Machines 2025, 13(8), 641; https://doi.org/10.3390/machines13080641 - 23 Jul 2025
Abstract
Supervisory control theory (SCT), based on Petri nets, offers a robust framework for modeling and controlling discrete-event systems but faces significant challenges in scalability, expressiveness, and practical implementation. This paper introduces General-purpose Petri Net Simulator and Real-Time Controller (GPenSIM), a MATLAB version 24.1.0.2689473 [...] Read more.
Supervisory control theory (SCT), based on Petri nets, offers a robust framework for modeling and controlling discrete-event systems but faces significant challenges in scalability, expressiveness, and practical implementation. This paper introduces General-purpose Petri Net Simulator and Real-Time Controller (GPenSIM), a MATLAB version 24.1.0.2689473 (R2024a) Update 6-based modular Petri net framework, as a novel solution to these limitations. GPenSIM leverages modular decomposition to mitigate state-space explosion, enabling parallel execution of weakly coupled Petri modules on multi-core systems. Its programmable interfaces (pre-processors and post-processors) extend classical Petri nets’ expressiveness by enforcing nonlinear, temporal, and conditional constraints through custom MATLAB scripts, addressing the rigidity of traditional linear constraints. Furthermore, the integration of GPenSIM with MATLAB facilitates real-time control synthesis, performance optimization, and seamless interaction with external hardware and software, bridging the gap between theoretical models and industrial applications. Empirical studies demonstrate the efficacy of GPenSIM in reconfigurable manufacturing systems, where it reduced downtime by 30%, and in distributed control scenarios, where decentralized modules minimized synchronization delays. Grounded in systems theory principles of interconnectedness, GPenSIM emphasizes dynamic relationships between components, offering a scalable, adaptable, and practical tool for supervisory control. This work highlights the potential of GPenSIM to overcome longstanding limitations in SCT, providing a versatile platform for both academic research and industrial deployment. Full article
(This article belongs to the Section Automation and Control Systems)
Show Figures

Figure 1

20 pages, 12036 KiB  
Article
Spatiotemporal Mapping of Grazing Livestock Behaviours Using Machine Learning Algorithms
by Guo Ye and Rui Yu
Sensors 2025, 25(15), 4561; https://doi.org/10.3390/s25154561 - 23 Jul 2025
Abstract
Grassland ecosystems are fundamentally shaped by the complex behaviours of livestock. While most previous studies have monitored grassland health using vegetation indices, such as NDVI and LAI, fewer have investigated livestock behaviours as direct drivers of grassland degradation. In particular, the spatial clustering [...] Read more.
Grassland ecosystems are fundamentally shaped by the complex behaviours of livestock. While most previous studies have monitored grassland health using vegetation indices, such as NDVI and LAI, fewer have investigated livestock behaviours as direct drivers of grassland degradation. In particular, the spatial clustering and temporal concentration patterns of livestock behaviours are critical yet underexplored factors that significantly influence grassland ecosystems. This study investigated the spatiotemporal patterns of livestock behaviours under different grazing management systems and grazing-intensity gradients (GIGs) in Wenchang, China, using high-resolution GPS tracking data and machine learning classification. the K-Nearest Neighbours (KNN) model combined with SMOTE-ENN resampling achieved the highest accuracy, with F1-scores of 0.960 and 0.956 for continuous and rotational grazing datasets. The results showed that the continuous grazing system failed to mitigate grazing pressure when grazing intensity was reduced, as the spatial clustering of livestock behaviours did not decrease accordingly, and the frequency of temporal peaks in grazing behaviour even showed an increasing trend. Conversely, the rotational grazing system responded more effectively, as reduced GIGs led to more evenly distributed temporal activity patterns and lower spatial clustering. These findings highlight the importance of incorporating livestock behavioural patterns into grassland monitoring and offer data-driven insights for sustainable grazing management. Full article
(This article belongs to the Section Smart Agriculture)
Show Figures

Figure 1

15 pages, 12546 KiB  
Article
Retrieval of Chlorophyll-a Concentration in Nanyi Lake Using the AutoGluon Framework
by Weibin Gu, Ji Liang, Lian Yang, Shanshan Guo and Ruixin Jia
Water 2025, 17(15), 2190; https://doi.org/10.3390/w17152190 - 23 Jul 2025
Abstract
The chlorophyll-a (Chl-a) concentration in lakes is a crucial parameter for monitoring water quality and assessing phytoplankton abundance. However, accurately retrieving Chl-a concentrations remains a significant challenge in remote sensing. To address the limitations of existing methods in terms of modeling efficiency and [...] Read more.
The chlorophyll-a (Chl-a) concentration in lakes is a crucial parameter for monitoring water quality and assessing phytoplankton abundance. However, accurately retrieving Chl-a concentrations remains a significant challenge in remote sensing. To address the limitations of existing methods in terms of modeling efficiency and adaptability, this study focuses on Lake Nanyi in Anhui Province. By integrating Sentinel-2 satellite imagery with in situ water quality measurements and employing the AutoML framework AutoGluon, a Chl-a inversion model based on narrow-band spectral features is developed. Feature selection and model ensembling identify bands B6 (740 nm) and B7 (783 nm) as the optimal combination, which are then applied to multi-temporal imagery from October 2022 to generate spatial mean distributions of Chl-a in Lake Nanyi. The results demonstrate that the AutoGluon framework significantly outperforms traditional methods in both model accuracy (R2: 0.94, RMSE: 1.67 μg/L) and development efficiency. The retrieval results reveal spatial heterogeneity in Chl-a concentration, with higher concentrations observed in the southern part of the western lake and the western side of the eastern lake, while the central lake area exhibits relatively lower concentrations, ranging from 3.66 to 21.39 μg/L. This study presents an efficient and reliable approach for lake ecological monitoring and underscores the potential of AutoML in water color remote sensing applications. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
Show Figures

Figure 1

18 pages, 5079 KiB  
Article
Graph Representation Learning on Street Networks
by Mateo Neira and Roberto Murcio
ISPRS Int. J. Geo-Inf. 2025, 14(8), 284; https://doi.org/10.3390/ijgi14080284 - 22 Jul 2025
Abstract
Street networks provide an invaluable source of information about the different temporal and spatial patterns emerging in our cities. These streets are often represented as graphs where intersections are modeled as nodes and streets as edges between them. Previous work has shown that [...] Read more.
Street networks provide an invaluable source of information about the different temporal and spatial patterns emerging in our cities. These streets are often represented as graphs where intersections are modeled as nodes and streets as edges between them. Previous work has shown that raster representations of the original data can be created through a learning algorithm on low-dimensional representations of the street networks. In contrast, models that capture high-level urban network metrics can be trained through convolutional neural networks. However, the detailed topological data is lost through the rasterization of the street network, and the models cannot recover this information from the image alone, failing to capture complex street network features. This paper proposes a model capable of inferring good representations directly from the street network. Specifically, we use a variational autoencoder with graph convolutional layers and a decoder that generates a probabilistic, fully connected graph to learn latent representations that encode both local network structure and the spatial distribution of nodes. We train the model on thousands of street network segments and use the learned representations to generate synthetic street configurations. Finally, we proposed a possible application to classify the urban morphology of different network segments, investigating their common characteristics in the learned space. Full article
Show Figures

Figure 1

16 pages, 4829 KiB  
Article
A New Monitoring Method for the Water-Filled Status of Check Dams Using Remote Sensing and Deep Learning Techniques
by Zhaohui Xia, Shu Yu, Naichang Zhang, Jianqin Wang, Yongxiang Cao, Fan Yue and Heng Zhang
Water 2025, 17(15), 2185; https://doi.org/10.3390/w17152185 - 22 Jul 2025
Abstract
China’s Loess Plateau is characterized by dense and widely distributed check dams. However, there has been a lack of large-scale and high-temporal-resolution monitoring for the water-filled status of check dams, which is strongly related to their safe operation. In this study, a deep [...] Read more.
China’s Loess Plateau is characterized by dense and widely distributed check dams. However, there has been a lack of large-scale and high-temporal-resolution monitoring for the water-filled status of check dams, which is strongly related to their safe operation. In this study, a deep learning-based remote sensing image interpretation technique was developed, which enables long-term, large-scale, and high-frequency monitoring of the water-filled status of check dams. Using object detection algorithms based on deep learning and YOLOv3, an object-detection model was constructed and optimized. Leveraging high spatial resolution optical remote sensing images, the water-filled status of check dams can be identified. The model prediction indicates that the average precision for the check dam and water-filled check dam detection models reached 90.27% and 91.89%, respectively. Case studies were conducted on the check dams in the Jiuyuangou and Xiwuselang small watersheds. The monitoring result based on remote sensing images in the year 2021 shows a good agreement with the actual number of check dams. The proposed monitoring method for the water-filled status of check dams was proven feasible. This provides a reliable and efficient technical approach for monitoring the water-filled status of check dams, which is promising for their daily management and safe operation. Full article
(This article belongs to the Section Soil and Water)
Show Figures

Figure 1

21 pages, 1672 KiB  
Article
TSE-APT: An APT Attack-Detection Method Based on Time-Series and Ensemble-Learning Models
by Mingyue Cheng, Ga Xiang, Qunsheng Yang, Zhixing Ma and Haoyang Zhang
Electronics 2025, 14(15), 2924; https://doi.org/10.3390/electronics14152924 - 22 Jul 2025
Abstract
Advanced Persistent Threat (APT) attacks pose a serious challenge to traditional detection methods. These methods often suffer from high false-alarm rates and limited accuracy due to the multi-stage and covert nature of APT attacks. In this paper, we propose TSE-APT, a time-series ensemble [...] Read more.
Advanced Persistent Threat (APT) attacks pose a serious challenge to traditional detection methods. These methods often suffer from high false-alarm rates and limited accuracy due to the multi-stage and covert nature of APT attacks. In this paper, we propose TSE-APT, a time-series ensemble model that addresses these two limitations. It combines multiple machine-learning models, such as Random Forest (RF), Multi-Layer Perceptron (MLP), and Bidirectional Long Short-Term Memory Network (BiLSTM) models, to dynamically capture correlations between multiple stages of the attack process based on time-series features. It discovers hidden features through the integration of multiple machine-learning models to significantly improve the accuracy and robustness of APT detection. First, we extract a collection of dynamic time-series features such as traffic mean, flow duration, and flag frequency. We fuse them with static contextual features, including the port service matrix and protocol type distribution, to effectively capture the multi-stage behaviors of APT attacks. Then, we utilize an ensemble-learning model with a dynamic weight-allocation mechanism using a self-attention network to adaptively adjust the sub-model contribution. The experiments showed that using time-series feature fusion significantly enhanced the detection performance. The RF, MLP, and BiLSTM models achieved 96.7% accuracy, considerably enhancing recall and the false positive rate. The adaptive mechanism optimizes the model’s performance and reduces false-alarm rates. This study provides an analytical method for APT attack detection, considering both temporal dynamics and context static characteristics, and provides new ideas for security protection in complex networks. Full article
(This article belongs to the Special Issue AI in Cybersecurity, 2nd Edition)
Show Figures

Figure 1

34 pages, 26037 KiB  
Article
Remote Sensing-Based Analysis of the Coupled Impacts of Climate and Land Use Changes on Future Ecosystem Resilience: A Case Study of the Beijing–Tianjin–Hebei Region
by Jingyuan Ni and Fang Xu
Remote Sens. 2025, 17(15), 2546; https://doi.org/10.3390/rs17152546 - 22 Jul 2025
Abstract
Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim [...] Read more.
Urban and regional ecosystems are increasingly challenged by the compounded effects of climate change and intensive land use. In this study, a predictive assessment framework for ecosystem resilience in the Beijing–Tianjin–Hebei region was developed by integrating multi-source remote sensing data, with the aim of quantitatively evaluating the coupled effects of climate change and land use change on future ecosystem resilience. In the first stage of the study, the SD-PLUS coupled modeling framework was employed to simulate land use patterns for the years 2030 and 2060 under three representative combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Building upon these simulations, ecosystem resilience was comprehensively evaluated and predicted on the basis of three key attributes: resistance, adaptability, and recovery. This enabled a quantitative investigation of the spatio-temporal dynamics of ecosystem resilience under each scenario. The results reveal the following: (1) Temporally, ecosystem resilience exhibited a staged pattern of change. From 2020 to 2030, an increasing trend was observed only under the SSP1-2.6 scenario, whereas, from 2030 to 2060, resilience generally increased in all scenarios. (2) In terms of scenario comparison, ecosystem resilience typically followed a gradient pattern of SSP1-2.6 > SSP2-4.5 > SSP5-8.5. However, in 2060, a notable reversal occurred, with the highest resilience recorded under the SSP5-8.5 scenario. (3) Spatially, areas with high ecosystem resilience were primarily distributed in mountainous regions, while the southeastern plains and coastal zones consistently exhibited lower resilience levels. The results indicate that climate and land use changes jointly influence ecosystem resilience. Rainfall and temperature, as key climate drivers, not only affect land use dynamics but also play a crucial role in regulating ecosystem services and ecological processes. Under extreme scenarios such as SSP5-8.5, these factors may trigger nonlinear responses in ecosystem resilience. Meanwhile, land use restructuring further shapes resilience patterns by altering landscape configurations and recovery mechanisms. Our findings highlight the role of climate and land use in reshaping ecological structure, function, and services. This study offers scientific support for assessing and managing regional ecosystem resilience and informs adaptive urban governance in the face of future climate and land use uncertainty, promotes the sustainable development of ecosystems, and expands the applicability of remote sensing in dynamic ecological monitoring and predictive analysis. Full article
Show Figures

Figure 1

19 pages, 8699 KiB  
Article
Study on the Spatio-Temporal Characteristics and Driving Factors of PM2.5 in the Inter-Provincial Border Region of Eastern China (Jiangsu, Anhui, Shandong, Henan) from 2022 to 2024
by Xiaoli Xia, Shangpeng Sun, Xinru Wang and Feifei Shen
Atmosphere 2025, 16(8), 895; https://doi.org/10.3390/atmos16080895 - 22 Jul 2025
Viewed by 56
Abstract
The inter-provincial border region in eastern China, encompassing the junction of Jiangsu, Anhui, Shandong, and Henan provinces, serves as a crucial zone that connects the important economic zones of Beijing–Tianjin–Hebei and the Yangtze River Delta. It is of great significance to study the [...] Read more.
The inter-provincial border region in eastern China, encompassing the junction of Jiangsu, Anhui, Shandong, and Henan provinces, serves as a crucial zone that connects the important economic zones of Beijing–Tianjin–Hebei and the Yangtze River Delta. It is of great significance to study the temporal variation characteristics, spatial distribution patterns, and driving factors of PM2.5 concentrations in this region. Based on the PM2.5 concentration observation data, ground meteorological data, environmental data, and socio-economic data from 2022 to 2024, this study conducted in-depth and systematic research by using advanced methods, such as spatial autocorrelation analysis and geographical detectors. The research results show that the concentration of PM2.5 rose from 2022 to 2023, but decreased from 2023 to 2024. From the perspective of seasonal variations, the concentration of PM2.5 shows a distinct characteristic of being “high in winter and low in summer”. The monthly variation shows a “U”-shaped distribution pattern. In terms of spatial changes, the PM2.5 concentration in the inter-provincial border region of eastern China (Jiangsu, Anhui, Shandong, Henan) forms a gradient difference of “higher in the west and lower in the east”. The high-concentration agglomeration areas are mainly concentrated in the Henan part of the study region, while the low-concentration agglomeration areas are distributed in the eastern coastal parts of the study region. The analysis of the driving factors of the PM2.5 concentration based on geographical detectors reveals that the average temperature is the main factor affecting the PM2.5 concentration. The interaction among the factors contributing to the spatial differentiation of the PM2.5 concentration is very obvious. Temperature and population density (q = 0.92), temperature and precipitation (q = 0.95), slope and precipitation (q = 0.97), as well as DEM and population density (q = 0.96), are the main combinations of factors that have continuously affected the spatial differentiation of the PM2.5 concentration for many years. The research results from this study provide a scientific basis and decision support for the prevention, control, and governance of PM2.5 pollution. Full article
(This article belongs to the Special Issue Atmospheric Pollution Dynamics in China)
Show Figures

Figure 1

24 pages, 4093 KiB  
Article
Community Structure and Influencing Factors of Macro-Benthos in Bottom-Seeded Marine Pastures: A Case Study of Caofeidian, China
by Xiangping Xue, Long Yun, Zhaohui Sun, Jiangwei Zan, Xinjing Xu, Xia Liu, Song Gao, Guangyu Wang, Mingshuai Liu and Fei Si
Biology 2025, 14(7), 901; https://doi.org/10.3390/biology14070901 - 21 Jul 2025
Viewed by 80
Abstract
To accurately assess the water quality, ecosystem status, distribution of large benthic organisms, and ecological restoration under human intervention, an analysis of benthic organisms on Caofeidian in September and November 2023 and January and May of the following year was conducted in this [...] Read more.
To accurately assess the water quality, ecosystem status, distribution of large benthic organisms, and ecological restoration under human intervention, an analysis of benthic organisms on Caofeidian in September and November 2023 and January and May of the following year was conducted in this work. By performing CCA (canonical correspondence analysis) and cluster and correlation coefficient (Pearson) analyses, the temporal variation characteristics of benthic abundance, dominant species, community structure and biodiversity were analyzed. A total of 79 species of macro-benthic animals were found in four months, including 32 species of polychaetes, cnidarians, 1 species of Nemertean, 19 species of crustaceans, and 24 species of molluscs. The use of conventional grab-type mud collectors revealed that the Musculus senhousei dominated the survey (Y > 0.02). While only a small number of Ruditapes philippinarum were collected from bottom-dwelling species, a certain number of bottom-dwelling species (Ruditapes philippinarum and Scapharca subcrenata) were also collected during the trawl survey. Additionally, a significant population of Rapana venosa was found in the area. It is speculated that the dual effects of predation and competition are likely the primary reasons for the relatively low abundance of bottom-dwelling species. The density and biomass of macro-benthos were consistent over time, which was the highest in May, the second highest in January, and the lowest in September and November. The main environmental factors affecting the large benthic communities in the surveyed sea areas were pH, DO, NO2-N, T, SAL and PO43−-P. Combined with historical data, it was found that although the environmental condition in the Caofeidian sea area has improved, the Musculus senhousei has been dominant. In addition, the abundance of other species is much less than that of the Musculus senhousei, and the diversity of the benthic community is still reduced. Our work provides valuable data support for the management and improvement of bottom Marine pasture and promotes the transformation of Marine resources from resource plunder to a sustainable resource. Full article
(This article belongs to the Special Issue Global Fisheries Resources, Fisheries, and Carbon-Sink Fisheries)
Show Figures

Figure 1

26 pages, 5535 KiB  
Article
Research on Power Cable Intrusion Identification Using a GRT-Transformer-Based Distributed Acoustic Sensing (DAS) System
by Xiaoli Huang, Xingcheng Wang, Han Qin and Zhaoliang Zhou
Informatics 2025, 12(3), 75; https://doi.org/10.3390/informatics12030075 - 21 Jul 2025
Viewed by 163
Abstract
To address the high false alarm rate of intrusion detection systems based on distributed acoustic sensing (DAS) for power cables in complex underground environments, an innovative GRT-Transformer multimodal deep learning model is proposed. The core of this model lies in its distinctive three-branch [...] Read more.
To address the high false alarm rate of intrusion detection systems based on distributed acoustic sensing (DAS) for power cables in complex underground environments, an innovative GRT-Transformer multimodal deep learning model is proposed. The core of this model lies in its distinctive three-branch parallel collaborative architecture: two branches employ Gramian Angular Summation Field (GASF) and Recursive Pattern (RP) algorithms to convert one-dimensional intrusion waveforms into two-dimensional images, thereby capturing rich spatial patterns and dynamic characteristics and the third branch utilizes a Gated Recurrent Unit (GRU) algorithm to directly focus on the temporal evolution features of the waveform; additionally, a Transformer component is integrated to capture the overall trend and global dependencies of the signals. Ultimately, the terminal employs a Bidirectional Long Short-Term Memory (BiLSTM) network to perform a deep fusion of the multidimensional features extracted from the three branches, enabling a comprehensive understanding of the bidirectional temporal dependencies within the data. Experimental validation demonstrates that the GRT-Transformer achieves an average recognition accuracy of 97.3% across three typical intrusion events—illegal tapping, mechanical operations, and vehicle passage—significantly reducing false alarms, surpassing traditional methods, and exhibiting strong practical potential in complex real-world scenarios. Full article
Show Figures

Figure 1

21 pages, 12252 KiB  
Article
Changes in Intra-Annual River Runoff in the Ile and Zhetysu Alatau Mountains Under Climate Change Conditions
by Rustam G. Abdrakhimov, Victor P. Blagovechshenskiy, Sandugash U. Ranova, Aigul N. Akzharkynova, Sezar Gülbaz, Ulzhan R. Aldabergen and Aidana N. Kamalbekova
Water 2025, 17(14), 2165; https://doi.org/10.3390/w17142165 - 21 Jul 2025
Viewed by 137
Abstract
This paper presents the results of studies on intra-annual runoff changes in the Ile River basin based on data from gauging stations up to 2021. Changes in climatic characteristics that determine runoff formation in the mountainous and foothill areas of the river catchment [...] Read more.
This paper presents the results of studies on intra-annual runoff changes in the Ile River basin based on data from gauging stations up to 2021. Changes in climatic characteristics that determine runoff formation in the mountainous and foothill areas of the river catchment have led to alterations in the water regime of the watercourses. The analysis of the temporal and spatial patterns of river flow formation in the basin, as well as its distribution by seasons and months, is essential for solving applied water management problems and assessing the risks of hazardous hydrological phenomena, such as high floods and low water levels. The statistical analysis of annual and monthly river runoff fluctuations enabled the identification of relatively homogeneous estimation periods during stationary observations under varying climatic conditions. The obtained characteristics of annual and intra-annual river runoff in the Ile River basin for the modern period provide insights into changes in average monthly water discharge and, more broadly, runoff volume during different phases of the water regime. In the future, these characteristics are expected to guide the design of hydraulic structures and the rational use of surface runoff in this intensively developing region of Kazakhstan. Full article
(This article belongs to the Section Water and Climate Change)
Show Figures

Figure 1

Back to TopTop