15 Years of Moss Biomonitoring for Air Quality Assessment in Albania by Pranvera Lazo, Sonila Shehu Kane, Flora Qarri, Shaniko Allajbeu, Lirim Bekteshi

“…The purpose of this research is to investigate the spatial patterns and temporal trends of trace metals, identify emission pathways, and evaluate the most likely influences on concentration levels throughout the entire territory of Albania. …”
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A Novel Human Action Recognition Model by Grad-CAM Visualization with Multi-level Feature Extraction Using Global Average Pooling with Sequence Modeling by Bidirectional Gated Recu... by Jayamohan Manoharan, Yuvaraj Sivagnanam

“…Most methods are based only on spatial or temporal features and cannot efficiently capture both in one framework, causing lower accuracy in realistic situations. …”
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The Impact of Assimilating Himawari-8 Radiance Data on the Prediction of a Severe Storm over Sichuan-Chongqing Region by Hao LIANG, Dongmei XU, Aiqing SHU, Xuewei ZHANG, Lixin SONG

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“Bias Correction Method” for Regional Correction Experiment of Warm Season Rainstorm in Zhejiang by Chengyan Mao, Xin Pan, Haowen Li, Weibiao Li, Haoya Liu

“…A frequency matching bias correction method is then applied to enhance the spatial and temporal accuracy of the objective consensus forecasting (OCF) 0.05° × 0.05° model precipitation forecast data for the region. …”
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The Driving Path of China’s Urban Resilience Enhancement in the Digital Economy Era by Liang Tang, Haifeng Jiang, Zhenling Zhang, Shanshan Hou, Bo Liu

“…This paper analyzed the spatial and temporal distribution characteristics and driving paths of urban resilience in 31 Chinese provinces, autonomous regions, and municipalities directly under the central government and proposed relevant measures to promote urban resilience. …”
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Spatiotemporal Rainfall Distribution of Soan River Basin, Pothwar Region, Pakistan by Fiaz Hussain, Ghulam Nabi, Ray-Shyan Wu

“…The results depicted that there is substantial year-to-year and season-to-season variability in rainfall patterns, and rainfall patterns are generally erratic in nature. …”
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Dynamic evolution characteristics and risk assessment of floor microseismic in close repeated mining by Jing JIA, Jinyan ZHOU, Qinghua NIU

“…The close proximity between upper and lower coal seams leads to secondary superposition failure during repeated mining, exacerbating the challenges associated with effective water control measures.Using 33200 working face of Pingmei 10 mine as a case study, this paper analyzes microseismic event spatial patterns, energy distribution, as well as extensive statistical data resulting from repetitive lower coal seam extraction actions. …”
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Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification by Zhuozheng Wang, Yunlong Wang

“…The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. …”
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Research on the Protection of Battlefield Sites in the Taihang Mountain Area of Southern Hebei Province, China: A Case Study of She County by Feifei Zheng, Yuan Kuang, Yue Hu

“…The aim is to reveal the architectural characteristics and spatiotemporal distribution patterns of battlefield sites, evaluate its current status and constraints, and propose targeted protection strategies and restoration measures. …”
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Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

“…The selected features were classified using a CNN-LSTM model, capturing both spatial and temporal patterns. The MLP-LSTM model was included only for benchmarking purposes. …”
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Using Vegetation Indices Developed for Sentinel-2 Multispectral Data to Track Spatiotemporal Changes in the Leaf Area Index of Temperate Deciduous Forests by Xuanwen Wang, Yi Gan, Atsuhiro Iio, Quan Wang

“…For high-resolution tracking of LAI in temperate deciduous forests at both temporal and spatial scales, future research is needed to incorporate additional information.…”
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Real-Time Coronary Artery Dominance Classification from Angiographic Images Using Advanced Deep Video Architectures by Hasan Ali Akyürek

“…Transformer-based models showed superior accuracy compared to convolution-based methods, highlighting their effectiveness in capturing spatial–temporal patterns in angiographic videos. …”
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HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones by Sarmela Raja Sekaran, Ying Han Pang, Ooi Shih Yin, Lim Zheng You

“…In this model, multiple enhanced TCN networks with diverse architectures are organised parallelly to capture hierarchical spatial-temporal patterns from raw inertial signals. …”
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Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI by Anming Dong, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun, Jiguo Yu

“…Compared to traditional CNNs, which rely solely on spatial features, or recurrent neural networks like long short-term memory (LSTM) and gated recurrent units (GRUs), which primarily capture temporal dependencies, the proposed CNN-BiGRU network integrates both spatial and temporal features concurrently. …”
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Quantifying the Cumulative Effects of Large-Scale Reclamation on Coastal Wetland Degradation by Linlin Cui, Guosheng Li, Miao Zhao, Zhihui Zhang

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Lightweight graph convolutional network with multi-attention mechanisms for intelligent action recognition in online physical education by Yuhao You

“…Existing action recognition models are often computationally intensive and struggle to generalize across diverse skeletal patterns. To address this, we propose a lightweight graph convolutional network (GCN) that integrates an improved Ghost module with multi-attention mechanisms, including a global attention mechanism (GAM) and a channel attention mechanism (CAM), to enhance spatial and temporal feature extraction. …”
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Assessment of relationship between sea surface temperature (SST) changes and precipitation types in Nigeria from 2000 to 2022 by Tertsea Igbawua, Fanan Ujoh, Solomon Kwaghfan Mkighirga, Grace Adagba

“…The results show that TOP, AVP, LSP, and CNP varied: spatially, the northern region received low moisture budget from the Atlantic Ocean while the temporal distribution of precipitation across different climatic zones indicate high variability in precipitation across these zones. …”
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Intrusion Detection and Mitigation Method for the Industrial Internet of Things Using Bidirectional Convolutional Long Short-Term Memory and Deep Recurrent Convolutional Q-Networks by Zhang Yan, Piyush Kumar Shukla, Prashant Kumar Shukla, Kanika Thakur, Anurag Sinha, Saifullah Khalid

“…The intrusion detection phase uses a combination of deep convolutional neural networks (DCNN) and bidirectional long short-term memory (BI-LSTM) networks to capture both spatial and temporal relationships in the data, while a hybrid feature selection technique improves the model’s performance. …”
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Medium-resolution Dynamic Habitat Indices from Landsat and Sentinel-2 satellite imagery by Elena Razenkova, Katarzyna E. Lewińska, Akash Anand, He Yin, Laura S. Farwell, Anna M. Pidgeon, Patrick Hostert, Nicholas C. Coops, Volker C. Radeloff

“…Biodiversity science requires effective tools to predict patterns of species diversity at multiple temporal and spatial scales. …”
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A comprehensive spatiotemporal and risk reduction drought assessment study utilizing SPEI index for Urmia Lake Basin, Iran by Sadaf Samiei, Mohammadali Alijanian

“…Hence, this study performs a comprehensive drought assessment, considering temporal, spatial, and risk reduction aspects. Using a 50-year (1971–2020) time series of multivariate Standardized Precipitation-Evapotranspiration Index (SPEI) data (3, 6, 9, and 12-month timescales), the study employs Mann-Kendall, modified Mann-Kendall, and Sen's slope analyses to determine drought trends, examines spatial patterns using Severity Area Frequency (SAF) curves, and finally, evaluates drought risk reduction using resilience, vulnerability, and exposure factors. …”
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