The Dynamics of Air Pollution in the Southwestern Part of the Caspian Sea Basin (Based on the Analysis of Sentinel-5 Satellite Data Utilizing the Google Earth Engine Cloud-Computin... by Vladimir Tabunshchik, Aleksandra Nikiforova, Nastasia Lineva, Polina Drygval, Roman Gorbunov, Tatiana Gorbunova, Ibragim Kerimov, Cam Nhung Pham, Nikolai Bratanov, Mariia Kiseleva

“…Additionally, the Gorgan basin exhibited elevated carbon monoxide levels, while the highest ozone concentrations were detected in the Sunzha basin. Our temporal analysis demonstrated a substantial influence of the COVID-19 pandemic on pollutant dispersion patterns. …”
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A Hybrid Regression–Kriging–Machine Learning Framework for Imputing Missing TROPOMI NO₂ Data over Taiwan by Alyssa Valerio, Yi-Chun Chen, Chian-Yi Liu, Yi-Ying Chen, Chuan-Yao Lin

“…By integrating dynamic meteorological variables and static land cover features, the hybrid RK–ML framework enhances the spatial and temporal completeness of satellite-derived air quality datasets. …”
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Shoreline time series analysis through the Isoradiometric method: Bridging landscape evolution and coastal management by F. Caldareri, N. Parrino, L. Balsamo, G. Dardanelli, S. Todaro, A. Sulli, A. Maltese

“…The increasing availability of remotely sensed data has enhanced our ability to monitor coastal evolution, yet extracting reliable time series for long-term analysis remains a challenge. This study evaluates the effectiveness of the Isoradiometric shoreline extraction Method in producing consistent time series data across different spatial and temporal scales. …”
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Spatiotemporal Dynamics and Simulation of Landscape Ecological Risk and Ecological Zoning Under the Construction of Free Trade Pilot Zones: A Case Study of Hainan Island, China by Yixi Ma, Mingjiang Mao, Zhuohong Xie, Shijie Mao, Yongshi Wang, Yuxin Chen, Jinming Xu, Tiedong Liu, Wenfeng Gong, Lingbing Wu

“…From 2015 to 2023, the spatial distribution of LER in the study area exhibited a pattern of high-risk peripheries (central mountainous areas) and low-risk central regions (coastal areas). …”
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Hyperspectral and LiDAR space-borne data for assessing mountain forest volume and biomass by Rodolfo Ceriani, Sebastian Brocco, Monica Pepe, Silvio Oggioni, Giorgio Vacchiano, Renzo Motta, Roberta Berretti, Davide Ascoli, Matteo Garbarino, Donato Morresi, Francesco Bassi, Francesco Fava

“…GEDI LiDAR proved to be a necessary input for accurate SV and AGB retrieval, and GPR was the best-performing ML algorithm. The resulting spatial maps were artifact-free and successfully delineated ecological gradients and management patterns. …”
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Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks by Chamalka Kenneth Perera, Alpha. A. Gopalai, Darwin Gouwanda, Siti. A. Ahmad, Pei-Lee Teh

“…The hip and knee are the drivers of STW, while the network architectures were selected for recognizing temporal and spatial relationships. Performance of the LSTMs were compared and evaluated against the STW strategies to accurately generate strategy-specific and user-oriented torque. …”
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The intervention of music education on adolescents’ subjective well-being from the perspective of sustainable development: a bibliometric review based on literature from 2000 to 20... by Lan Shen, Weijia Yang, Weijia Yang

“…The field’s trajectory was deconstructed along three dimensions: temporal (annual publication output and author contribution levels), spatial (national participation and institutional collaboration density), and content (high-frequency keyword clustering and evolution of emerging themes). …”
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Energy-Efficient Fall-Detection System Using LoRa and Hybrid Algorithms by Manny Villa, Eduardo Casilari

“…This study introduces a hybrid system that integrates a threshold-based model for preliminary detection with a deep learning-based approach that combines a CNN (Convolutional Neural Network) for spatial feature extraction with a LSTM (Long Short-Term Memory) model for temporal pattern recognition, aimed at improving classification accuracy. …”
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Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis by Fatin Nabilah Shaari, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Wan Aireene Wan Ahmed, Abdul Syafiq Abdull Sukor

“…However, baseline Convolutional Neural Network (CNN) commonly faced obstacles to fully capture the temporal dependencies present in sequential medical imaging data, limiting their diagnostic performance. …”
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Distance, weather, and forage conditions drive timing of autumn migration in female mule deer by Colby B. Anton, Nicholas J. DeCesare, Collin J. Peterson

“…Migration presents challenges for managing ungulates when the timing of autumn migrations varies across years and migrations cross management jurisdictions. Methods We evaluated the spatial and temporal patterns of autumn migration using GPS collar data collected during 2017–2019 from 68 female mule deer (Odocoileus hemionus) that migrated seasonally within three study areas in northwest Montana. …”
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Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. by Li Bo, Lv Junrui, Luo Xuegang

“…To overcome these limitations, we propose TGMHA (Tensor Decomposition and Gated Neural Network with Multi-Head Self-Attention), a novel hybrid model that integrates three key innovations: 1) Tensor-based Feature Extraction: We combine Standard Delay Embedding Transformation (SDET) with Tucker tensor decomposition to reconstruct raw time series into low-rank tensor representations, capturing latent spatio-temporal patterns while suppressing sensor noise. 2) Multi-Head Self-Attention for Inter-Indicator Dependencies: A multi-head self-attention mechanism explicitly models complex inter-dependencies among diverse water quality indicators (e.g., pH, dissolved oxygen, conductivity) via parallel feature subspace learning. 3) Efficient Long-Term Dependency Modeling: An encoder-decoder architecture with gated recurrent units (GRUs), optimized by adaptive rank selection, ensures efficient modeling of long-term dependencies without compromising computational performance. …”
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Analysis of Economic Vitality and Development Equilibrium of China’s Three Major Urban Agglomerations Based on Nighttime Light Data by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Zhenqing Wang, Zhuochen Wang, Yanchao Wang, Xinran Wang, Luoyao Hao, Futao Wang

“…Nighttime light (NTL) remote sensing data have been proven to be a good proxy variable for socio-economic development, and are widely used due to their advantages of convenient access and wide spatial coverage. Based on multi-source data, this study constructs an Economic Development Index (EDI) that comprehensively reflects regional economic vitality from two aspects, economic quality and development potential, combines the Nighttime Light Development Index (NLDI) as the evaluation indicators to measure the economic vitality and development equilibrium, analyzes the economic vitality and development equilibrium of 300 district and county units in China’s three major urban agglomerations from 2000 to 2020 and their temporal and spatial variation characteristics, and discusses the connotation of EDI and its availability. …”
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The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy by A. Dastoor, H. Angot, J. Bieser, F. Brocza, F. Brocza, B. Edwards, A. Feinberg, A. Feinberg, X. Feng, X. Feng, B. Geyman, C. Gournia, Y. He, I. M. Hedgecock, I. Ilyin, J. Kirk, C.-J. Lin, C.-J. Lin, I. Lehnherr, R. Mason, D. McLagan, D. McLagan, M. Muntean, P. Rafaj, E. M. Roy, A. Ryjkov, N. E. Selin, N. E. Selin, F. De Simone, A. L. Soerensen, F. Steenhuisen, O. Travnikov, S. Wang, S. Wang, X. Wang, S. Wilson, R. Wu, Q. Wu, Q. Wu, Y. Zhang, J. Zhou, W. Zhu, S. Zolkos

“…The primary goals of the project are to facilitate detection and attribution of recent (observed) and future (projected) spatial patterns and temporal trends of global environmental Hg levels and identification of key knowledge gaps in Hg science and modeling to improve future effectiveness evaluation cycles of the MEAs. …”
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STF: A spherical transformer for versatile cortical surfaces applications by Jiale Cheng, Fenqiang Zhao, Zhengwang Wu, Xinrui Yuan, Li Wang, John H Gilmore, Weili Lin, Xin Zhang, Gang Li

“…To handle longitudinal cortical surface data, we introduce the spatiotemporal self-attention mechanism, which jointly captures spatial context and temporal developmental patterns within a single layer. …”
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Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks by Kenan Honore Robacky Mbongo, Kanwal Ahmed, Orken Mamyrbayev, Guanghui Wang, Fang Zuo, Ainur Akhmediyarova, Nurzhan Mukazhanov, Assem Ayapbergenova

“…This study proposes a hybrid IDS model combining one-dimensional Convolutional Neural Networks (Conv1Ds), Gated Recurrent Units (GRUs), and Self-Attention mechanisms. A Conv1D extracts spatial features from network traffic, GRU captures temporal dependencies, and Self-Attention emphasizes critical sequence components, collectively enhancing detection of subtle and complex intrusion patterns. …”
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Differential Transcript Profiles of MHC Class Ib(Qa-1, Qa-2, and Qa-10) and Aire Genes during the Ontogeny of Thymus and Other Tissues by Breno Luiz Melo-Lima, Adriane Feijó Evangelista, Danielle Aparecida Rosa de Magalhães, Geraldo Aleixo Passos, Philippe Moreau, Eduardo Antonio Donadi

“…Aiming to characterize the transcriptional profiles of nonclassical MHC class I genes in spatial-temporal association with the Aire expression, we evaluated the gene expression of H2-Q7(Qa-2), H2-T23(Qa-1), H2-Q10(Qa-10), and Aire during fetal and postnatal development of thymus and other tissues. …”
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Assessing Coincidence of Satellite Acquisitions and Flood Events to Predict Suitability for Flood Map Synthesis by Lyle Prince, Riley C. Hales, Kel N. Markert, E. James Nelson, Gustavious P. Williams, Daniel P. Ames, Hyongki Lee, Amirhossein Rostami

“…For our processes, we used Forecasting Inundation Extents using REOF analysis (FIER), a data-driven method of synthesizing flood maps by correlating extracted spatial and temporal patterns from satellite imagery with historical hydrological variables. …”
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Relating satellite NO2 tropospheric columns to near-surface concentrations: implications from ground-based MAX-DOAS NO2 vertical profile observations by Bowen Chang, Haoran Liu, Chengxin Zhang, Chengzhi Xing, Wei Tan, Cheng Liu

“…This study enhances understanding of the spatial and temporal dynamics and influencing mechanisms of CNO2 and SNO2, supporting improved air quality assessments and pollution exposure evaluations.…”
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HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data by Nazish Ashfaq, Zeeshan Aziz, Muhammad Hassan Khan, Muhammad Adeel Nisar, Adnan Khalid

“…This study examines the role of multi-modalities in HAR using a hybrid convolutional multi-modal attention network (HCMMA-Net), designed to exploit spatial and temporal dependencies in sensor data. …”
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Soil moisture forecasting in wireless sensor networks via spatiotemporal graph convolutional networks by Weixuan Wang, Yating Wei, Longfei Hao, Zushuai Wei, Tianjie Zhao

“…However, due to the combined effects of internal factors like soil types, terrain, and vegetation cover, as well as external factors such as precipitation and temperature, soil moisture data in wireless sensor networks exhibit complex spatial and temporal interdependencies. Consequently, developing predictive models that can accurately capture these dependencies and enable precise forecasts of soil moisture within these networks poses a significant challenge. …”
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