A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empirical and ML Adopted Research by Md. Rajaul Karim, M. M. Mahbubul Syeed, Ashifur Rahman, Khondkar Ayaz Rabbani, Kaniz Fatema, Razib Hayat Khan, Md Shakhawat Hossain, Mohammad Faisal Uddin

“…This dataset can support meta-analysis of water quality models and can facilitate Machine Learning (ML) based data and model-driven investigation of the spatial and temporal drivers and patterns of surface water quality at a cross-regional to global scale.…”
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Episodic Gregariousness Leads to Level‐Dependent Core Habitats: A Case Study in Eastern Copperheads (Agkistrodon contortrix) by Tyler C. Christensen, Robert Kwait, Michael Van Clef, Brooke Maslo

“…Wildlife–habitat selection is influenced by multiple life‐history requirements, which act over varying spatial and temporal scales, and result in dispersion patterns that can differ across ecological levels. …”
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Multiscale Performance of Global Blended Satellite Precipitation Products Over Taiwan by Liping Wang, Haonan Chen, Zhe Li

“…To recognize the error patterns of the mainstream SPPs over Taiwan, this study evaluates the temporal and spatial performance of NOAA Climate Prediction Center (CPC) morphing technique (CMORPH V1) and NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG V07 Final) during 2019–2021, with an emphasis on exploring the multi-scale performance of CMORPH and IMERG and their dependence on precipitation intensity and season. …”
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Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model by Shanshan Wang, Jingwu Liu

“…Two datasets, Geo-Life Log (with location data) and Time-Life Log (without location data), were constructed to evaluate the impact of spatial information on classification performance. …”
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Spatiotemporal dynamics and driving mechanisms of coupled coordination between rural–urban integration and rural resilience in Southwest China by Zhengyong Yu, Jiqing Lin, Yifei Zhao, Wei Liu

“…The analysis of the temporal evolution of the degree of coupling coordination reveals a trend of gradually increasing regional coordination, while the analysis of the spatial evolution shows a spatial pattern of high in the centre and low on both sides. …”
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On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

“…Plant functional types were useful at the site level for the improved representation of spatial patterns but had a significant leverage on upscaling results – i.e., having a disproportionate impact on the spatial patterns, especially for XGBoost, but less for the LSTM model.…”
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Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model by Amir Miraki, Pekka Parviainen, Reza Arghandeh

“…Our framework employs a parallel feature extraction module that simultaneously captures temporal dynamics and spatial correlations, enabling improved forecasting accuracy. …”
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Integrating remote sensing, GIS, and machine learning for zoonotic cutaneous leishmaniasis modelling by Fatemeh Parto Dezfooli, Mohammad Javad Valadan Zoej, Fahimeh Youssefi, Ebrahim Ghaderpour

“…Utilizing data from 2014 to 2019, spatial and temporal patterns are investigated using Moran’s I, Getis-Ord Gi* statistics, and the Mann-Kendall (MK) test, while high-risk ZCL maps are generated through Extreme Gradient Boosting (XGBoost) and Random Forest (RF) models. …”
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Computational Analysis Method for Multi-subject Behavior in Public Spaces Based on Targeted Computer Vision Tracking by Chao YAN, Siyan LIU, Shanshu HE, Leiqing XU

“…Subsequently, computational analysis and visualization methods for revealing the interaction behaviors of different groups are explored, which involves a pedestrian identification threshold model based on human proportion characteristics that enables targeted identification and differentiation of children from adults, and a dedicated analysis module designed to visualize behavioral patterns of each identified crowd and thereby provide visual patterns for the spatial-temporal distributions of different crowds. …”
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Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia by K. K. W. Cheung, F. Ji, F. Ji, N. Nishant, J. Teng, J. Bennett, D. L. Liu

“…Both datasets reproduce daily timescale probability density distributions and spatial patterns of mean precipitation well with minor biases. …”
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Biologically inspired hybrid model for Alzheimer’s disease classification using structural MRI in the ADNI dataset by Houmem Slimi, Imen Cherif, Sabeur Abid, Mounir Sayadi

“…The model synergizes CNNs for hierarchical spatial feature extraction and SNNs for biologically inspired temporal dynamics processing. …”
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Improvement of SAM2 Algorithm Based on Kalman Filtering for Long-Term Video Object Segmentation by Jun Yin, Fei Wu, Hao Su, Peng Huang, Yuetong Qixuan

“…SAM 2’s fixed temporal window approach indiscriminately retains historical frames, failing to account for frame quality or dynamic motion patterns. …”
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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…However, the existing data-based weather prediction methods cannot adequately capture the spatial and temporal evolution characteristics of the target region, which makes it difficult for the existing methods to meet practical application requirements in terms of efficiency and accuracy. …”
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Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet by WEI Huanwei, ZHAO Jizhang, ZHENG Xiao, TAN Fang, LIU Cong

“…In the spatial domain, the Temporal Convolutional Network (TCN) models long-range dependencies by expanding causal convolutions, thereby capturing local and global spatial relationships. …”
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Variability of Rainfall and the Cropping Season of a Tropical Dry Zone River Basin: A Case Study in the Malwathu Oya River Basin in Sri Lanka by N. S. Abeysingha, S. D. Madurapperuma Arachchige, Ram L. Ray, L. P. Muthuwatta

“…The temporal and spatial rainfall variability impacts water resource management, agricultural productivity, and ecosystem health in river basins. …”
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Air quality prediction using stacked bi- long short-term memory and convolutional neural network in India by S Karkuzhali, Thendral Puyalnithi, R Nirmalan

“…The study aims to offer valuable insights into the temporal and spatial patterns of air quality in India, thereby aiding environmental policymakers, urban planners, and researchers in formulating effective strategies for air quality management. …”
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Hand Tremor Characterization from a Spatiotemporal Convolutional Representation by Jessica Pedraza Cadena, John Edinson Archila Valderrama, Franklin Sierra-Jerez, Alejandra Moreno Tarazona, Fabio Martínez Carrillo

“…Conclusions: This approach showed efficacy regarding the localization of tremor patterns, recovering movement information while preserving the spatial and temporal representation. …”
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Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China by Xiaohua Guo, Chang Liu, Shibo Bi, Yuling Tang

“…Despite a gradual reduction in disparities in green visibility, a spatial mismatch persisted between UGS growth and population distribution, leading to uneven patterns in UGS equity. …”
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Triggering mechanics and early warning for snowmelt-rainfall-induced loess landslide by Kaidierding Wulamu, Zizhao Zhang, Qianli Lv, Guangming Shi, Yanyang Zhang, Shichuan Liang

“…Instruments with higher spatial resolution can more effectively identify unstable areas within a landslide, while instruments with higher temporal resolution can provide detailed time-series deformation data. …”
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Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets by D. Dalmonech, E. Vangi, M. Chiesi, G. Chirici, L. Fibbi, F. Giannetti, G. Marano, C. Massari, A. Nolè, J. Xiao, A. Collalti

“…Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of carbon fluxes and stocks in forests. …”
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