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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Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China by Qinggang Meng, Xiaolan Chen, Hui Wang, Wanfang Shen, Peixin Duan, Xinyue Liu

“…However, regional disparities in PCCR, a persistent and dynamically evolving feature, manifested a spatial pattern of “higher in the east, lower in the west”. …”
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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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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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Extreme precipitation simulation capability of CMIP6 models and their future spatiotemporal evolution: Case study in Poyang Lake Basin, China by Yinxin Ge, Shanghong Zhang, Weiyi Zhou, Linyang Zhao, Qingqing Fang

“…This method demonstrably provides better simulations of interannual distribution and spatial pattern characteristics compared to single models and simple multi-model averages. …”
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Estimation of Cloud Water Resources in China by Jie Yu, Yuquan Zhou, Miao Cai, Jianjun Ou

“…Over the next three decades, cloud water resources in China are projected to exhibit an overall increasing trend, with the most pronounced enhancement occurring under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5). The spatial distribution pattern of cloud water resources is expected to remain largely consistent with that of the past two decades, while inter-model differences are primarily observed in southeastern China and the southern Tibetan Plateau. …”
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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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Assessing water clarity in the Chesapeake Bay using satellite-derived Secchi disk depth by Seunghyun Son, Nikolay P. Nezlin, Veronica P. Lance

“…Assessments of SDD from remotely-sensed ocean color data benefit from wide spatial and temporal coverage but are subject to low accuracy resulting from unstable relationship between water transparency and the color of water surface. …”
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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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Ensemble deep learning and anomaly detection framework for automatic audio classification: Insights into deer vocalizations by Salem Ibrahim Salem, Sakae Shirayama, Sho Shimazaki, Kazuo Oki

“…We gained invaluable insights into deer behavior by analyzing deer calls' frequency, timing, and duration. Additionally, the spatial distribution of deer calls in Taki enabled us to detect a breach in the city's protective fencing and an association between the spatial patterns of deer calls and crop damage in the two fields. …”
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When, where, and how prey pair antipredator behaviors to natural and anthropogenic mortality risks by Michael E. Egan, Abigail M. Weber, Nicole Gorman, Michael W. Eichholz, Daniel Skinner, Peter E. Schlichting, Guillaume Bastille-Rousseau

“…Despite prioritizing humans, mesopredators impacted behavioral state, suggesting that mesopredators still have important impacts on prey behavior. Finally, temporal patterns of avoidance align with other studies that indicate avoidance of predators is time-dependent, but further highlight the complex push–pull relationship of human modified areas on wildlife.…”
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Investigating the urban eco-environmental quality utilizing remote sensing based approach: evidence from an industrial city of Eastern India by Sharmistha Mondal, Kapil Kumar Gavsker

“…Moran’s scatter plot illustrated a highly positive clustered pattern of UEEQ across the city. Hotspots were mainly found in urbanized areas that had an “Average” urban eco-environment quality. …”
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Leaves and Tree Rings as Biomonitoring Archives of Atmospheric Mercury Deposition: An Ecophysiological Perspective by Fabrizio Monaci, Davide Baroni

“…This perturbation profoundly affects global atmospheric Hg concentrations, residence times, and spatial distribution patterns. While early investigations focused on forest stands near heavily polluted areas, contemporary research has expanded to diverse ecosystems, revealing that trees provide tissues that function as temporal archives for atmospheric-terrestrial Hg exchange. …”
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vBerlinV2N: Recreating a Cellular Network Measurement Campaign With Simulations by Christian L. Vielhaus, Mauri Seidel, Vincent Latzko, Alexander Grob, Peter Sossalla, Martin Reisslein, Frank H. P. Fitzek

“…We analyze the distributions as well as the temporal and spatial variations of the received signal quality metrics of the radio environments and compare the end-to-end throughput, delay, and handovers.…”
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Cerebral Amyloid Angiopathy Is Associated With Higher R2 Relaxation Rate: An MRI and Pathology Study by Md Tahmid Yasar, Arnold M. Evia, Mahir Tazwar, Sue E. Leurgans, David A. Bennett, Julie A. Schneider, Konstantinos Arfanakis

“…This study tested the hypothesis that CAA is associated with R2, extracted the spatial pattern of CAA‐related R2 abnormalities, and evaluated R2 at different CAA severity levels in a large number of community‐based older adults. …”
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Assessing the potential for traffic carbon emission reductions through residential travel mode shifts: insights from massive vehicle trajectory data and scenario simulations by Shaoying Li, Shaoli Li, Shuyuan Xu, Hanwen Xu, Xuanting Chen, Quan Mu, Zhangzhi Tan

“…This framework is designed to evaluate the impact of shifts in residential travel modes on carbon emissions at a fine spatial and temporal scale. …”
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Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models by Pei Jing Low, Bo Yan Ng, Nur Insyirah Mahzan, Jing Tian, Cheung-Chi Leung

“…The second approach leverages a multiple-frame <i>convolutional neural network</i> (CNN) to exploit temporal and spatial patterns in the video data. The third approach explores a 3D CNN-based deep learning model, which is capable of processing video data as volumetric inputs. …”
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Hybrid CNN–LSTM Model With Soft Attention Mechanism for Short‐Term Load Forecasting in Smart Grid by Syed Muhammad Hasanat, Muhammad Haris, Kaleem Ullah, Syed Zarak Shah, Usama Abid, Zahid Ullah

“…The proposed model leverages Convolution Neural Networks (CNNs) to extract spatial patterns, LSTMs to capture temporal dependencies, and attention mechanisms to prioritize important information, enhancing predictive performance. …”
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A Simulation Study of Low-Intensity Focused Ultrasound for Modulating Rotational Sense Through Acoustic Streaming in Semicircular Canal: A Pilot Study by Sion Cha, Wooksung Kim

“…Existing vestibular stimulation methods, such as galvanic vestibular stimulation (GVS), caloric vestibular stimulation (CVS), and magnetic vestibular stimulation (MVS), face limitations in spatial and temporal resolution, with unclear mechanisms. …”
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Incorporating Attention Mechanism Into CNN-BiGRU Classifier for HAR by Ohoud Nafea, Wadood Abdul, Ghulam Muhammad

“…The proposed methodology uses convolutional neural networks (CNN) and recurrent neural networks (RNN) to extract the spatial and temporal features. We propose to employ an attention mechanism based BiGRU. …”
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