Improving the accuracy of rainfall prediction using a regionalization approach and neural networks by Mohammad Arab Amiri, Christian Conoscenti, Mohammad Saadi Mesgari

“… Spatial and temporal analysis of precipitation patterns has become an intense research topic in contemporary climatology. …”
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A continual-learning-based multilayer perceptron for improved reconstruction of three-dimensional nitrate concentrations by X. Yu, X. Yu, H. Guo, J. Zhang, J. Zhang, Y. Ma, X. Wang, G. Liu, M. Xing, N. Xu, A. M. Seka, A. M. Seka, A. M. Seka

“…Then, the spatiotemporal distribution of the reconstructed 3D nitrate field from 2010 to 2023 reveals a spatial distribution pattern, an interannual upward trend, and the degree of consistency in vertical variation. …”
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Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina by Sabljić Luka, Lukić Tin, Bajić Davorin, Marković Slobodan B., Spalevic Velibor, Cvetković Vladimir M., Delić Dragica, Adžić Dragutin, Aleksova Bojana, Milevski Ivica, Srzentić Gordana Petković

“…The aim is to identify the spatial and temporal patterns of floods by integrating satellite-derived precipitation data, hydrological data, and Sentinel-1 imagery processed through Google Earth Engine. …”
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A Pretrained Spatio-Temporal Hypergraph Transformer for Multi-Stock Trend Forecasting by Yuchen Wu, Liang Xie, Hongyang Wan, Haijiao Xu

“…The proposed model employs a temporal encoder with an aggregation module to capture temporal patterns, utilizes self-attention to dynamically generate hyperedges, and selects cross-attention to implement hypergraph-associated convolution. …”
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Predictive goal coding by dentate gyrus somatostatin-expressing interneurons in male mice by Mei Yuan, Aurore Cazala, Sven Goedeke, Christian Leibold, Jonas-Frederic Sauer, Marlene Bartos

“…This function has been linked to goal-associated activity patterns of hippocampal principal cells. Using single-unit recordings from optogenetically identified somatostatin-expressing interneurons (SOMIs) in the dentate gyrus of head-fixed mice trained in a spatial goal-oriented reward-learning task in virtual realities, we show that SOMI activity temporally precedes reward-locations in expert mice characterized by goal-anticipatory behaviour. …”
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Simulating Co-Evolution and Knowledge Transfer in Logistic Clusters Using a Multi-Agent-Based Approach by Aitor Salas-Peña, Juan Carlos García-Palomares

“…Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. …”
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Deep learning-based object detection for environmental monitoring using big data by Wenbo Lin, Tingting Li, Xiao Li

“…EGAN constructs a spatiotemporal graph representation that integrates physical proximity, ecological similarity, and temporal dynamics, and applies graph convolutional encoders to learn expressive spatial features. These are fused with temporal representations using attention mechanisms, enabling the model to dynamically capture relevant patterns across modalities. …”
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Formation conditions of hydrocarbon systems in the Turkmen sector of the South Caspian basin by U. S. Serikova, M. A. Allanazarova

“…Hydrocarbon prospects were identified. The main spatial and temporal patterns in the development of generation, migration, and accumulation processes of hydrocarbon fluids were determined, along with the formation conditions of traps. …”
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Forecasting Stock Market Volatility Using CNN-BiLSTM-Attention Model with Mixed-Frequency Data by Yufeng Zhang, Tonghui Zhang, Jingyi Hu

“…The deep learning model can effectively capture both spatial and temporal patterns of multivariate time-series data, thus effectively improving prediction accuracy and generalization ability in stock market volatility forecasting. …”
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A Machine Learning-Reconstructed Dataset of River Discharge, Temperature, and Heat Flux into the Arctic Ocean by Zihan Wang, Fengming Hui, Xiao Cheng

“…Our understanding of these patterns is limited by substantial data gaps. To address this, we present the Reconstructed Arctic-draining river DIscharge and Temperature (RADIT) dataset, a comprehensive record of reconstructed daily discharge, temperature, and heat flux for 25 major Arctic rivers from 1950 to 2023. …”
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A Feature-Selective Independent Component Analysis Method for Functional MRI by Yi-Ou Li, Tülay Adali, Vince D. Calhoun

“…We also compare the task-related sources estimated from true fMRI data by a feature-selective ICA algorithm versus an ICA algorithm and show evidence that the feature-selective scheme helps improve the estimation of the sources in both spatial activation patterns and the time courses.…”
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DEVELOPMENT OF AN ECOLOGICAL, RISK‐BASED DECISION FRAMEWORK FOR RELEASING AND MANAGING WILD TURKEYS IN ONTARIO by Karen Bellamy, J. Bruce Pollard

“…Through knowledge gained from assessment of habitat suitability, overlaying habitat information on historic distribution, factoring in land‐use change, and by considering prevailing weather patterns, we developed a terrestrial ecozone framework to define the spatial extent for turkey range expansion. …”
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Sea lice infestation dataset for wild and farmed salmon populations on the Pacific coast of Canada (2001–2023) by Crawford W. Revie, Thitiwan Patanasatienkul, Gregor McEwan, Lance Stewardson

“…These data provide valuable insights into long-term trends, including spatial variability and demographic patterns within the sea lice populations observed on various host species along the BC coast.…”
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Mapping topographic structure in white matter pathways with level set trees. by Brian P Kent, Alessandro Rinaldo, Fang-Cheng Yeh, Timothy Verstynen

“…Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. …”
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Hydrocarbon systems in the Turkmen sector of the South Caspian basin by U. S. Serikova, M. A. Allanazarova, E. Z. Idiyatullina

“…Oil and gas prospects were identified, and the main spatial and temporal patterns in the development of generation, migration, and accumulation processes of hydrocarbon fluids, as well as the formation conditions of traps were established. …”
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Enhancing Time Series Anomaly Detection: A Knowledge Distillation Approach with Image Transformation by Haiwoong Park, Hyeryung Jang

“…In contrast, image anomaly detection has leveraged advancements in technologies for analyzing spatial patterns and visual features, achieving high accuracy and promoting research aimed at improving efficiency. …”
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Modeling the Impact of Urban Land Use Characteristics on Road Network Accessibility in a Case Study of Addis Ababa City by Kalkidan Kefale Berta, Yonas Minalu Emagnu

“…Geographic information system (GIS) techniques are used to gather and analyze precise geographical data on road networks, land use patterns, and other pertinent properties. In addition, a regression model is created to measure the effect of land use features on the accessibility of the road network. …”
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The interoceptive hippocampus: Mouse brain endocrine receptor expression highlights a dentate gyrus (DG)-cornu ammonis (CA) challenge-sufficiency axis. by Richard Lathe, Sheena Singadia, Crispin Jordan, Gernot Riedel

“…We studied the expression patterns in mouse brain of 250 endocrine receptors that respond to blood-borne ligands. …”
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Low-power-consumption road side unit deployment strategy for urban 2-D 5G Internet of vehicles by YANG Xinjie, WANG Shuai, MA Nan

“…A novel RSU deployment strategy was proposed that dynamically adjusted the operating mode and transmission power of RSU, based on the spatial distribution of vehicles. An adaptive mapping mechanism between RSU power consumption and vehicle distribution patterns was established that overcame the power consumption bottleneck caused by traditional RSU's fixed coverage to minimize the total power consumption of RSU. …”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…The study employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex spatial and temporal patterns, enabling more accurate and timely drought forecasting compared to traditional approaches. …”
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