Real-Time Fire Risk Classification Using Sensor Data and Digital-Twin-Enabled Deep Learning by In-Seop Na, Vani Rajasekar, Velliangiri Sarveshwaran

“…Advanced deep learning architectures such as convolutional neural networks (CNNs), deep CNNs (DCNNs), and recurrent neural networks (RNNs) are utilized to identify critical spatial and temporal patterns in the data. The models are trained on a comprehensive dataset encompassing environmental indicators, fire-prone area characteristics, and real-time meteorological data. …”
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Monthly methane emissions in Chinese mainland provinces from 2013–2022 by Duo Cui, Siyao Yang, Xuanren Song, Xiaoting Huang, Cuncun Duan, Mingrui Ji, Zhongyan Li, Siqi Yu, Zhu Deng, Piyu Ke, Xinyu Dou, Taochun Sun, Zhu Liu

“…Methane, a potent greenhouse gas, is emitted from diverse anthropogenic and natural sources, many of which exhibit pronounced temporal variability. In particular, emissions from rice cultivation, energy use, and livestock management show strong seasonal patterns, yet high-frequency and spatially detailed methane emission inventories have been lacking. …”
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Global biome changes over the last 21 000 years inferred from model–data comparisons by C. Li, C. Li, A. Dallmeyer, J. Ni, M. Chevalier, M. Willeit, A. A. Andreev, X. Cao, L. Schild, L. Schild, B. Heim, M. Wieczorek, U. Herzschuh, U. Herzschuh, U. Herzschuh

“…</p> <p>Overall, our reconstruction, with its relatively high temporal and spatial resolution, serves as a robust dataset for evaluating ESM-based paleo-megabiome simulations and provides potential clues for improving systematic model biases.…”
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A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces by Peiliang Gong, Pengpai Wang, Yueying Zhou, Daoqiang Zhang

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Multimodal sleep staging network based on obstructive sleep apnea by Jingxin Fan, Jingxin Fan, Jingxin Fan, Mingfu Zhao, Li Huang, Li Huang, Bin Tang, Bin Tang, Lurui Wang, Zhong He, Zhong He, Xiaoling Peng

“…The Multi-Scale Feature Extraction Module (MFEM) employs convolutional layers with varying dilation rates to capture spatial patterns from fine to coarse granularity. …”
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DDoS classification of network traffic in software defined networking SDN using a hybrid convolutional and gated recurrent neural network by Ahmed M. Elshewey, Safia Abbas, Ahmed M. Osman, Eman Abdullah Aldakheel, Yasser Fouad

“…The CNN-GRU model integrates a 1D convolutional layer for spatial pattern extraction and a GRU layer for temporal sequence learning, followed by dense layers with dropout regularization. …”
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Identifying gaps in protection from malaria vector biting in rural Cambodia using an entomological assessment and human behaviour observations by David J. McIver, Elodie A. Vajda, Dyna Doum, Nicholas W. Daniel, Molly Quan, Diane D. Lovin, Joanne M. Cunningham, Siv Sovannaroth, Allison Tatarsky, Neil F. Lobo

“…Conclusion This study demonstrates the importance of understanding spatial and temporal human exposure to mosquito bites, in the presence of proven vector control tools (LLINs, LLIHNs) and newly introduced bite prevention tools (VPSRs, ITCs, and TRs). …”
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Trends in Heavy Metal Pollution in Agricultural Land Soils of Tropical Islands in China (2000–2024): A Case Study on Hainan Island by Erping Shang, Yong Ma, Wutao Yao, Shuyan Zhang

“…Pb and As show similar spatial patterns, with higher concentrations in the west and lower concentrations in the east. …”
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Spatiotemporal analysis of thermal islands in a semi-arid city: A case study of Kermanshah, Iran using machine learning and remote sensing by Peyman Karami, Seyed-Mohsen Mousavi

“…Cold Islands (CIs) and Hot Islands (HIs) were identified for each image using LST and Getis-Ord Gi analysis, and their spatio-temporal changes were evaluated with the Kappa index and landscape metrics. …”
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Exploring habitat‐density relationships and model transferability for an alpine bird using abundance models by Håkon Brandt Fjeld, Jan Eivind Østnes, Erlend B. Nilsen

“…While several previous studies have evaluated the transferability of species distribution models, much less is known about how well models that predict spatially explicit population density transfer across contexts. …”
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Urban Heat Island Effect: Remote Sensing Monitoring and Assessment—Methods, Applications, and Future Directions by Lili Zhao, Xuncheng Fan, Tao Hong

“…The paper first analyzes the formation mechanisms and impacts of urban heat islands, then traces the evolution of remote sensing technology from early traditional platforms such as Landsat and NOAA-AVHRR to modern next-generation systems, including the Sentinel series and ECOSTRESS, emphasizing improvements in spatial and temporal resolution and their application value. …”
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Multi-Modal Fused-Attention Network for Depression Level Recognition Based on Enhanced Audiovisual Cues by Yihan Zhou, Xiaokang Yu, Zixi Huang, Feierdun Palati, Zeyu Zhao, Zihan He, Yuan Feng, Yuxi Luo

“…The FIE block utilizes ResNet-18 to enhance the feature representation of video frames and integrates two types of attention mechanisms to capture spatial-temporal patterns. Meanwhile, the VIE block processes the Mel spectrogram of the audio signal, followed by an optimized Swin transformer block to extract auditory features. …”
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Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin by Chandni Thakur, Venkatesh Budamala, KS Kasiviswanathan, Claudia Teutschbein, Bankaru-Swamy Soundharajan

“…Additionally, uncertainty estimates using the p-factor indicated that the extreme gradient boosting model exhibited lower uncertainty in reproducing the observed spatio-temporal patterns of climate variables. Overall, the proposed framework enhances the reliability of global climate model simulations, supporting robust regional-scale hydrological modeling and climate change impact assessments.…”
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A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging by Satyam Tiwari, Goutam Jain, Dasharathraj K. Shetty, Manu Sudhi, Jayaraj Mymbilly Balakrishnan, Shreepathy Ranga Bhatta

“…CNNs use specific filters to find spatial and temporal relationships in images, making understanding and interpreting them easier. …”
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Deep Learning Architectures for Single-Label and Multi-Label Surgical Tool Classification in Minimally Invasive Surgeries by Hisham ElMoaqet, Hamzeh Qaddoura, Mutaz Ryalat, Natheer Almtireen, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Thomas Neumuth, Knut Moeller

“…This study proposes a novel deep learning approach for surgical tool classification based on combining convolutional neural networks (CNNs), Feature Fusion Modules (FFMs), Squeeze-and-Excitation (SE) networks, and Bidirectional long-short term memory (BiLSTM) networks to capture both spatial and temporal features in laparoscopic surgical videos. …”
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SmartTrust: a hybrid deep learning framework for real-time threat detection in cloud environments using Zero-Trust Architecture by Umesh Kumar Lilhore, Sarita Simaiya, Roobaea Alroobaea, Abdullah M. Baqasah, Majed Alsafyani, Afnan Alhazmi, Md Monish Khan

“…SmartTrust integrates CNN, LSTM, and Transformer models to analyze spatial and temporal patterns in network traffic and user behaviours. …”
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Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin by Prashant Parasar, Akhouri Pramod Krishna

“…The main findings of this study are (1) KAN demonstrated high predictive performance with root mean squared error (RMSE) values ranging from 42.7 to 58.3 m3/s, Nash–Sutcliffe efficiency (NSE) between 0.80 and 0.87, mean absolute error (MAE) between 28.9 to 52.7 and R2 values between 0.84 and 0.90 across stations. (2) SHAP based feature contribution analysis identified Relative humidity (hurs), specific humidity (huss), and temperature (tas) as key predictors, while (pr) showed limited contribution due to spatial inherent inconsistencies in GCM precipitation data. (3) The bootstrapped SHAP distributions highlighted substantial variability in feature importance, particularly for humidity variables, revealing station specific uncertainty patterns in model interpretation. (4) The KAN framework results indicate strong temporal alignment and physical realism, confirming KAN’s robustness in capturing seasonal discharge dynamics and extreme flow events under monsoon influence environments. (5) In this study KAN with SHAP (SHapley additive exPlanations) is implemented for hydrological modeling under monsoon-influenced and data-limited regions such as SRB, offering improved accuracy, functional precision and efficiency compared to traditional models. …”
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Acoustic cues for person identification using cough sounds by Van-Thuan Tran, Ting-Hao You, Wei-Ho Tsai

“…The proposed architecture, CoughCueNet, is a convolutional recurrent neural network designed to capture both spatial and temporal patterns in cough sounds. The training process incorporates a hybrid loss function that combines supervised contrastive (SC) learning and cross-entropy (CE) loss to enhance feature discrimination. …”
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In vivo imaging and pharmacokinetics of percutaneously injected ultrasound and X-ray imageable thermosensitive hydrogel loaded with doxorubicin versus free drug in swine. by Jose F Delgado, Ayele H Negussie, Nicole A Varble, Andrew S Mikhail, Antonio Arrichiello, Tabea Borde, Laetitia Saccenti, Ivane Bakhutashvili, Joshua W Owen, Robert Morhard, John W Karanian, William F Pritchard, Bradford J Wood

“…Furthermore, US imaging revealed temporal changes, suggesting variations in acoustic intensity, heterogeneity, and echotextures. …”
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HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction by Yiying Wei, Xiangyu Zeng, Xirui Chen, Hui Zhang, Zhengan Yang, Zhicheng Li

“…This paper proposes a Habit-based Temporal–Spatial Attention Long Short-Term Memory (HTSA-LSTM) network, a novel framework that integrates a dual spatiotemporal attention mechanism to capture dynamic dependencies across time and space, coupled with a driving style analysis module. …”
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