Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…The time-distributed vision transformer learns the spatial feature and then feeds the result to the transformer to learn the temporal feature and make the final prediction afterward. …”
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Decoupling Urban Street Attractiveness: An Ensemble Learning Analysis of Color and Visual Element Contributions by Tao Wu, Zeyin Chen, Siying Li, Peixue Xing, Ruhang Wei, Xi Meng, Jingkai Zhao, Zhiqiang Wu, Renlu Qiao

“…To address this gap, this study employs semantic segmentation and color computation on a massive street-view image dataset encompassing 56 cities worldwide, comparing eight machine learning models in predicting Visual Aesthetic Perception Scores (VAPSs). …”
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Using electro-inductive sensor to trace moving and non-moving objects tracked by S. S. Kolmogorova, S. V. Biryukov

“…The research carries out a spatial model of path tracking using the dynamic parameters of the object's position, as well as the path of motion of electric field source. …”
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Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing by Xiaolu Zhu, Jinglin Li, Zhihan Liu, Fangchun Yang

“…The stacked auto-encoder model is employed for learning the latent spatial and temporal correlation features of demand. …”
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LoadSeer: Exploiting Tensor Graph Convolutional Network for Power Load Forecasting With Spatio-Temporal Characteristics by Jiahao Zhang, Bin Yu, Hanbin Lai, Lin Liu, Jinghui Zhou, Fengliang Lou, Yili Ni, Yan Peng, Ziheng Yu

“…Existing spatio-temporal prediction methods can only handle one factor in each dimension of time and space. …”
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Mapping near-real-time soil moisture dynamics over Tasmania with transfer learning by M. T. Widyastuti, J. Padarian, B. Minasny, M. Webb, M. Taufik, D. Kidd

“…Results showed that (1) models calibrated from the Australian dataset performed worse than Tasmanian models regardless of the type of DL approaches; (2) Tasmanian models, calibrated solely using local data, resulted in shortcomings in predicting soil moisture; and (3) transfer learning exhibited remarkable performance improvements (error reductions of up to 45 % and a 50 % increase in correlation) and resolved the drawbacks of the two previous models. …”
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Leaf area index-based phenotypic assessment of sweet potato varieties using UAV multispectral imagery and a hybrid retrieval approach by Philemon Tsele, Abel Ramoelo, Lucy Moleleki, Sunette Laurie, Whelma Mphela, Natasha Tshuma

“…However, the BRT performance in-comparison to KRR, captured more spatial variability of observed LAI with a better prediction accuracy across the 20 sweet potato varieties. …”
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PRODEM: an annual series of summer DEMs (2019 through 2022) of the marginal areas of the Greenland Ice Sheet by M. Winstrup, H. Ranndal, S. Hillerup Larsen, S. B. Simonsen, K. D. Mankoff, K. D. Mankoff, K. D. Mankoff, R. S. Fausto, L. Sandberg Sørensen

“…The PRODEMs are validated using leave-one-out cross-validation, and PRODEM19 is further validated against an external data set, showcasing their ability to correctly represent surface elevations within the associated spatially varying prediction uncertainties. We observe a general lowering of surface elevations during the 4-year PRODEM period, but the spatial pattern of change is highly complex and with annual changes superimposed. …”
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A comprehensive evaluation of satellite-based and reanalysis soil moisture products over the upper Blue Nile Basin, Ethiopia by Addis A. Alaminie, Sofie Annys, Jan Nyssen, Mark R. Jury, Giriraj Amarnath, Muluneh A. Mekonnen, Seifu A. Tilahun

“…Establishing these networks is essential for improving satellite retrievals and modelling in the upper Blue Nile Basin, Ethiopia.…”
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A multistudy analysis reveals that evoked pain intensity representation is distributed across brain systems. by Bogdan Petre, Philip Kragel, Lauren Y Atlas, Stephan Geuter, Marieke Jepma, Leonie Koban, Anjali Krishnan, Marina Lopez-Sola, Elizabeth A Reynolds Losin, Mathieu Roy, Choong-Wan Woo, Tor D Wager

“…In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. …”
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Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules by Khalfalla Awedat, Masoud Alajmi, Mustafa Elfituri

“…The U-Net-ASPP_Cent model is particularly effective for central anomaly detection, while the U-Net-ASPP_Diag model excels at identifying directional faults such as cracks. …”
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Hyper Spectral Camera ANalyzer (HyperSCAN) by Wen-Qian Chang, Hsun-Ya Hou, Pei-Yuan Li, Michael W. Shen, Cheng-Ling Kuo, Tang-Huang Lin, Loren C. Chang, Chi-Kuang Chao, Jann-Yenq Liu

“…After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. …”
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Revealing the early ice flow patterns with historical Declassified Intelligence Satellite Photographs back to 1960s by Shujie Wang, Hongxing Liu, Bailang Yu, Guoqing Zhou, Xiao Cheng

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Organoid‐Guided Precision Medicine: From Bench to Bedside by Boqiang Tao, Xiaolan Li, Ming Hao, Tian Tian, Yuyang Li, Xiang Li, Chun Yang, Qirong Li, Qiang Feng, Hengzong Zhou, Yicheng Zhao, Dongxu Wang, Weiwei Liu

“…Future integration of artificial intelligence, microfluidics, and spatial transcriptomics will enhance organoid scalability, reproducibility, and clinical relevance. …”
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Regional Pathways to Internationalization: The Role of Erasmus+ in European HEIs by Eleni Georgoudaki, Spyridon Stavropoulos, Dimitris Skuras

“…Employing hotspot analysis and a two-level random intercept model, this research analyses spatial patterns and the influences of regional characteristics and institutional variables on Erasmus mobility rates. …”
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Attention residual network for medical ultrasound image segmentation by Honghua Liu, Peiqin Zhang, Jiamin Hu, Yini Huang, Shanshan Zuo, Lu Li, Mailan Liu, Chang She

“…Finally, the predictive efficacy of the model was evaluated using publicly accessible breast ultrasound and thyroid ultrasound data. …”
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Low-Carbon-Economic Collaborative Optimal Dispatching of Microgrid Considering Electricity-Hydrogen Integration by Lingling TAN, Wei TANG, Dongqing CHU, Zihan YU, Xingquan JI, Yumin ZHANG

“…In the low-carbon demand response model, with the saved carbon emission costs by reduced carbon emissions of microgrid users as an incentive signal, a mapping relationship between energy flow and carbon emission flow of microgrid is established to realize the transfer and allocation of carbon emission responsibilities and fine evaluation of carbon emission characteristics of microgrid operations, so as to establish a low-carbon DR model which guides users to participate in carbon emission reduction strategies according to the spatial and temporal differences of load carbon emission intensity, and to deeply explore the synergy between low-carbon and economy of source-load of microgrid. …”
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Local Adaptation and Climate Change Vulnerability of the Relict Tree Species Taiwania cryptomerioides Provide Insights Into Its Conservation and Restoration by Yang Lu, Hao Dong, Saibin Fan, Lu Yuan, Yuhui Wang, Zhuang Zhao, Yong Lai, Shixin Zhu, Jinyong Huang, Caipeng Yue, Yongpeng Ma, Ningning Zhang

“…Here, we applied a landscape genomics approach to investigate the population genetic structure and predict adaptive capacity to climatic change for Taiwania cryptomerioides Hayata, a vulnerable Tertiary relict tree species in China. …”
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Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification by Rajendra Babu Chikkala, Chinta Anuradha, Patnala S. R. Chandra Murty, S. Rajeswari, N. Rajeswaran, M. Murugappan, Muhammad E. H. Chowdhury

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A Ship’s Maritime Critical Target Identification Method Based on Lightweight and Triple Attention Mechanisms by Pu Wang, Shenhua Yang, Guoquan Chen, Weijun Wang, Zeyang Huang, Yuanliang Jiang

“…The experimental results show that the average accuracy of this method in identifying seven types of targets—including buoys, bridges, islands and reefs, container ships, bulk carriers, passenger ships, and other ships—reached 92.1%, with a 12% reduction in the number of parameters. This enhancement improves the model’s ability to recognize and distinguish different targets and buoy colors.…”
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