DINOV2-FCS: a model for fruit leaf disease classification and severity prediction by Chunhui Bai, Chunhui Bai, Chunhui Bai, Lilian Zhang, Lilian Zhang, Lilian Zhang, Lutao Gao, Lutao Gao, Lutao Gao, Lin Peng, Lin Peng, Lin Peng, Peishan Li, Peishan Li, Peishan Li, Linnan Yang, Linnan Yang, Linnan Yang

“…However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability.MethodsIn light of the growing application of large model technology across a range of fields, this study draws upon the DINOV2 visual large vision model backbone network to construct the DINOV2-Fruit Leaf Classification and Segmentation Model (DINOV2-FCS), a model designed for the classification and severity prediction of diverse fruit leaf diseases. …”
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Housing, travel, and energy spatial-temporal simulation of Riyadh: Impacts of the New Murabba Project by Abdel Rahman Muhsen, John Abraham, Geraldine Fuenmayor, Paul McMillan, J. D. Hunt

“…The model, called the Riyadh PECAS model, was used to analyze the housing, travel, energy consumption, and related spatial impacts of a proposed megaproject, the New Murabba, consisting of 104,000 residences, 9,000 hotel rooms, and 4.8 million square meters of other non-residential space, anchored by a 400-meter cube-shaped megastructure with partially open interior space. …”
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Spatial Dynamics of Harbour Porpoise Phocoena phocoena Relative to Local Hydrodynamics and Environmental Conditions by Robert Mzungu Runya, Chris McGonigle, Rory Quinn, Morgane Pommier, Christian Armstrong

“…Using data derived from multibeam echosounders (MBES), particle size analysis of sediments, hydrodynamic modelling, and theodolite tracking observations, the study examines the influence of local hydrodynamics and environmental conditions on the spatial distribution of harbour porpoises. …”
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Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals by Yazeed Alkhrijah, Yazeed Alkhrijah, Shehzad Khalid, Shehzad Khalid, Syed Muhammad Usman, Amina Jameel, Muhammad Zubair, Haya Aldossary, Aamir Anwar, Saad Arif

“…The output of these classifiers is then fed into the model-agnostic meta learner ensemble classifier with LSTM as the base classifier for the final prediction of interictal and preictal states.ResultsThe proposed methodology is trained and tested on the publicly available CHB-MIT dataset while achieving 99.34% sensitivity, 98.67% specificity, and a false positive alarm rate of 0.039.DiscussionThe proposed method not only outperforms the existing methods in terms of sensitivity and specificity but is also computationally efficient, making it suitable for real-time epileptic seizure prediction systems.…”
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A cloud-based framework for the quantification of the spatially-explicit uncertainty of remotely sensed benthic habitats by Spyridon Christofilakos, Alina Blume, Avi Putri Pertiwi, Chengfa Benjamin Lee, Dimosthenis Traganos, Peter Reinartz

“…The calculation of the spatially-explicit uncertainty is based on the Shannon Entropy equation and the probability values of a successful prediction according to the ML model. …”
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Landslide assessment considering spatial calibration zoning of physical and mechanical parameters of rock and soil mass by Weimin YIN, Yuanyao LI, Xing LI, Ming LI, Le JU, Ou XIE

“…However, traditional SINMAP models overlook the spatial differences in rock and soil characteristics due to geological environmental changes, resulting in low accuracy in assessment results. …”
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Application of capsule networks based on reparameterized heterogeneous convolution in multi-scale heterogeneous environment matrix in predictive modeling of interdisciplinary compl... by Shuya Liu, Xiaoli Zhang

“…Abstract Predictive modeling of complex systems frequently encounters inadequate processing capabilities for multi-scale heterogeneous data, as conventional methods grapple with the effective integration of such data. …”
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Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin by S. Wongchuig, R. Paiva, V. Siqueira, F. Papa, A. Fleischmann, S. Biancamaria, A. Paris, M. Parrens, A. Al Bitar

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Mean limiting pressure factors determination in contiguous pile walls using RAFELA and nonlinear regression models in spatially random soil by Divesh Ranjan Kumar, Sittha Kaorapapong, Warit Wipulanusat, Suraparb Keawsawasvong

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Approaches to Proxy Modeling of Gas Reservoirs by Alexander Perepelkin, Anar Sharifov, Daniil Titov, Zakhar Shandrygolov, Denis Derkach, Shamil Islamov

“…On average, the ST-GNN method reduces computational time by a factor of 4.3 compared to traditional hydrodynamic models, with a median predictive error not exceeding 10% across diverse datasets, despite variability in specific scenarios. …”
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Prediction of Spatiotemporal Distribution of Electric Vehicle Charging Load Based on Multi-Source Information by WANG Qiang, BI Yuhao, GAO Chao, SONG Duoyang

“…The proposed charging demand gravity model optimizes users' charging station selection behavior by integrating factors such as charging station size， electricity price， and user time cost， resulting in a more reasonable spatial and temporal distribution of the charging load[Conclusions] This study constructed a spatial and temporal distribution prediction model for electric vehicle charging loads by integrating information from multiple sources. …”
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Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station by Zhicheng Dai, Dewei Li, Shiqing Feng

“…Emerging deep learning models offer valuable insights for accurately predicting passenger flow distribution. …”
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Modelling of spatially correlated weather-based electricity forecasting using combined frequency-based signal decomposition with optimized boosting approach by Indra A. Aditya, Didit Adytia

“…The primary contribution is a spatially correlation-driven feature selection technique to choose ideal weather input sites, coupled with the extraction of predominant frequency components from the load signal to enhance model input. …”
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Nonlocal fractional MGT-non-Fourier photothermal model with spatial and temporal nonlocality for controlling the behavior of semiconductor materials with spherical cavities by Mofareh Alhazmi, Ahmed E. Abouelregal

“…In this work, we present the nonlocal Moore-Gibson-Thompson photothermal (NMGTPT) theory, a novel framework that integrates spatial and temporal nonlocality to address limitations in both traditional and advanced thermoelastic models. …”
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Modelling the Spatial Distribution of <i>Dosidicus gigas</i> in the Southeast Pacific Ocean at Multiple Temporal Scales Based on Deep Learning by Mingyang Xie, Bin Liu, Xinjun Chen, Wei Yu, Jintao Wang, Jiawen Xu

“…With the advent of the big data era in ocean remote sensing and fisheries, there is a growing demand for finer temporal scales to predict spatial distribution of the jumbo flying squid (<i>Dosidicus gigas</i>). …”
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High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau by Zhenghao Li, Qiangqiang Yuan, Xin Su

“…As a key climate variable, soil moisture plays a crucial role in drought detection, flood warning, and crop yield prediction. In recent years, the demand for high-spatial-resolution soil moisture has increased, particularly in environmental management. …”
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Assessing spatial soil erosion under climate change using SWAT model in a semi-arid watershed of northeastern Tunisia by Fathia Jarray, Taoufik Hermassi, Haykel Sellami, Mohamed Ali Ben Abdallah, Mohamed Mechergui

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SFSCDNet: A Deep Learning Model With Spatial Flow-Based Semantic Change Detection From Bi-Temporal Satellite Images by K. S. Basavaraju, N. Sravya, Vibha Damodara Kevala, Shilpa Suresh, Shyam Lal

“…Existing deep learning-based methods, particularly those relying on triple-branch architectures, often struggle to accurately localize and predict changes in complex spatial environments characterized by diverse land-cover types. …”
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An EG-Tree Model Incorporating Spatial Heterogeneity for Analyzing Multifactorial Coupling Effects on Carbon Emissions Across Industries and Regions in China by Jinrui Zang, Xin Hu, Kun Qie, Zian Zhang, Shi Zhang

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Efficient room-level heat load prediction in buildings using spatiotemporal distribution characteristics by Xin Tan, Kaixuan Xu, Yahui Wang, Qihui Yu, Yongheng Yu, Guoxin Sun

“…A thermodynamic model built with DesignBuilder and a ResGRU neural network enables overall heat load prediction, with spatiotemporal matrix decomposition ensuring rapid room-level estimations. …”
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