Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…The spatiotemporal prediction of RZSM refers to the process of estimating its future spatial distribution and temporal variations using predictive models. …”
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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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Method for prediction magnetic silencing of uncertain energy-saturated extended technical objects in prolate spheroidal coordinate system by B. I. Kuznetsov, A. S. Kutsenko, T. B. Nikitina, I. V. Bovdui, K. V. Chunikhin, V. V. Kolomiets

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PVD-GSTPS: design of an efficient parallel vehicle detection based green signal time prediction system by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…These advancements are essential for effectively predicting vehicle Green Signal Time by considering accurate detection and tracking, Spatial Occupancy calculation, long-term dependencies, and non-linear relationships in traffic data. …”
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Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

“…The research intends to use the LSTM networks extensively that are proficient in spatial dependence capturing and integrate them with the collaborative learning framework of Federated Learning in an endeavor to augment the predictive competency. …”
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Modeling the Spatial Flows of Nitrogen: The Case of Xiamen by Yanmin Li, Tianqi Zhang, Shihang Wang, Yu-Sheng Shen, Shenghui Cui

“…Taking Xiamen as its research case, this study utilizes grid technology and spatial analysis to build a detailed spatial model for nitrogen flow at the grid scale. …”
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Research on a Crime Spatiotemporal Prediction Method Integrating Informer and ST-GCN: A Case Study of Four Crime Types in Chicago by Yuxiao Fan, Xiaofeng Hu, Jinming Hu

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Modeling the spatial distribution of anthrax in southern Kenya. by Fredrick Tom Otieno, John Gachohi, Peter Gikuma-Njuru, Patrick Kariuki, Harry Oyas, Samuel A Canfield, Jason K Blackburn, M Kariuki Njenga, Bernard Bett

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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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Development and Spatial External Validation of a Predictive Model of Survival Based on Random Survival Forest Analysis for People Living With HIV and AIDS After Highly Active Antiretroviral Therapy in China: Retrospective Cohort Study by Xiaoshan Li, Yanhui Li, Zhengping Zhu, Bingxin Tan, Xiaoyi Zhou, Hongjie Shi, Xin Li, Ping Zhu, Yuanyuan Xu

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Identifying leptospirosis hotspots in Selangor: uncovering climatic connections using remote sensing and developing a predictive model by Muhammad Akram Ab Kadir, Rosliza Abdul Manaf, Siti Aisah Mokhtar, Luthffi Idzhar Ismail

“…Machine learning algorithms, including support vector machine (SVM), Random Forest (RF), and light gradient boosting machine (LGBM) were employed to develop predictive models for leptospirosis hotspot areas. Model performance was then evaluated using cross-validation and metrics such as accuracy, precision, sensitivity, and F1-score. …”
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A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models by J. C. Vogeler, W. B. Cohen

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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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Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem. by Daniele Da Re, Marius Gilbert, Celia Chaiban, Pierre Bourguignon, Weerapong Thanapongtharm, Timothy P Robinson, Sophie O Vanwambeke

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Research on model predictive control strategy of three-level dual-active bridge DC-DC converter by Junrui Wang, Lu Tan, Jin Li, Changjiang Ji

“…Through the switching conduction mode of the three-level DAB converter, the working conditions of the inductor current in each mode are analyzed, and the range of zero-voltage turn-on of all switches of the three-level DAB under single phase shift (SPS) modulation is derived. The spatial equation of state is established according to each mode, the purpose of fast response is achieved through the cost function, and finally the predictive control strategy of the three-level DAB model with zero voltage turn-on is obtained discretically. …”
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Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging by Vijaya Bhaskar Sadu, Sathvik Bagam, Mohd Naved, Siva Krishna Reddy Andluru, Kamalakar Ramineni, Meshal Ghalib Alharbi, Sudhakar Sengan, Rahmaan Khadhar Moideen

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Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory by Yizhen Sun, Junyou Yang, Donghui Zhao, Moses Chukwuka Okonkwo, Jianmin Zhang, Shuoyu Wang, Yang Liu

“…The kinematic model for a single front steering‐wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. …”
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Evaluating the Uncertainty and Predictive Performance of Probabilistic Models Devised for Grade Estimation in a Porphyry Copper Deposit by Raymond Leung, Alexander Lowe, Arman Melkumyan

“…Probabilistic models are used to describe random processes and quantify prediction uncertainties in a principled way. …”
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Predicting species distributions in the open ocean with convolutional neural networks by Morand, Gaétan, Joly, Alexis, Rouyer, Tristan, Lorieul, Titouan, Barde, Julien

“…These findings show the adequacy of deep learning for species distribution modelling in the open ocean. Additionally, this purely correlative model was then analysed with explicability tools to understand which variables had an influence on the model’s predictions. …”
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Evaluation of the relationships between physico-chemical parameters and the abundance of Vibrio spp. in blue crabs (Callinectes sapidus) and seawater from the Maryland Coastal Bays by Jasmine Smalls, John Jacobs, Howard Townsend, Paulinus Chigbu, Salina Parveen

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