Evaluating PSA dynamics for predicting androgen deprivation failure with a patient specific prostate cancer model by Shengchao Zhao, Evan T. Keller, Tyler Robinson, Jinlu Dai, Alyssa Ghose, Ajjai Alva, Trachette Jackson, Harsh Vardhan Jain

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Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction by Shihu Jiao, Xiucai Ye, Tetsuya Sakurai, Quan Zou, Wu Han, Chao Zhan

“…By combining sequence embeddings from the ProtT5 language model and 3D structural data predicted by ESMFold, StrucToxNet can capture both sequential and spatial characteristics of peptides. …”
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A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data by Ningqing Pang, Hoiio Kong, Chanseng Wong, Zijun Li, Yu Du, Jeremy Cheuk-Hin Leung

“…The current reanalysis of temperature data faces difficulties in providing more accurate geographical temperature data due to insufficient spatial resolution (0.25° × 0.25°). In this study, a lightweight downscaling method incorporating a convolutional neural network is proposed to construct a high-resolution temperature prediction model for the Macau region based on ERA5 reanalysis data. …”
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AP-GRIP evaluation framework for data-driven train delay prediction models: systematic literature review by Tiong Kah Yong, Zhenliang Ma, Carl-William Palmqvist

“…The framework covers six key aspects across overall, spatial, temporal, and train-specific dimensions, providing a systematic approach for the comprehensive assessment of train delay prediction models. …”
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Calculation Methods of Topographic Factors Modification Using Data Digital Elevation Model (DEM) To Predict Erosion by Hengki Simanjuntak, Hendrayanto ., Nining Puspaningsih

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A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction by Yikun Fan, Wei Zhang, Wenting Zhang, Dejin Zhang, Li He

“…This study introduces a novel double-layer long short-term memory (LSTM) model to surmount the limitations of conventional prediction methods, which frequently overlook predicted vehicle behavior and interactions. …”
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Global lightning-ignited wildfires prediction and climate change projections based on explainable machine learning models by Assaf Shmuel, Teddy Lazebnik, Oren Glickman, Eyal Heifetz, Colin Price

“…In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. …”
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A Spatiotemporal Convolutional Neural Network Model Based on Dual Attention Mechanism for Passenger Flow Prediction by Jinlong Li, Haoran Chen, Qiuzi Lu, Xi Wang, Haifeng Song, Lunming Qin

“…The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. …”
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Spatiotemporal evolution and prediction of blue–green–grey-space carbon stocks in Henan Province, China by Kai Zhou, Kai Zhou, Xinyu Wei, Yanjie Wang, Jinhui Wang, Zhifang Wang, Zhifang Wang, Yichuan Zhang, Yichuan Zhang

“…Changes in blue–green–grey spaces use greatly influenced the carbon-storage capabilities of ecosystems, which is crucial for maintaining the carbon balance of regional ecosystems.By combining the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with the Patch-generating Land Use Simulation (PLUS) model, this study evaluates the spatiotemporal evolution of blue–green–grey spatial carbon stocks in Henan Province, China, and predicts the relationship between blue–green–grey spatial changes and carbon stocks under four future scenarios. …”
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Exploring malaria prediction models in Togo: a time series forecasting by health district and target group by Muriel Rabilloud, Nicolas Voirin, Anne Thomas, Tchaa Abalo Bakai, Tinah Atcha-Oubou, Tchassama Tchadjobo

“…Objectives Integrating malaria prediction models into malaria control strategies can help to anticipate the response to seasonal epidemics. …”
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Evaluation of Feature Selection and Regression Models to Predict Biomass of Sweet Basil by Using Drone and Satellite Imagery by Luana Centorame, Nicolò La Porta, Michela Papandrea, Adriano Mancini, Ester Foppa Pedretti

“…This study is among the first to combine multispectral data from both a drone equipped with Altum-PT camera and PlanetScope satellite imagery to predict fresh biomass in sweet basil grown in an open field, demonstrating the added value of integrating different spatial scales. …”
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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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Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning by Zipeng Zhao, Yuman Sun, Weiwei Jia, Jinyan Yang, Fan Wang

“…The 934 nm and 464 nm wavelengths were identified as the most critical spectral bands for predicting soil vanadium contamination. This integrated approach robustly delineates the spatial distribution characteristics of V and V5+ in soils, facilitating precise monitoring and ecological risk assessments of vanadium contamination through a comparative analysis of predictive accuracy across diverse models.…”
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Cultivated Land Suitability Prediction in Southern Xinjiang Typical Areas Based on Optimized MaxEnt Model by Yilong Tian, Xiaohuang Liu, Hongyu Li, Run Liu, Ping Zhu, Chaozhu Li, Xinping Luo, Chao Wang, Honghui Zhao

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Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma by Juan I. Pisula, Doris Helbig, Lucas Sancéré, Oana-Diana Persa, Corinna Bürger, Anne Fröhlich, Carina Lorenz, Sandra Bingmann, Dennis Niebel, Konstantin Drexler, Jennifer Landsberg, Roman Thomas, Katarzyna Bozek, Johannes Brägelmann

“…Risk stratification systems based on clinico-pathological criteria aim to identify high-risk patients, but accurate predictions remain challenging. Deep learning models present new opportunities for patient risk prediction, yet their interpretability has been largely unexplored. …”
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High-resolution simulation and prediction of urban private vehicles energy consumption system: Agent-based modelling by Chuang Tu, Jiayi Liu, Jing Wang, Jing Bai, Guangwen Hu

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Enhanced real-time Parkinson’s disease monitoring and severity prediction using a multi-faceted deep learning approach by Marreddy Naga Sabari, Deepak Ch

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A Secure and Efficient Framework for Multimodal Prediction Tasks in Cloud Computing with Sliding-Window Attention Mechanisms by Weiyuan Cui, Qianye Lin, Jiaqi Shi, Xingyu Zhou, Zeyue Li, Haoyuan Zhan, Yihan Qin, Chunli Lv

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High-resolution global modeling of wheat’s water footprint using a machine learning ensemble approach by Murat Emeç, Abdullah Muratoğlu, Muhammed Sungur Demir

“…The study revealed distinct outcomes for different clustering methods, demonstrating the model's robustness across varying spatial scales. …”
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Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration by Gökhan Deveci, Özgün Yücel, Ali Bahadır Olcay

“…The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. …”
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