Added wave resistance prediction and comparative study based on point cloud feature extraction by Mingfeng WU, Renchuan ZHU, Dekang XU

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AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data by Mohd Zubair Akhtar, Maximilian Schmid, Gordon Elger

“…Traditional Finite Element Analysis (FEA) techniques for predicting solder joint lifespan often rely on manual post-processing to identify high-risk regions for plastic strain accumulation. …”
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Development and Evaluation of Neural Network Architectures for Model Predictive Control of Building Thermal Systems by Jevgenijs Telicko, Andris Krumins, Agris Nikitenko

“…In this study, we adapt neural network architectures such as GRU and TCN for use in the context of building model predictive control. …”
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A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management by Muhammad Salman Haleem, Daphne Katsarou, Eleni I. Georga, George E. Dafoulas, Alexandra Bargiota, Laura Lopez-Perez, Miguel Rujas, Giuseppe Fico, Leandro Pecchia, Dimitrios Fotiadis, Gatekeeper Consortium

“…We achieved the multimodal architecture prediction results with Mean Absolute Point Error (MAPE) between 14 and 24 mg/dL, 19–22 mg/dL, 25–26 mg/dL in case of Menarini sensor and 6–11 mg/dL, 9–14 mg/dL, 12–18 mg/dL in case of Abbot sensor for 15, 30 and 60 min prediction horizon respectively. …”
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Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN-Based Model by Emilio Jiménez-Macías, Angel Sánchez-Roca, Hipólito Carvajal-Fals, Julio Blanco-Fernández, Eduardo Martínez-Cámara

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Continuous Joint Kinematics Prediction Using GAT-LSTM Framework Based on Muscle Synergy and Sparse sEMG by Meiju Li, Zijun Wei, Zhi-Qiang Zhang, Shuhao Ma, Sheng Quan Xie

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Hydrogen Safety in Solid Oxide Fuel Cells: an LSTM-Based Model for Predicting Temperature Anomalies and Change Points by Tomaso Vairo, Davide Clematis, Maria Paola Carpanese, Bruno Fabiano

“…The model efficacy in predicting temperature-related issues and detecting change points with high accuracy is verified by extensive runs in a laboratory scale plant. …”
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A Study on Predicting Key Times in the Takeout System’s Order Fulfillment Process by Dongyi Hu, Wei Deng, Zilong Jiang, Yong Shi

“…Their behaviors involve multiple key time points, and accurately predicting these critical moments in advance is essential for enhancing both user retention and operational efficiency on such platforms. …”
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Treegraph: tree architecture from terrestrial laser scanning point clouds by Wanxin Yang, Phil Wilkes, Matheus B. Vicari, Kate Hand, Kim Calders, Mathias Disney

“…Terrestrial laser scanning (TLS) offers millimetre‐level point cloud data, but current approaches to 3D tree reconstruction from TLS point clouds primarily focus on retrieving total volume at tree scale for aboveground biomass (AGB) estimation. …”
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Explosion resistance evaluation and damage prediction of middle partition walls in prefabricated frame tunnels by Zhen Huang, Yuzhu Zhou, Ziming Xiong, Hao Lu, Minqian Sun, Maojiang Qin

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Presentation of a Novel Method for Prediction of Traffic with Climate Condition Based on Ensemble Learning of Neural Architecture Search (NAS) and Linear Regression by Javad Artin, Amin Valizadeh, Mohsen Ahmadi, Sathish A. P. Kumar, Abbas Sharifi

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Advancing Neurodegenerative Disease Management: Technical, Ethical, and Regulatory Insights from the NeuroPredict Platform by Marilena Ianculescu, Lidia Băjenaru, Ana-Mihaela Vasilevschi, Maria Gheorghe-Moisii, Cristina-Gabriela Gheorghe

“…Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. …”
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Kinematic Integration Network With Enhanced Temporal Intelligence and Quality-Driven Attention for Precise Joint Angle Prediction in Exoskeleton-Based Gait Analysis by Lyes Saad Saoud, Irfan Hussain

“…Exoskeleton robots offer transformative potential in aiding the rehabilitation of patients with lower limb motor dysfunction, where precise and real-time prediction of knee joint angles is critical. Despite advances in deep learning for motion prediction, existing models struggle with balancing accuracy and real-time performance, particularly in wearable applications. …”
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Beyond the Backbone: A Quantitative Review of Deep-Learning Architectures for Tropical Cyclone Track Forecasting by He Huang, Difei Deng, Liang Hu, Yawen Chen, Nan Sun

“…Building on this framework, we conduct a critical cross-model analysis that reveals key trends, performance disparities, and architectural tradeoffs. Our analysis also highlights several persistent challenges, such as long-term forecast degradation, limited physical integration, and generalization to extreme events, pointing toward future directions for developing more robust and operationally viable DL models for TC track forecasting. …”
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The Fatigue Life Prediction of Welded Joints in Orthotropic Steel Bridge Decks Considering Weld-Induced Residual Stress and Its Relaxation Under Vehicle Loads by Wen Zhong, Youliang Ding, Yongsheng Song, Sumei Liu, Mengyao Xu, Xin Wang

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Event camera-based human pose estimation via hybrid spiking-point cloud neural architecture by Sichao Tang, Hengyi Lv, Xiangzhi Li, Yuchen Zhao, Yisa Zhang, Yang Feng

“…The system also incorporates a cross-modal adaptive fusion mechanism that dynamically adjusts weights across different modalities, and an asynchronous skeleton constraint module that leverages human anatomical prior knowledge to constrain prediction results. Compared to existing methods, our network architecture more effectively processes the sparse asynchronous characteristics of event data, achieving a better balance between accuracy and efficiency, particularly excelling in complex scenes and rapid motion scenarios. …”
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A multi-channel spatiotemporal SegNet model for short term wind power prediction with sequence decomposition and reconstruction by Xingdou Liu, Liang Zou, Li Zhang, Jiangong Wang, Zhiyun Han, Yong Li

“…The original SegNet network is improved into an encoder-predicting architecture with temporal learning capability to adapt to multi-dimensional spatiotemporal tensor joint training. …”
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Experimental test on 3D-printing components for Architectural Restoration by Valentina Tomei, Ernest Grande, Maura Imbimbo

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PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study... by Siva Senthuran, Lewis Campbell, Jennifer Fleming, Virginia Newcombe, Michael Reade, Silvia Manzanero, James Walsham, Nathan Brown, Xuan Vinh To, Clifford Pollard, Fatima Nasrallah, Judith Bellapart, Esther Jacobson, Jason Meyer, Janine Stuart, Tracey Evans, Shekhar S Chandra, Jason Ross, James McCullough

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Ultra-Short-Term Photovoltaic Power Prediction Based on Predictable Component Reconstruction and Spatiotemporal Heterogeneous Graph Neural Networks by Yingjie Liu, Mao Yang

“…A circuit singular spectral decomposition (CISSD) intrinsic predictable component extraction method is adopted to obtain specific frequency components in sensitive meteorological variables, a mechanism based on radiation characteristics and PV power trend predictable component extraction and reconstruction is proposed to enhance power predictability, and a spatiotemporal heterogeneous graph neural network (STHGNN) combined with a Non-stationary Transformer (Ns-Transformer) combination architecture to achieve joint prediction for different PV components. …”
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