Oxidative stability of biodiesel in the presence of leaves and fruit extracts by Isadora Guilherme Branco, Erica Signori Romagnoli, José Gonçales Filho, Ana Carolina Gomes Mantovani, Dionisio Borsato

“…The lack of fit was not significant at the same level with p1 = 0.132 and p2 = 0.653, showing that they can be used for predictive purposes. The analysis of the response surface and optimization using the super modified simplex method revealed that higher proportions of coffee leaves extract in the mixture yielded superior IP values and lower k values.…”
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NMPC-Based 3D Path Tracking of a Bioinspired Foot-Wing Amphibious Robot by Heqiang Cao, Hailong Wang, Zhiqiang Hu

“…To this end, a nonlinear model predictive control (NMPC) algorithm is employed to compute optimal control inputs, as it effectively addresses the challenges of strong nonlinearity, coupling effects, and multi-objective optimization. …”
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Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy by Lorenzo Villani, Martina Casciola, Davide Astiaso Garcia

“…This model enabled energy simulations using the Carrier–Pizzetti method and supported the design of a hybrid HVAC system—integrating VRF and hydronic circuits—further enhanced by a custom ML algorithm for adaptive, predictive energy management through BIM and IoT data fusion. …”
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Artificial intelligence-assisted nursing interventions in psychiatry for oral cancer patients: A concise narrative review by Abdulmalik Fareeq Saber, Sirwan Khalid Ahmed, Safin Hussein, Karzan Qurbani

“…AI-driven tools, such as predictive models, natural language processing, and mobile applications, provide novel means of early disease detection, risk assessment, personalized care planning, and real-time patient support. …”
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Assessment of the clinical efficacy of the heart spectrum blood pressure monitor for diagnosis of atrial fibrillation: An unblinded clinical trial. by Wei-Fong Kao, Sen-Kuang Hou, Chun-Yao Huang, Chun-Chieh Chao, Chung-Chih Cheng, Yi-Jung Chen

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Multi-field coupling model and evolution mechanism of thermal cracking in early-age mass concrete based on phase-field method by Wenqiang Xu, Sheng Qiang, Hui Wang, Ziang Zhao

“…The research outcomes provide theoretical foundations and technical support for crack prediction and prevention in hydraulic mass concrete structures.…”
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Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory by Qi Wang, Shunxiang Ji, Minqiang Hu, Wei Li, Fusuo Liu, Ling Zhu

“…The results show that this method can effectively improve the prediction accuracy of PV power and is of great significance to real-time economical dispatch.…”
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Hybrid nanofluid flow around a circular cylinder: A coupled ANN and numerical study of MHD, bioconvection, and thermal radiation by Muhammad Fawad Malik

“…This study provides significant information for researchers and engineers seeking to understand the characteristics of flow, its dynamics, and its predictive modeling.…”
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Robust Load Frequency Control in Cyber-Vulnerable Smart Grids with Renewable Integration by Rambaboo Singh, Ramesh Kumar, Utkarsh Raj, Ravi Shankar

“…In light of these concerns, this study proposes a cyber-physical LFC framework incorporating a fuzzy linear active disturbance rejection controller (F-LADRC), wherein the controller parameters are systematically optimized using the quasi-opposition-based reptile search algorithm (QORSA). Furthermore, the proposed approach integrates a comprehensive cyberattack detection and prevention scheme, employing Haar wavelet transforms for anomaly detection and long short-term memory (LSTM) networks for predictive mitigation. …”
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Optimisation of railway tamping scheduling by Mahdi Khosravi, Alireza Ahmadi, Ahmad Kasraei, Arne Nissen

“…To achieve this, we formulated the track geometry tamping scheduling problem as a mixed integer linear programming model and employed a genetic algorithm for its resolution. Key track quality indicators, including the standard deviation of the longitudinal level and single defects, were considered.We developed predictive models for the evolution of standard deviation and single defects over time, which were utilised to schedule preventive tamping activities and anticipate potential corrective actions. …”
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SorpVision: A Comprehensive Dataset for Cementitious Sorptivity Analysis Powered by Computer Vision by Hossein Kabir, Jordan Wu, Sunav Dahal, Tony Joo, Nishant Garg

“…The framework, trained using 1,440 data points from pastes with water-to-cement (w/c) ratios of 0.4–0.8 and curing durations of 1–7 days, achieves high predictive accuracy for initial and secondary sorptivities (R2 > 0.9 for cement pastes). …”
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Hybrid Paddy disease classification using optimized statistical feature based transformation technique with explainable AI by M Amudha, Brindha K

“…This research proposes a machine learning strategy based on Explainable AI that can assist farmers quickly diagnose and manage rice leaf diseases. To enhance predictive performance by transforming features, an improved Owl Search Optimization (IOSO) algorithm is used. …”
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Advanced hybrid computational analysis of febuxostat solubility using machine learning in supercritical processing method by Turki Al Hagbani, Rami M. Alzhrani, Majed Ahmed Algarni

“…With the R2 criterion, the GPR predictive model has a score of 0.819 and the KNN model has a score of 0.854, but the voting model has a score of 0.980, which shows that the combined voting method using the other two models has a better result than both of them. …”
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The role of artificial intelligence in enhancing disaster preparedness and trauma care by Mohamad Sorani

“…Furthermore, ethical considerations such as data privacy, algorithmic bias, and equitable access are crucial to ensuring that AI-driven solutions are implemented responsibly and inclusively.This review also explores future directions for AI, including the development of sophisticated predictive models, autonomous systems for high-risk tasks, immersive training programs utilizing virtual and augmented reality, and global collaboration aimed at standardizing AI tools and practices. …”
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Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network by Bin Luo, Susan Halabi

“…Simulation studies demonstrate that the proposed methods yield superior variable selection and predictive performance compared to traditional and outcome-specific approaches, while remaining robust to violations of the common predictor assumption. …”
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Development of a battery free, solar powered, and energy aware fixed wing unmanned aerial vehicle by Jackson Liller, Rishabh Goel, Abdul Aziz, Josiah Hester, Phuc Nguyen

“…We also derive the Greedy Energy-Aware Control (GEAC) and Predictive Energy-Aware Control (PEAC) solar control algorithm that overcomes power system brownouts and total-loss-of-thrust events, enabling the prototype to perform maneuvers without a battery. …”
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Location allocation approach based on influence of points of dispensing by Nusaybah Alghanmi, Reem Alotaibi, Sultanah Alshammari, Arif Mahmood

“…It also obtained the closest results to the case study in terms of number, location and predictive performance.…”
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Privacy, ethics, transparency, and accountability in AI systems for wearable devices by Petar Radanliev, Petar Radanliev

“…The integration of artificial intelligence (AI) and machine learning (ML) into wearable sensor technologies has substantially advanced health data science, enabling continuous monitoring, personalised interventions, and predictive analytics. However, the fast advancement of these technologies has raised critical ethical and regulatory concerns, particularly around data privacy, algorithmic bias, informed consent, and the opacity of automated decision-making. …”
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Enhancing Power Allocation in DAS: A Hybrid Machine Learning and Reinforcement Learning Model by S. Gnanasekar, K. C. Sriharipriya

“…The proposed method combines Machine Learning (ML) for predictive modeling with Multi-Agent Reinforcement Learning (MARL) for real-time coordination among RAUs. …”
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Multi-exit Kolmogorov–Arnold networks: enhancing accuracy and parsimony by James Bagrow, Josh Bongard

“…Here we introduce multi-exit KANs, where each layer includes its own prediction branch, enabling the network to make accurate predictions at multiple depths simultaneously. …”
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