Integrating data-driven and physics-based approaches for robust wind power prediction: A comprehensive ML-PINN-Simulink framework by Rajaperumal T. A, Christopher Columbus Chinnappan

“…Abstract This study presents a comprehensive hybrid forecasting framework that synergizes machine learning algorithms, MATLAB Simulink-based physical modeling, and Physics-Informed Neural Networks (PINNs) to advance wind power prediction accuracy for a 10 kW Permanent Magnet Synchronous Generator (PMSG)-based Wind Energy Conversion System (WECS). …”
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Methodical Approaches to Creation of Dividing Automation at Industrial Enterprises with Generating Power Plants by E. V. Kalentionok, Y. D. Filipchik

“…The paper considers a problem pertaining to creation of dividing automation at industrial enterprises which have their own generating plants. Algorithms for action of dividing automation that permit to ensure minimum possible power non-balance while using generating plants for autonomous operation and possible parameters for its response are proposed in the paper.…”
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Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN by David Herreros, Carlos Perez Mata, Chari Noddings, Deli Irene, James Krieger, David A. Agard, Ming-Daw Tsai, Carlos Oscar Sanchez Sorzano, Jose Maria Carazo

“…Traditional methods assume discrete conformations, while newer algorithms estimate conformational landscapes representing the different structural states a biomolecule explores. …”
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Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification by Santhakumar D, Gnanajeyaraman Rajaram, Elankavi R, Viswanath J, Govindharaj I, Raja J

“…These algorithms are based on natural processes: ACO mimics the foraging behavior of ants, while ALO models the hunting strategy of ant-lion larvae. …”
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A visualized bibliometric analysis on remote sensing monitoring of methane emissions in coal mines by CUI Ximin, GUO Wei, SHI Guobin, LI Qingqing, YAO Hansi, ZHANG Yuhuan, ZHAO Yuling, RIENOW Andreas

“…Remote sensing monitoring of methane emissions in coal mines has become a major field of research in the global efforts to monitor potent greenhouse gas emissions. While existing studies predominantly concentrated on the technological dimensions, this study proposes to review previous stu-dies, analyze major trends and pinpoint the current research priorities for this field of study. …”
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A Robust Conformal Framework for IoT-Based Predictive Maintenance by Alberto Moccardi, Claudia Conte, Rajib Chandra Ghosh, Francesco Moscato

“…At its core, this paper leverages the commercial modular aero-propulsion system simulation (CMAPSS) dataset to evaluate different artificial intelligence (AI) prognostic algorithms for remaining useful life (RUL) forecasting while supporting the estimation of a robust confidence interval (CI). …”
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Pilot-Aided Simultaneous Communication and Localisation (PASCAL) Under Practical Imperfections by Shuaishuai Han, Mohammad Al-Jarrah, Emad Alsusa

“…Two receiver processing approaches are proposed: (1) disjoint localisation and communication using maximum likelihood estimation and (2) joint localisation and data detection via newly developed algorithms. To assess localisation performance under gain-phase imperfections, the average Cramér-Rao lower bound (CRLB) is derived, while the average sum data rate (SDR) for UAV communication is obtained in closed form. …”
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Improved RT-DETR Framework for Railway Obstacle Detection by Peng Li, Yanhui Peng, Su-Mei Wang, Cheng Zhong

“…Obstacle intrusion detection in railway systems is a critical technology for ensuring the operational safety of trains. However, existing algorithms face challenges related to insufficient multiscale object detection, high model redundancy, and poor real-time performance. …”
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Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review by Rongrong Zhong, XiaoHui Wu, Jun Chen, Yiru Fang

“…They also tended to show higher activity in the morning and lower in the evening, while patients with UD showed the opposite pattern. …”
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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

“…The proposed hybrid model integrates Convolutional Neural Networks (CNNs) with Bidirectional Log-Short Term Memory (BiLSTM) to enhance energy-efficient schedulability and reduce energy usage while ensuring QoS provisioning. The CNN model efficiently extracts workload features from tasks and resources, while the BiLSTM captures complex sequential information, predicting optimal task placement sequences. …”
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Toward global rooftop PV detection with Deep Active Learning by Matthias Zech, Hendrik-Pieter Tetens, Joseph Ranalli

“…In the simulations, the entropy-based acquisition function shows the highest performance with only 3% of the data needed in case-imbalanced data, while remaining simple to implement. We believe that Deep Active Learning provides an elegant solution to maintain high model accuracy while reducing annotation effort substantially. …”
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Monitoring oral health remotely: ethical considerations when using AI among vulnerable populations by Colman McGrath, Chun Wang Reinhard Chau, Gustavo Fabián Molina

“…The accelerated adoption of these digital tools, particularly in the wake of the COVID-19 pandemic, has enhanced accessibility to care while simultaneously raising significant concerns regarding patient consent, data privacy, and algorithmic biases. …”
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How does artificial intelligence shape the productivity and quality of research in business studies? A systematic literature review and future research framework by Sugandha Agarwal, Qian Long Kweh, Dima Jamali, Walton Wider, Syed Far Abid Hossain, Muhammad Ashraf Fauzi

“…We also highlight significant challenges that require attention, including data privacy, intellectual property rights, and algorithmic bias. While we acknowledge limitations related to data usage and the generalizability of our reviews, especially given the rapid evolution of AI technologies, we recommend that researchers effectively integrate AI into their research activities and establish ethical frameworks for its application. …”
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Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming:... by Pattanun Chanpiwat, Fabricio Oliveira, Steven A. Gabriel

“…This study introduces a novel approach for optimizing residential energy systems by combining linear policy graphs with stochastic dual dynamic programming (SDDP) algorithms. Our method optimizes residential solar power generation and battery storage systems, reducing costs through strategic charging and discharging patterns. …”
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CLUMM: Contrastive Learning for Unobtrusive Motion Monitoring by Pius Gyamenah, Hari Iyer, Heejin Jeong, Shenghan Guo

“…Traditional approaches for human monitoring and motion recognition often rely on wearable sensors, which, while effective, are obtrusive and cause significant discomfort to workers. …”
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On the Choice of Training Data for Machine Learning of Geostrophic Mesoscale Turbulence by F. E. Yan, J. Mak, Y. Wang

“…Abstract Data plays a central role in data‐driven methods, but is not often the subject of focus in investigations of machine learning algorithms as applied to Earth System Modeling related problems. …”
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Advancing named entity recognition in interprofessional collaboration and education by Rui Zhang, Yifeng Shan, MengZhe Zhen

“…Each agent contributes dynamically to the collaborative process, adapting its knowledge, skills, and resources to optimize global utility while minimizing conflicts and enhancing synergy. …”
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How perceived discrimination and trust dynamics influence social integration in acculturation and reacculturation: the case of Chinese international student returnees by Tam-Tri Le, Ruining Jin, Ruining Jin

“…Generalized trust positively moderates this association while personalized trust has a negative moderating effect. …”
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A Trajectory Data-Driven Study on the Evolution Mechanism and Control Strategies of Lane-Changing Behavior in Intersection Areas of Expressways by Jiayou Wu, Yongwei Lei, Shaoliu Liu, Qiang Luo

“…After analyzing the sample data using traffic flow theory and statistical theory, the following results were obtained: the speeds increase upon entering and decrease upon exiting weaving zones, while headway distances consistently grow. Vehicles in the inner lanes exhibit smoother transitions and higher speeds, while lane-changing speeds range from 10 to 55 km/h (median: 29 km/h) and durations vary from 2 to 18 s (median: 8.5 s). …”
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Robust design of bicycle infrastructure networks by Christoph Steinacker, Mads Paulsen, Malte Schröder, Jeppe Rich

“…Interestingly, while the more complex optimization models yield better overall welfare results, the improvements over simpler methods are small. …”
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