Cooperative control method for multi-agent ground fracturing truck group based on offline reinforcement learning by RuYi Wang, HuiShen Jiao, YingCheng Tian, Yi Zhao, SiQi Wang, Ke Zhang, Bo Huang, QinRui Sun, DanDan Zhu

“…Abstract The increasing scale of unconventional oil and gas resource development has driven the demand for complex fracturing technologies. …”
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Explainable machine learning models for mortality prediction in patients with sepsis in tertiary care hospital ICU in low- to middle-income countries by Saumya Diwan, Vinay Gandhi, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta

“…Conclusions The proposed approach could provide preemptive estimations into prognostication and outcome prediction of patients with sepsis in low-resource settings. This will aid in clinical decision-making, resource allocation and research for new treatment modalities.…”
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Dispatch Decisions and Emergency Medical Services Response in the Prehospital Care of Status Epilepticus by Robert P. McInnis, Andrew J. Wood, Courtney L. Shay, Anna A. Haggart, Remle P. Crowe, Elan L. Guterman

“…However, it is unclear whether dispatch algorithms accurately identify those patients having a seizure-related medical emergency and how dispatch algorithms influence what prehospital resources are allocated for the encounter. …”
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The joint orchestration of edge applications and UPF CNFs over edge-cloud continuum infrastructure in 6G by Witold Jóźwiak, Andrzej Bęben, Maciej Sosnowski

“…We propose, implement, and evaluate new joint orchestration algorithms that take advantage of the abstract representation of edge-cloud continuum resources. …”
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Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning by Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan

“…Predicting the length of stay (LOS) for patients with LD and multiple long-term conditions (MLTCs) is critical for improving patient care and optimising medical resource allocation. However, there is limited research on the application of machine learning (ML) models to this population. …”
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APPLICATION OF NON-TEST METHODS FOR CHANEL ESTIMATION by M. L. Maslakov, M. S. Smal

“…The problem is currently  pressing  due  to increasing  demands of modern adaptive data  transmission systems to efficient  use of allocated  frequency-time resource  of radio channel. …”
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Approaches for handling imbalanced data used in machine learning in the healthcare field: A case study on Chagas disease database prediction. by André G Coimbra, Cleiane G Oliveira, Matheus P Libório, Hasheem Mannan, Laercio I Santos, Elisa Fusco, Marcos F S V D'Angelo

“…The application of these techniques aims to improve the quality of life for individuals, reduce healthcare costs, and allocate available resources more efficiently, making it a preventive action.…”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…This approach offers significant advantages in the preliminary assessment of aggregate suitability, contributing to more efficient resource allocation in construction projects.…”
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Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? by Efrain Noa-Yarasca, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, Douglas R. Smith

“…Accurate and reliable crop yield prediction is essential for optimizing agricultural management, resource allocation, and decision-making, while also supporting farmers and stakeholders in adapting to climate change and increasing global demand. …”
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Spatio-temporal distribution, prediction and relationship of three major acute cardiovascular events: Out-of-hospital cardiac arrest, ST-elevation myocardial infarction and stroke by Angelo Auricchio, Tommaso Scquizzato, Federico Ravenda, Ruggero Cresta, Stefano Peluso, Maria Luce Caputo, Stefano Tonazzi, Claudio Benvenuti, Antonietta Mira

“…Widespread implementation in clinical practice of prediction algorithms may allow to improve resource allocation, reduce treatment delays, and improve outcomes.…”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…By analyzing population-level data, AI can identify trends and hotspots of diabetes prevalence, assisting policymakers in resource allocation and the design of targeted interventions. …”
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Mental health evaluation during internet blackouts: A case study of Bangladesh Quota Movement by Rafi Mohammad Ariful Islam, Islam Tahidul

“…These findings highlight the utility of advanced algorithms in modelling mental health impacts, aiding policymakers in preparing targeted interventions and allocating resources to mitigate psychological effects during future disruptions.…”
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Comprehensive Load Regulation Strategy Considering Outage Risk in Power Tight Balance Scenarios by Shunjiang Wang, Zhongwei Li, Rongmao Wang, Huan Ma

“…Then, orderly consumption power (OCP) is determined by power balance analyzing and flexible resource accessing, reasonably allocated over the subsystems by considering the load types and energy efficiencies, and further specified by components of peak shifting, peak averting and power rationing. …”
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Artificial intelligence in nursing: an integrative review of clinical and operational impacts by Salwa Hassanein, Salwa Hassanein, Rabie Adel El Arab, Rabie Adel El Arab, Amany Abdrbo, Mohammad S. Abu-Mahfouz, Mastoura Khames Farag Gaballah, Mohamed Mahmoud Seweid, Mohamed Mahmoud Seweid, Mohammed Almari, Husam Alzghoul

“…Operationally, AI-based automation of routine tasks (e.g., scheduling, administrative documentation, and predictive workload classification) has streamlined resource allocation. These efficiencies have led to a measurable reduction in nurse burnout and improved job satisfaction, as nurses can devote more time to direct patient care. …”
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Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission by Chao-Yang Kuo, Emily Chia-Yu Su, Hsu-Ling Yeh, Jiann-Horng Yeh, Hou-Chang Chiu, Chen-Chih Chung

“…This novel, personalized approach to risk stratification elucidates crucial risk factors and has the potential to enhance clinical decision-making, optimize resource allocation, and ultimately improve patient outcomes.…”
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A novel multiscale feature enhancement network using learnable density map for red clustered pepper yield estimation by Chenming Cheng, Chenming Cheng, Jin Lei, Jin Lei, Zicui Zhu, Lijian Lu, Lijian Lu, Zhi Wang, Zhi Wang, Jiali Tao, Jiali Tao, Xinyan Qin, Xinyan Qin

“…IntroductionAccurate and automated yield estimation for red cluster pepper (RCP) is essential to optimise field management and resource allocation. Traditional object detection-based methods for yield estimation often suffer from time-consuming and labour-intensive annotation processes, as well as suboptimal accuracy in dense environments. …”
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Large Language Model-Assisted Reinforcement Learning for Hybrid Disassembly Line Problem by Xiwang Guo, Chi Jiao, Peng Ji, Jiacun Wang, Shujin Qin, Bin Hu, Liang Qi, Xianming Lang

“…Recycling end-of-life products is essential for reducing environmental impact and promoting resource reuse. In the realm of remanufacturing, researchers are increasingly concentrating on the challenge of the disassembly line balancing problem (DLBP), particularly on how to allocate work tasks effectively to enhance productivity. …”
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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

“…Conclusions The interpretable predictive model improves mortality risk stratification in ICU patients with hemorrhage, supporting clinicians in optimizing treatment plans and resource allocation. Enhanced model transparency through SHAP explanations may facilitate clinical adoption by improving trust in model reliability.…”
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Forecasting dengue in Bangladesh using meteorological variables with a novel feature selection approach by Mahadee Al Mobin

“…The necessity for advanced forecasting mechanisms has never been more critical to enhance the effectiveness of vector control strategies and resource allocations. This study formulates a dynamic data pipeline to forecast dengue incidence based on 13 meteorological variables using a suite of state-of-the-art machine learning models and custom features engineering, achieving an accuracy of 84.02%, marking a substantial improvement over existing studies. …”
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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks by Musallam M. AlZubi, Mohamed A. Mohamed, Hanan M. Amer, A. A. Salama

“…The main objective of this research is to enhance the management of energy resources within these networks, which traditional fuzzy logic methods often fail to do, leading to power failures and reduced reliability. …”
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