Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement Learning by Chengjin Ding, Yuzhen Guo, Jianlin Jiang, Wenbin Wei, Weiwei Wu

“…Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. …”
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A Matching Game for LLM Layer Deployment in Heterogeneous Edge Networks by Benedetta Picano, Dinh Thai Hoang, Diep N. Nguyen

“…Performance evaluation was conducted considering both simulations and a small-scale testbed to measure the effectiveness of the proposed algorithm compared to state-of-the-art alternatives. …”
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Using the clustering method to find the final environmental parameters coefficients in road construction projects by Eshagh Rasouli Sarabi, Ramin Vafaei Poursorkhabi, Mehdi Ravanshadnia

“…In the first phase, the Genetic Optimization Algorithm was implemented to determine convenient coefficients for the relevant parameters. …”
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Abiotic drivers of activity in a large, free-ranging, freshwater teleost, Murray cod (Maccullochella peelii). by Jason D Thiem, Ian J Wooden, Lee J Baumgartner, Gavin L Butler, Jamin Forbes, Matthew D Taylor, Robyn J Watts

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Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial by Caio César Souza Conceição, Camila Marinelli Martins, Mayck Medeiros Silva, Hugo Caire de Castro Faria Neto, Davide Chiumello, Davide Chiumello, Davide Chiumello, Patricia Rieken Macedo Rocco, Fernanda Ferreira Cruz, Pedro Leme Silva

“…BackgroundPredicting clinical improvement after hospital admission in patients with COVID-19 is crucial for effective resource allocation. Machine-learning tools can help identify patients likely to show clinical improvement based on real-world data. …”
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Research on Prediction and Optimization of Airport Express Passenger Flow Based on Fusion Intelligence Network Model by Jin He, Yinzhen Li, Yuhong Chao

“…The purpose of this paper is to optimize the accuracy of airport express passenger flow prediction so as to meet the need for the optimal allocation of traffic resources against the background of accelerated urbanization and the rapid development of airport express services. …”
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Research on multidimensional dynamic defense strategy for microservice based on deep reinforcement learning by Dacheng ZHOU, Hongchang CHEN, Weizhen HE, Guozhen CHENG, Hongchao HU

“…Aiming at the problem that it is hard for security defense strategies in cloud native to guarantee the quality of service under dynamic requests, a multidimensional dynamic defense strategy for microservice based on deep reinforcement learning, named D2RA strategy, was proposed to provide dynamic configuration schemes that ensure security defense performance and quality of service for microservices under dynamical requests.Firstly, based on the characteristics of multiple replicas and invocation chains of microservices, a microservices state graph was established to depict the maps between requests, system configuration and security performance, quality of service, and resource overhead of microservices.Secondly, the D2RA framework was designed and a dynamic strategy optimization algorithm based on deep Q-network was proposed for microservices to provide fast and optimal system configurations update scheme under dynamic requests.The simulation results show that D2RA effectively allocate resources under dynamic requests, and achieve 19.07% more defense effectiveness and 42.31% higher quality of service as compared to the existing methods.…”
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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…Although existing methods have achieved certain success in processing acoustic and vibration signals, they still face challenges such as insufficient feature fusion, inflexible weight allocation, lack of effective feature selection mechanisms, and low computational efficiency. …”
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A Transmit Beamforming and Nulling Approach with Distributed Scheduling to Improve Cell Edge Throughput by Wendy C. Wong, Qinghua Li, Shilpa Talwar

“…First, cell edge users suffering from severe interferences are identified. Next, the RRM unit allocates resource to serving cell edge users only. …”
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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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Predictive estimations of health systems resilience using machine learning by Alessandro Jatobá, Paula de Castro-Nunes, Paloma Palmieri, Omara Machado Araujo de Oliveira, Patricia Passos Simões, Valéria da Silva Fonseca, Paulo Victor Rodrigues de Carvalho

“…This research highlights the potential of ML in predictive modeling to inform strategic health decision-making, targeting interventions and more effective resource allocation. This study provides a robust framework for evaluating resilience, offering public health managers a valuable tool to strengthen health systems in the face of emerging challenges.…”
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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices by Muhammad Shoaib Farooq, Rizwan Pervez Mir, Atif Alvi, Kilian Tutusaus, Eduardo Garcia Villena, Fadwa Alrowais, Hanen Karamti, Imran Ashraf

“…The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. …”
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Leveraging machine learning to identify determinants of zero utilization of maternal continuum of care in Ethiopia: Insights from SHAP analysis and the 2019 mini DHS. by Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem, Meron Asmamaw Alemayehu

“…These insights can inform policies aimed at expanding female education, strengthening community-based maternal health programs, and prioritizing resource allocation to regions such as Somali where zero utilization is highest.…”
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Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…These models can be integrated into clinical decision support systems (CDSS) to give insights to clinicians, improve patient outcomes through personalized interventions and optimize resource allocation. Future work will be to validate this on different populations, include longitudinal patient data and integrate real time electronic health records (EHR) for deployment in hospitals.…”
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Radiology AI and sustainability paradox: environmental, economic, and social dimensions by Burak Kocak, Andrea Ponsiglione, Valeria Romeo, Lorenzo Ugga, Merel Huisman, Renato Cuocolo

“…Socially, AI risks perpetuating healthcare disparities through biases in algorithms and unequal access to technology. On the other hand, AI has the potential to improve sustainability in healthcare by reducing low-value imaging, optimizing resource allocation, and improving energy efficiency in radiology departments. …”
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A Study on Spatial and Temporal Changes and Synergies/Trade-Offs of the Production-Living-Ecological Functions in Mountainous Areas Based on the Niche Width Model by Yaling Li, Ruoying Song, Ping Ren

“…As a typical ecologically fragile mountainous area, Liangshan Yi Autonomous Prefecture in Sichuan Province faces challenges of irrational land resource allocation and uncoordinated urbanization. …”
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Dynamic Load Balancing for Enhanced Network Performance in IoT-Enabled Smart Healthcare With Fog Computing by Mohammed Alaa Ala'anzy, Raiymbek Zhanuzak, Ramis Akhmedov, Nader Mohamed, Jameela Al-Jaroodi

“…To address these limitations, an Optimised Load Balancing (OLB) algorithm is proposed, to allocate workloads effectively across fog nodes and to reduce communication and computational delays. …”
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Responsible artificial intelligence in public health: a Delphi study on risk communication, community engagement and infodemic management by Keyrellous Adib, David Novillo-Ortiz, Ben Duncan, Daniela Mahl, Mike S Schäfer, Stefan Adrian Voinea, Cristiana Salvi

“…Prioritised actions ranged from regulatory measures, resource allocation and feedback loops to capacity building, public trust initiatives and educational training.Conclusion To responsibly navigate the multifaceted opportunities, challenges and risks of AI for RCCE-IM in public health emergencies, clear guiding principles, ongoing critical evaluation and training as well as societal collaboration across countries are needed.…”
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Genetics of kidney stones and the role of genetic testing in prevention: a guide for urologists by Francesco Pintus, Noemi Giordano, Daniela Francesca Giachino, Giorgia Mandrile

“…This paper reviews the updated evidences in KSD genetics and suggest a diagnostic algorithm aimed to increase the diagnostic rate of genetic stones, allowing a personalized treatment and, in turn, a higher disease-free survival for the patients and a more efficient allocation of resources, analyzing the cost-effectiveness of genetic testing in urolithiasis. …”
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Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO by Umesh Kumar Lilhore, Surjeet Dalal, Magdalena Radulescu, Marinela Barbulescu

“…In smart grid management, precise stability prediction is a complicated task that adds to the effective allocation of resources with grid stability. Specifically, demand-side management is considered an essential element of the overall Smart Grids system. …”
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