Multi-Agent Deep Reinforcement Learning Cooperative Control Model for Autonomous Vehicle Merging into Platoon in Highway by Jiajia Chen, Bingqing Zhu, Mengyu Zhang, Xiang Ling, Xiaobo Ruan, Yifan Deng, Ning Guo

“…This study presents the first investigation into the problem of autonomous vehicle (AV) merging into existing platoons, proposing a multi-agent deep reinforcement learning (MA-DRL)-based cooperative control framework. …”
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A Systematic Review and Meta-Analysis of Perceptual Learning and Video Game Training for Adults with Monocular Amblyopia by Xiaolu Ming, Gantian Huang, Xiaohang Chen, Meng Liao, Longqian Liu

“…This meta-analysis aimed to analyze the effectiveness of perceptual learning and video game training for adults with amblyopia. …”
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Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques by Oana Vîrgolici, Daniela Lixandru, Andrada Mihai, Diana Simona Ștefan, Cristian Guja, Horia Vîrgolici, Bogdana Virgolici

“…<b>Methods</b>: The dataset includes medical records from patients in Bucharest hospitals, collected between 2012 and 2016. Several machine learning models, namely linear regression, polynomial regression, Gradient Boosting, and Extreme Gradient Boosting, were employed to predict changes in medical parameters as a function of body weight variation. …”
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A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection by Thanh-Dung Le, Clara Macabiau, Kevin Albert, Philippe Jouvet, Rita Noumeir

“…Among various SSL techniques&#x2014;including masking, contrastive learning, and DINO (self-distillation with no labels)&#x2014;contrastive learning exhibited the most stable and superior performance in small PPG datasets. …”
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Multi-Objective Optimal Scheduling of Water Transmission and Distribution Channel Gate Groups Based on Machine Learning by Yiying Du, Chaoyue Zhang, Rong Wei, Li Cao, Tiantian Zhao, Wene Wang, Xiaotao Hu

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Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing by Ruijin Sun, Yao Wen, Nan Cheng, Wei Wang, Rong Chai, Yilong Hui

“…Furthermore, to pull out the solution from the local optimum, our proposed SKDML updates parameters in LSTM with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.…”
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Similarity of immune-associated markers in COVID-19 and Kawasaki disease: analyses from bioinformatics and machine learning by Wang Li, Wenjie Zhu, Xiangting Tang, Zhiting Peng, Jiaqi Ye, Shuping Nie

“…This study used bioinformatics and machine learning to examine similarities in the molecular pathogenesis of COVID-19 and KD. …”
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Group 2 innate lymphoid cells drive inhibitory synapse formation with lasting effects on learning and memory by Johannes Steffen, Divija Deshpande, Henning Peter Düsedau, Janna Schmitz, Caio Andreeta Figueiredo, Laura Velleman, Claudia Pitzer, Christoph S.N. Klose, Ildiko R. Dunay

“…This early immune-mediated modulation may have lasting effects on neuronal circuitry and cognitive functions that persist into adulthood, emphasizing the long-term implications of neuro-immune interactions for normal cognitive development and function.…”
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Machine Learning Based Early Diagnosis of ADHD with SHAP Value Interpretation: A Retrospective Observational Study by Zhang X, Xiao X, Luo Y, Xiao W, Cao Y, Chang Y, Wu D, Xu H, Zhao J, Deng X, Jiang Y, Xie R, Liu Y

“…Clinical data, including complete blood count, liver and kidney function tests, blood glucose levels, serum electrolyte tests, and serum 25-dihydroxyvitamin D3 levels, were collected. …”
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Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning by Jian Li, Jian Lu, Hongkun Fu, Wenlong Zou, Weijian Zhang, Weilin Yu, Yuxuan Feng

“…This study accurately inverts key growth parameters of rice, including Leaf Area Index (LAI), chlorophyll content (SPAD) value, and height, by integrating multisource remote sensing data (including MODIS and ERA5 imagery) and deep learning models. Dehui City in Jilin Province, China, was selected as the case study area, where multidimensional data including vegetation indices, ecological function parameters, and environmental variables were collected, covering seven key growth stages of rice. …”
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Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs by Kailei Liu, Boyue Xu, Changjea Kim, Jing Fu

“…Since the properties of the unconventional reservoir are the function of time such as fluid properties and reservoir pressure, it is quite suitable to apply the time series analysis to understand the well production performance. …”
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Machine-learning aided calibration and analysis of porous media CFD models used for rotating packed beds by Ahmed M. Alatyar, Abdallah S. Berrouk

“…To this end, a direct sensitivity analysis is detailed to supplement a machine-learning (ML) algorithm built for calibrating resistance coefficients needed for porous media modelling. …”
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Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery by Muath Almaslamani, Jingyu Yang, Chi Soo Kang, Choong Mo Kang, Jung Mi Park, Sang-Keun Woo

“…This study aims to develop a graph neural network and attention mechanism-based radiopharmaceutical-protein (RP-protein) interaction prediction model for identifying an imaging candidate of mitochondrial function through targeting its core subunit NDUFS1. …”
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Investigation of ensembles of deep learning models for improved chronic kidney diseases detection in CT scan images by I.I. Ayogu, C.F. Daniel, B.A. Ayogu, J.N. Odii, C.L. Okpalla, E.C. Nwokorie

“…The loss of renal function is a growing public health issue that affects up to 10 % of the global population. …”
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A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information by Claudia Silva-Andrade, Sergio Hernández, Pedro Saa, Ernesto Perez-Rueda, Daniel Garrido, Alberto J. Martin

“…Despite its importance, there is a lack of computational methods capable of predicting its production as a function of the consortium composition. Here, we present a novel machine-learning approach leveraging automatically generated genome-scale metabolic models to tackle this limitation. …”
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Assessing the accuracy of a machine learning prediction for 2 different shoulder prostheses: an external validation study by Gianluca Caprili, MD, Andrea G. Calamita, MD, Michele Novi, MD, Domenico A. Campanacci, MD, PhD, Simone Nicoletti, MD

“…Background: The integration of machine learning in orthopedic surgery, including shoulder procedures, has garnered increasing interest. …”
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Towards Achilles Tendon Injury Prevention in Athletes with Structural MRI Biomarkers: A Machine Learning Approach by Norbert Kapinski, Karol Jaskulski, Justyna Witkowska, Adam Kozlowski, Pawel Adamczyk, Bartosz Wysoczanski, Agnieszka Zdrodowska, Adam Niemaszyk, Beata Ciszkowska-Lyson, Michal Starczewski

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Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks by Adriany A. F. Eduardo, Gustavo A. S. Martinez, Ted W. Grant, Lucas B. S. Da Silva, Wei-Liang Qian

“…The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. …”
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Land suitability assessment for sisal production: A machine learning and Analytical Hierarchy Process integrated approach by R.N. Mgohele, B.H. J. Massawe, M.J. Shitindi, H.G. Sanga, M.M. Omar

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Predicting cadmium enrichment in crops/vegetables and identifying the effects of soil factors based on transfer learning methods by Rui Chen, Zean Liu, Jingyan Yang, Tiantian Ma, Aihong Guo, Rongguang Shi

“…In the case of good prediction effect of transfer learning, available Cd is the most critical function, and available Cd is positively correlated with Cd in plants. …”
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