Optimization of State Clustering and Safety Verification in Deep Reinforcement Learning Using KMeans++ and Probabilistic Model Checking by Ryeonggu Kwon, Gihwon Kwon

“…The continuous state space is clustered using the KMeans++ algorithm, enabling efficient state space reduction. …”
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Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning by Pol Suárez, Francisco Alcántara-Ávila, Jean Rabault, Arnau Miró, Bernat Font, Oriol Lehmkuhl, Ricardo Vinuesa

“…The setup involves multiple zero-net-mass-flux jets and couples a computational fluid dynamics solver with a numerical multi-agent reinforcement learning framework based on the proximal policy optimization algorithm. Our results demonstrate up to 16% drag reduction at R e D  = 400, outperforming classical periodic control strategies. …”
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Deep learning can reduce acquisition time of T2-weighted image in brain imaging by Eman Hassan El-Saeed Abou-ELMagd, Sabry Alameldin Elmogy, Dina Gamal Abdelzaher

“…Abstract Background We used the deep learning-based reconstruction algorithm to reduce the scan time for brain T2-weighted images (T2WI) with reduction of image noise and preservation of image quality. …”
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Multidimensional Evaluation Framework and Classification Strategy for Low-Carbon Technologies in Office Buildings by Hongjiang Liu, Yuan Song, Yawei Du, Tao Feng, Zhihou Yang

“…The method includes four core components: (1) establishing three archetypal models—low-rise (H ≤ 24 m), mid-rise (24 m < H ≤ 50 m), and high-rise (50 m < H ≤ 100 m) office buildings—based on 99 office buildings in Beijing; (2) classifying 19 key technologies into three clusters—Envelope Structure Optimization, Equipment Efficiency Enhancement, and Renewable Energy Utilization—using bibliometric analysis and policy norm screening; (3) developing a four-dimensional evaluation framework encompassing Carbon Reduction Degree (CRD), Economic Viability Degree (EVD), Technical Applicability Degree (TAD), and Carbon Intensity Degree (CID); and (4) conducting a comprehensive quantitative evaluation using the AHP-entropy-TOPSIS algorithm. …”
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RATIONALE FOR PREVENTION OF CARDIO-CEREBRAL COMPLICATIONS IN THE METABOLIC SYNDROME BASED ON MATHEMATICAL FORECASTING TECHNIQUES by S. V. Chernavskii, A. N. Fursov, N. P. Potekhin, V. N. Yakovlev

“…The diagnostic and treatment algorithms developed based on the stratification of cardiovascular risk index, allow us to give sound recommendations for targeted prevention of cardio-cerebral complications.…”
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The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1) by M. Buehner, J.-F. Caron, E. Lapalme, A. Caya, P. Du, Y. Rochon, S. Skachko, M. Bani Shahabadi, S. Heilliette, M. Deshaies-Jacques, W. Chang, M. Sitwell

“…In addition to describing the current MIDAS applications, a sample of the results from these systems is presented to demonstrate their performance in comparison with either systems from before the switch to using MIDAS software or similar systems at other numerical weather prediction (NWP) centres. The modular software design also allows the code that implements high-level components (e.g. observation operators, error covariance matrices, state vectors) to easily be used in many different ways depending on the application, such as for both variational and ensemble DA algorithms, for estimating the observation impact on short-term forecasts, and for performing various observation pre-processing procedures. …”
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Machine learning integrates region-specific microbial signatures to distinguish geographically adjacent populations within a province by Li Luo, Li Luo, Bangwei Chen, Bangwei Chen, Shengyin Zeng, Shengyin Zeng, Yaxin Li, Yaxin Li, Xiaolin Chen, Xiaolin Chen, Jianguo Zhang, Xiangjie Guo, Shujin Li, Lei Ruan, Shida Zhu, Cairong Gao, Cuntai Zhang, Tao Li

“…Among the three ML algorithms, the random forest algorithm achieved the best performance, with an AUC of 0.943.ConclusionThe gut microbiota of individuals residing in the same province is significantly similar; however, pronounced differences in bacterial composition were noted between individuals. …”
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Variable Selection for Additive Quantile Regression with Nonlinear Interaction Structures by Yongxin Bai, Jiancheng Jiang, Maozai Tian

“…Effective variable selection is crucial for avoiding the curse of dimensionality and enhancing the predictive performance of a model. In this paper, we introduce a nonlinear interaction structure into the additive quantile regression model and propose an innovative penalization method. …”
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MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models by Devlin C. Moyer, Justin Reimertz, Daniel Segrè, Juan I. Fuxman Bass

“…Abstract Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. …”
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Machine learning approaches for modelling of molecular polarizability in gold nanoclusters by Abhishek Ojha, Satya S. Bulusu, Arup Banerjee

“…Our results demonstrate the efficacy of machine-learning in accurately predicting the polarizability of gold nanoclusters. …”
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An Enterprise-Naming Inspiration in the United States by Michael D. Sublett

“…Data collection tools involved standard online searches; Google’s predictive searching algorithm; business and nonprofit searching tools on the websites of the states’ secretaries of state; and several subscription databases, including Newspapers.com. …”
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Applications of AI Technology in Radiation Safety and Protection by Xiaowa WANG

“…Despite technical challenges and ethical issues such as accuracy, data processing, and algorithmic transparency, the application prospects of AI in radiation protection remain broad. …”
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Remote Sensing Applications in Ocean Observation (Second Edition) by Chung-Ru Ho

“…This information is valuable for developing predictive models and informing policy decisions related to climate change mitigation and adaptation.…”
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Mapping Data to Concepts: Enhancing Quantum Neural Network Transparency with Concept-Driven Quantum Neural Networks by Jinkai Tian, Wenjing Yang

“…Experimental results demonstrate that CD-QNN maintains high predictive accuracy while offering clear and meaningful explanations of its decision-making process. …”
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How to crack a SMILES: automatic crosschecked chemical structure resolution across multiple services using MoleculeResolver by Simon Müller

“…MoleculeResolver systematically crosschecks structures retrieved from multiple reputable chemical databases, implements rigorous identifier plausibility checks, standardizes molecular structures, and intelligently selects the most accurate representation based on a unique resolution algorithm. Scientific contribution Benchmarks across diverse datasets confirm that MoleculeResolver significantly enhances precision, recall, and overall reliability compared to traditional single-source methods, proving its utility as a valuable resource for chemists, data scientists, and researchers engaged in high-quality molecular data analysis and predictive model development. …”
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Differential Evolution for Optimizing Model Parameters in Simulation of Direct Dimethyl Carbonate Synthesis by Outi Ruusunen, Riitta Keiski, Mika Ruusunen

“…When validated with data of a real-world batch process, the model accurately predicted evolution of reaction composition ratios in time, offering a tool for predictive optimization. …”
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Three-Dimensional Impact Time and Angle Control Guidance Based on MPSP by Yang Li, Hao Zhou, Wanchun Chen

“…The guidance law is devised using the model predictive static programming (MPSP) method, and the control saturation constraint is incorporated in the MPSP algorithm. …”
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A New Finite-Difference Method for Nonlinear Absolute Value Equations by Peng Wang, Yujing Zhang, Detong Zhu

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Deep Ocean Learning of Small Scale Turbulence by Ali Mashayek, Nick Reynard, Fangming Zhai, Kaushik Srinivasan, Adam Jelley, Alberto Naveira Garabato, Colm‐cille P. Caulfield

“…Here, we show that supervised machine learning algorithms can be trained on the existing turbulence data to develop skillful predictions of the key properties of turbulence from T, S, Z, and topographic data. …”
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Natural Killer (NK) Cell Alloreactivity in Haploidentical Stem Cell Transplantation by Mar Luis-Hidalgo, José Luis Piñana, Carlos Solano, Dolores Planelles

“…These models consider the presence or absence of specific ligands or receptors and/or <i>KIR</i> genes in the donor and recipient to predict alloreactivity. Determining the best model for predicting KIR alloreactivity and its significance in donor selection algorithms for haploidentical transplantation is still under investigation.…”
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