Solving differential‐algebraic equations in power system dynamic analysis with quantum computing by Huynh T. T. Tran, Hieu T. Nguyen, Long T. Vu, Samuel T. Ojetola

“…We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.…”
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Benchmarking Variants of Recursive Feature Elimination: Insights from Predictive Tasks in Education and Healthcare by Okan Bulut, Bin Tan, Elisabetta Mazzullo, Ali Syed

“…To help researchers better understand and apply RFE more effectively, this study organizes existing variants into four methodological categories: (1) integration with different machine learning models, (2) combinations of multiple feature importance metrics, (3) modifications to the original RFE process, and (4) hybridization with other feature selection or dimensionality reduction techniques. …”
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Random forest algorithm integrated with an initial basic feasible solution in buckling analysis of a two-dimensional functionally graded porous taper beam by Ravikiran Chinthalapudi, Jagadesh Kumar Jatavallabhula, Geetha Narayanan Kannaiyan, Bridjesh Pappula, Seshibe Makgato

“…This work provides practical insights for the design and optimization of advanced graded structures where conventional models fall short, establishing a novel pathway for the integration of machine learning in structural mechanics.…”
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Tunable Energy-Efficient Approximate Circuits for Self-Powered AI and Autonomous Edge Computing Systems by Shubham Garg, Kanika Monga, Nitin Chaturvedi, S. Gurunarayanan

“…Moreover, this problem becomes more complex while deploying computationally intensive heavy machine learning (ML) models on energy-constrained edge devices. …”
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Unraveling morphological brain network disparities Parkinsonian tremor from essential tremor: an artificial intelligence approach for clinical differentiation by Moxuan Zhang, Siyu Zhou, Huizhi Wang, Pengda Yang, Jinli Ding, Xiaobo Wang, Xuzhu Chen, Chaonan Zhang, Anni Wang, Yuan Gao, Qiang Liu, Yueping Li, Tianqi Xu, Zeyu Ma, Yin Jiang, Lin Shi, Chunlei Han, Yuchen Ji, Guoen Cai, Tao Feng, Jianguo Zhang, Fangang Meng

“…Finally, by incorporating these specific characteristics, we developed a machine learning model capable of accurately distinguishing between different tremor types, providing valuable insights for clinical practice.…”
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Single-cell multiomics reveals simvastatin inhibits pan-cancer epithelial-mesenchymal transition via the MEK/ERK pathway in XBP1+ mast cells by Sen Lin, Huimin Zhang, Ruiqi Zhao, Zhulin Wu, Weiqing Zhang, Mengjiao Yu, Bei Zhang, Lanyue Ma, Danfei Li, Lisheng Peng, Weijun Luo

“…A prognostic model was established using WGCNA and 12 machine learning algorithms to identify potential mast cell targets. …”
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Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information for Mental Health Professionals: Observational Study of TikTok... by Alexandre Hudon, Keith Perry, Anne-Sophie Plate, Alexis Doucet, Laurence Ducharme, Orielle Djona, Constanza Testart Aguirre, Gabrielle Evoy

“…ResultsDisinformation was predominantly found in videos about neurodevelopment, mental health, personality disorders, suicide, psychotic disorders, and treatment. A machine learning model identified weak predictors of disinformation, such as an initial perceived intent to disinform and content aimed at the general public rather than a specific audience. …”
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Single-cell mitochondrial morphomics reveals cellular heterogeneity and predicts complex I, III, and ATP synthase Inhibition responses by Ratneswary Sutharsan, Maddi Biaut Hontaas, Yan Li, Hao Xiong, Hartwig Preckel, Carolyn M. Sue, Gautam Wali

“…By incorporating the most affected cells into machine learning models, we significantly improved the prediction accuracy of mitochondrial dysfunction outcomes − 81.97% for antimycin, 75.12% for rotenone, and 94.42% for oligomycin. …”
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Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks by Bowen Bao, Hui Yang, Qiuyan Yao, Lin Guan, Jie Zhang, Mohamed Cheriet

“…In this paper, benefiting from machine learning, we propose a resource allocation with edge-cloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. …”
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From Policy to Prices: How Carbon Markets Transmit Shocks Across Energy and Labor Systems by Cristiana Tudor, Aura Girlovan, Robert Sova, Javier Sierra, Georgiana Roxana Stancu

“…Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. …”
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A systematic review of computational simulation methods for predicting the toxicity of chemical compounds by Akram Tabrizi, Fatemeh Paridokht, Yaser Khorshidi Behzadi, Rezvan Zendehdel

“…Various methods, including Quantitative Structure-Activity Relationship (QSAR), machine learning, and molecular dynamics, were widely used to predict the toxicity of chemical compounds, with the predictive accuracy of these models generally being high. …”
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Pseudocell Tracer-A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination. by Derek Reiman, Godhev Kumar Manakkat Vijay, Heping Xu, Andrew Sonin, Dianyu Chen, Nathan Salomonis, Harinder Singh, Aly A Khan

“…Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. …”
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Host‐Microbial Cometabolite Ursodeoxycholic Acid Protects Against Poststroke Cognitive Impairment by Xuxuan Gao, Feng Zhang, Jiafeng Zhang, Yu Ma, Yiting Deng, Jiaying Chen, Yueran Ren, Huidi Wang, Boxin Zhao, Yan He, Jia Yin

“…Patients with mild acute ischemic stroke who developed PSCI exhibited significant alterations in gut microbiota and plasma bile acid profiles during the acute stroke phase, including a notable reduction in UDCA level. Through feature selection and machine learning, we constructed a predictive model for PSCI incorporating plasma UDCA level, the relative abundance of Clostridia, Bacilli, and Bacteroides, as well as age, educational level, and the presence of moderate to severe white matter lesions. …”
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Soft-Computing Analysis and Prediction of the Mechanical Properties of High-Volume Fly-Ash Concrete Containing Plastic Waste and Graphene Nanoplatelets by Musa Adamu, Yasser E. Ibrahim, Mahmud M. Jibril

“…Hence, this study employed two machine-learning (ML) models, namely Gaussian Process Regression (GPR) and Elman Neural Network (ELNN), to forecast the mechanical properties of HVFAC. …”
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Caffeine induces age-dependent increases in brain complexity and criticality during sleep by Philipp Thölke, Maxine Arcand-Lavigne, Tarek Lajnef, Sonia Frenette, Julie Carrier, Karim Jerbi

“…We analyzed sleep electroencephalography (EEG) in 40 subjects, contrasting 200 mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrum’s 1/f-like slope, and a reduction in long-range temporal correlations. …”
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Impact of Concrete Sealer and Salt Usage on Concrete Bridge Deck Condition and Life Cycle Cost by Wei Huang, Hao Wang, Danny Xiao

“…To achieve this goal, machine learning models were built to predict the evolution of bridge deck rating. …”
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How Can Business Analytics Enhance Decision-Making in Oil and Gas Surface Facilities? by Agung Prasetya, Meditya Wasesa, Yos Sunitiyoso

“…Predictive analytics enables proactive maintenance through machine learning, while prescriptive analytics optimizes operations using simulations and multi-objective decision models. …”
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A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening by Emre Yalçın, Serpil Aslan, Mesut Toğaçar, Süleyman Cansun Demir

“…The final features are classified using machine learning algorithms, including Bagged Trees and Naive Bayes. …”
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EFLOP: a sparsity-aware metric for evaluating computational cost in spiking and non-spiking neural networks by Simon Narduzzi, Friedemann Zenke, Shih-Chii Liu, L Andrea Dunbar

“…Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been proposed as candidates for these tasks. …”
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Integrating Ambient In-Home Sensor Data and Electronic Health Record Data for the Prediction of Outcomes in Amyotrophic Lateral Sclerosis: Protocol for an Exploratory Feasibility S... by William E Janes, Noah Marchal, Xing Song, Mihail Popescu, Abu Saleh Mohammad Mosa, Juliana H Earwood, Vovanti Jones, Marjorie Skubic

“…ObjectiveThis study aims to describe a federated approach to assimilating sensor and EHR data in a machine learning algorithm to predict decline among people living with ALS. …”
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