Biomolecular Detection Using AlN/<italic>&#x03B2;</italic>-Ga₂O₃ MOSHEMT: A Machine Learning-Assisted Analytical and Simulation Framework by Aishwarya Tomar, Apurba Chakraborty, Rahul Kumar

“…In comparison to the AlGaN/GaN MOSHEMT, the AlN/<inline-formula> <tex-math notation="LaTeX">$\beta -Ga_{2}O_{3}$ </tex-math></inline-formula> MOSHEMT showed improved sensitivity in terms of drain current. Additionally, the machine learning (ML) model created for this investigation correlates strongly with the simulation results. …”
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Framingham Risk Score Prediction at 12 Months in the STANDFIRM Randomized Control Trial by Thanh G. Phan, Velandai K. Srikanth, Dominique A. Cadilhac, Mark Nelson, Joosup Kim, Muideen T. Olaiya, Sharyn M. Fitzgerald, Christopher Bladin, Richard Gerraty, Henry Ma, Amanda G. Thrift

“…Methods and Results We used machine learning regression methods to evaluate 35 variables encompassing demographics, risk factors, psychological, social and education status, and laboratory tests. …”
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IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting Under Solar-Balanced and Storm-Aware Conditions by Mert Can Turkmen, Yee Hui Lee, Eng Leong Tan

“…While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. …”
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Research on the impact of green finance on regional carbon emission reduction and its role mechanisms by Huiyun Li, Zongbao Yu, Gang Chen, Yingjun Nie

“…Therefore, taking the green financial reform and innovation pilot zone as a quasi-natural experiment, we select 270 cities from 2010 to 2021 as research samples and empirically assess the effects of the green finance policy on reducing regional carbon emissions through the double debiased machine learning (DDML) model. This study demonstrates that (1) green finance policy plays a significant role in promoting regional carbon emission reduction, and this conclusion remains valid after a variety of robustness tests; (2) the mechanism of action indicates that green finance policy contributes to regional carbon emission reduction by supporting green technological innovation and promoting the optimization of the industrial structure; (3) the analysis of heterogeneity reveals that green finance policy has a more pronounced effect on carbon emission reduction in the eastern region and in non-resource-based cities than in the central and western regions and in resource-dependent cities; and (4) the pilot policy of “Broadband China”, the pilot policy of information consumption, and the comprehensive experimental zone of big data has a synergistic effect on carbon reduction and emission reduction with green finance policy. …”
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Assessing the Impact of Climate Change on Winter Wheat Production in the North China Plain from 1980 to 2020 by Jinhui Zheng, Shuai Zhang

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A lightweight neural attention-based model for service chatbots by Sinarwati Mohamad Suhaili, Mohamad Nazim Jambli

“…Abstract The growing demand for efficient service chatbots has led to the development of various deep learning techniques, such as generative neural attention-based mechanisms. …”
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MetaStackD A robust meta learning based deep ensemble model for prediction of sensors battery life in IoE environment by D. Gayathri, S. P. Shantharajah

“…Abstract Advancements in Artificial Intelligence, Machine Learning, and Deep Learning have paved the way for ample applications in real-time. …”
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Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning... by Feiyang Xia, Tingting Fan, Mengjie Wang, Lu Yang, Da Ding, Jing Wei, Yan Zhou, Dengdeng Jiang, Shaopo Deng

“…However, the interpretation of the diverse microbial communities in relation to complex pollutants is still challenging, and there is limited research in multi-polluted groundwater. Advanced machine learning (ML) algorithms help identify key microbial indicators for different pollution types (CAHs, BTEX plumes, and mixed plumes). …”
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Interdisciplinary framework for cyber-attacks and anomaly detection in industrial control systems using deep learning by Qawsar Gulzar, Khurram Mustafa

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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. …”
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Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures by Eslam Deef-Allah, Magdy Abdelrahman

“…The framework predicted the rheological resistance of the binders to rutting and cracking using linear and nonlinear machine learning models. The nonlinear models outperformed the linear models for the three rheological parameters. …”
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Balancing Explainability and Privacy in Bank Failure Prediction: A Differentially Private Glass-Box Approach by Junyoung Byun, Jaewook Lee, Hyeongyeong Lee, Bumho Son

“…Traditional machine learning models often lack transparency, which poses challenges for stakeholders who need to understand the factors leading to predictions. …”
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Combining single-cell ATAC and RNA sequencing for supervised cell annotation by Jaidip Gill, Abhijit Dasgupta, Brychan Manry, Natasha Markuzon

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Deeper Effects of fiscal multidimensional poverty reduction: household characteristics, financial lags and elite capture. by Wenjie Jiang, Hong Yang, Chunyu Liu

“…The research results indicate that fiscal investment in agriculture can effectively alleviate multidimensional relative poverty among rural households, and this conclusion still holds after the robustness and endogeneity tests of traditional measurement and Double Machine Learning. However, differences in household characteristics affect the performance of fiscal poverty alleviation. …”
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Proactive Data Placement in Heterogeneous Storage Systems via Predictive Multi-Objective Reinforcement Learning by Suchuan Xing, Yihan Wang

“…Through comprehensive evaluation using both synthetic and real-world traces from deep learning training workloads, our method demonstrates substantial improvements over state-of-the-art algorithms: achieving up to 45.1% reduction in average I/O latency, 32.5% improvement in throughput for critical applications, and 28.8% reduction in storage costs. …”
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Overcoming cloud obstruction: Fast forest-damage assessment in post-tropical cyclone optical remote sensing by Tianchu Wang, Wentao Yang, Ziteng Xu, Wenwen Qi, Liam Taylor, Ming Wang, Wei Wu

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Predicting Hospitalization Length in Geriatric Patients Using Artificial Intelligence and Radiomics by Lorenzo Fantechi, Federico Barbarossa, Sara Cecchini, Lorenzo Zoppi, Giulio Amabili, Mirko Di Rosa, Enrico Paci, Daniela Fornarelli, Anna Rita Bonfigli, Fabrizia Lattanzio, Elvira Maranesi, Roberta Bevilacqua

“…Radiomics, combined with machine learning (ML), offers a promising approach by extracting quantitative imaging features from CT scans. …”
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Sleep disturbances and PTSD: identifying baseline predictors of insomnia response in an intensive treatment programme by Philip Held, Ashby Boland, Sarah A. Pridgen, Dale L. Smith

“…PTSD severity, depression, posttrauma cognitions, neurobehavioral symptoms). Machine learning models (neural net, random forest, elastic net, and ensemble) were trained to classify participants with clinically meaningful insomnia improvements.Results: Veterans reported large average PTSD severity reductions (d = 0.96), whereas depression and insomnia symptoms reduced moderately (d = 0.57) and modestly (d = 0.34), respectively. …”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, <i>n</i> = 14,863) and patients hospitalized with heart failure (patients’ dataset, <i>n</i> = 8220). …”
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Mental Health Issues and 24-Hour Movement Guidelines–Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Ma... by Lin Luo, Junfeng Yuan, Chen Xu, Huilin Xu, Haojie Tan, Yinhao Shi, Haiping Zhang, Haijun Xi

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