A Machine Learning-Based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations by Seyit Alperen Celtek, Seda Kul, A. Ozgur Polat, Hamed Zeinoddini-Meymand, Farhad Shahnia

“…The paper evaluates various machine learning algorithms for real-time RUL estimation regarding accuracy, computation time, and memory usage. …”
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Ethical and Legal Challenges of Implementing AI in Science and Math Education in Central Asia by Dilfuza M.Makhmudova, Xilola R. Sharipova, Nosirjon K. Hojiyev, Azamat E.Ergashev, Yulduzknon Kh.Satvaldieva, Khosiyat U.Mamatkulova, Egambergan M. Khudoynazarov

“…Additionally, 64% of respondents expressed serious concerns about student data privacy, while 71% supported the need for formal AI ethics training. …”
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REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS by Maria Nascimento CUNHA, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, Isabel OLIVEIRA

“…AI and ML applications, such as chatbots for 24/7 customer service and predictive analytics for tailoring travel recommendations, have greatly improved customer interaction and operational efficiencies. While the industry benefits from technological advancements, there are ongoing challenges such as concerns over data privacy and the need for constant updates to algorithms to keep pace with evolving market conditions.…”
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A Distributed Machine Learning Model for Blue and Green Water Resources With Transferable Applications in Similar Climatic Zones by Zhibin Li, Haroon Sahotra, Sajjad Ahmad, Wei Wang, Zhe Yang, Pute Wu, Eakalak Khan, La Zhuo

“…The modeling thoroughly accounts for the influence of human activities, incorporating 5 scales (grid, county, city, province, and cluster), 4 algorithms, and 2 model integration methods (Stacking and Bayesian). …”
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Can We Teach Machines to Select Like a Plant Breeder? A Recommender System Approach to Support Early Generation Selection Decisions Based on Breeders’ Preferences by Sebastian Michel, Franziska Löschenberger, Christian Ametz, Herbert Bistrich, Hermann Bürstmayr

“…The target trait was the retrospective binary classification of selected versus non-selected breeding lines during a period of five years, while the selection decisions of the breeder were predicted by various machine learning models. …”
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Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis by Lengyun Wei, Dashuai Li, Hongjin Chen, Yajing Pu, Qun Wang, Jintao Li, Meng Zhou, Chenfeng Liu, Pengpeng Long

“…In this study, we employed a suite of machine learning algorithms and statistical methods to construct a robust prognostic model for BC based on insulin resistance-related genes (IRGs). …”
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Theory of an Automatic Seepage Meter and Ramifications for Applications by Vitaly A. Zlotnik, D. Kip Solomon, David P. Genereux, Troy E. Gilmore, C. Eric Humphrey, Aaron R. Mittelstet, Anatoly V. Zlotnik

“…On this basis, changing the ASM geometry by increasing the radius and decreasing tube insertion depth may enable ASM field test protocols that estimate interface flux and hydraulic conductivity faster while maintaining desired accuracy. Potential applications of joint parameter estimation are suggested.…”
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Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach by Shuai Liu, Peng Zhang, Anbin Wang, Keke Tang, Shuo Chen, Chensen Lin

“…This leads to significant economic and time costs, while the complexity of instruments and algorithms further raises the barrier for DLD design and optimization. …”
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Towards a digital twin: Digitization and model-based optimization of the innovative high-gradient magnetic separatorMendeley Data by Marko Tesanovic, Torben Bardel, Robin Karl, Sonja Berensmeier

“…Optimization results indicated a theoretical 4% productivity gain and a 3 percentage point yield improvement, while exposing critical design constraints in the HGMS chamber.These findings underscore the potential of digital twins to accelerate process optimization and reduce development costs through in-silico experimentation. …”
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Blockchain and NFT-Based Solution for Genomic Data Management, Sharing, and Monetization by Ahmad Musamih, Khaled Salah, Raja Jayaraman, Samer Ellaham, Mohammed Omar, Ibrar Yaqoob

“…We present various diagrams and algorithms to illustrate the functionality of our solution. …”
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Analysis of follow-up data in large biobank cohorts: a review of methodology by Anastassia Kolde, Anastassia Kolde, Merli Koitmäe, Merli Koitmäe, Meelis Käärik, Märt Möls, Krista Fischer, Krista Fischer

“…We address four primary issues: left-truncation of the data, computational inefficiency of standard model-fitting algorithms, relatedness among individuals, and model misspecification. …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

“…However, invasive methods, while accurate, are often painful and impractical for routine use. …”
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A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space by Zheng Luo, Jiahao Mai, Caihong Feng, Deyao Kong, Jingyu Liu, Yunhong Ding, Bo Qi, Zhanbo Zhu

“…Effective predictive methods assist learners in targeted studying based on forecast results, while effective analytical methods help educators design appropriate educational content. …”
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Diagnostic Utility of ¹⁸F-FDG PET/CT in Infective Endocarditis by Corina-Ioana Anton, Alice-Elena Munteanu, Mihaela Raluca Mititelu, Militaru Alexandru Ștefan, Cosmin-Alexandru Buzilă, Adrian Streinu-Cercel

“…Its integration into diagnostic algorithms may improve clinical management and outcomes in complex IE cases.…”
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Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects by Soheil Mohseni, Alan C. Brent

“…These integrated resource planning endeavours primarily aim to minimise total discounted system costs while adhering to a network of interconnected technical constraints, encompassing considerations of reliability, resilience, and the integration of renewable energy sources. …”
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Selection and Validation of Stable Reference Genes for RT-qPCR in <i>Scotogramma trifolii</i> (Lepidoptera: Noctuidae) by Anpei Yang, Hang Zhang, Weiwei Bai, Ruifeng Ding, Weipeng Li, Guangkuo Li

“…To address the lack of validated reference genes in <i>S. trifolii</i>, we evaluated six candidate genes (<i>β-actin</i>, <i>RPL9</i>, <i>GAPDH</i>, <i>RPL10</i>, <i>EF1-α</i>, and <i>TUB</i>) across four developmental stages (egg, larva, pupa, and adult) and six adult tissues (head, thorax, abdomen, wings, legs, and antennae) using geNorm, NormFinder, BestKeeper, and RefFinder algorithms. Stability analysis identified <i>β-actin</i>, <i>RPL9</i>, and <i>GAPDH</i> as the most reliable reference genes for developmental stage normalization, while <i>RPL10</i>, <i>GAPDH</i>, and <i>TUB</i> were validated for adult tissues. …”
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Dynamic time-varying transfer function for cancer gene expression data feature selection problem by Hao-Ming Song, Yu-Cai Wang, Jie-Sheng Wang, Yu-Wei Song, Shi Li, Yu-Liang Qi, Jia-Ning Hou

“…Subsequently, to assess the generalizability of our proposed approach across 12 cancer gene expression datasets for testing purposes five algorithms (AOA, COA, PSO, WOA and ZOA) are employed. …”
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Unsupervised Machine Learning for Classifying CHIME Fast Radio Bursts and Investigating Empirical Relations by Da-Chun Qiang, Jie Zheng, Zhi-Qiang You, Sheng Yang

“…The Chow test results reveal that while some repeaters and nonrepeaters share similar empirical relationships, the overall distinctions between the two groups remain significant, reinforcing the classification of FRBs into repeaters and nonrepeaters. …”
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Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data by Michele Croci, Manuele Ragazzi, Alessandro Grassi, Giorgio Impollonia, Stefano Amaducci

“…TR prediction was more challenging while RF showed promising results in LOGOCV (nRMSE = 22.1 %), all ML models were outperformed by the NullModel in the more realistic LOYOCV scenario. …”
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“Image-Spectral” fusion monitoring of peanut leaf spot disease level based on deep learning by Yongda Lin, Jiangtao Tan, Hong Li, Xi Li, Jianguo Wang, Tingting Chen, Lei Zhang

“…RGB images offer high-resolution macroscopic indicators such as color, shape, and texture, while hyperspectral reflectance captures early-stage physiological and biochemical changes invisible to the naked eye. …”
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