Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models by Kennedy C. Onyelowe, Viroon Kamchoom, Tammineni Gnananandarao, Krishna P. Arunachalam

“…The high precision and Phi Correlation Coefficient further affirm the reliability and accuracy of the model’s predictions. …”
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Deep Multi-Component Neural Network Architecture by Chafik Boulealam, Hajar Filali, Jamal Riffi, Adnane Mohamed Mahraz, Hamid Tairi

“…The architecture’s efficiency is further demonstrated by its reduced trainable parameters and robust handling of multimodal and variable-length inputs, making it a versatile solution for classification tasks.…”
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Prox-STA-LSTM: A Sparse Representation for the Attention-Based LSTM Networks for Industrial Soft Sensor Development by Yurun Wang, Yi Huang, Dongsheng Chen, Longyan Wang, Lingjian Ye, Feifan Shen

“…For deep learning based soft sensors, the spatiotemporal attention (STA)-LSTM is a newly emerged technique which provides efficient predictions for quality variables of industrial processes. …”
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Active RIS-NOMA Uplink in URLLC, Jamming Mitigation via Surrogate and Deep Learning by Ghazal Asemian, Mohammadreza Amini, Burak Kantarci

“…The complexity of the optimization problem, involving numerous interacting variables, leads us to develop a deep regression model to predict optimal network configurations, providing a computationally efficient approach as well as reducing the signaling overhead. …”
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Calculation and Forecast of Glacial Feeding in River Basins by V. G. Konovalov

“…Index δ for the upper reaches of the Rhone River turned out to be not only a representative characteristic of changes in the vegetation period and annual runoff of the river, but also an efficient argument for the super-long-range prediction of these variables for 2025–2054 years.…”
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Neurocognitive factors of new drone Pilots: Identifying candidates with expert potential by Miguel A. Ramallo-Luna, Sara Gonzalez-Torre, Álvaro Rodríguez-Mora, Gabriel G. de la Torre

“…Identifying the neurocognitive variables that influence the performance of these pilots can optimize selection and training processes. …”
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On the development of a practical Bayesian optimization algorithm for expensive experiments and simulations with changing environmental conditions by Mike Diessner, Kevin J. Wilson, Richard D. Whalley

“…The method is validated on two synthetic test functions, and the effects of the noise level, the number of environmental parameters, the parameter fluctuation, the variability of the uncontrollable parameters, and the effective domain size are investigated. …”
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Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka by Amila T. Peiris, Jeevani Jayasinghe, Upaka Rathnayake

“…This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. …”
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On the Nonlinear Forced Vibration of the Magnetostrictive Laminated Beam in a Complex Environment by Nicolae Herisanu, Bogdan Marinca, Vasile Marinca

“…The nonlinear differential equations were studied using an original, explicit, and very efficient technique, namely the optimal auxiliary functions method (OAFM). …”
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Advancing the modified face name associative memory exam in cognitive aging research: insights into connectomic correlates and task reliability by Yilin Liu, Yilin Liu, Mark H. Sundman, Mark H. Sundman, Dalen Hinderaker, Allison Yu-Chin Chen, Allison Yu-Chin Chen, Jacob M. Green, Jacob M. Green, Lisbeth G. Haaheim, Lisbeth G. Haaheim, Hannah M. Siu, Hannah M. Siu, Catherine Jezerc, Kaitlyn Lai, Carol Chen, Parker Guss, Ying-hui Chou, Ying-hui Chou, Ying-hui Chou

“…Experiment 2 revealed significant associations between mFNAME performance and network properties like global efficiency, local efficiency, and system segregation in the default mode network (DMN) and medial temporal network (MTN). …”
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Fabrication of Wood-Derived Carbon Aerogel/Mg(OH)₂ Bio-Composite and Its High Performance for Adsorption and Separation of Cadmium Ions by Ran An, Jinyue Liu, Haomiao Ma, Yuqing Yan, Yuanru Guo, Qingjiang Pan, Shujun Li

“…The resulting wood-derived carbon aerogels (WCA) were functionalized with Mg(OH)₂ to boost their environmental remediation potential. …”
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A Comprehensive Performance Evaluation Method Based on Dynamic Weight Analytic Hierarchy Process for In-Loop Automatic Emergency Braking System in Intelligent Connected Vehicles by Dongying Liu, Wanyou Huang, Ruixia Chu, Yanyan Fan, Wenjun Fu, Xiangchen Tang, Zhenyu Li, Xiaoyue Jin, Hongtao Zhang, Yan Wang

“…At the criterion level, a bivariate variable–weight function, incorporating the vehicle speed and road surface adhesion coefficient, was developed to enable the dynamic coupling modeling of road environment parameters. …”
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Association between metabolic parameters and erection in erectile dysfunction patients with hyperuricemia by Guo-Wei Du, Pei-Ning Niu, Zhao-Xu Yang, Xing-Hao Zhang, Jin-Chen He, Tao Liu, Yan Xu, Jian-Huai Chen, Yun Chen

“…Fasting plasma glucose (β = −0.628, P < 0.001), uric acid (β = −0.552, P < 0.001), triglycerides (β = −0.088, P = 0.047), low-density lipoprotein cholesterol (β = −0.164, P = 0.027), glycated hemoglobin (HbA1c; β = −0.562, P = 0.012), and smoking history (β = −0.074, P = 0.037) exhibited significant negative impacts on erectile function. The coefficient of determination (R²) for the model was 0.239, and the adjusted R² was 0.230, indicating overall statistical significance (F-statistic = 26.52, P < 0.001). …”
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Bivariate Epanechnikov-exponential distribution: statistical properties, reliability measures, and applications to computer science data by H. M. Barakat, M. A. Alawady, I. A. Husseiny, M. Nagy, A. H. Mansi, M. O. Mohamed

“…The statistical properties of the EP-EX-FGM model are examined, including product moments, coefficient of correlation between the internal variables, moment generating function, conditional distribution, concomitants of order statistics (OSs), mean residual life function, and vitality function. …”
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Signatures of Rapidly Rotating Stars with Chemically Homogeneous Evolution in the First Galaxies by Boyuan Liu, Yves Sibony, Georges Meynet, Volker Bromm

“…We find that the rest-frame UV luminosity of star-forming galaxies can be significantly enhanced by a factor of  ∼3−6 when CHE stars above a minimum initial mass of ${m}_{\star ,\min }^{\mathrm{CHE}}\sim 2-10\,{M}_{\odot }$ account for more than half of the total stellar mass following a Salpeter initial mass function. As a result, the UV luminosity functions observed at z  ∼ 12−16 can be reproduced with less extreme values of star formation efficiency and UV luminosity stochastic variability. …”
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A Lie Symmetry Classification of a Nonlinear Fin Equation in Cylindrical Coordinates by Saeed M. Ali, Ashfaque H. Bokhari, F. D. Zaman

“…The nonlinear fin equation in cylindrical coordinates is considered. Assuming a radial variable heat transfer coefficient and temperature dependent thermal conductivity, a complete classification of these two functions is obtained via Lie symmetry analysis. …”
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Molecular and immune landscape of melanoma: a risk stratification model for precision oncology by Miao Sun, Junliang Wu

“…Knockdown efficiency was confirmed by qRT-PCR. Functional assays revealed that KIR2DL4 silencing significantly reduced cell proliferation, as assessed by MTT assay, and impaired migratory capacity, as demonstrated by wound healing assay. …”
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ANALYTICAL SOLUTIONS OF NON-LINEAR HEAT CONDUCTION PROBLEMS by V. A. Bondarev

“…An example of calculations in the case of variable coefficients of thermal conductivity and radiative heating transfer on the boundaries is considered in the paper. …”
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Unsupervised selective labeling for semi-supervised industrial defect detection by Jian Ge, Qin Qin, Shaojing Song, Jinhua Jiang, Zhiwei Shen

“…This has motivated a shift towards semi-supervised learning (SSL), which leverages labeled and unlabeled data to improve learning efficiency and reduce annotation costs. This work proposes the unsupervised spectral clustering labeling (USCL) method to optimize SSL for industrial challenges like defect variability, rarity, and complex distributions. …”
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Mass transpiration impact on effectiveness of heat transport of ternary hybrid nanofluid with velocity slip by S. Bilal, Muhammad Yasir

“…Furthermore, to raise the efficiency and to optimize the performance of the aforementioned mechanisms insertion of the composition of distinctively natured ternary nanoparticles comprising of Cu, TiO2 and Ag are induced. …”
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