Prediction of Expected Fouling Time During Transmembrane Transition in Reverse Osmosis Systems by Jozsef Lakner, Gabor Lakner

“…However, due to the porous structure of membranes, they are prone to fouling, which reduces their efficiency and can eventually render the membranes incapable of functioning. …”
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Psychophysiological and Performance Effects of Biofeedback and Neurofeedback Interventions in a Top 100 Female Chess Player by Juan Pedro Fuentes-García, Santos Villafaina

“…Specifically, the sensorimotor rhythm stimulation (12–15 Hz) can enhance cognitive functions such as selective attention and working memory. …”
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Nonlinear Creep Constitutive Model of Rock Considering Hardening Effect by Dipeng Zhu, Zhiyong Hu, Shuguang Zhang, Wei Qiu, Yijie Wang, Donglan Chen, Fan Mingzhuo, Shutian Zhao, Ye Sun, Wenbo Liu

“…To precisely capture the nonlinearity of rock creep and the law of accelerated deformation, a hardening function and a damage variable are introduced. Based on traditional rheological models, creep mechanisms, and damage laws, an accelerated creep constitutive model integrating hardening and damage effects is established. …”
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Exploring individual and organizational contributors to workplace deviant behavior by D. Daryono, R. Gunawan, U. Udin

“…The novelty and originality of this research lie in its integration of these three variables to understand their combined impact on workplace deviant behavior. …”
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Gross Tonnage-Based Statistical Modeling and Calculation of Shipping Emissions for the Bosphorus Strait by Kaan Ünlügençoğlu

“…These EPA-estimated values served as dependent variables in the modeling process. The modeling framework followed a three-step strategy: (1) outlier detection using Rosner’s test to reduce the influence of outliers on model accuracy, (2) curve fitting with 12 regression models representing four curve types—polynomial (e.g., linear, quadratic), concave/convex (e.g., exponential, logarithmic), sigmoidal (e.g., logistic, Gompertz, Weibull), and spline-based (e.g., cubic spline, natural spline)—to capture diverse functional relationships between GT and emissions, and (3) model comparison using difference performance metrics to ensure a comprehensive assessment of predictive accuracy, consistency, and bias. …”
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Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer by Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Eltjona Mane, Mariia Ivanova, Giulia Cursano, Francesca Maria Porta, Alberto Concardi, Arnaud Gerard Michel Ceol, Annarosa Farina, Carmen Criscitiello, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco

“…The DL model exhibited high accuracy in tissue quantification and TP53 status prediction, outperforming traditional methods in terms of precision and efficiency. DL-based approaches offer significant promise for enhancing biomarker testing and precision oncology by reducing intra- and inter-observer variability, but further validation is required to optimize their integration into real-world clinical workflows. …”
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The Brain Behind the Grid: A Comprehensive Review on Advanced Control Strategies for Smart Energy Management Systems by Gowthamraj Rajendran, Reiko Raute, Cedric Caruana

“…This paper presents a systematic review of key digital technologies—such as artificial intelligence, the Internet of Things, blockchain, and digital twins—employed in AES, providing a critical assessment of their individual functionalities, interdependencies, and collective contributions to the energy sector. …”
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Robust IoT Activity Recognition via Stochastic and Deep Learning by Xuewei Wang, Shihao Wang, Xiaoxi Zhang, Chunsheng Li

“…Our approach begins with a rigorous preprocessing pipeline—encompassing filtering and normalization—to enhance data quality, followed by the application of probability density functions and key statistical measures to capture intrinsic sensor characteristics. …”
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Factors Determining Trade Credit Dynamics During Crisis: Panel Data Analysis for Macedonian Firms by F. Deari, V. V. Lakshina

“…The study includes a review of the literature on commercial credit factors; elaborately analyzed descriptive statistics of the collected data and dependent variable variance; tests for unobservable effects and their functional form; evaluation of panel regression and interpretation of the results. …”
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Feasibility Assessment of Novel Multi-Mode Camshaft Design Through Modal Analysis by Merve Üngör, Osman Taha Sen, Akif Yavuz, Cemal Baykara

“…Camshafts in internal combustion engines are critical components not only for mechanical functionality but also for dynamic performance and vibration characteristics. …”
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Maturity Classification and Quality Determination of Cherry Using VNIR Hyperspectral Images and Comprehensive Chemometrics by Yuzhen Wei, Siyi Yao, Feiyue Wu, Qiangguo Yu

“…Based on the spectral principal components, three classifiers were built to classify the maturity level: support vector machine, backpropagation neural network, and radial basis function neural network. Excellent results with a classification accuracy of 100% were obtained. …”
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Approximation by interpolation spectral subspaces of operators with discrete spectrum by M.I. Dmytryshyn

“…Applications to spectral approximations of the regular elliptic operators with variable smooth coefficients in the space $L_q(\Omega)$ over an open bounded set $\Omega\subset\mathbb{R}^n$ and some self-adjoint ordinary elliptic differential operators in a bounded interval $\Omega=(a,b)$ are shown.…”
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Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals. by Antonio Rivero-Juárez, David Guijo-Rubio, Francisco Tellez, Rosario Palacios, Dolores Merino, Juan Macías, Juan Carlos Fernández, Pedro Antonio Gutiérrez, Antonio Rivero, César Hervás-Martínez

“…This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. …”
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A Mimetic Assay of Neutrophil Extracellular Trap Degradation Using YOYO-1-Stained DNA-Histone Surface Webs by Katherine H. Nguyen, Midori L. Wasielewski, Srilakshmi Yalavarthi, Xianggui Qu, Jason S. Knight, Shuichi Takayama

“…This paper describes a simplified, lower variability mimetic assay with DNA–histone structures, referred to as surface webs, that performs functionally similarly to traditional NET degradation assays with increased scalability, ease of use, shorter preparation time, and lowered costs. …”
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Cardiometabolic risk factors in predicting obstructive coronary artery disease in patients with non-ST-segment elevation acute coronary syndrome by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, E. D. Emtseva, A. A. Vishnevskiy

“…The predictors of this model were 7 categorical (total cholesterol (CS) ≥5,9 mmol/L, low-density lipoprotein cholesterol >3,5 mmol/L, waist-to-hip ratio ≥0,9, waist-to-height ratio ≥0,69, atherogenic index ≥3,4, lipid accumulation product index ≥38,5 cm*mmol/L, uric acid ≥356 pmol/L) and 2 continuous (high density lipoprotein cholesterol and insulin resistance index) variables.Conclusion. The developed algorithm for selecting predictors made it possible to determine their significant predictive threshold values and weighting coefficients characterizing the degree of influence on endpoints. …”
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Family Resilience and Its Influencing Factors Among Patients With Lung Cancer Based on Double ABC‐X Theoretical Framework by Ziyi Shen, Yan Fang, Chengcheng Li, Xin Luo, Junling Cui, Yanchang Liu, Jingfang Hong

“…Relationships between variables and pathways were also explored based on the Double ABC‐X as the theoretical framework. …”
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Inversion and validation of soil water-holding capacity in a wild fruit forest, using hyperspectral technology combined with machine learning by Tingwei Song, Liang Guo, Qian Sun, Guizhen Gao, Jing Chen, Qikun Zhang

“…The results revealed a significant negative correlation between SWHC and the smoothed leaf spectral reflectance, with the best correlation coefficient was r = − 0.59. The use of third-order derivatives and logarithmic second-order derivatives further enhanced this correlation, yielding optimal coefficients of r = − 0.78 and r = − 0.72, respectively. …”
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Advances in nursing care for post-stroke limb dysfunction rehabilitation by Jie Bai, Kecheng Chen

“…Clinically validated assessment tools demonstrate variable utility across recovery phases, with the Pittsburgh Rehabilitation Participation Scale offering efficiency in acute settings and the Motivation in Stroke Patients for Rehabilitation Scale providing comprehensive evaluation during subacute and chronic phases. …”
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COSP-RTTOV-1.0: flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design by J. K. Shaw, J. K. Shaw, D. J. Swales, S. DeSouza-Machado, D. D. Turner, J. E. Kay, J. E. Kay, D. P. Schneider

“…Outputs from COSP-RTTOV are consistent with instrument spectral response functions, orbit sampling, and the physics of the host model. …”
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Flexible Reconfiguration for Optimal Operation of Distribution Network Under Renewable Generation and Load Uncertainty by Behzad Esmaeilnezhad, Hossein Amini, Reza Noroozian, Saeid Jalilzadeh

“…Minimizing the operational costs is difficult when there is a high penetration of renewable resources and variability of loads, which introduces uncertainty. …”
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