Digital mapping of Ghana’s soil properties and nutrients: performance of spline and weighted average approaches by Kora B.D. Simperegui, Anselme K.K. Kouame, Benjamin Kwesie, Prem S. Bindraban, William Adzawla, Eric Asamoah, Mohamed El Gharous

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Probabilistic analysis of active earth pressures in spatially variable soils using machine learning and confidence intervals by Tran Vu-Hoang, Tan Nguyen, Jim Shiau, Duy Ly-Khuong, Hung-Thinh Pham-Tran

“…Abstract This study introduces a probabilistic framework for assessing active earth pressures in soils exhibiting spatial variability in friction angles and unit weight. These properties are modeled using random fields with log-normal distributions and spatial correlation lengths. …”
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Median nerve somatosensory evoked potentials studies on latency variability as a function of subject height, limb length and nerve conduction velocity: estudos de variabilidade das... by Joaquim P. Brasil-Neto

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Application of physics-informed neural networks for two-phase flow model with variable diffusion and experimental validation by Daulet Kalesh, Timur Merembayev, Sagyn Omirbekov, Yerlan Amanbek

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Upper-Bound Analysis of Slope Reliability Considering Anisotropic Spatial Variability and Reinforcement with Anti-slide Piles by ZHAO Zi-hao, WANG Jia-rui, FU Tao-tao, ZHU En-lin

“…To ensure that the random field demonstrates general rotational anisotropy, the autocorrelation function was transformed through a coordinate system conversion before the decomposition of the autocorrelation coefficient matrix. …”
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Construction and analysis of one class of cryptographic functions by Zhi-hui OU, Ya-qun ZHAO, Xu LI

“…A novel class of n+t -variable Boolean functions G (x,y) through adding t variables while concatenating t+ 1 Boolean functions (called basic function) was constructed and the Walsh spectrum and autocorrelation coefficient of G(x,y)were given.The relationship between G(x,y)and basic functions by Krawtchouk polynomial and Krawtchouk matrix was studied.Moreover,their cryptographic properties:correlation immunity,propagation and algebraic immunity were investigated.Specially,the detailed relationship between G (x,y) and basic functions when t= 2 was analyzed.In additional,a novel class of multioutput Boolean functions by generalizing the method was constructed and the general Walsh spectrum of the class of multioutput Boolean functions was proposed.Correlation immunity and algebraic immunity of the class of multioutput Boolean functions were analyzed.…”
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Construction and analysis of one class of cryptographic functions by Zhi-hui OU, Ya-qun ZHAO, Xu LI

“…A novel class of n+t -variable Boolean functions G (x,y) through adding t variables while concatenating t+ 1 Boolean functions (called basic function) was constructed and the Walsh spectrum and autocorrelation coefficient of G(x,y)were given.The relationship between G(x,y)and basic functions by Krawtchouk polynomial and Krawtchouk matrix was studied.Moreover,their cryptographic properties:correlation immunity,propagation and algebraic immunity were investigated.Specially,the detailed relationship between G (x,y) and basic functions when t= 2 was analyzed.In additional,a novel class of multioutput Boolean functions by generalizing the method was constructed and the general Walsh spectrum of the class of multioutput Boolean functions was proposed.Correlation immunity and algebraic immunity of the class of multioutput Boolean functions were analyzed.…”
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Safety Performance Functions for Low-Volume Roads by Gianluca Dell’Acqua, Francesca Russo

“…The research study presented here has been used to calibrate crash prediction models (CPMs) per kilometer per year. The coefficients of the CPMs are estimated using a non-linear multi-variable regression analysis utilizing the least – square method. …”
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Enhanced thermal performance of variable cross-section solar collectors: a case study in Santiniketan, India by Pranab Kanti Roy

“…Abstract We present analytical models for variable cross-section absorber plate solar collectors, where the thermal conductivity, overall heat loss coefficient, and incident solar heat flux are power-law functions of temperature. …”
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Using Wearable Inertial Sensors to Estimate Kinematic Parameters and Variability in the Table Tennis Topspin Forehand Stroke by Ziemowit Bańkosz, Sławomir Winiarski

“…The large intraindividual variability in movement was probably functional (i.e., motor adjustment and injury avoidance). …”
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Interobserver Variability in Manual Versus Semi-Automatic CT Assessments of Small Lung Nodule Diameter and Volume by Frida Zacharias, Tony Martin Svahn

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Functional Analysis of Thyme Production in Denizli Province by Arif Semerci, Enver Ken

“…Among the variables in the production function, the variables with marginal efficiency coefficient above 1 were determined as the production area (3.20) and the fertilizer factor (1.33). …”
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A domain free of the zeros of the partial theta function by V. Kostov

“…For each $q$ fixed, $\theta$ is an entire function of order $0$ in the variable~$x$. When $q$ is real and $q\in (0,0.3092\ldots )$, $\theta (q,.)$ is a function of the Laguerre-P\'olya class $\mathcal{L-P}I$. …”
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Predicting Wear Rate and Friction Coefficient of Li₂Si₂O₅ Dental Ceramic Using Optimized Artificial Neural Networks by Marko Pantić, Saša Jovanović, Aleksandar Djordjevic, Suzana Petrović Savić, Milan Radenković, Živče Šarkoćević

“…A genetic algorithm (GA) was used to optimize the ANN’s hyperparameters, improving its ability to model complex, nonlinear relationships between input variables, including normal load and velocity and output properties such as wear rate and friction coefficients. …”
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High-Order Spectral Method of Density Estimation for Stochastic Differential Equation Driven by Multivariate Gaussian Random Variables by Hongling Xie

“…In this paper, we propose a high-order algorithm of gPC-based density estimation where SPDE driven by multivariate Gaussian random variables. Our main techniques are (1) we build a new multivariate orthogonal basis by adopting the Gauss–Schmidt orthogonalization; (2) with the newly constructed orthogonal basis in hand, we first assume the unknown function in the SPDE has the stochastic general polynomial chaos (gPC) expansion, second implement the stochastic gPC expansion for the SPDE in the multivariate Gaussian measure space, and third we obtain and numerical calculation deterministic differential equations for the coefficients of the expansion; (3) we used high-order algorithm of gPC-based for density estimation and moment estimation. …”
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Heart Rate Variability as a Marker of Disease Severity in Pulmonary Arterial Hypertension: A Prospective Cohort Study by Arun Jose, Alex D. Moseley, Manisha Das, Robert E. O'Donnell, Jean M. Elwing

“…Comparable relationships were observed between heart rate captured using HRV device and these CMR measures (RVEF coefficient −0.37, p = 0.007; coupling ratio coefficient −0.013, p = 0.016). …”
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A Data-Driven Iterative Feedforward Tuning Strategy with a Variable-Gain Feedback Controller for Linear Servo Systems by Jiaqian Fu, Shanhu Li

“…Theoretical verification and experimental results indicate the excellent iterative efficiency and accuracy of IFFT with a variable-gain feedback controller.…”
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Genetic and molecular basis of abnormal BOLD signaling variability in patients with major depressive disorder after electroconvulsive therapy by Siyu Fan, Yulin Zhang, Rui Qian, Jie Hu, Hao Zheng, Wentao Dai, Yang Ji, Yue Wu, Xiaohui Xie, Si Xu, Gong-Jun Ji, Yanghua Tian, Kai Wang

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Exploring patterns of heart rate variability in major depressive disorder: A short-term study in Jaipur, Rajasthan by Sunidhi sharma, Sudhanshu Kacker, Neha Saboo

“…Methods: This observational study was conducted at RUHS College of Medical Sciences and Associated Hospitals, Jaipur, from July 2022 to January 2023, on a major depressive disorder population of either sex in the age group of 20-40 years. Cognitive functions were assessed using a questionnaire, and AD instruments recorded heart rate variability variables (time & frequency domain) using a digital physiograph (MLT004/ST). …”
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Beyond Global Metrics: The U-Smile Method for Explainable, Interpretable, and Transparent Variable Selection in Risk Prediction Models by Katarzyna B. Kubiak, Agata Konieczna, Anna Tyranska-Fobke, Barbara Więckowska

“…Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. …”
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