Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network by Bin Luo, Susan Halabi

“…For linear marginal hazard models, we develop a penalized pseudo-partial likelihood approach with a group LASSO-type penalty applied to the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="sans-serif-italic">ℓ</mi><mn>2</mn></msub></semantics></math></inline-formula> norms of coefficients corresponding to the same covariates across marginal hazard functions. …”
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Radiative heat and mass transfer of second-grade nanofluid slip flow with variable thermal properties by Zia Ullah, Md. Mahbub Alam, Aamir Abbas Khan, Shalan Alkarni, Feyisa Edosa Merga

“…The objective of this study is to provide deeper insights into how these variables influence fluid flow characteristics and heat transfer in nanofluid. …”
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Sharp L2 Norm Convergence of Variable-Step BDF2 Implicit Scheme for the Extended Fisher–Kolmogorov Equation by Yang Li, Qihang Sun, Naidan Feng, Jianjun Liu

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A Neutrosophic Multi-Objective Optimization Framework for Sustainable Design of Water Supply Systems by Jesús Rafael Hechavarría Hernández, Maikel Yelandi Leyva Vázquez

“…Through the application of MMN, optimal solutions are identified that not only minimize costs and losses but also maximize efficiency and service quality, considering variability in demand and risks associated with climate change and infrastructure failures. …”
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Minute Ventilation/Carbon Dioxide Production Slope Could Predict Short- and Long-Term Prognosis of Patients After Acute Decompensated Heart Failure by Sheng-Hui Tuan, I-Ching Huang, Wei-Chun Huang, Guan-Bo Chen, Shu-Fen Sun, Ko-Long Lin

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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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A Method for Solving LiDAR Waveform Decomposition Parameters Based on a Variable Projection Algorithm by Ke Wang, Guolin Liu, Qiuxiang Tao, Luyao Wang, Yang Chen

“…First, using a variable projection algorithm, we separated the linear (amplitude) and nonlinear (center position and width) parameters in the Gaussian function model; the linear parameters are expressed with nonlinear parameters by the function. …”
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New feedback functions for synchronizing chaotic maps by M. K. Ali

“…Synchronization of chaotic maps is studied using the method of variable feedback. A general method is presented for generating feedback functions for maps. …”
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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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CFD-based optimization of dynamic cyclones with variable vortex length using GMDH artificial neural network by Hamed Safikhani, Somayeh Davoodabadi Farahani, Lakhbir Singh Brar, Faroogh Esmaeili

“…In the second phase, data obtained from the numerical simulations is used to construct objective function models, focusing on minimizing pressure drop and maximizing collection efficiency. …”
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An adaptive sliding mode fault-tolerant control of variable speed reaching law for steer-by-wire systems by Jinwen Yang, Yinquan Yu, Dequan Zeng, Yiming Hu, Junhui Liu, Kai Liu, Giuseppe Carbone, Shaohua Luo, Xiaofeng Zhu

“…By integrating an adjustment function into the constant-velocity reaching polynomial and designing an adaptive law, dynamic updating of the control law is achieved. …”
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New Numerical Algorithm to Solve Variable-Order Fractional Integrodifferential Equations in the Sense of Hilfer-Prabhakar Derivative by MohammadHossein Derakhshan

“…In this article, a numerical technique based on the Chebyshev cardinal functions (CCFs) and the Lagrange multiplier technique for the numerical approximation of the variable-order fractional integrodifferential equations are shown. …”
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Structural optimization of stiffened panel structures with continuous and discrete design variables using deep reinforcement learning by Ryota NONAMI, Mitsuru KITAMURA

“…To overcome such an environment, in this study, an optimization flow for structural optimization is considered, and states, actions, and rewards appropriately representing design variables, constraint conditions, and objective functions are discussed. …”
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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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Analysis of Current Ratio and Debt to Equity Ratio on Return On Assets with Company Size as a Moderating Variable by Mardiyani Mardiyani, Maiyaliza Maiyaliza, Rahmat Rian Hidayat, Muhammad Dwika Pramudya

“…This means that an increased liquidity, along with the proportion of debt can reduce the efficiency and profitability of the company. Company size strengthens the effect by functioning as a moderating variable. …”
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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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An Operational Matrix Technique Based on Chebyshev Polynomials for Solving Mixed Volterra-Fredholm Delay Integro-Differential Equations of Variable-Order by Kamal R. Raslan, Khalid K. Ali, Emad M. H. Mohamed, Jihad A. Younis, Mohamed A. Abd El salam

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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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Symmetric physically unclonable functions of the arbiter type by V. N. Yarmolik, A. A. Ivaniuk

“…The proposed approach to the construction of symmetrical physically unclonable functions, based on the implementation of the Generate, Select and Switch functions by various components of the base element, has shown its efficiency and promise. …”
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