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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Hereditary angioedema diagnosis evaluation score (HADES): A new clinical scoring system for predicting hereditary angioedema with C1 inhibitor deficiency by Ricardo Zwiener, MD, Rafael Zamora, MD, Carlos María Galmarini, MD, PhD, Laura Brion, PhD, Laura Arias, MD, Andrea Pino, MD, Paula Rozenfeld, PhD

“…Blood samples were analyzed for C1-INH/C1q levels and C1-INH function. A predictive score was developed from the odds ratios derived from multivariate logistic regression analysis. …”
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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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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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Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle by Chenguang Lu

“…The G theory is based on the P-T probability framework and, therefore, allows for the use of truth, membership, similarity, and distortion functions (related to semantics) as constraints. Based on the study of the <i>R</i>(<i>G</i>) function and logical Bayesian Inference, this paper proposes the Semantic Variational Bayesian (SVB) and the Maximum Information Efficiency (MIE) principle. …”
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Efficient bit labeling in factorization machines with annealing for traveling salesman problem by Shota Koshikawa, Aruto Hosaka, Tsuyoshi Yoshida

“…Abstract To efficiently determine an optimum parameter combination in a large-scale problem, it is essential to convert the parameters into available variables in actual machines. …”
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Methods of evaluating maturity level of the organization based on fuzzy modeling by Lyudmila Viktorovna Borisova, Lyubov Azatovna Dimitrova, Inna Nikolaevna Nurutdinova

“…Membership functions of all the linguistic variables are developed according to the estimates of four experts for which purpose the typical trapezoidal functions are used. …”
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Efficient curve fitting with penalized B-splines for oceanographic and ecological applications by Kwan-Young Bak, Dong-Young Lee, Ju-Seong Lee, Hee-Jung Jee, R. Jisung Park, Ja-Yong Koo, Jae-Hwan Jhong

“…The total variation penalty controls curve smoothness by penalizing abrupt changes in the estimated function, while the group penalty ensures that all response variables share a consistent set of knots, enhancing interpretability. …”
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The dichotomy of human decision-making: An experimental assessment of stone tool efficiency. by David Nora, João Marreiros, Walter Gneisinger, Antonella Pedergnana, Telmo Pereira

“…This strongly suggests that each raw material used in archaeological contexts to produce blanks should be evaluated for its efficiency. In addition, it may be pertinent to extend this approach to other blunt artefactssuch as scrapers, burins, anvils, and hammerstones when investigating aspects of interconnected behaviours such as artefact variability, resource economy, group mobility, and site function. …”
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PRODUCTIVITY AND EFFICIENCY OF MAIZE (ZEA MAYS) FARMERS IN ADAMAWA STATE, NIGERIA by Abdu Karniliyus TASHIKALMA, Dengle Yuniyus GIROH

“…Education and extension contact were statistically significant (p≤0.05) and increase technical efficiency among respondents. Furthermore, the stochastic cost function analysis indicated that 80.24% variations in allocative efficiencies were as a result of the variables included in the model. …”
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Technical Efficiency of Sweet Potato Production: A Stochastic Frontier Analysis by Godfrey C. Onuwa, Solomon T. Folorunsho, Ganiyu Binuyo, Mercy Emefiene, Onyekwere P. Ifenkwe

“…Data collected was analyzed using descriptive statistics and stochastic frontier production function. The socioeconomic variables of the respondents affected their farm efficiency and level of farm output. …”
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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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An Efficient Sparse Twin Parametric Insensitive Support Vector Regression Model by Shuanghong Qu, Yushan Guo, Renato De Leone, Min Huang, Pu Li

“…Similar to twin parametric insensitive support vector regression (TPISVR), STPISVR constructs a pair of nonparallel parametric insensitive bound functions to indirectly determine the regression function. …”
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Computationally Efficient Hybrid Downscaling of Surf Zone Hydrodynamics: Methodology and Evaluation by E. R. Echevarria, S. Contardo, B. Pérez‐Díaz, R. K. Hoeke, B. Leighton, C. Trenham, L. Cagigal, F. J. Méndez

“…Abstract We present a hybrid surf‐zone model that combines numerical simulations and statistical/machine learning techniques, enabling accurate calculations of nearshore wave and hydrodynamic parameters with high computational efficiency. The approach involves defining representative forcing conditions, carrying out numerical model (XBeach) simulations for these cases, and training machine learning models capable of predicting selected model output variables. …”
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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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Machine learning-based smart irrigation controller for runoff minimization in turfgrass irrigation by Sambandh Dhal, Jorge Alvarado, Ulisses Braga-Neto, Benjamin Wherley

“…The synthetic data were derived from observations collected from irrigation plots at the Texas A&M University Turfgrass Laboratory in Texas, United States, with Soil Wetting Efficiency Index (SWEI) serving as the target variable. …”
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THE JUSTIFICATION OF THE EFFICIENCY OF FINANCIAL SUPPORT OF EDUCATION IN THE CONTEXT OF THE NATIONAL ECONOMY DEVELOPMENT by A. Vorontsova, T. A. Vasylieva, T. Mayboroda

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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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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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