Showing 281 - 300 results of 2,280 for search 'variables function (coefficiency. OR efficiency.)', query time: 0.17s Refine Results
  1. 281

    Significance of dissipative flow on a second-grade nanofluid with variable thermal properties on the stretching surface by Zia Ullah, Aamir Abbas Khan, Shalan Alkarni, Abhinav Kumar, N. Beemkumar, Tushar Aggarwal, Ashwin Jacob, Jajneswar Nanda, Feyisa Edosa Merga

    Published 2025-05-01
    “…Boundary conditions are used for the analysis of heat and mass transmission. Stream functions and similarity variables are utilized to reduce the complexity of the governed PDEs (partial differential equations) and altered into ODEs (ordinary differential equations). …”
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    Article
  2. 282

    Prognostic Significance of Pulse Pressure Variability During Mechanical Thrombectomy in Acute Ischemic Stroke Patients by Benjamin Maïer, Guillaume Turc, Guillaume Taylor, Raphaël Blanc, Michael Obadia, Stanislas Smajda, Jean‐Philippe Desilles, Hocine Redjem, Gabriele Ciccio, William Boisseau, Candice Sabben, Malek Ben Machaa, Mylene Hamdani, Morgan Leguen, Etienne Gayat, Jacques Blacher, Bertrand Lapergue, Michel Piotin, Mikael Mazighi

    Published 2018-09-01
    “…Moreover, pulse pressure (PP) has not been considered as a potent hemodynamic parameter to describe BP variability during MT. We assessed the impact of PP variability on functional outcome in acute ischemic stroke patients with large vessel occlusion during MT. …”
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    Article
  3. 283

    Free Vibration Analysis of Moderately Thick Rectangular Plates with Variable Thickness and Arbitrary Boundary Conditions by Dongyan Shi, Qingshan Wang, Xianjie Shi, Fuzhen Pang

    Published 2014-01-01
    “…Under the current framework, the one displacement and two rotation functions are generally sought, regardless of boundary conditions, as an improved trigonometric series in which several supplementary functions are introduced to remove the potential discontinuities with the displacement components and its derivatives at the edges and to accelerate the convergence of series representations. …”
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    Article
  4. 284

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

    Published 2025-05-01
    “…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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  5. 285

    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

    Published 2025-03-01
    “…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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  6. 286
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  8. 288

    Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle by Chenguang Lu

    Published 2025-06-01
    “…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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    Article
  9. 289

    Efficient bit labeling in factorization machines with annealing for traveling salesman problem by Shota Koshikawa, Aruto Hosaka, Tsuyoshi Yoshida

    Published 2025-07-01
    “…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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  10. 290

    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

    Published 2025-07-01
    “…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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    Article
  11. 291

    The dichotomy of human decision-making: An experimental assessment of stone tool efficiency. by David Nora, João Marreiros, Walter Gneisinger, Antonella Pedergnana, Telmo Pereira

    Published 2025-01-01
    “…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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  12. 292

    Methods of evaluating maturity level of the organization based on fuzzy modeling by Lyudmila Viktorovna Borisova, Lyubov Azatovna Dimitrova, Inna Nikolaevna Nurutdinova

    Published 2017-03-01
    “…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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  13. 293

    PRODUCTIVITY AND EFFICIENCY OF MAIZE (ZEA MAYS) FARMERS IN ADAMAWA STATE, NIGERIA by Abdu Karniliyus TASHIKALMA, Dengle Yuniyus GIROH

    Published 2024-01-01
    “…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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    Article
  14. 294

    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

    Published 2020-01-01
    “…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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  15. 295

    Technical Efficiency of Sweet Potato Production: A Stochastic Frontier Analysis by Godfrey C. Onuwa, Solomon T. Folorunsho, Ganiyu Binuyo, Mercy Emefiene, Onyekwere P. Ifenkwe

    Published 2021-08-01
    “…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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  16. 296

    An Efficient Sparse Twin Parametric Insensitive Support Vector Regression Model by Shuanghong Qu, Yushan Guo, Renato De Leone, Min Huang, Pu Li

    Published 2025-07-01
    “…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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  17. 297

    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

    Published 2025-06-01
    “…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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  18. 298

    Machine learning-based smart irrigation controller for runoff minimization in turfgrass irrigation by Sambandh Dhal, Jorge Alvarado, Ulisses Braga-Neto, Benjamin Wherley

    Published 2024-12-01
    “…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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