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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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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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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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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The Impact of the Preferential Attachment Level on the Innovation Network Structure and Innovation Efficiency by Lei Hua, Zhong Yang, Jiyou Shao, Dan Jin

“…In the formation process of innovation networks, the level of preferential attachment between agents has an important impact on the resulting structural and functional characteristics of a network. A simulation model of innovation networks with variable preferential levels of attachment is established to study these impacts. …”
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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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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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Experimental analysis of innovative perforated heat sinks for enhanced photovoltaic efficiency by Sebastian-Valeriu Hudișteanu, Nelu-Cristian Cherecheș, Florin-Emilian Țurcanu, Iuliana Hudișteanu, Marina Verdeș, Ana-Diana Ancaș

“…The experimental setup was placed on a roof terrace, exposing the panels to real-world weather and functional conditions. Each cooling solution, designed with variable characteristics, aimed to efficiently dissipate heat generated by the PV panels, particularly during warmer seasons. …”
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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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Weight illusions explained by efficient coding based on correlated natural statistics by Paul M. Bays

“…We show that the precision with which human observers estimate object weight varies as a function of both mass and volume in a manner consistent with the estimated joint distribution of those properties among everyday objects. …”
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Enhanced Lightweight YOLO Model for Efficient Vehicle Detection in Satellite Imagery by Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Elmi Abu Bakar, Ahmad Faizul Hawary

“…Vehicle detection in satellite images is a challenging task due to the variability in scale and resolution, complex background, and variability in object appearance. …”
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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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Engineering novel CRISPRi repressors for highly efficient mammalian gene regulation by Andrew Kristof, Krithika Karunakaran, Christopher Allen, Paula Mizote, Sophie Briggs, Zixin Jian, Patrick Nash, John Blazeck

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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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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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DANNET: deep attention neural network for efficient ear identification in biometrics by Deepthy Mary Alex, Kalpana Chowdary M., Hanan Abdullah Mengash, Venkata Dasu M., Natalia Kryvinska, Chinna Babu J., Ajmeera Kiran

“…This shift has highlighted the need for reliable biometric systems that can function effectively even when facial features are partially obscured. …”
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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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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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