Effect of uniaxial stress on bursting energy absorption of paper

The overall usefulness of the bursting energy absorption (BEA) was studied for a better analysis of paper strength properties. Additionally, the changes of the BEA during more complex deformations of paper products, e.g., preliminary or simultaneous tensile and burst, were determined. For the purpose of the research, an experimental setup was designed. The results showed that the correlation between BEA and bursting strength was linear, but the proportionality strongly depended on paper grade. Thus, a more accurate method to characterize the bursting resistance (BR) of paper was proposed. The BR parameter is described by the three following parameters: average bursting strength, average bursting energy absorption, and the slope of the fitted linear regression curve (relationship between the bursting energy absorption and the bursting strength). This method revealed new mechanical behaviors of papers related to their preloading.

Neutron-Induced Nuclear Cross-Sections Study for Plasma Facing Materials via Machine Learning: Molybdenum Isotopes

In this work, we apply a machine learning algorithm to the regression analysis of the nuclear cross-section of neutron-induced nuclear reactions of molybdenum isotopes, 92Mo at incident neutron energy around 14 MeV. The machine learning algorithms used in this work are the Random Forest (RF), Gaussian Process Regression (GPR), and Support Vector Machine (SVM). The performance of each algorithm is determined and compared by evaluating the root mean square error (RMSE) and the correlation coefficient (R2). We demonstrate that machine learning can produce a better regression curve of the nuclear cross-section for the neutron-induced nuclear reaction of 92Mo isotopes compared to the simulation results using EMPIRE 3.2 and TALYS 1.9 from the previous literature. From our study, GPR is found to be better compared to RF and SVM algorithms, with R2=1 and RMSE =0.33557. We also employed the crude estimation of property (CEP) as inputs, which consist of simulation nuclear cross-section from TALYS 1.9 and EMPIRE 3.2 nuclear code alongside the experimental data obtained from EXFOR (1 April 2021). Although the Experimental only (EXP) dataset generates a more accurate cross-section, the use of CEP-only data is found to generate an accurate enough regression curve which indicates a potential use in training machine learning models for the nuclear reaction that is unavailable in EXFOR.

Human Performance in Critical Scenarios as a Benchmark for Highly Automated Vehicles

Before highly automated vehicles (HAVs) become part of everyday traffic, their safety has to be proven. The use of human performance as a benchmark represents a promising approach, but appropriate methods to quantify and compare human and HAV performance are rare. By adapting the method of constant stimuli, a scenario-based approach to quantify the limit of (human) performance is developed. The method is applied to a driving simulator study, in which participants are repeatedly confronted with a cut-in manoeuvre on a highway. By systematically manipulating the criticality of the manoeuvre in terms of time to collision, humans’ collision avoidance performance is measured. The limit of human performance is then identified by means of logistic regression. The calculated regression curve and its inflection point can be used for direct comparison of human and HAV performance. Accordingly, the presented approach represents one means by which HAVs’ safety performance could be proven.

Main Factors Affecting Mechanical Characteristics of Silicon Carbide Sludge-Based Geopolymer Revealed by Experimental Design and Associated Statistical Analysis

In recent years, many researches have been analyzed on the subject of geopolymer materials. As far as we know, the design of experiments (DOE) methods had not been used for the analysis of geopolymer containing silicon carbide sludge (SCS) waste and metakaolin components. This study was used to quantify the effects the interaction between the constituent factors and macroscopic / microstructure properties of SCS-based geopolymers (SCSGPs), by the DOE methods. Compares the correlation between the factor parameters and physical properties, which were analyzed the microstructure analysis of SCSGPs. The results of statistical analysis showed that the influencing factors of compressive strength of SCSGPs were mainly Na / Si mole ratio (NSR), Na / Al mole ratio (NAR), followed by dissolution rate of Al (DRA). In the regression curve analysis results, when the SCS replacement levels of 20%, the coefficient of b was the most influential, because the synergistic effect between metakaolin (MK) and SCS. In this study, the multivariate adaptive regression splines model provided a valid reference for the application of and future improvements in SCSGPs.

Electromagnetic Energy Harvester for Transportation Infrastructure Using Vehicle-Tread Force

We propose a novel electromagnetic energy-harvesting device (EHD) for structural health monitoring systems of transportation infrastructures. The EHD is embedded in the road surface and uses the tread force of cars as the input force when tires of cars pass over it. Because the input force is very fast, the proposed EHD can generate a large amount of energy. The footprint of the device is 20 × 20 mm, its height is 7.5 mm, and its volume is 2.4 cm3. We measured the energy generated when a bicycle passed over the EHD 34 times at various speeds between 5 and 15 km/h. Subsequently, we obtained the regression curve from the results, which showed the relationship between the bicycle speed and generated energy, and estimated the electric energy generated by car at higher speeds. The results showed that, even though the size of the EHD was small, electric energies of 100 μJ, and 1.0 mJ could be generated at car speeds of 17 km/h, and 52 km/h, respectively.

The Curve Estimation of Combined Truncated Spline and Fourier Series Estimators for Multiresponse Nonparametric Regression

Nonparametric regression becomes a potential solution if the parametric regression assumption is too restrictive while the regression curve is assumed to be known. In multivariable nonparametric regression, the pattern of each predictor variable’s relationship with the response variable is not always the same; thus, a combined estimator is recommended. In addition, regression modeling sometimes involves more than one response, i.e., multiresponse situations. Therefore, we propose a new estimation method of performing multiresponse nonparametric regression with a combined estimator. The objective is to estimate the regression curve using combined truncated spline and Fourier series estimators for multiresponse nonparametric regression. The regression curve estimation of the proposed model is obtained via two-stage estimation: (1) penalized weighted least square and (2) weighted least square. Simulation data with sample size variation and different error variance were applied, where the best model satisfied the result through a large sample with small variance. Additionally, the application of the regression curve estimation to a real dataset of human development index indicators in East Java Province, Indonesia, showed that the proposed model had better performance than uncombined estimators. Moreover, an adequate coefficient of determination of the best model indicated that the proposed model successfully explained the data variation.

Spline and Kernel Mixed Nonparametric Regression for Malnourished Children Model in West Nusa Tenggara

Health sector development is essential to improve human life quality, especially in West Nusa Tenggara (NTB) Province. Based on data from the NTB Provincial Health Office from 2011 to 2016, children under five suffering from malnutrition continued to increase, caused by several factors that affected the incident. Therefore, appropriate analysis is needed to model children who suffer from malnutrition in NTB Province in 2016, consisting of 10 districts based on the variables that influence it. The analysis in this study was carried out using a nonparametric regression mixed-model spline truncated and kernel. The estimation of the nonparametric regression curve depends on the optimal knot points and bandwidths parameter. Therefore, in determining the optimal knot points and bandwidths obtained from Generalized Cross-Validation (GCV). Based on the analysis that has been done, we obtained a nonparametric regression mixed-model spline truncated and kernel optimal knot points, such as for each variable and optimum bandwidths, such as and , with the value of GCV. The mixed model acquired has a good model by considering the values of and MSE. Besides, the MAPE value indicated a high degree of accuracy, so that the model obtained has an excellent forecast.

ESTIMASI KURVA REGRESI SPLINE PADA DATA LONGITUDINAL DENGAN METODE KUADRAT TERKECIL

This article examines the estimation of spline regression, especially its use in longitudinal data. Longitudinal data is data obtained based on observations made as many as n objects that are independent with each object being repeatedly observed in different time periods and between observations in the same object are dependent besides longitudinal data is data that can distinguish the diversity of responses caused by The regression curve of the spline was estimated using the least squares. It can be seen that the estimation of the spline regression curve for longitudinal data is a class of linear estimation in response observations and is highly dependent on the knots point k1,k2,...,Kn

Truncated Spline Estimation of Percentage Poverty Modeling in Papua Province

In estimating the regression curve there are three approaches, namely parametric regression, nonparametric regression and semiparametric regression. Nonparametric regression approach has high flexibility. Nonparametric regression approach that is quite popular is Truncated Spline. Truncated Spline is a polynomial pieces which have segmented and continuous. One of the advantages of Spline is that it can handle data that changes at certain sub intervals, so this model tends to search for data estimates wherever the data pattern moves and there are points of knots. In reality, data patterns often change at certain sub intervals, one of which is data on poverty in the Papua Province. Papua Province is ranked first in the percentage of poor people in Indonesia. The best of model Truncated Spline in nonparametric regression for the poverty model in Papua Province is using a combination of knot.