On Enhanced GLM-Based Monitoring: An Application to Additive Manufacturing Process

Innovations in technology assist the manufacturing processes in producing high-quality products and, hence, become a greater challenge for quality engineers. Control charts are frequently used to examine production operations and maintain product quality. The traditional charting structures rely on a response variable and do not incorporate any auxiliary data. To resolve this issue, one popular approach is to design charts based on a linear regression model, usually when the response variable shows a symmetric pattern (i.e., normality). The present work intends to propose new generalized linear model (GLM)-based homogeneously weighted moving average (HWMA) and double homogeneously weighted moving average (DHWMA) charting schemes to monitor count processes employing the deviance residuals (DRs) and standardized residuals (SRs) of the Poisson regression model. The symmetric limits of HWMA and DHWMA structures are derived, as SR and DR statistics showed a symmetric pattern. The performance of proposed and established methods (i.e., EWMA charts) is assessed by using run-length characteristics. The results revealed that SR-based schemes have relatively better performance as compared to DR-based schemes. In particular, the proposed SR-DHWMA chart outperforms the other two, namely SR-EWMA and SR-HWMA charts, in detecting shifts. To illustrate the practical features of the study’s proposal, a real application connected to the additive manufacturing process is offered.

Traditional Games on Character Building: Integrating Hide and Seek on Learning

The purpose of this study was to determine the effect on learning of incorporating the traditional game of Hide and Seek. This type of research employs a mixed-methods. This study used several variables as research categories, including response, peace-loving character, and patriotism, and enrolled a total of 44 students. Descriptive and inferential statistics were used in the data analysis. The integration took place at an Elementary School and a Madrasah Ibtidaiyah in Batang Hari, as evidenced by the response, which reflected the peace-loving nature of patriotism. The result is that each response variable, peace, love, and patriotism, has a significant effect, with a value of sig 0.05, and that each variable is dominant in the good category.

A New Generalization of the Student’s t Distribution with an Application in Quantile Regression

In this work, we present a new generalization of the student’s t distribution. The new distribution is obtained by the quotient of two independent random variables. This quotient consists of a standard Normal distribution divided by the power of a chi square distribution divided by its degrees of freedom. Thus, the new symmetric distribution has heavier tails than the student’s t distribution and extensions of the slash distribution. We develop a procedure to use quantile regression where the response variable or the residuals have high kurtosis. We give the density function expressed by an integral, we obtain some important properties and some useful procedures for making inference, such as moment and maximum likelihood estimators. By way of illustration, we carry out two applications using real data, in the first we provide maximum likelihood estimates for the parameters of the generalized student’s t distribution, student’s t, the extended slash distribution, the modified slash distribution, the slash distribution generalized student’s t test, and the double slash distribution, in the second we perform quantile regression to fit a model where the response variable presents a high kurtosis.

SimBetaReg Web-Tool: The Easiest Way to Implement the Beta and Simplex Regression Models

When the response variable is defined on the (0,1) interval, the beta and simplex regression models are commonly used by researchers. However, there is no software support for these models to make their implementation easy for researchers. In this study, we developed a web-tool, named SimBetaReg, to help researchers who are not familiar with programming to implement the beta and simplex regression models. The developed application is free and works independently from the operating systems. Additionally, we model the incidence ratios of COVID-19 with educational and civic engagement indicators of the OECD countries using the SimBetaReg web-tool. Empirical findings show that when the educational attainment, years in education, and voter turnout increase, the incidence ratios of the countries decrease.

Vinasse: from a residue to a high added value biopolymer

This work aimed to study the feasibility of using vinasse for polyhydroxybutyrate (PHB) production by Bacillus megaterium. To optimize the culture medium, a Box–Behnken design was employed considering carbon (C), nitrogen (N), and phosphorus (Ph) concentrations as independent variables and PHB productivity as the response variable. The productivity decreased when C or N were increased, probably due to the presence of phenolic compounds and the limitation of N for the production of PHB by Bacillus sp. bacteria. An additional experimental design to optimize the C/N ratio and growing conditions (fermentation time and temperature) was carried out. Fermentation time had a statistically significant effect on PHB productivity reaching 10.6 mg/L h. On the other hand, the variability in physicochemical properties of vinasse samples led to significant differences in PHB productivity. Lower productivity values were obtained when vinasse had higher values of DBO. Therefore, biopolymers production from vinasse is a feasible alternative to valorize this bioethanol by-product. Graphical Abstract

PCR, PLS, or OPLS Evaluation of different regression techniques for hypothesis generation

In the current era of ‘big data’, scientists are able to quickly amass enormous amount of data in a limited number of experiments. The investigators then try to hypothesize about the root cause based on the observed trends for the predictors and the response variable. This involves identifying the discriminatory predictors that are most responsible for explaining variation in the response variable. In the current work, we investigated three related multivariate techniques: Principal Component Regression (PCR), Partial Least Squares or Projections to Latent Structures (PLS), and Orthogonal Partial Least Squares (OPLS). To perform a comparative analysis, we used a publicly available dataset for Parkinson’ disease patien ts. We first performed the analysis using a cross-validated number of principal components for the aforementioned techniques. Our results demonstrated that PLS and OPLS were better suited than PCR for identifying the discriminatory predictors. Since the X data did not exhibit a strong correlation, we also performed Multiple Linear Regression (MLR) on the dataset. A comparison of the top five discriminatory predictors identified by the four techniques showed a substantial overlap between the results obtained by PLS, OPLS, and MLR, and the three techniques exhibited a significant divergence from the variables identified by PCR. A further investigation of the data revealed that PCR could be used to identify the discriminatory variables successfully if the number of principal components in the regression model were increased. In summary, we recommend using PLS or OPLS for hypothesis generation and systemizing the selection process for principal components when using PCR.rewordexplain later why MLR can be used on a dataset with no correlation

Pemodelan Determinan Pernikahan Dini di Daerah Pedesaan dengan Pendekatan Regresi Logistik Biner

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> The behavior of early marriage in Indonesia is still high and most prevalent in rural areas. In addition to violating the law, a marriage performed before reaching 19 years also has many negative effects. One of them is the death of the mother and the baby. Using data from the Demographic and Health Survey 2017, this study aims to analyze the determinants of early marriages in rural areas in Indonesia. The response variable used is binary categorical data, namely the status of early marriage and not early marriage, so we use a binary logistic regression. The steps performed on this model include estimates of parameters, parameter testing either simultaneously or partially, and a test of the goodness of fit. The results show that the variables of education level, internet access, and wealth index significantly affected early marriage status in rural areas in Indonesia in 2017. Based on the goodness of fit result, this model is proper for modeling early marriage behavior in Indonesia. The study results can be used as a reference for the government in formulating policies to overcome the problem of early marriage in rural areas in Indonesia.</p><p> <strong>Keywords</strong><strong>: </strong>early marriage, rural area, categorical response variable, binary logistic regression</p>

Optimal Quick-Response Variable Structure Control for Highly Efficient Single-Phase Sine-Wave Inverters

This paper puts forward an optimal quick-response variable structure control with a single-phase sine-wave inverter application, which keeps harmonic distortion as low as possible under various conditions of loading. Our proposed solution gives an improvement in architecture in which a quick-response variable structure control (QRVSC) is combined with a brain storm optimization (BSO) algorithm. Notwithstanding the intrinsic resilience of a typical VSC with respect to changes in plant parameters and loading disruptions, the system state convergence towards zero normally proceeds at an infinitely long-time asymptotically, and chattering behavior frequently takes place. The QRVSC for ensuring speedy limited-time convergence with the system state to the balancing point is devised, whilst the BSO will be employed to appropriately regulate the parametric gains in the QRVSC for the elimination of chattering phenomena. From the mix of both a QRVSC together with a BSO, a low total harmonic distortion (THD) as well as a high dynamic response across different types of loading is generated by a closed-loop inverter. The proposed solution is implemented on a practicable single-phase sine-wave inverter under the control of a TI DSP (Texas Instruments Digital Signal Processor). It has experimentally shown the simulation findings as well as the mathematical theoretical analysis, displaying that both quick transient reaction as well as stable performance could be obtained. The proposed solution successfully inhibits voltage harmonics in compliance with IEEE 519-2014’s stringent standard of limiting THD values to less than 5%.

Incorporating Clustering Techniques into GAMLSS

A method for statistical analysis of multimodal and/or highly distorted data is presented. The new methodology combines different clustering methods with the GAMLSS (generalized additive models for location, scale, and shape) framework, and is therefore called c-GAMLSS, for “clustering GAMLSS. ” In this new extended structure, a latent variable (cluster) is created to explain the response-variable (target). Any and all parameters of the distribution for the response variable can also be modeled by functions of the new covariate added to other available resources (features). The method of selecting resources to be used is carried out in stages, a step-based method. A simulation study considering multiple scenarios is presented to compare the c-GAMLSS method with existing Gaussian mixture models. We show by means of four different data applications that in cases where other authentic explanatory variables are or are not available, the c-GAMLSS structure outperforms mixture models, some recently developed complex distributions, cluster-weighted models, and a mixture-of-experts model. Even though we use simple distributions in our examples, other more sophisticated distributions can be used to explain the response variable.