The question of the ergonomic use of virtual models

The comfort and functionality between man and his surroundings are the main goals of ergonomics, making it are increasingly in the production process. Many companies have tried Virtual Reality Center (VRC) to assist the analysis and development of ergonomic products. The use of virtual models brings many benefits to the design of products, including greater flexibility, speed and quality, and facilitates the control and reporting, also integrating all stages of the project. There are still some difficulties for the deployment of these resources, like the need for more powerful computers and specialized professionals. Possibly, the VR will be indispensable to the construction in some years, becoming a tool of fundamental importance to the professionals.     

On Asymptotic Normality of the Local Polynomial Regression Estimator with Stochastic Bandwidths

Nonparametric density and regression estimators commonly depend on a bandwidth. The asymptotic properties of these estimators have been widely studied when bandwidths are non stochastic. In practice, however, in order to improve finite sample performance of these estimators, bandwidths are selected by data driven methods, such as cross-validation or plug-in procedures. As a result, nonparametric estimators are usually constructed using stochastic bandwidths. In this article, we establish the asymptotic equivalence in probability of local polynomial regression estimators under stochastic and nonstochastic bandwidths. Our result extends previous work by Boente and Fraiman (1995 Boente , G. , Fraiman , R. ( 1995 ). Asymptotic distribution of data-driven smoothers in density and regression estimation under dependence . Can. J. Statist. 23 : 383 – 397 .[Crossref], [Web of Science ®] , [Google Scholar]) and Ziegler (2004 Ziegler , K. ( 2004 ). Adaptive kernel estimation of the mode in nonparametric random design regression model . Probab. Mathemat. Statist. 24 : 213 – 235 . [Google Scholar]).     

Optimal Design of Acceptance Sampling Plans by Variables for Nonconforming Proportions When the Standard Deviation Is Unknown

This article presents an optimization-based approach for the design of acceptance sampling plans by variables for controlling nonconforming proportions when the standard deviation is unknown. The variables are described by rigorous noncentral Student’s t-distributions. Single and double acceptance sampling (AS) plans are addressed. The optimal design results from minimizing the average sampling number (ASN), subject to conditions holding at producer’s and consumer’s required quality levels. The problem is then solved employing a nonlinear programming solver. The results obtained are in close agreement with previous sampling plans found in the literature, outperforming them regarding the feasibility.     

Aggregated functional data model for near-infrared spectroscopy calibration and prediction

Calibration and prediction for NIR spectroscopy data are performed based on a functional interpretation of the Beer–Lambert formula. Considering that, for each chemical sample, the resulting spectrum is a continuous curve obtained as the summation of overlapped absorption spectra from each analyte plus a Gaussian error, we assume that each individual spectrum can be expanded as a linear combination of B-splines basis. Calibration is then performed using two procedures for estimating the individual analytes’ curves: basis smoothing and smoothing splines. Prediction is done by minimizing the square error of prediction. To assess the variance of the predicted values, we use a leave-one-out jackknife technique. Departures from the standard error models are discussed through a simulation study, in particular, how correlated errors impact on the calibration step and consequently on the analytes’ concentration prediction. Finally, the performance of our methodology is demonstrated through the analysis of two publicly available datasets.     

A strange tale

We are going through such challenging times that dealing with reality is sometimes easier through fiction. Therefore, we tell the tale of a faraway land where poor women — mostly black — who make a living as domestic servants are particularly impacted by the COVID‐19 pandemic. Their abject bodies are part of a form of necropolitics that separates those who must live from those who can die. Their bodies do not matter, as they are perceived as mere working tools. And what sounds like a strange tale is the true reality of millions of women during the pandemic, in a land that is not so far away as it seems.     

A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling

In this paper, we consider a Bayesian mixture model that allows us to integrate out the weights of the mixture in order to obtain a procedure in which the number of clusters is an unknown quantity. To determine clusters and estimate parameters of interest, we develop an MCMC algorithm denominated by sequential data-driven allocation sampler. In this algorithm, a single observation has a non-null probability to create a new cluster and a set of observations may create a new cluster through the split-merge movements. The split-merge movements are developed using a sequential allocation procedure based in allocation probabilities that are calculated according to the Kullback–Leibler divergence between the posterior distribution using the observations previously allocated and the posterior distribution including a ‘new’ observation. We verified the performance of the proposed algorithm on the simulated data and then we illustrate its use on three publicly available real data sets.     

GMM estimation of spatial autoregressive models in a system of simultaneous equations with heteroskedasticity

This paper proposes a GMM estimation framework for the SAR model in a system of simultaneous equations with heteroskedastic disturbances. Besides linear moment conditions, the proposed GMM estimator also utilizes quadratic moment conditions based on the covariance structure of model disturbances within and across equations. Compared with the QML approach, the GMM estimator is easier to implement and robust under heteroskedasticity of unknown form. We derive the heteroskedasticity-robust standard error for the GMM estimator. Monte Carlo experiments show that the proposed GMM estimator performs well in finite samples.     

建设项目的费用控制以及项目费用管理系统的需求

对于许多项目而言,费用严重超支、交付成果低劣的现象更凸显了需要对项目管理流程进行有效的管理和控制。本文叙述了项目从概念设计阶段到完工整个生命周期的有效费用控制,并介绍了进行这种控制所需要的项目费用管理系统在大型建设项目上的应用。     

Psychological Interventions for Transgender Persons: A Scoping Review

This study aimed to investigate psychological interventions for transgender persons, aiming to highlight their goals, theoretical background and methods. A scoping review was conducted in four databases, ranging from 1980 to 2015. Selection led to a final sample of 36 articles. The goals of psychological interventions are unclearly described in many studies. Different background theories were identified and in some studies, these were not clearly explained and presented no epistemological considerations. The publishing of more empirical studies, with an affirmative standpoint toward gender identity and stricter scientific rigor, is needed to support and strengthen interventions.