Explaining Income Inequalities in Developing Countries: the Role of Human Capital

Explaining Income Inequalities in Developing Countries: the Role of Human Capital

Parametric study of self -consolidating concrete

The research investigation presented herein was intended to study the influence of parameters, such as aggregate size, admixture source, hauling time, and temperature on the fresh and hardened properties of three distinct groups of self-consolidating concretes (SCC). Within each group, the selected SCCs were made with a constant water-to-cementitious materials ratio, a uniform cementitious materials (cement and fly ash) content, and a constant coarse-to-fine aggregate ratio that provided the optimum aggregate gradation. Three coarse aggregate sizes (ASTM C 33 #8, #7, and #67) obtained from two different quarries were investigated. Four sources of polycarboxylate-based high range water-reducing admixtures (HRWRA) and viscosity modifying admixtures (VMA) were used. All raw materials were evaluated for their physicochemical characteristics; The investigation presented herein was divided into two major phases. The first phase aimed at: (1) comparing the optimum dosage requirements of four different sources of polycarboxylate-based HRWRA and VMA in attaining the target slump flow of 508 mm (20 inches), 635 mm (25 inches), and 711 mm (28 inches), T50 of 2 seconds or more, and a visual stability index (VSI) of 0 (Highly stable concrete) or 1 (Stable concrete), (2) evaluating the flow rate/plastic viscosity, static and dynamic stabilities, passing ability, and filling ability of the selected self-consolidating concretes, and (3) examining the properties of the trial self-consolidating concretes as related to air content, bleeding, time of setting, adiabatic temperature, demolded unit weight, compressive strength and modulus of elasticity; In the second phase, the influence of hauling time, temperature, and combined hauling time and temperature on the fresh properties of the selected self-consolidating concretes was evaluated. Seven different temperatures (43, 36, 28, 21, 14, 7, and -0.5°C (109, 96, 83, 70, 57, 44, and 31°F)) and nine different hauling times (10, 20, 30, 40, 50, 60, 70, 80, and 90 minutes) were used to determine the loss in unconfined workability, dynamic stability, and flow ability rate of the designed matrices. The adverse effect of the above-mentioned variables was remediated by way of overdosing and retempering techniques which resulted in achieving the desired fresh characteristics of the designed self-consolidating concretes for different hauling times and temperatures

Immigration Attorneys\u27 Perceptions and Attitudes About Delays in Removal Proceeding Hearings

Abstract Immigration courts in the United Sates are struggling to resolve 610,524 removal proceedings cases with approximately 330 judges located in 58 immigration courts nationwide. Due to the limited number of judges, case backlogs have increased steadily, with the wait time being 854 days in 2017 for the first hearing and much longer for case resolution. The purpose of this case study was to explore the perceptions and attitudes of immigration attorneys about delays in removal proceeding hearings in an immigration court in the southwest. Kettl\u27s transformation of governance theory served as the theoretical foundation for this study, which explored immigration attorneys\u27 perceptions about the effects of delays on the welfare of immigrant clients, the effects of delays on client-attorney relationships, and potential solutions to the delay crisis. Data were collected through semistructured interviews with a snowball sample of 10 participants as well as deportation hearing observations and court document reviews. Data were analyzed using the open coding technique. Findings indicated that legal representation was challenging for undocumented immigrants as the lack of proper documents often dissuaded immigrants from seeking legal guidance and they experienced challenges in navigating workplaces, schools, and society. Findings also indicated inadequacies in immigration courts and the need for more funding and resources such as judges, staff training, online application submission system, and judicial system restructuring. The implications for positive social change are directed at immigration policymakers and decision makers as a better understanding of the delay crisis may help them to focus attention and resources in helping to reduce the backlog and improve the judicial process

Immigration Attorneys’ Perceptions and Attitudes about Delays in Removal Proceedings Hearings

The problem investigated in this study is the myriad of backlogged cases that immigration courts in the United States (U.S.) need to adjudicate. Participants shared that due to the overwhelming number of undocumented immigrants, government agencies are overburdened with identifying, vetting, and processing deportations, putting a strain on the resources allocated. The participants expressed genuine concern for the well-being of their clients and often discussed the ways in which they sought to help their clients.https://scholarworks.waldenu.edu/symposium2018/1007/thumbnail.jp

Supervised Classification Using Finite Mixture Copula

Use of copula for statistical classification is recent and gaining popularity. For example, statistical classification using copula has been proposed for automatic character recognition, medical diagnostic and most recently in data mining. Classical discrimination rules assume normality. But in this data age time, this assumption is often questionable. In fact features of data could be a mixture of discrete and continues random variables. In this paper, mixture copula densities are used to model class conditional distributions. Such types of densities are useful when the marginal densities of the vector of features are not normally distributed and are of a mixed kind of variables. Authors have shown that such mixture models are very useful for uncovering hidden structures in the data, and used them for clustering in data mining. Under such mixture models, maximum likelihood estimation methods are not suitable and regular expectation maximization algorithm is inefficient and may not converge. A new estimation method is proposed to estimate such densities and build the classifier based on mixture finite Gaussian densities. Simulations are used to compare the performance of the copula based classifier with classical normal distribution based models, logistic regression based model and independent model cases. The method is also applied to a real data

Linear Dependency for the Difference in Exponential Regression

In the field of reliability, a lot has been written on the analysis of phenomena that are related. Estimation of the difference of two population means have been mostly formulated under the no-correlation assumption. However, in many situations, there is a correlation involved. This paper addresses this issue. A sequential estimation method for linearly related lifetime distributions is presented. Estimations for the scale parameters of the exponential distribution are given under square error loss using a sequential prediction method. Optimal stopping rules are discussed using concepts of mean criteria, and numerical results are presented

Copula-Based Models for Bivariate and Multivariate Zero-inflated Count Time Series Data

Count time series data have multiple applications. The applications can be found in areas of finance, climate, public health and crime data analyses. In some scenarios, count time series come as multivariate vectors that exhibit not only serial dependence within each time series but also with cross correlation among the series. When considering these observed counts, analysis presents crucial challenges when a value, say zero, occurs more often than usual. There is presence of zero-inflation in the data. In this presentation, we mainly focus on modeling bivariate zero-inflated count time series model based on a joint distribution of the two consecutive observations. The bivariate zero-inflated models are constructed through copula functions. Such Gaussian copula can accommodate both serial dependence and cross-sectional dependence in zero-inflated count time series data. We consider the first order Markov chains with zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB) and zero-inflated Conway-Maxwell-Poisson (ZICMP) marginals. Bivariate copula functions such as the bivariate Gaussian and t-copula are chosen to construct the distribution of consecutive observations. Likelihood based inference is used to estimate the model parameters with the bivariate integrals of the Gaussian or t-copula functions being evaluated using standard randomized importance sampling method. To evaluate the superiority of the model, simulated (under positive and negative cross-correlations) are provided and presented. Real data examples are also shared. Extensions for high dimensional scenarios are discussed by introducing the copula autoregressive model (COPAR) with pair copula construction and vine tree structure. Structure matrices of the COPAR of orders 1 and 2 are shown. Simulations are conducted to validate the models.https://digitalcommons.odu.edu/gradposters2023_sciences/1026/thumbnail.jp

On Weighted Distributions and Mean Advantage Over Inferiors Functions

In this note, some fundamental results including relationship be-tween weighted distribution functions and mean advantage over inferi-ors functions are established. Ordering of reliability and/or distribution functions via mean advantage over inferiors functions and related func-tions for parent and weighted reliability functions are presented. Some applications and examples are given

The Joint Distribution of Bivariate Exponential Under Linearly Related Model

In this paper, fundamental results of the joint distribution of the bivariate exponential distributions are established. The positive support multivariate distribution theory is important in reliability and survival analysis, and we applied it to the case where more than one failure or survival is observed in a given study. Usually, the multivariate distribution is restricted to those with marginal distributions of a specified and familiar lifetime family. The family of exponential distribution contains the absolutely continuous and discrete case models with a nonzero probability on a set of measure zero. Examples are given, and estimators are developed and applied to simulated data. Our findings generalize substantially known results in the literature, provide flexible and novel approach for modeling related events that can occur simultaneously from one based event