Paid Sick Days: Attitudes and Experiences

Analyzes survey findings on Americans' views on the importance of paid sick days as a basic workers' right and support for legislation guaranteeing paid sick days by age, race/ethnicity, income, education, family structure, and political affiliation

Effective Scheduling of Grid Resources Using Failure Prediction

In large-scale grid environments, accurate failure prediction is critical to achieve effective resource allocation while assuring specified QoS levels, such as reliability. Traditional methods, such as statistical estimation techniques, can be considered to predict the reliability of resources. However, naive statistical methods often ignore critical characteristic behavior of the resources. In particular, periodic behaviors of grid resources are not captured well by statistical methods. In this paper, we present an alternative mechanism for failure prediction. In our approach, the periodic pattern of resource failures are determined and actively exploited for resource allocation with better QoS guarantees. The proposed scheme is evaluated under a realistic simulation environment of computational grids. The availability of computing resources are simulated according to real trace that was collected from our large-scale monitoring experiment on campus computers. Our evaluation results show that the proposed approach enables significantly higher resource scheduling effectiveness under a variety of workloads compared to baseline approaches

An Efficient Approach for Solving Mesh Optimization Problems Using Newton’s Method

We present an efficient approach for solving various mesh optimization problems. Our approach is based on Newton’s method, which uses both first-order (gradient) and second-order (Hessian) derivatives of the nonlinear objective function. The volume and surface mesh optimization algorithms are developed such that mesh validity and surface constraints are satisfied. We also propose several Hessian modification methods when the Hessian matrix is not positive definite. We demonstrate our approach by comparing our method with nonlinear conjugate gradient and steepest descent methods in terms of both efficiency and mesh quality

Learning Delaunay Triangulation using Self-attention and Domain Knowledge

Delaunay triangulation is a well-known geometric combinatorial optimization problem with various applications. Many algorithms can generate Delaunay triangulation given an input point set, but most are nontrivial algorithms requiring an understanding of geometry or the performance of additional geometric operations, such as the edge flip. Deep learning has been used to solve various combinatorial optimization problems; however, generating Delaunay triangulation based on deep learning remains a difficult problem, and very few research has been conducted due to its complexity. In this paper, we propose a novel deep-learning-based approach for learning Delaunay triangulation using a new attention mechanism based on self-attention and domain knowledge. The proposed model is designed such that the model efficiently learns point-to-point relationships using self-attention in the encoder. In the decoder, a new attention score function using domain knowledge is proposed to provide a high penalty when the geometric requirement is not satisfied. The strength of the proposed attention score function lies in its ability to extend its application to solving other combinatorial optimization problems involving geometry. When the proposed neural net model is well trained, it is simple and efficient because it automatically predicts the Delaunay triangulation for an input point set without requiring any additional geometric operations. We conduct experiments to demonstrate the effectiveness of the proposed model and conclude that it exhibits better performance compared with other deep-learning-based approaches

Social-science research and the general social surveys

'Social-science research has been transformed over the last generation by the advent and expansion of the general social surveys (GSS). The GSS model of research has created a infrastructure for the social sciences designed to address the interests and research agenda of scholars and their students; cover a wide range of topics; utilize reliable, valid, and generalizable measurement; and provide data both across nations and across time. This design in turn has generated widespread analysis and notably contributed to our understanding of social processes and societal change.' (author's abstract)

A Derivative-Free Mesh Optimization Algorithm for Mesh Quality Improvement and Untangling

We propose a derivative-free mesh optimization algorithm, which focuses on improving the worst element quality on the mesh. The mesh optimization problem is formulated as a min-max problem and solved by using a downhill simplex (amoeba) method, which computes only a function value without needing a derivative of Hessian of the objective function. Numerical results show that the proposed mesh optimization algorithm outperforms the existing mesh optimization algorithm in terms of improving the worst element quality and eliminating inverted elements on the mesh

The general social survey-national death index: an innovative new dataset for the social sciences

<p>Abstract</p> <p>Background</p> <p>Social epidemiology seeks in part to understand how social factors--ideas, beliefs, attitudes, actions, and social connections--influence health. However, national health datasets have not kept up with the evolving needs of this cutting-edge area in public health. Sociological datasets that do contain such information, in turn, provide limited health information.</p> <p>Findings</p> <p>Our team has prospectively linked three decades of General Social Survey data to mortality information through 2008 via the National Death Index. In this paper, we describe the sample, the core elements of the dataset, and analytical considerations.</p> <p>Conclusions</p> <p>The General Social Survey-National Death Index (GSS-NDI), to be released publicly in October 2011, will help shape the future of social epidemiology and other frontier areas of public health research.</p

An Iterative Mesh Untangling Algorithm Using Edge Flip

Existing mesh untangling algorithms are unable to untangle highly tangled meshes. In this study, we address this problem by proposing an iterative mesh untangling algorithm using edge flip. Our goal is to produce meshes with no inverted elements and good element qualities when inverted elements with poor element qualities are produced during mesh generation or mesh deformation process. Our proposed algorithm is composed of three steps: first, we iteratively perform edge flip; subsequently, optimization-based mesh untangling is conducted until all inverted elements are eliminated; finally, we perform mesh smoothing for generating high-quality meshes. Numerical results show that the proposed algorithm is able to successfully generate high-quality meshes with no inverted elements for highly tangled meshes

Is safety education in the E-learning environment effective? Factors affecting the learning outcomes of online laboratory safety education

Safety education is essential to prevent accidents and casualties in modern society. The coronavirus disease (COVID-19) pandemic has increased the importance of e-learning in safety education. However, there is limited research on the effectiveness of this type of education. This study aims to develop a model that predicts the factors affecting the learning outcomes of safety education in an e-learning environment. An online survey of research workers who participated in laboratory safety education at a university was conducted to empirically test the developed model. Partial least squares-based structural equation modeling (PLS-SEM) was used for analysis and hypothesis testing. Face-to-face interviews were conducted to supplement the findings. The results revealed the following. (1) Intention to use online laboratory safety education (OLSE) was significantly and positively predicted by attitude toward online education and satisfaction, but not by attitude toward safety education. (2) Respondents preferred safety education through e-learning not because of its effectiveness, but to avoid the stress and inconvenience of traditional training. (3) Negative perceptions of education and lack of motivation to learn are the root causes of low effectiveness. The findings revealed the current status of OLSE with deficient learning effects and learners&apos; negative perceptions. This study identifies the adverse effects of mandatory safety education in e-learning environments. This study provides insights into the low effectiveness of mandated online safety education and argues for the need to increase learner motivation and improve legislation. We suggest several practical implications to improve the effectiveness of such education

Disability of Older Koreans: Evidence on Prevalence and the Role of Education from Five Data Sets

This paper investigates how educational attainment may affect the prevalence of disability among older Koreans, a population for whom the association between health and education has been little studied. It performs descriptive and logistic regression analysis on five nationally representative data sets, all collected between 2004 and 2006, regarding education and disability among Koreans at least 65 years of age. It finds the relationship between education and disability to be strongest between less than primary school graduates and primary school graduates. Beyond the primary school level, the educational gradient on disability is weak.