University of North Carolina at Charlotte

About: University of North Carolina at Charlotte is a education organization based out in Charlotte, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8772 authors who have published 22239 publications receiving 562529 citations. The organization is also known as: UNC Charlotte & UNCC.

Rooted in Poverty?: Terrorism, Poor Economic Development, and Social Cleavages 1

Abstract: This study evaluates the popular hypothesis that poverty, inequality, and poor economic development are root causes of terrorism. Employing a series of multiple regression analyses on terrorist incidents and casualties in ninety-six countries from 1986 to 2002, the study considers the significance of poverty, malnutrition, inequality, unemployment, inflation, and poor economic growth as predictors of terrorism, along with a variety of political and demographic control variables. The findings are that, contrary to popular opinion, no significant relationship between any of the measures of economic development and terrorism can be determined. Rather, variables such as population, ethno-religious diversity, increased state repression and, most significantly, the structure of party politics are found to be significant predictors of terrorism. The article concludes that “social cleavage theory” is better equipped to explain terrorism than are theories that link terrorism to poor economic development.

International supply chain agility ‐ Tradeoffs between flexibility and uncertainty

Abstract: Firms operating in an international environment face a host of uncertainties that make it difficult to meet deadlines reliably. To be reliable in an uncertain and changing environment, firms must be able to quickly respond to changes. The ability to do this in a useful time frame is called agility. Unfortunately, measures taken to increase agility often lead to increases in complexity, which works against agility. We propose a theoretical construct linking elements of uncertainty with aspects of agility, pointing out the two‐edged nature of the requisite capabilities. We illustrate our points with examples from five case studies.

Surface texture metrology for metal additive manufacturing: a review

Abstract: A comprehensive analysis of literature pertaining to surface texture metrology for metal additive manufacturing has been performed. This review paper structures the results of this analysis into sections that address specific areas of interest: industrial domain; additive manufacturing processes and materials; types of surface investigated; surface measurement technology and surface texture characterisation. Each section reports on how frequently specific techniques, processes or materials have been utilised and discusses how and why they are employed. Based on these results, possible optimisation of methods and reporting is suggested and the areas that may have significant potential for future research are highlighted.

Time series classification using multi-channels deep convolutional neural networks

Abstract: Time series (particularly multivariate) classification has drawn a lot of attention in the literature because of its broad applications for different domains, such as health informatics and bioinformatics. Thus, many algorithms have been developed for this task. Among them, nearest neighbor classification (particularly 1-NN) combined with Dynamic Time Warping (DTW) achieves the state of the art performance. However, when data set grows larger, the time consumption of 1-NN with DTW grows linearly. Compared to 1-NN with DTW, the traditional feature-based classification methods are usually more efficient but less effective since their performance is usually dependent on the quality of hand-crafted features. To that end, in this paper, we explore the feature learning techniques to improve the performance of traditional feature-based approaches. Specifically, we propose a novel deep learning framework for multivariate time series classification. We conduct two groups of experiments on real-world data sets from different application domains. The final results show that our model is not only more efficient than the state of the art but also competitive in accuracy. It also demonstrates that feature learning is worth to investigate for time series classification.