Industrial internet of things (IIoT) has ushered us into a world where most machine parts are now embedded with sensors that collect data. This huge data reservoir has enhanced data-driven diagnostics and prognoses of machine health. With technologies like cloud or centralized computing, the data could be sent to powerful remote data centers for machine health analysis using artificial intelligence (AI) tools. However, centralized computing has its own challenges, such as privacy issues, long latency, and low availability. To overcome these problems, edge computing technology was embraced. Thus, instead of moving all the data to the remote server, the data can now transition on the edge layer where certain computations are done. Thus, access to the central server is infrequent. Although placing AI on edge devices aids in fast inference, it poses new research problems, as highlighted in this paper. Moreover, the paper discusses studies that use edge computing to develop artificial intelligence-based diagnostic and prognostic techniques for industrial machines. It highlights the locations of data preprocessing, model training, and deployment. After analysis of several works, trends of the field are outlined, and finally, future research directions are elaborated

This article presents a new method for prediction of an event involving a future bivariate observation. The method combines nonparametric predictive inference (NPI) applied to the marginals with a parametric copula to model and estimate the dependence structure between two random quantities, as such, the method is semiparametric. In NPI, uncertainty is quantified through imprecise probabilities. The resulting imprecision in the marginals provides robustness with regard to the assumed parametric copula. Due to the specific nature of NPI, the estimation of the copula parameter is also quite straightforward. The performance of this method is investigated via simulations, with particular attention to robustness with regard to the assumed copula in case of small data sets. The method is further illustrated via two examples, using small data sets from the literature. This article presents several novel aspects of statistical inference. First, the link between NPI and copulas is powerful and attractive with regard to computation. Second, statistical methods using imprecise probability have gained substantial attention in recent years, where typically imprecision is used on aspects for which less information is available. This article presents a different approach, namely, imprecision mainly being introduced on the marginals, for which there is typically quite sufficient information, in order to provide robustness for the harder part of the inference, namely the copula assumptions and estimation. Third, the setup of the simulations to evaluate the performance of the proposed method is novel; key to these are frequentist comparisons of the success proportion of predictions with the corresponding data-based lower and upper predictive inferences. All these novel ideas can be applied far more generally to other inferences and models, while also many alternatives can be considered. Hence, this article presents the starting point of an extensive research program towards powerful predictive inference methods for multivariate data.

Background: Modifiable risk factors are associated with cardiovascular mortality (CVM) which is a leading form of global mortality. However, diverse nature of urbanization and its objective measurement can modify their relationship. This study aims to investigate the moderating role of urbanization in the relationship of combined exposure (CE) of modifiable risk factors and CVM. Design and methods: This is the first comprehensive study which considers different forms of urbanization to gauge its manifold impact. Therefore, in addition to existing original quantitative form and traditional two categories of urbanization, a new form consisted of four levels of urbanization was duly introduced. This study used data of 129 countries mainly retrieved from a WHO report, Non-Communicable Diseases Country Profile 2014. Factor scores obtained through confirmatory factor analysis were used to compute the CE. Age-income adjusted regression model for CVM was tested as a baseline with three bootstrap regression models developed for the three forms of urbanization. Results: Results revealed that the CE and CVM baseline relationship was significantly moderated through the original quantitative form of urbanization. Contrarily, the two traditional categories of urbanization could not capture the moderating impact. However, the four levels of urbanization were objectively estimated the urbanization impact and subsequently indicated that the CE was more alarming in causing the CVM in levels 2 and 3 urbanized countries, mainly from low-middle-income countries. Conclusion: This study concluded that the urbanization is a strong moderator and it could be gauged effectively through four levels whereas sufficiency of two traditional categories of urbanization is questionable.

A Comparative Study on Box-Jenkins and Garch Models in Forecasting Crude Oil Prices