Tutorials

A New Signal Analysis ARS and Its Applications for IoT with Machine Learning Techniques

Abstract. We proposed a new method for signal analysis termed as Accumulation for real-time serial-to-parallel converter (ARS) which is suitable for applications on the Internet of Things (IoT). ARS is advantageous in terms of the low computational complexity and the high resolution in low frequency band, comparing with conventional methods such as the discrete Fourier transform (DFT). Such features of ARS enable us, not only to realize edge computing architecture, but also to exploit ARS as a new method for producing features to be fed into machine learning techniques. This course delivers the principle of ARS and one of the future applications of ARS to the emotion estimation.

COVID-19, modeling, logistic systems, and social responsibility: A Holistic Overview

Abstract. The COVID-19 brought many dangers and uncertainties to life and livelihood. The emergence of COVID-19 revealed a higher degree of disruptions at multiple levels in comparison to outbreaks that had happened previously. The reason is in today’s interconnected world, where many socio-spatial, ecological, and economic factors play a role as catalysts for speeding epidemic outbreaks. Globalization, population mobility, population density, climate change, and urbanization are a few examples of such catalysts that accelerate the spread of communicable diseases. The supply chains (SC) and logistic operations (LO) are of utmost importance to controlling any pandemics, be it before, during, or after outbreaks. For example, if anthrax spreads, the procurement and distribution of medications must be there within two days, while if smallpox spreads, there is a need for vaccinating the population within only four days. Therefore, establishing the LO’s decision-support systems (DSS), evaluating the current situation, forecasting the spread of the outbreak, building emergency SC mechanisms, and deploying monitoring technologies are central to controlling pandemics. In order to efficiently manage the epidemic and analyze and forecast its dimensions, mathematical epidemic models play a central role. There is also a strong challenge to effectively and holistically link the influence of COVID-19 dynamics with the dynamics of material flows running via SCs to make the right prediction of possible scenarios projected into the future. In these contexts, the contribution is dedicated to reviewing mathematical epidemiology models most commonly applied to deal with COVID-19. The paper also investigates the literature about modeling approaches conducted in the context of SCs and logistic systems and the impact of the pandemic on social responsibility.