The development of Taiwanese non-profit food banks during the world pandemic: Crisis and opportunities

The world has been affected badly by Covid-19 in 2020. Taiwan, fortunately, has not been that badly affected by the virus. However, there are questions surrounding how the pandemic has impacted Taiwanese food banks so far and whether Taiwanese food banks have experienced any crisis caused by the pandemic or gained momentum owing to the relatively safer circumstances. Based on practices, interviews, and questionnaires, this research aims to answer them. The results show most food banks were more or less impacted by the coronavirus between February and early May. The majority of food banks reported an increase in the recipients of their help and did not report a shortage of volunteers. In 2020, more food banks have been established and many existing food banks have expanded their services. Food Banking is gaining popularity and recognition in Taiwan. © 2023 Author(s).

Community vulnerability is the key determinant of diverse energy burdens in the United States

Low-income households generally experience a high energy burden;however, the factors influencing energy burdens are beyond socio-economics. This study explores the relationships between the multidimensionality of community vulnerability factors and energy burden across multiple geospatial levels in the United States. Our study found the distribution of energy burden in 2020 showed a great deal of variety, ranging from a minimum of 2.93 % to a maximum of 30.45 % across 3142 counties. The results of non-spatial and spatial regressions showed that the vulnerability ranks of socioeconomic, household composition and disability, minority and language, household type and transportation, and COVID mortality rate are significant predictors of energy burdens at the national level. However, at the regional level, only socioeconomic, minority and language significantly influence energy burdens. Minority and language negatively impact energy burdens except for the South East-Central region. Additionally, our analyses highlight the need to consider community vulnerability indicators' spatial homogeneity and heterogeneity. At the national level, only the epidemiological factors index is a spatially homogeneous predictor;on the regional and state level, the spatially homogeneous predictors such as socioeconomic status, household composition and disability, and household type and transportation vary by region. Such a region-sensitive relationship between energy burden and the predictors indicates spatial heterogeneity. This study suggests policy recommendations through the lens of the multidimensionality of community vulnerability factors. Implementing flexible national energy policies while making particular energy assistance policies for the vulnerable population at the regional or state levels is essential. © 2023

A Cyber-Physical-Social Perspective on Future Smart Distribution Systems

An increasing number of distributed energy resources (DERs), such as rooftop photovoltaic (PV), electric vehicles (EVs), and distributed energy storage, are being integrated into the distribution systems. The rise of DERs has come hand-in-hand with large amounts of data generated and explosive growth in data collection, communication, and control devices. In addition, a massive number of consumers are involved in the interaction with the power grid to provide flexibility. Electricity consumers, power networks, and communication networks are three main parts of the distribution systems, which are deeply coupled. In this sense, smart distribution systems can be essentially viewed as cyber-physical-social systems. So far, extensive works have been conducted on the intersection of cyber, physical, and social aspects in distribution systems. These works involve two or three of the cyber, physical, and social aspects. Having a better understanding of how the three aspects are coupled can help to better model, monitor, control, and operate future smart distribution systems. In this regard, this article provides a comprehensive review of the coupling relationships among the cyber, physical, and social aspects of distribution systems. Remarkably, several emerging topics that challenge future cyber-physical-social distribution systems, including applications of 5G communication, the impact of COVID-19, and data privacy issues, are discussed. This article also envisions several future research directions or challenges regarding cyber-physical-social distribution systems.

Towards an Electrochemical Immunosensor for the Detection of Antibodies against SARS-CoV-2 Spike Protein

Herein we report the electrochemical system for the detection of specific antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins in blood serum patient samples after coronavirus disease 2019 (COVID-19). For this purpose, the recombinant SARS-CoV-2 spike protein (SCoV2-rS) was covalently immobilised on the surface of the gold electrode pre-modified with mixed self-assembled monolayer (SAMmix) consisting of 11-mercaptoundecanoic acid and 6-mercapto-1-hexanol. The affinity interaction of SCoV2-rS with specific antibodies against this protein (anti-rS) was detected using two electrochemical methods: cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The anti-rS was detected with a detection limit of 2.53 nM and 1.99 nM using CV and EIS methods, respectively. The developed electrochemical immunosensor is suitable for the confirmation of COVID-19 infection or immune response in humans after vaccination.

Using Content Analysis and Machine Learning to Identify COVID-19 Information Relevant to Low-income Households on Social Media

COVID-19 pandemic has caused great distress in the lives of many populations. Low-income households are among the most severely impacted groups in the United States and across the globe. Using social media, this paper aims to identify and organize the information about the impact of the pandemic on low-income households. We use content analysis to derive an annotation protocol and manually annotate a tweet dataset using this protocol. Furthermore, we use machine learning to learn models from the annotated dataset. We also employ a human-in-the-loop data augmentation procedure to improve the model's performance for the underrepresented classes. Our results show that using carefully annotated data, automated machine learning models can be trained and employed to identify information relevant to low income households, potentially in real time. © 2021 IEEE.