IEEE PELS Atlanta Chapter Hosts In-Person PCB Design and Manufacturing Workshop

The IEEE Power Electronics Society (PELS) Atlanta Chapter recently partnered with the NCAB Group to deliver a workshop on printed circuit board (PCB) design and manufacturing on 4 October 2022. The event was held at Georgia Institute of Technology (Georgia Tech) and was significant for the PELS Atlanta Chapter because it was its first in-person event since the Covid-19 pandemic and one of the IEEE Day events.

Discovery and translation of functional nucleic acids for clinically diagnosing infectious diseases: Opportunities and challenges

Functional nucleic acids (FNAs) are short, single-stranded nucleic acids that can be derived from synthetic nucleic acid libraries using test-tube selection experiments. Due to their excellent chemical stability, high binding affinities and specificities, compatibility with a variety of signal-transduction mechanisms, and ease of synthesis and modification, FNAs have a great potential to overcome some of the limitations of current pathogen diagnostic methods by acting as molecular recognition elements (MREs) for point-of-care testing. This review summarizes the development of FNA-based biosensors for viral and bacterial detection in clinical samples. We first discuss examples of selecting FNAs for recognizing biomarkers of viral and bacterial pathogens. This is followed by discussion on integrating FNAs into fluorescent, colorimetric, and electrochemical biosensors and applying these sensors towards clinically diagnosing infectious diseases caused by many important bacterial and viral pathogens. Finally, the challenges of making FNA-based biosensors for infectious diseases are provided. (c) 2022 Elsevier B.V. All rights reserved.

A Deep Spatio-Temporal Fuzzy Neural Network for Subway Passenger Flow Prediction with COVID-19 Search Engine Data

Passenger flow prediction is of great significance in the operation and management of subways, especially in reducing energy consumption and improving service quality. Due to the impact of COVID-19, subway passenger flow fluctuates a lot, which makes passenger flow estimation or forecasting a very challenging task. This paper mainly carries out two aspects of work to solve the task of subway passenger flow prediction under pandemic. First, this paper introduces search engine data as a new data source and provides a systematic method to extract valid quires and search volumes that are closely associated with subway passenger flow under pandemic. Second, this paper combines the fuzzy theory and neural network to propose a deep learning architecture called ‘Deep Spatio-Temporal Fuzzy Neural Network (DST-FNN)’to deal with the complex Spatio-temporal features and uncertain external data of subway passenger flow prediction. Experiments on the actual data set of the Beijing subway prove the superiority of the model and the effectiveness of search engine data in subway passenger flow forecasting. IEEE

Evaluating YouTube as a source of information on COVID-19: analysis with latent Dirichlet allocation method.

OBJECTIVE: The global impact of COVID-19 pandemic has gained momentum rapidly. People have little information about SARS-CoV-2 (Coronavirus). Internet has become a frequently used tool to obtain information in recent years, while YouTube is one of the popular sources of information with many videos on its platform. This study aims to identify the topics regarding Coronavirus that people learned about on YouTube. The videos about Coronavirus were also evaluated in terms of the reliability of their source of information. METHODS: In total, 160 videos on Coronavirus that had 500,000 or more views were analysed. The latent Dirichlet allocation method was used in the process of identifying the topics that were then compared in terms of video parameters. The reliability of the source of information provided by videos was assessed with a modified DISCERN tool. RESULTS: A proportion of 15.6 % of these videos had a scientific content, while 45 % of these videos were about the process entailed by the COVID-19 pandemic. In terms of video reliability, the difference between video types was found to be significant; videos with scientific content had more reliable sources of information (p<0.001). CONCLUSION: It has been determined that the videos about the symptoms, diagnosis and treatment of COVID-19, and those with scientific content have the most reliable source of information on Coronavirus (Tab. 5, Fig. 1, Ref. 35). Text in PDF www.elis.sk Keywords: coronavirus, SARS-CoV-2, COVID-19 pandemic, coronavirus pandemic, latent Dirichlet allocation, information, YouTube.

[Using Markov Chain Monte Carlo methods to estimate the age-specific case fatality rate of COVID-19].

Objectives: The COVID-19 epidemic has swept all over the world. Estimates of its case fatality rate were influenced by the existing confirmed cases and the time distribution of onset to death, and the conclusions were still unclear. This study was aimed to estimate the age-specific case fatality rate of COVID-19. Methods: Data on COVID-19 epidemic were collected from the National Health Commission and China CDC. The Gamma distribution was used to fit the time from onset to death. The Markov Chain Monte Carlo simulation was used to estimate age-specific case fatality rate. Results: The median time from onset to death of COVID-19 was M=13.77 (P(25)-P(75): 9.03-21.02) d. The overall case fatality rate of COVID-19 was 4.1% (95%CI: 3.7%-4.4%) and the age-specific case fatality rate were 0.1%, 0.4%, 0.4%, 0.4%,0.8%, 2.3%, 6.4%, 14.0 and 25.8% for 0-, 10-, 20-, 30-, 40-, 50-, 60-, 70- and ≥80 years group, respectively. Conclusions: The Markov Chain Monte Carlo simulation method adjusting censored is suitable for case fatality rate estimation during the epidemic of a new infectious disease. Early identification of the COVID-19 case fatality rate is helpful to the prevention and control of the epidemic.