A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough

The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.

On the Volatility and Market Inefficiency of Bitcoin During the COVID-19 Pandemic

The COVID-19 pandemic has been causing unprecedented economic downturn worldwide. As it wreaks havoc on every aspect of global economic activities, stakeholders are wondering how its impact can be quantified to craft viable responses. In the exotic field of cryptocurrencies, prior to the pandemic, everyone was excited about Bitcoin and its multitude of potentials. However, a day after COVID-19 was officially announced by the World Health Organization as a pandemic, the rate of return to Bitcoin dropped by an unheard-of one-day decline of-46.5%, and people started to rethink the prospects of Bitcoin. A day after this steep decline, Bitcoin recovered and started a sustained bull run which lasted for almost a year and even posted an all-time high daily uptick of 59.6%. By the end of July 2021, the price reached its all-time high but lost more than half of it at the end of the sample period. This study aims to empirically analyze the risk-return profile and the market efficiency of Bitcoin utilizing a 1,306-day data set conveniently subdivided into pre-pandemic and pandemic periods. The general conclusion of the study is: During the pandemic, Bitcoin is extremely volatile and does not subscribe to the efficient market hypothesis. © 2023 by De La Salle University.

Pathology Detector System in the Bronchopulmonary System of Asthmatic patients visualized through a radiofrequency of the chest

The arrival of COVID-19 caused devastation to humanity by spreading rapidly around the world and seriously affecting the entire health system. To date, the peculiar symptoms of COVID-19 and the problems it generates in those asthmatic people are already known, which is complicated if they have not had an adequate treatment of their disease, since bronchial asthma is one of the complex bronchopulmonary diseases and for its diagnosis some methods are used that do not provide enough information about the patient's condition, being inefficient methods, therefore, it is necessary to use tools to diagnose pathologies to patients in a comfortable way for an efficient treatment by providing the greatest amount of information about the patient's condition for continuous treatment and in addition to facilitating constant access to several patients with asthma. In view of this problem, in this article a pathology detection system was made in the bronchopulmonary system of asthmatic patients visualized through a radiofrequency of the chest, in such a way that an early diagnosis is made, and some pathological change can be detected in the patient's bronchopulmonary system, with this, an efficient treatment of the patient can be carried out. Through the development of the system, it was possible to observe that the operation was done correctly in the tests conducted, the positioning equipment will move the radiant module on the patient's body for the detection of some pathology with an accuracy of 97.86% efficiency. © 2023 IEEE.

BIST Tourism and BIST Electricity Index Relationship

In this study, which is based on daily data, the relationship between BIST electricity index and BIST tourism index was measured between 2012:M9 – 2022:M9 periods. The aim of the study is to measure the relationship between BIST electricity index and BIST tourism index. VAR Granger causality test was applied to determine whether there is any causal relationship between the variables. It has been determined as a result of the analysis that the BIST electricity index has no effect on the BIST tourism index. Two-way ineffectiveness was determined among the variables. In addition, it was obtained as a result of the analysis that the applied correlation relationship was weak between these variables. The results obtained from the study are important in terms of measuring the effects among BIST indices.

COVID-19 SEIAR Model with Sensitivity Analysis

Beside the unexpected toll of mortality and morbidity caused by COVID-19 worldwide, low- and middle-income countries are more suffering from the devastating issues on economic and social life. This disease has fostered mathematical modelling. In this paper, a SEIAR mathematical model is presented to illustrate how policymakers may apply efficient strategies to end or at least to control the devastating wide spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Bank of Japan's exchange traded fund purchases: a help or hindrance to market efficiency?

We examine the impact of the Bank of Japan's exchange traded fund (ETF) purchases on two aspects of market efficiency—long-range dependence and price delay—of the TOPIX and Nikkei 225 indices. An increase in ETF purchases results in lower long-range dependence for both indices while the impact on the price delay varies according to index and measure. A sub-period analysis shows that the impact on market efficiency varies over time, with the dominant pattern being a delayed harmful effect, followed by a positive impact and thereafter a negative effect. The implications of these findings are discussed.

Dysphagia rehabilitation in post-COVID patients: Review of the literature

COVID-19 causes acute respiratory failure syndrome (SIRA), leading patients to require intubation in the intensive care unit (ICU). A common complication of this ventilatory support is dysphagia, which has a prevalence of up to 30%.This work aims to describe rehabilitation methods in patients with coronavirus infection based on levels of evidence according to the GRADE System, so a systematic review of the literature was carried out. The selected articles were divided into the following subtopics: diagnosis of dysphagia and rehabilitation in COVID patients. The gold standard for the diagnosis of dysphagia is the videofluoroscopic swallowing study (VFS). Fiberoptic Evaluation of Swallowing Assessment (FEES) has high sensitivity and specificity, although they have the disjunction of an aerosol-generating procedure (AGP);however, in a pandemic situation, the study of choice in the literature is VF. Once the diagnosis is made, it is necessary to initiate rehabilitation as soon as possible, even from hospitalization in patients who have hemodynamic stability to prevent long-term effects and promote normal swallowing even before discharge. In patients with COVID-19 infection dysphagia, the risk-benefit of assessment tools and therapy used for diagnosis should be decided to help to maintain social distancing. It becomes imperative to carry out clinical studies with high levels of evidence that allow us to generate Clinical Practice Guides for the benefit of our patients.Copyright © 2021 Sociedad Medica del Hospital General de Mexico. Published by Permanyer.

Asian perspective of capital market performance amid the COVID 19 pandemic

PurposeThe purpose of this research is to investigate the short-term capital markets' reactions to the public announcement first local detection of novel corona virus (COVID 19) cases in 12 major Asian capital markets.Design/methodology/approachUsing the constant mean return model and the market model, an event study methodology has been implied to determine the cumulative abnormal returns (CARs) of 10 pre and post-event trading days. The statistical significance of the data was assessed using both parametric and nonparametric test statistics.FindingsFirst discovery of local COVID 19 cases had a substantial impact on all 12 Asian markets on the event day, as shown by statistically significant negative average abnormal return (AAR) and cumulative average abnormal return (CAAR). The single factor ANOVA result has also demonstrated that there is no variability among 12 regional markets in terms of short-term market responses. Furthermore, there is little evidence that these major Asian stock market indices differ significantly from the FTSE All-World Index which might suggest possible spillover impact and co-integration among the major Asian capital markets. The study further discovers that market capitalization and liquidity did not have any significant impact on market reaction to announcement.Research limitations/implicationsThe study's contribution might have been compromised by the absence of socio-demographic, technical, financial and other significant policy factors from the analysis.Practical implicationsThese findings will be considerably helpful in tackling this unprecedented epidemic issue for personal and institutional investors, industrial and economic experts, government and policymakers in assessing the market in special circumstances, diversifying risk and developing financial and monetary policy proposals.Originality/valueThis paper is the first to examine the effects of local COVID 19 detection announcement on major Asian capital markets. This study will add to the literature by investigating unusual market returns generated by infectious illness outbreaks and the overall market efficiency and investors' behavioral pattern of major Asian capital markets.

An adaptive testing strategy for efficient utilization of healthcare resources during an epidemic.

Lockdowns are found to be effective against rapidly spreading epidemics like COVID-19. Two downsides to strategies rooted in social distancing and lockdowns are that they adversely affect the economy and prolong the duration of the epidemic. The extended duration observed in these strategies is often due to the under-utilization of medical facilities. Even though an under-utilized health care system is preferred over an overwhelmed one, an alternate strategy could be to maintain medical facilities close to their capacity, with a factor of safety. We explore the practicality of this alternate mitigation strategy and show that it can be achieved by varying the testing rate. We present an algorithm to calculate the number of tests per day to maintain medical facilities close to their capacity. We illustrate the efficacy of our strategy by showing that it reduced the epidemic duration by 40% in comparison to lockdown-based strategies.

An unbiased regression type estimator of proportion in randomized response sampling by using analysis of variance mechanism

In this article, two new estimators of population proportion of a sensitive characteristic are introduced by using a method analogous to Analysis of Variance (ANOVA). Then, a new unbiased regression type estimator is developed by utilizing these two estimators. The proposed estimator is, then, compared with its competitor at the same level of protection of the respondents. Also included is a study, based on data collected during summer 2021, of the currently hot topic of estimating the proportion of students, 18 years and older, returning to schools in fall 2021, who tested positive for COVID-19.

Artificial Intelligence-enabled Internet of Medical Things for COVID-19 pandemic data management

The dreaded coronavirus (COVID-19) disease traceable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) has killed thousands of people worldwide, and the World Health Organization (WHO) has proclaimed the viral respiratory disease a human pandemic. The adverse flare of COVID-19 and its variants has triggered collaborative research interests across all disciplines, especially in medicine and healthcare delivery. Complex healthcare data collected from patients via sensors and devices are transmitted to the cloud for analysis and sharing. However, it is pretty difficult to achieve rapid and intelligent decisions on the processed information due to the heterogeneity and complexity of the data. Artificial intelligence (AI) has recently appeared as a promising paradigm to address this issue. The introduction of AI to the Internet of Medical Things (IoMT) births the era of AI of Medical Things (AIoMT). The AIoMT enables the autonomous operation of sensors and devices to provide a favourable and secure environmental landscape to healthcare personnel and patients. AIoMT finds successful applications in natural language processing (NLP), speech recognition, and computer vision. In the current emergency, medical-related records comprising blood pressure, heart rate, oxygen level, temperature, and more are collected to examine the medical conditions of patients. However, the power usage of the low-power sensor nodes employed for data transmission to the remote data centres poses significant limitations. Currently, sensitive medical information is transmitted over open wireless channels, which are highly susceptible to malicious attacks, posing a significant security risk. An insightful privacy-aware energy-efficient architecture using AIoMT for COVID-19 pandemic data handling is presented in this chapter. The goal is to secure sensitive medical records of patients and other stakeholders in the healthcare domain. Additionally, this chapter presents an elaborate discussion on improving energy efficiency and minimizing the communication cost to improve healthcare information security. Finally, the chapter highlights the open research issues and possible lines of future research in AIoMT.

A Secure and Efficient Scheme for Mutual Authentication for Integrity of Exchanged Data in IoMT

The emergence of pandemic diseases like Covid-19 in recent years has made it more important for Internet of Medical Things (IoMT) environments to build contact between patients and doctors in order to control their health state. Patients will be able to send their healthcare data to the cloud server of the medical service provider in remote medical environments through sensors connected to their smart devices, such as watches or smartphones. However, patients' worries surrounding their data privacy protection are still present. In order to ensure the security and privacy of patients' healthcare data in remote medical environments, a number of different schemes have been proposed by researchers. However, these schemes have not been able to take all security requirements into account. Consequently, in this study, we have proposed a secure and effective protocol to safeguard the privacy of patients' medical data when it is sent to the server. This protocol entails two components: mutual authentication of the patient and the server of the medical service provider, as well as the integrity of the exchanged data. Also, our scheme satisfies security requirements and is resistant to well-known attacks. Following this, we used the Scyther tool to formally analyze our proposed scheme. The results showed that the scheme is secure, and in the section on performance analysis, we demonstrated that the proposed scheme performs better than comparable schemes. © 2023 IEEE.

The separation membranes in artificial organs

Separation membranes play a crucial role in the functioning of artificial organs, such as hemodialysis machines, membrane oxygenators, and artificial liver models. The current COVID-19 pandemic has highlighted the importance of these technologies in the medical community. However, membrane technology in artificial organs faces significant challenges, such as the clearance of low-middle-molecule and protein-bound toxins and limited blood compatibility. In this review, we will discuss the separation mechanisms, separation performance, and biocompatibility of different types of separation membranes used in artificial organs. We will also highlight the opportunities and challenges for next-generation membrane technology in this field, including the need for improved clearance of toxins and increased blood compatibility, as well as the potential for microfluidic devices.