NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis

This paper examines the efficiency and asymmetric multifractal features of NFTs, DeFi, cryptocurrencies, and traditional assets using Asymmetric Multifractal Cross-Correlations Analysis covering the period from November 2017 to February 2022. Considering the full sample with a significant variation among asset classes, the study reveals DeFi-DigiByte is the most efficient while the cryptocurrency-Tether is the least efficient. However, S&P 500 showed high efficiency before COVID-19, and DeFi-Enjin Coin advanced as the most efficient asset during COVID-19. The volatility dynamics of NFTs, DeFi, and cryptocurrencies follow strong nonlinear cross-correlations, but evidence of weaker nonlinearity exists in traditional assets. Additionally, the sensitivity to smaller events in bull markets is high for NFTs and DeFi. The findings have significant implications for portfolio diversification when an investor's portfolio set includes traditional assets and cryptocurrency and relatively new blockchain-based assets like NFTs and DeFi. © 2023 The Authors

A bioinformatics approach to characterize a hypothetical protein Q6S8D9_SARS of SARS-CoV

Characterization as well as prediction of the secondary and tertiary structure of hypothetical proteins from their amino acid sequences uploaded in databases by in silico approach are the critical issues in computational biology. Severe acute respiratory syndrome-associated coronavirus (SARS-CoV), which is responsible for pneumonia alike diseases, possesses a wide range of proteins of which many are still uncharacterized. The current study was conducted to reveal the physicochemical characteristics and structures of an uncharacterized protein Q6S8D9_SARS of SARS-CoV. Following the common flowchart of characterizing a hypothetical protein, several sophisticated computerized tools e.g., ExPASy Protparam, CD Search, SOPMA, PSIPRED, HHpred, etc. were employed to discover the functions and structures of Q6S8D9_SARS. After delineating the secondary and tertiary structures of the protein, some quality evaluating tools e.g., PROCHECK, ProSA-web etc. were performed to assess the structures and later the active site was identified also by CASTp v.3.0. The protein contains more negatively charged residues than positively charged residues and a high aliphatic index value which make the protein more stable. The 2D and 3D structures modeled by several bioinformatics tools ensured that the proteins had domain in it which indicated it was functional protein having the ability to trouble host antiviral inflammatory cytokine and interferon production pathways. Moreover, active site was found in the protein where ligand could bind. The study was aimed to unveil the features and structures of an uncharacterized protein of SARS-CoV which can be a therapeutic target for development of vaccines against the virus. Further research are needed to accomplish the task.

Student Attention Base Facial Emotion State Recognition Using Convolutional Neural Network

E-learning system advancements give students new opportunities to better their academic performance and access e-learning education. Because it provides benefits over traditional learning, e-learning is becoming more popular. The coronavirus disease pandemic situation has caused educational institution cancelations all across the world. Around all over the world, more than a billion students are not attending educational institutions. As a result, learning criteria have taken on significant growth in e-learning, such as online and digital platform-based instruction. This study focuses on this issue and provides learners with a facial emotion recognition model. The CNN model is trained to assess images and detect facial expressions. This research is working on an approach that can see real-time facial emotions by demonstrating students' expressions. The phases of our technique are face detection using Haar cascades and emotion identification using CNN with classification on the FER 2013 datasets with seven different emotions. This research is showing real-time facial expression recognition and help teachers adapt their presentations to their student's emotional state. As a result, this research detects that emotions' mood achieves 62% accuracy, higher than the state-of-the-art accuracy while requiring less processing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Model Development for Predicting the Crude Oil Price: Comparative Evaluation of Ensemble and Machine Learning Methods

The crude oil market is unstable, and its price is highly volatile. Due to the Covid-19 pandemic, the price of crude oils goes up and down in a short period of time. Future plans and projects' policies depend directly and indirectly on the future price of crude oil. So, the aim of this study is to predict the price of crude oil by using machine learning and ensemble algorithm, as well as to show the comparison of performance of Ada Boost, Bagging Lasso and Support Vector Regression model. The study uses crude oil price time series data for analysis and to form a model to predict future price. The actual vs. predicted curve is used to show the performance of each algorithm individually. Analysis shows that the ensemble AdaBoost algorithm displays better performance than other algorithms. The result is validated using mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), two accuracy score function variance score, and R2 score. This study will help the stakeholders of the crude oil industry in making decisions and formulating policies based on forecasted crude oil prices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Impacts of COVID-19 on Textile Industry Workers in Bangladesh: Socio-Economic Perspective

This study is designed to explore the impacts of the COVID pandemic on the socio-economic status of textile workers and to suggest ways for balancing the turnover rate. For the study, a cross-sectional study design and mixed-method approach were employed. The researchers conducted a questionnaire survey of 357 textile workers using convenience sampling and, 5 KIIs, 20 IDIs, and 5 FGDs using purposive sampling. The study area was the Gazipur district of Bangladesh, a hub of the textile industry. Findings from Principle component analysis revealed that 54.38% of the total observed variation can be explained by five components. Working Environment (r = –0.699), Payment System (r = .987), Savings (r = .920), Employee Turnover Rate (r = .769), and Social Status (r = .558) of employees are identified by PCA as the most strongly correlated variables that have significant impacts on the socio-economic condition of textile workers due to Covid 19 pandemic. Finally, through Logistic Regression Analysis, the study has found that Safe Working Environment (OR = 0.203, 95% CI 0.098-0.419), Satisfactory Payment Structure (OR = 2.196, 95% CI 1.354-3.561), and Provident Fund Facilities (OR = 2.908, 95% CI 1.497-5.651) can reduce the turnover rate of textile workers. Additionally, effective labor unions and adequate training facilities can also balance the adverse socio-economic impacts on the textile workers. © 2023 DMICS.

Cash-aid Support of Bangladesh Government During COVID-19 Pandemic, Effectiveness and Challenges

The outbreak of COVID 19 has affected a number of countries throughout the world. The widespread disruptions of COVID 19 on the world economy and people's livelihood are devastating, and, the lower-middle and lower-income people endure the worst hit of the pandemic. Bangladesh also, has undergone tremendous economic recession, unemployment, and poverty, and the marginal poor people in Bangladesh has become the worst bearer of COVID 19 Pandemic implications. The nationwide restrictions to contain the infection have made the situation unbearable for these poor people. To mitigate these sufferings and social inequality, the government intervened for ensuring the basic needs of the lower-income people. This intervention was carried out through a massive stimulus scheme. Among them, the PM's Cash-Aid is one intervention that was initiated to protect the mass people who were affected and lose informal jobs due to lockdown during COVID 19. In previous, several other Cash Assistance programs have been provisioned in Bangladesh for the vulnerable groups, however, controversies arose regarding the effectiveness of those programs. Becoming conscious of this issue, the government has digitalized the service process of PM's Cash-Aid Program by adopting Digital Financial Service (DFS) system to support the grassroots people directly. Hence, the performance of the cash aid program to reach the targeted people was quite well, though controversies were also grown on the program effectiveness. This article based on a qualitative approach would look to assess the effectiveness and challenges of the Cash-Aid Program. After such assessment, we would like to draw the thrusting through the depiction of the dynamic process of how standard performance can be ensured in Cash Assistance Programs. © 2023 DMICS.

Covid-19 pandemic and stock returns in India

This paper examines whether the Covid-19 pandemic has had a homogeneous or heterogeneous effect on stock returns in India. We consider panel data by using 1,318 companies that are listed on the National Stock Exchange of India. We find that the daily growth rate in Covid-19 cases and Covid-19 deaths are negatively associated with stock returns. Further, we observe that the average stock returns during Lockdown 2 are positive and highly significant, while the returns during Lockdowns 3 and 4 are negative. Moreover, our results show that the chemical, technology, and food and beverage industries earn higher returns. In contrast, the banking and finance, automotive, services, and cement and construction industries yield lower returns for the overall period. Interestingly, all industry groupings in this study earn a positive return during the lockdown period. In particular, the chemical, technology, automotive, metals and mining, and food and beverage industries provide higher returns during the lockdown period. Finally, this study supports the claim that the Covid-19 pandemic has had a heterogeneous effect in the Indian stock markets. © 2022, Academy of Economics and Finance.

Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network

This paper aims to develop an artificial neural network-based forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007-2020, including the Covid-19 period. Empirical findings show that the FTDNN model outperforms existing baselines and artificial neural network-based models in forecasting West Texas Intermediate and Brent crude oil prices and National Balancing Point and Henry Hub natural gas prices. As a result, we demonstrate the predictability of energy commodity prices during the volatile crisis period, which is attributed to the flexibility of the model parameters, implying that our study can facilitate a better understanding of the dynamics of commodity prices in the energy market.

Posttraumatic Stress Symptoms, Anxiety, and Depression Among COVID-19 Survivors After Discharge from Hospital

Background: The coronavirus disease 2019 (COVID-19) pandemic can lead to psychological issues;however, few studies have evalu-ated the mental health status of COVID-19 patients after discharge from the hospital. Objectives: This study aimed to assess the psychological status of COVID-19 survivors and determine the risk factors associated with adverse psychological outcomes. Methods: Through a web-based cross-sectional survey, the data were collected from 158 COVID-19 survivors one month after discharge from the hospital using demographic information, the Hospital Anxiety and Depression Scale (HADS), and the Posttraumatic Stress Disorder Checklist for the Fifth Edition of Diagnostic and Statistical Manual of Mental Disorders (PCL-5). Data analysis was conducted in SPSS software (version 24) using logistic regression modeling. Results: The mean age of the participants was 42.02 ±10.56 years, and the majority of patients were male (58.2%) and married (79.1%). According to the HADS, 32 (20.3%) and 21 (13.3%) patients had anxiety and depression, respectively. Using the PCL-5, 45 (28.5%) patients experienced posttraumatic stress disorder (PTSD) after discharge from the hospital. A positive history of psychiatric disorders, sub-stance abuse, and smoking were the related risk factors for depression, anxiety, and PTSD (P < 0.05). Conclusions: Based on the results, it might be concluded that COVID-19 survivors, especially the aforementioned groups, need more targeted interventions regarding psychological health during and after discharge to avoid COVID-19-related psychiatric injuries. © 2022, Author(s).

Covid-19 pandemic and stock returns in India

This paper examines whether the Covid-19 pandemic has had a homogeneous or heterogeneous effect on stock returns in India. We consider panel data by using 1,318 companies that are listed on the National Stock Exchange of India. We find that the daily growth rate in Covid-19 cases and Covid-19 deaths are negatively associated with stock returns. Further, we observe that the average stock returns during Lockdown 2 are positive and highly significant, while the returns during Lockdowns 3 and 4 are negative. Moreover, our results show that the chemical, technology, and food and beverage industries earn higher returns. In contrast, the banking and finance, automotive, services, and cement and construction industries yield lower returns for the overall period. Interestingly, all industry groupings in this study earn a positive return during the lockdown period. In particular, the chemical, technology, automotive, metals and mining, and food and beverage industries provide higher returns during the lockdown period. Finally, this study supports the claim that the Covid-19 pandemic has had a heterogeneous effect in the Indian stock markets. © 2022, Academy of Economics and Finance.

Strategies for the development of photonic sensors for COVID-19

Beyond the optical and analytical performance of the sensor itself, the development of an optical detection tool in response to a pressing research or diagnostic need requires consideration of a host of additional factors. This talk will provide an overview of two photonic sensor systems developed for profiling the human immune response to COVID-19 infection and/or vaccination. One, focused on the design goal of high multiplexing (many targets per sensor), was built on the Arrayed Imaging Reflectometry (AIR) platform. AIR is a free-space optics technique that relies on the creation and target molecule binding-induced disruption of an antireflective coating on the surface of a silicon chip. The second method, focused on low cost and high speed, uses a small (1 x 4 mm) ring resonator photonic chip embedded in a plastic card able to provide passive transport of human samples. This “disposable photonics” platform is able to detect and quantify anti-COVID antibodies in a human sample in a minute, making it attractive for high-throughput testing applications. © 2022 SPIE

Privacy-Preserved Credit Data Sharing Integrating Blockchain And Federated Learning For Industrial 4.0

The rapidly increasing volume of user credit-related data generated by connected devices in the Industrial Internet of Things (IIoT) paradigm opens up new possibilities for improving the quality of service for emerging applications through credit data sharing. However, security and privacy issues (such as credit data leakage) are significant barriers to credit data providers and applications sharing their data in wireless networks. Leakage of private credit data can lead to serious problems, not only in terms of financial loss for the data provider, but also in terms of illegal use of personal credit data. In particular, the economic recovery after the global COVID-19 pandemic has further boosted the demand for efficient, secure credit models for Industry 4.0, which could alleviate the potential credit crisis under financial pressure. IEEE

Media discourse in China and Japan on the COVID-19 pandemic: comparative analysis of the first three months

Purpose: This study aims to analyze how English-language versions of e-newspapers in the first two countries affected, China and Japan, which are non-English-speaking countries and have different socio-economic and political settings, have highlighted Coronavirus disease 2019 (COVID-19) pandemic news and informed the global community. Design/methodology/approach: A text-mining approach was used to explore experts’ thoughts as published by the two leading English-language newspapers in China and Japan from January to March 2020. This study analyzes the Opinion section, which mainly comprises editorial and the op-ed section. The current study groups all editorial discussions and highlights into ten major aspects, which cover health, economy, politics, culture and others. Findings: Within the first three months, the media in both China and Japan shifted their focus from health and preparedness to the economy, politics and social welfare. Governance and social welfare were key concerns in China’s news media, while, in contrast, global politics received the highest level of attention from experts in Japan’s news media. Environment and technologies aspects did not receive much attention by the expert’s columns. Originality/value: At the initial stage of a world crisis, how leading nations and initially affected nations deal with the problem, how media play their role and guide mass population with experts’ thoughts are highlighted here. The understanding developed in this study can provide guidance to news media in other countries in playing effective roles in the management of this health crisis and catastrophes. © 2021, Emerald Publishing Limited.

Relationship between outcomes of covid19, its susceptibility and blood groups

COVID-19, a viral infection spread across the world affecting many people around the world. In ABO blood type, certain types are more prone to infections and causes severe symptoms. Relationship between ABO blood type and COVID-19 still needs to be found out. A prospective cohort study was conducted to evaluate a relationship between ABO blood type and COVID-19. Data was collected from 148 patients who presented for COVID testing through PCR or nasal swab tests. COVID positive patient’s blood test was performed to find out ABO blood group/type and their symptoms with which they presented. The blood group distributions, age and gender of these patients were recorded. It was seen that there is a statistically significant association between COVID and blood group A+, A-, B+ with p-value of 0.01, 0.03 and 0.01 respectively and no statistical significance was found between B-, O+, O-, AB+ and AB-with P-value of 0.06, 0.1, 0.9, 0.7 and 0.8 respectively. Multi variate analysis performed showed age, blood group and ICU stay to be significantly associated with COVID with p-value of <0.01, 0.05, <0.01 and gender to be non-significantly associated with COVID with p-value of 0.7. Blood group A+, A-and B+ are more prone to contract COVID virus with more severe symptoms. Fever and cough have been to be positively associated with COVID cases and found to be affecting patient’s health. Age is also found be affecting patient’s life, with a higher chance of contracting COVID-19 as the increases.

Novel Coronavirus (COVID-19) Pandemic: The Role of Printing Media in Asian Countries

During all critical incidents, the media frame our understanding and create powerful forces at both individual and societal levels. The mental health of readers and viewers can also be affected by the media after tragic events. Potentially, the media have a proactive role in shaping the actions of the mass population and thereby influencing policy actions. The print media especially are considered a key avenue for taking information to the masses. However, in this information and communications technology (ICT) era, people are increasingly reluctant to carry hard-copy newspapers, instead preferring e-newspapers. At the present time, entire newspapers, and especially their opinion sections, are deluged by concerns about the novel coronavirus disease 2019 (COVID-19) pandemic. After China and Japan first encountered COVID-19, other Asian countries began their COVID-19 fight at different times between January and March 2020. All affected countries sought to manage the pandemic in their own way, following lessons learned from China and Japan. Every form of media in affected countries highlighted concerns by presenting news, perceptions, and opinions related to the pandemic. With opinion sections and editorials, the key sections of e-newspapers to reflect experts' perceptions and thoughts, this study aims to examine experts' views in the e-newspapers of five different countries in Asia, in relation to China and Japan. Considering the diversity of socioeconomic and geopolitical settings, five countries-South Korea, Singapore, Iran, India, and Bangladesh-are selected, each represented by one leading English-language e-newspaper. This study explores how experts' perceptions in the studied countries present different aspects of life. It also examines which e-newspaper emphasized which aspect of life and in which period of the outbreak. By intensive text mining in each selected e-newspaper, the study found that experts' opinions addressed diverse issues with regard to COVID-19. These issues are grouped under the following eight categories: health and drugs, preparedness and awareness, social welfare and humanity, the economy, governance and institutions, politics, the environment and wildlife, and innovation and technology. This pioneering study of five different e-newspapers in Asian countries from January to March 2020 presents a similar picture of experts' concerns and their roles in shaping responses to health crises;thus, it plays a role in contributing to policy actions.