Variations of environmental parameters and zooplankton abundance in port dickson coastal waters over a decade of development

Environmental parameters and zooplankton abundance in Port Dickson coastal waters in 2010 were compared with those in 2020 to detect marine environmental changes over the 10-year period in this rapidly developing tourist destination. The first COVID-19 movement control order (MCO) began in March until June 2020, after which the MCO was relaxed to allow tourism. Turbidity, nutrients and chlorophyll-a were significantly higher in 2020 compared to those in 2010. Within the year 2020, nutrients and chlorophyll-a were relatively low immediately after the MCO, but steadily increased as human activities intensified in the area. Nutrient enrichments were highest in September 2020, approximately three months after the first MCO. With the increase of primary production, zooplankton density concomitantly increased from a mean density of 559.9 in.m-3 in 2010 to 2717.0 in.m-3 in 2020. In both years, copepods were the most dominant zooplankton group, contributing to 79.7% and 82.5% of the total zooplankton in 2010 and 2020, respectively. This study illustrated that Port Dickson coastal waters showed nutrient enrichment over the years. The temporary absence/decrease of human activities related to COVID-19 pandemic resulted in relatively low nutrients and chlorophyll-a, but rapidly increased again with the relaxation of the restrictive movement order. © 2023 Author(s).

Re-examining oil and BRICS' stock markets: new evidence from wavelet and MGARCH-DCC

This study examines how the relationship between oil and stock market return of BRICS behaves at different investment horizons. Using data ranging from 2006 to 2020, the wavelet and MGARCH-DCC found that the stock markets' return of Russia, Brazil, and South Africa are comparatively more correlated with oil price return across the investment horizons and more volatile particularly during the Covid-19 period. However, the stock markets' return of China and India is less correlated with oil price return and less volatile. It is also revealed that oil price return leads the BRICS' stock markets' return and both are positively correlated.

COVID-19, Supply Chain, and Profitability: A Survey Based on an Emerging Economy

COVID-19 has stunned the whole economy and has been unveiled as a colossal threat to global supply chain management. This paper has tried to assess the implications of COVID-19 on different areas of the supply chain and the profitability of different industries in Bangladesh. The study is based on a questionnaire survey of three major industries of Bangladesh: textile, pharmaceuticals, and banking sectors conducted from December 2021 to February 2022. Descriptive statistics and t-tests for mean differences were used to analyze the data and they are displayed across tables and graphs. It is found that supply chain aspects like raw material, labor, manufacturing facility, import/export, product/service, revenue, and profit are significantly impinged by the pandemic. Furthermore, the supply chain of the manufacturing sector is more affected than that of the service sector, and statistically, significant differences are evidenced between manufacturing industries. The findings and recommendations of this study will have policy implications for regulators, companies, suppliers, investors, and customers. This is a pioneer study to investigate the implications of the pandemic on the supply chain fields.

An IoT Based Smart Vault Security and Monitoring System with Zero UI

Recent advances have introduced IoT as one of the key technologies globally. As safety remains a critical issue for those who spend much time outside. Automated security systems are very useful where safety is an important issue. With a prospect of a Zero User Interface (UI) model this work represents a novel IoT based smart vault security system. The system is built and designed based on IoT combining with Arduino-Uno and Bluetooth module. This system involves LDR sensor, IR sensor and Sonar sensor for monitoring. The vault provides security on three levels. Password protected entry to connect with the smartphone using Bluetooth module, IR sensor array to use 'secret gesture pattern' to unlock the door, tracking number of transactions from the vault using Sonar sensor and LDR was used as a switch. To avoid the replication of physical unlocking of objects IR sensor array was used to introduce 'secret gesture pattern' unlocking system through touchless interfaces for the avoidance of transmissive diseases like COVID-19. This novel system has substantial possibility as a security vault system for industrial and residential use in a contactless manner. © 2023 IEEE.

COVID-19 pandemic and the dynamics of major investable assets: What gives shelter to investors?

The rapid spread of coronavirus (COVID-19) significantly destabilized the global financial and economic conditions, prompting investors to seek safer investments for their portfolios. This study examines the contagion effect and dynamic relationship between DJI, DJIM, gold, silver, oil, bitcoin, ethereum and cryptocurrency index using data obtained before (18 June 2019–29 January 2020), during the first phase (30 January – 11 September 2020) and after the first phase of COVID-19 pandemic (12th September 2020 - 3rd January 2022). We employed three main methodologies: MGARCH-DCC, MODWT and Wavelet Coherence analysis, to investigate the safe haven property of major investable assets during the pandemic. The findings revealed that gold offers a safe trading opportunity due to its lower volatility and correlation across the different stock market investment horizons (with a few exceptions), contrary to other assets, which exhibits high volatility and correlation. The phase pattern demonstrated the existence of a dynamic asymmetric relationship between the studied investable assets and stock market returns. Furthermore, the study also found that the dynamic lead-lag relationship of investable assets with the stock market returns varies depending on the investment horizons before and during the pandemic. © 2023 Elsevier Inc.

Idea Generation using Transformer Decoder Models

Our work aims to generate new ideas to explore in a specific domain using generative language models. For example, doctors can write about known symptoms as cues to the system, and then the system will generate ideas based on the cues. Similar scenarios can be thought of for other scientific domains. We used transformer-based decoders, especially GPT3-based transformer decoders, as the language models and generators. As the data, we used COVID-19 open research dataset [18]. We finetuned GPT-NEO-125M and GPT-NEO-1.3B models with 125 million and 1.3 billion parameters, respectively. The later model generated more coherent text and could link ideas relevant to the same problem better. We report here our findings with examples generated from our finetuned models. © 2022 ACM.

Human gut virome and COVID-19: Modulating the extent of infection

Coronavirus disease 2019 (COVID-19) caused by the enveloped RNA virus SARS-CoV-2 (severe acute respiratory syndrome-related coronavirus 2) primarily infects human lungs and then gut where active viral replication has been reported. The human gut also harbors different communities of commensal viruses (collectively known as the gut-virome (GV)) that play significant role in regulating host immunity. A gut-dysbiosis signature is observed with increased opportunistic pathogens and massive viral shifts in COVID-19 patients. The intensive two-way dialogues between lungs and gut involving the biome and immune cells of the both compartments are majorly responsible for massive systemic inflammation and immunity breakdown during a lung infection. Thence, during SARS-CoV-2 disease, the host GV might respond to the viral infection and subsequently influence disease progression as well as treatment outcome with additional manifestations. This chapter aims to explain the role of GV in altered homeostasis associated with disease severity and recovery processes based on existing records. Since evidence on direct linkage between human GV and SARS-CoV-2 infection is yet to be explored, understanding intestinal virome perturbation that underlies worse COVID-19 outcomes and severity is an unmet necessity. © 2023 Elsevier Inc. All rights reserved.

Disruption of 3CLpro protease self-association by short peptides as a potential route to broad spectrum coronavirus inhibitors.

Coronaviruses have posed a persistent threat to human health over the last two decades. Despite the accumulated knowledge about coronavirus-related pathogens, development of an effective treatment for its new variant COVID-19 is highly challenging. For the highly-conserved and main coronavirus protease 3CLpro, dimerization is known to be essential for its catalytic activity and thereby for virus proliferation. Here, we assess the potential of short peptide segments to disrupt dimerization of the 3CLpro protease as a route to block COVID-19 proliferation. Based on the X-ray structure of the 3CLpro dimer, we identified the SPSGVY126QCAMRP dodecapeptide segment as overlapping the hotspot regions on the 3CLpro dimer interface. Using computational blind docking of the peptide to the 3CLpro monomer, we found that the SPSGVY126QCAMRP peptide has favourable thermodynamic binding (ΔG= -5.93 kcal/mol) to the hotspot regions at the 3CLpro dimer interface. Importantly, the peptide was also found to preferentially bind to the hotspot regions compared to other potential binding sites lying away from the dimer interface (ΔΔG=-1.31 kcal/mol). Docking of peptides corresponding to systematic mutation of the V125 and Y126 residues led to the identification of seven peptides, SPSGHAQCAMRP, SPSGVTQCAMRP, SPSGKPQCAMRP, SPSGATQCAMRP, SPSGWLQCAMRP, SPSGAPQCAMRP and SPSGHPQCAMRP, that outperform the wild-type SPSGVY126QCAMRP peptide in terms of preferential binding to the 3CLpro dimer interface. These peptides have the potential to disrupt 3CLpro dimerization and therefore could provide lead structures for the development of broad spectrum COVID-19 inhibitors.Communicated by Ramaswamy H. Sarma.

Detection of COVID-19 from X-Ray Images Using Machine Learning Models

Corona virus disease (COVID-19) is one of the deadliest scourge mankind have ever seen. It's a highly infectious influenza virus which may transmit from one person to another without causing any symptoms. In compliance with WHO (World Health Organization) data, corona virus (COVID-19) was first found in China in 2019 and has spread swiftly to individuals in other countries, with an estimated total of 349,641,119 cases (till 25 January) globally. As counter measures to this condition, screening afflicted people is mandatory which requires time and is also costly. Radiological scanning is a plausible measure for achieving this. In this case, the chest X-Ray is the most at hand and cost-effective alternative. In this work, we present a Deep CNN (Convolutional Neural Network) based method for perceiving COVID-19 infected people by analyzing chest X-Ray images. Here, four pre-trained CNN models (AlexNet, VGG16, InceptionV3, and EfficientNetB4) are suggested to analyze chest X-ray radiographs. Among these models, EfficientNetB4 gives us the highest accuracy to detect COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Improved Outpatient Medical Visit Compliance with Sociodemographic Discrepancies in Vascular Telehealth Evaluations.

OBJECTIVE: The coronavirus pandemic has led to a rapid expansion in telemedicine across all medical fields but has also exposed tele-healthcare disparities with differing access to technology across racial and ethnic groups. The objective of our study was to investigate the impact of telehealth on vascular visit compliance and to explore the effect of sociodemographic factors on vascular surgery outpatient telehealth utilization during the COVID-19 pandemic. METHODS: Consecutive patients undergoing outpatient vascular surgery evaluation between February 24, 2020 (the launch of our telemedicine program) and December 31, 2020, were reviewed. Baseline demographic and outcomes were obtained from the electronic medical record. Telehealth or in-person evaluations were defined according to the patient's index visit during this period. Medical visit compliance was established upon completion of the telehealth or in-person encounter. Chi square tests and logistic regression analyses were used. RESULTS: There were 23,553 scheduled outpatient visits for 10,587 patients during the study period. 1,559 of those were scheduled telehealth encounters compared to 21,994 scheduled in-persons encounters. Out of the total scheduled outpatient encounters, 13,900 medical visits (59.0%) were completed: 1,183 telehealth visits and 12,717 in-person visits. The mean travel distance saved for telehealth visits were 22.1±27.1 miles and the mean travel time saved was 46.3±41.47 minutes. We noted no sociodemographic differences between patients scheduled for telehealth vs. in-person visits. There was a trend towards a lower proportion of African American patients among the telehealth group vs. in-person group (7.8% vs. 10.6%, P=.116) respectively; however, this did not reach statistical significance. There was a significantly higher rate of medical visit completion among the telehealth group compared to the in-person group (79.5% vs. 59.4%, P<.001). Among patients who were scheduled for an outpatient medical visit, having a scheduled telemedicine evaluation (vs. in-person) was associated with 2.3 times the odds of completing the medical visit (Odds Ratio (OR)=2.31, 95% Confidence Interval (CI): 2.05-2.61) adjusting for age, sex, race, ethnicity, language and the distance between the patient's home zip code and the outpatient vascular center zip code. Selecting for scheduled telemedicine visits, African American patients were associated with decreased odds of telemedicine utilization (OR=0.73, 95%CI:0.59-0.90) after adjusting for age, sex, ethnicity, language and visit type. CONCLUSIONS: The vascular surgery outpatient telehealth evaluation appears to improve medical visit completion in our region with apparent sociodemographic disparities. Further studies are needed to confirm if telemedicine expansion has improved access to care in other geographical areas.

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh's 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city's high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

ESL Teachers and Students Perceptions towards Blended Learning during COVID-19 Pandemic

Undoubtedly, the COVID-19 epidemic's worldwide disturbance has brought about countless, indescribable, and widespread problems in the education sector. During the corona virus 2019 limitation, the significance of blended learning (BL) has indeed been highlighted. But there is very little research on students' and teachers experiences with BL, especially when utilizing WhatsApp as among the educational tools. This study examined the perspectives of ESL students and teachers toward utilizing different BL approaches. In an Indonesian university, a mix of in-person lectures and virtual learning is used. The study described in this paper attempts to investigate how students experience about their learning in a BL environment that uses the social media platforms Google Classroom and WhatsApp as teaching tools. It combined descriptive research design with a mixed method to examine how students' and teachers' perspectives of BL education relate to one another. According to the findings, students had a favorable opinion of their BL experience. Students express their issues about the internet connectivity and the clarity of the teaching in the online course resources. The greatest benefit, in the opinion of the teachers, was that it was difficult for them to prepare the material and use the media (93%). It can be suggested that BL can be practiced even after the COVID 19 period. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Adapting to Changing Expectations: Accounting Students in the Digital Learning Environment

As a developing nation, Malaysia is looking forward to transforming its direction towards the Industrial Revolution 5.0. Aligned with the transformation process that is mainly driven by the rapid modern industrial transformation, Malaysian education system is faced with a variety of challenges especially in equipping young adults with innovative, digitally literate, and high critical thinking skills to make them future-ready. Additionally, the growing digital world economy has resulted in the loss and transformation of jobs which necessitates the transformation of Malaysian education for future employability. Moreover, the ongoing global COVID-19 pandemic has accelerated the digitalization of the education sector. Hence, this study aims to explore, analyze, and evaluate first-year Bachelor in Accountancy students' perspectives with regard to their online learning experiences in digitalized classrooms by looking at six dimensions: 1) Self-Directed Learning, 2) Learner Readiness toward Online Learning, 3) Learner Control, 4) Online Communication, 5) Computer/Internet Self-Efficacy, and 6) Motivation for Learning. Using a qualitative research design, online interviews via Google Meet involving 43 first-year students were conducted. The recorded interviews were then transcribed and analyzed using ATLAS.ti 8. The results of the study suggest that online learning has its advantages and disadvantages based on the respondents' perspectives and experiences. A majority of the respondents reported that they enjoyed online learning and were well prepared for it while some of them perceived online learning as a complicated approach due to some factors. It can be concluded that these students were ready for online learning. © 2023 by the authors.

COVID-19 SPATIOTEMPORAL SIR MODEL: REGIONAL OPTIMAL CONTROL APPROACH

In the present work, we consider a spatio-temporal model to describe the evolution of covid19 in an area Ω (Ω can be a city, a country,..). Taking into account the financial means of the considered country, we suppose that the number of available vaccines is destined to a region ω1 ⊆Ω (ω1 can be an industrial city, a university city. ..) and we suppose that the available treatments are dedicated to a region ω2 ⊆ Ω (ω2 can be a military city,..), it is not excluded that ω1 = ω2 . To minimize the number of infection with minimal cost, we apply an optimal regional control strategy to stop the death of infected individuals in the considered area. Much of this work has been devoted to mathematical study, where the existence of the optimal controls and the solutions of the state system are proven, an optimal control characterization in terms of state and adjoint functions are provided, and the optimality system is solved numerically using a forward-backward sweep method. Our numerical results suggest that when vaccination and treatment procedures are used together, the control approach becomes more effective in protecting a specific region from epidemic transmission from neighboring regions. © 2022 the author(s).

Ameliorative Effects of 25-Hydroxycholecalciferol Levels on Broiler Chickens Challenged with Infectious Bronchitis Coronavirus: A Novel Approach

SUMMARY This study aimed to see how 25-hydroxyvitamin D3 [25(OH)D] supplementation affected the performance, immunity, and vitamin D status of broilers, with and without infectious bronchitis virus challenge. Day-old chicks (n = 1000) were divided into four dietary treatments. All experimental treatments were containing 5000 IU of D3 and supplemented with 0, 100, 300, or 500 g/tonne of Bio D® containing 0, 13.94, 41.82 or 69.70 mg 25(OH)D, respectively. In unchallenged birds, the inclusion of 25(OH)D significantly improved survival rates and EPEF values, and no significant difference in LBW, BWG, FC, or FCR was observed. In infectious bronchitis virus (IBV)-challenged birds, the 25(OH)D treatment showed less deterioration in BWG, and FC compared with the control, especially 25(OH)D500 group. The lowest mortality was observed in the 25(OH)D500 group. The health status of chicks fed diets supplemented with 25(OH)D post-challenge had significantly better CD4, CD8, and immunoglobulin (IgG, IgM) levels, H/L ratio, total leucocytic count (TLC) in peripheral blood and IBV titers, cytokine (including interleukin (IL) 10) and (IFN)-γ expression, and reduced IBV shedding. After IBV challenge, all 25(OH)D groups recorded higher levels of plasma vitamin D than the control. Furthermore, 25(OH)D groups showed significantly higher antibody titers than the control group. In general, dietary supplementation with different levels of 25(OH)D had a positive effect on inflammatory reaction and stimulated the systemic innate immune responses of chickens during IBV challenge. In turn, recovery in the challenged birds with 25(OH)D rapidly occurred compared with that in the control group.

CEO duality and firm performance during the 2020 coronavirus outbreak.

Stewardship theory suggests that CEO duality can provide strong leadership and facilitate the development and coordination of firm strategy. These benefits should affect firm risk and financial performance, particularly when the firm has high information-gathering costs. We use the 2020 coronavirus outbreak as a natural experiment to determine whether CEO duality is beneficial during crisis periods. We find that in 2020, S&P 1500 firms with CEO duality exhibit smaller increases in default probability risk than firms with non-duality in the presence of high information costs. Firms with CEO duality experience a smaller decrease in profitability when information costs are high. We also find that firms with CEO duality offer cumulative abnormal returns significantly higher than those of other firms. CEO duality is more valuable in firms with higher information costs. Our results indicate that CEO duality is valuable during crisis periods, particularly when information costs are high. These results are consistent with stewardship theory and indicate that the concentration of power from CEO duality is beneficial during crisis periods.

Computer modeling and simulation to predict COVID-19 propagation patterns via factual cellular automata

Computer modelling and simulation methods are very important and play a critical role in the mitigation and response to the ongoing COVID-19 pandemic. In this study, we propose a computational modeling technique based on cellular automata (CA) with realistic proposed rules. The rules are designed to simulate the propagation of COVID-19 disease through a bounded area. Our proposed CA rules are novel in many respects. For example, we introduce rules regarding surface states not only the states of the person rules to the proposed model. In addition on, the classification of neighbors to nearest neighbors and range of neighbors based on cellular layers is explained. Moreover, the concepts of time generation and access time are deployed for the first time to model the propagation of the disease over time in this work. Further details of the proposed model including the topology of the defined area, the initial states of the cells and four-layer transfer mechanism are explained as well. This work may be considered a criterion of spreading for COVID-19 from point source in a defined population area. The results of the proposed algorithm represent the percentage of the population whose infectious status is described by different cellular state objects after a defined generation time. The results are compared under different circumstances and analyzed equanimity. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

An Intelligent Vaccine Distribution Process in COVID-19 Pandemic through Blockchain-SDN Framework from Bangladesh Perspective

Currently, vaccine distribution is the most concerning issue of Coronavirus (SARS-CoV-2) diseases in every stage of peoples properly. There are several strategies is proposed by the researchers. Still, the public is facing various difficulties in taking the vaccine. However, the current technologies such as the Internet of Things (IoT), Software-Defined Networking (SDN), and Blockchain (BC) are the intelligent solution for vaccine distribution efficiently. In light of these considerations, in this paper, we propose the "DistVAC-COVID" architecture incorporating a distributed Blockchain-based SDN-IoT framework for COVID-19's vaccine distribution considerably. In the presented system, the SDN benefits to divide the entire network into two distinct planes such as the data and control plane for distributing COVID data smartly. Further, a distributed Blockchain provides the desired system to be prominently secured and more confidential. Therefore, the "DistVAC-COVID" model combining SDN, IoT, and BC can be handled by several COVID-19 zones-red, hazard, and green securely. Finally, we evaluate the "DistVAC-COVID" architecture via an experimental evaluation process and show the effectiveness of the suggested solution in comparison with baseline system.

A Comprehensive Study to Investigate Student Performance in Online Education during Covid-19

During the recent Covid-19 pandemic, there has been a tremendous increase in online-based learning (e-learning) activities as nearly every educational institution has transferred its programs to digital platforms. This makes it crucial to investigate student performance under this new mode of delivery. This research conducts a comparison among the traditional educational data mining techniques to detect the best performing classifier for analyzing as well as predicting students’ performance in online learning platforms during the pandemic. It is achieved through extracting four datasets from X-University student information system and learning platform, followed by the application of 6 classifiers to the extracted datasets. Random Forest Classifier has demonstrated the highest accuracy in the first two out of the four datasets, while Simple Cart and Naïve Bayes Classifiers presented the same for the remainder two. All the classifiers have demonstrated medium to high TP rates, class precision and recall, ranging from 60% to 100% for almost all of the classes. This study emphasized the attributes that have a direct impact on students’ performance. The outcomes of this study will assist the instructors and educational institutions to identify important factors in the analysis and prediction of student performance for online program delivery. © 2022 MECS.

Mortality rate of COVID-19 infection in end stage kidney disease patients on maintenance hemodialysis: A systematic review and meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been the most talked-about disease of the past few years. Patients with significant comorbidities have been at particular risk of adverse outcomes. This study looked at the outcomes and risk factors for adverse outcomes among patients on chronic hemodialysis for end-stage renal disease, a group of patients known to be particularly susceptible to infectious complications. AIM: To assess outcomes and risk factors for adverse outcomes of COVID-19 infection among patients on chronic hemodialysis. METHODS: We searched PubMed/MEDLINE, EMBASE, Reference Citation Analysis (https://www.referencecitationanalysis.com/) and Web of Science databases for relevant terms and imported the results into the Covidence platform. From there, studies were assessed in two stages for relevance and quality, and data from studies that satisfied all the requirements were extracted into a spreadsheet. The data was then analyzed descriptively and statistically. RESULTS: Of the 920 studies identified through the initial database search, only 17 were included in the final analysis. The studies included in the analysis were mostly carried out during the first wave. We found that COVID-19 incidence among patients on hemodialysis was significant, over 10% in some studies. Those who developed COVID-19 infection were most likely going to be hospitalized, and over 1 in 5 died from the infection. Intensive care unit admission rate was lower than the infection lethality rate. Biochemical abnormalities and dyspnea were generally reported to be associated with adverse outcomes. CONCLUSION: This systematic review confirms that patients on chronic hemodialysis are very high-risk individuals for COVID-19 infections, and a significant proportion was infected during the first wave. Their prognosis is overall much worse than in the general population, and every effort needs to be made to decrease their exposure.