Influence of COVID-19 on Tertiary Orthopaedic Centres.

Coronavirus disease 2019 (COVID-19) is a highly contagious lethal infection that has successfully spread all across the world. The novel coronavirus that is behind the menace and spread of COVID-19, is the next in the lineage of the Coronaviridae family of viruses, which had previously given two deadly viruses with limited geographical extent. After sustaining for more than two years, the virus is still active and keeps on mutating to evade human immunity. The impact of COVID-19 is felt not only by patients of COVID-19 who go through the trauma but also by non-COVID-19 patients due to the non-pharmacological interventions (NPIs) enforced. Patients in the orthopedic departments suffered a huge blow as their rehabilitation practices were stalled due to a lack of health professionals and also restrictions imposed. But to soften the blow, usage of telemedicine was done in some instances so that the essential therapies can continue despite the movement restrictions imposed. COVID-19 has disrupted many aspects of human life including clinical practices and this endeavor is to review those aspects and provide conclusions if any. The aim of the study is to review the available resources regarding Indoor orthopedic practice during the COVID-19 pandemic and draw a conclusion that can help further research on the aforementioned topic.

Analyze the Effects of COVID-19 on Energy Storage Systems: A Techno-Economic Approach

During the COVID-19 pandemic, the U.S. power sector witnessed remarkable electricity demand changes in many geographical regions. These changes were evident in population-dense cities. This paper incorporates a techno-economic analysis of energy storage systems (ESSs) to investigate the pandemic's influence on ESS development. In particular, we employ a linear program-based revenue maximization model to capture the revenues of ESS from participating in the electricity market, by performing arbitrage on the energy trading, and regulation market, by providing regulation services to stabilize the grid's frequency. We consider five dominant energy storage technologies in the U.S., namely, Lithium-ion, Advanced Lead Acid, Flywheel, Vanadium Redox Flow, and Lithium-Iron Phosphate storage technologies. Extensive numerical results conducted on the case of New York City (NYC) allow us to highlight the negative impact that COVID-19 had on the NYC power sector. © 2023 IEEE.

A Study To Assess The Effect Of Emotional Freedom Technique (EFT) On Stress & Anxiety Among Nurses Working In Covid Ward At Selected Hospital

The impact of COVID-19 pandemic on nurses has been significant on their mental & physical health. To minimize their psychological impact Emotional Freedom Technique (EFT) is cost effective, self-administrative successful treatment in very minimum time and highly effective on stress and anxiety. Aim(s): To assess the effect of Emotional Freedom Technique (EFT) on stress & anxiety among nurses working in covid ward. The focus of the study was analysis of data related to the effect of Emotional Freedom Technique (EFT) on stress & anxiety among nurses in covid ward at Dr. D.Y. Patil Hospital in Pune city. Methodology: An evaluative with One- group pre-test post-test research design was adopted to this study.A Non-probability purposive sampling technique was used for selecting 60 samples of covid ward nurses who met the designated set of criteria in the period of data collection in Dr. D.Y. Patil Hospital. In this study there was one experimental group, 60 covid ward nurses were taught EFT method by the researcher and advised to apply this technique continue for 10days twice a day, early morning & night time before sleep. Telephonic follow up was taken with record. Result(s): In pre-test, 20% of the nurses working in covid ward had mild stress levels, 75% of them had moderate stress and 5% of them had severe stress. In post-test, 90% of them had mild stress levels and 10% of them had moderate stress and in pre-test, 73.3% of the nurses working in covid ward had mild anxiety levels, 25% of them had moderate anxiety and 1.7% of them had severe anxiety. In post-test, all of them had mild anxiety. The findings related to association of stress and anxiety levels with demographic variables said that p-value corresponding to number of children is small (<= 0.05), the demographic variable number of children and family type was found to have significant association with the stress among nurses working in COVID ward, and the association of stress with demographic variables said that p-values are large (greater than 0.05) So, there is no significant association with the anxiety among the nurses working in covid ward. Conclusion(s): The research found that in pre-test nurses working in covid ward had mild stress levels and in post-test, all of them had mild anxiety.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Use of fuzzy logic for psychological support to migrant workers of Southern Odisha (India)

People migrate with lot of ambitions to turn their economic status around. In Odisha, particularly in the southern part of the Odisha state, there are many families where the migrant worker is the only earning member in the family and also migration is the only option to choose. Even if the government and NGOs have taken so many steps for the betterment of the migrant workers, improper way of working continues and also migrant workers are being taken for granted. The pandemic caused by COVID-19 has shattered the dreams of migrant workers as they have been the hardest hit of the virus both from lives and livelihoods fronts. This paper is to present the psychological status of migrant workers during COVID-19 and for the purpose, the migrant workers of two southern districts (Khurdha and Gajapati) of Odisha have been considered. Moreover, how fuzzy logic can be of help in improving the psychological state of such migrants has also been highlighted in this paper. © 2023 Scrivener Publishing LLC. All rights reserved.

Educational Data Mining tools and Techniques used for Prediction of Student's Performance: A Study

In the digital era, enormous amounts of data have been generated in education that has led to data driven approaches which in turn help effective decision making. Educational data mining has been used as a very effective tool for identifying the hidden patterns in educational data, predicting students' performance and to enhance the teaching /learning environment. Educational data mining tools and techniques help institutes in providing information about students e.g., about enrolment of students, weak students can be identified earlier so that various corrective strategies can be applied, and various resources can be allocated to enhance their performance and their success in courses they enrolled. This paper examines the research efforts that have been made in the field of educational data mining and the various educational data mining tools and techniques used in recent years for predicting student's performance. We live in a world where enormous volumes of data are collected, but if these data are not further examined, they remain nothing more than enormous amounts of data We may use this data, analyze it, and gain a significant edge by using new approaches and procedures. Data mining is the ideal approach in this situation. Extraction of hidden and valuable information and patterns from massive data sets is known as data mining. It has already been widely used in several fields, including banking, sales, marketing, telecommunications, and finance. This essay aims to introduce a unique use of data mining for education, known as educational data mining. An interdisciplinary study topic called Educational Data Mining (EDM) was established to apply data mining to the educational sector. To examine the data gathered during teaching and learning, it employs a variety of tools and methodologies from machine learning, statistics, data mining, and data analysis. The process of turning large educational databases' raw data into useful information that can be used for decision-making in educational systems as well as for a better understanding of students and their learning circumstances is known as educational data mining. © 2022 IEEE.

Equitable Access to eLearning during Covid-19 Pandemic and beyond. A Comparative Analysis between Rural and Urban Schools in Zimbabwe

This paper was motivated by the increased digital disparity between rural and urban learners in Zimbabwe. It was further exacerbated by the advent of Covid-19 pandemic in 2020, which disenfranchised rural learners as schools had no other alternative, except adopting eLearning platforms. The study's main objectives were to determine the preparedness of rural schools in adopting eLearning and conducting a comparative analysis between rural and urban schools on equitable access to eLearning during Covid-19 crisis. In that respect, the study pursued a mixed research methodology grounded on a pragmatist philosophical view. In line with that, an exploratory case study strategy became prime in gathering both qualitive and quantitative from 112 participants found in Goromonzi district schools of Zimbabwe. Ethnography was used to gather qualitative data between 2020 and 2021, whilst a survey contributed to quantitative data gathering. In that respect, findings from the study suggests that rural schools were largely disadvantaged by the adoption of eLearning during the Covid-19 pandemic period, as very few learners could attend online schools. More so, there was an increased educational inequality between rural and urban learners, mainly attributed to lack of ICT infrastructure and resources. Therefore, the study proposed an eLearning adoption strategy, which could be adopted by key stakeholders in the school education system during crisis periods. © 2022 IST-Africa Institute and Authors.

How the Covid-19 crisis shaped research collaboration behaviour

The paper explores the impact of Covid-19 on scientists' collaboration behaviour in the 14 countries with the largest research output. The approach is bibliometric, taking the unit of analysis of collaborations as the individual researchers and the co-authorships in their preprints. The time plot of the data confirms a clear discontinuity in the number of preprint depositions after the Covid-19 outbreak. Less evident is the discontinuity in average number of co-authors per preprint, and also in propensity to collaborate. Investigating further, a multivariate econometric analysis shows that for propensity to national collaboration (both intra- and extramural) there has been a positive “effect” from the pandemic, but negative one on international collaboration. The same analysis conducted by country, however, reveals that these effects are significant only in some countries and often with discordant signs. © 2022, Akadémiai Kiadó, Budapest, Hungary.

Customer Prioritization Integrated Supply Chain Optimization Model with Outsourcing Strategies.

Pre-COVID-19, most of the supply chains functioned with more capacity than demand. However, COVID-19 changed traditional supply chains' dynamics, resulting in more demand than their production capacity. This article presents a multiobjective and multiperiod supply chain network design along with customer prioritization, keeping in view price discounts and outsourcing strategies to deal with the situation when demand exceeds the production capacity. Initially, a multiperiod, multiobjective supply chain network is designed that incorporates prices discounts, customer prioritization, and outsourcing strategies. The main objectives are profit and prioritization maximization and time minimization. The introduction of the prioritization objective function having customer ranking as a parameter and considering less capacity than demand and outsourcing differentiates this model from the literature. A four-valued neutrosophic multiobjective optimization method is introduced to solve the model developed. To validate the model, a case study of the supply chain of a surgical mask is presented as the real-life application of research. The research findings are useful for the managers to make price discounts and preferred customer prioritization decisions under uncertainty and imbalance between supply and demand. In future, the logic in the proposed model can be used to create web application for optimal decision-making in supply chains.

The impact of COVID-19 on the interdependence between US and Chinese oil futures markets

The oil futures market plays a vital role in the global financial system, especially after the negative future oil price rose during the COVID-19 pandemic. This paper investigates the COVID-19 impact on the interdependence between the US and Chinese oil futures markets by extending the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models with incorporating COVID-19 variables and by applying vector autoregression (VAR) models. Our study reveals that the COVID-19 pandemic enhanced the long-run correlation between the two oil markets. In contrast, daily changes in pandemic severity had a negative effect on the short-term transient correlation. Our results show that COVID-19 changed the one-direction causality from the US oil market to the Chinese market in the pre-COVID period to a bidirectional causal relation between the two markets during the COVID period. It strengthened the volatility spillover effect from the Chinese to US markets. These findings are helpful to regulars' monitoring oil supply chain risk and investors' cross-market hedging of spillover risks from a systematic risk perspective.

Impact of Covid-19 Lockdown on Engineering Education System;A Survey Based Case Study in Pakistan

The education system in Pakistan is suffering a lot of challenges in this modern world of advanced technologies. The engineering and technical education system is implementing modern techniques to improve the quality of education. The current disaster of coronavirus has badly affected the operation of engineering education in Pakistan, and student and faculty members are suffering from several issues in implementing a successful online education of digital semester. Each stakeholder has their own problems to make online education efficient. A questionnaire survey has been conducted with students as well as faculty members of engineering disciplines from different institutions. Different problems and issues are discussed with both stakeholders to observe the flaws which have been suffered by Pakistan’s higher education in the engineering sector. The effect of teacher’s academic profile, their attitude, professional field experience and advanced skills were discussed with 1000 students randomly. Similarly, 100 faculty members were questioned regarding the problems of the digital education system and pandemic effects on their performance. Various observations are concluded with this short survey-based research, and some suggestions are provided to improve the quality of education in this pandemic situation. The proposed solutions include the training sessions for teachers to get quipped with digital technologies, enforcement of lab sessions by opening all institutions at least one day a week, and professional experience requirements for the eligibility of academic staff. © Pakistan Academy of Sciences.

The Perceived Effects of Covid-19 on the Preferences and Expectations of Customers of Grocery Stores in Metro Manila

The novel coronavirus disease 2019 (COVID-19, caused by SARS-CoV-2) has spread globally and its impact cause many brick-and-mortar retailers including grocery stores to develop different strategies to operate amidst pandemic which includes revamping the area of facility management and layout, systems design and retail marketing. The objective of the study is (1) determine the relationship between the perceived COVID-19 effects and the buying expectations and preference of grocery customers, (2) determine the significance of the New Normal grocery operations strategy elements and practices and its moderating effect on the grocery customer buying expectations and preferences in view of the Covid-19 repercussions and (3) recommend a grocery operations strategy implementation platform to address the new buying preferences and expectations of grocery customers toward the new normal. With a sample size of 303 respondents of various individual that have experience buying grocery during pandemic answered the online questionnaire. Using Structural Equation Modeling (SEM), the result of the study showed that the COVID-19 effect have direct significant to the buying expectations and preference of grocery customers. Additionally the New Normal grocery operations strategy elements and practices also have significant direct effect to the buying expectations and preference of grocery customers. © IEOM Society International.

Impact of COVID-19 pandemic on Saudi medical students' career choices and perceptions of health specialties: findings from a national cross-sectional study.

BACKGROUND: The COVID-19 pandemic has had a devastating effect on people across the globe. Its impact on medical students' education has also been profound. Here, we aimed to comprehensively determine the nature of this impact on their choice of specialty. METHOD: A cross-sectional study was conducted among medical students in Saudi Arabia during the pandemic from May to June 2021. Data collected from 1984 medical students were analyzed. RESULTS: Of the total sample, 810 (40.8%) respondents reported that the pandemic could affect their choice of specialty, with the majority being in the third year (n = 235). Across all class-years, the most common reason chosen was the inability to explore specialties of interest (n = 539, 66.5%). Another reason cited was the inability to support residency application (n = 175, 21.6%). A majority expressed concerns regarding enrollment in research activities. As high as 17.9% (n = 356) of the respondents admitted that they were trying to avoid specialty with frontline exposure to COVID-19, while 353 students (17.8%) were considering local training programs only. While examining certainty levels, of the 1174 (59.2%) students who reported not being affected by the pandemic, 924 (78.7%) had a weak certainty level. The majority were in the third (54.8%, n = 342) and fourth years (44.8%, n = 212). CONCLUSIONS: This study is the first attempt to thoroughly examine the effect of COVID-19 on medical students' choice of specialty. This effect unfurled in 4 out of 10 surveyed students. Many students reported concerns regarding the inability to explore medical specialties and the inadequacy of obtained clinical knowledge. However, a subsidiary effect was observed among students who were assertive about their choice of specialty. These findings shed new light on the exigency of establishing a career counseling framework designed to meet individual learner needs, thereby galvanizing their morale. Further research could explore the long-term implications of the Saudi Commission for Health Specialties Matching System.

Information asymmetry, an obstacle to government support in the time of COVID-19 in Mexico

The COVID-19 pandemic has paralyzed businesses, causing a global economic crisis. In this paper, the support that the Mexican government intends to give to companies that fell into crisis is analyzed. The problem lies in a lack of knowledge of these companies: those that were in crisis before the epidemic and those that went into crisis because of it. To avoid economic and social losses, an asymmetric game is presented, the results show a mechanism to incorporate signals and improve uncertainty. The analysis deduces a threshold that determines a percentage of companies to support, showing that it is optimal to support 46% of the approximately 4 million MSMEs that exist in Mexico. The scarce official information limits the results, in addition to the fact that the model only shows a resource allocation mechanism and not an equilibrium, since the company player only emits an exogenous signal.

Effect of Covid 19 over Indian Domestic Power Sector: A Comprehensive Study

The year 2020 will be remembered a battle for existence of mankind against a super spreading virus Covid-19. While health-workers fought from the front, power industry stood like backbone to ensure proper support to handle the crisis. The covid-19 brought lots of changes in people's sociocultural, economic, day to day life. The fear of the pandemic along with its counter measure pushed many people to work from home. On the other hand, health care industry faced an unprecedented demand of oxygen, medicine, transportation, PPE, life support system etc. In this paper it has been shown that how the pandemic affected the different regions of Indian power industry by changing energy and power demand, load pattern, generation resource sharing and creating transients. Also, it describes how Indian Power Industry stood tall by successfully handling all these unprecedented situations. © 2021 IEEE.

Baseline Evaluation of COVID-19 Impact on Developing Countries Workforce by Machine Learning

A skilled workforce is the ultimate strength of the economy. COVID-19 has affected the continuity of the regular workforce remarkably across the world. A large number of the workforce had been splashed from operations, and a significant number had been continued operations from remotely. The aim of this research is to analyze the COVID-19 effects on the developing countries workforce like Bangladesh. In this paper, we have performed the statistical analysis and developed a machine learning model based on Bangladeshi IT company daily activities. The dataset has been separated into three parts based on periods such as pre-COVID-19, lockdown and post-lockdown periods. We have used several supervised learning algorithms such as SVM, LR, RF and DT for both classification and regression problem. From statistical analysis, it has been observed that regular operations of pre-COVID-19 have been disrupted during the lockdown period, and to minimize the damages of the lockdown period, company workforces are putting extra effort during the post-lockdown period. From machine learning analysis, it has been observed that random forest (RF) has performed better than other classifiers for both classification and regression problem. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The ambiguity of covid-19 effect on the latvian fishery sector

The dramatic change in various spheres of daily life caused by the COVID-19 virus epidemic has had many ambiguous effects on the Latvian fisheries sector. As part of a national research program project reCOVery-LV to study the virus’s effect on the supply chain, LLU researchers concluded that Latvian fish processing demonstrates a multidirectional effect. The interpretation of statistics and the interviews conducted confirmed the hypothesis that the virus has had negative and positive effects within one sector of the economy. This industry heterogeneity places high demands on support criteria, making them more targeted for successful risk management. The research aims to analyze the fishery sector and identify the COVID-19 pandemic effect on Latvia’s fish sector. As part of the study, all stages of the fish food chain were studied, risks were identified, their relative relevance was determined, and measures were proposed to neutralize these risks. This article summarizes the results of the study, prioritizes the implementation of countermeasures that reduce risks and are recommended by the results of the risk assessment, and complement the results of the study, identifies measures aimed at the long-term and sustainable development of the industry, based on the lessons of the COVID-19 pandemic. © 2021, Latvia University of Life Sciences and Technologies. All rights reserved.