Forecasting gold prices in India using an ARIMA model

Over the years, the gold prices have been increasing rapidly. Covid-19 and its impact leads to rise in the prices of Gold in the year 2020. So many variables are mindful for increasing the gold cost in India and it leads to investment decisions of individuals and enterprises. Autoregressive Integrated Moving Average (ARIMA) is useful method to gauge time series data. In the paper we mainly focus on daily costs of Gold from the year 2018 to 2020 to determine and forecast the daily gold prices in 2021. Also estimate the error (%) between the observed and estimated values through ARIMA model. This study will provide the estimates of suitable ARIMA model (0,1,2) along with Autocorrelation function (ACF) & Partial autocorrelation function (PACF) from the selected data, The auxiliary source information shows the positive patterns for getting effectiveness, For quantitative examination and speculation selections of financial backers. © 2023 Author(s).

Spatial mapping of the incidence, case-fatality rate and recovery rate of COVID-19 in West Bengal: An epidemiology study

Information on the spatial and temporal distributions of COVID-19 cases is important for improved control, social distancing strategies and developing targeted prevention strategies. Towards this objective, we analyzed the spatial and temporal growth pattern of COVID-19 incidence and death counts in districts of West Bengal. This paper also analyzes the current trend or pattern of COVID-19 transmission in West Bengal. For this approach, COVID-19 data have been compiled from several sources, including the WHO, Ministry of Health and Family Welfare (MoHFW), and demographic data from Census of India (2011). This analytical study was conducted based on detailed data from 23 districts of West Bengal from May 31, 2020, till December 31, 2021. We used ArcGIS Software for map-making and different formulas to measure Incidence, CFR, and CRR, considering all possible scenarios. Up to December 31, 2021, Kolkata, the origin of the COVID-19 epidemic, had reported 337767 COVID-19 cases, while the confirmed cases in the surrounding districts North 24 Parganas, South 24 Parganas, and Howrah were 337091, 104268, and 102048, respectively. The top five districts with the highest incidence were Kolkata (7.51%), Darjeeling (3.66%), North 24 Parganas (3.36%), Kalimpong (2.85%), and Jalpaiguri (1.79%), had high risks of COVID-19. Therefore, identification of the case fatality, recovery rates, and spatiotemporal trends should be the first step to evaluate disease severity and develop effective policies to manage and control any new epidemic. These results are informative locally and useful for the rest of the world. © 2020 The author (s). Published by Zagazig University. This is an open access article under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/.

Significant variation in air quality in South Indian cities during COVID-19 lockdown and unlock phases.

With the spread of COVID-19 pandemic worldwide, the Government of India had imposed lockdown in the month of March 2020 to curb the spread of the virus furthermore. This shutdown led to closure of various institutions, organizations, and industries, and restriction on public movement was also inflicted which paved way to better air quality due to reduction in various industrial and vehicular emissions. To brace this, the present study was carried out to statistically analyze the changes in air quality from pre-lockdown period to unlock 6.0 in South Indian cities, namely, Bangalore, Chennai, Coimbatore, and Hyderabad, by assessing the variation in concentration of PM2.5, PM10, NO2, and SO2 during pre-lockdown, lockdown, and unlock phases. Pollutant concentration data was obtained for the selected timeframe (01 March 2020-30 November 2020) from CPCB, and line graph was plotted which had shown visible variation in the concentration of pollutants in cities taken into consideration. Analysis of variance (ANOVA) was applied to determine the mean differences in the concentration of pollutants during eleven timeframes, and the results indicated a significant difference (F (10,264) = 3.389, p < 0.001). A significant decrease in the levels of PM2.5, PM10, NO2, and SO2 during the lockdown phases was asserted by Tukey HSD results in Bangalore, Coimbatore, and Hyderabad stations, whereas PM10 and NO2 significantly increased during lockdown period in Chennai station. In order to understand the cause of variation in the concentration of pollutants and to find the association of pollutants with meteorological parameters, the Pearson correlation coefficient was used to study the relationship between PM2.5, PM10, NO2, and SO2 concentrations, temperature, rainfall, and wind speed for a span of 15 months, i.e., from January 2020 to March 2021. At a significant level of 99.9%, 99%, and 95%, a significant correlation among the pollutants, rainfall had a major impact on the pollutant concentration in Bangalore, Coimbatore, Hyderabad, and Chennai followed by wind speed and temperature. No significant influence of temperature on the concentration of pollutants was observed in Bangalore station.

Trends Analysis of Virtual Reality Research between 1987 and 2021

The COVID-19 pandemic has dramatically altered the way how we communicate with others. From ZOOM to Meta-verse, an increasing number of people are shifting to the virtual world for work and personal life. However, as a technology, virtual reality is still considered merely a device for immersive gaming for the young generation. Thus, despite itspotential, virtual reality is hardly discussed as acore technology enabling Metaverse, which provides a virtual world for everyone. Therefore, it is necessary to examine prior studies for an understanding full spectrum of virtual reality research. There are three primary aims of this study: 1. To trace the history of virtual reality research for providing a holisticview oftheresearch trajectory. 2. Todiscover prevalent topics during the last 34 years as well as highly cited papers and authors. 3. To find hub topics for identifying the direction of interdisciplinary research. © 2022, Tamkang University. All rights reserved.

Machine Learning Algorithm for Trend Analysis in Short term Forecasting of COVID-19 using Lung X-ray Images

With the development of medical technology, the diagnosis of lung diseases relies more on the determination of medical images. With increasingly huge data, a powerful data processing model is urgently needed to provide favorable support for this field. The goal of this study is to develop a computer-assisted method to identify COVID-19 from X-ray pictures of the lungs at the very beginning of the disease. The architecture is implemented as a software system on a computer that can assist in the affordable and accurate early identification of cardiac illness. The performance of CNN architecture is best among all other classification algorithms to detect COVID-9 from Lung X-ray images. The datasets consist of COVID-19 established cases for 4 weeks which included the X-ray images of the chest. Then the distribution of the data was examined according to the statistical distribution. For this prediction, time series models are used for forecasting the pandemic situation. The performances of the methods were compared according to the MSE metric and it was seen that the Convolutional Neural Networks (CNN) achieved the optimal trend pattern.

Digital Transformation Strategies, Practices, and Trends: A Large-Scale Retrospective Study Based on Machine Learning

The purpose of this research is to identify the areas of interest, research topics, and application areas that reflect the research nature of digital transformation (DT), as well as the strategies, practices, and trends of DT. To accomplish this, the Latent Dirichlet allocation algorithm, a probabilistic topic modeling technique, was applied to 5350 peer-reviewed journal articles on DT published in the last ten years, from 2013 to 2022. The analysis resulted in the discovery of 34 topics. These topics were classified, and a systematic taxonomy for DT was presented, including four sub-categories: implementation, technology, process, and human. As a result of time-based trend analysis, "Sustainable Energy”, "DT in Health”, "E-Government”, "DT in Education”, and "Supply Chain” emerged as top topics with an increasing trend. Our findings indicate that research interests are focused on specific applications of digital transformation in industrial and public settings. Based on our findings, we anticipate that the next phase of DT research and practice will concentrate on specific DT applications in government, health, education, and economics. "Sustainable Energy” and "Supply Chain” have been identified as the most prominent topics in current DT processes and applications. This study can help researchers and practitioners in the field by providing insights and implications about the evolution and applications of DT. Our findings are intended to serve as a guide for DT in understanding current research gaps and potential future research topics.

A Study on the Productivity Improvement of PropTech Forum Construction Company in Korea

Currently, in the domestic construction market, several companies are actively investing in the proptech sector to develop real estate development and high-tech technology, and the domestic proptech sector is continuously growing. However, the construction market is still dominated by sales projects that rely on short-term finance, and there is a lack of discussion between the government and companies on ways to improve productivity. In addition, productivity improvement, operation, and management are vulnerable due to reckless investment focused only on profitability of small companies, and there are significant differences in productivity, operational efficiency, and size among domestic companies. This study aims to study the operational status and productivity improvement measures of domestic proptech construction companies. Therefore, among domestic construction companies, construction companies belonging to the Korea PropTech Forum were selected as analysis targets and an analysis of corporate productivity was conducted. As an analysis method, the Malmquist productivity index analysis, which can analyze changes in productivity and efficiency over a specific period through various outputs, was used. As variables used in the analysis, capital and number of employees was derived from input factors, assets, operating income, and net income were derived from output factors. Through the Mamquist productivity index analysis, trend analysis was conducted according to time point changes, and the degree of change in productivity between different time points was compared and analyzed. Results show that productivity improved due to the overall increase in MPI by year, but the index of flows such as MPI during the entire period was different by period. This means that the factors affecting productivity improvement have changed by period, sensitively reacting to external factors such as the construction industry or the COVID-19 situation. This study is significant because it identifies changes in the productivity index of proptech construction companies and suggests efficient operation of companies and ways to improve productivity. © 2023, Success Culture Press. All rights reserved.

An overview and trend analysis of research on the relationship between urban streets and residents' health in China pre- and post COVID-19 pandemic.

Introduction: After the outbreak of COVID-19, the international community has been faced with various problems it has brought to cities. A large number of research projects and corresponding management measures were launched globally, trying to reduce the impact of COVID-19 on society. Among them, exploring how to maintain the health of residents by managing and updating the design of urban streets is one of the important issues regarding urban sustainability in the post-epidemic era. Methods: This study uses bibliometric analysis techniques to obtain an overview of the knowledge structure of 898 Chinese urban streets and residents' health relationship studies from the China National Knowledge Infrastructure (CNKI) database for two periods (1999-2019 and 2020-2022). Five aspects were analyzed in terms of the keyword domain co-occurrence network, topic evolution path, emergent terms, hierarchical clustering, and confusion matrix. Results and discussion: The findings revealed that studies focused on six broad themes: community residents, health surveys, health education, COVID-19, healthy city, and public health. Based on these findings, the paper compares and discusses research priorities before and after the outbreak and highlights areas for further research and attention.

Trends in Depressive Symptoms and Suicidality of South Korean Adolescents: Comparison of Expected and Observed Prevalence During the COVID-19 Pandemic.

PURPOSE: Depressive symptoms and suicidality of adolescents during the COVID-19 pandemic are emerging public health issues. However, there is a lack of representative studies on adolescents' mental health that considers the preceding secular trends. METHODS: This descriptive study used nationally representative cross-sectional data of Korean adolescents from the Korea Youth Risk Behavior Survey from 2005 to 2020 (N = 1,035,382). We utilized joinpoint regression analysis to explore the temporal prevalence trends of depressive symptoms, suicidal ideation, and suicide attempts. Based on the annual percentage change until 2019, the expected and actual prevalence in 2020 (N = 54,948) was compared to describe departures of prevalence from the trend line. These trends between sex, school level, ethnic status, and socioeconomic status were also compared. RESULTS: Considering the recent increase in secular trends until 2019, the actual observed values in 2020 were lower than expected by 13% in depressive symptoms, 20% in suicidal ideation, and 40% in suicide attempts. The gap between sexes, school levels, ethnic status, and socioeconomic groups was similar or narrowed in 2020 compared to previous trends. DISCUSSION: We observed a lower prevalence of depressive symptoms and suicidality among Korean adolescents than expected about 9 months from the beginning of the COVID-19 pandemic despite the recent increase in secular trends.

Multi-Institutional Trends in Gynecological Robotic Surgery in India: A Real-World Scenario.

Background Robot-assisted laparoscopic surgery in gynecology has grown exponentially compared to laparoscopic surgery. The probable reasons for the increased uptake of robotics are a shorter learning curve, three-dimensional vision, and increased dexterity compared to laparoscopic surgery, and precise surgery as compared to open surgery. This study compares the time trends of various parameters in robotic gynecological surgery in India over a decade. Material and methods In India, a retrospective analysis of all robot-assisted laparoscopic surgery for gynecologic diseases in five tertiary care hospitals was conducted between July 2011 and June 2021. Data were collected regarding demographic profiles, clinical and disease characteristics, and indications for surgery. Details related to surgery were collected, such as the number of ports, console and docking time, the procedure performed, total operative time, average blood loss, blood transfusion, and length of hospital stay. All the parameters collected were grouped into five years, and a comparison was made between the first five years (2011-2015) and the second five years (2016-2021). Statistical analysis, including descriptive statistics and trend analysis, was performed. Results During the 10 years, the total number of cases included was 1,501, out of which 764 were benign cases and 737 were pre-malignant/malignant cases. The common indications were uterine leiomyoma (31.2%) and carcinoma endometrium (28%). The mean age for benign cases was significantly lower than that for malignant cases (40.84 years and 55.42 years, respectively). Mean blood loss was significantly lower for benign indications (97.48 mL) than for oncological surgery (184.67 mL) and needed fewer transfusions. The mean length of stay (LOS) for benign (2.07 days) and malignant/ pre-malignant cases (2.32 days) and the mean BMI for benign (28.40) and for oncological patients (28.47) were similar in both groups. The docking time reduced significantly in the last five years. Conclusion The current retrospective study demonstrates an increasing uptake of robotic technology in gynecological surgery in India. Of the total cohort of cases, 70.9% of patients underwent gynecological robotic surgery in the last five years. A burst of adaptability happened for malignant cases in 2017 and benign cases in 2018, probably due to the increased availability of robotic platforms and improved awareness of technology and training among medical professionals. The number of cases has grown exponentially over the last five years in both benign and malignant/ pre-malignant scenarios; however, there has been a downward trend in the robotic surgery performed in the previous couple of years due to the uncertainty of the COVID pandemic.

Healthcare management and COVID-19: data-driven bibliometric analytics

Healthcare management and COVID-19 has been broadly studied during the recent few days, especially after declaration of the COVID-19 outbreak in almost all countries in the world. Therefore, the present research article aims to provide an extensive overview of the scientific literature about the study of healthcare management and COVID-19 for choosing the new topic of related research. It conducts four types of analyses where the first analysis is a trend analysis and other three analyses are related to network and density maps. The second analysis is analyzed decisively in order to produce all keywords, author keywords and index keywords co-occurrence network map and country co-authorship network map and tables summarizing the significant scientific trends under the present topics. The third analysis is analyzed purposefully in order to produce all documents, journals, authors and countries bibliographic coupling network maps and tables summarizing the significant scientific trends. The last analysis provides valuable approaching of the most significant used keywords on the research topic and the links among them using keyword co-occurrence network and density maps respectively.

The Effect of Abrupt Changes to Sources of PM 10 and PM 2.5 Concentrations in Three Major Agglomerations in Mexico

In the three major urban agglomerations in Mexico (Mexico City, Monterrey, and Guadalajara), a significant change to anthropogenic sources of air pollution happened in March–May 2020, when policies implemented to stop the spread of the COVID-19 virus in Mexico caused the reduction of some anthropogenic sources of air pollution. We study the effect of these significant changes to air pollution sources using satellite-retrieved aerosol optical depth (AOD) and particulate matter (PM10 and PM2.5) concentrations from ground stations. The Chow test was applied to study trend changes in PM concentrations from 1 January to 30 May 2020. The Mann–Whitney non-parametric test was then used to compare average PM concentrations in April and May pre-lockdown, during lockdown in 2020, and post-lockdown in 2021. The assessment was further performed by evaluating the exceedance of national air quality standard maxima. The trend analysis showed that PM10 concentrations were reduced during lockdown in Mexico City and Monterrey, whereas no change was found for PM10 in Guadalajara and PM2.5 in the three cities. Further analysis showed that in Mexico City and Guadalajara, average PM10 and PM2.5 concentrations decreased by 12% in April and May 2020. However, in Monterrey, average PM10 and PM2.5 concentrations increased by 2.76% and 11.07%, respectively, in April 2021 due to a severe drought that caused dry soils and dust around the city. The results of this research can be used to implement policies for reducing anthropogenic sources to improve the air quality in urban areas. [ABSTRACT FROM AUTHOR] Copyright of Atmosphere is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pre and Post-COVID-19 Financial Performance of Oil and Gas Companies: An Absolute and Relational Study of Financial Variables

The outbreak of COVID-19 affected all aspects of individuals at a worldwide level. It harmed the physically and economically of individuals, and lower the growth of economies, globally. India the third largest consumer of oil and gas was affected by the pandemic, negatively. The study is based on the data collected from the financial statements of Indian oil and gas companies available on the websites. The purpose of the study is to know the financial performance of the Indian oil and gas companies pre and post-COVID-19 pandemic period. The absolute and relational financial variables are applied to get the absolute trend and relational growth of the financial performance of Indian oil and gas companies in graphical form. To explore the profit-earning capacity, short-term paying ability, and long-term paying ability of the Indian oil and gas companies, profitability (profit before tax ratio) ratio, liquidity (current ratio) ratio, and solvency (Debt-Equity ratio) ratio were applied. From 2015 to 2021, the absolute values of revenues, total expenses, and profit before tax were applied to get the trend in graphical form while stacked column charts were prepared to compare the profitability, liquidity, and long-term paying ability of the Indian oil and gas companies. During the pandemic period, total revenues, total expenses, profits, and profitability declined while liquidity and solvency status was unaffected by COVID-19 in Indian oil and gas companies. The profitability of the smaller Indian oil and gas companies improved more than the larger Indian oil and gas companies after the COVID-19 pandemic. It is found that the large-scale production of Indian oil and gas companies was affected more than the smallerscale production of Indian and gas companies by the negativity of the pandemic COVID-19 due to fixed expenses. © 2023 WITPress. All rights reserved.

Medical waste generation during COVID-19 pandemic in selected member countries of Arabian Gulf region, Google trend analysis

The variant SARS-CoV-2 (COVID-19) has affected the entire globe;it has caused acute respiratory illnesses and has led to massive environmental problems associated with extra medical waste and single-use plastics. Herein the total quantity of waste generated in the selected GCC countries from different sources has been calculated. The popularity of the medical waste issue through using relative search volume was investigated to associate it with the quantities of Waste. Data was collected from the first COVID-19 case in the respective country until March 26, 2022, from the public domains, "Worldmeter”, "Our World in Data” and "Google Trends”. Autoregressive Integrated Moving Average (ARIMA) was used to predict the amount of CMW. The calculated values are 1107.980 kg/day from the facemask used by the public, 2,554,812 kg from vaccination, 2,595,772 kg from PCR tests, also the predicted daily total amount of COVID-19 related Medical Waste (CMW) by the end of 2022 (December 31, 2022) to be 3667.25 kg/day. The trend in the generation of CMW has been identified in selected countries. Generally, the waste is expected to get minimized, but it is a lesson for future planning. Especially With extensive vaccination, the cases are drastically decreased, and life is resuming in the regions. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study.

BACKGROUND: Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. Past research has linked misinformation to increased hesitancy and lower vaccination rates. Gaps remain in our knowledge about the main drivers of vaccine misinformation on social media and effective ways to intervene. OBJECTIVE: Our longitudinal study had two primary objectives: (1) to investigate the patterns of prevalence and contagion of COVID-19 vaccine misinformation on Twitter in 2021, and (2) to identify the main spreaders of vaccine misinformation. Given our initial results, we further considered the likely drivers of misinformation and its spread, providing insights for potential interventions. METHODS: We collected almost 300 million English-language tweets related to COVID-19 vaccines using a list of over 80 relevant keywords over a period of 12 months. We then extracted and labeled news articles at the source level based on third-party lists of low-credibility and mainstream news sources, and measured the prevalence of different kinds of information. We also considered suspicious YouTube videos shared on Twitter. We focused our analysis of vaccine misinformation spreaders on verified and automated Twitter accounts. RESULTS: Our findings showed a relatively low prevalence of low-credibility information compared to the entirety of mainstream news. However, the most popular low-credibility sources had reshare volumes comparable to those of many mainstream sources, and had larger volumes than those of authoritative sources such as the US Centers for Disease Control and Prevention and the World Health Organization. Throughout the year, we observed an increasing trend in the prevalence of low-credibility news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. Tweets by a small group of approximately 800 "superspreaders" verified by Twitter accounted for approximately 35% of all reshares of misinformation on an average day, with the top superspreader (@RobertKennedyJr) responsible for over 13% of retweets. Finally, low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts. CONCLUSIONS: The wide spread of misinformation around COVID-19 vaccines on Twitter during 2021 shows that there was an audience for this type of content. Our findings are also consistent with the hypothesis that superspreaders are driven by financial incentives that allow them to profit from health misinformation. Despite high-profile cases of deplatformed misinformation superspreaders, our results show that in 2021, a few individuals still played an outsized role in the spread of low-credibility vaccine content. As a result, social media moderation efforts would be better served by focusing on reducing the online visibility of repeat spreaders of harmful content, especially during public health crises.

Impact of COVID-19 pandemic on breast and cervical cancer screening in Denmark: A register-based study.

Background: Denmark was one of the few countries where it was politically decided to continue cancer screening during the COVID-19 pandemic. We assessed the actual population uptake of mammography and cervical screening during this period. Methods: The first COVID-19 lockdown in Denmark was announced on 11 March 2020. To investigate possible changes in cancer screening activity due to the COVID-19 pandemic, we analysed data from the beginning of 2017 until the end of 2021. A time series analysis was carried out to discover possible trends and outliers in the screening activities in the period 2017-2021. Data on mammography screening and cervical screening were retrieved from governmental pandemic-specific monitoring of health care activities. Results: A brief drop was seen in screening activity right after the first COVID-19 lockdown, but the activity quickly returned to its previous level. A short-term deficit of 43% [CI -49 to -37] was found for mammography screening. A short-term deficit of 62% [CI -65 to -58] was found for cervical screening. Furthermore, a slight, statistically significant downward trend in cervical screening from 2018 to 2021 was probably unrelated to the pandemic. Other changes, for example, a marked drop in mammography screening towards the end of 2021, also seem unrelated to the pandemic. Conclusions: Denmark continued cancer screening during the pandemic, but following the first lockdown a temporary drop was seen in breast and cervical screening activity. Funding: Region Zealand (R22-A597).

Changes in work-related stressors before and during the COVID-19 pandemic: differences by gender and parental status.

PURPOSE: The COVID-19 pandemic changed people's working conditions worldwide and research suggests increases in work stressors. However, it is not known to what extent these changes differ by gender or parental status. In the present study, we investigate trends in work stressors and whether these differ by gender and parental status. METHODS: We used cross-sectional time series data of the European Working Conditions Survey of 2015 and Living, Working and COVID-19 survey of spring 2020 to examine trends in work stressors by gender and parental status. Work stressors were working in leisure time, lack of psychological detachment and work-life conflict. We applied three-way multilevel regressions reporting prevalence ratios and reported predicted probabilities and average marginal effects to show trends and differences in changes in work stressors. RESULTS: Our multilevel regression results showed elevated prevalence ratios during the pandemic for working leisure time (PR: 1.43, 95% CI 1.34-1.53), psychological detachment (PR: 1.70, 95% CI 1.45-1.99) and work-life conflict (PR: 1.29, 95% CI 1.17-1.43) compared to before the pandemic. Except for working in leisure time, the increase was more significant among women and mothers. The proportion of work-life conflict in 2020 was 20.7% (95% CI 18.7-22.9) for men and 25.8% (95% CI 24.0-27.6) for women, equalling a difference of 5.1% (p < 0.001). CONCLUSIONS: There is evidence that work stressors increased disproportionately for women and mothers. This needs to be monitored and addressed to prevent widening gender inequalities in the quality of work.

Driving Financial Inclusion: Technology as an Indicator of Financial Ecosystem Development During the COVID-19 Pandemic in India

This paper examines India's level of digital access to financial services as compared to other Asian countries. The study also intends to analyse whether COVID-19 has influenced the usage trend of the selected digital payment indicators in India. Data has been collected from the World Bank Global Findex Database and RBI bulletins. Cross country descriptive analysis was used for studying India's digital financial access against the other Asian countries. Event study methodology followed by trend analysis was employed to examine whether COVID-19 has impacted the digital payment indicators' usage in India. The findings of the study indicated that India's position in digital financial access needs to be improved. It was further identified that COVID-19 has increased the usage of digital modes for financial transactions in India. There has been a significant increase in the usage volume of mobile banking after the declaration of the pandemic. Govt. can frame its action plans to make use of the opportunity created through the pandemic to improve digital financial access in India.

Reshaping University Continuing Education: Leadership Imperatives for Thriving in a Changing and Competitive Market

University continuing and distance education is at a crossroads. This article asserts that a radical disruption in the competitive landscape for university-based continuing education (CE) is on the immediate horizon. University CE is threatened by external trends beyond its control and will likely not survive in its present form, or survive at all, without adapting to the changing marketplace. This disruption is occurring at the same time and is partially due to the increasing university demands on CE units to produce more income in part by scaling new markets and increasing customers in new ways. The signals and drivers of that change will be detailed, followed by a description of the competitive landscape that university CE will soon face. Universities will experience barriers in combating this competition, which, if not overcome, will spell the decline of many CE units and disruptions will also manifest themselves into major problems for the university as a whole. This article concludes with recommendations for CE and university leaders that may allow them to thrive and prosper as a part of that dynamic landscape.

Text Mining Approach for Trend Tracking in Scientific Research: A Case Study on Forest Fire

Scientific studies are increasing day by day with the development of technology. Today, more than 171 billion academic records are made available to researchers via the Web of Science database, which is frequently followed by the scientific community, and is where records of articles, proceedings, and books in many different fields are kept. More than 40 thousand studies are reached when a search is made for research on forest fires in the relevant database. It is unfeasible to examine and read so many publications and understand what topics are important in the relevant field, what is trending, or whether there is a difference between the subjects studied based on years and/or regions/countries. The most effective and scientific method of deriving information from such large and unstructured data is text mining. In this study, text mining is used to reveal where the research on forest fires in the Web of Science database concentrates, which study topics have emerged, how an issue's level of importance changes over the years, and which topics different countries focus on. Therefore, the s of approximately 32 thousand articles published in English were collected and analyzed based on the country of the authors and the published years. Over 600 words in the s were indexed for each article and their importance was calculated according to inverse document frequency. A size reduction was made to determine the main concepts of the articles by using the singular value decomposition and a total of 29 different concepts were found. Among these, important concepts can be mentioned such as damage to vegetation and species affected, post-fire actions, fire management, and post-fire structural changes. Considering all the articles, studies on soil, fuel (biofuel), treatment, emissions, and species were found to be important. The results we have obtained in this study are by no means a summary of the research carried out in the field;they do, however, allow statistical due diligence concerning, for example, which subjects are important in the relevant field, the determination of increasing and decreasing trending topics, which countries attach importance to in the same research, and so on. Thus, it will function as be a guide in terms of the direction, timing, and budget allocation of research plans in a specific area in the future.