Events In India Research Articles

Exploring the Future Rainfall Characteristics over India from Large Ensemble Global Warming Experiments

We investigated rainfall patterns over India for the period from 1951 to 2010 and predicted changes for the next century (2051–2100) with an assumed 4K warming from large ensemble experiments (190 members). We focused on rainfall patterns during two periods of present-day climate (1951–1980 and 1981–2010) and their projected changes for the near and far future (2051–2080 and 2081–2110). Our analysis found that the northeastern region of India and some southern regions received higher rainfall during the period of 1951–2010, which is consistent with daily observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE). In the warming climate, rainfall events in India are predicted to carry more precipitation, with the northeast and southern regions experiencing stronger rainfall events. The frequency and intensity of these events (with more than 20 mm of rainfall per day, on average) are also expected to increase. Overall, our study suggests that water-related disasters such as flooding and landslides could be much worse in India in the future due to climate warming.

Modelling volatility effects between stock, oil, gold and forex markets: Evidence from India

Although several studies on the integration of diverse stock markets have been conducted in the financial literature, most of them have focused on the integration and volatility spillovers across established stock markets. The present study explores the dynamics of integration and volatility spillover across gold, oil, forex, and stock markets during four significant events in India: the pre-changed government regime, the post-changed government regime, the post-Brexit referendum date, and the COVID era. Daily data from 2010 to 2022 is divided into four categories using the Chow test. This is done to examine if these events’ financial turmoil affects market interconnectivity. The unit root test determines data stationarity. The ARCH LM test examines series volatility clustering, and the BEKK GARCH test examines market volatility spillover. Results indicate that gold cannot be considered a hedge or safe haven. Secondly, market interconnectedness increased during the crisis period. Third, domestic political and geopolitical conditions globally do not increase the scale of spillover amongst financial assets, though they impact the spillover’s magnitude. The results of this study have several important implications for portfolio diversification and risk management.

1960s Bombay stars and fashion cultures: The queens in our hearts

This article looks at the influence of Hollywood stardom on Bombay films in the 1960s that gave birth to Indian female star figures: the influence of Audrey Hepburn and the political events in India had major roles to play. Stars, such as Sadhana Shivdasani, Sharmila Tagore and Mumtaz, occupy an important role in the advent of economic and sexual liberalization that became central to feminist (re)presentations. They also played a key role in creating the image of the ‘ideal’ Indian woman. Despite its many patriarchal-centred narrations, Bombay cinema occupied a prominent place in feminist engagement as it created more opportunities for dialogues and awareness for women than were generally available at the time in Indian culture.

아미타브 고시의 『그림자 선』에 나타난 인도 내셔널리즘과 포스트식민 윤리

This paper examines the ways in which Amitav Ghosh’s The Shadow Lines portrays the lives of its main characters in terms of ethics and freedom in the aftermath of the partition of India in 1947 and the partition of Pakistan in 1971. Instead of focusing on the binary opposition between nationalism and cosmopolitanism, this research aims to redefine the notion of them, thus raising the need for a new concept of ethics against the background of the historical events in India. By comparing the main characters’ ethical practices in the postcolonial context, the paper shows how cosmopolitanism based on alternative imagination and ethical life can contribute to bringing about the possibility of reconciliation and solidarity between the first world and the third world.

National biomechanics day in India – Lessons learned from a collaborative approach

National Biomechanics Day (NBD) is an educational outreach event aimed at raising awareness of the field of biomechanics among high school students. NBD celebrations growing internationally acted as a catalyst for us to conduct the event in India, where STEM-based education is emphasized. With a truly global collaborative effort and perhaps historically for the first time, virtual and in-person NBD events were successfully conducted in India. The current article provides perspectives from different stakeholders from the collaborative team about the successes and challenges associated with conducting these events and the path forward to growing biomechanics in India and elsewhere.

Assessment of knowledge, attitude, and practices toward pharmacovigilance among under graduate medical students of a tertiary care teaching hospital of central India

Background: Pharmacovigilance is the science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other possible drug-related problems. Adverse drug reaction (ADR) is global problem. It affects majority of population causing morbidity and mortality. Aims and Objectives: The present study was conducted to evaluate undergraduate student’s knowledge, attitudes, and practices (KAP) about pharmacovigilance and ADR reporting. Materials and Methods: KAP-based questionnaire was shared among undergraduate medical students using Microsoft forms to assess the information about knowledge, KAP of pharmacovigilance. The KAP questionnaire was analyzed question-by-question, and the percentage values were calculated with the help of Microsoft Excel software. Results: In our study, 238 under graduate responded to questionnaire, and 80.25% of the total participants defined pharmacovigilance up to the mark, 75.63% were aware of the purpose of pharmacovigilance. Majority of the students (92.02%) knew that which ADRs should be reported. However, most of them did not have any idea of number of days within which serious adverse event in India should report. About 94.12% students agreed that reporting of ADR is necessary and 97.06% of them thought that pharmacovigilance reporting should be made compulsory. Among the respondents, 25.21% had come across ADR in their ward postings, 64.29% under graduate were not trained on reporting ADR, and only 11.34% ever reported ADRs from. Conclusion: From this study, it was clear that undergraduate medical students have good knowledge, positive attitude but lack of practice toward pharmacovigilance.

Observational Evidence of Increasing Compound Tropical Cyclone‐Moist Heat Extremes in India

AbstractMoist heat stress can lead to the inability of the human body to cool itself due to the impact of high temperature and humidity. The co‐occurrence of tropical cyclones (TCs) and moist heat stress has considerable implications for India's dense population and infrastructure. However, the crucial linkage between TCs and moist heat extremes remains unrecognized. We used the cyclone eAtlas and ERA5 reanalysis to examine the temporally compounding TC and moist heat extremes over India from 1980 to 2021. We find that TC‐Heat stress events in India have increased recently, which can be attributed to the high‐intensity TCs originating from the Arabian Sea. The risk of TC‐Heat stress events is higher (than in other parts of the world) in India due to an overlap of peak moist heat and TCs occurrence during the pre‐monsoon (April–June) season. Landfalling TCs alter the thermodynamic environment causing the moist heat to peak over the region with increased frequency and intensity. The direct and compounded influence of TCs on moist heat can have substantial implications.

Modes of summer temperature intraseasonal oscillations and heatwaves over the Indian region

Over the Indian region, the pre-monsoon (i.e. April–May) is a dry summer season. The heatwaves, as well as local temperature variations during this season, are not associated with significant large-scale convective heating like the monsoonal modes, and several studies identified several drivers of heatwaves. Heatwaves are extreme events. Are these extremes arising from low-frequency intraseasonal modes, in the same way, extreme rainfall occurs on a synoptic or intraseasonal mode during monsoon? Studies do not explicitly point out the existence of temperature intraseasonal modes during April–May over the Indian region, and it is not clear if some of the drivers of heatwaves can also explain the April–May temperature variations as derivative of some modes. This study identifies the dominant pair of the intrinsic mode of temperature intraseasonal oscillations, which can also explain the heatwave spikes. The empirical orthogonal function based modes are isolated in the detrended surface temperature data to remove the global warming mode. It was found that the subtropical jet acting as a Rossby wave guide drives the first mode with pan India spatial modal signature, while the second mode is driven by the extratropical Rossby wave modes originating from the latitudes of the eddy-driven jet. Another important result is that the first (second) mode principal component shows a significant decreasing (increasing) trend from 1981–2020 period. The observed spatial heterogeneity in warming and the trend in the spatial distribution of extreme temperature events in India could also be explained by the trend in the two modes of oscillation.

Analyzing Extreme Events in Power Systems: An Open, Cross-Domain Data-Driven Approach

Over the past several years the electric power sector has been challenged by a number of extreme events around the globe. Significant societal and economic shocks were due to the rapid spread of COVID-19 around the world. In addition to the pandemic, there have been several extreme weather and societal disruptions to the electricity sector, such as the February 2021 Texas power outage and the 9 p.m. nine-minute blackout event in India.

Historical, Political and Cultural Consciousness in the Autobiographies of Nirad C. Chaudhuri

The proposed research paper seeks to trace the public, political, historical and cultural cognizance in two autobiographies of Nirad C. Chaudhuri. Nirad C Chaudhuri is one of the few Indian English writers who have used English language for non-fictional purpose alone. The Autobiography of an Unknown Indian (1951) and Thy Hand, Great Anarch! (1987) remained his best works. The Autobiography of an Unknown Indian gave an account of Chaudhuri’s childhood and student days till 1921. Thy Hand, Great Anarch! takes the account up to 1952.Both the autobiographies give a portrait of the slow but triumphant self-discovery of a powerful writer. During this process Nirad C Chaudhuri was in the grip of dramatic public events in India. Independence and partition of the continent in 1948 were widely hailed as statesmanship and the reparation of imperial wrongdoings, but Chaudhuri drew quite another conclusion- that his country had no future. Nirad C. Chaudhuri’s both autobiographies are more of a national than personal history as it reveals precedence to the environment over the product. And it is more of an exercise in descriptive anthology than autobiographies. Keywords: Autobiography, Indian Independence, character delineation, culture, British Empire

Investigating political polarization in India through the lens of Twitter.

Social media plays a pivotal role in shaping communication among political entities. Substantial research has been carried out for examining the impact of politicians’ social media usage and interactions on political polarization. Analysing political polarization is particularly significant for fragmented political systems like India where collaboration between parties is essential for winning support in parliament. Different topics of discussion between political entities may induce different levels of polarization. This study aims to examine the presence of polarization on Twitter social media platform with respect to different topics of political discussions among Indian politicians. The investigation is based upon two conflicting notions about social media in influencing political polarization. The first notion regards social media as a medium for interaction between different ideological users. The second opinion on the other hand focuses on prevalence of selective exposure in social media leading to polarization. The study will investigate the use of Twitter for forming communication ties in and between parties and the extent of divergence of opinions during political discourse. The investigation performs social network analysis and content analysis of the tweets posted by Indian politicians during some major events in India from 2019 to 2021. For an unbiased topic-specific analysis of polarization, some important topics related to Indian government policies, national security and natural disaster events have been considered. The findings of the study suggest that Twitter not only opens up communication spaces to Indian political users but also makes online political discussions among them polarized. Moreover, the extent of polarization varies with respect to topics of political discussions. Polarization is more for controversial and debatable topics than non-controversial ones.

Super Predictor of Indian Premier League (IPL) using Various ML techniques with help of IBM Cloud

Abstract: Indian Premier League matches are one in every of the foremost important events in India. it's professional cricket league in India contested by eight teams representing the various cities in India. The paper focuses on the performance analysis of the eight contesting IPL teams supported the runs of the team, wickets, decisions usurping winning the toss and Duckworth Lewis rule analysis. The IPL data from 2008 to 2019 is used for the player analysis. The team performances are visualized graphically using data analytics to render the interpretation in an exceedingly good manner. The performance data using visual analytics helps in selecting players for future matches and provides additional information on player yet as team profiles. Almost every IPL team’s Management use Analytics for better games. Not only team’s owners, there are several betting and fantasy cricket platform, which are highly rely upon analytics for his or her success. Analytics can help all of them for his or her success. The research paper tries to predict the IPL matches using machine learning models with variables like match id, inning, batting team, bowling team, over, ball, batsman, non-striker, bowler is super over, wide runs, bye runs, leg by runs, bowling runs, penalty runs, batsman runs, extra runs, total runs, player dismissed, dismissal kind fielder. to appear out the result it uses different machine learning models like statistical method model, Random Forest. The results of the study shows that for IPL game, Teams, Venue, Winning Toss, Venue of the Match and Decision after winning the toss are important influencers to win a match. Different Machine Learning helps to predict outcome of a match. Right selection of Machine Learning Model helps to extend Accuracy of Prediction. From Different Classification Models, Support Vector Machine, Decision Tree and Random Forest are best to predict outcome of an IPL games. All of the subsequent gives almost 88% accuracy Level. The study has been conducted from data of Kaggle. Secondary data has been used for the analysis.

Acceptance of electric vehicles: A dual-factor approach using social comparison theory and technology acceptance model

This study intends to explore the acceptance of electric vehicles by adopting social comparison theory and Technology Acceptance Model. To fulfill the objectives of the study, 400 data were collected from a four-day auto expo event in India, and Structural Equation Modelling approach was used to analyze the data. The results show that informational influence, value-expressive influence, and product innovativeness showed a significant relationship with electric vehicle acceptance. Perceived usefulness signified a mediation effect on the relationships of informational influence, value-expressive influence, and product innovativeness with the electric vehicle acceptance. Further, positive moderation effect of age, income and gender was identified on the paths between perceived usefulness and electric vehicle acceptance. Holistically, the study produces significant contributions to the factors used in the study and the electric vehicle sector at large.

Public Health Issue of Indoor Dilution Ventilation for Disease Prevention Versus PM2.5 in Intake Air in Auditoriums of Premier Engineering Institutes in India.

Background: Dilution ventilation by enhancing fresh air intake has been prescribed to reduce airborne infection spread during the COVID-19 pandemic. This is all the more important in assembly spaces like auditoriums. Premier technology institutes have large campuses with large auditoriums for academic and cultural events in India. These institutes serve as role models for society, where gatherings are essential, but there is also the possibility of transmission of all airborne respiratory infections, including tuberculosis, into the community. The fresh air taken in should also be filtered for pollution to prevent other lung issues.Aims: Fresh air intake and filtration have been studied in order to understand whether the outside air supplied indoors is filtered for PM2.5, which is a major ambient polluter in India.Settings and design/methods: In this study, the Right to Information Act of 2005 has been used to obtain first-hand information from the institutes with respect to the heating, ventilation, and air conditioning (HVAC) systems in their auditoriums. Twelve of the 19 institutes fall in cities with non-attainment of ambient air quality standards.Results: Eleven out of all those had recently integrated fresh air supply, and six replied in the negative. Only one out of all of them had appropriate filters.Conclusion: This study highlights the need for a possible trade-off between the use of air conditioners for thermal comfort + assumed protection against PM2.5, which is the switching off of air conditioners and manually opening up windows and using fans for ventilation. Indian HVAC design for gathering spaces, especially educational institutes, needs to factor in fresh air for dilution ventilation as well as PM2.5 filtration.

Potential Use of Extreme Rainfall Forecast and Socio-Economic Data for Impact-Based Forecasting at the District Level in Northern India

Due to the increase in extreme rainfall events in India, there is an urgent need for prior communication of the expected impacts and appropriate responses in order to mitigate the losses of lives and damage to property. Extreme rainfall events cause numerous casualties, damage to property and infrastructure and vast displacement of people. Hence, the development of an approach where the rainfall forecasts are well analyzed, associated risks are identified, and the probable impacts are clearly communicated to relevant stakeholders is required. In this study, we aim to develop a framework for generating the impact-based forecasts (IBF) and associated warning matrices over the selected districts of eastern Uttar Pradesh, India, by integrating the rainfall forecasts and the socio-economic characteristics such as population, economy and agriculture. The selected districts lack proper infrastructure, have poor socio-economic conditions and have been historically prone to frequent extreme rainfall. The basic idea is to estimate the impacts that could occur over various sectors of population, economy and agriculture and suggest appropriate actions in order to mitigate the severity of the impacts. To this end, we identify the vulnerable districts based on the frequency of the number of extreme rainfall forecasts (ERFs) in the past four years (2017–2020) and the nature of socio-economic conditions. We selected three vulnerable districts based on the expected impacts, i.e., Shravasti (high category), Gorakhpur (medium category) and Jaunpur (low category) and subsequently, the corresponding IBFs are generated. Furthermore, a warning matrix is created for each district which provides updated information regarding the potential risk for a district a few days in advance. This study is significant since it identifies the different levels of potential impact over multiple sectors of society, presents a framework to generate impact-based forecasts and warnings, informs about the expected impacts, and suggests mitigation actions to reduce potential damage and losses.

Super Predictor of Indian Premier League (IPL) using Various ML techniques with help of IBM Cloud

Abstract—Indian Premier League matches are one in every of the foremost important events in India. it's professional cricket league in India contested by eight teams representing the various cities in India. The paper focuses on the performance analysis of the eight contesting IPL teams supported the runs of the team, wickets, decisions usurping winning the toss and Duckworth Lewis rule analysis. The IPL data from 2008 to 2019 is used for the player analysis. The team performances are visualized graphically using data analytics to render the interpretation in an exceedingly good manner. The performance data using visual analytics helps in selecting players for future matches and provides additional information on player yet as team profiles. Almost every IPL team’s Management use Analytics for better games. Not only team’s owners, there are several betting and fantasy cricket platform, which are highly rely upon analytics for his or her success. Analytics can help all of them for his or her success. The research paper tries to predict the IPL matches using machine learning models with variables like match id, inning, batting team, bowling team, over, ball, batsman, non-striker, bowler is super over, wide runs, bye runs, leg by runs, bowling runs, penalty runs, batsman runs, extra runs, total runs, player dismissed, dismissal kind fielder. to appear out the result it uses different machine learning models like statistical method model, Random Forest. The results of the study shows that for IPL game, Teams, Venue, Winning Toss, Venue of the Match and Decision after winning the toss are important influencers to win a match. Different Machine Learning helps to predict outcome of a match. Right selection of Machine Learning Model helps to extend Accuracy of Prediction. From Different Classification Models, Support Vector Machine, Decision Tree and Random Forest are best to predict outcome of an IPL games. All of the subsequent gives almost 88% accuracy Level. The study has been conducted from data of Kaggle. Secondary data has been used for the analysis.

Extreme Flood Events in India and Attribution to Climate Change: An Analysis of News Media Coverage from 2013–2019 during Bihar Floods

AbstractThis study analyzes the news media coverage of floods in a major flood-prone state of India, Bihar. Although news media helps shape public perception and political actions, little analysis ...

Exploring the marketing related stimuli and personal innovativeness on the purchase intention of electric vehicles through Technology Acceptance Model

Consumers and marketers are at the cross-roads as both are engaged in purchasing and marketing eco-friendly products. Inter alia, e-vehicles are eco-friendly products that are a panacea to most of the environment related problems. Prior studies that intend to study the purchase intention of e-vehicles used Technology Acceptance Model. However, this research composed all the marketing related stimuli; namely relative product innovativeness, relative product advantage, relative price advantage, the incentives provided by the government and the personal innovativeness on the Technology Acceptance Model. An explanatory design was adopted and 402 samples were drawn from the four-day auto expo event in India. Data were analyzed with the use of structural equation modelling through SPSS 23 and AMOS. Results showed that all marketing related stimuli and perceived incentives played significant role in the purchase intention of e-vehicles.

IMPACT OF PANDEMIC CRISES ON CORPORATE RESTRUCTURING EVENTS IN INDIA

Coronavirus (Covid19) pandemic broke out in February march 2020 in India. To overcome the spread of this disease many social, travel, transportation and economical restriction were applied which affected Global as well Indian economy badly. Financial distress was the main impact of this disaster in Indian market. This paper aimed to analyse the impact of this pandemic on corporate restructuring events (M&A) in Indian economy. It was found by the study that in post pandemic, M&A events plummeted in India as the pandemic halted the whole economy which resulted as the company’s market value and shareholders wealth is drastically decreased. But in some industries which were not affected by the pandemic (like pharmaceutical), corporate restructuring emerged as reviving tool for such industries of India

Landslide Susceptibility Mapping using Frequency Ratio, a case study of Vythiriblock in Wayanad, the northern part of Kerala, India

Landslides are common and disastrous events in India's Western Ghats during the rainy season and various factors influence the occurrence of landslides. The present research aims to establish landslide susceptibility zones to control landslide hazards and risk effectively. Landslides occurred in the entire area of the Wayanad district. The study was carried out in vythiri taluk to do detailed surveys using remote sensing and GIS, GPS and field investigation. A total of 162 landslide locations are used to develop a landslide susceptibility map of this area. Landslide susceptibility map of this area was done by using the frequency ratio (FR) model. The landslide susceptibility map is based on factors that affect directly or indirectly land subsidence process. There are twelve landslide influencing factors taken for this study: Slope, Aspect, Land use/land cover, Rainfall, Vegetation, Soil, Distance to lineament, Distance to the road, Elevation, Curvature, Geomorphology and Lithology. The resulting landslide susceptibility map shows that 3.6 % of the region is in a low susceptible zone, 40.5 % is in a moderate susceptible zone, 49 % is in a high susceptible zone and 6.9 % is in a very high susceptible zone to landslides. According to this model, the area under the curve (AUC) for the success rate is 75.3 %. This study would support the relevant authorities in identifying potential risks in particular parts which could be used for landslide mitigation activities as construction development in the region increases.