Volume 12 | Issue 9 |

 Abstract

From the ancient times, sacred books of literature in Sanskrit and regional languages reflect India as multilingual, multireligious and multicultural nation. In our myth and religion land has been represented as a place to inhabit. In modern context, with migration from one place to another, land has also become a source to describe dislocation, alienation and suffering. It is the life of multitudes of individuals and societies that collectively forms the dominant ethos of an era and finds due expression in various forms of art. The art of fiction lies in the way it savors of humanity in whatever age it is written. Indian English novel, a sibling of its western counterpart was in its early stages influenced by the western art form of the novel. In the later phase of the twentieth century a new term Post-Colonial Literature came into literary jargon. With the advent of the new millennium it has been witnessed that young authors are dabbling in commercial or popular fiction and setting new benchmarks for bestsellers. Chetan Bhagat is one of those new-age Indian novelists who have redefined the concept of bestsellers in India. There are several parameters of computing the bestseller quotient. Applying the theoretical paradigms of popular literature versus high art to the fiction of Chetan Bhagat reveals that the author is a highly successful story teller as all his novels in a row have far exceeded the benchmark of a bestseller.

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Keywords: Literature, the Art of Fiction, Millennium Writings, Chetan Bhagat, Popular Fiction

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The present study investigates the policy preferences of older voters in Kolkata and Chennai, exploring how these preferences are associated with the rational choice theory and political behaviour. The survey examined various policy areas, including healthcare, social security, and education, to recognise patterns and differences in voter preferences between the two cities and the factors influencing these policy areas. Utilizing a sample of 914 respondents from Kolkata and Chennai, a regression analysis was conducted to assess the impact of these perceptions alongside demographic factors such as age, gender, income, education, caste, and city of residence. The model revealed significant predictors, with positive perceptions of senior citizen welfare, old-age schemes, and job opportunities showing strong influences on policy preferences. Additionally, age, gender, and income emerged as significant demographic factors, indicating that older individuals, females, and higher-income groups are more supportive of policies benefiting senior citizens. Findings revealed distinct policy preference trends among older voters in Kolkata and Chennai, highlighting the impact of regional variations on electoral choices.

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 Keywords

Rational Choice Theory; political behaviour; survey analysis; urban India

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Rapid development of technologies and the widespread use of mobile phones have resulted in various risks, such as spam and phishing attacks. SMS (short message service) is a text message service available in smartphones as well as keypad phones. So, the traffic of SMS increased drastically. Many fraudulent URLs and short links are sent through SMS, and we don't know if they are safe or not. So, spam classification has special attention and it is very important to classify HAM and SPAM for the benefit of every individual in the society. In this paper, we used different machine learning techniques and deep learning technique for SMS spam detection/classification. With the help of machine learning techniques such as Naive Bayes, logistic regression, and other classifiers, the spam detection model has been developed in this project. We used a dataset to train the machine learning and NLP method and Naive Bayes, Decision Tree and Voting Classifier.

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 Keywords

SMS, SPAM., CLASSIFICATION OF SPAM , SPAM DETECTION METHODOLOGY

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Air pollution is a pressing global problem that poses severe challenges to the environment and human health. It results from the release of harmful pollutants into the atmosphere, primarily through human activities. The burning of fossil fuels, industrial emissions, vehicular exhaust, and agricultural practices are among the major contributors to air pollution. As these pollutants accumulate in the air, they form a toxic mixture that degrades the air quality and poses a threat to ecosystems, wildlife, and human populations. Air quality monitoring systems and early warning systems can be Implemented for extensive air quality monitoring and they can provide real-time information on pollution levels. Combined with advanced modeling techniques, this data can help predict future levels of pollution and provide communities with an early warning so they can take precautions. Methods for predicting pollution levels include statistical models (e.g., linear regression, time series analysis), machine learning algorithms (e.g., support vector machines, random forests), Gaussian process regression, hybrid models, etc. Each method offers unique approaches to analyzing pollution data and making predictions. GP is a data-driven approach that excels in predicting future pollution levels due to its ability to analyze large datasets, capture complex nonlinear relationships, automatically select relevant features, optimize the model structure, and generate interpretable models.GP's flexibility, adaptability, and ability to handle diverse data types make it a promising method for accurately predicting pollution levels and aiding in decision-making and mitigation strategies. However, the choice of prediction method may benefit from combining multiple approaches and domain knowledge for optimal results. In our project, we have used GP for the prediction of NOx and SO2 for the Karve Road Ambient Air Quality Monitoring Station with promising results.

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Air Pollution, Genetic Programming, Prediction Model, Particulate Matter (PM), Air Quality Index (AQI), Data Mining, Machine Learning, Meteorological Variables, Environmental Monitoring, Statistical Methods, Synthetic Minority Oversampling Technique (SMOTE), Decision Tree Algorithm, Artificial Neural Networks (ANN), Performance Evaluation, Environmental Parameters.

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A study to assess the awareness regarding hepatitis and its prevention among general population in selected area of Moradabad (U.P), towards the partial fulfillment of the requirement for the degree of bachelors in nursing , at Teerthanker Mahaveer College Of Nursing, Teerthanker Mahaveer University, Moradabad, U.P during the year 2017-2018. The objectives of the study was to assess the awareness regarding hepatitis and its prevention among general population in selected area of Moradabad. And to find out the association between the levels of awareness about hepatitis in general population in selected demographic variable.A qualitative research approach was used to evaluated the awareness about hepatitis and it's prevention in general population in selected area of Moradabad, and to find out the association between the levels of awareness regarding hepatitis among general population in selected demographic variable. The research way for present study were Descriptive. The target number of people for study was general population in selected area of Moradabad. The sample size of the study was 100 and for gathering the data questionnaire checklist were prepared and the sampling technique used in this study was purposive sampling technique . Data analysis was done both the expressive and graphic statistics on the initiating of the objective and study hypothesis and to computed information, master data sheets were prepared. The association between demographic variables and awareness level was determined by chi square test. The result shows that 11% of general population having excellent level of awareness, 67% having good level of awareness and 22% having poor level of awareness. The maximum general has good level of awareness. And there was no significant association between level and demographic variables.

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Key Terms: Hepatits , Prevention , General Population , Moradabad

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This comprehensive study presents a rigorous mathematical analysis of shockwave formation and propagation, elucidating the intricate dynamics governing nonlinear hyperbolic systems. Employing a combination of asymptotic expansions and singular perturbation techniques, novel scaling laws governing shockwave initiation and evolution are derived. These scaling laws provide valuable insights into the nonlinear interactions between compressible flow variables, leading to the emergence of discontinuous shock profiles. The research investigates the mathematical structure of shockwaves in one spatial dimension, utilizing the compressible Euler equations as the governing framework. A high-order finite difference scheme is employed to numerically solve the Euler equations, validating the asymptotic analysis through careful comparison with numerical simulations.

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Shockwave dynamics, Nonlinear hyperbolic systems ,Asymptotic analysis, Singular perturbation techniques,Compressible Euler equations, Finite difference scheme

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This study discusses the relevance of the Pr?lambam hasta, a mudra frequently used in Bharatanatyam. Mudras, or hand gestures, are fundamental to Bharatanatyam, serving as a powerful means of expression and storytelling. They are used to convey specific meanings, emotions, and narratives. Each gesture has a distinct significance, allowing dancers to tell intricate stories without words. The Pr?lambam mudra is mentioned only in the 18th-century text B?lar?mabharata? of Maharaja Kartika Tirunnal Balaramavarma. This work is an extension of earlier classical texts like the Natyasastra by Bharata Muni and the Abhinayadarpanam by Nandikeshwara. Balaramabharatam is not merely a compilation but an adaptation of these ancient principles to the contemporary contexts of its time. It includes novel ideas on action, dramaturgy, and theatrics, reflecting the author's profound scholarship in music and dance.

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Pr?lamba hasta, B?lar?mabharata?, mudras.

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Gig Workers include independent freelancers, workers via online platforms, workers supplied by contract firms, call workers on demand, and temporary workers. The phrase "gig economy" describes a labour market where freelance or contract work dominates over full-time employment. In the gig economy, individuals typically work on a temporary, flexible basis, often as independent contractors, or freelancers, rather than being employed by a single employer. Workers, consumers, and business entities make up the gig economy. The gig economy serves as a link between unemployed people and full-time employment. Digital platforms do the labour in our economy. One of the primary attractions of the gig economy is the flexibility it offers workers. They can choose when, where, and how much they want to work, allowing for greater work-life balance and the ability to pursue multiple sources of income simultaneously.

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 Keywords

Gig economy, Redefining the work in the digital era, Employment opportunities, Platform workers, flexibility in working hour.

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ABSTRACT Disposable baby diapers are the most widely used type of diaper, occupying around 95% of the world market. This translates to an annual consumption of over 21 billion units in the European Union alone. Almost 700 kit of raw materials are consumed annually to manufacture disposable baby diapers, excluding packaging. The volume of diaper waste (dry weight) generated every year in the EU is equivalent to the annual municipal waste produced by over 1.4 million people. Likewise, the manufacture, distribution, use and waste management of diapers emit 2.7 Mt of greenhouse gases (GHG) per year. Pre-experimental one group pre-test and post-test design was used to assess the effectiveness of planned teaching programme on knowledge regarding diaper care among mothers of neonates in selected hospitals at Bangalore. In the view of the nature of the problem and to accomplish the objective of the study, the structured teaching programme was prepared focusing on diaper care from the usage to the disposal among mothers of neonates, the structured questionnaire was prepared to assess the effectiveness of structured teaching programme. Reliability of the tool was computed by using split half technique employing spearman browns prophecy formula, and validity was ensured in consultation with guides and expert's in the field of nursing. The study was carried out at Jayanagar general hospital 4thblock, Jayanagar, Bangalore. Structured questionnaire was used to collect the needed data, collected data was analysed using descriptive and inferential statistics. The results shown in pre-test out of 30 respondents the majority of the mothers of neonates 26 (86.67%) had moderate level of knowledge, about 4 (13.33%) of the samples had inadequate knowledge, and about 0(0%) had adequate knowledge. In post-test out of 30 respondents 28(94%) had adequate level of knowledge, about 2 (6%) of the samples had moderate level of knowledge, and 0(0%) had inadequate knowledge. The post-test mean value 28.40 was higher than the pre-test value 12.97. The mean difference between pre-test and post-test was 15.53 and the obtained paired t value is 37.499(p<0.001) was highly significant. The mean value of post-test was significantly higher than the mean value of pre-test. It is inferred that mothers of neonates had significantly gained in post-test knowledge of diaper care after structured teaching programme. The structured teaching programme was effective.

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Key words; Effectiveness; Structured Teaching Programme; Diaper care;

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Abstract: The rapid advancement of artificial intelligence (AI) technologies has significantly transformed various sectors, with the service industry being one of the most affected. This review article provides a comprehensive analysis of the impact of AI on the service industry, focusing on key areas such as customer service, operational efficiency, and personalization. We examine how AI-driven innovations, including chatbots, machine learning algorithms, and predictive analytics, are reshaping service delivery and enhancing customer experiences. The review highlights both the positive outcomes, such as improved service speed and tailored interactions, and the challenges, including ethical concerns and the potential for job displacement. By synthesizing current research and industry case studies, this article offers insights into the evolving dynamics of the service sector in the age of AI, providing a critical understanding of the technology's implications for businesses and consumers alike.

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Keywords: Artificial Intelligence, Industry: Services; Customer Service; Operational Efficiency; Personalization; Chatbots; Machine Learning; Predictive Analytics; Service Delivery; Customer Experience; Job Displacement; Ethical Considerations.

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