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An Energy Optimized Clustering approach for Communication in Vehicular Cloud Systems
Vehicular cloud networks are considered to possess faster transitional topology and mobility thereby adhering to its features as an ad hoc network. Many times, it is difficult to monitor vehicular nodes that results in internetworking concerns as a result of power inadequacy during real computation. This leads to lots of energy wastage issues encountered during routing which degrades lifetime of nodes. Thus in this study a new clustering based energy optimization method is proposed to enhance the efficiency of vehicular communication. K-medoid cluster analysis along with dragonfly approach is applied to the system model to optimize energy. On the basis of simulation undertaken, it is recorded that the network lifetime, packets delivered, processing delay and throughput are increased using the proposed model. 2023 IEEE. -
Child sexual abuse prevention involving mothers: A Quasi-experimental study with Protection Motivation Theory-based intervention
Child sexual abuse (CSA) is a worldwide problem. The safety of children is everybody's responsibility. Parental and family involvement is crucial in the CSA prevention process. Parental lack of knowledge may increase the risk of abuse among children. Educating parents is crucial as children are mostly dependent on them. The role of mothers in educating children on CSA is of relevance as they are identified as more sensitive and responsible caregivers. The present study aimed to promote knowledge and attitude towards CSA prevention among mothers through a psychoeducation program. A pre- and posttest design (n = 67) was used with an intervention based on Protection Motivation Theory (PMT). A CSA knowledge and attitude scale was developed for the assessment of the participants for the study. There were significant changes in knowledge and attitude among the participants after the intervention. The prevention of CSA will be effective with knowledge gains after the PMT-based intervention. The intervention programs that involve mothers in CSA education have benefits. The findings of this study can be helpful to incorporate public health approaches to devise evidence-based parental programs in community settings. 2022 Wiley Periodicals LLC. -
IoT based heart monitoring and alerting system with cloud computing and managing the traffic for an ambulance in India
Global Burden of Disease Report, released in Sept 2017, shows that Cardiovascular Diseases caused 1.7 million deaths (17.8%) in 2016 and it is the leading cause of deaths in India [1]. According to the Indian Heart Association, 25% of all heart attacks happen under the age of 40. In most cases, the initial heart attacks are often ignored. Even post-diagnosis, as per government data [2], 50% of heart attack cases reach the hospital in more than 400 minutes against the ideal window time of 180 minutes; post which damage is irreversible. The delay is often attributed to delay in reaching a hospital or receiving primary aid. In India, traffic conditions also add to the grimace of the situation. Although the government is taking various measures; a holistic solution is required to minimize the delay at each of the steps like accessing the patient situation, contacting the Medical aid or making available the nearest aid possible. In this paper, we aim at providing the holistic solution using the Internet of Things technology (IOT) along with data analytics. IoT enables real-time capturing and computation of medical data from smart sensors built-in wearable devices. The amalgamation of Internet-based services with Medical Things (Smart sensors) enhance the chances of survival of patients. The proposed system analyses the inputs collected from the sensors fit with the patients prone to cardiovascular diseases to ascertain the emergency situation. In addition, to these data, the system also considers age, maximum and minimum heart rate. Based on computational results received from the input parameters, the system triggers the alert to emergency contacts such as the close relatives of the patient, doctors, the hospitals and nearby ambulance. The proposed system combines with the optimized navigation platform to guide the medical assistance to find the fastest route. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Smart-Beta Strategies in India: Analysis of Performance and Exposure of BSE Strategy Indices
The study examines the performance and exposure of BSE strategy indices and also compares them between the pre and post-covid periods. Though the monthly returns of some of the indices were significant, no indices outperformed the Sensex across the sample periods. On the risk-adjusted terms, quality, momentum, and low-volatility indices offered a significant alpha in the pre-covid, but post covid, all indices failed. The indices, except momentum, performed better in post covid compared to pre-covid but are insignificant due to the post covid high volatility. Momentum and value indices offered a predominant performance in the pre and post covid periods respectively, while the dividend index failed in both periods. While the market factor is the prominent driver of return for the indices, the size effect on their performance is insignificant across the sample periods. The indices, irrespective of their pre-covid exposure, gained significant exposure to loser stocks post covid. Though the exposure varied between pre and post covid, the change in the exposure was not significant except for the quality and dividend indices. They offer limited intended factor exposure and some extent of unintended exposure, as in the case of the momentum index in the pre-covid period. Indian Institute of Finance.. -
Impact of COVID-19 on NSE Sectoral Indices
The paper examined the level of impact of COVID-19 on different sectors of the Indian economy using NSE sectoral indices under the event study methodology. Multiple events and event windows were considered to examine the sectoral impact of the pandemic on different events and at various stages of the outbreak. Amongst the specific events considered, the lockdown announcement had a significant impact on sectoral indices, whereas the stimulus package announcement had limited impact and the 1st COVID-19 case had no impact on sectoral indices. The impact was intensive in the prelockdown period leading to the intensive lockdown, there was not much impact in other phases. The overall impact of the COVID-19 outbreak was significantly negative on Banks, Financial Services, and Realty indices, and positive on Pharma and FMCG indices. The media index showed no significant impact of the pandemic. Indian Institute of Finance. -
Factor investing: evidence of long-only factor portfolios from the Indian market
The study examines the performance of long-only factor portfolios in the Indian market. An extended 8-factors model and well-known factor models are used to analyse the exposure and risk-adjusted performance of factor portfolios. The results reveal a mixed portfolio performance: market-driven factors like illiquid, winner, stable, and small offered better performance than those based on fundamental data like value, strong, and conservative. While the market factor is the primary return driver, the SMB and HML factors are the other standard return drivers. The portfolios showed exposure to the specific factor they are constructed upon, except for the strong portfolio. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Smart Beta Investing in India : Portfolio Construction, Implementation, and Evaluation
The smart beta strategies, having marked their footprint in the developed markets in the last decades on the backdrop of the failure of active investing, are capturing emerging markets such as India recently. In this regard, the study attempts to examine the performance of smart beta strategies in long-only, multifactor, and alternative indexing frameworks in India. The study builds alternatively weighted (AW) univariate portfolios. Firstly, the cap-weighted (CW) single-factor portfolios are built. Subsequently, the portfolios are alternatively weighted and compared to the CW portfolio. Next, the CW multifactor portfolios are built and compared with singlefactor portfolios. Finally, the AW multifactor portfolios are built and newlinecompared with CW multifactor portfolios. All the portfolios are tested for their significant performance relative to the risk-free rate, market, and alpha under factor models. The portfolios were constructed from the constituents of NIFTY 500, adjusting for survivorship bias. The sample period spanned over 21 years from 01/10/2000 to 31/09/2021. The hypotheses were tested using the One-Sample T-test or Wilcoxon Signed Rank test for the difference in return, based on return distribution, and the Wald test for the difference in alpha and exposure using the Seemingly Unrelated Regression framework. The portfolios were constructed and analyzed using Python. We find mixed evidence of factor presence; the factor portfolios built on market data such as Illiquid, Winner, Stable, and Size offered better performance than those built on fundamental data such as Value, Strong, and Conservative. The Integrated portfolio does not differ from Mixed and single-factor portfolios, except for underperformance against the Illiquid portfolio. The alternative weighting offered mixed performance at single and multifactor levels. -
Smart beta investing in India portfolio construction implementation and evaluation
The smart beta strategies, having marked their footprint in the developed markets in the last decades on the backdrop of the failure of active investing, are capturing emerging markets such as India recently. In this regard, the study attempts to examine the performance of smart beta strategies in long-only, multifactor, and alternative indexing frameworks in India. The study builds alternatively weighted (AW) univariate portfolios. Firstly, the cap-weighted (CW) single-factor portfolios are built. Subsequently, the portfolios are alternatively weighted and compared to the CW portfolio. Next, the CW multifactor portfolios are built and compared with singlefactor portfolios. Finally, the AW multifactor portfolios are built and newlinecompared with CW multifactor portfolios. All the portfolios are tested for their significant performance relative to the risk-free rate, market, and alpha under factor models. The portfolios were constructed from the constituents of NIFTY 500, adjusting for survivorship bias. The sample period spanned over 21 years from 01/10/2000 to 31/09/2021. The hypotheses were tested using the One-Sample T-test or Wilcoxon Signed Rank test for the difference in return, based on return distribution, and the Wald test for the difference in alpha and exposure using the Seemingly Unrelated Regression framework. The portfolios were constructed and analyzed using Python. We find mixed evidence of factor presence; the factor portfolios built on market data such as Illiquid, Winner, Stable, and Size offered better performance than those built on fundamental data such as Value, Strong, and Conservative. The Integrated portfolio does not differ from Mixed and single-factor portfolios, except for underperformance against the Illiquid portfolio. The alternative weighting offered mixed performance at single and multifactor levels. -
Modelling the nexus of macro-economic variables with WTI Crude Oil Price: A Machine Learning Approach
Crude oil price shocks have a significant impact on aggregate macroeconomic indices like GDP, interest rates, investment, inflation, unemployment, and currency rates, according to empirical evidence. Various factors like GDP, CPI, and Gold prices show a considerable impact on the Crude old prices. The correlation analysis between these variables can help the machine learning model to find the highly impacting factor of the target variable. The advanced machine learning algorithms can be used to find the most relevant variable impacting the crude oil price followed by predicting the crude oil price. Time series analysis algorithms can forecast the crude oil prices for the specific period ahead. In the current study it was observed that US dollar and CPI show a high impact on Crude oil prices. The study has implemented six machine learning algorithms out of which the ARIMAX was found to be the most efficient model. VAR and ARIMA models are used to successfully forecast the crude oil prices for the next 5 years. From the current research, a machine learning model has been obtained as an outcome of the study, which will help economists in the future to understand the dynamics of crude oil prices driver and forecast it for the near future. 2022 IEEE. -
Database aware memory reconstruction for object oriented programming paradigm
Data storage is a big challenge in front of industries and researchers when its growing enormously. Traditional data storage strategy was fulfilling the business needs till the data was in structured format. But now due to Internet of Things (IoT) compatible devices unstructured data is more than structured one. In such cases traditional data storage strategy won't work efficiently. Initially data storage devices used to store the data irrespective of its logical storage. It means the record was stored either in array format or block format. Such type of storage was not matching physical and logical structure. Logically, structured data is generated as an attribute of particular entity, but physically it gets stored in a sequential memory storage either as file or as memory block. Object Based Storage pattern(OBS) stores the data in the way object gets generated by the programmer and accordingly memory is allocated for that object. Object contains all the data related to that particular entity which makes it easy to retrieve it back. Current study includes comparative advantages, operations and study of different service providers of object-based storage. We also focused on the current need of object-based storage structure for big data analysis and cloud computing. International Journal of Scientific and Technology Research. All rights reserved. -
Natural Language Processing on Diverse Data Layers through Microservice Architecture
With the rapid growth in Natural Language Processing (NLP), all types of industries find a need for analyzing a massive amount of data. Sentiment analysis is becoming a more exciting area for the businessmen and researchers in Text mining NLP. This process includes the calculation of various sentiments with the help of text mining. Supplementary to this, the world is connected through Information Technology and, businesses are moving toward the next step of the development to make their system more intelligent. Microservices have fulfilled the need for development platforms which help the developers to use various development tools (Languages and applications) efficiently. With the consideration of data analysis for business growth, data security becomes a major concern in front of developers. This paper gives a solution to keep the data secured by providing required access to data scientists without disturbing the base system software. This paper has discussed data storage and exchange policies of microservices through common JavaScript Object Notation (JSON) response which performs the sentiment analysis of customer's data fetched from various microservices through secured APIs. 2020 IEEE. -
The Metamorphic Influence of Cause-Related Marketing: Empowering Consumers as Catalysts for Societal Transformation
Purpose: In a market where prices and quality are fiercely competitive, companies have overflowed the market with a number of suitable brands. In the competitive business world of today, marketing tactics must always evolve to meet changing times and circumstances. Researchers have been motivated to discover the underlying aspects driving cause-related marketing strategy due to its global acceptability. This research elucidated the concept of cause-related marketing and emphasized the elements that motivate consumers to engage in such initiatives and influence their choice of products. Design/Methodology/Approach: The study was conducted with the help of a questionnaire sent to 480 respondents, out of which 432 questionnaires were found to be complete. Furthermore, the study examined the significance of each factor and its impact on decision-making using the confirmatory factor SEM model to analyze the data. Findings: The study revealed that Commitment was the utmost preferred attribute for the preference of cause-related marketing products. Practical Implications: A competitive edge may be obtained through cause-related marketing. Companies may provide financial support, increased awareness, and motivated actions for significant causes by making the most of their resources and efforts. This collaborative effort between companies and consumers has the potential to improve society significantly. Originality: The confirmatory factor SEM model has been used in this work to address urgent problems, new trends, or important information gaps. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Decoding Retail Realities: Traditional Retailers' Outlook on Sales Erosion to Modern Retail Economy
The traditional retail landscape in Indian metropolises has changed significantly in the last several decades, mostly due to the modern retail economy's growth, including corporate chain stores and e-commerce sites. Small merchants have been gradually displaced as a result of this paradigm shift, which has been exacerbated by changed Foreign Direct Investment (FDI) laws that have brought significant money into the Indian market, as well as a rise in consumer disposable income and the wave of digitalization. This study explores small merchants' consequences as they contend with the growing power of organized retail and e-commerce behemoths. Despite earlier research studies mostly focusing on the organized trade's exponential rise due to changing customer behavior, this paper fills this gap by illuminating the traditional retailers perspective towards the contemporary retail landscape and highlighting the threats to small businesses with a traditional focus. The study uses empirical analysis using tools like SPSS and SEM models to examine the initial troubles faced by small retailers of fast-moving consumer goods (FMCG), highlighting the difficulties they face in competing with the powerful forces of deep discounting, massive sales events, and evolving consumer tastes. This exploratory research analyzed the undermining factors like utilitarian and hedonic, purchasing patterns, menaces, hindrances, pecuniary and location as reasons for the retail paradigm from traditional to modern trade. The outcome emphasized that utilitarian factors like ambience, experience, status, variety, payment modes, single-store distribution and assortment are the drivers behind the explosion of traditional trade by the modern trade in retail economy. 2024 The Author(s). -
Analysing the Ascendant Trend of Veganism: A Comprehensive Study on the Shift towards Sustainable Dietary Choices
Background: Veganism has become a prominent social and culinary movement due to concerns about animal welfare, environmental sustainability, and ones own health. Vegans strive to consume only plant-based meals in order to lessen the suffering of animals, stop the environmental damage caused by the animal agriculture sector, and enhance their own health. Objective: This chapter aspires to understand various dynamics of consumer consciousness towards veganism through social media analysis (Twitter) and research opinions. Materials and Methods: This chapter used a qualitative approach and a three-part methodology. Firstly, a literature review examines the impact of veganism on human health, ethical needs and sustainable food choices. Secondly, the authors extracted tweets and analysed them using data visualisation software- NVivo with the essential parameters being themes, sentiment, world map, and word cloud. Results: Sentiment analysis explained consumer perception towards veganism as a storming blackball result of 36.1 present positive insights. Word map analysis describes veganism as a global phenomenon. The third part analysed the Scopus research data and identified food, diet and meat as major themes in veganism. The Scopus database sentimental analysis also re-emphasised the growing positive insights towards it. Conclusion: This study highlighted the significance of veganism as a sustainable dietary choice for addressing urgent global issues while promoting a thoughtful and compassionate approach to eating. It is also emerging as a powerful tool for positive change in preserving and promoting biodiversity. 2024 selection and editorial matter, Mourade Azrour, Jamal Mabrouki, Azidine Guezzaz, Sultan Ahmad, Shakir Khan and Said Benkirane; individual chapters, the contributors. -
Service industry alchemy: A symphony of digital innovations in customer engagement
The emergence of digitization, automation, and artificial intelligence has transformed service delivery, allowing businesses to increase productivity, tailor client experiences, and provide cutting-edge solutions. The delivery, use, and accessibility of services are changing in various service sectors due to innovations. Among them, healthcare, education, and finance have received considerable attention in recent years. To synthesize prior research on innovations in the service industry, the chapter attempts a thematic, sentiment, and bibliometric analysis of the research domain. For the analysis, data was extracted from the Scopus database and was filtered by application of inclusion-exclusion, with the use of NVivo and Bibliometric software VOS viewer. Most productive and influential articles, authors, journals, and affiliations were recognized. Thematic mapping and trend analysis revealed past and present research subdomains that were used for the prediction of future research agendas. 2024, IGI Global. All rights reserved. -
Sacred Roots: Rethinking Urban Landscapes via Ethnobotanical Narratives
This chapter discusses the synthesis of interdisciplinary research on the integration of sacred ethnobotanical knowledge with artificial intelligence (AI) into the present-day urban planning. This chapter draws upon a wide range of literature in the field of ethnobotany, ecotheology, urban ecology and digital innovation to explore how relationships between religious worldviews (including TEK), AI and green infrastructure can be used toward the enhancement of sustainable development of the urban. Review of current academic discourse on sacred plant landscape is emphasized above all, also examining the academic discourse on the nature of faith based ecological ethics and AI assisted urban greening strategies. I begin by reviewing ethnographic approaches and field-based studies that discuss the cultural and spiritual significance of sacred plants in Hindu, Islamic and Christian traditions, then examine service and trust as both a source and outcome for social infrastructure. It is critically analyzed how theological frameworks are ecologically applicable on the plural urban context. The review of AI integrated urban gardening initiatives provides a glimpse of how sensor data, machine learning models as well as mobile platforms are used to monitor plant health and plant biodiversity and how these can also be problematic on ethical front, justice, appropriation of knowledge and autonomy of community. The case studies from projects in Tokyo, Singapore, Ethiopia and Barcelona are placed within a global context and globally applied with a thematic synthesis in order to explore how, in practice, the coalescence of sacred ecological values and technological interventions occurs. The chapter discusses challenges of implementing policy, of cultural commodification, and of current interfaith collaboration models. The end of the review discusses the best practices and policy recommendations that can assist cities to join spiritual stewardship with digital ecological management to coalesce inclusive, biodiverse, and culturally grounded urban ecosystems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
In-vitro antioxidant analysis of Aristolochia indica, Ipomoea obscura, Tylophora indica, Glinus oppositifolius and Abroma augustum from Bankura district, West Bengal
Five therapeutic plants that have been utilized traditionally across the Sonamukhi Block of Bankura District, West Bengal, were tested for antioxidant activity using three assays: ABTS radical scavenging activity, FRAP reduction power, and DPPH free radical scavenging. According to the DPPH assay, Glinus oppositifolius (68.4%) and Ipomoea obscura (23.83%) showed moderate radical-scavenging activity, whereas Aristolochia indica (73.07%), Abroma augustum (52.87%), and Tylophora indica (25%) demonstrated the highest levels. While Glinus oppositifolius (0.685) and Ipomoea obscura (0.401) showed moderate activity in the FRAP assay, Abroma augustum (0.459), Tylophora indica (0.637), and Aristolochia indica (0.545) demonstrated significant reducing power. According to the ABTS assay, Aristolochia indica (90.37%) and Glinus oppositifolius (98.7%) had the highest levels of radical scavenging activity. These findings support the traditional medical usage of these plants, especially Glinus oppositifolius and Aristolochia indica, which showed the most antioxidant qualities. The results highlight the importance of these plants in traditional medicine, shed light on their therapeutic potential, and lay the groundwork for further research on natural antioxidant treatments. Authors CC4-NC-ND, ScienceIN. -
Herbal healing traditions: A study of folk medicines used by traditional healers of Sonamukhi block, Bankura district, West Bengal, India
This ethnobotanical study aims to dive into the traditional medicinal practices used by tribals in Sonamukhi Block, Bankura district, West Bengal, India. Focusing on the use of medicinal herbs, the study carefully investigates the intergenerational wisdom kept by traditional healers, who play an important role in healthcare administration. Through conversations with traditional healers and patients, a thorough list of medicinal plants used to treat various diseases has been developed. The study demonstrates that traditional healers successfully use these medicinal plants to produce herbal medications, offering insights into the painstaking process of herbal medicine preparation that was explored in depth during the investigation. These findings highlight the significance of joint efforts to preserve indigenous knowledge and encourage the incorporation of traditional healing techniques into contemporary healthcare. The research study focused on the complicated junction of scientific methodologies and traditional beliefs, indicating the possibility for effective collaboration between scientific institutions and indigenous populations to improve healthcare practices. The studies' thorough examination of medicinal plant usage and herbal medication manufacturing emphasizes the importance of preserving, recording, and using this unique traditional knowledge for the benefit of world healthcare. 2024 Horizon e-Publishing Group. All rights reserved. -
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Synthesis, impurity profiling, simultaneous NP-HPTLC method development, molecular modelling study and EGFR tyrosine kinase inhibitory profile: An integrated approach to characterize desethynyl erlotinib process impurity
The study presents an integrated approach to characterizing Desethynyl Erlotinib, a process impurity in the synthesis of Erlotinib, a potent EGFR tyrosine kinase (EGFR TK) inhibitor used in lung cancer treatment. A normal phase high-performance thin-layer chromatography (NP-HPTLC) method was developed and validated for the simultaneous profiling of Erlotinib and its Desethynyl Erlotinib impurity. The optimized method utilized ethyl acetate, methanol, and glacial acetic acid (9: 0.5: 0.5 v/v/v) as the mobile phase for effective separation and quantification. The method demonstrated excellent linearity for Erlotinib and its impurity over a concentration range of 200-1200 ng/spot, with R2 values of 0.9979 and 0.9998, respectively. Validation confirmed precision with intra-day and inter-day % RSD values of less than 2% and robustness. The limits of detection (LOD) and quantification (LOQ) were 5.18 ng/spot and 15.70 ng/spot for Erlotinib and 7.07 ng/spot and 21.43 ng/spot for the impurity. In-vitro assays against the A549 lung cancer cell line expressing wild-type EGFR tyrosine kinase (WT EGFR TK) showed that the Desethynyl Erlotinib impurity exhibits significant inhibition compared to Erlotinib, suggesting the potential toxicity of the Desethynyl Erlotinib impurity and causing side effects such as diarrhea, skin rashes and interstitial lung disease due to WT EGFR tyrosine kinase (WT EGFR TK) inhibition. Molecular docking and molecular dynamics simulations further corroborated greater stability in the Desethynyl Erlotinib impurity with WT EGFR tyrosine kinase (WT EGFR TK). Clinically, these findings highlight the importance of monitoring and minimizing impurities like Desethynyl Erlotinib to prevent adverse effects and maintain the therapeutic safety of Erlotinib in lung cancer treatment. This research underscores the necessity for rigorous quality control in Erlotinib production to ensure purity and therapeutic effectiveness. 2025 Har Krishan Bhalla & Sons.