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Impact of the Pandemic on Entrepreneurial Ecosystems
Entrepreneurship is crucial for the global economy, as it helps ideas develop from the drawing board to an executable stage. An excellent economic state of a country is the outcome of a well-designed system where the stakeholders interact with each other towards innovation and social development. This study is an empirical investigation into the COVID pandemic and its effects on the Indian entrepreneurial eco-system. Primary data was collected from 155 entrepreneurs of India, who were independent and first movers of entrepreneurs in their family during COVID times. Poor planning, exhausting resources, a slowdown in productivity, lower employment, and employee retention were the after-effects of the pandemic. It was found that the pandemic negatively affected the entrepreneurial ecosystem and its stakeholders. However, constant support by the government and well-designed policy measures would help assist existing businesses affected due to COVID-19 and encourage the entrepreneurial future in India. Copyright 2022, IGI Global. -
Optimized Multi-Scale Attention Convolutional Neural Network for Micro-Grid Energy Management System Employing in Internet of Things
The combination of micro-grid energy management systems (EMSs) with the Internet of Things (IoT) offers a promising way to improve energy use and distribution. However, challenges such as device compatibility and the difficulty of managing energy efficiently make it hard to implement these systems effectively. This study offers a significant advancement in energy management by using IoT for microgrid systems. An Optimized Multi-scale Attention Convolutional Neural Network for microgrid EMS employing IoT (OMACNN-MGEMS-IoT) is proposed in this study, which enables efficient monitoring and control of energy resources. The proposed model's input data are gathered from the MQTT dataset. This research employs a Regularized Bias-aware Ensemble Kalman Filter (RBAEKF) for pre-processing input data, ensuring the removal of outliers and updating missing values. The MACNN is then used for effective fault detection within the microgrid. To enhance its performance, the Sheep Flock Optimization Algorithm (SFOA) is introduced to optimize the MACNN parameters, ensuring accurate fault detection. Implemented on the MATLAB platform, the performance of the OMACNN-MGEMS-IoT method is assessed through various performance metrics, demonstrating significant improvements. Notably, the proposed method achieves higher cost reductions of 25%, 22%, and 26% compared to existing approaches such as the IoT platform for energy management in multi-micro grid systems (IoT-PEM-MMS), a micro-grid system infrastructure implementing IoT for efficient energy management in buildings (MSII-IoT-EEM) and a hybrid deep learning-based online energy management scheme for industrial microgrids (HDL-OEM-IM). The findings highlight the impact of the proposed OMACNN-MGEMS-IoT method in enhancing energy efficiency and cost-effectiveness in microgrid systems. 2025 John Wiley & Sons Ltd. -
Edge Computing in Aerial Imaging A Research Perspective
Internet of Drones (IoD) is a field that has a vast scope for improvement due to its high adaptability and complex problem statements. Aerial vehicles have been employed in various applications such as rescue operations, agriculture, crop productivity analysis, disaster management, etc. As computing and storage power have increased, satellite imaging and drone imaging have become possible, with vast datasets available for study and experiments. The recent work lies in the edge computing sector, where the captured aerial images are processed at the edge. Our paper focuses on the algorithms and technologies that easily facilitate aerial image processing. The applications and their architectures are focused on which can efficiently function using aerial processing. The various research perspectives in aerial imaging are concentrated on paving the way for further research. 2024 Scrivener Publishing LLC. All rights reserved. -
Enhancing IoT Security Through Multilayer Unsupervised Learning and Hybrid Models
This research addresses the challenge of limited unsupervised learning in current IoT security research, which heavily relies on labelled datasets, hindering the detection of unknown threats. To overcome this constraint, the study proposes a sophisticated methodology integrating K-means clustering, autoencoders, and a hybrid model (combining both). The aim is to enhance detection capabilities without being reliant on prior labelled data. Emphasizing the need to go beyond traditional models, the research underscores the significance of incorporating a diverse range of smart home IoT devices to gain comprehensive insights. Tests conducted on the N-BaIoT dataset, which incorporates authentic traffic data from nine commercial IoT devices afflicted with Mirai and BASH-LITE infections, demonstrate the effectiveness of the suggested models. K-means clustering demonstrates excellence in precision, recall, and F1-scores, particularly in Doorbell and Thermostat categories. The Hybrid model consistently achieves high precision and recall metrics across various device categories by leveraging the strengths of both Kmeans and autoencoder techniques. Notably, the Autoencoder model stands out for its exceptional ability to achieve a perfect 100% detection rate for anomalies across all devices. This study highlights the robust performance of the proposed unsupervised learning models, emphasizing their strengths and potential areas for refinement in enhancing IoT network security. 2024 IEEE. -
Finance future patterns in the market using artificial intelligence /
Patent Number: 202221052876, Applicant: Dr.Shiva Johri.
A methodology that makes use of natural language processing (N.L.P.) methods to extract information from online news feeds and then makes use of the information that was thus extracted to anticipate changes in stock prices or volatilities. This approach is known as news feed mining. These forecasts may be used to develop trading strategies that result in a profit. Parsing or pattern matching may be done on terms in or close to the phrase that contains the business name in order to identify the company name and automatically fill up basic templates that describe the acts that the organization does. -
Practices for measuring business in construction engineering organizations /
Patent Number: 202221034683, Applicant: Dr. Anil Zende.
The fundamental drives of every organization are profitability and achievement. The sustainability among these organizations relies on numerous elements that seem to have a substantial influence on performance. Estimating the implementation of sustainability organizations helps to discover weaknesses in terms of enhancing its productivity and profitability. Because of the enormous diversity of construction companies, it is harder for development organizations to develop or sustain a scientific approach for measuring their present effectiveness. Previous research utilized questionnaires and scientific and management consultations. -
Two-dimensional Ti3C2Tx MXenes as a catalyst support for the synthesis of 1,4-disubstituted-1,2,3-triazoles via azide-nitroalkene oxidative cycloaddition
Two-dimensional transition metal carbides/nitrides: MXenes have become a prime choice for researchers to exploit their outstanding properties for various applications in different fields majorly including energy, health and environment. Interestingly, there are no reports of utilizing 2D materials especially MXenes as a catalyst support for organic transformations. In the present study, we have utilized 2D Ti3C2Tx MXenes as a catalyst support for the synthesis of 1,4-disubstituted-1,2,3-triazoles via azide-nitroalkene cycloaddition for the first time. Reusability of Ti3C2Tx MXene catalyst up to five cycles without the loss of catalytic activity with appreciable yields of the product is the noteworthy feature of the present protocol. The synthesized 1,2,3-triazole derivatives possessing long alkyl chain upto fourteen carbon atoms on terminal nitrogen in triazole ring could become a good precursors to give a liquid crystalline properties beyond its biological properties. Nontoxic catalyst, catalyst reusability, broad substrate scope, and good yield are some of the salient features of the present protocol. 2023 Elsevier B.V. -
Research Potentials and Future Trends of Digital Sustainability
A once-in-a-century pandemic has left scars on countries already roiled by the increasingly erratic weather patterns induced by the climate crisis, wreaking havoc on sectors as diverse as food security, industrial production, and defence. Countries are torn between ameliorating COVID-19's devastating impact on education, health, and livelihoods of citizens, and finding their footing in a new global order. But from this ferment are emerging technologies, ideas, and solutions that will drive the world of the future; innovation and big ideas are building a vision that is bold and transformative. As the digital technologies evolve, its comprehensive impact on the environment needs to be considered to harness its full potential. Technology is transforming our world, but at the same time it brings new opportunities as well as challenges for sustainability. The unintended negative environmental impacts emerging from technologies are likely to be outweighed by potential of technology to solving it. Advances in technology, coupled with artificial intelligence, innovation in analytics, and data generation, is likely to have positive sustainability impacts. This chapter highlights the research potential and future trends of digital technologies for sustainability purposes. We intend to evaluate the implications of digital technology such as cloud computing, blockchain, Internet of Things, big data analytics, and artificial intelligence on pollution reduction, sustainable farming practices, conservation of biodiversity, and natural disaster management. Using real-life cases, we will investigate how digital technologies can be both an obstacle and enabler to global sustainability, which will enable devising appropriate digitalization strategies geared towards the achievement of sustainability. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
Cryptocurrency Market Anomaly: The Day-of-the-Week-Effect
Cryptocurrency has emerged as a fad amongst investors, academicians and policy-makers as a financial asset, making it important to empirically test the price behaviour of this emerging market. This paper is designed to investigate the presence of a well-known day-of-the-week effect in the young and emerging cryptocurrency market returns from August 2015 to March 2019. Using varied statistical techniques, this anomaly is examined for six cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, Stellar and Tether). The study applies both parametric and non-parametric statistical tests, i.e.,Bar Graph, Heat map, Students t-test, Analysis of Variance (ANOVA),regression analysis with dummy variables and the Kruskal Wallis Test. The studys findings show that no sample cryptocurrency returns exhibit the day-of-the-week effect phenomenon.The statistically insignificant result of the day-of-the-week effect in thecryptocurrency returns showcases the evidence of market efficiency in the cryptocurrency market. Indian Institute of Finance. -
Does Google Trend Affect Cryptocurrency? An Application of Panel Data Approach
Cryptocurrency has emerged globally as the most profitable investment asset of the decade. The media exposure and reportage on cryptocurrency are frequent, and it seems that prices of cryptocurrencies could only rise higher. In today's digital world, any individual's first go-to information-seeking platform is the Google search engine. Thus, it is imperative to understand how Google's search trend affects an investable asset and its market as a whole. Researchers have explored varied sentiment measurement proxies such as news coverage, Facebook and Twitter posts, and, most importantly, Google searches. Numerous research studies show increasing interest in Google search volume and its predictive ability to understand investment returns and economic outcomes. In a behavioural finance context, the present research uses Pearson's correlation and panel regression to examine the association of cryptocurrency returns (Bitcoin, Ethereum, and Ripple) and their varied characteristics with the Google search intensity. The study's findings reveal that investors searching for information on Cryptocurrency online drive the price increase in cryptocurrency and push the trading volume up and increase the volatility of the cryptocurrency returns. Furthermore, investor sentiment has a statistically significant impact on cryptocurrencies' trading volume and weekly volatility in periods of high or greedy investor sentiment. The findings imply that the 'price pressure hypothesis' given by Barber and Odean (2008) as a stock market research finding is also present in the cryptocurrency market. 2023 SCMS Group of Educational Institutions. All rights reserved. -
Effectiveness of Telemedicine in Disaster Relief Response Management
Due to climate change many parts of the worlds are prone to natural disasters. Thus, disaster management is the need of the hour. Effectiveness of Telemedicine in Disaster Relief response management shows the demand for telemedicine in the current time to tackle disasters. This paper investigates the history and evolution of telemedicine, their types, demand, challenges and its prospects. The proposed model, CrisisResponsive E-Health Recovery, places an approach on a concise way to manage disaster in the least time without giving up accuracy. The suggested model has the best response time as compared to the other existing model. Wide implementation of this model will result in better recovery rates in disasters. 2024 IEEE. -
Investigation of Spectroscopic Parameters and Trap Parameters of Eu3+-Activated Y2SiO5 Phosphors for Display and Dosimetry Applications
Using the solid-state reaction technique, varied Y2SiO5 phosphors activated by europium (Eu3+) ions at varied concentrations were made at calcination temperatures of 1000 C and 1250 C during sintering in an air environment. The XRD technique identified the monoclinic structure, and the FTIR technique was used to analyze the generated phosphors. Photoluminescence emission and excitation patterns were measured using varying concentrations of Eu3+ ions. The optimal strength was observed at a 2.0 mol% concentration. Emission peaks were detected at 582 nm and 589 nm for the 5D0?7F1 transition and at 601 nm, 613 nm, and 632 nm for the 5D0?7F2 transition under 263 nm excitation. Because Eu3+ is naturally bright, these emission peaks show how ions change from one excited state to another. This makes them useful for making phosphors that emit red light for use in optoelectronics and flexible displays. Based on the computed (1931 CIE) chromaticity coordinates for the photoluminescence emission spectra, it was determined that the produced phosphor may be used in light-emitting diodes. The TL glow curve was examined for various doping ion concentrations and durations of UV exposure levels, revealing a broad peak at 183 C. Using computerized glow curve deconvolution (CGCD), we calculated the kinetic parameters. 2024 by the authors. -
A Study on the Factors Affecting Infants' Health-Related Issues and Child Mortality using Machine Learning
Child mortality and infant health-related issues remain significant challenges worldwide. Understanding the factors that influence these outcomes is crucial for implementing effective interventions and improving child health outcomes. In this study, we employ machine learning techniques to identify and analyze the key factors affecting infants' health-related issues and child mortality. Further, we identify several significant factors that influence infants' health-related issues and child mortality. These factors include maternal health indicators, access to healthcare services, socioeconomic status, environmental factors, and demographic characteristics. The machine learning models provide insights into the relative importance of these factors, enabling policymakers and healthcare professionals to prioritize interventions and allocate resources effectively. Additionally, we investigate the potential interaction effects among these factors and their impact on child health outcomes. This analysis helps in understanding the complex relationships and causal pathways involved in infants' health-related issues and child mortality. The findings of this study contribute to the existing knowledge by leveraging machine learning techniques to identify and analyze the factors affecting infants' health-related issues and child mortality. The insights gained from this research can inform evidence-based policies and interventions aimed at reducing child mortality rates and improving infant health outcomes globally. By addressing the underlying factors identified through this study, we can work towards achieving better health outcomes for infants and reducing the burden of child mortality worldwide. 2023 IEEE. -
From physical to digital: what drives generation Z for mobile commerce adoption?
Purpose: This study aims to identify factors affecting generation Z as the early adopters of mobile commerce (m-commerce). The research seeks to explore their behavioral intention to adopt m-commerce in India with consideration of gender differences while providing empirical validation for the theory of planned behavior (TPB). Design/methodology/approach: In this study, a modified TPB model has been used to explain generation Zs intention to adopt m-commerce. The proposed model was tested using a survey method with a sample of 245 students from a private university in Northern India. Subgroup analysis was performed to find gender differences in the process of adopting m-commerce. Findings: All three independent constructs have a positive influence on the behavioral intention of generation Z to adopt m-commerce. Further, the male subgroup has a lower beta value for attitude and higher beta value for subjective norm in comparison to the female subgroup. For perceived behavioral control, no significant difference in beta value across gender could be established. Practical implications: A better understanding of generation Z behavioral intentions will be of great use to telecom companies, marketers and electronic commerce companies to formulate strategies to expedite the use of m-commerce. As gender plays an important role in attitude and subjective norms, companies are advised to target their communication tactics in accordance to gender. Originality/value: To the best of the authors knowledge, this study is one of the first to test TPB and generation Z association in the context of m-commerce adoption in India. Data regarding the difference between the two genders has also shed light on the uniqueness of the context. 2021, Emerald Publishing Limited. -
Examining the Domain of Green Finance Through Bibliometric Research Analysis of 22 Years (20002022): An Analytical Retrospective
Green finance has evolved as a concept aiming to integrate environmental protection and economic profits. Growing global concern towards climate change, greenhouse gas emissions, industrial pollution control, waste management, and environmental protection has caught the attention of countries and policymakers towards innovative financial products and services that are used to address a broader range of environmental concerns. Financial instruments such as green bonds, green stocks and loans effectively guide capital investment towards environment-friendly projects and promote the United Nations sustainable development goals. Thus, green finance is considered a compelling concept that plays a vital role in promoting sustainability. The authors used the Scopus database to perform a bibliometric review of green finance from 2001 to 2022 to determine the current trend and progress in the field. The article presents a thorough bibliometric and temporal analysis that provides inputs that other researchers on this topic have not evaluated. VoS Viewer and Biblioshiny. The software was used to visually analyse the data and identify patterns of co-occurrences and prominent research themes. The graphical and systematic mapping illustrates the evolution in publications over time and identifies areas of current research interests. The findings show that the research on green finance has gained momentum from 2014 onwards. The analysis provides a comprehensive overview of the green finance-related research, which will help the researchers and policymakers to examine better the trends and future direction of the development of green finance. 2023 MDI. -
FACTORS AFFECTING ORGANIC FARMING CULTIVATION AND THEIR ROLE IN ACHIEVING SUSTAINABLE DEVELOPMENT GOALS (SDG)
Objectives: The paper aims to explore the factors which influence the organic farming cultivation in India and how they lead towards attainment of SDG. Theoretical Framework: This paper has adopted Theory of Planned Behavior to identify the factors which influence the organic farming and define their relationship with SDG. Method: The paper attempts to explore the factors which influence the organic farming intention among the farmers. It further explores the relationship between intention for organic farming and adoption of organic farming. The data was captured through detailed questionnaire which was captured through 347 respondents. The data analysis was performed with the help of SPSS 29 and AMOS 29 The mediating role of motivation was also explored using process Macro 4.2. Results & Discussions: The findings revealed that environment and cost influence the organic farming intention while attitude and perception had no effect on organic farming intention. Adoption of organic farming shared a positive relationship with organic farming intention The Cultivation of organic products lead to attainment of following goals No Poverty (SDG 1), zero Hunger (SDG2), Good Health and Well Being (SDG3) and Responsible Consumption and Production (SDG 12). Research Implications: The study reveals that there is a need to raise the awareness level among the farmers regarding the role of organic farming, its environmental impacts and health benefits associated with it. The findings underscore the importance of implementing additional training and awareness programs targeted at novice and less-experienced farmers. Such initiatives aim to familiarize them with diverse aspects of marketing, economics, and social factors associated with organic farming. 2025, Editora Alumni In. All rights reserved. -
A phenomenological exploration of Indian women's body image within intersecting identities in a globalizing nation
The goal of the study was to examine Indian women's body image experiences utilizing an intersectional framework. Using phenomenological method, the study attempted to explore how experiences of gender oppression intersect with salient social identities to produce experiences of body dissatisfaction in Indian women. Thirty-Five Indian women in the age group 1840 years participated in semi-structured interviews. Overall, women experienced and discussed their bodies in terms of physical features they liked and disliked. Three themes emerged that comprised body image experiences of Indian women- (a) Beautiful, thin and fair- three social imperatives for women, (b) Internalization and (c) Body image management. Each of these impacted women negatively and contributed to greater body monitoring, increased indulgence in unhealthy behaviours and heightened body dissatisfaction. Women also discussed coping techniques for managing such experiences. Researchers and practitioners are encouraged to take into account culturally constructed beauty norms and unique socio-cultural factors for Indian women that determine body image. Findings are interpreted in the context of evolving socio-cultural norms that have recolonised Indian women's embodiment in a globalizing nation. 2023 Elsevier Ltd -
Mental Health Stigma: Strategies for Destigmatization in Healthcare Settings
Mental illness is one of the most common disabilities in the world. The term "mental illness stigma"describes harmful practices and misconceptions that lead to a detrimental effect on the mental health, motivation, and self-worth of those who suffer from mental illnesses. Health care services are important for treating and reducing the negative stigma of mental health, as they are areas where patients seek relief and support. The study aims to investigate the causes and how to reduce them. Explores ways to disrupt the health care environment, specifically the RESHAPE program, which focuses on the concept of "critical". This review paper looks at 8-10 papers on mental health and stigma and how stigma will be reduced. The results show that a large number of doctors and students are stigmatized, negatively affecting the lives of people affected by mental illness. RESHAPE, KAP, and IBH therapies are also effective ways to minimize mental health stigma. This intervention aims to educate public health workers, promote social cohesion, and integrate treatment into primary health care, improving treatment into primary health care, improving treatment quality and patient outcomes. The study draws attention to the importance of stigma reduction efforts in the long term in health education and practice emphasis. 2024 IEEE. -
European VLBI Network observations of the peculiar radio source 4C 35.06 overlapping with a compact group of nine galaxies
Context. According to the hierarchical structure formation model, brightest cluster galaxies (BCGs) evolve into the most luminous and massive galaxies in the Universe through multiple merger events. The peculiar radio source 4C 35.06 is located at the core of the galaxy cluster Abell 407, overlapping with a compact group of nine galaxies. Low-frequency radio observations have revealed a helical, steep-spectrum, kiloparsec-scale jet structure and inner lobes with less steep spectra, compatible with a recurring active galactic nucleus (AGN) activity scenario. However, the host galaxy of the AGN responsible for the detected radio emission remained unclear. Aims. We aim to identify the host of 4C 35.06 by studying the object at high angular resolution and thereby confirm the recurrent AGN activity scenario. Methods. To reveal the host of the radio source, we carried out very long baseline interferometry (VLBI) observations with the European VLBI Network of the nine galaxies in the group at 1.7 and 4.9 GHz. Results. We detected compact radio emission from an AGN located between the two inner lobes at both observing frequencies. In addition, we detected another galaxy at 1.7 GHz, whose position appears more consistent with the principal jet axis and is located closer to the low-frequency radio peak of 4C 35.06. The presence of another radio-loud AGN in the nonet sheds new light on the BCG formation and provides an alternative scenario in which not just one but two AGNs are responsible for the complex large-scale radio structure. The Authors 2024. -
Lightweight Sybil attack detection framework for wireless sensor network with cluster topology
The development of communication and networking technology has made it possible for wireless sensor networks to play a significant role in many fields. Wireless sensor networks are vulnerable to a variety of security threats because of their remote hostile features. The Sybil attack, which generates several identities to gain access to wireless sensor networks, is one such devastating but simple to spread exploit. This Paper proposes a novel identity and trust-based system to ensure protection against Sybil attacks. Analysis of the RSSI and location parameter increases the accuracy. It recognises the attackers and broadcasts information about them to all adjacent sensor nodes. Additionally, it offers other crucial security features. 2025 Author(s).