bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultra Compact Square Slotted Flexible Antenna for Bio Electronics Application
Authors: Pathak Rajeev Shankar , Mishra Sadhana , Singh Vinod Kumar , Dwivedi Raghav
Published: September 2024

In this research, the authors introduce a novel wearable flexible square slotted antenna, enhanced by utilizing a unique substrate made from jeans material. The antenna operates at a resonant frequency of 5.29 GHz, making it suitable for various applications including satellite broadcasting, weather information systems, radar communication, and military operations. The designed antenna achieves a high directivity of 3.28 dBi at 5.29 GHz. A simple and concise comparison of simulated results with the measurement has been performed to verify the proposed antenna performance. The bandwidth of the textile antenna is 88.50%, which is highly noticeable as well. Here the polar plots of directivity patterns at phi=90° and phi=0° show that it always gives a good performance. Biomimetic antenna designs like the one reported here can bring with them significant potential for bio-electronic integration, delivering a life-like quality in flexible and high-performance wearable technology.
Development of Antenna Using Flexible Material for Wide Band Application
Authors: Kumar Neetendra , Yadav Neeraj , Singh Mayank , Verma Ramesh Kumar
Published: September 2024

This chapter illustrates a wearable antenna operating WLAN, C-band. Consumers today are looking for items they can take on the go without needing to break their backs carrying them. This makes broad range antenna demand broadband property in the lower power IoT apparatus, and hence, a wideband flexible dipole antenna intended for low-control internet of things gadgets has been produced on a fun substrate because some customers have requested it. This was to permit a flexible-structured substrate antenna. Naturally, the antenna is fairly small too, and works at a frequency band of 3.85 to 9.38 GHz. The range of frequencies it can handle is simply too wide. The designed antenna will accomplish a resonant frequency of 4.95 GHz. The directivity of the antenna is found as 3.198 dBi. The antenna has a broadband width of 83.59%. It will be an especially good fit for internet of things applications.
Prototype of Textile Antenna for Flexible Electronics and Mobile Health System Applications
Authors: Vishwakarma Akancksha , Tiwari Ratnesh , Singh Vinod Kumar
Published: September 2024

The ever-expanding field of flexible electronics requires that their components be adaptable and of high-performance, especially for communication networks. In this light, this study presents a novel textile antenna prototype designed for inclusion in modern flexible electronics, specifically for applications in advanced communication networks. The antenna will be fabricated using novel and flexible textile-oriented materials for it to be ideal for wearable and portable devices. Additionally, unsurpassed performance indicators such as bandwidth, directivity, frequency range, resonant frequency among others have been thoroughly researched for enhanced functionality. Specifically, the prototype exhibits a wide bandwidth of 110.34%, directivity of 5.14 dBi, and suitable in a frequency range 5.2-17.92 GHz, with the resonant frequency at 11.24 GHz. The applicability of the proposed antenna is in a variety of fields such as telemedicine and mobile health systems, and present-day communication systems in a smartphone system.
AI-Powered Business Evolution
Authors: Karulkar Yashodhan , Shah Arshi , Naik Rishab
Published: September 2024

This chapter, as per the authors, examines the multidimensional function of artificial intelligence (AI) in many industries, focusing on its strategic integration for organizational success, decision-making improvement, and data management efficiency. The chapter examines how AI-driven solutions are altering old processes and catalyzing innovation in the logistics, aeronautics, agriculture and the banking industry. In logistics, AI enables predictive analytics and route optimization, resulting in better supply chain management and operational efficiency. AI in aeronautics improves safety measures, optimizes fuel usage, and revolutionizes flight operations. In addition, the use of AI in agriculture provides farmers with precision farming techniques which allow optimized resource allocation and higher crop yields. Finally, AI in the banking industry streamlines operations, enhances customer experience, and enables data-driven decision making for personalized services and fraud detection.
The Effect of AI on HRM Practices
Authors: AlHarthi Tharaya Said
Published: September 2024

In the age of digital technologies and transformation, artificial intelligence (AI) has emerged as essential technology to be integrated into various business activities and practices. The integration of AI into human resource management (HRM) practices has a profound and transformative shift in the way organizations approach workforce management. This technological evolution has not only revolutionized traditional HR methods, but has also introduced novel efficiencies, reshaping the landscape of talent acquisition, employee engagement, and overall organizational effectiveness. In this era of rapid technological advancement, understanding the multifaceted impact of AI on HRM is essential for businesses aiming to stay competitive, foster a more responsive workforce and build its capabilities. By reviewing the existing literature, this chapter offers a systematic analysis to gain insights on the impact of AI on various HRM practices. In addition, it aims to explore how AI technologies assist organizations to tackle numerous challenges associated with HRM.
An Analytical Study on Emerging Growth of Artificial Intelligence in India
Authors: Gayathri J. , Yazhini J.
Published: September 2024

Artificial intelligence (A.I.) is a multidisciplinary field aimed at automating tasks that currently need human intelligence. Computerized medical diagnosticians and systems that automatically tailor hardware to specific user requirements are examples of recent AI accomplishments. A.I. has spawned a slew of important technical concepts that unify these disparate fields. The search is made complex because of the need to determine the relevance of information and because of the frequent occurrence of uncertain and ambiguous data. Heuristics provide the A.I. system with a mechanism for the focusing its attention and controlling its searching processes. A total number of respondents 206 samples in the age group 18-35 years were selected randomly and forwarded the analyzed question online randomly through a convenient sampling method. The use of SPSS software to analyze and present the data collected from the frequency Graphs,chi-square tests.
Reinforcement Learning Approaches in Cyber Security
Authors: Safeer Ehtisham
Published: September 2024

Reinforcement learning (RL) allows defense mechanisms to adapt to changing threats and has shown promise in tackling cyber security issues. This study presents a thorough introduction which includes foundations, uses, and difficulties to RL in cyber security. The efficacy of RL in making decisions is also emphasized in the introduction. Then the foundation for comprehending RL's use in cyber security, the fundamentals of the technology, and algorithm classifications is clarified. The study then delves into a number of RL applications in cyber security. Then a number of RL applications in cyber security and issues in RL is discussed. Along with prospects for improving cyber security safeguards through the application of RL methodologies, to successfully manage increasing cyber threats, future research directions are proposed with the integration of blockchain technology and generative adversarial networks (GANs). This work emphasizes the importance of RL in supporting cyber security and research to improve cyber defenses.
Reshaping Cybersecurity of Wireless Sensor Networks Using Energy-Optimized Approach Against Wormhole Attack
Authors: Shahid Hafsa , Ashraf Humaira , Jhanjhi N. Z. , Zam Qurat-ul Ain Zam
Published: September 2024

Security is a major problem in wireless sensor networks (WSN) because they are vulnerable to a variety of dangerous threats, the deadliest of which is the wormhole assault. These networks support functions like localization and other networking protocols. These networks are made up of many small sensors that collect and share data with central repositories. In such a setting, the detection of wormholes becomes more difficult because of low resource availability in detecting equipment. The authors propose to develop an algorithm for wormhole detection which performs efficiently within the given resource constraints in WSN environment. This research has initially performed a survey using systematic literature review (SLR) to study the schemes against wormhole attacks. Articles have been searched using reliable data sources and applied quality assessment measures to refine the article collection. The SLR has helped to find the shortcomings and limitations in the literature. This research elaborates a comparison of the wormhole attack prevention techniques based on results, findings, and limitations in each category of schemes in literature. Finally, the problem of high energy consumption is taken under consideration for the purpose of research. The proposed technique for handling wormhole has been designed to give good performance and consume less energy of the network. This method uses the transmission duration between the sender and recipient nodes to identify suspicious nodes. Then, it uses the hop count and hop interval time to detect suspicious nodes. The simulation's findings indicate a 100% packet delivery ratio and detection rate. Comparing the proposed technique to a good existing technique in the considered base article, it has enhanced throughput, lowered end-to-end delay, and consumed less energy.
Reshaping Secure Coding Through Generative AI Approach to Minimizing Programming Challenges
Authors: Riskhan Basheer , Saufan Halawati Abd Jalil , Ladan Jazuli Bello , Sheikh Md Amin Ullah , Hussain Khalid , Hussain Manzoor
Published: September 2024

A combination of technical expertise, creativity, and problem-solving skills is needed to succeed in the complicated and demanding profession of programming. As a result, there are several challenges that programmers may run against when creating software or computer systems. The literature on how to optimize and reduce the problems and difficulties in computer programming is reviewed in this chapter. The issue has a global scope and keeps becoming worse on a local scale. Even though there are numerous instructional tools available to support the teaching and learning of computer programming, the issue still exists. Computer introduction courses had high failure and dropout rates even from the beginning. This situation's justification includes the student's inability to solve problems. To overcome the challenges of learning computer programming, these two factors must be taken into account concurrently. This chapter will find out the ways to minimize these challenges.
Generative AI for Secure User Interface (UI) Design
Authors: Sindiramutty Siva Raja , Prabagaran Krishna Raj V. , Akbar Rehan , Hussain Manzoor , Malik Nazir Ahmed
Published: September 2024

Generative AI, which is equipped with unique capabilities, is about to put the world of secure user interface (UI) design upside down and turn it into something full of endless possibilities in which users will be able to use the same opportunities and experienced solutions to protect their interaction in digital from any future security threats. This chapter takes a deep plunge into the merger of the generative AI with the secure user interface design, on the whole, presenting a complete exposition of the principals involved, methodologies applied, practical embodiment, and ultimate ramifications. The beginning will explore the building blocks of UI design principles and the user-centred iterative approach, wherein a robust framework for understanding Generative AI as a critical part of building secure, intuitive, and engaging user experiences is implemented. Further, it provides an overview of different types of generative AI approaches that could be deployed for secure UI design, such as GANs, VAEs, and autoregressive models, with their capabilities expanding the scope of security measures, which include authentication protocols, encryption, and user access rights while retaining usability and aesthetic appeal. Moreover, it surveys instance applications of the generative AI that support the Secure design of GUI, among the automatic generation of safe layout patterns, the dynamic change of the interface according to emerging threats, and the creation of cryptographic keys and secure symbols.
Future Trends and Trials in Cybersecurity and Generative AI
Authors: T. Venkat Narayana Rao , G. Harsh Vardhan , A. N. Krishna Sai , Sangers Bhavana
Published: September 2024

The relationship between the fields of generative (AI) and cybersecurity offers both opportunity and danger as technology continues to grow at an unimaginable rate. The emergence of 5G networks and the spread of internet of things (IoT)-connected devices are changing the challenging landscape in the field of cybersecurity. The predictions point to an increase in complex cyberattacks using AI-driven strategies like adversarial machine learning and deepfake methods. Simultaneously, content manipulation are being revolutionized by the rise of generative AI technologies such as deep learning and GANs (generative adversarial networks). The AI-generated synthetic media contributes to existing problems with digital trust and authenticity by posing moral questions about identity theft and disinformation. In conclusion, managing the intersection of cybersecurity and generative AI requires proactive steps to fully utilize AI's potential while minimizing its inherent risks. This chapter includes solutions like anomaly detection systems and AI-powered threat intelligence.
Optimizing eCommerce Data
Authors: Sharma Chirag , Kaur Amanpreet , Datta Priyanka , Gulzar Yonis
Published: September 2024

The process of gathering and analyzing data, on variables in a manner to address research inquiries, test hypotheses and evaluate results is referred to as data collection. After obtaining the required data the next important step is pre-processing it. Data pre-processing involves converting data into a dataset before running algorithms. It prepares the dataset by handling missing values, noisy data and inconsistencies. Sometimes additional adjustments such as handling outliers and creating subcategories are needed for generating outcomes. This process can be seen as a step in organizing data effectively. The methods mentioned above demonstrate that research entails steps typically carried out manually or using applications by researchers. This article will explore an instances where Artificial Intelligence (AI) and Machine Learning (ML) are utilized for cleaning and pre-processing data. By examining the data AI and ML can detect patterns efficiently. Visual representations play a role, in understanding customer satisfaction and happiness levels within a shopping platform
Artificial Intelligence and Machine Learning in eCommerce
Authors: Johnpaul M. , Miryala Raam Sai Bharadwaj , Mazurek Marica , Jayaprakashnarayana G. , Miryala Ramesh Kumar
Published: September 2024

This chapter examines the transformative implications of artificial intelligence (AI) and machine learning (ML) on eCommerce security and operations. AI and ML technologies are transforming the eCommerce sector by streamlining supply chains, enhancing inventory management, and providing better customer experiences through personalized recommendations. Using real-time data and predictive algorithms, businesses can efficiently manage stock levels and forecast demand. In terms of security, machine learning (ML) and artificial intelligence (AI) provide effective means of detecting and mitigating fraud, protecting personal data, and ensuring secure transactions. This chapter delves into the numerous applications and benefits of artificial intelligence (AI) and machine learning (ML) in eCommerce, highlighting their critical roles in increasing efficiency, improving consumer satisfaction, and protecting operations in a fast-paced online market.
AI and Machine Learning Applications for Preserving Privacy and Data Leakage of E-Commerce Data
Authors: Arora Jatin , Kaur Gaganpreet , Sethi Monika , Singh Saravjeet
Published: September 2024

The e-commerce industry has been expanding at an exponential rate, evolving and adapting even during times of national lockdowns and worldwide pandemics. In a relatively short period of time, the Indian e-commerce ecosystem has grown to be vital to both the country's internet users and economy. The e-commerce industry has also seen a rise in cyberattacks. These attacks frequently result in data leaks that compromise data integrity, violate customers' rights and privacy, and foster mistrust between online retailers and their clientele. The roots of e-commerce security problems are highlighted in this paper, along with the potential benefits of several cutting-edge technologies including machine learning (ML) and artificial intelligence (AI) in reducing security risks. To increase e-commerce security, the study suggested a number of methods, including as data encryption, secure payment gateways, multi-factor authentication (MFA), regular software updates and patches, user privacy policies, and compliance.
Enhancing E-Commerce Data Privacy in India's Rapidly Evolving Cybersecurity Landscape Through AI-Driven Intrusion Detection Systems
Authors: Selvalakshmi B. , Sudhakar G. , Anbalagan Anand , Subashini K. , Vijayalakshmi P. , Kavin F.
Published: September 2024

The rise of sophisticated cyber threats poses significant challenges to the safety and integrity of goods and services in today's interconnected digital environment. This paper introduces “Secure by Intelligence,” a paradigm that utilizes Artificial Intelligence (AI) to enhance security and mitigate risks. India's rapidly growing e-commerce sector is transforming business practices, with AI playing a crucial role in its technological advancements. As e-commerce applications increase in complexity, maintaining usability becomes more challenging. This paper proposes integrating AI with Intrusion Detection Systems (IDS) to ensure data safety in e-commerce. By employing advanced machine learning techniques like deep learning and anomaly detection, AI-driven IDS can strengthen the resilience of e-commerce systems against cyber threats. The study examines the architecture and advantages of these systems while addressing implementation challenges. Ultimately, integrating AI-driven IDS will enhance consumer trust, protect sensitive data, and ensure the reliability of e-commerce ecosystems.
Predictive Analysis-Based AI-Driven Data Security Authentication and Authorization for Medical Warehousing Mechanisms
Authors: Senthil G. A. , Prabha R.
Published: September 2024

Maintaining data security is important for several reasons, as it shields patients, first and foremost, from fraud, identity theft, and prejudice due to their medical history. This paper presents a comprehensive framework for enhancing data security authentication and authorization within medical warehousing mechanisms in the context of e-commerce, leveraging predictive analysis, artificial intelligence (AI), and blockchain technology. In an era where the integrity and confidentiality of medical data are paramount, the proposed framework integrates advanced predictive analysis models and AI-driven authentication mechanisms with the immutable nature of blockchain to ensure robust security measures. The proposed solution presents an innovative approach to enhance data security authentication and authorization within medical warehousing mechanisms, leveraging predictive analysis and AI algorithms within the context of e-commerce-enabled blockchain. Specifically, the study focuses on employing Naive Bayes, LSTM, and XGBoost for predictive analysis to fortify security measures.
Deep Learning for Identification of Behavioral Changes
Authors: Pokkuluri Kiran Sree , N. S. S. S. N. Usha Devi , Khang Alex
Published: September 2024

This study explores the application of long short-term memory (LSTM) networks for the identification of behavioral changes. LSTM networks, a type of recurrent neural network (RNN), excel at modeling sequential data and capturing long-range dependencies, making them well-suited for analyzing temporal patterns in human behavior. The research investigates how LSTM networks can effectively learn from sequential behavioral data, such as activity logs, physiological signals, or speech patterns, to detect deviations from normal behavioral patterns. By leveraging LSTM's ability to retain information over extended time intervals, the study aims to develop models capable of recognizing subtle shifts in behavior that may indicate changes in mental health, emotional states, or lifestyle habits. Additionally, the research explores techniques to enhance the interpretability of LSTM-based behavioral change detection models, addressing challenges related to model transparency and explainability.
Healthcare Expenditure and Its Impact on SDG3
Authors: Gupta Meenakshi , Kumari Isha
Published: September 2024

The current study focusses on SDG3 (good health and well-being). The reason for the same is that SDG3 by addressing the basic need for good health and well-being acts as a catalyst for stimulating the progress across all SDGS. Thus, the study first investigated the trends in healthcare expenditure and life expectancy and later on the association between different sources of healthcare expenditure and life expectancy at birth using panel ARDL analysis for South Asian economies. It was observed that there were huge disparities across different South Asian economies in terms of the selected variables. Sri Lanka and Maldives have relatively higher life expectancies while in Afghanistan and Bangladesh there is limited access to healthcare services. The findings highlight the critical role of health expenditure, emphasizing the need for governments to prioritize and support this sector for overall better health outcomes.
Influence of Social Factors on Health Behavior
Authors: Rangan Varidhi
Published: September 2024

This chapter explores the complex impact of social influences on health behavior. This study examines the influence of social norms, cultural beliefs, socioeconomic status, social networks, and interactions with others on individuals' decisions and actions linked to their mental health. This chapter will analyze the ways in which social factors influence health behaviors in various situations and people, using research from sociology, psychology, public health, and epidemiology. Furthermore, it will investigate the impact of socioeconomic determinants of health on inequalities and disparities in health outcomes. This chapter seeks to offer a thorough comprehension of the intricate relationship between social determinants and health behavior, using an interdisciplinary approach. The findings will have consequences for interventions and the formulation of policies.
Neuroeconomic Insights on Addiction
Authors: Singha Ranjit , S. Yogesh Kanna
Published: September 2024

Addiction is considered a result of behaviour that has wandered at many levels, including neurophysiological, genetic predisposition, and environment. By taking a comprehensive approach, community-based initiatives and policy implications call for more than just curing. This chapter outlines the role of typical understanding in addiction treatment, prevention, and recovery. The multi-dimensional addiction behaviour scale, a method for the diagnosis of addictive behaviours, views addiction from different angles, such as psychological factors such as mental illness and genetic dispositions brought to us through biological inheritance and neurobiological changes by long-term drug abuse. Psychological factors, biological factors, social and environmental factors, cognitive and behavioural patterns, motivation and readiness for change are the possible multi-dimensions of addiction behaviour.
Social Cognition and Behavioral Economics
Authors: Thacker Richa , Reddy K. Jayasankara
Published: September 2024

This chapter delves into the intersection of social cognition and behavioral economics, aiming to elucidate the profound impacts of social influences and decision-making processes. By integrating insights from both fields, the authors comprehensively explore how social contexts shape individuals cognitive processes and how these influences manifest in economic decision-making. The chapter begins by providing an overview of key social cognition and behavioral economics concepts, laying the groundwork for understanding the complex interplay between social factors and decision-making behavior. They delve into various aspects of social cognition, such as the theory of mind, social perception, empathy, and decision-making biases, to elucidate how social cues influence an individual's perceptions and judgements. Throughout the chapter, they highlight the practical implications of understanding social influences on decision-making in various domains. They aim to provide valuable insights for practitioners and policymakers seeking to design interventions that promote better decision outcomes.
The Painful Dilemma
Authors: Bhuvaneswari Sayee , Vetrivel Muralidharan
Published: September 2024

Pain, a complex phenomenon extending beyond mere nociception, is substantially modulated by physiological, psychological, and social factors that shape its perceived intensity and overall impact. This chapter explores the intricate interplay between the neural pathways of pain, emotional networks, and the cognitive neuroscience of decision-making, ultimately examining the implications for healthcare economics. Through a comprehensive review of existing literature and an in-depth analysis of select case studies and anecdotes, this chapter highlights how pain perception intertwines with emotional responses to influence decision-making processes from the perspective of neuroeconomics. Understanding this dynamic interplay is crucial for the development of more effective strategies for pain management and for comprehending and planning the economic aspects that impact healthcare spending.
The Vital Role of Emotions in Health Decision-Making
Authors: Ghosh Oindrilla , Raj Manjeri Subin Sunder
Published: September 2024

Emotions play a vital role in the process of making health-related decisions. From choosing a healthy lifestyle to deciding on medical treatments, emotions influence our behavior, perceptions, and even physical well-being. Patients who approach their treatment with a hopeful mindset tend to experience better outcomes compared to those who succumb to despair or pessimism. The complex interplay between emotions and cognitive processes shapes our choices and actions in ways that are often underestimated but profound. Empathy and compassion, on the other hand, are emotions that can foster supportive relationships between healthcare providers and patients, ultimately enhancing treatment adherence and outcomes. Recognizing and managing emotions effectively is crucial for making sound health decisions. In conclusion, emotions play a multifaceted role in shaping health decision-making processes. By understanding the influence of emotions, acknowledging their impact, and learning to manage them effectively, individuals can make informed choices that promote their overall wellbeing and treatment outcomes.
Fundamentals of Data Visualization and Its Applications in Business
Authors: Singh Arun Kumar
Published: September 2024

Data and information visualization involves creating clear and understandable visual representations of complex quantitative and qualitative data using static, dynamic, or interactive visuals. These visualizations are based on data from specific areas of expertise and are designed to help a wide audience explore and understand data structures, patterns, and relationships. Effective data visualization is accurate, simple, and visually appealing, using deliberate choices of shapes, colors, and other elements. New technologies like virtual and augmented reality can enhance the immersive and interactive nature of data visualization. The goal of data visualization is to present and explore non-physical data from various sources, differentiating it from scientific visualization, which focuses on rendering realistic images based on physical data.
Empowering Business Insights
Authors: S. Lara Priyadharshini , Kumar Anuj
Published: September 2024

This research explores the critical role of data visualization tools, particularly Tableau, in enhancing business decision-making and strategic planning. It traces the evolution of data visualization and its growing importance in the context of big data. The study reviews literature and case studies to highlight Tableau's benefits, including improved decision-making, real-time visualization, and user-friendly interfaces, while addressing challenges like the learning curve, costs, and integration issues. It also examines future trends, such as AI and machine learning integration, the rising importance of data governance, and Tableau's role in big data and IoT. The research concludes with practical recommendations for businesses considering Tableau adoption and suggests areas for future research on the impact of emerging technologies on data visualization tools.
Role of Artificial Intelligence and Blockchain on Cyber Security
Authors: Pathak Juhi P. , Singh Kriti , Roy Swarnendu
Published: September 2024

This chapter explores the integration of artificial intelligence (AI) and blockchain technologies in enhancing cybersecurity through a PRISMA-compliant systematic literature review. It reviews key studies, highlighting various AI techniques such as expert systems, neural networks, and machine learning algorithms used for intrusion detection, malware monitoring, and decision support. The advantages of AI, including early intrusion prevention and enhanced decision-making, are discussed alongside challenges like bias and security threats. Additionally, the role of blockchain in securing IoT, data privacy, and network security is examined. The chapter concludes by emphasizing the promising future of AI and blockchain in defending against sophisticated cyber threats while acknowledging the need for broader research and policy frameworks.
Virtual Collaboration Evolution
Authors: Asrifan Andi , Syamsu T. , Apriani Eka , Vargheese K. J. , Rasyid Rustam Efendy , Alaqad Mohammed H.
Published: September 2024

This chapter covers the major advances in virtual cooperation driven by current exploratory research and technology. Digital communication tools, social networks, and immersive technologies have transformed how organizations and individuals collaborate. The chapter comprises individual aspects of virtual collaboration, virtual team practices, and organization-enabling aspects. It analyzes communication styles, virtual team dynamics, and the adoption of AI, VR, and blockchain. These technologies bring security and privacy risks and potential for future organizational structures and work practices, which the chapter explores. In a quickly changing digital ecosystem, firms can use virtual collaboration to boost productivity, cut costs, and innovate by anticipating and comprehending these trends and developments.
Metaverse-Based Virtual Workspace to Support Team Collaboration in the Laboratory
Authors: Narayanamma K. Laxmi , Nagpal Amandeep , Adnan K. , Mahenderreddy M. , Raju K. Butchi
Published: September 2024

This research set out to discover how a virtual workplace based on the metaverse may facilitate communication and cooperation in a health informatics lab in an academic institution. Concurrent triangulation was used to analyze a survey of fourteen lab members. Personas representing the various kinds of lab members were created by organizing the qualitative survey results depending on the opportunity, capability, motivation, and behaviour (COM-B) model. To supplement the results of the survey, planned work hours were also statistically analyzed. The survey data was used to create four fictional characters who stand in for various kinds of remote workers. The many perspectives expressed by the participants on virtual work were mirrored in these avatars, which were also used to classify the most frequent comments. Only a small fraction of the potential cooperation possibilities were used, according to the work hours schedule sheet study.
Ethical Measures to Prevent Intellectual Ethics in Virtual Teams
Authors: Narayanamma K. Laxmi , Kumar C. Praveen , Singh Navdeep , Ahmed S. , Saha Amrita , Gopalakrishna P.
Published: September 2024

There have been profound shifts in all aspects of human life as a result of the information society and the new economy. In addition, the COVID-19 pandemic and crisis brought about massive changes across all areas, endangering social harmony while simultaneously straining how quickly society is being modernised and the expansion of the new economy. This difficult matter is brought to notice since a properly controlled connection of this kind might provide a significant boost to the knowledge-based economy. Societal importance also rests on the interplay between leadership ethics, teamwork, and efficiency in interaction, output, and success. This study explains how recent college grads may make a difference in their organisations by applying their ethical principles to the roles of management, collaboration, effective communication, and production. There is a noticeable lack of study that addresses all four of these aspects of ethics at the same time.
Shaping Future Trends Metaverse as Experiential Marketing for Gen Z
Authors: Arora Madhu , Khurana Poonam , Gupta Jaya , Kapoor Renuka
Published: September 2024

The study is an attempt to highlight the future of marketing through artificial intelligence considering specifically Metaverse. Gen Z is considered as the target group for the study as they are the consumers of this tech-dominated marketing practices. This study makes use of primary data collected through a questionnaire from 644 respondents and SPSS software was used to analyze the result. This chapter aims to study the factors impacting willingness to purchase like smell, act, sense, feel, loyalty, relate, and word of mouth. It found that willingness to purchase has positive correlation with the act and a very small positive correlation with loyalty and word of mouth (WOM). Moreover, willingness to purchase has a negative correlation with smell, feel, and relate. There is no relationship between smell and feeling with willingness to purchase especially in online purchase.
From Purchase to Metamorphosis
Authors: Somani Pritesh Pradeep , Behare Nitesh , Iyer Vishwanathan Hariharan
Published: September 2024

The present chapter deals with the transformative journey from initial purchase to brand loyalty through the lenses of consumer engagement and experience. In an era where market competition is fierce and consumer choices are abundant, understanding the mechanisms that foster customer loyalty is critical for business success. The study delves into the multifaceted nature of consumer engagement, highlighting its components such as direct interactions, personalized communication, and participatory activities. It also emphasizes the critical role of delivering exceptional consumer experiences that exceed expectations and turn routine transactions into memorable encounters. Key factors such as consistency, authenticity, and effective feedback loops are identified as pivotal in building trust and credibility. By synthesizing current research, the chapter outlines strategies for brands to cultivate emotional connections, ensure continuous value delivery, and create a sense of community among their customers.
Whistleblowing e estruturas de remuneração de executivos na prevenção de fraudes contábeis
Authors: Silva Gilson Rodrigues da , Barreto de Queiroz Dimas , Amilton Maia Leite Filho Paulo , Paes de Barros Câmara Renata
Published: September 2024

Objetivo: O presente artigo tem como objetivo analisar o efeito da estrutura de remunerações de executivos, compostas por ações restritas, na intenção de whistleblowing de gestores em casos de fraudes contábeis.
 
Metodologia: Foi conduzido um experimento com 95 profissionais de contabilidade, com vínculo de trabalho efetivo. Os participantes da pesquisa foram distribuídos em dois grupos, um de controle (remuneração variável a partir de ações sem restrições) e outro de tratamento (remuneração variável a partir ações restritas). O instrumento de coleta de dados utilizado foi um questionário estruturado e os dados foram tratados com testes não paramétricos.
 
Lacuna: Ausência de estudos sob a ótica da Teoria da Agência, no Brasil, que analise a estrutura de remuneração como aliada aos requisitos para eficácia dos canais de denúncia, na prevenção e detecção de fraudes contábeis.
 
Relevância: A forma de remuneração restrita corrobora com a redução dos conflitos de agência, sendo um componente de controle essencial nos sistemas denúncias, pois estimula o executivo a reportar problemas comportamentais e fraudes identificadas.
 
Impacto: Os resultados indicam que organizações (públicas ou privadas) devem estimular a institucionalização de whistleblowing no Brasil para prevenir e identificar casos de fraudes e corrupção, sobretudo, pois os achados mostram que fatores motivacionais preditivos a denúncia como cultura ética, percepção moral e a materialidade do delito estão associadas ao grau de instrução e idade do respondente.
 
Resultados e contribuições: Os resultados revelam que os gestores possuem maior propensão a denunciar, quando suas estruturas de remuneração são compostas por ações restritas, com diferenças estatisticamente significantes entre as médias dos grupos. Os gestores podem relutar em denunciar ilegalidades quando têm sua remuneração atrelada a ações sem restrições de curto prazo, pois a divulgação de whistleblowing, afeta o valor de mercado e a imagem da entidade, reduzindo seu valor de liquidação.
 
Palavras-chaves: Remuneração de Executivos; Ações Restritas; Whistleblowing; Canais de Denúncias; Fraudes.
Understanding the Role of miR-29a in the Regulation of RAG1, a Gene Associated with the Development of the Immune System
Authors: Roy Urbi , Desai Sagar Sanjiv , Kumari Susmita , Bushra Tanzeem , Choudhary Bibha , Raghavan Sathees C
Published: September 2024

The process of Ag receptor diversity is initiated by RAGs consisting of RAG1 and RAG2 in developing lymphocytes. Besides its role as a sequence-specific nuclease during V(D)J recombination, RAGs can also act as a structure-specific nuclease leading to genome instability. Thus, regulation of RAG expression is essential to maintaining genome stability. Previously, the role of miR29c in the regulation of RAG1 was identified. In this article, we report the regulation of RAG1 by miR-29a in the lymphocytes of both mice (Mus musculus) and humans (Homo sapiens). The level of RAG1 could be modulated by overexpression of miR-29a and inhibition using anti-miRs. Argonaute2-immunoprecipitation and high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation studies established the association of miR-29a and RAG1 with Argonaute proteins. We observed a negative correlation between miR-29a and RAG1 levels in mouse B and T cells and leukemia patients. Overexpression of pre–miR-29a in the bone marrow cells of mice led to the generation of mature miR-29a transcripts and reduced RAG1 expression, which led to a significant reduction in V(D)J recombination in pro-B cells. Importantly, our studies are consistent with the phenotype reported in miR-29a knockout mice, which showed impaired immunity and survival defects. Finally, we show that although both miR-29c and miR-29a can regulate RAG1 at mRNA and protein levels, miR-29a substantially impacts immunity and survival. Our results reveal that the repression of RAG1 activity by miR-29a in B cells of mice and humans is essential to maintain Ig diversity and prevent hematological malignancies resulting from aberrant RAG1 expression in lymphocytes.
Erythema Multiforme post-Orf viral infection: a comprehensive review of the literature with a case report highlight
Authors: Alkallabi Mona , Nabil Nagshabandi Khalid , Alobaid Shaden , Bin Idris Reem , Fathaddin Amany
Published: September 2024

Orf, also known as ecthyma contagiosum, is a highly contagious zoonotic infection linked to direct contact with infected sheep, goats, or reindeer, posing a significant risk to farmers, veterinarians, shepherds, and butchers. This study presents a case of a 41-year-old female who developed Orf, complicated by the rare occurrence of Erythema Multiforme (EM), after handling sheep meat. The patient initially presented with a progressive painful skin eruption on the left dorsum of the little finger, which subsequently worsened and led to the development of generalized painful and itchy skin lesions, including target lesions typical of EM, on the palms, soles, and extremities. The diagnosis was confirmed through clinical examination and skin biopsy. The patient was treated with antihistamines and topical corticosteroids, resulting in a complete resolution of symptoms within three weeks. Our underscores the importance of recognizing EM as a potential complication of Orf infection, particularly in individuals with occupational exposure to animals.
The relationship between academic expectation stress and self-defeating behavior and cognition: the mediating role of emotional self-awareness
Authors: Dehban Ziba , Maleki Pirbazari Masoume , Ashournejad Fatemeh
Published: September 2024

Self-defeating behaviors and cognitions can significantly hinder individuals’ ability to achieve their goals. This study aims to explore the relationship between academic expectation stress and self-defeating behavior and cognition, with a focus on the mediating role of emotional self-awareness. Using a correlational design and structural equation modeling, we surveyed 200 first-year secondary school girls from villages in Roudbane city during the 2022-2023 academic year. Participants were selected through convenience sampling. Data were collected using the Academic Expectations Stress Inventory, the Self-Defeating Behaviors and Cognitions Scale, and the Self-Awareness Outcomes Questionnaire. Analyses were conducted using Pearson correlation and structural equation modeling with SPSS v26 and AMOS v24 software. Results indicated that both academic expectation stress and emotional self-awareness directly influence self-defeating behavior and cognition. Additionally, academic expectation stress indirectly affects these outcomes through the mediating role of emotional self-awareness. These findings suggest that educational psychologists and school counselors should consider both academic stress and emotional self-awareness when developing strategies to prevent self-defeating behaviors and cognitions in students.
Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response
Authors: Kirtipal Nikhil , Seo Youngchang , Son Jangwon , Lee Sunjae
Published: September 2024

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.
Investigating the influencing factors of evidence-based healthcare practice adoption
Authors: Motsi Lovemore
Published: September 2024

Background: In healthcare facilities, evidence-based healthcare practice (EBHP) is becoming more widely acknowledged as a critical element of patient care delivery. An increasingly important component of EBHP is the implementation of electronic health records (EHRs).Objectives: This study aims to investigate factors that influence EBHP adoption in public healthcare institutions in South Africa.Method: Four hundred and fifty patients were self-administered to healthcare professionals at an academic public hospital in Gauteng and used in this study. A total of 300 responses were available for use in the final analysis following the data cleaning procedure. Utilising structural equation modelling (SEM), the collected data were analysed.Results: Perceived ease of use (PEOU) and perceived usefulness (PU) were found to be major variables in the adoption of EBHP along with technological, organisational and environmental factors. The technology context relative advantage (RELA) was shown to have a positive significant influence on the adoption of evidence-based healthcare practice by the PEOU and PU, with the environmental context government laws and regulations (GLRS) and organisational context organisational readiness (ORGR) coming in second and third, respectively.Conclusion: Perceived ease of use, PU, ORGR, and GLRS are regarded as a vital variables in the implementation of EBHP in South African public hospitals.Contribution: The study’s conclusions would be helpful to policymakers as they redefine nursing practice. Furthermore, the findings heighten the consciousness of healthcare practitioners regarding the significance of employing evidence-based practice while making decisions.
Artificial intelligence’s challenges to the essence of humanity from the perspective of Martin Luther’s anthropology in Chinese context
Authors: Huang Paulos Z.Z.
Published: September 2024

This article argued the following points. Firstly, the challenge posed by artificial intelligence (AI) to the essence of humanity is serious. Secondly, it is important to analyse the external context and internal dynamics of the history of interaction between knowledge and power. Thirdly, it is necessary to trace the intellectual history of humanity becoming god-like. Finally, by combining Martin Luther’s anthropology with insights from social science and philosophical theology, this article advocated for guiding human beings to use their capabilities for good rather than evil through ethical and legal constraints. Efforts in Chinese context should be made to resolve the conflict between humanity’s pursuit of omnipotence and its failure to develop towards complete goodness, so as to avoid catastrophic consequences for humanity.Contribution: The present article’s special contribution was the theological reflection referring to AI’s challenges to the essence of humanity from Luther’s anthropology in Chinese context. The conflict between omnipotence and omnibenevolence has been highlighted as the key problem which human beings need to solve in the face of AI’s challenge.