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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. -
Carbon Nanotubes for Supercapacitors
Supercapacitors are energy storage devices that boast significant capacitance, enhanced energy density, rapid charge/discharge cycles, minimal heat generation, safety, sustainability with no expendable components, and extended durability. Supercapacitors, due to their unique characteristics, are increasingly favoured in consumer electronics and as alternate energy solutions. Carbon nanotubes (CNTs) have emerged as a promising material for supercapacitor electrodes, thanks to their remarkable features like exceptional conductivity, large surface area, robust mechanical strength, and chemical stability. The objective is to offer a comprehensive understanding of the pros and cons of supercapacitor materials involving CNTs and to pinpoint ways to boost their efficiency. This also entails examining how the inherent physical and chemical traits of pure CNTs, such as their size, quality, imperfections, shape, modifications, and treatment processes, influence their capacitance. Moreover, a deeper dive into composites, like CNTs combined with oxides, polymers, and other hybrid materials, aims to customize their composition and characteristics to optimize capacitance while ensuring the devices longevity. This section also compiles the latest studies on various CNT composites as potential supercapacitor electrode materials. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Pathway toDetect Cancer Tumor byGenetic Mutation
Cancer detection is one of the challenging tasks due to the unavailability of proper medical facilities. The survival of cancer patients depends upon early detection and medication. The main cause of the disease is due to several genetic mutations which form cancer tumors. Identification of genetic mutation is a time-consuming task. This creates a lot of difficulties for the molecular pathologist. A molecular pathologist selects a list of gene variations to analyze manually. The clinical evidence strips belong to nine classes, but the classification principle is still unknown. This implementation proposes a multi-class classifier to classify genetic mutations based on clinical evidence. Natural language processing analyzes the clinical text of evidence of gene mutations. Machine learning algorithms like K-nearest neighbor, linear support vector machine, and stacking models are applied to the collected text dataset, which contains information about the genetic mutations and other clinical pieces of evidence that pathology uses to classify the gene mutations. In this implementation, nine genetic variations have been taken, considered a multi-class classification problem. Here, each data point is classified among the nine classes of gene mutation. The performance of the machine learning models is analyzed on the gene, variance, and text features. The gene, variance, and text features are analyzed individually with univariate analysis. Then K-nearest neighbor, linear support vector machine, and stacking model are applied to the combined features of a gene, variance, and text. In the experiment, support vector machine gives better results as compared to other models because this model provides fewer misclassification points. Based on the variants of gene mutation, the risk of cancer can be detected, and medications can be given. This chapter will motivate the readers, researchers, and scholars of this field for future investigations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Study of Bard-Marangoni Convection in a Microfluid with Coriolis Force
The convection of micro-structured fluid particles and the Coriolis force has been investigated in the problem. The eigenvalues are calculated for upper free velocity and adiabatic temperature boundary conditions and lower rigid velocity and isothermal temperature boundary conditions. The analysis is based on solving linear disturbance equations. The impact of different micropolar fluid variables and the Taylor number based on the convection has also been investigated. The study could observe that while the coupling and micropolar heat conduction parameters along with rotational parameters have a stabilizing effect, the couple stress parameter results in a destabilizing effect. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Selfipendant and Extremal Pendant Graphs
[No abstract available] -
Exploring ARIMA Models with Interacted Lagged Variables for Forecasting
Including interactions among the explanatory variables in regression models is a common phenomenon. However, including interactions existing among lagged variables in autoregressive models has not been explored so far. In this paper, Autoregressive Integrated Moving Average (ARIMA) model with interactions among the lagged variables is proposed for improving forecast accuracy. The methodology for identifying the interacted lagged variables and including them in the ARIMA model is suggested. Using five different data sets of different types, the paper explores the effect of interacted lagged variables in ARIMA model. The experimental results exhibit that when interactions do actually exist, ARIMA model with interactions improves the forecast accuracy as compared to ARIMA model without interactions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Novel Hybrid Model for Time Series Forecasting Using Artificial Neural Network and Autoregressive Integrated Moving Average Models
Enhancing forecast accuracy while using time series is a potential area of research. Evidences exist in the literature to show that hybrid models can significantly improve the forecasting performance, as they combine the exclusive strengths of different models. This paper presents a novel hybrid model by combining forecasts from Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN) models with suitable weights, thereby improving the forecast accuracy. The methodology employs appropriate error metrics to construct the weights. The paper further demonstrates the efficiency of the proposed methodology through an empirical study, based on two real-world time series data sets. Thus, the new methodology can be used for enhancing the forecast accuracy in a number of fields of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Bacterial Pigments as Antimicrobial Agents
In this chapter, we discuss various bacterial derived secondary metabolites pigments which has antimicrobial properties. Though these metabolites were identified more than several decades ago, attention into their bioactivities has emerged in the last few decades. Their increasing acceptance is an outcome of their cost-effectiveness, biodegradability, noncarcinogenic property, and eco-friendly characteristics. This chapter has made an attempt to take an in-depth observation into the current bacterial derived pigments and their bioactivity against various microorganisms. 2024 selection and editorial matter, Mohammed Kuddus, Poonam Singh, Raveendran Sindhu and Rachana Singh; individual chapters, the contributors. -
Eradication of Global Hunger at UN Initiative: Holacracy Process Enriched byHuman Will and Virtue
The researchers have directed their attention to the UNs 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs), with a specific focus on two critical objectives: hunger and poverty alleviation. While the UN has been vocal about eradicating hunger and poverty, the researchers believe that a fundamental shift in human perspective is needed. They propose a novel approach rooted in holacracy to revolutionize food production, distribution, and management. At the core of their proposal lies the ancient Indian principle, Vasudhaiva Kutumbakam, which translates to The World Is One Family. While it may seem utopian, the researchers see it as a reachable goal through holacracy. Their hypothesis centres on producing food for all and collectively utilizing it, transcending national boundaries and individual interests. The researchers advocate for a transformation in the way the UN operates by embracing holacracy as a practical social technology rather than a mere concept. Holacratic organizations, they argue, have the potential to remove barriers obstructing progress. The implementation of their vision begins with the UN functioning as a global nerve centre for data, with its 193 member nations acting as equal and interdependent contributors. This Centre would display the worldwide food landscape and foster a moral and ethical awakening, emphasizing the shared responsibility for all humanity. Real-time data on food availability, supply chains, and consumption would be accessible on a public website. Holacracy, they contend, should inspire individuals to prioritize love for humanity as a panacea. Power circles interconnect to collaboratively address issues. The UN could serve as a catalyst for this transformation. The knowledge nerve centre would provide critical data on arable land, water resources, and supply chain infrastructure to facilitate problem-solving at various levels. Timely responses and actions would be driven by the principles of holacracy and advanced digital technologies, addressing concerns hindering the achievement of UN goals. This data-driven approach, coupled with actionable plans, aims to eliminate food shortages and subsequently combat poverty and hunger worldwide. In conclusion, the researchers envision a future where holacracy and a shared sense of responsibility propel humanity towards ending hunger and poverty, with the UN playing a pivotal role as a catalyst for change and a provider of essential data and guidance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Reshaping the Education Sector of Manipur Through Blockchain
The use of technology in education has been over a century, yet blockchain is in its nascent stage in education. Over the years, technology has enhanced the teaching-learning method, and blockchain can improve even in the administrative section of education. The states of North East, India, in general, lag behind the rest of Indian states in almost all sectors, and the lack of transparency in the administrative sector significantly contributed to it. If blockchain is incorporated into the education department at the administrative level, it could pave the way for a faster, more transparent, and smoother administration. Given the harsh reality that transportation is hard and expensive, a standardised blockchain can alleviate the need to be physically present for any academic-related activity. The attempt of this study would be to show how blockchain can be beneficially used even at the institutional level, where unabated printing could be reduced and adopting to e-paper be maximised. Besides the educational institutions, the administrative sector in education could profitably use them in offices, thereby avoiding red tape for the common people. The chapter points out how blockchain can be a trailblazer in reshaping the education sector in Manipur. Educational institutions must take the lead towards a sustainable future, and blockchain can aid in bringing some visible change in the educational sector. This chapter uses an interdisciplinary approach to substantiate the importance and need for blockchain in the context of Manipur to change for a sustainable future. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
EFFECTIVENESS OF COGNITIVE BEHAVIOURAL THERAPY FOR ADULTS WITH DEPRESSION AND ANXIETY DURING COVID-19: A Systematic Review of Randomised Controlled Trials
Introduction: The COVID-19 pandemic has forced the administration of Cognitive Behavioural Therapy (CBT) either face-to-face or online. This systematic review aims to assess the effectiveness of CBT and Internet-Delivered CBT (iCBT) in treating depression and anxiety disorders during the COVID-19 outbreak. Methods: Three independent reviewers searched the Web of Science, PubMed, Cochrane Library, and Clinical Trial Databases using specific search phrases. PubMed searches included Cognitive Behavioural Therapy/Intervention and COVID-19 and 2019 Coronavirus Disease or 2019-nCoV, internet-administered/internet-based cognitive behavioural therapy, CBT, cognitive behavioural treatment. Two independent reviewers evaluated the risk of bias at the study level, with disagreements settled through discussion with other research team members. The study findings were reported as per the PRISMA guidelines. Results: Thirty-one studies met the inclusion criteria, and 17 were randomised controlled trials. The studies demonstrated that CBT and iCBT effectively treated depression and anxiety disorders during the COVID-19 pandemic. However, a hybrid CBT modality was more beneficial from a long-term perspective. Conclusion: The findings suggest that CBT and iCBT effectively treat depression and anxiety disorders during the COVID-19 pandemic. However, further research is needed to establish these interventions long-term effectiveness and identify the optimal mode of delivery for different populations. 2024 selection and editorial matter, Dr Rajesh Verma, Dr Uzaina, Dr Tushar Singh, Dr Gyanesh Kumar Tiwari, and Prof Leister Sam Sudheer Manickam. -
Beyond Teacher Quality: Understanding the Moderating Role of Infrastructure in Student Learning Outcomes in Secondary Education
Education is an essential resource for individuals and societies, and it plays a significant role in shaping the future of any nation. Depriving a generation of young children of their basic right to quality education can easily be regarded as the highest form of injustice in a society. Bihar, which was once the epitome of education and knowledge across the world, is now counted among the states with the lowest literacy rates and the poorest educational infrastructure. While a list of reasons can be enumerated behind this downfall, including historic and social reasons, it is prudent to act on those that we can effectively alter and improve upon, such as infrastructure and teaching quality. The quality of education provided to students is influenced by various factors, such as infrastructure, teacher quality, and student-teacher relationships. This study explores the moderating effect of infrastructure on the relationship between teacher quality and student outcome in secondary education in Bihar, mapping an intriguing contrast with Kerala, the state with the highest literacy rate in India. With the help of a simple moderation analysis and drawing on the resource dependency theory, our findings indicate that the moderation effect of infrastructure on student outcome is stronger in Bihar than in Kerala. This study highlights the urgent need to prioritise consolidating and enhancing the quality of education in schools in Bihar rather than adding up a number of concrete blocks. 2024 Patliputra School of Economics. -
Transforming Industry 5.0: Real Time Monitoring and Decision Making with IIOT
This chapter explores the transformative potential of Industry 5.0 by leveraging real-time monitoring and decision-making capabilities through the use of IIoT dashboards. It extends in examining how IIoT dashboards enable organizations to gain real-time insights into their operations, facilitating data-driven decision-making and improving overall efficiency. By embracing IIoT dashboards, businesses can effectively transform Industry 5.0, unlocking new levels of productivity, agility, and competitiveness. In this chapter, important challenges such as data integration, data security, scalability, and user experience are identified. It highlights key considerations for implementing IIoT dashboards and offers practical methods for successful adoption of this technology. Remarkable achievements in implementing this technology include applications such as crude oil production with IIoT and edge computing, as well as IIoT-enabled smart agriculture dashboards. Adopting IIoT dashboards may involve initial costs, but they offer long-term benefits and cost-effectiveness, particularly in the era of Industry 5.0 transformation. 2024 selection and editorial matter, C Kishor Kumar Reddy, P R Anisha, Samiya Khan, Marlia Mohd Hanafiah, Lavanya Pamulaparty and R Madana Mohana. -
Machine Learning in Cyber Threats Intelligent System
Cybercriminals disrupt services, exfiltrate sensitive data, and exploit victim machines and networks to perform malicious activities against organizations. A malicious adversary seeks to steal, destroy, or compromise business assets that have a specific financial, reputational, or intellectual value. As a result, organizations are complementing their perimeter defenses with threat intelligence platforms to address these security challenges and eliminate security blind spots for their systems. Any type of information useful for identifying, assessing, monitoring, and responding to cyber threats is considered cyber threat intelligence. Organizations can benefit from increased visibility into cyber threats and policy violations. An organizations threat intelligence allows them to prevent or mitigate various types of cyberattacks. The use of machine learning and artificial intelligence is a key component of cybersecurity conflict, which together allows attackers and defenders to function at new speeds and scales. In spear-phishing attacks, relatively frivolous machine learning algorithms have been used to overwhelming effect as adversarial artificial intelligence. This chapter discusses the various cyber threats, cyber security attack types, publicly available datasets for research work, and machine learning techniques in cyber-physical systems. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
The Women Leadership: A Catalytic Role of Digital Divide Through Digital Ecosystem
The digitalization of the macro environment of an organization and digital upskilling and digital capital formation in the micro level of human capital paves women an opportunity to ladder up in the organization in various job roles leading to women leadership. The sociodemographic, psychological, socioeconomic, and cultural factors to the digital divide predominantly determine digital capital formation. Every attempt of women to surpass the digital divide obstacle to digital capital formation enables women capital to leadership characteristics. The study proposes a conceptual framework by extricating the past scholarly works on digital divide, digital capital, technology leadership, and women leadership. The preferences and choices of women leading to leadership skills through technology by encapsulating digital capital formation are meritorious in the research inquiry. The individual factors and organization factors are taken to the moderating variables in the association of digital capital to the women leadership. The study finds the need of ICT and digital upskilling among the women professionals in industry and catalytic role of digital capital formation by surpassing the digital divide. The empirical study on the concept formulation is highly recommendable for the future study. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Voices of the Future: Generation Zs Views on AIs Ethical and Social Impact
As artificial intelligence (AI) becomes increasingly integral to modern society, its profound implications are coming to the forefront of discussions. This research paper investigates the perspective of Generation Z on the multifaceted societal and ethical impacts of AI. Gen Z is the first generation to fully embrace AI across all facets of life. Therefore, understanding their attitudes, concerns, and expectations towards AI is imperative for cultivating a responsible, adaptable, and ethically conscious society in the AI-driven era. This study addresses a significant research gap by exploring Gen Zs perceptions of the challenges associated with AI, such as issues related to privacy, data security, transparency, bias, public fear and more. It also examines the impact of AI on employment dynamics, specifically on job displacement and the necessity for reskilling in the face of AI-driven automation. The paper adopts a global perspective, acknowledging the variations in perception influenced by cultural, economic, and historical factors. Leveraging a sample size of approximately 200250 respondents aged 1825years, the research aims to provide a comprehensive view of Gen Zs viewpoints on AIs ethical and societal ramifications. Findings emphasize the need for transparent and accountable AI systems, as Gen Z is uncomfortable with the ambiguity in AI algorithms. Concerns about privacy and data security highlight the necessity for robust safeguards. They also advocate for strategies to address job displacement and ensure harmonious coexistence between humans and AI. In education, Gen Z sees AI as transformative, endorsing personalized learning. They stress the importance of regulatory frameworks to combat AI bias. They recognize AIs potential to enhance human connections and combat social isolation. The studys findings contribute to policy discussions, educational strategies, and business practices, offering insights into how to harness AIs benefits while mitigating its potential pitfalls. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Green Data Centers: A Review of Current Trends and Practices
A green data center is a facility that makes use of eco-friendly techniques and technologies to lessen its carbon footprint and environmental impact. A data center can consume as much electricity as a small city and contains thousands of servers. These server farms require an enormous amount of processing power to operate, which presents numerous difficulties, including high energy costs, greenhouse gas emissions, backups, and recovery. This paper clarifies the various green data center best practices, including energy efficiency, cooling systems, renewable energy, sustainable building techniques, and carbon footprint. The need for green data centers in todays internet, commercial, financial, and business applications is also covered in the paper. The reality and myths of green data centers are alsoexamined. The paper delves into the metrics for each characteristic used to gauge how green and effective data centers are. The discussion has concluded with case studies of companies that have implemented green data centers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Application of AI-Based Learning in Automated Applications and Soft Computing Mechanisms Applicable in Industries
The term artificial intelligence is used to describe a method through which computers may teach themselves new skills and develop themselves, without the help of humans or any predetermined instructions. Machines are fed data and trained to look for patterns; these patterns are then used as templates for further learning. They get the agency to choose their own actions and alter their habits accordingly. The term soft computing refers to a group of computational techniques that draw inspiration from both AI and natural selection. Solutions to difficult real-world situations that have no simple computer solution are provided, and they are both practical and cost-effective. Soft computing is an area of study in mathematics and computer science that has been around since the early 1990s. The idea for this project sprang from the fact that people can think of solutions that are close to the ones in the actual world. It is via the use of approximations that the science of soft computing is able to solve difficult computational challenges. Industrial automation is used by a diverse variety of industries and companies to improve the effectiveness of their processes by leveraging a number of technology developments. Many routine tasks are being changed by industrial applications. Industrial automation that reduces breakdowns and repairs quickly might help a business save money. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
AI in Forensics A Data Analytics Perspective
Artificial intelligence (AI) is rapidly becoming the most significant science in all areas of life, and forensic science is one of the fields benefiting from it. Forensics can be defined as a study of crime via the use of scientific methods and techniques. Around the globe, governments invest a large amount of money in developing forensics techniques to prove criminal activities and track criminals effectively. It is now becoming a practice to involve artificial intelligence in supporting the forensic application. It involves a smart and intelligent examination of massive volumes of very complicated data. As a result, AI is becoming an excellent solution for addressing many of the complicated issues that now exist in forensics. For example, AI proves more effective in skeleton-based human identification compared to the traditional skull/skeleton superimposition method. AI can be used to pool meta-data generated from multiple sources connected to forensic science and do a meta-analysis on it to simplify complex data. AI finds patterns and uses them to identify/recognize/predict something that is required in crime tracking or criminal/victim recognition. Complex analytics and probabilistic reasoning are used to recognize patterns. Among the most crucial things to forensic science is the identification of specific sorts of patterns in enormous amounts of data. This could include image pattern recognition, in which the program attempts to distinguish between distinct components of an image or a person. Other types of pattern recognition, such as finding patterns in text, may also exist. Artificial intelligence aids in the more accurate recognition of such patterns in complex data. This chapter introduces the reader to several aspects of artificial intelligence that can be used in forensics. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors.