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                <text>Eradication of Global Hunger at UN Initiative: Holacracy Process Enriched byHuman Will and Virtue</text>
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                <text>Holacracy; Hunger; Poverty; Sustainable Development Goals (SDGs); UN (United Nations)</text>
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                <text>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.</text>
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                <text>Green Energy and Technology, Vol-Part F2272, pp. 477-494.</text>
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                <text>Nair R., School of Arts and Humanities, Christ University, Bangalore, India; Joy J., School of Business and Management, Christ University, Bangalore, India</text>
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                <text>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.</text>
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                <text>Madhavan A.; Arun K.B.; Reshmy R.; Abhinand K.; Menon A.M.; Nair B.G.; Sindhu R.; Kuriakose L.L.; Awasthi M.K.; Pandey A.; Binod P.</text>
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                <text>Microbial Pigments: Applications in Food and Beverage Industry, pp. 150-157.</text>
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                <text>&lt;a href="https://doi.org/10.1201/9781003353980-9" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781003353980-9&lt;/a&gt;
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                <text>Madhavan A., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, India; Arun K.B., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Reshmy R., Department of Science and Humanities, Providence College of Engineering, Kerala, Chengannur, India; Abhinand K., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, India; Menon A.M., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, India; Nair B.G., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, India; Sindhu R., Department of Food Technology, T K M Institute of Technology, Kerala, Kollam, India; Kuriakose L.L., Department of Food Technology, T K M Institute of Technology, Kerala, Kollam, India; Awasthi M.K., College of Natural Resources and Environment, Northwest A &amp;amp; F University, Shaanxi, Yangling, China; Pandey A., Center for Innovation and Translational Research, CSIR-Indian Institute of Toxicology Research, Lucknow, India, Sustainability Cluster, School of Engineering, University of Petroleum and Energy Studies, Uttarkhand, Dehradun, India, Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow, India; Binod P., Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Kerala, Trivandrum, India</text>
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                <text>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.</text>
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                <text>Thangarajan B.; Nagaraja M.S.; Dhandra B.V.</text>
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                <text>Trends in Mathematics, Vol-Part F2357, pp. 747-754.</text>
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                <text>ISSN: 22970215</text>
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                <text>Thangarajan B., CHRIST (Deemed to be University), Bengaluru, India; Nagaraja M.S., CHRIST (Deemed to be University), Bengaluru, India; Dhandra B.V., CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>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.</text>
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                <text>Thangarajan B.; Nagaraja M.S.; Dhandra B.V.</text>
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                <text>Trends in Mathematics, Vol-Part F2357, pp. 735-745.</text>
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                <text>Thangarajan B., CHRIST (Deemed to be University), Bengaluru, India; Nagaraja M.S., CHRIST (Deemed to be University), Bengaluru, India; Dhandra B.V., CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>GLE; Micropolar fluid; URBMC</text>
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                <text>Jestine S.; Pranesh S.</text>
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                <text>Trends in Mathematics, Vol-Part F2357, pp. 355-365.</text>
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                <text>Jestine S., Department of Mathematics, Centre for Mathematical Needs, CHRIST(Deemed to be University), Bangalore, India; Pranesh S., Department of Mathematics, Centre for Mathematical Needs, CHRIST(Deemed to be University), Bangalore, India</text>
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            <description>A name given to the resource</description>
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              <elementText elementTextId="149979">
                <text>Selfipendant and Extremal Pendant Graphs</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="149980">
                <text>05C38; 05C40; 05C70; Equipendant graphs; Eulerian graphs; Extremal pendant graphs; Graph decomposition; Path decomposition; Pendant number; Self-complementary graphs; Selfipendant graphs</text>
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            </elementTextContainer>
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            <description>An account of the resource</description>
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              <elementText elementTextId="149981">
                <text>[No abstract available]</text>
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            <name>Creator</name>
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            <elementTextContainer>
              <elementText elementTextId="149982">
                <text>Kottarathil J.; Naduvath S.</text>
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            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="149983">
                <text>Trends in Mathematics, Vol-Part F2357, pp. 879-888.</text>
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              <elementText elementTextId="149992">
                <text>Kottarathil J., Department of Mathematics, St. Josephs College, Moolamattom, Kerala, Arakulam, India; Naduvath S., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, India</text>
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                <text>Study of Bard-Marangoni Convection in a Microfluid with Coriolis Force</text>
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                <text>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.</text>
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                <text>Trends in Mathematics, Vol-Part F2357, pp. 367-378.</text>
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                <text>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.</text>
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                <text>Mohanty A., Computer Science and Engineering, CHRIST (Deemed to be) University, Bengaluru, India; Prusty A.R., DGT, RDSDE, NSTI(W), West Bengal, Kolkata, India; Dasig D., Graduate Studies College of Science and Computer Studies, De La Salle University, Dasmarinas, Philippines</text>
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                <text>Carbon nanotubes (CNTs); CNTs/oxide and CNTs/polymer composite; Electrode materials; Energy storage; Supercapacitor</text>
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                <text>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.</text>
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                <text>Engineering Materials, Vol-Part F2348, pp. 217-236.</text>
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                <text>ISSN: 16121317</text>
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                <text>Simon S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Aswathi V.P., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Sunny S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Sreeja P.B., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India</text>
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                <text>Analysing the Ascendant Trend of Veganism: A Comprehensive Study on the Shift towards Sustainable Dietary Choices</text>
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                <text>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.</text>
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                <text>IoT, Machine Learning and Data Analytics for Smart Healthcare, pp. 71-84.</text>
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                <text>&lt;a href="https://doi.org/10.1201/9781003430735-6" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781003430735-6&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186723448&amp;amp;doi=10.1201%2F9781003430735-6&amp;amp;partnerID=40&amp;amp;md5=0163bc90a31a90cf3202b7cf70707747" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186723448&amp;amp;doi=10.1201%2f9781003430735-6&amp;amp;partnerID=40&amp;amp;md5=0163bc90a31a90cf3202b7cf70707747&lt;/a&gt;</text>
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                <text>Bhagat S., School of Business and Management, Christ University, India; Ravi S.S., School of Business and Management, Christ University, India; Rai R., School of Business and Management, Christ University, India</text>
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          <element elementId="50">
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                <text>Wearable Smart Technologies: Changing the Future of Healthcare</text>
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                <text>Wearable smart technologies are the innovative solutions for the issues of healthcare services. In this chapter, a review of the innovative wearable healthcare devices and applications has been done. Wearable devices are used for supervision and illness control. These innovative wearable technologies can straightforwardly affect the medical dynamic, can upgrade the quality of treatment for patients, and can reduce the expenses incurred in it. The large health record generated by the wearable devices provides an opportunity for data analysts to apply machine learning techniques for prediction on the data generated by sensors. Today's wearable smart technologies are capable of being integrated into eyeglasses, cloths, shoes, belts, watches, etc. Sensors can be inserted in these objects to be worn. The advanced forms of wearable technologies can be attached to the skin of the wearer. A smartphone is mainly utilized to collect data and communicate it to a server situated at a remote area for greater capacity and investigation. Maximum innovations related to wearable technologies are still in the prototyping phase. The study covers almost every aspect of wearable technologies, which could be helpful in the future for innovation and research in this area.  2024 selection and editorial matter, Ankur Beohar, Ribu Mathew, Abhishek Kumar Upadhyay, and Santosh Kumar Vishvakarma -individual chapters, the contributors. All rights reserved.</text>
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              <elementText elementTextId="150049">
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                <text>Manimaran A., Department of Mathematics, School of Advanced Sciences, VIT-AP University, Andhra Pradesh, Amaravati, India; Goundar S., School of Science, Engineering, and Technology, RMIT University, Hanoi, Viet Nam; Chandramohan D., Department of Computer Science &amp;amp; Engineering, Thapar Institute of Engineering &amp;amp; Technology, Punjab, Patiala, India; Arulkumar N., Department of Computer Science, Christ University, Bangalore, India</text>
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                <text>This paper describes a new way to improve the performance of an EDN by integrating distributed battery energy storage systems (BESs) in the best way possible. This method is based on the Dandelion Algorithm (DA). The search space for BES locations is first predetermined using loss sensitivity factors (LSFs), and then DA is used to determine the optimal locations and sizes. The reduction of real power distribution loss is regarded as the primary objective function, and the impact of BESs is extended to examine the network voltage profile, voltage stability, and GHG emissions. IEEE 33-busEDN is used to calculate the computational efficiency of LSF-DA. Results show that DA is more efficient than Archimedes optimization (AOA), future search algorithm(FSA), pathfinder algorithm(PFA), and butterfly optimization algorithm(BOA) algorithms. Furthermore, the results show that the proposed DA enhances all technological and environmental factors and RDN performance.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.</text>
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                <text>Investment decisions are always complex in nature. Investment assets are volatile in nature there are less volatile, medium volatile and high volatile investment assets in the financial market. In the current study how, the behavioral biases of the investors affecting their investment decisions in the less volatile asset classes is examined using an extensive survey method among the IT professionals in the Bangalore city. The relationship between the demographic variables and behavioral biases is tested. Also, a detailed study is conducted to examine the risk-taking behavior of the investors in the less volatile assets. There are basically three type of investors on the basis of their risk-taking behavior i.e. Risk seeking, Risk Neutral and Risk averse investors. Current study reveals that investors in the less volatile asset classes are very much cautious about the risk factor and therefore they are risk averse in nature.  The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024.</text>
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                <text>Studies in Systems, Decision and Control, Vol-487, pp. 807-818.</text>
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                <text>Shaji A.K., Department of Commerce, CHRIST (Deemed to be University), Karnataka, Bangalore, India, Kristu Jayanti College (Autonomous), Karnataka, Bangalore, India; Uma V.R., Department of Commerce, CHRIST (Deemed to be University), Karnataka, Bangalore, India</text>
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                <text>Attention to Economic Factors and Its Response to Foreign Portfolio Investment: An Evidence from Indian Capital Market</text>
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                <text>Equity and debt market; FPI; India; Macroeconomic factors</text>
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                <text>Stock market consists of a variety of investors. Among these, Foreign Portfolio Investors (FPIs) is a key investment influx. These investments can change or fluctuate due to several macroeconomic factors which can cause a shift in the dynamics of the markets in India. This paper examines the factors influencing for foreign portfolio investment in long run as well as short run. The sample comprises of 120 monthly observations on Foreign Portfolio Investment (FPIs) and Macro economic variables such as Oil prices (OP), Gross Domestic Product (GDP), Interest Rate (IR), Exchange rate of Indian Rupee with USD (ER), Inflation (CPI), Nifty Index (NSEI), 10year Bond Prices (BP) and Index of Industrial production (IIP) over a period of 10years, spanning from January 2013 to November 2022. The study employed Autoregressive Distributed Lag model (ARDL) to establish the long run association with error correction models. The result indicates that there is long run association between the Foreign Portfolio Investment and macro-economic variables. Among this, NSEI, IIP and ER played a significant role to determine FPI investments in the long run, whereas in the short run, FPI was impacted by ER and NSEI significantly.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.</text>
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                <text>Pachiyappan S.; Kandral A.; Shylaja H.N.; Raj V J.P.; Vellayan S.</text>
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                <text>Studies in Systems, Decision and Control, Vol-440, pp. 659-676.</text>
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                <text>Pachiyappan S., School of Business and Management, CHRIST (Deemed to be University), Bannerghatta Road Campus, Bangalore, India; Kandral A., School of Business and Management, CHRIST (Deemed to be University), Bannerghatta Road Campus, Bangalore, India; Shylaja H.N., Department of Commerce, Manipal Academy of Higher Education, Manipal, India; Raj V J.P., School of Business and Management, CHRIST (Deemed to be University), Bannerghatta Road Campus, Bangalore, India; Vellayan S., School of Business and Management, CHRIST (Deemed to be University), Bannerghatta Road Campus, Bangalore, India</text>
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                <text>This chapter is dedicated to cross-silo private parameter aggregation. ML/DL has demonstrated promising results in a variety of application domains, especially when vast volumes of data are collected in one location, such as a data center or a cloud service. The goal of FL is to improve the quality of ML/DL models while minimizing their drawbacks. Participating devices in an FL task could range in size from a single smartphone or watch to a global corporation housing multiple data centers. It was originally believed that just a little amount of information about the original training data would be carried over into subsequent model updates as FL interactions occurred. The differential privacy framework is concerned with restricting the release of private information while sharing the outcomes of computations or queries performed on a dataset. Recently, many researchers have begun to employ differential privacy while training models in a federated setting.  2024 Saravanan Krishnan, A. Jose Anand, R. Srinivasan, R. Kavitha and S. Suresh.</text>
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                <text>Vijayalakshmi K.; Sitharselvam P.M.; Thamarai I.; Ashok J.; Sathish G.; Mayakannan S.</text>
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                <text>Handbook on Federated Learning: Advances, Applications and Opportunities, pp. 80-105.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179282227&amp;amp;doi=10.1201%2F9781003384854-4&amp;amp;partnerID=40&amp;amp;md5=59abf2775fcdf01df2776ae412f0cc85" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179282227&amp;amp;doi=10.1201%2f9781003384854-4&amp;amp;partnerID=40&amp;amp;md5=59abf2775fcdf01df2776ae412f0cc85&lt;/a&gt;</text>
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                <text>ISBN: 978-100383750-3; 978-103247162-4</text>
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                <text>Vijayalakshmi K., Department of Electrical and Communication Engineering, College of Engineering, National University of Science and Technology, Muscat, Oman; Sitharselvam P.M., RVS Educational Trusts Group of Institutions, Tamilnadu, Dindigul, India; Thamarai I., Department of Computer Science and Engineering, Panimalar Engineering College, Chennai City Campus, Tamil Nadu, Chennai, 600029, India; Ashok J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Sathish G., Department of Information Technology, St. Martins Engineering College, Telangana, Secunderabad, India; Mayakannan S., Department of Mechanical Engineering, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, Namakkal, India</text>
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                <text>Individual attitude; Internet memes; Memes marketing; Social media; Social media memes</text>
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                <text>Somasundaram T., Department of Management, Kristu Jayanti College (Autonomous), Bengaluru, India; Vijayasairam P., Department of Management Studies, NMS S Vellaichamy Nadar College (Autonomous), Madurai, India; Pachiyappan S., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>Revisiting the Nexus Between Suicides and Economic Indicators: An Empirical Investigation</text>
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                <text>In India, as of 2021, there was a 7.2% increase in suicides. With the economic burden inflicted by the pandemic and increasing suicides, a systematic investigation needs to be done. This empirical investigation uses the Autoregressive Distributed Lag (ARDL) model to obtain long and short-term estimates for the relationship between suicides and prominent economic indicators. The findings suggest that economic indicators like GDP per capita growth, age dependency ratio, and unemployment rate have a significant dynamic relationship with suicides. In this regard, preventive measures can be formulated and implemented in such a way that focuses on improving the countrys economic scenario which will in turn reduce suicides. Organizations and governments can plan training and mental health care programs for farmers, workers, and students. Mental health care services require attention from the government so that at a macro level the problem of increasing suicides can be handled.  The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024.</text>
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                <text>Studies in Systems, Decision and Control, Vol-487, pp. 751-760.</text>
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                <text>Sannapureddy K., CHRIST (Deemed to be University), Bangalore, India; Shaw J., CHRIST (Deemed to be University), Bangalore, India; Bashir S., CHRIST (Deemed to be University), Bangalore, India; Vijayalakshmi S., CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>Quality Efficacy Issues in Mangoes: Decoding Retailers Supply Chain</text>
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                <text>This research article tries to uncover the elements and compelling reasons causing supply chain inefficacy concerning low quality at the retailer level of the mangoes supply chain in Karnataka. The descriptive research approach was used in work. The research was conducted in the biggest mango-producing areas of Karnataka. Factors were discovered by factor analysis. A systematic questionnaire was used to determine how much the mango sector may improve supply chain efficacy. Contingent on the factor analysis, four variables for low quality were identified: functional difficulties, knowledge, Manpower, and resources. It was also discovered that the functional component is the compelling factor causing supply chain inefficacy. The study is confined to the retailer level of the Mango supply chain, focusing on four Mango-producing districts in Karnataka. Furthermore, the measures for the key causes under each aspect causing hindrances in supply chain efficiency in terms of quality have been discovered. There is a scarcity of materials to enhance the supply chain efficiency of merchants in Indias mango business. This research attempted to address a literature gap and help practitioners improve the mango supply chain in underdeveloped nations. This paper also serves the 2nd goal, Zero Hunger, End starvation, improve food security and nutrition, and promote sustainable agriculture of sustainable development.  The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024.</text>
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                <text>Studies in Systems, Decision and Control, Vol-487, pp. 795-805.</text>
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