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Attention to Economic Factors and Its Response to Foreign Portfolio Investment: An Evidence from Indian Capital Market
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. -
Behavioral Bias as an Instrumental Factor in Investment Decision-An Empirical Analysis
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. -
Facile Synthesis of Polymer Dot and Its Antibacterial Action Against Staphylococcus aureus
Antimicrobial resistance (AMR) rising from nosocomial infections is an escalating threat to human life nowadays due to the overuse of drugs. The multidrug-resistant pathogenic bacteria have increased morbidity and mortality rates, becoming a crucial global clinical challenge. Gram-positive Staphylococcus aureus bacteria is one of the nosocomial pathogens that cause severe invasive diseases and skin infections to human health worldwide. Herein, a non-conjugated polymer dot (NCPD) was synthesized from less toxic and biocompatible polyvinyl alcohol (PVA) via hydrothermal treatment. The fluorescence of the polymer dots was enhanced by nitrogen doping. The as-synthesized nitrogen-doped polymer dots (PDs) exhibit excitation-dependent luminescence emission and show green color fluorescence under UV light. The average size of the synthesized functionalized non-conjugated polymer dot is obtained as 4.08nm, and they exhibit an amorphous structure. No antibacterial property was observed for bulk polymer, but the doped polymer dots showed antibacterial activity against Gram-positive Staphylococcus aureus bacteria. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Dandelion Algorithm for Optimal Location and Sizing of Battery Energy Storage Systemsin Electrical Distribution Networks
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. -
Application Areas, Benefits, and Research Challenges of Converging Blockchain and Machine Learning Techniques
In recent years, machine learning (ML) has become a hot topic of research and application. ML model and huge amount of data growth difficulties still follow ML development. With the lack of new data and constant training, published ML models may soon become obsolete; unscrupulous data contributors may upload incorrectly labelled data, leading to poor training results; and data leakage and abuse are all possible outcomes. These issues can be effectively addressed by using blockchain, a new and rapidly evolving technology. With the advancement of various smart devices and the field of artificial intelligence and machine learning, interdisciplinary collaboration with blockchain technology may be incredibly valuable for future investigations. Collaborative ML and blockchain convergence can be studied here, with emphasis on how these two technologies can be combined and their application areas. On the other hand, look at the existing researchs shortcomings and future enhancements. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
Wearable Smart Technologies: Changing the Future of Healthcare
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. -
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.