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Exploring the Influence Dynamism of Economic Factors on Fluctuation of Exchange Rate-An Empirical Investigation for India Using ARDL Model
The Indian Foreign Exchange Market has experienced significant changes over the past decade, due to high degree of instability of the Indian Rupee leading to its devaluation against major global currencies. Exchange rate is considered as one of crucial indicators to determine the economic growth. Volatility of exchange rate of each day is influenced by various factors such as demand and supply, Gross Domestic Product, Interest rate, employment rate, public debt, balance of payments, inflation etc. Though there are multiple causes to determine the movement of exchange rate, but still the accurate level of causation is unpredictable. Keeping this in mind, this paper tries to attempt the relationship that exists between the exchange rate and select macroeconomic factors. To analyse the extent of influence of the selected variables on the exchange rate, the research paper uses 10 years of data spanning from Jan 2013 to Nov 2022. Further, the study uses monthly data of above-mentioned variables to bring out the analysis to meet the objectives. Descriptive statistics is used to find the characteristics of the data, correlation analysis and Ordinary Least Square method is used to find the relationship and impact level select macroeconomic factors on exchange rate. Autoregressive Distributed Lag (ARDL) model is used to find if any short run and long run association exists between the variables and the exchange rate. 2023, ASERS Publishing House. All rights reserved. -
Measurement Model of CO-PO Attainment in Higher Education: A Simplified Approach
The educational system in most countries are moving toward Outcome-Based Education (OBE) which is a student-centric teaching and learning methodology. The basic idea behind the adoption of OBE model is that the graduates should possess a sound knowledge in their respective disciplines and also have global mobility and acceptance. The Outcome-Based Education (OBE) should be based on the vision and mission of the institution. The institutions should clearly spell out the learning objectives of the program and course. The Course Outcome (CO), Program Outcome (PO), Program Specific Outcome (PSO) and Program Educational Objectives (PEO) determine clearly what the students are expected to accomplish, post their course or program respectively. This study aims to provide the simplified approach on assessment, evaluation and calculating the attainment levels of students through COs and POs in a management program. To assess the CO attainment for management courses, the authors have identified the subject Entrepreneurship Development offered in the first semester from the 2018-2020 batch of 60 students from the MBA program of an autonomous institute. The Course Outcome (CO) and Program Outcome (PO) are mapped with the Continuous Internal Assessments (CIA) and Semester Exam End (SEE) and thus the attainment levels of each CO are measured. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
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. -
Autoregressive Distributed Lag Approach for Estimating the Nexus between Net Asset Value of Mutual Fund and Economic Determinants in India
India has seen a phenomenal growth in cumulative mutual fund investment from Rs 7.93 trillion in 2012 to Rs 40.38 trillion in 2022, which is more than a five-fold increase since last 10 years. Retail investors are now realizing the power of savings and Systematic Investment Plans (SIP) to build long term wealth. A financial literacy wave which is sweeping across India has projected mutual funds as a significant contributor and beneficiary of this phenomenon. The evolving economic landscape of India provides investors with excellent opportunities to capitalize on these fluctuations through systematic investment in safe investment vehicles like mutual funds. The market associated with mutual funds is always subjected to economic risks. The erratic fluctuations in macroeconomic variables can largely explain the Volatility in Net Asset Value (NAV) of equity oriented mutual fund schemes. With this background, this paper examines the impact of select macroeconomic variables on mutual funds performance in India. To analyse this, monthly observations of select macroeconomic variables, average NAV of large cap, mid cap, and small cap funds collected for a period of 10 years starting from January 2013 to November 2022. Descriptive statistics is used to probe the characteristics of the variable. In addition, correlation and ordinary least square method is applied to check the existing relationship and impact level of macroeconomic factors on NAV of select schemes. Lastly, short and long run relationship is analysed using Autoregressive Distributed Lag Model (ARDL). 2023, ASERS Publishing House. All rights reserved. -
Does Green Financing affect the Sustainable Economic Growth of Emerging Economies? Evidence from Panel ARDL Model
This study examines the nexus between green finance determinants and sustainable economic growth in Brazil, India, China, and South Africa using a panel Autoregressive Distributed Lag (ARDL) approach. These rapidly developing countries face the dual challenge of maintaining economic growth while addressing environmental sustainability. The analysis focuses on five key independent variables: Comparative Advantage in Low Carbon Technology Products, Total Trade in Low Carbon Technology Products, Trade Balance in Low Carbon Technology Products, Annual CO2 Emissions, and Lack of Coping Capacity. Short-run results indicate that Total Trade in Low Carbon Technology Products negatively affects GDP, suggesting that while green trade is expanding, it currently lacks stable, revenue-generating mechanisms. Annual CO2 Emissions and Lack of Coping Capacity positively influence GDP in the short term, reflecting continued dependence on emission-intensive industries and limited infrastructure for resilience. Comparative Advantage and Trade Balance in Low Carbon Technology Products are statistically insignificant in the short run, implying delayed economic benefits. In the long run, none of the green finance indicators show a significant relationship with GDP, possibly due to the substantial upfront investments required for green projects, which delay economic returns. The study underscores the need for strategic investments in technology, infrastructure, and governance to align economic growth with long-term sustainability goals. 2025 Sathish Pachiyappan et al., published by Oikos Institut d.o.o. -
Golden Insights: Analyzing the Influence of Economic Indicators on Sovereign Gold Bond Performance in India
India has been the leading consumers of gold with the consumption of around 774 metric tons in 2022. The demand for gold in India is majorly associated with its culture, tradition, attractiveness, and the source for financial security (GJC,n.d.)The gold market in India plays a vital role in the economy as a stable asset and hedge against inflation due to its ability to hold value over time. In order to limit the import of gold and reduce the countrys current deficit, the Indian Government introduced Sovereign Gold Bonds in 2015 as a substitute to physical gold. As SGBs export-import values are backed by Reserve Bank of India (RBI) they are considered as an inflation hedging tool. The study aims to examine the effectiveness of SGBs, in the changing economy by understanding the impact of key economic indicators Inflation Rate, Exchange Rate, Per Capita Income, Gold Prices, and GDP Growth Rateon the performance of Sovereign Gold Bonds (SGBs) in India. 36 months observations of the selected macroeconomic variables and series wise released prices are collected for a period starting from September 2021 till August 2024 for the analysis. Descriptive statistics is applied to understand the characteristics of the variables. Further, correlation and ordinary least square method is used to check the existing relationship and impact level of macroeconomic variables on SGBs. Lastly, both long run and short run relationships of these variables are analyzed using the Autoregressive Distributed Lag Model (ARDL). 2025, Iquz Galaxy Publisher. All rights reserved. -
The Macro Lens: Exploring the Impact of Macroeconomic Variables on Indias Small Cap, Mid Cap, and Large Cap Indices
Subject and Purpose of Work: This study explores the intricate relationship between key macroeconomic variables and Indias equity market segments, specifically the NIFTY Small-cap, Mid-cap, and Large-cap indices. The primary objective is to evaluate how selected macroeconomic factors influence market dynamics and investor sentiment in the Indian context. Materials and Methods: The research analyses monthly data spanning five years, from January 2019 to January 2024. The macroeconomic indicators considered include Foreign Institutional Investment (FII), Domestic Institutional Investment (DII), Consumer Price Index (CPI), Purchasing Managers Index (PMI), Treasury Bill Rate, Gold Price, and Reverse Repo Rate. Statistical techniques such as the Unit Root Test, Ordinary Least Squares (OLS), and Granger Causality Test are employed to assess the short-term and long-term impacts of these variables on market indices. Results: The findings reveal that GDP, CPI, PMI, and Gold Price exhibit no statistically significant influence on the NIFTY Small-cap, Mid-cap, or Large-cap indices, aligning with certain earlier studies. However, variables like FII, DII, Treasury Bill Rate, and Reverse Repo Rate show varying degrees of influence across the indices, highlighting the complex and segmented nature of the Indian equity market. Conclusion: These insights are valuable for investors, policymakers, and financial analysts in refining investment strategies, informing policy frameworks, and enhancing market forecasting models. The study underscores the need for continuous evaluation of macroeconomic influences to better navigate market volatility and investor behaviour. 2025 Sathish Pachiyappan et al., published by John Paul II University of Applied Sciences. -
Driving profitable business growth through economical optimization, energy management, and industrial 5.0 innovations
The chapter emphasizes the significance of economic optimization, energy efficiency, and Industrial 5.0 innovations in driving sustainable growth and profitability in today's business landscape. It highlights the strategic allocation of resources to maximize efficiency and minimize costs, using lean management principles, automation, and data analytics. Energy management is crucial for reducing operational costs and mitigating environmental impact, using renewable energy sources and smart technologies. Industrial 5.0, a new era of industrial transformation, combines automation, connectivity, and data exchange, with technologies like artificial intelligence, IoT, and blockchain. 2024, IGI Global. -
Scripts influence on reading processes and cognition: a preamble
[No abstract available] -
Editorial: Methods and applications in cognitive science
[No abstract available] -
A Multifaceted Approach at Discerning Redditors Feelings Towards ChatGPT
Generative AI platforms like ChatGPT have leapfrogged in terms of technological advancements. Traditional methods of scrutiny are not enough for assessing their technological efficacy. Understanding public sentiment and feelings towards ChatGPT is crucial for pre-empting the technologys longevity and impact while also providing a silhouette of human psychology. Social media platforms have seen tremendous growth in recent years, resulting in a surge of user-generated content. Among these platforms, Reddit stands out as a forum for users to engage in discussions on various topics, including Generative Artificial Intelligence (GAI) and chatbots. Traditional pedagogy for social media sentiment analysis and opinion mining are time consuming and resource heavy, while lacking representation. This paper provides a novice multifrontal approach that utilises and integrates various techniques for better results. The data collection and preparation are done through the Reddit API in tandem with multi-stage weighted and stratified sampling. NLP (Natural Language processing) techniques encompassing LDA (Latent Dirichlet Allocation), Topic modelling, STM (Structured Topic Modelling), sentiment analysis and emotional analysis using RoBERTa are deployed for opinion mining. To verify, substantiate and scrutinise all variables in the dataset, multiple hypothesises are tested using ANOVA, T-tests, KruskalWallis test, Chi-Square Test and MannWhitney U test. The study provides a novel contribution to the growing literature on social media sentiment analysis and has significant new implications for discerning user experience and engagement with AI chatbots like ChatGPT. 2024 Padarha et al., licensed to EAI. -
Interconnected Intelligence: Navigating Through Power Quality Checking and Control Using Smart Intelligence-Based Methods
Globally, power quality issues incur substantial costs. In the United States, power quality problems contribute to a $150 billion annual cost, covering lost productivity, equipment damage, and safety hazards. Smart intelligence-based methods can potentially cut these costs by up to 50%. In India, power quality disturbances result in a $10 billion annual cost involving equipment damage, productivity losses, and customer dissatisfaction. The adoption of smart intelligence-based power quality methods in India is projected to grow annually by 25% for the next 5years due to increasing grid demands. In todays intricate power landscape, dependable electrical systems are crucial. Power quality disturbances, including voltage variations, harmonics, and flicker, can disrupt sensitive equipment, resulting in financial losses and safety risks. Addressing these challenges, smart intelligence-based methods emerge as promising solutions. This chapter systematically explores the application of artificial intelligence, machine learning, and data analytics for elevated power quality monitoring, assessment, and regulation. Such intelligent approaches optimise power system performance, reduce downtimes, and ensure a consistent supply of high-quality electrical energy. The assimilation of smart intelligence-based methods emerges as a promising avenue to address these challenges effectively. Harnessing the capabilities of these intelligent paradigms empower power systems to attain optimal performance, curtail downtimes, and ensure a steadfast provision of high-grade electrical energy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges
In the age of digital transactions, credit cards have emerged as a prevalent form of payment in smart cities. However, the surge in online transactions has heightened the challenge of accurately discerning legitimate from fraudulent activities. This paper addresses this crucial concern by introducing a pioneering system for detecting fraudulent credit card transactions, particularly within highly imbalanced datasets, in the realm of cybersecurity. This paper proposes a hybrid model to effectively manage imbalanced data and enhance the detection of fraudulent transactions. This paper emphasizes the efficacy of the hybrid approach in proficiently identifying and mitigating fraudulent activities within highly imbalanced datasets, thereby contributing to the reduction of financial losses for both merchants and customers in smart cities. As cybersecurity in smart cities evolves, this paper underscores the significance of ensemble learning and cross-validation techniques in optimizing credit card transaction analysis and fortifying the security of digital payment systems. 2024, Taru Publications. All rights reserved. -
Legal conundrums of space tourism
Private commercial space tourism carrying passengers to outer space is no longer a distant or far-fetched fantasy, rather it is at verge of becoming an affordable reality with exponential development in space technology including development of Reusable Launch Vehicle (RLV), increasing involvement of private companies like Virgin Galactic, SpaceX, Blue Origin etc. into research and funding of space tourism explorations and applications. It is also receiving huge attention from the public. These developments reflect the infinite possibilities and inevitability of space tourism in near future. However, space tourism may also pose many critical legal issues which must be addressed to ensure the consistent and sustainable development of space tourism, and to secure the rights of all stakeholders involved including operators, passengers, launching State etc. The research paper would highlight the crucial legal issues associated with the space tourism. The paper would critically analyze the efficiency of the present international space treaties in dealing with these issues. At the end, the paper would also attempt to provide few suggestions and solutions to these legal conundrums relating to space tourism. 2021 IAA -
Humour as a Moderator Between Hassles and Well-Being
Humour is a universal phenomenon that offers several physiological and psychological benefits across cultures. The objectives of this study were to examine the relationships between daily hassles, humour and well-being; and to investigate the moderating effect of humour on the relationship between hassles and well-being. A correlational design was adopted to collect data from 644 participants (men = 300, women = 344), aged between 18 and 58years using purposive and snowballing sampling techniques. The Daily Hassles Scale, Sense of Humour Questionnaire (SHQ-R) and the Personal Well-Being IndexAdult (PWI-A) were administered to the sample. The self-report measures were appropriately scored and the collective data were analyzed. Statistical analyses revealed a positive relationship between sense of humour and well-being. A negative relationship was observed between sense of humour and hassles; and between well-being and hassles. Further, sense of humour was found to be moderating the relationship between daily hassles and well-being. This study highlights the role of humour in softening the impact of hassles on the well-being of the Indian population. This strengthens the construct of humour in the context of positive psychology. The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India 2024. -
A hybrid crypto-compression model for secure brain mri image transmission
Medical image encryption is a major issue in healthcare applications where memory, energy, and computational resources are constrained. The modern technological architecture of digital healthcare systems is, in fact, insufficient to handle both the current and future requirements for data. Security has been raised to the highest priority. By meeting these conditions, the hybrid crypto-compression technique introduced in this study can be used for securing the transfer of healthcare images. The approach consists of two components. In order to construct a cutting-edge generative lossy compression system, we first combine generative adversarial networks (GANs) with oearned compression. As a result, the second phase might address this problem by using highly effective picture cryptography techniques. A randomly generated public key is subjected to the DNA technique. In this application, pseudo-random bits are produced by using a logistic chaotic map algorithm. During the substitution process, an additional layer of security is provided to boost the techniques fault resilience. Our proposed system and security investigations show that the method provides trustworthy and long-lasting encryption and several multidimensional aspects that have been discovered in various public health and healthcare issues. As a result, the recommended hybrid crypto-compression technique may significantly reduce a photos size and remain safe enough to be used for medical image encryption. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Optical design studies for national large optical-IR telescope
A 1012 m class national large optical-IR telescope (NLOT) is envisaged to meet the growing scientific requirements in astronomy and astrophysics. Telescopes of such dimensions can only be made by segmenting the primary mirror, as it eases a more prominent primary mirrors fabrication, transportation, operation, and maintenance process. This paper presents the various optical designs analyzed for NLOT that can be fabricated using the India TMT Optics Fabrication Facility (ITOFF) at the Centre for Research and Education in Science and Technology (CREST) campus. We present the primary mirror segmentation details, its ideal optical performance, and study each designs advantages and technical complexities. Based on the above analysis, we have narrowed it down to an optimal design, and its performance analysis is also discussed. Indian Academy of Sciences 2024. -
The role of religious and financial factors in eudaimonic well-being among Indian adults
Purpose: The available literature has explored the various psychosocial determinants of well-being to some extent. The earlier works have focused primarily on hedonic well-being with little focus on eudaimonic aspects. Therefore, this study aims to understand the role of parentchild religious attendance during childhood, religious connectedness, and financial-material stability on eudaimonic well-being among adults in India. Design/methodology/approach: The authors used the India data from Global Flourishing Study Wave 1 that addressed flourishing among adults above 18 years. The authors considered 9,076 Indian adults and used descriptive and correlation statistics. In addition, the authors conducted path analysis and t-test. Findings: The likelihood of eudaimonic well-being increased with parentchild religious attendance during childhood (= ?0.044, p < 0.01) along with religious connectedness (= ?0.112, p < 0.01) and financial-material stability (= 0.145, p < 0.01) as an adult. In addition, a significant difference existed in terms of religious connectedness and eudaimonic well-being with income and perceived feelings about income. Originality/value: This study emphasizes financial stabilitys relevance in well-being and suggests the importance of considering religious factors during childhood and adulthood. Emphasizing factors influencing eudaimonic well-being is relevant due to its influence on mental health and quality of life. 2024, Emerald Publishing Limited. -
The role of religious and financial factors in eudaimonic well-being among Indian adults
Purpose: The available literature has explored the various psychosocial determinants of well-being to some extent. The earlier works have focused primarily on hedonic well-being with little focus on eudaimonic aspects. Therefore, this study aims to understand the role of parentchild religious attendance during childhood, religious connectedness, and financial-material stability on eudaimonic well-being among adults in India. Design/methodology/approach: The authors used the India data from Global Flourishing Study Wave 1 that addressed flourishing among adults above 18 years. The authors considered 9,076 Indian adults and used descriptive and correlation statistics. In addition, the authors conducted path analysis and t-test. Findings: The likelihood of eudaimonic well-being increased with parentchild religious attendance during childhood (= ?0.044, p < 0.01) along with religious connectedness (= ?0.112, p < 0.01) and financial-material stability (= 0.145, p < 0.01) as an adult. In addition, a significant difference existed in terms of religious connectedness and eudaimonic well-being with income and perceived feelings about income. Originality/value: This study emphasizes financial stabilitys relevance in well-being and suggests the importance of considering religious factors during childhood and adulthood. Emphasizing factors influencing eudaimonic well-being is relevant due to its influence on mental health and quality of life. 2024, Emerald Publishing Limited. -
A Model for Detecting Type 2 Diabetes Using Mixed Single-Cell RNA Sequencing with Optimized Data
Diabetes is a critical disease and is crucial to personage agility. Type 2 Diabetes (T2D) accounts for 92% of epithetical cases. This paper proposes an optimized type 2 diabetes detection model using mixed single-cell RNA sequencing (scRNA-seq) technology. Diabetes is a chronic metabolic disorder affecting millions of people worldwide. Early detection of the disease can greatly improve treatment outcomes, but current diagnostic methods have limitations. Our proposed model integrates scRNA-seq data from both human pancreatic beta cells to identify gene expression patterns associated with diabetes. Our study shows that the proposed model is highly accurate in identifying diabetes, achieving an area under the curve (AUC) of 0.98. We employed an optimized model to improve the detection of diabetes at an early stage, leading to better treatment outcomes and an improved quality of life for patients. We initially incorporated optimal features from the dataset using the Monte Carlo (MC) feature selection method. This method helped us to estimate the relative importance (RI) score of each gene or feature, which is then used to rank the features. Further, we proposed an optimized deep belief network (ODBN) as a classification model to classify T2D and non-diabetes. To improve the performance of ODBN, an adaptive chimp optimization algorithm (AChOA) is introduced to optimize the weight parameters and achieved a performance accuracy of 96.57%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.