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SIGNIFICANCE OF NURTURING PERMA FLOURISHING IN HIGHER EDUCATION: AN INTEGRATIVE REVIEW
This integrated review explored the significance of PERMA, a multidimensional well-being framework, and PERMA-based interventions in promoting student well-being within higher education contexts. The literature search resulted in 16 studies, and the synthesizing of key research findings supports the effectiveness of PERMA-based intervention on students overall well-being. The interventions centered on cultivating PERMA (positive emotions, engagement, relationship, meaning, accomplishment) offered as semester courses, classroom-based curricula, or intervention programs were found successful in improving wellbeing, happiness, life satisfaction, motivation, relationship building, engagement in learning, and reducing negative emotions, stress, academic boredom, anxiety, depression. Overall, the review findings demonstrate that embedding a PERMA-based well-being program as a holistic approach in education would foster a supportive learning environment and social connection in promoting individual and collective well-being among the students. Future studies could strengthen the present findings and respond to the limitations of the existing studies, which would provide a better understanding of the application and effects of PERMA-based programs. Copyright: The Author(s). -
Development of a Thin Layer Chromatography method to detect the presence of spiked Steroid drugs in Herbal Crude extracts
This study aimed to develop a better, faster and more efficient TLC method for detecting steroids in the herbal samples. The steroid drugs dexamethasone and prednisolone were considered possible adulterants and were spiked into herbal extracts of neem and amla. Optimization of the TLC mobile phases for better resolution for prominent visualisation of spots was performed with several combinations of solventsnamely, methanol, chloroform, ethyl acetate, toluene, ethanol, isopropyl alcohol, acetic acid and petroleum ether and, spray reagents such as iodine, potassium permanganate, sulphuric acid and, methanol. The optimal result was obtained using chloroform and methanol (9:1, v/v) as the mobile phase and methanol-sulphuric acid (9:1, v/v) as the spraying agent. The method was also able to separate a mixture of dexamethasone and prednisolone in the ratio of 1:1. The retention factors (Rf) for the steroids dexamethasone and prednisolone were within the range of 0.50-0.55 and 0.73-0.83 respectively. The lowest detection limit for the steroid drugs when mixed with herbal samples (neem and amla) was 0.1 g/ml. The developed TLC method is robust and can be conveniently utilised for detecting steroid adulterants in herbal samples. 2026, World Researchers Associations. All rights reserved. -
Isolation and Screening of Chromium-Tolerant Bacteria from Wetland Rhizosphere for use in Sustainable Bioremediation
Heavy metal contamination by hexavalent chromium [Cr(VI)] remains a major ecological threat due to its toxicity and persistence. This study investigated chromium-tolerant rhizospheric bacteria associated with Typha domingensis collected from Doddagubbi Lake, Bengaluru. Twelve isolates were obtained and exhibited diverse colony morphologies, where Gram staining classified the isolates into six Gram-positive and six Gram-negative strains. Physiological screening revealed broad adaptability with several isolates (DL4, DL6, DL11) tolerating up to 7.5% NaCl and DL11 showing thermotolerance up to 50C. pH profiling indicated that multiple strains (DL2, DL4, DL6, DL7, DL8) sustained growth up to pH 10. Biochemical tests showed widespread catalase activity and notable proteolysis, citrate utilisation and amylase production. Molecular identification grouped the isolates into eight taxa, including Brevibacillus brevis, Bacillus subtilis, Paenibacillus cookii, Kocuria rhizophila and several Pseudomonas species. Chromium tolerance assays demonstrated clear differentiation between highly tolerant isolates (DL1, DL2, DL8, DL9), which showed uninterrupted growth from 10100 mg/L Cr(VI) and moderately tolerant isolates that declined beyond 60 mg/L. Quantitative OD??? measurements further confirmed that the highly tolerant group retained OD values >0.55 at 80100 mg/L, whereas sensitive strains dropped below 0.05. These results highlight the strong bioremediation potential of T. domingensisassociated rhizobacteria for Cr(VI)-contaminated environments. 2026, World Researchers Associations. All rights reserved. -
Phytochemicals in Lemongrass (Cymbopogon citratus) Contributing to Growth and Disease Resistance in Goldfish (Carassius auratus Linn. 1758): Integration of Molecular Docking and Statistical Analyses
The ornamental fish industry has experienced significant growth with species like goldfish (Carassius auratus) gaining popularity for their vibrant appearance and ease of care. However, bacterial infections, particularly those caused by Aeromonas hydrophila, pose a significant threat to fish health and market value. In this study, visibly diseased goldfish exhibiting symptoms such as fin rot, black spots, tail rotting and skin lesions were divided into control and treated groups. The treated group was fed lemongrass (Cymbopogon citratus)-coated pellets, while the control group received standard feed. Over a three-week trial, visual improvements, including the healing of fin rot were documented, demonstrating the effectiveness of lemongrass-enhanced feed in promoting recovery and growth. GC-MS analysis of fresh lemongrass leaves identified key bioactive compounds, including citral, tetra decanoic acid, trans-verbenol and 1-undecanol, known for their antimicrobial properties. These findings confirmed the presence of phytochemicals with potential therapeutic applications against bacterial infections. Molecular docking studies further evaluated the interactions of prominent lemongrass phytochemicals: Procyanidin B2, Diosmin, Catechin and Tricin, with A. hydrophila outer membrane protein (3OD9). The results demonstrated strong binding affinities with Procyanidin B2 showing the highest (-8.0 kcal/mol), followed by Diosmin (-7.8 kcal/mol), Catechin (-7.6 kcal/mol) and Tricin (-7.6 kcal/mol), indicating their potential to inhibit bacterial pathogenicity. This study highlights the dual role of lemongrass as a natural growth promoter and antibacterial agent, emphasizing its potential as a sustainable and eco-friendly alternative to antibiotics in aquaculture. By effectively managing bacterial infections and improving fish health, lemongrass offers a promising solution for enhancing sustainability in aquaculture. 2026, World Researchers Associations. All rights reserved. -
Pediococcus pentaceous-mediated fermentation of Gracilaria corticate: A sustainable reutilisation of renewable resource to enhance its nutritional profile, optimised through response surface methodology for improved growth and pathogenic resistance in Oreochromis niloticus
Seaweed, as a functional food and a sustainable alternative to synthetic additives, is gaining attention. It can enhance the nutritive value, improve antioxidant properties and mitigate oxidative stress induced by pathogens. This study investigates the utilisation of fermented seaweeds in feed formulations to reduce oxidative stress, improve fish health and enhance disease resistance. Seaweeds Gracilaria corticate, rich in bioactive compounds such as polyphenols and antioxidants, were fermented using probiotic Pediococcus pentosaceus MK459540. Nile tilapia (Oreochromis niloticus) was fed a diet supplemented with fermented seaweed, which indicates lower levels of Superoxide Dismutase (SOD), Glutathione (GSH) and Glutathione-S-Transferase (GST) activities compared to control and non-fermented seaweeds when challenged with Vibrio harveyi, Aeromonas hydrophila and a mixture of both pathogens. These findings highlight the potential of seaweed, a sustainable and renewable marine resource in advancing aquaculture practices by promoting fish health and immunity. 2026, World Researchers Associations. All rights reserved. -
Study of cognitive adaptiveness of isolated Plant Growth Promoting Bacteria in nutritionally stress condition
The biological processes behind bacterial memory in different species are still under terra incognita. Additionally, the ability of learning through association in prokaryotes is still unknown. Cross-fertilization between the study of multicellular creatures' cognitive capacities and that of bacteria is possible. Therefore, Plant Growth Promoting Bacteria (PGPB) can be used to analyze this cognitive adaptation of bacteria under stress because PGPB is crucial to the maintenance of plant physiology and growth under a variety of stress scenarios. This study focuses on analyzing preliminary evidence of cognitive adaptability in PGPB under nutritional stress conditions. The isolated PGPB were treated with nutritional deprivation in both periodical and non-periodical manners and their performance was compared with the control group. The characteristics of PGPB, such as ammonia production, siderophore production, phosphate solubilization and indole-3-acetic acid, as well as anti-oxidant activities such as DPPH activity, hydroxyl radical scavenging activity and hydrogen peroxide scavenging activities, were analysed and compared to periodically and non-periodically stressed PGPB with control. In the isolated PGPB post-nutrition deprivation treatment, it was evident that the periodically stressed performed better than the non-periodically stress-exposed PGPB compared to the control wherein the isolates produced as high as 2.5510 mol mL-1 ammonia, 23.0406 mgL?1 indole-3-acetic acid, 69.16 0.71 psu siderophore and 123.5780.429mgL-1 phosphate solubilised. Out of the four isolated PGPB, the two novel strains, Paenibacillus alvei SJ6 and Paenibacillus alvei SJ8, have shown to possess the supreme ability to adapt to periodic nutritional stress compared to the other isolates in our study. 2025 World Researchers Associations. All rights reserved. -
Isolation and characterization of plant growth promoting bacteria (PGPB) from the rhizosphere of Spinacea oleracea L.
As the years pass by, there is an increase in abiotic stress conditions around the environment that directly or indirectly affect agriculture around the world. Therefore, there is a dire need to increase the sustainability of plants. Plant Growth Promoting Bacteria (PGPB) play an important role in maintaining the physiology and growth of plants under various stress conditions. This study looks into the isolation and characterization of different PGPB from Spinacia oleracea L. and their tolerance against salinity and commonly used commercial pesticides against the Spinacia family. The techniques used are isolation by serial dilution, 16sRna sequencing, characterization of different PGPB assays for confirmation such as ammonia production, catalase test, phosphate solubilisation, potassium solubilization, siderophore production, indole-3-acetic acid production, biofilm formation assay, halotolerance and tolerance study using Minimal Inhibitory Concentration (MIC). PGPB were isolated and characterized from Spinacia oleracea L., which was under an abiotic stress environment. Isolates were Bacillus clarus, Bacillus licheniformis, Paenibacillus alvei SJ6 and Paenibacillus alvei SJ8, having quantities as high as 78.10.004 mgL-1 phosphate solubilization, 43.8 mgL?1 of indole-3-acetic acid production, 14.5660.011 psu of siderophore production and 0.62 0.027 mol mL?1 of ammonia production. All isolates also had considerable amounts of halotolerance up to 10%, whereas Bacillus licheniformis had 12.5% halotolerance. The bacterial isolates had considerable tolerance against commonly used commercial pesticides against green leafy vegetables such as chlorpyriphos + cypermethrin combination and fungicides such as mancozeb. Therefore, this study looks into the isolation of potential plant growth promoting bacteria that have considerable amount of halotolerance and pesticide tolerance. 2025 World Researchers Associations. All rights reserved. -
Enterococcus faecalis CGz3 alleviating steatosis via BSH-mediated modulation in HepG2 cell-lines
The study aimed to evaluate the therapeutic potential of bile salt hydrolase (BSH)-producing probiotic Enterococcus faecalis CGz3 in alleviating steatosis in HepG2 hepatocarcinoma cells, with non-alcoholic fatty liver disease (NAFLD) induced by cholesterol and oleic acid (OA), focusing on its effects on lipid accumulation, metabolic gene expression and, inflammatory pathways. HepG2 cells were treated with cholesterol and OA to induce lipid accumulation, mimicking non-alcoholic fatty liver disease (NAFLD) conditions. Cells were then incubated with E. faecalis CGz3 for 6 hours at 37C and 5% CO2. Lipid levels were quantified using Oil Red O staining and cholesterol uptake assays, while gene expression of lipogenic, inflammatory and metabolic markers was assessed via quantitative real-time polymerase chain reaction (qRT-PCR). Treatment with E. faecalis CGz3 significantly reduced lipid accumulation from 42.961.35 mg/mL in NAFLD-induced cells to 29.731.26 mg/mL. It down regulated lipogenic genes (SREBP-1c, FAS and ACC) and inflammatory markers (TNF-?, IL-6, CRP, TLR4, TLR9, NF-?B, JNK, ERK) while upregulating PPAR? and AMPK, promoting fatty acid oxidation. No significant cytotoxicity was observed at 6 hours, though prolonged exposure (1224 hours) reduced cell viability. This study introduces E. faecalis CGz3, a novel BSH-producing probiotic isolated from chicken gizzard, as a promising candidate for NAFLD intervention. Its selective modulation of lipid metabolism and inflammation via BSH activity offers a new perspective on probiotic-based therapies for NAFLD, warranting further in vivo and clinical exploration. 2025, World Researchers Associations. All rights reserved. -
Novel Ovate Antenna for Wireless Communication: Characteristic Mode and Time Domain Analyses
In this article, a novel ovate-shaped microstrip antenna (OMSA) is presented for the application in wireless communication. It covers the evolution of a new shape and delves deeper into the resonance mechanism of the proposed design using characteristic mode analysis (CMA). The OMSA resonates at 2.45 GHz and 2.69 GHz with the return loss of ?18.82 dB and ?31.84 dB, respectively. It offers an ultra-wideband performance with 91.46% measured bandwidth. The characteristic impedance and VSWR at 2.4 GHz are 49 ? and 1.3, respectively. By introducing performance enhancement techniques such as ground truncation and a notch in the patch, the antenna resonance characteristics have been enhanced. A prototype of the proposed OMSA has been fabricated and validated experimentally. The time domain characteristics of the proposed OMSA have been simulated for both face-to-face (FtF) and side-by-side (SbS) configurations. The FtF configuration offers better performance, showcasing the group delay of the OMSA < 2 ns and minimal variation along the operating band. The phase linearity is also maintained, minimizing any distortions. The time domain results demonstrate a maximum fidelity factor of 90.62%, reaffirming the suitability of the antenna for wireless communication. The suitability of the proposed OMSA for wireless applications is also validated experimentally by analyzing the group delay and S21 phase linearity of the received signal. 2026, Electromagnetics Academy. All rights reserved. -
Artificial Intelligence Driven Drug Delivery Systems: Recent Advances and Emerging Trends
Drug Delivery Systems (DDS) play a critical role in ensuring the therapeutic efficacy and safety of pharmaceutical agents. Conventional drug delivery approaches often suffer from limitations such as poor bioavailability, non-specific targeting, and systemic toxicity. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the design and optimization of drug delivery platforms. AI-driven methods enable predictive modeling, intelligent nanocarrier design, and personalized therapeutic strategies by analyzing large biomedical datasets. These technologies facilitate optimized drug formulation, controlled release mechanisms, and targeted delivery, thereby improving treatment outcomes. AI algorithms such as Support Vector Machines (SVM), random forests, Convolutional Neural Networks (CNN), and reinforcement learning are increasingly applied in nanoparticle design, pharmacokinetic modeling, and clinical decision support systems. Additionally, emerging concepts such as self-driving laboratories, autonomous drug delivery systems, and AI-guided nanomedicine are reshaping pharmaceutical research. This review provides a comprehensive analysis of recent advances in AI-driven drug delivery systems, covering computational techniques, nanocarrier optimization, clinical applications, and emerging research trends. Comparative analysis tables summarize key algorithms, delivery platforms, and research developments reported in the literature. Finally, major challenges including data quality, regulatory issues, and interpretability of AI models are discussed along with future directions for the integration of AI in precision medicine and smart therapeutics. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved. -
Performance Evaluation of Machine Learning Models for Detecting Vulnerabilities in Internet of Things Network
Security threats and attacks are a growing concern in the field of Internet of Things (IoT) infrastructure. Internet-based automated network application models are used across various domains; commensurately, different security vulnerabilities and anomaly attacks are also increased at the same level. These attacks could cause failures in IoT infrastructure and network systems. In the modern world, Machine Learning (ML) models support various predictive analyses, providing more accurate results for future forecasting in various fields. In this article, we compare existing classical Machine Learning (ML) algorithms supported by Artificial Intelligence (AI) to evaluate and predict the performance and accuracy of different vulnerabilities in IoT infrastructure. We considered and compared the results of Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) using publicly available datasets. Through this evaluation, we obtained an accuracy of 99.4% from DT, RF, and ANN. Additionally, RF demonstrated a highest accuracy of F1 is 0.994 and lowest STD variance is 0.014 than compared models in the selected dataset. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved. -
Nurturing Rural Women as Entrepreneurs: Jharkhand State Livelihood Promotion Society (JSLPS) Based Case Evidences
This study examines the dynamics of rural womens entrepreneurship in Jharkhand (India), emphasising the role of the Jharkhand State Livelihood Promotion Society (JSLPS) under the National Rural Livelihoods Mission (NRLM). With merely 0.68 per cent of rural women entrepreneurs nationwide situated in Jharkhand, the report highlights a primary developmental necessity. Data was obtained from 521 Rural Women Entrepreneurs (RWEs) across six districts of Jharkhand, using a qualitative study design supplemented by five illustrative case studies. The analysis informed by the Resource-Based View (RBV) framework demonstrates that both tangible resources (such as access to micro-credit, training and digital platforms) and intangible resources (including empowerment, peer support and social capital) are essential for enterprise sustainability and growth. JSLPS interventions, including the Palash brand, SHG federations and market access through trade fairs and internet platforms, have facilitated womens move from informal employment to empowered entrepreneurship. The case studies, encompassing bakeries, soap production, incense manufacturing and handicraft businesses, underscore context-specific issues and adaptive tactics such as digital onboarding, communal ownership and skill development. Despite infrastructural and societal challenges, these businesses demonstrate scalable, replicable, and robust forms of grassroots entrepreneurship. The study indicates that a tailored, locally integrated and digitally inclusive support structure is essential for promoting RWEs. It proposes that forthcoming interventions incorporate institutional collaborations, policy changes and multi-sectoral frameworks to fully capitalise on rural womens entrepreneurial potential and convert them into catalysts for inclusive and sustainable development. 2025, National Institute of Rural Development. All right reserved. -
Fabrics of Power: Cutting Through the Noise in the Classroom
The hijab, purdah and veil though differently named constitute a continuum of meanings shaped by social, cultural and personal contexts. A womans decision to adopt or reject these garments situates her within a shifting spectrum of religious expression and secular alternatives. The volatility of these meanings renders the garments vulnerable to political appropriation, transforming them into contested symbols that are difficult to address pedagogically, therefore becoming a fabric of power. The hijab controversy that unfolded in Karnatakas educational institutions in early 2022 sharpened these complexities, prompting extensive public commentary on the purpose of education, the responsibilities of institutions, and the rhetorics of liberty, secularism, nation and religion. This article examines these commentarial responses ranging from editorials to columns in Kannada and the English media while reflecting on the parallel experience of teaching concepts such as liberty, dissent, secularism and religion during the period of unrest. In doing so, it foregrounds the paradox inherent in the politics of teaching literature, the framing of literature as political, and the pedagogical negotiations required when instruction unfolds within a charged and highly politicised atmosphere. 2025, Unisa Press. All rights reserved. -
Economics of Farming in Mahatwar, Uttar Pradesh
Recent policy efforts have focussed on transforming eastern Uttar Pradesh, an acknowledgement of the relative backwardness of the regions agricultural development. Despite this, there has been little discussion in the literature of agrarian relations and their implications for the economics of farming. Taking Mahatwar village in eastern Uttar Pradesh as a case study, this article examines disparities across socio-economic classes in incomes and the costs of cultivation. We found substantial inequality, with landlord and big capitalist farmer households earning nearly 30 times the annual income of lower peasant and manual worker households. These disparities arise primarily from differences in costs: poor peasant and manual worker households bear a disproportionate rental burden, rely excessively on family labour, and use much of their produce for self-consumption. Our findings highlight the need for rent reduction and yield enhancement, along with support measures such as minimum support prices (MSPs), to provide meaningful incomes to low-income farmers. 2025, Tulika Books. All rights reserved. -
METROLOGICAL IMPACT ON ORANGE FRUIT: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR PREDICTING FRUIT DISEASES
This research offers a comprehensive comparative analysis of efficient deep learning models for predicting diseases in orange fruits, with a particular emphasis on the impact of meteorological factors on disease prevalence. Citrus diseases such as Blackspot, Canker, and Greening are significantly influenced by environmental conditions. Recognizing the crucial role of weather conditions in the development and spread of these diseases, we concentrate on enhancing prediction accuracy by integrating Convolutional Neural Networks (CNNs) with various classification algorithms to develop hybrid models that account for meteorological impacts. Specifically, we assess the performance of a CNN combined with Gradient Boosting (CNN-GB) and compare it against other hybrid models such as CNN integrated with Long Short-Term Memory networks (CNN-LSTM), Support Vector Machines (CNN-SVM), and Random Forest classifiers (CNN-Random Forest). These models are evaluated using different optimization algorithms to determine the most effective approach for disease prediction under varying meteorological conditions. A meticulously curated dataset comprising 1,600 training images and 300 testing images of orange fruits exhibiting a variety of disease symptoms was utilized for evaluation. The dataset reflects diverse environmental conditions to capture the meteorological impact on disease manifestation. All the models tested, the CNNGB hybrid model with NDAM optimizer exhibited superior performance (Accuracy 98.03) in comparison of other models like CNN (Accuracy 96.03), CNN+LSTM (Accuracy 96.16), CNN + SVM (Accuracy 97.13) and CNN + Random Forest (Accuracy 97.79). The exceptional performance of the CNN-GB model suggests that integrating CNNs with powerful classification algorithms like Gradient Boosting, along with considerations of meteorological data, can significantly enhance disease detection in crops. This advancement contributes to more proactive and effective disease management strategies, ultimately reducing economic losses and increasing productivity in the agricultural sector. 2025 Published by Faculty of Engineering. -
METROLOGICAL IMPACT ON ORANGE FRUIT: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR PREDICTING FRUIT DISEASES
This research offers a comprehensive comparative analysis of efficient deep learning models for predicting diseases in orange fruits, with a particular emphasis on the impact of meteorological factors on disease prevalence. Citrus diseases such as Blackspot, Canker, and Greening are significantly influenced by environmental conditions. Recognizing the crucial role of weather conditions in the development and spread of these diseases, we concentrate on enhancing prediction accuracy by integrating Convolutional Neural Networks (CNNs) with various classification algorithms to develop hybrid models that account for meteorological impacts. Specifically, we assess the performance of a CNN combined with Gradient Boosting (CNN-GB) and compare it against other hybrid models such as CNN integrated with Long Short-Term Memory networks (CNN-LSTM), Support Vector Machines (CNN-SVM), and Random Forest classifiers (CNN-Random Forest). These models are evaluated using different optimization algorithms to determine the most effective approach for disease prediction under varying meteorological conditions. A meticulously curated dataset comprising 1,600 training images and 300 testing images of orange fruits exhibiting a variety of disease symptoms was utilized for evaluation. The dataset reflects diverse environmental conditions to capture the meteorological impact on disease manifestation. All the models tested, the CNNGB hybrid model with NDAM optimizer exhibited superior performance (Accuracy 98.03) in comparison of other models like CNN (Accuracy 96.03), CNN+LSTM (Accuracy 96.16), CNN + SVM (Accuracy 97.13) and CNN + Random Forest (Accuracy 97.79). The exceptional performance of the CNN-GB model suggests that integrating CNNs with powerful classification algorithms like Gradient Boosting, along with considerations of meteorological data, can significantly enhance disease detection in crops. This advancement contributes to more proactive and effective disease management strategies, ultimately reducing economic losses and increasing productivity in the agricultural sector. 2025 Published by Faculty of Engineering. -
Experimentally optimizing a spinning disk by manipulating its mass distribution and radius
The scientific method enables the experimental study of complex phenomena by isolating key variables. This work explores the significant properties of spinning bodies. Optimizing spinning disks is the primary aim of this work. Optimization is achieved by manipulating the moment of inertia (MOI) of the disk, allowing a longer duration of spin and lowering the rate of energy dissipation. Experiments are designed and conducted to explore the relationship between the radius and mass distribution of the disk and the angular deceleration experienced by it. Effects of the same on energy retention is analyzed. Empirical data is interpreted graphically while accounting for systematic and random uncertainties. Percentage change in duration of spin as a result of percentage change in physical quantities is studied. Moving mass away from the central axis of the spinning disk increases its duration of spin from a constant initial angular velocity. Energy retention is also improved. Increasing the radius of the disk increases the duration of spin and reduces the rate of energy dissipation. The above conclusions are drawn from experiments where the mass and thickness of the disk are controlled along with other necessary factors that can influence the results. The experiments confirm the existing theory relating to the moment of inertia, angular quantities, resistive torques and kinetic energy of spinning disks. The experiments provide insights into the behavior of spinning disks in practical situations, especially in problems concerned with optimization in the field of mechanical engineering. 2026 Veeresha et al., published by Paradigm. -
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. -
Green Growth Nexus: Analysing Environmental Performance and GDP Trends in OECD Economies
Subject and Purpose of the Work: Over the past two decades, OECD countries such as Italy, Germany, France, and the United Kingdom have experienced consistent economic growth, averaging 2% annually in GDP. This upward trend has been driven by various factors, including government spending, investment rates, and favourable global conditions. Recently, environmental performance has emerged as a critical factor influencing economic development. This study aims to examine the relationship between environmental performance indicators and GDP growth in selected OECD countries, focusing on the growing emphasis on environmental sustainability. Materials and Methods: The analysis uses panel data from the OECD and World Bank, spanning 25 years (20002024), for four OECD nations. The study employs a Panel Autoregressive Distributed Lag (ARDL) model, which allows for the estimation of both short-run and long-run dynamics. GDP growth is the dependent variable, while the independent variables include environmental tax revenue (TAX), greenhouse gas emissions (EMI), air quality (QUA), government expenditure on environmental protection (EXP), and the share of renewable energy in total energy supply (REN). Results: The empirical findings indicate that TAX and EXP have minimal positive impact on GDP growth, suggesting potential inefficiencies in the allocation or effectiveness of environmental funds. In contrast, other indicators such as air quality and renewable energy share show a stronger link with economic growth. Conclusion: The study highlights the growing significance of environmental performance in shaping economic outcomes. It contributes to the sustainable development literature by demonstrating that targeted environmental efforts can positively influence long-term economic growth. 2025 K Keerthana et al., published by John Paul II University of Applied Sciences. -
NET ZERO TRANSITION TOWARDS DECARBONIZATION IN CONTEXT OF ENERGY SECTOR
The study provides an identification and analysis of potential enablers that facilitate transition towards net zero in the energy sector through Multi Criteria Decision-Making (MCDM) framework. The identified enablers and causal relationships between them in terms of decarbonization initiatives are studied using the DEMATEL method and combining trapezoidal fuzzy numbers (TFNs). The research design involves an overarching review of thirteen potential enablers to net zero transition within the energy sector, in order of their impact and causality. Top-ranked enablers that would have the greatest impact in achieving the energy transition were carbon pricing mechanisms, waste-to-energy conversion, decentralized energy systems and circular procurement policies. The research indicates that the enablers show causal pathways that are interconnected and can take place as both causes and effects in the decarbonization framework. Application of DEMATEL method using TFNs increases the strength of causal relationship derivation. The study adds to the literature on enabling net zero transition in energy and highlights the importance of a conceptual approach involving a combination of policy, technology and principles of the circular economy. Such lessons can guide policy makers, industry players and academics in planning and speeding up the process to sustainable energy systems and world climate targets. 2026 Sciendo. All rights reserved.