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NEXT-GEN SUSTAINABILITY: ANALYSING ATTITUDES AND ACTIONS OF GEN Z TOWARDS CIRCULAR ECONOMY AND ECO-FRIENDLY PRACTICES
Generation Zs (Gen Z) role in the creation in maintenance of the circular economy is very important, as they have the potential to shape sustainable practices for future generations. This study aims to comprehend Gen Zs role in promoting environmental sustainability within the framework of the circular economy. An explanatory sequential research design has been adopted in order to achieve the objectives of this research article. The researchers have initially collected quantitative data from 941 respondents using a questionnaire. The respondents were selected based on a stratified random sampling approach. Following the quantitative analysis, qualitative data was collected via interviews with 18 Gen Z participants. Based on the quantitative data analysis, the study found that Gen Z exhibits a strong commitment to promoting circular economy. The results of the Structural Equation Modelling (SEM) shows that recycling activity has the highest impact on achieving the principles of circular economy. Based on the qualitative analysis, this study discovered four main themes. The first theme was centred on Awareness of Circular Economy. The second theme explored the Integration of the Circular Economy on Personal Life. The third theme, probed into the Impact of the Circular Economy on Peoples Lives. While the final theme addressed Steps Towards Building an Active Circular Economy. UMT Press -
Effect of Premixing Process on the Uniform Distribution of Nano hBN and Carbon Fiber Reinforcements in AA7050 Matrix
In Aluminium metal matrix composites, achieving a homogeneous dispersion of reinforcements remains a significant challenge, especially when mixing fibrous and nanoscale reinforcements. The effect of the premixing procedure on the homogeneous dispersion of carbon fiber (CF) and nano hexagonal boron nitride (hBN) reinforcements in AA7050 matrix is studied in this work. Before composites are prepared, a multi-stage process for premixing is used, which consists of ultra-sonication, magnetic stirring, and mechanical mixing in order to minimize particle clustering. This also improves the wetting between the reinforcement and the matrix. Field emission scanning electron microscope (FESEM) was used to characterize the premixed powders to assess agglomeration behaviour, interfacial integrity, and dispersion uniformity. Due to the premixing process, better densification of nearly 95.2% and enhancement of 33.3% of micro-hardness are reported for 0.25 wt.% CF and 0.5 wt.% hBN addition. The results reveal that after the premixing process, particle dispersion was improved, leading to high-quality composites in the subsequent sintering process. The premixing process offers a better way to disperse the nano reinforcement particles in the production of aluminium metal matrix nanocomposites, which directly influences the properties of the composites. -
Vertex Removal on Perfect Italian Domination and ?pI-Stability of Graphs
Perfect Italian Domination (PID) is a domination concept where all vertices are assigned one of the labels among 0, 1 and 2 such that the sum of the labels in the neighbourhood of every vertex labelled 0 should be exactly 2. We examine a few graph classes of graphs and discuss the criticality of Perfect Italian Domination. We also define ?pI stable graphs and PID critical graphs. Following our definitions of ?pI-stable and PID critical graphs, we have grouped some graph classes. We characterise a family of trees that is ?pI-stable.. MatDer. -
On a Mixture of the Lindley and Modified Lindley Distributions: Properties and Estimation
In this article, we investigate a novel three-parameter lifetime distribution constructed from a mixture of the original Lindley and modified Lindley distributions. The concept behind this construction is to combine the contrasting properties of these two well-known distributions to provide a new statistical modeling option for lifetime data analysis. In particular, it provides a natural alternative to the three-parameter, two-component mixture of the Lindley distribution, which has attracted attention in the recent statistical literature. We investigate its main properties from both a theoretical and practical point of view. The shapes of the corresponding probability density and hazard rate functions and the formulas for the moments, moment generating functions and characteristic functions are discussed. The distribution is then subjected to statistical analysis, considering it as a semi-parametric model. The maximum likelihood approach is used to estimate the parameters. In a simulation analysis, the numerical behavior of the bias and the mean square error of the obtained estimates are studied. The new model is tested on three data sets and the results show that it has a better fit behavior than its main competitor, the three-parameter two-component mixture of the Lindley model. 2025 YU -
Evaluating the effectiveness of virtual reality-based rehabilitation programs for post-injury recovery in adolescent athletes: a mixed-methods study; [Evaluaci de la eficacia de los programas de rehabilitaci basados en realidad virtual para la recuperaci de deportistas adolescentes tras lesiones: un estudio de modos mixtos]
Introduction: the importance of post-injury rehabilitation for teenage athletes demands innovative methods because traditional practices fail to sustain student athlete participation. VR-based rehabilitation creates interactive recovery programs which might advance physical healing together with mental drive. Objective: the research investigates how well VR-based rehabilitation works against traditional approaches for both physical healing and psychological involvement in adolescent athletes. Methodology: sixty adolescent athletes (aged 1318) received their rehabilitation through random assignment into two groups: one involved traditional approaches while the other received VR-based rehabilitation. The research measured recovery outcomes at three time points: baseline, 4 weeks and 8 weeks. The measured outcomes included range of motion (ROM), muscle strength, return to sport (RTS) time and pain perception. The VR group members shared their experiences through semi-structured interview methods. Results: the subjects in the VR group achieved greater improvements in ROM (p = 0.02) and muscle strength (p = 0.03) and RTS time (p = 0.01). People who used VR reported stronger motivation and engagement although these benefits brought increased worry about re-injuring their knee. Subject participants achieved better results in their rehabilitation by using immersive VR interventions. Conclusions: virtual reality-based rehabilitation enables adolescent athletes to restore physical well-being as well as emotional well-being. The interactive features of this approach improve patient commitment which accelerates their recovery time. Future investigations need to analyze extended advantages and expanded medical applications within sports medicine. 2025 Federacion Espanola de Docentes de Educacion Fisica. All rights reserved. -
Mainstreaming Reproductive Mental Health of Women: The Unmet Need of the Hour
Background: While the existing research is limited, over recent years, there has been growing awareness to understand the mental health of women during menstruation, menopause, and postpartum. Methodology: A woman's distinct reproductive life stages adversely affect her psychological well-being, aggravated by other underlying social, economic, and cultural factors. Drawing upon the analysis of governing laws and womens reproductive health literature. Results: The existing reproductive health law, educational, and workplace frameworks in India are inadequate for supporting the reproductive mental health of women. Conclusion: It is of critical importance to adopt a holistic approach and call for mainstreaming the reproductive mental health of women through urgent legal and healthcare reforms. The Author(s). -
Medical Awareness in Telemedicine: A Legal Perspective
Telemedicine is the technology to provide remote delivery of the healthcare services such as consultation, diagnosis, treatment, etc., to the individuals. It is done through telecommunication technology such as video call, audio calls, messaging. It will help in providing the services, especially in rural or underserved areas, while maintaining patient-provider communication and continuity of care. Medical negligence in telemedicine is an emerging area of concern as healthcare increasingly shifts toward virtual platforms. In order to attract the liability under medical negligence, various essentials such as duty of care, breach of duty, causation and damages must be fulfilled. This paper aims to analyse various legal and ethical challenges that could be faced by the individuals in the process of telemedicine. This research paper was created using the doctrinal approach of research. In order to comprehend, analyze, and organize the law, this research style focuses on analyzing legal doctrines, legislation, case laws, and legal principles. The medical negligence in telemedicine could be in several ways such as misdiagnosis or delayed diagnosis, lack of informed consent, failure to refer the patient for in-person treatment, improper prescription or data breaches. These factors complicate diagnosis, treatment, and legal accountability in virtual healthcare settings, increasing risks for both patients and providers. The Author(s). -
TRANSFORMING GREEN TRANSPARENCY INTO GREEN BRAND LOYALTY AND REPURCHASE INTENTIONS: THE ROLE OF BRAND IMAGE AND CREDIBILITY AMONG ELECTRIC VEHICLE USERS
The present study leverages the Stimulus-Organism-Behavior-Consequence (SOBC) framework to investigate how green transparency influences green brand loyalty and repurchase intention among electric vehicle consumers. Specifically, it examines the mediating roles of brand image and brand credibility in the relationships between green transparency, green brand loyalty, andrepurchase intention. Data collected from 386 electric vehicle users were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results reveal that green transparency positively impacts brand image and brand credibility, which subsequently enhances green brand loyalty and repurchase intention. Mediation analysis further highlights brand image and brand credibility as critical mechanisms linking green transparency to green brand loyalty. This study extends the SOBC framework to green marketing, offering theoretical and practical insights into fostering sustainable consumer behavior. By emphasizing the role of green transparency in building credible and compelling brand narratives, the findings guide marketers in cultivating consumer trust and loyalty while supporting policymakers in formulating transparency regulations for a sustainable marketplace. 2025 Journal of Applied Structural Equation Modeling. -
Convolutional Neural Network based Di-Strategy Cheetah Optimization Algorithm for Automatic Diabetes Prediction
Diabetes is a chronic metabolic disease characterized by elevated blood sugar levels. Diabetes prediction leverages patient data to assess the risk of developing the condition, facilitating early diagnosis and intervention. However, existing models struggle to capture the complex interactions between risk factors due to limited feature representation, leading to inaccurate predictions. This research proposes a Convolutional Neural Network-based Di-Strategy Cheetah Optimization Algorithm (CNN-DS-COA) for automatic diabetes prediction using patient data. The COA is enhanced with tent chaotic mapping and an adaptive search agent, which improves population diversity distribution and convergence speed. Initially, the Pima Indians Diabetes Database (PIMA) and Germany datasets are employed to evaluate the performance of CNN-DS-COA. Min-max normalization is applied to scale the data within a uniform range while preserving relationships among values. The CNN is then used for automatic diabetes prediction, with DS-COA fine-tuning the CNNs parameter values effectively using two strategies. The proposed CNN-DS-COA achieves superior accuracy, with 99.90% and 99.72% on the PIMA and Frankfurt Hospital, Germany datasets, respectively, outperforming existing methods such as stacked ensemble approaches and statistical predictive models. 2025, Research Institute of Intelligent Computer Systems. All rights reserved. -
Predictive Modeling of Student Learning Outcomes Through Cognitive and Emotional Skill Integration
The interplay of factors, including both cognitive and non-cognitive, plays a significant role in the learning patterns of students. However, the majority of the research conducted on such issues mainly puts forward the role of cognitive skills but forgets that a very important role is played by the non-cognitive factor, specifically motivation and emotional intelligence. Therefore, this study focuses on bridging that gap by investigating the combined influence of cognitive and non-cognitive factors on the learning capacities of engineering students during their transition to higher education. A two-year longitudinal study on engineering students of AITAM, Tekele, India was considered in relation to their academic performance, learning preference, and socio-emotional aspects. The approach adopted makes use of predictive analytics. It is deployed here as machine learning algorithms in the form of Logistic Regression (LR), Naive Bayes, k-Nearest Neighbors (k-NN), Decision Trees (DT), and Support Vector Machines (SVM) to classify the learners into very fast, fast, average, and slow learners. The algorithm of k-NN also achieved the highest accuracy classification and showed good robustness for learning the students' learning rates. This study underscores the combination of new teaching approaches as well as personalized self-learning methods to enhance learning performance, especially for slow learners. Indeed, the outcome gives avenues for much more extensive studies done on large datasets using advanced algorithms which can be applied across a range of educational fields to support tailored learning interventions. 2025, Iquz Galaxy Publisher. All rights reserved. -
Assessing the Role of Organizational Support and Job Satisfaction in Mitigating Work-Life Imbalance Among Gazetted Police Officers
The police profession entails significant obligations and prolonged work hours, which increase stress levels and affect officers' health. Police officers engage with various entities and make instantaneous, life-altering judgments, which exert pressure on their physical and psychological health. The police officers face challenges achieving work-life balance due to the demands of round-the-clock duties. The study examines the relationship between organizational support and work-life balance and the mediating role of job satisfaction in this relationship. Gazetted police officers working in the State of Karnataka were the population of the study. Using a sample of 242 officers determined through the Krejcie and Morgan formula and proportional quota sampling method, data were collected via a standardized questionnaire and analyzed using SPSS PROCESS MACRO. The findings reveal that organizational support positively impacts work-life balance, with job satisfaction serving as a partial mediator. Supportive programs, policies, and promotional opportunities within the police organization enhance job satisfaction, which, in turn, contributes to achieving work-life balance. The study underscores the critical role of organizational support in promoting the work-life balance and job satisfaction of gazetted police officers, emphasizing the need for targeted interventions to have work-life balance. Study suggests implementing the programs and policies like flexible work schedules, family friendly policies and addressing understaffing for work-life balance of gazetted police officers. 2025, Iquz Galaxy Publisher. All rights reserved. -
Personality Traits and Financial Literacy: Impact on Equity Investment Intention among Planters in India
This paper examines the relationship of personality and financial aspects toward investment intentions among Indian planters, including long-term and short-term investment decisions. This quantitative study is based on a random survey of 568 planters for the role of Openness, Agreeableness, Extraversion, Neuroticism, financial knowledge, financial skills, and financial attitudes. The findings indicate that Extraversion and financial knowledge are significant predictors of risk perception, mediating their influence on investment intentions. Planters with a higher level of financial literacy and an extroverted personality are more likely to perceive risk appropriately, making better investment decisions. Agreeableness was insignificant concerning risk perception, while Openness positively correlated with short-term investment decisions. The findings demonstrate that risk perception acts as a meaningful force towards LTI and STII since planters with the capability to perceive risk appropriately tend to make better decisions in investment. In addition, these results support the idea of financial education's importance in influencing investment behaviour. Such financial literacy programs, targeted towards improving the ability of farmers to assess risk and investment strategies, will be the initiative of the highest priority to bring about better financial outcomes in the Indian agricultural context. In this regard, this research will add knowledge of psychological and financial factors that impact investment decisions in India, and it will provide valuable insights into building an effective financial literacy program to target the enhancement of the investment strategies of planters in the Indian market. 2025, Iquz Galaxy Publisher. All rights reserved. -
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. -
Coati Optimization Algorithm for Detecting Pediatric Kidney Abnormalities using Ultrasound Images
This study aimed to classify pediatric ultrasound images as normal or abnormal by identifying the optimal number of image texture features for analysis and developing an effective classification system using selected features. The experiment identified a successful feature selection and classification algorithm with a good performance. This study introduced a new approach for computer-assisted ultrasound image classification. Initially, a Gaussian median filter enhances the image quality and removes noise. For feature extraction, various features, including first-order derivatives, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Dependence Matrix (GLDM), Gray Level Size Matrix (GLSZM), and Neighbouring gray tone difference matrix (NGTDM), were extracted using the Pyrandiomics Python package. The Coati optimization algorithm (COA) was employed as a feature selection technique. The Classification was performed using Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-nearest Neighbor (KNN), Nae Bayes (NB), and Extreme Gradient Boosting (XG-Boost) algorithms. Therefore, this study proposed a new machine learning classifier, the Extreme Gradient Neighborhood classifier (XGNC), using NB, KNN, and XG-Boost, with a classification accuracy of 97.91%, which outperformed the other classifiers mentioned in the study. The results indicated that the optimal feature selection and classifier choice yielded the most accurate computer-aided diagnosis of kidney abnormalities. 2025, Iquz Galaxy Publisher. All rights reserved. -
Global Financial Cycle and Its Determinants: A VECM Approach
The determinants of the global financial cycle are empirically investigated in this study report. The presence of concurrent changes in capital flows, asset prices, and global bank leverage is associated with the Global Financial Cycle (GFCy). According to the research now in publication, the Chicago Board of Exchange's VIX (Volatility Index), which gauges market uncertainty and risk aversion, indicates this cycle. The Federal Reserve's monetary policy decisions are the driving force behind this cycle, and the literature already in existence has examined the ramifications of these decisions. The GFCy and, thus, the financial circumstances of emerging market economies (EMEs) could be impacted by additional global shocks. Other global shocks have the potential to impact the global financial cycle and analysis of the same is required to make the existing literature more robust. Our analysis, which includes a study of identifying the potential global shocks for a period of 23 years data (quarterly), indicates that the global financial cycle is driven by global liquidity and global economic policy uncertainty. VECM, Granger Causality, Impulse Response functions were applied. There is a unidirectional causal relationship between the global financial cycle and global liquidity, as well as a unidirectional relationship between the global financial cycle and global economic policy uncertainty. 2025, Iquz Galaxy Publisher. All rights reserved. -
Featuring Machine Learning Models to Evaluate Employee Attrition: A Comparative Analysis of Workforce Stability Relating Factors
Employee attrition is a problem for most organizations as it affects morale, productivity, and business continuity. In addressing this, the study made use of machine learning techniques such as Clear AI, Random Forest, and logistic regression in designing a prediction model to predict who is the next to leave within an organization. The HR data relating to demographics, performance metrics, job roles, and conditions of work was sourced from publicly available website Kaggle.com for the study. Data preprocessing included scaling, outlier detection, and balancing the dataset using SMOTE. Multiple machine learning models were trained and evaluated by checking on accuracy, F1-score, and the ROC-AUC curve. The best model that was tested was Random Forest, which gave an accuracy of 85.71%. Additional insights from feature importance highlighted the significant effect of overtime, marital status, and stock options on attrition. Among the remaining key drivers are workload, work-life balance, and financial incentives. These findings suggest the need for focused HR strategies, such as reduction of overtime, mentorship programs, and career development opportunities, to reduce attrition rates and improve employee satisfaction. This study provides a robust methodology in predicting attrition and delivers actionable insights into designing interventions that improve workforce stability and organizational efficiency. 2025, Iquz Galaxy Publisher. All rights reserved. -
Evaluating the Potential of Cordycepin as a Therapeutic Agent for Cancer: In-Silico Analysis of EGFR and VEGFR Interactions
Due to multipotent activity, Cordycepin, a nucleoside isolated from Cordyceps fungi (Cordyceps militaris), has recently attracted considerable interest as a compound for antitumor. Cordycepin is also known as 3-deoxyadenosine, which is known to inhibit tumor growth, but the actual mechanism is not known. The present work aims to evaluate the cordycepin as an anticancer candidate by analyzing its impact on the major oncogene receptors EGFR and VEGFR through an in-silico approach. In the analysis, computational docking was performed with AutoDock Vina 1.5.7, which estimated the binding constants of cordycepin with EGFR and VEGFR and got binding energies of -6.8 kcal/mol and -5.5 kcal/mol, respectively, relative to a reference Leucovorin molecule. In addition, molecular dynamics simulations were also performed for the best complex (Cordycepin-EGFR) to examine the conformational dynamic behavior of the cordycepin-EGFR complex. The functionality and architecture of the cordycepin-EGFR complex were illustrated: their interaction might serve as a base for therapy. Also, ADMET predictions show that cordycepin follows Lipinskis rules, which supports the drug-likeness of cordycepin compounds. Accordingly, the findings presented here will confirm and draw the attention of the scientific community to use the cordycepin as a possible treatment for cancer and its potential use in scientific pharmacology. 2025, Iquz Galaxy Publisher. All rights reserved. -
Investment Intentions and Influential Factors among University Students
This study investigates the investment intentions of university students in Delhi NCR and the factors influencing their decision-making, guided by the Theory of Planned Behavior (TPB). Specifically, the research examines how financial attitude, risk tolerance, and academic background contribute to students' intent to invest, alongside demographic factors such as gender, family income, and family structure. A structured questionnaire was administered to 454 university students, and data were analyzed using one-way ANOVA, chi-square tests, and multiple linear regression. Findings indicate that financial attitude and risk appetite significantly influence investment intention, with financial attitude showing the strongest negative effect. While the course of study did not significantly predict general investment intention, it showed a meaningful association with preference for equity investments. Gender differences were statistically significant, with male students more likely to invest both generally and in equities. In contrast, no significant differences were found for family income or family structure. The regression model explained 40.7% of the variance in investment intention, reinforcing TPBs attitudinal and control constructs. The study highlights the importance of integrating behavioral finance elements into education and encourages a shift beyond theoretical literacy toward experiential learning. Although variables such as social influence, financial self-efficacy, and digital platform awareness were not included in this study, their relevance is acknowledged for future research. These insights have practical implications for financial education policies under the NEP 2020 and for designing student-targeted financial awareness programs. 2025, Iquz Galaxy Publisher. All rights reserved. -
Analysing Young Adults Preferences for AI-Generated and Human-Created Art in India: A Comparative Study Using the Mixed Method Approach
Artificial intelligence (AI) has emerged as a transformative tool in creating art, blending computational precision with creative processes. This study explores the appeal of AI-generated art compared to human-created physical and digital art among young adults in India, particularly focusing on visual art students. Additionally, the research addresses critical questions regarding the aesthetic appreciation and criticism of AI-generated art, its impact on human creativity, and its challenges to traditional art and its future. The research employed a mixed-method approach to understand preferences, motivations, and perceptions regarding these two art forms. The Art Reception Survey (ARS) was utilised to measure individuals engagement with visual aesthetics and their preferences. The qualitative approach using Multimodal Critical Discourse Analysis (MCDA) enabled deeper analysis, which helped examine how meaning, perceptions, and visual cues must have shaped their responses. The findings indicate a strong preference for original works involving creative thought processes and artistic skills-factors that lean towards a preference for traditional artwork. The findings suggest that despite rapid advancements in AI, people still significantly value human effort and creativity. The participants also acknowledged that blending both art forms can open new avenues of opportunity for the artists. The study suggests that traditional art will likely remain highly valued and argues that AI should not be seen in opposition to conventional art but as complementary tools for artistic innovation. While human-created art remains strongly appreciated, embracing AI would be the way forward, as outright rejection may not always be feasible or beneficial. 2025, Iquz Galaxy Publisher. All rights reserved. -
Factors Influencing Equity Investment Intention: A Behavioral Perspective
Many financial and psychological factors influence equity investment decisions. The present study examines the influence of Personality, risk attitude, and financial literacy on equity investment intention. Questionnaire responses were collected from Bengaluru investors. The present study uses the Big Five Personality Traits (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience) to categorise individual behaviour tendencies. Risk attitude is examined as a mediator variable, and financial literacy (Financial Knowledge, Financial Skill, and Financial Attitude) is examined as a moderator variable. The results show that extraversion, conscientiousness, and openness to experience positively affect equity investment intention, and Neuroticism negatively affects equity investment intention. Risk-taking propensity also moderates the personality-investment intention relationship and shows that individuals with high risk-taking propensity invest in equities. Financial literacy also moderates the relationship and implies that financial knowledge and ability are key determinants of investing. These results have policy and practice implications for investment educators, policymakers, and financial planners and indicate the value of investor-specified advice founded on psychological and financial literacy profiles. Financial literacy programs can assist investors in making effective investment decisions and managing risk. This research contributes to the behavioural finance literature by integrating personality psychology and financial literacy as investment decision-making frameworks. 2025, Iquz Galaxy Publisher. All rights reserved.