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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. -
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. -
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. -
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. -
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. -
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). -
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). -
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. -
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 -
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. -
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. -
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 -
COVID-19 COMICS AS GRAPHIC NETWORKS OF NON-EMERGENT HEALTHCARE: A MULTIMODAL DISCOURSE ANALYSIS OF PANDEMIC COMICS
COVID-19 comic discourses articulating lived experiences during lockdown, isolation, and quarantine constructed a creative space of self and collective care. This research article explores how the multimodality within COVID-19 comic discourses aids in constructing a networked public of graphic care. Resorting to Dannah Boyds (2008; 2011) methodological framework of networks of graphic care, comics on the themes of self-care, coping mechanisms, and mental well-being from the work COVID Chronicles: A Comics Anthology (Boileau & Johnson, 2021) is analysed. A Multimodal Discourse Analysis (MDA) is performed to decipher the visual and verbal comic devices employed in constructing this graphic network of care using Thierry Groensteen (2007) and Scott McCloud's (2008) comic theories. The analysis highlights the various visual and linguistic techniques that enhance the communicative effectiveness of COVID-19 comics as self-care tools. By intervening in the visuality of COVID-19 comics as a tool of mental healthcare on individual and collective levels, this study illustrates the potential of comic narratives as a sustainable mode of sustenance and care in times of adversity and uncertainty. Furthermore, drawing from the results, the article also proposes the potentiality of comic discourses as non-emergent healthcare tools and a pedagogical tool for the successive policy implementation of mental healthcare measures. Copyright 2025 Albeena Stephen, Reshma Jacob. -
Electromagnetic Radiation-Driven Plastic Degradation and Energy Recovery for Sustainable Waste Management
The persistent accumulation of plastic waste presents a severe global environmental challenge. This study presents a non-thermal photodegradation and energy-recovery system that selectively cleaves 82 5% of CC/CH bonds in polyethene (PE), polypropylene (PP), and polystyrene (PS) within 30 min of UVC (254 nm) exposure. The bond-dissociation energy is harvested via thermoelectric generators (TEGs), delivering 10 W, and via photoelectric cells, yielding 5 W (10 mA.cm- at ? < 2 eV), for a combined recovery of 15 W. Emissions are held below 0.5 ppm VOCs and 0.1 mg.m- microplastics. A lab-scale prototype processes 0.5 kg.h-1 of mixed plastic per 0.1 m reaction area equivalent to 30 Wh.kg-1 of electrical energy and is scalable to 5 kg.h-1 in a pilot module. Real-time FTIR, Raman, and UV-VIS spectroscopy, integrated with an IoT-PID feedback loop, ensures autonomous optimization. Life-cycle assessment indicates a 25% reduction in greenhouse gas emissions compared to conventional recycling methods. A circular-economy framework envisions recovering oligomeric and monomeric fragments for direct reintegration into polymer production. Feature work will implement digital-twin simulations to refine process control, maximize throughput, and ensure long-term system reliability. 2026 by the authors Licensee: Technoscience Publications. -
Land Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier
Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data. 2025 by the authors. -
Classification of irreducible Harish-Chandra modules over full toroidal Lie algebras and higher-dimensional Virasoro algebras
In this paper, we classify the irreducible Harish-Chandra modules over the full toroidal Lie algebra, which is a natural higherdimensional analogue of the affine-Virasoro algebra. In particular, we complete the classification of irreducible bounded modules studied by Billig for non-zero level modules [Int. Math. Res. Not. 2006]. As a by-product, we also obtain the classification of irreducible Harish-Chandra modules over the higher-dimensional Virasoro algebra, which was introduced by RaoMoody [Comm. Math. Phys. 1994], thereby generalizing the well-known result of O. Mathieu [Invent. Math. 1992] for the classical Virasoro algebra. More precisely, we show that any irreducible Harish-Chandra module over the higher-dimensional Virasoro algebra turns out to be either a quotient of a module of tensor fields on a torus or a highest weight type module up to a twist of an automorphism, as conjectured by Eswara Rao in 2004. 2025, International Press, Inc. All rights reserved. -
Identification of Dynamics of Tractor Chassis Structure through Ground Vibration Testing
This study investigates the vibrations arising from mass imbalance and variable inertia forces within dynamic systems, specifically focusing on agricultural tractors. Impulse test method is used to determine the modal characteristics of the tractors structure, including its frequencies, damping and mode shapes. The random responses of various components, such as the chassis, bonnet, muffler, seat and axle, were measured at engine speeds of 800, 1500 and 2500 rpm. The results indicate that the vibration amplitude depends on material properties and operational conditions, with the maximum random vibration response observed at the highest engine speed of 2500rpm. The overall root-mean-square acceleration (grms) was used to quantify vibration levels, revealing significant acceleration values of 2-4 grms across the entire tractor structure. Increased vibrations, particularly at high engine speeds, lead to amplified noise, dynamic stresses and accelerated wear on the chassis and subsystems, necessitating periodic maintenance and part replacements. The study also assessed the impact of road and field surface conditions on the vibration levels. The dynamic modes identified provide insights into potential improvements in tractor performance by implementing semiactive or active vibration control mechanisms utilizing smart materials without changing the existing engine dynamics. 2025. Carbon Magics Ltd. -
Investigation of Diesel Engine Performance, Emissions and Combustion Characteristics Utilizing Emulsified Biodiesel at Varied CRs
In this experimental investigation, a varied CR (CR) diesel engine is fuelled with palmyra biodiesel B20 (20% palmyra methyl ester + 80% diesel) and emulsified palmyra biodiesel (85% B20 + 10% water + 5% surfactant), with span 80 and tween 80 (hlb of 6.43) used as surfactants. The study aims to evaluate the performance, emissions and combustion characteristics of the engine at varying CR of 17, 17.5 and 18 with the standard CR set at 17.5. Results show that increasing the CR leads to an improvement in Brake Thermal Efficiency (BTE), with a 3.89% higher BTE observed at a CR of 18 compared to 17. Additionally, higher CRs result in significant reductions in emissions, including hydrocarbons by 25.49%, carbon monoxide by 28.35% and smoke by 11.82% compared to running on neat diesel. These findings highlight the potential of emulsified palmyra biodiesel at higher CRs to improve the engine efficiency and reduce emissions, emphasizing its viability as a sustainable alternative fuel. 2025. Carbon Magics Ltd. -
Mindfulness in treatment-seeking adults with comorbid obsessive-compulsive and major depressive disorders: Mediating effects of obsessive beliefs and mental well-being
Background: Mindfulness-based interventions have shown promise in alleviating symptoms associated with obsessive?compulsive disorder (OCD) and major depressive disorder (MDD). However, the specific mechanisms that drive these effects, mainly through obsessive beliefs and mental well?being, are seldom examined. Aim: To explore the mechanisms by which mindfulness influences symptom severity in adults with comorbid OCD and MDD, focusing on the mediating roles of obsessive beliefs and mental well-being. Methods: Primary data from 60 treatment-seeking adults with comorbid OCD and MDD were analyzed. Ordinary least-squares path analysis was employed to examine the mediating roles of obsessive beliefs and mental well-being in the relationship between mindfulness and the severity of OCD and MDD symptoms. Results: Mindfulness was significantly associated with reduced symptom severity for both OCD (? = ? 0.40, P < 0.001) and MDD (? = ? 0.49, P < 0.001). For MDD, obsessive beliefs (? = ? 0.20, P < 0.001) and mental well?being (? = ? 0.33, P < 0.001) significantly mediated the relationship. In contrast, no significant indirect effects were observed for OCD symptoms through obsessive beliefs (? = ? 0.10, P = 0.16) or mental well?being (? = ? 0.08, P = 0.20). Conclusion: These findings highlight the distinct mechanisms of mindfulness in comorbid OCD and MDD, underscoring the importance of customized interventions based on specific pathways. 2025 Indian Journal of Psychiatry. -
Breaking down Vicarious Trauma: Supporting Trauma Workers Who Work among Survivors of Sex Trafficking in India
Trauma workers who work at the grassroots levels of sex trafficking rehabilitation are face to face with the survivors struggle. Their roles are essential to lay the foundations of recovery and reintegration for the survivors. This article focuses on what vicarious trauma means and how acknowledging its presence as an occupational hazard will help shape organization policies and structures in a way that empowers trauma workers to continue to bring quality work from an intentional, supported, and grounded space. 2025 Indian Journal of Social Psychiatry.