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On the Investigation of Environmental Effects of ChatGPT Usage via the Newly Developed Mathematical Model in Caputo Sense
This study explores the interconnection between the variables of ChatGPT usage, energy consumption, water consumption, and carbon dioxide CO2 emissions. A new integer and fractional order model using the Caputo derivative is proposed to comprehend the long-term dependencies of these variables. Boundedness, and global and local stability are examined for the fractional order model. The equilibrium points of these variables are shown to determine the stability of the model. The RungeKutta 7 numerical method is employed for the integer order model, whereas the semi-implicit linear interpolation (L1) method is used for the fractional order model. The parameter sensitivity is conducted on the systems parameters to understand the variables impact by varying the relevant parameters for the system. To increase the efficacy of our analysis, we used machine learning approaches to model and predict the dynamics of CO2 emissions, energy and water consumption, and ChatGPT usage. The Prophet ML model stood out among the other methods because it is adept at identifying long-term growth trends, seasonal changes, and the impact of outside variables in intricate time-series data. It is extremely beneficial for research centered on sustainability, where accurate projections are essential for wellinformed decision-making, because it can produce robust, interpretable forecasts against missing values and outliers. Using the Prophet ML model, our research guarantees precise and expandable predictions and provides valuable information that can direct tactics to balance environmental sustainability and technological progress. 2026 by the authors. -
GEMS: Gas-Enhanced Marine Search for Optimizing Fusion Mamba-Attention Networks for Fake Review Classification
The rise of fake reviews has become a major problem for trust in e-commerce sites. As for traditional machine learning solutions, they fail to capture the nuanced language that separates real reviews from fake reviews. In this work, we introduce a new hybrid metaheuristic algorithm that optimizes the Fusion Mamba-Attention Network (FMA-Net) for fake review detection, called GEMS (Gas-Enhanced Marine Search). GEMS is a unique combination of the exploration capabilities of the Enhanced Marine Predators Algorithm and the exploitation process of Henry Gas Solubility Optimization, offering a dual-phase optimization design for high-dimensional, asymmetric, metaheuristic-configured GEMS-optimized FMA-Net. Geometric enhancement of GEMS optimization provides GEMS-optimized FMA-Net with an accuracy of 96.8%, F1-score of 95.4%, and AUC-ROC of 97.2%, marking 37% improvement over the current best models for fake review detection on the Yelp, Amazon, and Google Reviews datasets. We lower the average time of hyperparameter optimization using GEMS with FMA-Net to achieve 68% reduction in overall time spent in grid search and 42% lower for complexity in comparison to genetic algorithms. The contributions of this work are the first hybrid metaheuristic for transformers, a mathematically formulated GEMS algorithm, and an extensive empirical study for proving multi-dimensional metric plausibility. 2026 by the authors. -
Predictive Analysis of Voice Pathology Using Logistic Regression: Insights and Challenges
Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individuals quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, strain, and gender on a binary diagnosis outcome (Yes/No). The analysis was performed on the Perceptual Voice Qualities Database (PVQD), a comprehensive dataset containing voice samples with perceptual ratings. Two widely used voice quality assessment tools, CAPE-V (Consensus Auditory-Perceptual Evaluation of Voice) and GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain), were employed to annotate voice qualities, ensuring systematic and clinically relevant perceptual evaluations. The model revealed that age (odds ratio: 1.033, p < 0.001), loudness (odds ratio: 1.071, p = 0.005), and gender (male) (odds ratio: 1.904, p = 0.043) were statistically significant predictors of voice pathology. In contrast, severity and voice quality-related features like breathiness, pitch, roughness, and strain did not show statistical significance, suggesting their limited predictive contributions within this model. While the results provide valuable insights, the study underscores notable limitations of logistic regression. The model assumes a linear relationship between the independent variables and the log odds of the outcome, which restricts its ability to capture complex, non-linear patterns within the data. Additionally, logistic regression does not inherently account for interactions between predictors or feature dependencies, potentially limiting its performance in more intricate datasets. Furthermore, a fixed classification threshold (0.5) may lead to misclassification, particularly in datasets with imbalanced classes or skewed predictor distributions. These findings highlight that although logistic regression serves as a useful tool for identifying significant predictors, its results are dataset-dependent and cannot be generalized across diverse populations. Future research should validate these findings using heterogeneous datasets and employ advanced machine learning techniques to address the limitations of logistic regression. Integrating non-linear models or feature interaction analyses may enhance diagnostic accuracy, ensuring more reliable and robust voice pathology predictions. 2025 by the authors. -
A Comprehensive Analysis on Computational Thinking in Education: Open Issues and Challenges
Computational thinking (CT) is a cognitive approach for solving problems using the concepts of algorithmic thinking, decomposition of a problem into components, identifying patterns among commonly occurring activities, and abstraction. CT promotes interdisciplinary learning and enhances problem-solving and logical reasoning abilities. In this study, a comprehensive analysis of the current issues and challenges of applying CT in the educational landscape is presented with a focus on the various assessment tools and their implementation in teaching methods. The study identifies the various techniques that can be used by educators to evaluate the skills of students based on their ability to solve problems that require CT. A systematic review of the available literature and related works was conducted to analyze their importance in CT, as well as their issues and challenges. This study finds that there is a need for a unified definition and implementation guidelines on CT. The available assessment tools mainly focus on programming constructs, leaving little room for evaluating abstract concepts as challenges in the field; hence, designing and developing assessment mechanisms are also required for effective implementation of CT in an academic context. 2025 by the authors. -
Wireless Soil Health Beacons: An Intelligent Sensor-Based System for Real-Time Monitoring in Precision Agriculture
Precision agriculture is a modern technology that focuses on the crop by meeting the specific needs of the field. This research presents the Wireless Soil Health Beacons design that can be used in precision agriculture to enhance the production and real-time monitoring of the soil and field parameters. The proposed system integrates bio and physical sensors into an IoTenabled Wireless Soil Health Beacons (WSHB) to provide detailed and real-time soil health parameters. The beacons are compact and are powered by solar, which is weather-resistant and interconnected via wireless nodes. A set of beacons will be implanted to capture biological and environmental data. The biosensor module detects key soil microbiological parameters such as nitrogen-fixing microbial activity, soil pathogen presence, and general microbial population shifts indicative of soil fertility and disease conditions. The physical sensor module continuously measures soil moisture levels, temperature, and salinity. The data is passed from the nodes to a processing module, which collects and analyses the critical parameters directly related to plant growth, water management, and fertiliser optimisation. A mobile interface assists the farmers and stakeholders with the required information, such as field maps, real-time soil health indicators, and critical alerts related to drought, salinity stress, or pathogen hotspots. The proposed system forms as a multidimensional soil profiling tool capable of supporting precision agriculture. Most existing soil monitoring systems rely on environmental parameters, while the proposed system allows the continuous tracking of ecological and microbial dynamics in the area. The mesh network architecture helps the system to be redundant and enhances the outcomes. The proposed system helps with sustainable agriculture and improves the yields with minimal environmental degradation, enabling an adaptive and precise farm management system. 2025 by the authors. -
Predicting Nitrogen Flavanol Index (NFI) in Mentha arvensis Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture
Crop growth monitoring at various growth stages is essential for optimizing agricultural inputs and enhancing crop yield. Nitrogen plays a critical role in plant development; however, its improper application can reduce productivity and, in the long term, degrade soil health. The aim of this study was to develop a non-invasive approach for nitrogen estimation through proxies (Nitrogen Flavanol Index) in Mentha arvensis using UAV-derived multispectral vegetation indices and machine learning models. Support Vector Regression, Random Forest, and Gradient Boosting were used to predict the Nitrogen Flavanol Index (NFI) across different growth stages. Among the tested models, Random Forest achieved the highest predictive accuracy (R2 = 0.86, RMSE = 0.32) at 75 days after planting (DAP), followed by Gradient Boosting (R2 = 0.75, RMSE = 0.43). Model performance was lowest during early growth stages (1530 DAP) but improved markedly from mid to late growth stages (4590 DAP). The findings highlight the significance of UAV-acquired data coupled with machine learning approaches for non-destructive nitrogen flavanol estimation, which can immensely contribute to improving real-time crop growth monitoring. 2025 by the authors. -
Harnessing Wild Jackfruit Extract for Chitosan Production by Aspergillus versicolor AD07: Application in Antibacterial Biodegradable Sheets
A fungal strain with comparably high chitosan yield was isolated from the Shivaganga hills and identified as Aspergillus versicolor AD07 through molecular characterization. Later, the strain was cultivated on Sabouraud Dextrose Broth (SDB) and wild jackfruit-based media to evaluate its potential for chitosan production. Among the various media formulations, the highest chitosan yield (178.40 1.76 mg/L) was obtained from the jackfruit extract medium with added peptone and dextrose. The extracted chitosan was characterized through FTIR, XRD (reported a crystallinity index of 55%), TGA/DTG, and DSC analysis, confirming the presence of key functional groups and high thermal resistance. The extracted chitosan was fabricated into a sheet incorporated with 1% lemongrass oil; the sheet exhibited strong antibacterial activity against Escherichia coli (30 mm) and Bacillus megaterium (48 mm). The biodegradation studies reported a weight loss of 38.93 0.51% after 50 days of soil burial. Further, the chitosan film was tested as a packaging material for paneer, demonstrating better preservation by maintaining nutritional quality and reducing microbial load over a 14-day storage period. These findings highlight the potential of A. versicolor AD07-derived chitosan, cultivated on a waste substrate medium, as a sustainable biopolymer for food packaging applications. 2025 by the authors. -
Exploring electric vehicle consumer behavior: impact of digital innovation, environmental concern, perceived value, and social influence on purchase intentions
Background: Understanding the drivers and boundary conditions of electric vehicle (EV) adoption is critical to fostering sustainable transportation. Building on perceived value and planned behavior theories, this study proposes a moderated mediation model in which perceived value influences both sustainability perception and purchase intentions, with household income, technology trust, and environmental knowledge serving as moderators. Methods: A cross-sectional survey of 496 licensed drivers familiar with EVs was conducted using validated multi-item scales. Data were analyzed in R using confirmatory factor analysis and structural equation modeling (lavaan), incorporating product-indicator interactions and 5,000-sample bootstrapping to test the direct, moderating, and mediating effects. Results: Consumers perceived value has a positive effect on sustainability perception (0.122, p?<?0.001) and purchase intentions (0.002, p?<?0.001). Household income also strengthens the relationship between perceived value and purchase intention (0.043, p?<?0.001). Digital innovation (0.285, p?<?0.001) and environmental concerns (0.411, p?<?0.001) dynamically influenced the perception of sustainability at a significant level, although social influence was not significant. Compared with other variables, sustainability perception had the greatest effect on consumers intention to buy an electric car (0.624, p?<?0.001) and served as a mediator in three out of four indirect connections between perceived value and purchase intention. The moderating effects of technology trust and environmental knowledge were not supported. Conclusion: These findings highlight the central roles of value and sustainability perceptions in EV adoption and identify income as a key boundary condition. Practical implications include tailoring incentives by income segment, investing in user-centric digital platforms, and emphasizing both economic and environmental benefits. Theoretically, this study extends technology acceptance models by integrating sustainability constructs and underscores the nuanced impact of socioeconomic factors on green consumer behavior. Copyright 2025 Kottala, Chanagala, Balaji, Reddy and Babu. -
Artificial intelligence attitudes and resistance to use robo-advisors: exploring investor reluctance toward cognitive financial systems
Introduction: The study investigates resistance towards Financial Robo-Advisors (FRAs) among retail investors in India, grounded in innovation resistance theory. The study examines the impact of functional barriers and psychological barriers on resistance to FRAs, while considering users attitudes towards Artificial Intelligence (AI) as a moderator. It further evaluate the influence of such resistance on users intentions to use and recommend FRAs. Methods: Utilizing purposive sampling data was collected from 409 investors and further analyzed using structural equation modelling. Results: The findings revealed that all barriers under study, expect value barrier, substantially derive resistance towards robo-advisors, with inertia being the strongest determinant. Further, this resistance impedes both the intention to use FRAs and to recommend them. Moderation analysis results finds that users attitude towards AI significantly weakens the influence of inertia, overconfidence bias and data privacy risk on resistance, with no such impact on other relationships. Discussion: Overall, the study enriches IRT in Fintech context and provides theoretical and practical insights to enhance FRAs adoption in emerging markets. Copyright 2025 Verma, Schulze, Goswami and Upreti. -
Mental health literacy and happiness among university students: a social work perspective to promoting well-being
The present study tried to assess university students mental health literacy (MHL) and happiness levels and whether a relationship existed between these. The study used a descriptive quantitative methodology, utilizing Likert-type scales to collect data. A private university in Istanbuls Faculty of Health Sciences had a sample of 443 students. Information was collected using a Personal Data Collection PR Form, the Oxford Happiness Questionnaire Short Form (OHQ-SF), and the Mental Health Literacy Scale (MHLS). Descriptive statistics and one-way analysis of variance (ANOVA) were used to analyze the data. The participants mean MHLS score was 23.00 4.70, and the OHQ-SF score was 23.50 4.70. We detected a significant difference in the MHL subscale owing to age, gender, department, class, maternal education, maternal employment status, income level, academic success, family attitude, smoking status, and exercise status. There were also differences in OHQ-SF scores by students department, class level, mothers education level, fathers income level, academic success status, resident status, family attitude type smoking status, health perception of chronic illness, family history of chronic illness, exercise habit, nutritional status psychological problems, and family mental illness history. Knowledge-oriented and belief-oriented MHL subscales were weak but significantly negatively related, according to the findings. A weak correlation but a significant one was found for subscale Resource-Oriented MHL with happiness level and MHL Total. According to the above-stated research, people who can access mental health resources are more likely to be happy. These findings highlight how making mental health resources available could improve peoples mental well-being with a prolonged social work perspective. As happiness is a primary goal of life, more research contributing to our understanding of it is essential. The mental health literacy indicators for university students relate to realizing happiness and fostering well-being. Copyright 2025 Elkin, Mohammed, K?l?nl, Soydan, Tanr?ver, lik and Ranganathan. -
Digital arrest in the cyber age: a psychological perspective on fear, authority, and consciousness
Digital arrest is an emerging form of cyber deception wherein cybercriminals impersonate law enforcement or other authorities to falsely claim legal authority to arrest individuals through digital means, often via phone calls or online communication channels. As digital technology increasingly permeates daily life, such deceptive tactics pose serious threats to individuals' security and psychological well-being. This paper proposes a conceptual framework for understanding the phenomenon of digital arrest, differentiating it from other cybercrimes like phishing, vishing, and social engineering. The paper examines the psychological mechanisms underlying such scams, including the exploitation of fear, authority, and urgency, as well as the social implications of digital deception. Ultimately, the paper highlights the necessity for future research to empirically assess and evaluate the effectiveness of preventive measures and strategies aimed at reducing victimization. This conceptual paper aims to raise awareness of digital arrest as a distinct form of cyber threat and contribute to the growing body of literature on digital scams and their psychological consequences. Copyright 2026 Robert, Singh, Pandey and Bhuyan. -
Characterizing Ultimatum Game responders: a scoping review of factors that influence decision-making through an evolutionary lens
The Ultimatum Game is a widely used tool for studying conflict resolution within a bargaining framework. This scoping review aims to comprehensively examine the various internal and external factors influencing the responders behavior in this game and compile the status quo of the knowledge space. 31 pertinent research articles were identified from databases like Google Scholar, PubMed and JStor, using the following keywords ultimatum game, responder behavior, emotions and the ultimatum game, fairness in the ultimatum game, social norms and the ultimatum game, punishment game, impunity game, outside options in the ultimatum game. An analysis of the same yielded two broad domains of influencing factors: internal and external. Internal factors encompassed emotions, personality traits, and cognitive capabilities, showcasing their significant influence on decision-making. External factors, including ownership, social norms, power dynamics, outside options, gender, and attraction, revealed how the context of the game shaped responder choices. This review investigates how internal and external factors influence bargaining behavior within the Ultimatum Game, distinguishing between typical and atypical responder behavior. Invoking Kahnemans dual system theory offer insights into the evolutionary roots and modern cognitive processes guiding decision-making. The interplay between these systems reveals nuanced responses to fairness, reciprocity, and self-interest, challenging traditional economic models. While acknowledging the oversimplification of brain dynamics in these studies and also the need for cultural integration, the current review compiles a framework that advances our understanding of human behavior across disciplines, particularly for economics, psychology, and evolutionary biology. Refining this model promises deeper insights into decision-making processes amidst societal complexities. Copyright 2026 Chowdhury, Rangaswamy and Kolte. -
Screens and scars: SEM analysis of the relationship between childhood trauma, emotion regulation, and social media addiction
Background: Addiction is an increasingly significant global public health concern, affecting individuals across diverse age groups and demographics. With the rapid rise of digital technology, social media addiction has emerged as a growing behavioral issue, impacting mental health, interpersonal relationships, and daily functioning. Methods: This study employed an online cross-sectional self-report questionnaire, with university students aged 1635?years as the target population. Data were collected using Google Forms questionnaires, accessible via the university registration system, and sent to the participating students smart phones. The data collection instruments included the Social Media Addiction Scale (SMAS), the Childhood Trauma Scale (CTS), and the Difficulty in Emotion Regulation Scale (DERS). Results: Data from 318 university students were analyzed. The analysis of sociodemographic data revealed a mean participant age of 21.2?years, with 87.3% being female. An analysis of the relationship between social media addiction and childhood trauma revealed that participants with childhood trauma had higher social media addiction. The linear regression model, including childhood traumas and emotion regulation difficulties for social media addiction scores, was statistically significant. A positive correlation was observed between social media addiction and difficulty in emotion regulation. Conclusion: These findings suggest that individuals who struggle with emotion regulation tend to use social media more frequently. Furthermore, the negative effects of childhood trauma on emotion regulation capabilities during adulthood contribute to the development of social media addiction. Copyright 2025 Elkin, Mohammed Ashraf, K?l?nl, K?l?nL, Ranganathan, Sakarya and Soydan. -
Cyber-victimizationinfluence of parental rules and impact on mental health among Indian adolescents
Introduction: In the contemporary digital age, cyberspace offers numerous benefits but also presents significant risks, including cyber-victimization. Adolescents, as frequent internet users, are particularly vulnerable to such experiences. This study examines the relationship between parental regulations on internet usage and the incidence of cyber-victimization among Indian adolescents, while also assessing the impact of cyber-victimization on mental health outcomes such as stress, anxiety, and depression. Methods: A sample of 224 adolescents (Mean age?=?16.5?years SD?=?2.34) was surveyed using standardized measures of cyber-victimization and mental health. Results: Multiple linear regression analyses revealed that written-verbal cyber-victimization was a significant predictor of stress (??=?0.18, p?<?0.05), while impersonation, written-verbal cyber-victimization, and online exclusion significantly predicted anxiety (p?<?0.05). However, none of the cyber-victimization subtypes significantly predicted depression, and the overall model accounted for only 4% of its variance. Discussion: These findings suggest that while cyber-victimization is linked to stress and anxiety, its influence on depression may be more complex. Furthermore, the Pearson correlation analysis indicated a negligible association between cyber-victimization and parental rules on internet usage (r?=?0.039), suggesting that parental regulations alone may not effectively mitigate cyber-victimization risks. Given these findings, interventions focusing on resilience-building, digital literacy, and peer support may be more effective in protecting adolescents from the adverse effects of cyber-victimization. Future research should explore alternative protective factors and preventive strategies to promote adolescent well-being in digital spaces. Copyright 2025 Tamarana, Mathur, Madhusudan and Annapurna Kiranmai. -
Signal-aware deep learningbased respiratory motion prediction for lung tumor management
Introduction: Respiratory motion management in radiotherapy for lung cancer patients remains a significant challenge, as it directly affects accurate tumor targeting. Furthermore, unaccounted tumor motion during treatment planning and delivery can lead to imaging artifacts and biased dose distributions, which compromises the accuracy of image-guided radiotherapy. This issue places clinicians in a dilemma between expanding treatment margins, which increases radiation exposure to healthy tissue or risking reduced targeting precision. Methods: In this work, a hybrid deep learning model composed of dilated convolutional layers, bidirectional long-short term memory layers, and a generative autoencoder module is proposed to jointly model the spatial and temporal characteristics of respiratory motion, while enabling reconstruction of the physiologically coherent respiratory signals. Each architectural component learns complementary motion-related patterns from respiratory signals to support tumor motion prediction. The model performs motion-range classification, captures abnormal breathing patterns across spatial and temporal domains, reconstructs physiologically coherent respiratory cycles, and predicts tumor motion within an algorithmic validation framework. Results: Experimental evaluation demonstrates high motion-range classification performance of 98.37%, including low root-mean square error in motion prediction, while maintaining stable performance across long and complex respiratory signals over multiple breathing cycles. Discussion: This study focuses on algorithmic feasibility and establishes a computational foundation for future clinically calibrated and dosimetrically validated models. The findings indicate that the proposed approach can support future motion-aware radiotherapy planning strategies by improving motion characterization at the algorithmic level. Copyright 2026 Das, J. and Medhi. -
Development and validation of screening tool for excessive and problematic use of internet and digital devices (STEPS-IDD) based on the WHO framework (ICD-11) for addictive behaviours
Background: The widespread use of internet and digital devices has been accompanied by growing concern regarding harms associated with their excessive or problematic use. The World Health Organization has also formally included some of these in its latest classificatory system (ICD-11) under the category of disorders due to addictive behaviours. However, a validated, comprehensive screening tool aligned with ICD-11 that screens for these potentially addictive behaviours is lacking. This study aimed to develop and validate the Screening Tool for Excessive and Problematic use of Internet and Digital Devices (STEPS-IDD), designed to assess multiple addictive behaviours based on ICD-11 criteria. Methods: STEPS-IDD was developed based on the ICD-11 framework for disorders due to addictive behaviours It was applied to assess well-established behavioural addictions like gaming and gambling disorder, as well as less-established but widely researched ones such as problematic use of social media, online shopping/buying, OTT content watching, and pornography watching. Face validity was established through expert review and feedback. Construct validity was evaluated through exploratory factor analysis (EFA), and Cronbach's alpha coefficients were estimated to assess internal consistency. To examine concurrent validity, correlations between scores obtained on the newly developed STEPS-IDD sub-sections and the previously validated Gaming Disorder and Hazardous Gaming Scale (GDHGS) and modified GDHGS for other behaviours were assessed. Receiver Operating Characteristic (ROC) analyses were conducted to determine optimal STEPS-IDD cut-off scores for different behaviours. Results: Data from a total of 112 college students (64.3% female) with a mean age of 20.5 years were analyzed. STEPS-IDD demonstrated good construct validity, with EFA revealing predominantly unidimensional factor structure for most behavioural domains. Internal consistency was excellent (Cronbach's ? = 0.860.91 across sub-sections). Concurrent validity was supported by moderate to strong positive correlations (r = 0.440.76) of STEPS-IDD sub-sections with corresponding GDHGS and modified GDHGS scores. ROC analyses yielded optimal cut-off scores with high sensitivity and acceptable specificity for different behaviours, and fair to excellent overall diagnostic accuracy. Conclusion: STEPS-IDD is a psychometrically robust, brief yet comprehensive screening tool grounded in the ICD-11 framework, for the risk stratification in the context of addictive behaviours related to the use of the internet and digital devices. 2026 Balhara, Singh, Majumdar, Ayoob and Singh. -
Gen AI Gen Z: understanding Gen Zs emotional responses and brand experiences with Gen AI-driven, hyper-personalized advertising
Introduction: Gen Z, a tech-savvy consumer group, has highly evolved in its approach to new-age advertising. The rise of Generative Artificial Intelligence (Gen AI) has revolutionized advertising by enabling hyper-personalized content, making it essential to understand its influence on Generation Z (Gen Z) population. This study explores the responses of Gen Z participants in India to Generative Artificial Intelligence based, hyper-personalized advertisements, with a specific focus on emotional responses and brand interactions which are significant predictors of advertisement success. Methods: Using qualitative research methods, semi-structured interviews were conducted with 40 Gen Z participants. Thematic analysis of the data was performed to understand the major themes pertaining to emotional responses and brand interactions to this form of Gen AI-driven advertising. Results: Two major themes and five sub-themes were revealed through thematic analysis. The first theme, diverse emotional responses, encompassed two sub-themes, curiosity and interest as well as fear and suspicion. The second major theme, enhanced brand experience, encompassed three sub-themes of advanced targeted marketing; initial attraction and brand engagement; and brand connection and loyalty, as perceived by the participants. Discussion: Findings imply that brands can harness Gen AI-driven, hyper-personalized advertisements to evoke meaningful emotions, enhancing consumer loyalty and building stronger, more personal connections with their audience. Copyright 2025 Peter, Roshith, Lawrence, Mona, Narayanan and Yusaira. -
Heavy metal contaminants in eggs and hatchlings of olive ridley turtles (Lepidochelys olivacea) at a mass nesting rookery in India
Heavy metal pollution has emerged as a prominent threat in recent times with high metal levels widely reported in species across ecosystems. The threat of rapid biomagnification is particularly enhanced in species such as olive ridley turtles, which occupy a higher trophic position, increasing their exposure to heavy metals. In the current study, we examine the presence of heavy metals in adults, in-utero and oviposited eggs, hatchlings as well as the nesting beach at two important olive ridley rookeries in India Devi and Rushikulya. We collected muscle and in-utero egg samples from stranded adult olive ridleys at the two rookeries, while oviposited eggs, hatchling and sand samples were obtained from hatchery nests at Rushikulya. We compared concentrations of 9 heavy metals (Cr, Mn, Ni, Co, Cu, As, Se, Cd, and Pb) across different sample types in an Inductively Coupled Plasma Mass Spectrometer (ICP-MS). We found mean metal concentrations of sand to be highest among all samples analysed, followed by muscle tissue. Arsenic was the most prominent metal in adult turtles suggesting bioaccumulation, while Selenium was found to be higher in egg components. Heavy metals (HM) were found in-utero eggs, providing evidence of maternal transfer. Most HMs were similar for in-utero and oviposited eggs, though a few metals were higher in in-utero eggs suggesting potential leaching out during development. Sand and hatchling samples show a high correlation for Mn suggesting potential environmental transfer. These findings emphasise the risk posed by heavy metals to adult and early life stages of olive ridleys and highlight the urgent need for mitigation of these threats. Copyright 2026 Pradhan, Pusapati and Shanker. -
Mediating Role of Mathematics and Science Engagement in the Relationship between Attitude toward STEM Education and Subjective Well-being of Adolescents
Science, technology, engineering, and mathematics (STEM) education has become a focal point of global discussions in the field of education. It emphasizes an interdisciplinary approach to learning. Subjective well-being of adolescents is characterized as joy to learn, close connectedness in schools, perception of the purpose of education, and the estimation of academic efficiency. This study investigates the mediating role of mathematics and science engagement in the relationship between the attitude toward STEM education and subjective well-being of school students in Kerala. Drawing upon theoretical frameworks from psychology, education, and sociology, this study employs a quantitative approach to data collection and analysis. A sample of 363 secondary and senior secondary students was administered standardized survey tools, measuring attitudes toward STEM education, subjective well-being, and their engagement in mathematics and science classes. Regression and mediation analyses resulted in indicating the positive, mediating effect of mathematics and science engagement in the relationship between the attitude toward STEM education and subjective well-being. Practically, the study suggests that educators should foster positive STEM attitudes through engaging teaching techniques and hands-on activities. Cultivating a positive STEM culture in schools can contribute to students well-being and equip them for future success in STEM fields. 2025 International Council of Associations for Science Education (ICASE). All rights reserved. -
Impact of Endothelial Cell Repair Mechanisms on Doxorubicin-Induced Cardiomyopathy: Exploring Molecular Docking and Simulation studies of Angiogenic Factors
Doxorubicin (Dox), despite being an effective anti-cancer drug, also causes cytotoxicity to noncancerous tissues. ECs are highly abundant in the heart; thus, endothelial dysfunction is a major cause of doxorubicin-induced cardiomyopathy. The release of EPCs triggered by endothelial dysfunction, participates in the growth of new blood vessels and the repair of damaged endothelium to promote repair mechanism. The current study aimed to evaluate the effects of doxorubicin on SDF1/CXCR4 pathway via in silico molecular docking and simulation studies. Remarkably, heparin binding site of SDF1at LEU: 29 might be preoccupied by doxorubicin leads to poor expression because SDF1 activity heavily depends on its binding sites. On the other hand, active sites of CXCR4 at ASP: 171 and GLU: 288 also engaged by dox, leading to the assumption that doxorubicin restrict the receptor activity. Additionally, the interaction of doxorubicin at the proton acceptor site and ATP binding sites of VEGFR1, including ASP: 1022, GLY: 836, ALE: 837 and PHE: 838, suppresses the function of the receptor in the MAPK1/ERK2 and AKT1 signaling cascades. The co-expressing factor involved in SDF1/CXCR4 like VEGFR2, ANGPT1 and SHIP2 were also affected by specific amino acid interactions. This induces alterations in several vital biochemical pathways, leading to metabolic chaos. Taken together, it is hypothesize that doxorubicin-mediated functional inactivity of SDF1 via its receptor CXCR4 and VEGFR1 impaired the cardiac EPCs regulation on angiogenesis and vascularization. (2025), (DergiPark). All rights reserved.