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Investigation of Spectroscopic Parameters and Trap Parameters of Eu3+-Activated Y2SiO5 Phosphors for Display and Dosimetry Applications
Using the solid-state reaction technique, varied Y2SiO5 phosphors activated by europium (Eu3+) ions at varied concentrations were made at calcination temperatures of 1000 C and 1250 C during sintering in an air environment. The XRD technique identified the monoclinic structure, and the FTIR technique was used to analyze the generated phosphors. Photoluminescence emission and excitation patterns were measured using varying concentrations of Eu3+ ions. The optimal strength was observed at a 2.0 mol% concentration. Emission peaks were detected at 582 nm and 589 nm for the 5D0?7F1 transition and at 601 nm, 613 nm, and 632 nm for the 5D0?7F2 transition under 263 nm excitation. Because Eu3+ is naturally bright, these emission peaks show how ions change from one excited state to another. This makes them useful for making phosphors that emit red light for use in optoelectronics and flexible displays. Based on the computed (1931 CIE) chromaticity coordinates for the photoluminescence emission spectra, it was determined that the produced phosphor may be used in light-emitting diodes. The TL glow curve was examined for various doping ion concentrations and durations of UV exposure levels, revealing a broad peak at 183 C. Using computerized glow curve deconvolution (CGCD), we calculated the kinetic parameters. 2024 by the authors. -
Bayesian and Non-Bayesian Parameter Estimation for the Bivariate Odd Lindley Half-Logistic Distribution Using Progressive Type-II Censoring with Applications in Sports Data
The Bivariate Odd Lindley Half-Logistic (BOLiHL) distribution with progressive Type-II censoring provides a powerful statistical tool for analyzing dependent data effectively. This approach benefits society by enhancing engineering systems, improving healthcare decisions, and supporting effective risk management, all while optimizing resources and minimizing experimental burdens. In this paper, the likelihood function derived under progressive Type-II censoring is generalized for the BOLiHL distribution. The well-known maximum likelihood estimation method and Bayesian estimation are applied to evaluate the parameters of the distribution. A study utilizing simulation techniques is performed to evaluate the performance of the estimators, using statistical analysis metrics for censored observations under a progressive Type-II censoring scheme with varying sample sizes, failure times, and censoring schemes. Additionally, a real dataset is studied to validate the proposed model, delivering impactful analyses for practical applications. 2025 by the authors. -
Study of Expression of MST3 in Myeloid Leukaemia
Myeloid leukaemia (ML) is a cancer that occurs by the accumulation of abnormally multiplied myeloid cells in bone marrow, peripheral blood, and other related tissue. MST3 is a gene of the GCK family that has a role in apoptosis, along with other cellular functions like cellular differentiation, cell cycle, metabolism, and others. Objectives: The objectives of this study were to count RBCs and WBCs, study MST3 expression in ML and control samples, and perform an in silico correlation study on the KRAS and NRAS genes. Methods: The counting of RBCs and WBCs was carried out using a hemacytometer, the expression of MST3 was studied using RT-PCR, and a correlation study was carried out using GEPIA. Results: RBC and WBC levels in ML differed from the control levels, and the expression of MST3 was found to be upregulated in ML in comparison to controls, with a 2.908.65-fold change, with a significant p-value > 0.05. A positive correlation in expression was also found between MST3 and KRAS and NRAS genes, with a significant r value correlation. Conclusions: From this study, it could be deduced that MST3 might have a role in ML pathogenesis, but further research is needed to study its role in the progression of the disease. 2025 by the authors. -
Climate Performance and Firm Valuation: A Meta-Analysis of Tobins Q in the Post-IPCC AR6 Era
This study examines whether corporate climate performance is reflected in firm valuation by synthesising recent empirical evidence, using Tobins Q as a forward-looking indicator of market expectations. Employing a random-effects meta-analysis of 30 peer-reviewed studies published between 2020 and 2025 across multiple industries and regions, the findings reveal a modest yet statistically significant positive association between stronger climate performance and higher market valuations, suggesting that investors increasingly incorporate climate-related information into firm pricing. Contrary to prevailing assumptions in the literature, proactive climate strategies, such as emissions-reduction initiatives, do not systematically generate greater valuation benefits than disclosure-oriented approaches; both exhibit comparable positive effects. Similarly, valuation outcomes do not differ materially between self-reported and externally verified climate data. Meta-regression analysis identifies data source as the only statistically significant moderator, although its influence remains nuanced. Overall, the results indicate that climate performance enhances firm valuation in a context-dependent manner, challenging the view that only proactive strategies or externally verified data are uniquely rewarded by financial markets. The study contributes to the sustainable and corporate finance literature by clarifying how capital markets price climate-related corporate behaviour under heterogeneous strategic responses. 2026 by the authors. -
Bridging Financial and Operational Gaps in Supply Chain Finance: An Information Processing Theory Perspective
This paper explores the integration of financial and operational flows in Supply Chain Finance (SCF) through the lens of Information Processing Theory (IPT). Despite increasing adoption of SCF solutions like reverse factoring and trade credit, existing literature lacks a unified theoretical framework that captures both financial and organizational complexities. Drawing from 47 peer-reviewed articles in leading supply chain journals, this study identifies key SCF dimensionstask characteristics, environment, and interdependenceas primary sources of uncertainty and information processing needs. It then examines how IT systems, coordination mechanisms, and organizational design enhance processing capacity, enabling firms to build SCF capabilities such as risk assessment, supplier onboarding, and financial process standardization. These capabilities facilitate financial supply chain integration through data connectivity, embedded flows, and collaborative planning. The study contributes a comprehensive conceptual model that connects SCF uncertainties, processing strategies, and performance outcomes, addressing theoretical and managerial gaps. It further provides a foundation for future empirical research and strategic design of SCF systems to enhance supply chain resilience and financial efficiency. 2025 by the authors. -
Comprehensive Study of Silver Nanoparticle Functionalization of Kalzhat Bentonite for Medical Application
The characterization and biomedical modification of bentonite clays from the Kalzhat deposit (Kzh), which is situated in Kazakhstans Zhetysu region, are the main objectives of this work. In order to improve the raw materials structural qualities, the montmorillonite fraction was enriched, and coarse impurities were eliminated using the Salo method. The presence of meso- and micropores that guarantee high dispersity and specific surface area, as well as the prevalence of montmorillonite and kaolinite, was all confirmed by physicochemical analysis. Particle size measurements indicated finely dispersed structures with a propensity to aggregate, whereas thermal analysis demonstrated resilience under heating. After effective functionalization with silver nanoparticles, a porous hybrid system with improved surface reactivity was produced. These enhancements demonstrate the modified bentonites usefulness as a multifunctional carrier for the immobilization and controlled release of pharmaceuticals, with potential uses in drug delivery systems, antimicrobial coatings, and wound-healing materials. The material has potential use in sorption and environmental protection technologies in addition to its biomedical application. Overall, Kzhs structural and functional performance is greatly improved by the combination of purification and functionalization with silver nanoparticles, highlighting its promise as a useful element in the development of next-generation polymercomposite systems. 2025 by the authors. -
AdaptiveNet: A Novel Architecture for Reducing Computation Complexity to Fake Review Classification
The exponential rise of e-commerce platforms has resulted in a dramatic increase in online reviews, which creates a challenge in distinguishing fake reviews that erode consumer confidence and harm commerce ecosystems. Traditional approaches for fake review detection employ computationally expensive deep learning networks which are resource-intensive and difficult to use in practice. In this paper, we describe AdaptiveNet, a new lightweight neural architecture that achieves fake review detection with much lower computational resources while maintaining a higher detection and classification precision. The model proposed in this paper is based on three original innovations: a Multi-Scale Semantic Fusion (MSSF) layer for hierarchical feature extraction, Dynamic Attention Scaling (DAS) with complexity measure attention, and Adaptive Parameter Sharing (APS) context-gated networks. With thorough evaluation on Amazon, Yelp, and TripAdvisor datasets of reviews totalling 1.2 million reviews, AdaptiveNet attains 94.8% accuracy while achieving 65% computational overhead in comparison to traditional models. The architecture outperformed all other state-of-the-art models, BERT-base (92.1%), RoBERTa (91.8%), and other more recent efficient models, requiring 70% lower parameters and 60% lower energy consumption. This work markedly advances the other efficient deep learning architectures for text classification and allows for the practical implementation of fake review detection systems in resource-limited settings as process innovation. 2026 by the authors. -
Rv1899c, an HDAC1ZBTB25-Interacting Protein of Mycobacterium tuberculosis, Promotes Stress Resistance and Immune Evasion in Infected Macrophages
Rv1899c, a previously identified HDAC1ZBTB25-interacting protein of Mycobacterium tuberculosis, plays a crucial role in bacterial adaptation and immune modulation. Recombinant M. smegmatis-expressing Rv1899c (MS_ Rv1899c) showed enhanced survival under acidic and oxidative stress compared to vector controls, along with improved early intracellular growth in THP1-derived macrophages. This was accompanied by reduced reactive oxygen species (ROS), diminished cytokines associated with inflammation and downregulation of autophagy proteins ATG5, Beclin, and LC3, which ultimately skewed the immune response, suppressing the pro-inflammatory M1 macrophage population. Targeting Rv1899c with 3-aminobenzamide (3-AB) impaired intracellular bacterial survival and restored IL-12B expression, while its combination with the HDAC inhibitor C1994 significantly enhanced bacterial clearance. Structural modelling confirmed the high stereochemical quality of the Rv1899c macrodomain, and computational studies identified 3-AB as the strongest ligand (?5.75 kcal/mol), stabilized through hydrogen bonding and hydrophobic interactions with key residues. Molecular dynamics simulations conducted for 200 ns demonstrated stable proteinligand interactions with consistent parameters, while MM/GBSA analysis indicated favourable binding energy (?G_bind = ?6.6 kcal/mol), largely influenced by van der Waals and electrostatic forces. Together, these findings highlight Rv1899c as a mediator of stress resistance and immune evasion and propose it as a potential therapeutic target against M. tuberculosis. 2025 by the authors. -
Enhancement of Phenolic and Polyacetylene Accumulation in Lobelia chinensis (Chinese lobelia) Plantlet Cultures Through Yeast Extract and Salicylic Acid Elicitation
Lobelia chinensis (Lour.) is a medicinal plant that contains phytochemicals, such as phenolics and polyacetylene compounds, with beneficial biological activities. In vitro cultures are typically employed for biomass generation and plant multiplication. However, the current biotechnological approaches for producing these chemicals are ineffective, which is why bioelicitors are being used to boost synthesis of these molecules. Plantlet cultures were established in vitro using Murashige and Skoog medium supplemented with 3% sucrose (w/v). Following 4 weeks of culture initiation, the plantlet cultures were treated with 0, 25, 50, 100, or 200 mg L?1 of yeast extract (YE) or with 0, 25, 50, 100, or 200 M of salicylic acid (SA) for 1 week to boost the synthesis of bioactive compounds. The amounts of total phenolics, total flavonoids, specific phenolics including catechin, phloretic acid, linarin, and polyacetylenes, including lobetyolinin and lobetylin, were considerably elevated in the plantlet cultures treated with 50 mg L?1 YE and/or 25 M SA. The 2,2 Diphenyl 1 picrylhydrazyl (DPPH) radical scavenging assay, 2,2?-azino-bis (3-ethybenzothiazoline-6-sulphonic acid) (ABTS) assay, and ferric reducing antioxidant power (FRAP) assay were performed to assess the antioxidant properties of the plantlets. The elicitor-treated plantlets were found to have higher antioxidant activity. Thus, plantlet biomass produced in vitro can be used as a raw material to produce medicinal and nutraceutical products. 2025 by the authors. -
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.