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Adherence to the WHO Guidelines on Suicide Reporting: A Content Analysis from Bengaluru, India
Background: Media, with its power to influence the masses, is found to have an impact on how the readers perceive suicide, and evidence suggest that suicidal behavior is contagious. However, studies have shown that it is possible to intervene by implementing media guidelines for suicide reporting. Unfortunately, the guidelines are mostly not being adhered to by the media. The current study attempts to assess if there has been any change in reporting after the Press Council of India issued guidelines on suicide reporting in 2019. Methodology: Content analysis of the newspaper articles reporting on suicide was done for 3 months (October 1, 2019, to December 31, 2019). Nineteen newspapers published in Bengaluru, Karnataka, were selected for the study based on the language and readership. These included six English, five Kannada, two Malayalam, two Hindi, two Tamil, and two Telugu newspapers. A total of 1198 reports were found and analyzed. Results: The study found that the majority of the reports did not adhere to the guidelines. It was observed that the news reports on suicide mostly resorted to sensationalization. Majority of the reports portrayed suicide in a harmful manner by mentioning the suicide method and the site in detail and focused on monocausal explanations. The significant connection between suicide and mental illness was also overlooked. Conclusion: Irresponsible reporting of suicide creates risks for the public and collaborative efforts should be designed to decrease the negative impact media can have on suicide prevention initiatives. 2025 Indian Journal of Social Psychiatry. -
The Psychosocial Experiences of Family Caregivers of Cancer Patients
Introduction: This study explores the psychosocial experiences of family caregivers of cancer patients, focusing on changes in their lives postdiagnosis, motivations for caregiving, challenges faced, coping mechanisms, and perceptions of caregiving. Understanding these experiences is crucial for enhancing support services for caregivers. Methods: A qualitative, exploratory study employing interpretative phenomenological analysis was conducted with 10 family caregivers. In?depth interviews were used to gather data on their lived experiences, which were analyzed to identify key themes. Results: Findings revealed significant emotional shifts, lifestyle adjustments, and changes in caregivers perceptions of the patient following the cancer diagnosis. Motivations for caregiving stemmed from external influences and personal attributes. Caregivers encountered multiple challenges, including lifestyle disruptions, emotional strain, financial difficulties, and a lack of support. Coping strategies involved prioritization, reliance on personal strengths, spirituality, family support, and rationalization, highlighting resilience and adaptability. Conclusion: Despite the difficulties, caregivers viewed their role as noble and transformative, maintaining a positive outlook and a deep concern for their loved ones well?being. These insights emphasize the need for healthcare providers to develop targeted support interventions to assist family caregivers in managing their responsibilities effectively 2025 Indian Journal of Social Psychiatry. -
Social Media Addiction and ParentPeer Attachment in Telangana Adolescents: A Cross-sectional Investigation
Background: The ubiquity of social media in contemporary life has raised concerns about its potential negative impacts, particularly among adolescents. While the impact of attachment on adolescents social media use has been studied in Western and South Asian contexts, there is a paucity of research on this relationship in the Indian context. Aims: This study aimed to find the relationship between sociodemographic factors, attachment to parents and peers, and social media addiction among adolescents in Telangana, India. Methods: A random cluster sampling method was used to survey 264 6th to 12th grade students in two schools. Data was collected using the Parent and Peer Attachment Inventory and the Social Media Disorder Scale. Chi-square analysis, Pearsons correlation, and multiple regression analysis were done to achieve the research objective. Results: The study found no association between sociodemographic factors (age, gender, socioeconomic status, family type, and number of siblings) and social media addiction. However, significant negative correlations were found between social media addiction and dimensions of attachment to parents and peers, except for communication with friends. Multiple regression revealed that attachment dimensions explained 15.7% of the total variance. The variables, Trust in the father and Alienation from the mother independently and significantly predicted social media addiction. Conclusion: The findings underscore the importance of attachment relationships in understanding social media addiction among Indian adolescents. The results reveal that fathers and mothers attachments to adolescents predict adolescent social media addiction differentially. Further research, especially longitudinal studies, is needed to explore these relationships in greater depth. 2025 Indian Journal of Social Psychiatry. -
Empowering Adolescents and Communities: Integrating Mental Health Awareness and Destigmatisation into Curriculum and Building a Foundation for Life
Adolescence, a critical period of growth, has seen an increase in mental health issues. Despite some successes in government initiatives, stigma continues to hinder help?seeking. To address this, programs should focus on anti?stigma efforts and emerging needs. A comprehensive mental health curriculum can help normalize discussions, reduce stigma, and encourage help?seeking. However, overcoming challenges such as time constraints, parental resistance, and a shortage of trained educators is essential. Organizing awareness events and sharing recovery stories can help combat stigma. Although social media has its drawbacks, it offers accessible and anonymous outreach. A concerning trend is the romanticization of mental health, which can trivialize real struggles and potentially lead adolescents to pretend mental illness for attention. A two?tiered solution is necessary to address this issue, involving certified educators, first?aid responders, and curriculum integration. This system allows professionals to focus on prevention and promotion while specialists handle diagnosis and treatment, ultimately fostering a long?term, inclusive, and mentally healthy society. 2025 Indian Journal of Social Psychiatry. -
Stress and Decision-Making among Civil Aviation Pilots in India: Mediating Role of Cognitive Flexibility
Objective: The study aims to investigate the difference between stress, decision-making, and cognitive flexibility based on demographic factors and the mediating role of cognitive flexibility on the association of stress and decision-making among civil aviation pilots. Methods: Data was collected from 372 commercial pilots from India through an online survey. The survey comprises standardized tools, including perceived stress, decision-making, and cognitive flexibility. Results: No significant gender difference was found in stress, decision-making, and cognitive flexibility. Age and work experience influenced stress levels, with mid-career pilots reporting the highest stress. Stress has a negative impact on pilots' decision-making ability. Cognitive flexibility partially mediates this relationship. Conclusion: Integrating cognitive flexibility training and stress management interventions into pilot training programs could significantly improve decision-making under pressure for safer aviation practices. 2025 Indian Journal of Occupational and Environmental Medicine. -
Indias Recent Free Trade Agreements and Alcohol Control: Implications for Public Health and SDG Commitments
Background: India accounts for 20% of the worldwide deaths due to alcohol use, and alcohol use among adolescents and young adults is on the rise. In May 2025, the Government of India negotiated Free Trade Agreements (FTAs) with the United Kingdom (UK), the European Union (EU) and a proposed agreement with the United States of America (USA), stipulating sweeping reduction in alcohol tariffs. This paper reviews the public health implications of such reduced tariffs under the FTAs. Materials and Methods: Existing evidence on the public health burden of alcohol use, along with the World Health Organization (WHO) and Government of India policies on alcohol control, and published information on the proposed FTAs, is reviewed. Result: No level of alcohol consumption is safe. Alcohol use disorder affects roughly 9% of Indian men. Extensive legal and constitutional safeguards are available for alcohol control in India. Taxation is one of the most cost-effective interventions to reduce alcohol-related harm. Reduction in alcohol tariffs proposed under the recent FTAs is inconsistent with Sustainable Development Goal 3, WHO policy guidelines, and national legal and constitutional framework for public health and alcohol control. Conclusion: Given the public health burden of alcohol use and Indias commitment to domestic and international alcohol control policies, it must keep cheap imports of alcohol out of the FTAs with the UK, the EU, and the USA. 2026 Indian Journal of Community Medicine. -
Measuring employee attrition intention in an auto-component manufacturing organisation
Orientation: The auto-component manufacturing sector, a critical contributor to industrial growth, faces persistent challenges related to employee attrition, affecting operational efficiency and workforce stability. This study examines the influence of job satisfaction, work-life balance, and job stress on attrition intention among employees in Indian auto-component manufacturing organisations. Research purpose: To identify the key factors contributing to employee turnover and evaluate their relative impact on attrition intention. Motivation for the study: Amid rising concerns over attrition in the manufacturing industry, this research aims to explore how work-life balance and job stress influence employees intentions to leave their organisations. Research approach/design and method: Data were collected from 192 employees across 10 auto-component manufacturing companies in Pune, Maharashtra, India, using a structured questionnaire. The responses were analysed through structural equation modelling (SEM) using SPSS and AMOS. Main findings: The study reveals that work-life balance and job stress significantly impact attrition intention. Employees with poor work-life balance and high job stress are more likely to consider leaving. However, job satisfaction does not have a direct effect on attrition intention. Practical/managerial implications: Organisations should prioritise improving work-life balance and managing job stress by implementing flexible work policies, wellness programmes, and realistic workload distribution. Contribution/value-add: This study underscores the importance of addressing work-life balance and job stress in retention strategies, offering actionable insights for HR managers to mitigate attrition in the auto-component manufacturing sector. 2025. The Authors. -
The Metaverse Marketplace: Exploring the Drivers of Consumer Purchase Behavior in Metaverse
This research explores factors influencing consumer intention to shop in Metaverse E-commerce, an area with limited existing research despite its potential for novel consumer experiences. A quantitative study involving 1,070 respondents used PLS-SEM to analyze a model based on technology readiness dimensions and Metaverse-specific variables. Key findings indicate that optimism and innovativeness are positively associated with consumer shopping intention in Metaverse E-commerce. Conversely, discomfort and insecurity show a negative association. Additionally, a sense of immersion, perceived interactivity, perceived personalization, perceived enjoyment, and perceived serendipity were found to significantly influence shopping intention within Metaverse E-commerce. This study enhances the academic literature on Metaverse shopping by integrating technology readiness dimensions and Metaverse-related constructs. The findings also offer practical insights for managers and marketers in developing effective Metaverse E-commerce strategies. 2025 IGI Global. All rights reserved. -
Enhancing English Learning Through Digital Storytelling in Indian Schools
This study examines the effectiveness of the Digital Storytelling (DST) teaching approach in improving English learning among ninth graders in four schools in Bengaluru, India. Using a sequential mixed-methods design, the quantitative phase included a non-randomized, post-test-only quasi-experimental design with 200 students divided into a DST-based experimental group and a traditional control group of 100 students each. Quantitative data were collected using a 12-item survey questionnaire, while qualitative data included self-reflection logs from 100 and interviews with 20 students from the experimental group. The results show that DST significantly improves language development and student satisfaction. This is evidenced by higher and more consistent post-test scores in the experimental group, with statistical significance confirmed by the Wilcoxon test. Increased engagement, understanding, and motivation reported by students are consistent with the quantitative improvements. 2025 IGI Global. All rights reserved. -
Early CKD Prediction Using Ensemble and Basic Machine Learning Models
Chronic kidney disease (CKD) is a progressive illness that often remains undiagnosed until advanced stages and represents a significant global health burden. Proper and timely diagnosis of CKD can significantly improve patient prognosis and reduce treatment costs. This study evaluates several machine learning (ML) models, including support vector machine (SVM), random forest (RF), gradient boosting (GB), Nae Bayes (NB), AdaBoost, and a multilayer perceptron (MLP) neural network. Additionally, it proposes a stacking ensemble model combining RF and GB for accurate CKD prediction using a publicly available Kaggle dataset. Missing value handling and feature normalisation are performed during data preprocessing, and model performance is evaluated using an 80:20 traintest split with metrics such as the area under the curve (AUC), classification accuracy (CA), F1-score, precision, recall, and Matthews Correlation Coefficient (MCC). Experimental results indicate that RF and GB achieve the strongest individual performance, while the proposed stacking ensemble attains the highest CA of 99.4%. These findings highlight the potential of artificial intelligence (AI)-driven predictive models to support proactive CKD diagnosis and enhance clinical decision-making in healthcare systems. 2026 by the authors of this article. Published under CC-BY. -
Federated Learning with Adaptive Intermediate Model Selection for Predicting IVIG Resistance in Kawasaki Disease
Kawasaki disease (KD), a rare pediatric illness affecting children under five, is treated with intravenous immunoglobulin (IVIG). But 1020% of patients are resistant to IVIG, and these resistant kids face a higher risk of coronary artery abnormalities. Identifying resistance early is vital, yet data scarcity, class imbalance, and the diseases rarity necessitate nationwide collaboration, which is often hindered by country-specific privacy policies. Federated learning (FL) provides a practical way for different parties to collaborate on training a model while keeping their raw data private and secure. To enhance model adaptability across diverse clinical populations, we propose an adaptive intermediate model selection strategy in federated learning. Each client retains the versionglobal or locally fine-tunedthat performs best on its own data, using customizable performance metrics such as F1-score or recall. The system was implemented using the Flower FL framework, with three simulated clients and a shared convolutional neural network (CNN) architecture. Experiments demonstrated that the global model achieved stronger performance than conventional models, and several clients obtained further gains by selecting intermediate models aligned with their data. This approach introduces a novel balance between worldwide collaboration and local personalization in FL, offering a flexible and clinically meaningful solution for IVIG resistance prediction. 2026 by the authors of this article. -
Privacy-Preserving Federated Learning for Prognostic Modeling in Rare Diseases: A Scalable Case Study on Kawasaki Disease
Predictive modeling in rare diseases faces major challenges, including data scarcity, class imbalance, and strict privacy regulations that limit cross-border collaboration. These challenges are particularly critical in Kawasaki disease (KD)a rare vasculitis in childrenwhere 10% to 20% of patients are resistant to intravenous immunoglobulin (IVIG), the standard first-line treatment. This significantly increases the risk of coronary artery abnormalities (CAA), making early and accurate prediction of resistance to IVIG essential for improving patient outcomes. Our work proposes a federated learning (FL) approach to address the constraints imposed by security and privacy concerns. We investigate convolutional neural networks (CNN) as the shared model, collaboratively trained across clients. Coupled with strategies to address class imbalance resulting from the rarity of the condition, the federated approach yielded promising results when evaluated against conventional machine learning (ML) models. The proposed approach demonstrated strong performance, achieving 94% accuracy, 93% precision, 89% recall, and 91% F1 score. To ensure robustness and generalizability, an independent dataset was also used, where the proposed model excelled similarly. These results highlight the potential of FL to overcome data privacy barriers and provide a scalable, secure solution for predictive modeling in rare diseases, supporting its integration into medical prediction workflows. 2025 by the authors of this article. -
Metabolomics Pathway Prediction Using Enhanced-Graph Convolutional Networks with Graph Attention Networks
Metabolomics, the comprehensive study of small molecules in biological systems, has a central role to play in the diagnosis of diseases, biomarker detection, and the design of new drugs. Although there have been major breakthroughs in analytical toolsets such as mass spectrometry (MS) coupled with chromatography, it is hard to predict metabolomics pathways because biochemical interactions are inherently complex. To meet this end, the current research suggests a deep learning-based approach using graph neural networks (GNN), which have shown high efficiency for graph-structured biological data. We specifically propose an enhanced graph convolutional network integrated with graph attention networks (EGCNGAT) to enhance pathway prediction performance. The hybrid framework employs graph convolutional networks (GCN) to represent molecular structural data and graph attention networks (GAT) to provide context-sensitive feature importance, thus improving the models capacity for learning complex pathway patterns. Comparative experiments against current deep learning approaches show that the introduced EGCN-GAT model obtains an accuracy of 98.90 percent, which is a 0.26 percent increase compared to the baseline MLGL-MP model. In addition, it demonstrates a 0.94 percent gain in precision as well as a slight gain in recall. The findings validate the performance of the proposed method and highlight its utility for developing pathway-level predictions in metabolomics studies. 2025 by the authors of this article. Published under CC-BY. -
Stability and bifurcation analysis of a fractional-order preypredator model with ratio-dependent functional response
This paper explores the dynamics of a fractional preypredator system with a ratio-dependent functional response with memory and hereditary effects in predatorprey interactions. The model is developed by the Caputo fractional derivative, and the existence, uniqueness, positivity, and boundedness of solutions are proven to satisfy biological reality. Stability conditions for local and global stability of both predator-free and coexistence equilibria are proven through linearization and Lyapunov function techniques. The fractional order is used as a bifurcation parameter, and the appearance of Hopf bifurcations is analytically explained with demonstration of the influence of memory on oscillations. To examine discrete-time dynamics, the piecewise constant argument is used to derive a discrete counterpart of the fractional model. The discrete model indicates a wide range of rich complex oscillatory phenomena, including period-doubling and NeimarkSacker bifurcations, leading to periodic, quasiperiodic, and chaotic oscillations. Numerical computations, including bifurcation diagrams, phase portraits, and Lyapunov exponents, verify the analytical results and describe the routes of transition to chaos. A comparative analysis to compare integer- and fractional-order cases indicates that memory effects enhance dynamical richness and sensitivity to parameters. The study provides a unified framework relating continuous fractional dynamics and their discrete implementations and provides additional insight into how memory and discretization interact to modify stability and bifurcation in ecological models. 2026 the Author(s), -
A comparative study of bayesian and classical methods for the weighted Lindley distribution under unified hybrid censoring with survival data applications
In survival analysis and reliability engineering, censoring schemes play a crucial role in efficient data collection and analysis. This study investigated the unified hybrid censoring scheme (UHCS), a versatile framework that integrates multiple censoring strategies, to evaluate the suitability of the Weighted Lindley (WL) distribution for modeling lifetime data. Maximum likelihood estimates (MLEs) and their corresponding asymptotic confidence intervals are derived for the parameters of the WL distribution. In the Bayesian framework, parameter estimation was performed under a squared error loss function. A detailed Monte Carlo simulation study was conducted to compare the performance of classical and Bayesian estimators across various sample sizes and censoring schemes. The simulation results revealed that Bayesian estimators consistently yielded lower mean squared errors (MSEs) than their classical counterparts, and the associated credible intervals were generally narrower than the frequentist confidence intervals. To demonstrate the practical applicability of the proposed methods, the analysis was applied to real-world survival datasets. The results highlighted the effectiveness of the WL distribution under UHCS, offering valuable insights for researchers and practitioners in reliability and survival analysis. 2025 the Author(s), licensee AIMS Press. -
X-Ray Spectral Variability of 13 TeV High-energy-peaked Blazars with XMM-Newton
We present a comprehensive study of the X-ray spectral variability observed in 13 TeV photon-emitting high-energy-peaked BL Lacertae objects (HBLs). These data come from 54 XMM-Newton EPIC-pn pointed observations made during its operational period from 2001 June through 2023 July. We performed spectral studies in the energy range of 0.6-10 keV by fitting X-ray spectra of the pointed observations with power-law and log-parabolic (PL and LP) models. We found at a 99% confidence level that 31 of these X-ray spectra were best fitted with a range of LP models with local photon indices (at 1.0 keV), ? ? 1.75-2.66, and convex curvature parameter ? ? 0.02-0.25. PL models with photon index ? ? 1.78-2.68 best described the spectra of 14-pointed observations. Nine PN spectra resulted in negative curvature parameters in fitting an LP model, and eight among them were significant (? ? 2?err). We fitted broken power-law models to these eight X-ray spectra and found spectral hardening in the range of ?? ? 0.06?0.54 for these observations. EPIC-MOS spectra were also studied for those eight observations to search for similar trends, and we were able to find them in only two, one observation each of PKS 0548-322 and Mrk 501. This indicates the possibility of the coexistence of an inverse Compton component along with the dominant synchrotron component for these two observations. We also performed correlation studies between various LP spectral parameters and briefly discuss their possible implications. 2025. The Author(s). Published by the American Astronomical Society. -
X-Ray Spectral Variability of the TeV High-energy Peaked Blazar PG 1553+113 with XMM-Newton
We present an extensive X-ray spectral variability study of the TeV photon-emitting high-energy-peaked BL Lacertae object PG 1553+113, using the data from the EPIC-PN camera of XMM-Newton, which observed the source during its operational period from 2001 September to 2024 November. X-ray spectra in the energy range, 0.67.0 keV, were fitted with absorbed power-law (PL) and absorbed log-parabola (LP) models. We found with 99% confidence that 14 of them were fit well by LP models having parameters in the range ??2.132.80, and ??0.040.18, one spectrum flavors an LP model with ?<0, while simple PL models with ??2.532.69 were sufficient to describe the X-ray spectra of the remaining 15. Of these 30 observations, 2 showed strong signatures of an additional inverse Compton component, while 1 showed weaker indications. On fitting joint Optical Monitor and EPIC-PN data with LP models, we found synchrotron peaks in the energy range of ?s?4.5948.61 eV. This indicates that the spectral evolution is probably caused by variations in particle acceleration or cooling conditions within the jet. 2026. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the https://creativecommons.org/licenses/by/4.0/. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. -
Spectrotemporal Evolution in XTE J1701-462 during Its 2022 Outburst as Revealed by NICER
We present a comprehensive spectrotemporal study of the 2022 outburst of the transient neutron star low-mass X-ray binary (NS-LMXBs) XTE J1701?462 using 57 NICER observational epochs (E1?E57). The 0.8?10 keV lightcurve exhibits a FRED-like profile with multiple rebrightenings and intensity dips, indicating a nonmonotonic evolution of the accretion flow. Broadband spectral modeling with an absorbed Comptonized disk-blackbody model reveals a coherent evolution of spectral parameters consistent with changes in the disk?corona geometry driven by a varying mass accretion rate. The ??Fbol diagram shows distinct clustering, enabling the identification of six accretion states: LHS-1, IMS-1, HSS, IMS-2, LHS-2, and QS. These states trace the expected cycle of disk truncation, inward propagation, and recession, with notable deviations such as sustained coronal heating in IMS-1 and the HSS, likely caused by changes in coronal geometry or the limited bandpass of NICER. State-resolved hardnessintensity diagrams reveal that XTE J1701?462 exhibits a hybrid phenomenology: island and banana branches characteristic of atoll-state early in the outburst, followed by well-defined horizontal and normal branches during IMS-1 and the HSS. As the source decays through IMS-2 and LHS-2, the HID returns to isolated clumps with increasing hardness before entering quiescence. We detected a quasiperiodic oscillation (QPO) at ?27 Hz with a quality factor Q ? 3.4 during epochs E30 and E31. A Crab-based cross-calibration between NICER and NuSTAR shows that XTE J1701?462 reached a peak accretion rate of ?1.21 (Formula presented) ?Edd, suggesting near- or super-Eddington luminosities consistent with its 2006 outburst. 2026. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the https://creativecommons.org/licenses/by/4.0/. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. -
Tracing Fe K X-Ray Reverberation Lag in the Energy-resolved Spectra of Narrow-line Seyfert 1 Galaxy Ton S180
We report the Fe K relativistic reverberation feature for the first time in the narrow-line Seyfert 1 galaxy Ton S180. Using a long observation from XMM-Newton we find that the Fe K emission lag peaks at 117 49 s in the lag energy spectrum computed for frequencies (0.3-8.5) 10?4 Hz. The lag amplitude drops to 22.85 14.20 s as the frequency increases to (8.5-30) 10?4 Hz. The time-averaged spectrum of the source shows a relatively narrow Fe K line at ?6.4 keV, indicating a low black hole spin ( a = 0 . 4 3 ? 0.14 + 0.10 ) based on the reflection modeling. We perform general relativistic transfer function modeling of the lag energy spectra individually. This provides an independent timing-based measure of the spin at a = 0.3 0 ? 0.17 + 0.34 , a black hole mass M BH = 0.2 9 ? 0.16 + 0.01 1 0 8 M ? , comparable to the previous measurement, and a coronal height h = 2.5 9 ? 0.33 + 5.17 r g . Further, we observe that the Fe K lag and the black hole mass fit well in the linear lag-mass relation shown by other Seyfert 1 galaxies. 2026. The Author(s). Published by the American Astronomical Society. -
Spin Frequency of Neutron Star in the Bright Atoll Source 4U 1705-44 Using NICER Observation
We conducted a systematic study of two thermonuclear type I X-ray bursts (B1 and B2) and the spectral properties of the bright atoll type neutron star low-mass X-ray binary 4U 1705?44 using NICER observation. In our analysis, a burst oscillation at ?702 Hz was detected during the double-peaked profile of burst B1. This provides the first strong evidence for the spin frequency of the neutron star, which establishes 4U 1705?44 as a rapid rotator. The measured convexity parameters of the bursts indicate that both bursts likely ignited at off-equatorial latitudes. The ignition depth of burst B1 was nearly twice that of B2, indicating substantial fuel consumption between the two events. The recurrence time of B2 (?1 hr) categorizes it as a short waiting time burst. The complementary spectral analysis of persistent emission, modeled with tbabsnthcomp+ diskbb), showed a hard state with a photon index ? < 1.12. The hardnessintensity diagram was consistent with the source being in the island state. Notably, the accretion disk appeared to extend close to the neutron star surface. 2026. The Author(s). Published by the American Astronomical Society.