Volume 14 | Issue 5 |

 Abstract

In the current research work, an ultra sensitive method of detecting magnetic and electric fields through Quantum Metrology has been developed. This frame work combines quantum measurement theory concepts such as recoherence free quantum measurements, quantum adoptive estimations, entangled states for quantum sensing, quantum decoupling, and machine learning based noise reduction for enhancing sensitive, coherence and resilience.

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Quantum Metrology. Quantum Fensing, Magentic field detection, electric field detection, quantum fisher information, docherence, suppression, adaptive quantum estimation, NV centers, Rydberg Atoms, Quantum Coherence, Maching Learning assisted noise filtering

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This research aims to examine the link between humour styles and coping with social rejection in young adults aged 18-30 years. Humour is a crucial psychological resource that people use in different ways, which may have implications for dealing with difficult social situations like rejection or exclusion. This study will focus on four humour styles: affiliative, self-enhancing, aggressive, and self-defeating. A quantitative correlational study approach will be adopted. The study will collect data from young adult participants using the Humour Styles Questionnaire (HSQ) and the Adult Rejection Sensitivity Questionnaire (A-RSQ). The HSQ is a scale designed to measure individual differences in humour styles, while the A-RSQ is a scale designed to measure rejection sensitivity and expectations about social rejection. Descriptive statistics, tests for normality, correlation analysis, and gender comparisons, if necessary, will form the main body of statistical analysis. The results of this study are likely to contribute to a better understanding of the link between different humour styles and rejection sensitivity in young adults. This research may help to provide information about adaptive and maladaptive humour styles and their implications for coping with social rejection. The results may also have implications for psychological interventions to promote more adaptive ways of coping with social situations. Keywords: Humour Styles, Social Rejection, Rejection Sensitivity, Coping, Young Adults, Humour Styles Questionnaire (HSQ), Adult Rejection Sensitivity Questionnaire (A-RSQ), Gender Differences

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Humour Styles, Social Rejection, Rejection Sensitivity, Coping, Young Adults, Humour Styles Questionnaire (HSQ), Adult Rejection Sensitivity Questionnaire (A-RSQ), Gender Differences

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one of the most influential religious practices in contemporary Hindu society. Traditionally,Bhagwat Katha is one of the most influential devotional traditions in Hinduism, rooted in the ?r?mad Bh?gavata Pur??a. In contemporary society, it has evolved from a localised religious discourse into a global spiritual and cultural phenomenon. This research paper examines the historical development of Bhagwat Katha, analyses it from a sociological perspective, studies its role in spreading Hinduism globally, and evaluates the influence of media and globalisation on Bhagwat Katha traditions. The study is based on secondary sources, including books, journals, research papers, and online religious discourse. The findings suggest that Bhagwat Katha functions not only as a religious activity but also as a medium of cultural identity, social integration, and global dissemination of Hindu values. Modern communication technologies and globalisation have significantly transformed Bhagwat Katha into an international spiritual movement.

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Bhagwat Katha, Hinduism, Bhakti Movement, Globalisation, Media, Sociology, Hindu Diaspora, Social integration.

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Supply chain management (SCM) occupies a strategically critical position in the pharmaceutical sector, where product quality failures or distribution interruptions can translate directly into adverse patient outcomes. This paper presents a structured analysis of SCM practices within the Indian pharmaceutical industry, drawing on comparative observations from two representative organizations--Harman Finochem Limited, a leading Active Pharmaceutical Ingredient (API) manufacturer, and Sun Pharmaceutical Industries, a globally integrated formulations company. Using a descriptive-analytical research design supported by primary survey data and secondary financial and operational data, the study identifies persistent inefficiencies including siloed departmental functioning, inadequate outsourcing governance frameworks, and suboptimal digital integration. The cost architecture of pharmaceutical SCM is quantified: manufacturing accounts for the largest share (25%), followed by procurement, inventory management, and distribution (each 20%), with transportation contributing the remainder (15%). The study evaluates a portfolio of technological interventions--including Artificial Intelligence for demand sensing, blockchain-based track-and-trace, IoT-enabled cold chain monitoring, and ERP-integrated planning--and maps these against documented operational deficiencies. Key findings indicate that organizations adopting integrated digital SCM platforms demonstrate measurably superior performance in order fulfillment rates, inventory turnover, and regulatory compliance. The paper concludes by proposing a multi-dimensional SCM improvement framework aligned with Good Distribution Practice (GDP) standards and India's evolving regulatory landscape

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Supply Chain Management; Pharmaceutical Industry; Active Pharmaceutical Ingredients; Cold Chain Logistics; Blockchain Traceability; Demand Forecasting; Inventory Optimization; Digital Transformation; Sun Pharma; Harman Finochem

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The global escalation of heavy metal and radionuclide contamination represents one of the most persistent environmental challenges of the twenty-first century. Industrialization, mining operations, metallurgical activities, phosphate fertilizer application, fossil fuel combustion, and nuclear fuel cycle processes have substantially increased the release of toxic metals and radioactive elements into terrestrial and aquatic ecosystems. Unlike organic pollutants, heavy metals and radionuclides are non-biodegradable, persisting indefinitely in environmental compartments and driving chronic ecological degradation, food-chain contamination, and adverse human health outcomes. Conventional remediation technologies-including soil excavation, chemical immobilization, electrokinetic treatment, vitrification, and soil washing-are frequently hampered by high economic costs, significant secondary ecological disturbance, and limited applicability across large contaminated landscapes. Phytoremediation has emerged as an environmentally sustainable and economically viable alternative that exploits the natural physiological and biochemical capacities of plants to remove, stabilize, transform, or immobilize environmental contaminants . Among candidate species, Chrysopogon zizanioides (Vetiver grass) has attracted considerable scientific interest due to its extensive root architecture exceeding three to five meters in depth, exceptional biomass production, tolerance to multiple abiotic stresses, and demonstrated capacity to accumulate and immobilize heavy metals and radionuclides]. By contrast, Allmania nodiflora has recently gained attention as a potentially valuable phytoremediation species because of its rapid growth characteristics, adaptive stress responses, and capacity to survive in contaminated environments, although molecular and field-scale evidence remains limited. This review synthesizes current knowledge regarding the physiological, biochemical, molecular, and ecological mechanisms underlying contaminant uptake and detoxification in both species. Furthermore, it grounds theoretical transport mechanisms in real-world contexts by evaluating pollutant dynamics in Kadapa industrial soils, and provides quantitative comparative metrics (Bioconcentration and Translocation Factors) for both botanical agents. Particular emphasis is placed on metal transport systems, transcriptomic responses, antioxidant defense pathways, phytochelatin-mediated sequestration, rhizosphere interactions, and emerging multi-omics approaches. Furthermore, recent advances in microbial-assisted phytoremediation, rhizosphere engineering, synthetic biology, genome editing, and systems-level analyses are critically evaluated. Comparative analyses reveal that Vetiver currently possesses greater technological maturity and field applicability, whereas A. nodiflora represents a promising but underexplored candidate requiring substantial molecular characterization and field validation. Future integration of genomics, microbiome engineering, artificial intelligence, and precision environmental biotechnology may significantly accelerate the development of next-generation phytoremediation systems.

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Phytoremediation; Chrysopogon zizanioides; Vetiver grass; Allmania nodiflora; Heavy metals; Uranium; Cadmium; Lead; Molecular mechanisms; Transcriptomics; Rhizosphere engineering; Environmental biotechnology

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This paper looks at how information literacy works in faith-based education through technology-driven teaching methods, focusing on insights from Sunday school settings. In a digital world filled with information, faith educators need to promote critical thinking, ethical judgment, and responsible use of information based on spiritual values. The paper reviews existing studies on the use of digital tools and multimedia in religious teaching. It points out the chances for interactive and relevant learning, as well as difficulties with digital skills, information trustworthiness, and theological consistency. It stresses the importance of a tailored information literacy framework in faith-based education that encourages both technology use and moral growth. The review also finds gaps in teacher training and support from institutions, suggesting areas for future research and practice.

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Information Literacy; Faith-based Education; Sunday School; Sunday School Teachers; Technology-Enabled Learning.

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Crime Investigations rely on human testimony as a primary source of evidence. That testimony becomes the pillar of the investigation. In Forensic Psychology, forensic interviewing and statement analysis address two linked but different questions. First, how to obtain a testimony which should be as accurate and uncontaminated as possible and second, how to evaluate the content of an obtained testimony for indicators of veracity or fabrication. Forensic interviewing and statement analysis play different but related roles in crime investigation. How that testimony is taken and how its content is interpreted have intense implications for the accuracy, fairness, and legal outcomes of the testimony and its findings. Forensic interviewing. Forensic interviewing consists of the live, protocolized elicitation of witness and victim narratives intended to maximize accuracy and minimize contamination whereas statement analysis is the post hoc, content focused assessment of those narratives to identify linguistic and contextual markers associated with plausibility or fabrication. This article evaluates how interview fidelity affects analytic validity. Compares contrasts diagnostic performance and methodological requirements. This article clarifies conceptual boundaries between forensic interviewing and statement analysis, their interdependence, and outlines practical and legal implications.

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Forensic Psychology, Forensic interviewing, statement analysis, Criminal Investigation, Criminal Behaviour Analysis

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The aim of this study is to examine the value relevance of corporate water disclosure in the Indian setting. To this end, the research utilizes secondary data from the Carbon Disclosure Project (CDP) India water disclosure scores and financial data from ProwessIQ database for the period 2025. The study employed a multiple regression to study the relationship. The study found a strong positive association between water disclosure and corporate value. The finding indicates that, with an increased level of transparency measures are indicative of better corporate governance practices. The study is significant as it highlights the prominent role played by transparency in water as a strategy in the backdrop of emerging economic environment.

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Corporate water disclosure, firms value, Multiple regression, corporate governance, economic environment.

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Abstract: This research introduces an integrated machine learning framework for predicting multiple diseases based on patient data. By combining various healthcare data sources, including electronic health records, laboratory results, genomic information, and lifestyle factors, our approach develops classification models for diseases such as diabetes, heart disease, breast cancer, liver disease, and Parkinson's disease [1]. These models are deployed in an intuitive web application built with Stream lit, enabling users to input relevant health parameters and receive immediate risk predictions. Model performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. Our findings indicate that a unified multi-disease prediction system is feasible and can support early detection, targeted interventions, and context-aware decision-making in healthcare [2][3]. Our results demonstrate the feasibility of a multi-disease prediction system in facilitating early detection and targeted interventions. This highlights the growing importance of data-driven, context-aware ML models in healthcare [4][5].

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Keywords--Machine learning, disease prediction, healthcare, multi-disease, Streamlit.

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Women in India are recognized as one of the nation's most valuable human resources. While working women in India symbolize progress and empowerment, they often face multiple challenges that impact their health and safety. Among these challenges, the health concerns of women working night shifts have emerged as a critical issue requiring immediate attention from public health experts and policymakers. This group is particularly vulnerable to a range of health problems, including overweight and obesity, cardiovascular diseases, diabetes, sleep disturbances, stress, depression, anxiety, and workplace-related injuries. Beyond the physical and mental toll, these women also encounter significant social difficulties. Collectively, these issues contribute to a broader burden on Indian society, leading to increased rates of morbidity, disability, mortality, and socioeconomic costs. There is an urgent need for health promotion policies and targeted programs that specifically address the needs of night-shift working women. This paper aims to explore the genesis and implications of night shift work in the Indian context and examines its effects on women's health. The primary aim of the research to analysis working conditions of the study is to analyze the working conditions of women in night shifts and assess the impact on their overall well-being.

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A STUDY ON CHALLENGES FACED BY WOMEN IN NIGHT SHIFT EMPLOYMENT IN DELTA ELECTRONICS WITH SPECIAL REFERENCE TO VELLORE DISTRICT

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