Veer Kunwar Singh University

Although nitrogen (N), phosphorus (P), and potassium (K) are important nutrients for the creation of high crop yields that support the growing global population, overuse of these nutrients and their underuse cause ecological damage. Therefore, optimization of NPK use efficiency should be the focus for better sustainable agricultural practices. At Uttaranchal University in Dehradun, Uttarakhand, India, a field experiment named "A comparative study of nutrient use efficiency in wheat (Triticum aestivum L.) variety DBW-187 affected by different doses of NPK" was carried out during the Rabi season (October to March) of 2023–2024. Seven treatments and three replications were used in the randomized complete block design (experiment: T1 was control (no fertilizer), T2 was 25% RDF (recommended dose of fertilizers), T3 was 50% RDF, T4 was 75% RDF, T5 was 100% RDF, T6 was 125% RDF, and T7 was 150% RDF. On December 6, 2023, the DBW-187 cultivar was planted with inorganic fertilizers, specifically urea, DAP (Diammonium Phosphate), and MOP (Muriate of Potash). Seventy five kg ha-1 of nitrogen, 40 kg ha-1 dosages of potassium, and 60 kg ha-1 phosphorus were applied at the base, with the remaining nitrogen being top-dressed at 40 DAS before the second watering. Results showed that application of 100% RDF (150:60:40 kg ha⁻¹ N:P:K) significantly improved crop performance, recording the highest grain yield (75.8 q ha⁻¹), biological yield (189.3 q ha⁻¹), leaf area index (1.97 at 90 DAS), and benefit–cost ratio (2.54) compared with the control. Grain protein content increased with higher fertilizer doses, ranging from 8.24% (control) to 10.75% (150% RDF); however, yields did not increase proportionately beyond 100% RDF. Excess fertilizer application (125–150% RDF) resulted in taller plants and higher protein content but lower nutrient use efficiency and economic returns. Overall, 100% RDF was identified as the optimum fertilizer dose for maximizing yield, nutrient use efficiency, and profitability of wheat variety DBW-187 under irrigated conditions.

Using data from the Scopus database, this study analyzed top 100 cited peer-reviewed documents published between 2000 and 2024. Bibliometric tools such as VOSviewer and Biblioshiny were employed to map the co-authorship, keyword co-occurrence, country collaborations, and citation patterns within this field. The results revealed a significant growth in religious identity research over the past two decades, with notable contributions from psychology, sociology, and political science. The USA and the UK were the most productive countries, while scholars such as Verkuyten and Ysseldyk emerged as the most influential contributors. The common themes revealed were relationship between religiosity and identity, behavior (e.g., delinquency, health, volunteering), social attitudes, and measurement tools. Collaboration networks indicated increasing internationalization, though the dominance of Western scholars and institutions. The study underscores the interdisciplinary nature of religious identity research and the growing interest in understanding its implications in diverse social and political contexts. This bibliometric review not only consolidates current knowledge but also provides a theoretical roadmap for future investigations by identifying emerging areas, research gaps, and potential interdisciplinary linkages.

Purpose The purpose of this study is to examine the extent to which Sustainable Inbound Logistics Supply Chain Management (SILSCM) practices enhance environmental performance in the Indian dairy sector. The study further assesses the mediating role of market orientation (MO) in strengthening the relationship between sustainable logistics practices and environmental performance. Design/methodology/approach Data were collected from 107 executives representing 30 dairy processing plants across Uttar Pradesh. Four managers from each plant operations, procurement, IT and plant heads were selected through judgmental sampling based on firm size, market relevance and capacity criteria. Their expertise ensured an informed assessment of SILSCM practices, environmental performance and MO. The proposed relationships were analysed using structural equation modelling (SEM), allowing examination of both direct and mediating effects. Findings Results indicate that SILSCM practices significantly enhance environmental performance by reducing emissions, minimizing wastage, improving cold-chain efficiency and optimizing inbound transportation. MO exhibits a partial mediating effect, suggesting that organizations with stronger customer awareness, competitor monitoring and cross-functional collaboration derive greater environmental benefits from sustainable logistics. Findings also highlight that institutional arrangements, governance structures and power relations influence sustainability adoption, validating the relevance of critical theory in understanding environmental performance outcomes. Research limitations/implications This study is limited to dairy organizations in Uttar Pradesh, which restricts generalizability. Future research should expand to other regions and agro-based sectors, include small and medium dairy enterprises, and adopt longitudinal designs to capture capability development over time. Further work may explore the influence of digital technologies, inter-organizational collaboration and structural barriers such as infrastructure and cold-chain gaps. Examining moderating factors like market dynamics, institutional policies and organizational culture would also deepen understanding of when sustainable inbound logistics most effectively enhance environmental performance. Originality/value This research fills a critical gap by empirically linking inbound logistics sustainability practices, MO and environmental performance within the Indian dairy sector, an area of high economic importance yet limited scholarly coverage. The incorporation of a critical theory lens adds a novel dimension by revealing how institutional constraints, governance asymmetries and power relations condition sustainability outcomes. The study provides actionable and theoretical contributions for scholars, practitioners and policymakers committed to advancing sustainability in developing-country agri-food systems.

The rapid growth of digital platforms and data-driven business environments has significantly transformed the analysis of consumer behavior. Traditional statistical models, while theoretically grounded, often fail to capture the complex, nonlinear, and high-dimensional patterns present in modern consumer data. This study investigates the effectiveness of machine learning algorithms for predictive modeling of consumer behavior using structured data derived from digital interactions. A comprehensive framework is developed encompassing data preprocessing, feature engineering, model training, and performance evaluation. Multiple supervised and ensemble learning algorithms—including Logistic Regression, Support Vector Machines, Random Forests, Gradient Boosting, and XGBoost—are comparatively evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The results demonstrate that ensemble and nonlinear models outperform traditional linear approaches in predictive accuracy, particularly in capturing dynamic behavioral patterns. Feature importance analysis reveals that behavioral and temporal variables play a more significant role than static demographic factors in predicting consumer actions. In addition to predictive performance, the study emphasizes model interpretability and ethical considerations, highlighting the importance of transparency, fairness, and data privacy in consumer analytics. The findings provide valuable insights for both academic research and practical decision-making, offering a balanced approach that integrates accuracy, interpretability, and responsible use of machine learning in predicting consumer behavior.

Semiconductors with dimensions in the nanometer range are turns out to be the important field of studies due to their long range of applications. Optical properties of ZnO are of great interest for application in optoelectronics, photovoltaics and biological sensing. Various chemical synthetic methods have been developed to prepare such nanoparticles ZnO nanoparticles are synthesized by hydrothermal method. In this method zinc acetate salt is used with surfactant to form the ZnO precipitates. The obtained precipitates are vacuum dried at room temperature. The prepared sample is characterized by XRD techniques and characteristics peaks are obtained. Furthermore, The FT-IR and Raman studies confirm the formation of Zno nanoparticles. The particles obtained are of the orders of 20 nm. Furthermore, The prepared sample tested for photo response measurement and ZnO sample has photo response of 5% at 30 Mw/cm2.

Smart agriculture involves real-time environmental monitoring that combines the Internet of Things (IoT) technologies and Wireless Sensor Networks (WSNs) to change the traditional method of farming to become an accurate, computerized, and data-driven solution. Conventional farming methods involve manual observations which are usually periodically in nature, so making it prone to everything including over-irrigation and ineffective fertilization leading to low crop yields due to the ever changing climatic conditions. IoT based WSNs use low-cost and low power sensor nodes to deploy in fields to constantly monitor vital parameters that include, soil moisture, temperature, humidity, lights intensity, pH and nutrient content (NPK) and send the real-time data to cloud applications to analyze it. This information-driven solution allows farmers to make precise decisions, automate the process of irrigation, shading, ventilation, and fertilizer application, and act in a dynamic way to the environment. Research shows that WSN-based threshold irrigation may save 28-65 liters by the method, solar-powered nodes may save energy and are 5.75 to 115.75 days more efficient in their work, and data-based interventions in real time can increase crop yields by 30 per cent. Machine learning also helps in improving accuracy of data by reducing redundancy by 35 percent and anomaly detection by 40 percent and reliable communication networks with a lower packet loss rate of 4 percent ensure consistent monitoring. It is not only able to optimize the use of resources and cut operating costs but also allows identifying pests, diseases, or environmental stress early enough, which enhances climate-resilient and sustainable farming methods.

This study examines the role of JEEViKA Didis women members of Bihar’s Self-Help Group (SHG)–based livelihood mission—in strengthening grassroots democracy through enhanced political participation, awareness, and influence in local governance. Drawing on a mixed-methods approach, the research collected quantitative data from 400 respondents (200 JEEViKA Didis and 200 non-members) across four districts, complemented by interviews, focus group discussions, and observations of Gram Sabha meetings. The findings reveal substantial differences between JEEViKA and non-JEEViKA women in multiple dimensions of democratic engagement. JEEViKA Didis exhibited significantly higher attendance in Gram Sabha meetings, greater likelihood of speaking or raising issues, and stronger collective visibility compared to non-members. Their political awareness scores were markedly higher across categories such as knowledge of the 73rd Constitutional Amendment, Panchayat functions, government schemes, and budgeting processes. Moreover, JEEViKA Didis demonstrated moderate to high influence in local decision-making, particularly in mobilizing community members, monitoring public schemes, and negotiating with frontline officials. Qualitative insights further show that group solidarity, training, and institutional support help women overcome traditional socio-cultural barriers that have historically limited their public participation. These findings indicate that JEEViKA functions not only as a livelihood intervention but also as a critical platform for political empowerment, transforming women into active stakeholders in village-level governance. By expanding women’s agency, voice, and leadership, JEEViKA contributes significantly to the deepening of decentralized democracy in Bihar. The study highlights the potential of women’s collectives as engines of political inclusion and suggests the need for continued policy support to strengthen their role in governance.

In digital banking, the augmented reality (AR) can be used to reach clients in a way that was not possible with the current non-immersive and uneasy mobile applications. With legacy offerings, financial data is presented only in static dashboards that leaves space for problems in terms of engagement and trust. To mitigate those gaps, we developed a proof of concept (PoC) banking application utilizing Unity and ARCore/ARKit with secure banking APIs. The technical aspects of latency time, frame rate and tracking stability were measured to assess the system's capability. Trust, ease of use and behavioral intention were some of the features used to measure the adoption by user. To the best of our knowledge, we are the first to explore the AR approach with present larger-scale experimental datasets (400 subjects) and task-based experiments, in combination with surveys that experimentally demonstrate its effectiveness. The results resulted in an average latency of 85 ms, a stable track, and a mean SUS score well over 80. Structural Equation Modeling confirms that usability enhances trust, and trust is an important driving factor of system adoption. The paper presents an AR bank architecture, the empirically validated one and design principles for future realisation.

Heavy metal ion pollutants pose serious threats to human health and the environment due to their toxicity. An intuitive and convenient technique for detecting heavy metal ions is vital for qualitative monitoring and food safety. In this work, carbon dots (L-CDs) are prepared from a green source (lemon leaves) using a hydrothermal process, yielding a quantum of approximately 12.4%. These L-CDs exhibited excellent selectivity and sensitivity towards Hg2+ ions, with a limit of detection (LOD) of 0.23 nM and a wide linear range of 0-8 nM. This detection system may follow a dynamic quenching mechanism, characterized by fluorescent lifetime decay and zeta potential analysis. In addition, it was applied to detect Hg2+ ions in real water samples (coal water and industrial water), with recovery rates ranging from 98 to 102%, and relative standard deviations were 0.2-0.6%. Furthermore, the low cytotoxicity and small particle size of the L-CDs also make them suitable for use in antioxidant activity as well as HeLa cell imaging. Overall, this research represents a promising candidate in the field of environmental monitoring, food safety, and biomedical research.