Vignan's Foundation for Science, Technology & Research

Air particulate matter is linked to several health risks globally. Its sources are known to include industrial and vehicular emissions, biomass burning, and even the long-range transport of pollutant gases. Source apportionment studies are essential for accurately tracking sources and enabling effective mitigation. Common techniques of receptor modeling, such as Unmix, Chemical Mass Balance (CMB), and Positive Matrix Factorisation (PMF), have evolved, with increased reliability and accuracy. Recent advancements in modeling techniques, along with high-resolution measurements, have also been instrumental in enhancing the understanding of secondary aerosol formation. Despite these developments, challenges remain due to variations in source emissions, secondary atmospheric processes, and measurement uncertainties. This review examines recent advances in receptor modeling over the past decade. It also discusses their applications under different atmospheric conditions. Also, it highlights key research gaps that need to be addressed in future studies. Ongoing improvements in these models have made them more effective tools for policymaking and in the design of measures to reduce health risks linked to PM.

Generative AI is increasingly used in financial analytics to interpret large datasets and support time-sensitive decision making. However, access to financial databases still depends heavily on structured query language (SQL), which limits usability for non-technical users. Existing natural-language interfaces often perform well on simple requests but degrade on complex, nested, or domain-specific financial queries, especially under real-time constraints. This paper presents Kestrel AI, a natural-language-driven analytics system that combines advanced NLP with retrieval-augmented generation to produce executable SQL and corresponding visualizations. The system is designed for large-scale financial data, concurrent workloads, and low-latency execution through a modular architecture with GPU-accelerated inference, parallel retrieval, and caching. Experimental evaluation across multiple financial datasets reports an average SQL accuracy of 92 https://github.com/veerababulara/Natural-Language-Driven-Data-Visualization-Using-Kestrel-AI.git and archived with DOI https://doi.org/10.5281/zenodo.19642245 .

This study introduces a dual-hybrid COVID-19 forecasting modeling approach that integrates an eight-compartment SEAIQHRD model with Gaussian Process Regression (GPR) and ARIMA-based residual learning to enhance predictive performance. A central methodological contribution is the incorporation of convergence and stability diagnostics, demonstrating reliable parameter estimation through multi-start optimization and bootstrap analysis. Although the SEAIQHRD model captures core disease progression, it is limited in representing nonlinear multi-wave patterns and reporting inconsistencies. The SEAIQHRD–ARIMA hybrid improves short-term linear adjustments, while the SEAIQHRD–GPR hybrid effectively models nonlinear residual structure and provides uncertainty-aware forecasts. Using COVID-19 data from India, both hybrids outperform the standalone model, with the GPR variant yielding the greatest accuracy. Forecast superiority, confirmed by DM, CW, GW, Wilcoxon, and Friedman tests, underscores the robustness and applicability of the proposed modeling approach for public-health. Clinical trial Not applicable.

This study investigates the flow of mixed convective Casson nanofluid with the Cattaneo-Christov model and gyrotactic microorganisms, addressing the effects of thermal radiation, activation energy, and Darcy-Forchheimer. Results obtained by employing MATLAB's bvp5c tool, illustrate that the heat distribution profile is elevated with increasing radiation and thermal source. Additionally, the motile microorganism profile exhibits a downward trend with increasing Peclet and Bioconvective Lewis numbers. Also, the thermal transmission rate increases with boosted Radiation, while the motile density is escalated against the Peclet and Schmidt numbers. This investigation has practical applications in biomedical engineering, chemical and medicinal industries, and biofuel technologies.

This research is an analysis of the relationship between sustainable urbanization and economic inequality through smart city initiatives in developing countries such as India. Rapid urbanization in developing countries tends to have a detrimental impact on socioeconomic inequalities, and the effort to build smart cities may inadvertently increase exclusion when it is not planned with inclusiveness in mind. To reach this goal, an integrated Multi-Criteria Decision-Making (MCDM) approach using a combination of AHP, TOPSIS, and DEMATEL is adopted to systematically identify, assess, and identify the key criteria that affect the inclusive urban development. This study’s results show that infrastructure, governance, digital accessibility, and social inclusion play a key role in mitigating urban disparities and facilitating sustainable development. In particular, good governance and the availability of equitable digital infrastructure appear to be one of the critical factors in the reduction in inequalities and long-term urban resilience. This research provides policy-oriented insights for policymakers in designing inclusive smart city policies in accordance with the Sustainable Development Goals, as well as theoretical contributions to urban sustainability research.