Published Papers

Circular fashion refers to an approach within the fashion industry that aims to create a closed-loop system, reducing waste and maximizing the lifespan of clothing and textiles. It aligns with the principles of the Circular Economy by rethinking how garments are designed, produced, used, and disposed of (Archana Puri,2024). This paper, by compiling and analysing prior research on the ongoing implementation of the circular economy in the fashion industry and on consumer behaviour and responses to these initiatives, presents a review of the existing literature, evaluates the success of current implementations, and identifies areas where further research and data are required for the growth of the circular fashion framework. The paper also applies relevant economic theories to analyse consumer responses to brand-led initiatives aimed at circularising production and design.

This study examines the predictability of silver prices using time series and machine learning models. Daily silver price data from 2000 to 2025, comprising 6,360 observations, were analysed using ARIMA, Random Forest, and XGBoost models. The dataset was split chronologically into 70% training and 30% testing sets. Model performance was evaluated using RMSE, MAE, and MAPE. Results show that the ARIMA model achieved the lowest prediction errors, indicating strong performance in capturing short term price dynamics. Machine learning models were able to follow general trends but showed reduced accuracy during periods of high volatility. These findings suggest that traditional time series models remain effective for short term forecasting of silver prices, although incorporating additional explanatory variables may improve machine learning performance. The study highlights the challenges of forecasting volatile commodity markets and suggests directions for future research. 

This conceptual study critically examines the transformative impact of big data and artificial intelligence (AI) on organizational strategy, consumer engagement, and digital marketing. Drawing from a broad synthesis of recent scholarly and industry literature, this paper explores significant advantages, including enhanced personalization, predictive analytics, operational efficiency, and sustainability, alongside substantial challenges such as algorithmic bias, data privacy concerns, integration complexities, and workforce readiness. Emphasis is placed on the critical role of managers and stakeholders in mitigating technological risks, fostering ethical governance, and navigating complex regulatory frameworks. The study further highlights blockchain technology’s emerging role in improving data transparency, trust, and loyalty programs, thereby reshaping customer relationships. The paper provides comprehensive recommendations for managerial practice and policy, emphasizing adaptive governance, robust data stewardship, and collaborative innovation. By integrating these insights, organizations can leverage AI and big data responsibly to capitalize on their full potential, ensuring competitive advantage while addressing ethical and operational risks in an evolving digital landscape. 

This study examines industry-level valuation through the framework of the Capital Asset Pricing Model (CAPM) using monthly data for 49 U.S. industry portfolios from January 2000 to November 2025. First, a graphical analysis of total risk and return provides preliminary evidence of a positive risk–return relationship across industries. To isolate systematic risk, CAPM regressions are estimated to obtain industry betas and Jensen’s alphas. The Security Market Line (SML) is then used to assess whether industry returns are consistent with market risk exposure. The results indicate that while beta explains a substantial portion of return variation, several industries exhibit statistically meaningful positive or negative alphas, suggesting deviations from CAPM predictions. These findings highlight cross-industry differences in risk-adjusted performance and provide insights into industry valuation and the limitations of the single-factor model. 

Air pollution exposure varies significantly across micro-environments and directly impacts respiratory health, yet conventional monitoring approaches rely on sparse fixed stations that provide city-level measurements without capturing individualized exposure conditions or physiological responses. This lack of personalized monitoring limits the ability to assess how localized pollution affects individual respiratory health in real time. To address this gap, this paper presents LungHero, a portable multimodal sensing system that integrates localized air quality measurement with physiological respiratory indicators for personalized exposure assessment. The system combines low-cost particulate matter and gas sensors with temperature-humidity sensing, pulse oximetry monitoring, and acoustic cough analysis. A weighted Air Quality Index (AQI) is computed from normalized environmental sensor readings, while cough signals captured through a mobile device are analyzed using Mel-spectrogram features and a convolutional neural network to assess respiratory severity. Experimental evaluation demonstrates stable air quality sensing, effective cough classification with 89.44% accuracy, and observable correlations between degraded air quality, reduced SpO2 levels, and adverse cough patterns. The results suggest that multimodal sensing at the personal scale can provide meaningful insights into respiratory health under polluted conditions, offering a practical alternative to traditional station-based monitoring systems.

Efficient cellulose hydrolysis for bioethanol production is limited by its end product glucose as it inhibits β-glucosidases, the final enzyme in the multi-enzyme hydrolysis. Therefore, a glucose tolerant β-glucosidase with improved activity at high glucose concentrations is essential to overcome this product inhibition. Although a variety of such β-glucosidases have been identified or engineered, a kinetic model that can compare their performance under industrially relevant conditions is valuable for optimizing enzyme selection. In this study, a glucose-stimulated kinetic model was developed by extending an existing activation model to incorporate glucose inhibition effects. The model response was evaluated using previously reported β-glucosidase hydrolysis data of p-nitrophenyl-β-D-glucopyranoside (pNP-Glu). The resulting kinetic parameters, including glucose inhibition constants and maximum specific activity provide a quantitative framework for selecting suitable β-glucosidases for industrial bioethanol production.