Authors - Sambhram Pattanayak, Pallavi Mishra, Ruhi Sethi, Prachi Trivedi Abstract - Rapid advances in Artificial Intelligence (AI) have significantly transformed high-end camera systems, particularly in autofocus, exposure control, and image processing. This study examines the growing integration of AI in high-end camera systems, focusing on its impact on these areas. By leveraging deep learning models and edge-based computational frameworks, modern cameras perform real-time scene analysis, subject recognition, and predictive parameter optimization. The research adopts a hybrid methodology that combines controlled experimental evaluation, particularly for AI-assisted autofocus, with a systematic review of contemporary industry and academic developments. Key performance indicators such as focus accuracy, response latency, exposure consistency, and subject-tracking reliability are analyzed under challenging conditions, including low light, dynamic motion, and complex scene compositions. The results demonstrate that AI-driven imaging systems significantly outperform traditional manual and semi-automated approaches by improving precision, reducing operational complexity, and enabling intelligent decision-making at the point of capture. The study also highlights AI’s dual role as a technical enabler and a creative support tool, allowing photographers and cinematographers to focus more on artistic expression while maintaining high technical standards. Overall, the research contributes to the evolving field of computational cinematography by offering a balanced evaluation of AI’s technical benefits and its implications for creative workflows.
Authors - Mohamed Boujarfaoui, Amal Azeroual, Mourad Azhari, Abdessamad Dibi, Mustapha Esghir Abstract - Amid the progressive adoption of results-based management and the steady expansion of public expenditure on education, the question of allocative efficiency has become increasingly central to educational governance in Morocco. Despite substantial financial investment, the Moroccan education system continues to face persistent challenges related to learning outcomes, territorial inequalities, and school dropout rates. This paper examines the role of the Educational Management Information System (EMIS) as a strategic instrument for improving governance mechanisms and enhancing the allocation of educational resources. The analysis identifies several structural limitations affecting the current system, including fragmented data structures, weak interoperability between digital platforms, limited analytical exploitation of information, and insufficient integration of pedagogical, administrative, and budgetary dimensions. The study further explores the potential contribution of artificial intelligence (AI) to the transformation of educational governance. Through predictive analytics, intelligent dashboards, and budget simulation models, AI technologies can support more accurate decision-making, strengthen territorial targeting, and improve the anticipation of school dropout risks. Such tools also offer new opportunities for optimizing the distribution of financial and human resources while reinforcing performance-oriented public management. The paper argues that the modernization of Morocco’s educational information system, combined with the integration of AI, could significantly enhance the efficiency, equity, and responsiveness of educational policies. However, this transition requires substantial improvements in data governance, analytical capabilities, interoperability standards, and ethical safeguards to ensure transparency, accountability, and data security.
Authors - Z. Aadhil, T.A. Alka, M. Suresh Abstract - This research explores the factors leading to less brand sacralization in zero-waste lifestyle product brands among Gen Z consumers by examining the influence relationships among these factors. For this, a quantitative analysis using Grey Influence Analysis (GINA) is conducted, and the grey influence and response coefficients are calculated to understand interdependencies among the critical, ideal, and typical models. The study identified that Lack of Emotional Connection and Low Trust in the brand’s Green Claims are the most influential factors in the system. These are influenced by other factors, including utility perception, expected value, sustainability, weak visuals and design, and the feeling that the price feels high. The findings reveal that for brand sacralization, the emotional connection or meaning and authenticity are important compared to the functional attributes. Therefore, this study provides decision-making to practitioners like brand managers to develop zero-waste lifestyle brands with more credibility, emotional resonance, storytelling, brand narratives in the case of Gen Z consumer perception and values, transparency, building trust, and calls for pol-icymakers’ actionable interventions on standardized policy measures to enhance trust and reduce greenwashing. The study identified the future research scope in conducting longitudinal research and statistical analysis to understand the perception of the brand, cross-cultural differences, barriers to the zero-waste life-style products, and the role of digital platforms in increasing emotional connection and trust for higher brand sacralization among the millennials and Gen Z consumers. Hence, this research, by providing a system-based understanding of brand sacralization, is novel and highly contributes.
Authors - Mariya Joseph, Vinodkumar K., Dayana Das Abstract - The beauty and cosmetics industry has emerged as one of the fastest-growing consumer sectors, significantly influencing the consumption behaviour of working women. Cosmetic purchases are no longer limited to grooming needs but have become associated with workplace confidence, professional identity, social media visibility, and lifestyle aspirations. This study investigates the financial impact of cosmetic purchases on working women, with special emphasis on monthly budget allocation, savings behaviour, financial stress, and impulse purchase tendencies. A quantitative sur-vey-based methodology is proposed using a structured questionnaire among 250 working women across urban sectors. Statistical tools including descriptive analysis, reliability testing, correlation, regression, and ANOVA are used to examine the relationship between cosmetic spending and personal financial well-being. The findings indicate that frequent cosmetic expenditure significantly reduces discretionary savings and con-tributes to moderate financial stress, especially among younger professionals and middle-income groups. The study contributes to consumer behaviour literature by integrating beauty expenditure with financial decision-making among women professionals.
Authors - Sreesankar R S, Pranav Vinod, S Kailas, Vivek V, Durgalashmi C V Abstract - The insertion of the Goods and Services Tax (GST) in 2017 was assumed not only to simplify India’s indirect tax system but also to encourage small firms to shift from the informal to the formal economy. For micro and small enterprises, enactment through GST registration can open doors to bank credit, digital payment systems and government schemes, all of which are central to financial inclusion. Kerala offers a particularly interesting setting because it combines a strong micro-enterprise culture with active financial inclusion initiatives such as self-help groups, microfinance and financial literacy programmes supported by institutions like Kerala bank and NABARD. This paper explores the influence of goods and services tax (GST) on small business formalization in Kerala, and its impact on their access to formal financial services. The study which is based on primary data collected from small enterprises that are both in and out of GST cover selected districts in Kerala also supplemented with secondary data mainly derived from official and policy sources, aims to examine what trends emerge in relation to patterns of GST adoption, perceived costs and benefits associated formalization as well as changes in bank accounts credit digital transaction access post-GST. Objective of the findings is to provide evidence on whether GST acts as a facilitator for small businesses in accessing formal finance and then identifying policy actions that could make tax reforms more enabling and supportive for grassroots entrepreneurship.
Authors - Melly Azwari, Abdurrahman Faris Indriya Himawan Abstract - Operational sustainability is a strategic imperative for ports, requiring efficiency, cargo integrity, environmental responsibility, and stakeholder trust. This study investigates how integrating halal and sustainable supply chain practices influences operational sustainability at Tanjung Priok Port, Indonesia, using the Triple Bottom Line perspective. Halal supply chain employs physical segregation, traceability via barcode/RFID, and segregated waste handling, while sustainable supply chain implements energy efficiency, emission reduction, and green logistics practices. A quantitative associative design was employed with 169 purposively sampled port logistics operations respondents. Data were analyzed using multiple linear regression. Results indicate that both halal supply chain (B = 0.248; t = 4.016; p < 0.001) and sustainable supply chain (B = 0.702; t = 10.281; p < 0.001) significantly and positively affect operational sustainability, with sustainable supply chain emerging as the dominant predictor. Jointly, the integration explains 60.5% of the variance (F = 129.567; p < 0.001). These findings offer practical insights for port managers seeking responsible, resilient, and integrated port operations.
Authors - Suramrit Kohli, Nikunj Parikh Abstract - Microplastics have been found in human plasma under various research studies, with 77% of the samples tested containing concentrations greater than 1,000 particles/litre. Microplastics are so small that they are able to pass through cell walls, and without a standardized clinical test for them, there is a heightened risk of inflammation, oxidative stress, neurotoxicity, haemolysis, and damage to the DNA. The aim of this proof-of-concept study was to test the feasibility of using PolyEthylene Glycol-coated chitosan Super Paramagnetic Iron Oxide Nanoparticles (PEG-chitosan SPIONs) to selectively capture and extract Polyvinyl Chloride(PVC) microplastics from blood-like solutions such as synthetic plasma Fetal Bovine Serum(FBS). The PEG-chitosan SPION approach was evaluated with respect to external magnetic dialysis and by measuring the efficiency with which they bound to and separated from PVC microplastics. The data demonstrated that PEG-chitosan SPIONs are highly effective in removing PVC microplastics from blood-like fluids with a decrease of 85.1% in turbidity and a reduction of 45.6% in the total contaminants present, while also capturing approximately 0.17 g of PVC microplastics within the SPION-PVC-microplastic mixture. These data suggest that PEG-chitosan SPIONs are able to bind and remove PVC microplastics efficiently and have considerable biocompatibility under in vitro conditions. These preliminary data will serve as a foundation for conducting future studies. The results suggest that magnetically assisted nano-separation systems hold promise for large-scale development of commercial microplastic removal options in the future.
Authors - Anamika Saini, Kavita Rathi Abstract - Video steganography has emerged as an effective approach for secure multimedia communication by concealing secret information inside video frames while maintaining visual imperceptibility. This work presents a UCF101 datasetbased video steganography framework using a 2LSB embedding technique for hiding secret image data inside video frames. 101 video samples from the UCF101 benchmark dataset were utilized to evaluate the robustness and scalability of the proposed framework under diverse motion and background conditions. The visual quality of stego frames was analyzed using Peak Signal-to- Noise Ratio (PSNR), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). In addition, machine learning-based steganalysis models including Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Extreme Gradient Boosting (XGBoost) were implemented to detect hidden information from cover and stego frames. Experimental results demonstrate that the proposed embedding method maintains high visual quality with low distortion values. However, the steganalysis results indicate that advanced machine learning approaches, particularly XGBoost, can effectively identify hidden embedding patterns present in stego frames. The study highlights the trade-off between visual imperceptibility and resistance against machine learning-based steganalysis in modern video steganography systems.
