Volume 14 | Issue 3 |

 Abstract

Innovation is crucial to a country's economic progress. Innovation makes the country more competitive in the international market, increases productivity of both labour and capital, increases demand for skilled labour, increases efficiency of the factors of production, and creates new businesses and products. Increasing women`s empowerment boosts the economic progress of the country through increased women`s participation in the labor market, increased family health and welfare, reduces the gender gap, and decreases poverty. This paper investigated the impact of both innovations and women`s economic empowerment on Kenya`s economic performance over the period 1980-2025. A multiple regression model was used in this study. The findings of the study revealed that innovations and women`s economic empowerment have a positive effect on Kenya`s economic performance. The policy implications are that policymakers and the Kenyan government should implement policies and regulations that boost innovation in the country and promote women's economic empowerment.

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 Keywords

Innovations, Women`s economic empowerment, economic performance, Kenya J

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Blended learning is an integration of textbooks along with digital platforms. This is of- late very much in need for higher education system as the learning behavior of the young minds is changing. The reading - learning practice is happening only from filtered content from AI tools. While the conventional reading practice enhances the in-depth knowledge in the topic learnt. So, this study will be a perspective study to solve the gap in learning between textbook reading, and AI generated study content. This is a perspective study based on exploration of AI supported QR code embedded in textbooks for personalized assessment and student engagement in learning and application of learnt material. This study investigates the integration of Artificial Intelligence (AI) supported QR codes embedded in printed textbooks to enable personalized assessment in higher education institutions in India, this study also gives hand to NEP 2020 on an account of digital inclusion. A survey was conducted among 136 students from BBA and MA HRM programs. Pearson correlation analysis reveals statistically significant positive relationships between perceived usefulness, readiness for personalized assessment, perceived assessment effectiveness, and teacher benefit perception. The strongest relationship was observed between perceived assessment effectiveness and teacher benefit (r = 0.649, p < 0.01). The findings indicate that AI-supported QR textbooks enhance engagement, formative assessment quality, learner autonomy, and teacher analytics efficiency from student perspective. The study aligns with NEP 2020 objectives promoting technology-enabled, learner-centric, and competency-based education.

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 Keywords

QR code AI supported textbooks, Personalized learning, Blended learning, Higher education alignment with NEP 2020

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The present study focused on overview of the Public Distribution System in the Sirmour District of Himachal Pradesh, which is one of the major food security schemes of India. It plays vital role to reduce poverty, food insecurity and malnutrition. The study is based on secondary data for the year 2016 to 2020. The data analyzed through percentages& annual growth rate and presented through tabular form & bar graph. The results of the study shown that allocation, distribution and subsidy of all essential food items, wheat flour witnessed the highest increase and salt registered very little variability. While in expenditure, edible oils had shown the highest expenditure and lowest was on salt from 2016 to 2020. The study has shown that there is a gap between allocation and distribution of food items which is due to distribution of food items in advance. Similarly, the gap between expenditure and subsidy has been accounted by multiple costs involved in it.

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Keywords: Allocation, distribution, expenditure, subsidy, poverty, beneficiaries

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Strotasa, a distinctive concept elucidated in Ayurveda, refers to the channels (both micro and macro) through which the elements necessary for the nourishment of Sharira bhava (body constituents) are perpetually circulated. The operation of Purishvaha Strotasa (the channels responsible for the transportation and transformation of feces) pertains to the processes of formation, retention, and evacuation of feces. Symptoms arising from the vitiation of Purishvaha Strotasa are mainly related to defecation.Purishvaha Srotas, which is responsible for the formation and expulsion of stool, encompasses the Pakvashaya (colon/large intestine) and Guda (rectum/anus). The Dusti (derangement) of this system is the primary cause of numerous common gastrointestinal disorders such as Atisara (diarrhea), Arsa (piles), Pravahika (dysentery), Krimi (worms), and Grahani (malabsorption syndrome).

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 Keywords

Strotasa, Purishvaha Strotasa, Nidana, Purishvaha Strotodushti Lakshana,Arsha, Atisara

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DC-DC converters are becoming increasingly popular in renewable energy applications and solar PV systems. This report introduces a non-isolated, non-coupled inductor-based high-gain DC-DC boost converter with the desirable features of low voltage stress on controlled power switches and high voltage gain at lower duty ratios. The proposed converter is well suited for boosting the low-input DC voltage obtained from distributed generation units like photovoltaic (PV) or fuel cells to substantially higher DC voltage. The converter comprises only two switches, and a single PWM signal governs its operation. These characteristics result in a topology that is more compact, less costly, lighter weight, and easier to control. A Fuzzy Logic Controller based voltage control loop is introduced for load voltage regulation under varying input conditions. The proposed converter is compared with conventional controllers across various performance parameters. The simulation is carried out in MATLAB/Simulink software, and a hardware prototype is developed to validate the results experimentally.

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 Keywords

DC-DC Converter, Fuzzy Logic Controller, High-Gain, Microgrid, PWM, Renewable Energy.

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The Multi-Angle Accident Risk Detection System is an intelligent video surveillance platform designed to detect and predict potential vehicle collision scenarios in real-time using multi-camera perspective fusion with deep learning-based object detection and tracking. The system processes live video feeds from multiple camera angles at traffic intersections or roads, employs YOLOv8 for state-of-the-art object detection, implements ByteTrack for sophisticated multi-object tracking, and calculates multi-dimensional risk scores based on vehicle dynamics (speed, proximity, trajectory patterns). This Final Year Project integrates a modular 7-day development pipeline where each processing stage adds a new dimension of intelligence, culminating in unified multi-angle risk aggregation and intelligent alerting for improving traffic safety.

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Real-time accident detection, multi-object tracking, YOLOv8, ByteTrack, fusion algorithms, risk assessment, traffic safety, computer vision, deep learning, vehicle dynamics

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Ultrasound biomicroscopy; Intumescent cataract; Phacomorphic glaucoma; Lens vault; Angle closure; Anterior segment

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In today's fast-paced digital marketplace, providing a smooth and dependable online shopping experience is critical, particularly in the health-focused food sector. This project introduces Harvest Bites, a full-stack e-commerce web application specifically developed for the sale of functional millet-based cookies, aiming to encourage healthier food choices through a convenient and engaging digital platform. The system leverages React for the frontend and Django for the backend, delivering a responsive, scalable, and mobile-friendly interface compatible with both web and handheld devices. Key features of Harvest Bites include user authentication, product browsing, category filtering, shopping cart management, order processing, and administrative functionalities. The application prioritizes usability and performance to ensure seamless interaction across diverse screen sizes. It follows modern web development standards and incorporates RESTful APIs, Axios for client-server communication, and Bootstrap for an adaptive and responsive UI. A secure payment workflow simulation is included to replicate real-world online transactions. Thorough testing across all modules ensures robustness, data integrity, and user satisfaction, making Harvest Bites a comprehensive example of full-stack development applied to health-oriented e-commerce solutions

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Full-Stack E-Commerce System - Health-Oriented Food Platform - Millet- Based Cookies

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This study examines the determinants influencing capital structure decisions across major Indian manufacturing industries. Using panel data from 100 companies across 10 industries over the period 2010-2020, the research applies a static panel regression approach to analyze the relationship between leverage and firm-specific as well as macroeconomic variables. The findings reveal that determinants of capital structure vary significantly across industries, indicating that no universal financing pattern exists. The study contributes to existing literature by emphasizing industry-specific financial behavior in emerging markets like India.

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 Keywords

Capital Structure Determinants, Indian manufacturing industries, financial behavior leverage, macroeconomic variables

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The proliferation of deepfake technology and the degraded image quality in digital media have necessitated comprehensive solutions for both detection and restoration. This study presents an integrated AI-driven system that combines deepfake detection with GAN-based image restoration capabilities. Our approach uses EfficientNetB0 for deepfake classification and LPDGAN (Learned Primal- Dual Generative Adversarial Network) for image deblurring. The detection module uses transfer learning from ImageNet, achieving robust feature extraction on a dataset of 3,000 images (1,500 real and 1,500 fake). The restoration module employs 2,500 blur-sharp image pairs to train the LPDGAN architecture. The integrated system was deployed as a Flask-based web application, providing end-to-end processing from image upload to final classification through restoration. Experimental results demonstrate the effectiveness of preprocessing degraded images prior to detection. The system provides a restored visual output for forensic analysis while successfully classifying images with high confidence. The modular architecture enables flexible deployment for real-world applications in digital forensics, journalism, and cybersecurity.

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 Keywords

Deepfake detection, Metadata, Exiftool Image restoration, EfficientNetB0, LPDGAN, GAN-based deblurring, Deep learning.

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Creative Commons Attribution 4.0 and The Open Definition