Volume 13 | Issue 6 |

 Abstract

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Women who underwent cesarean delivery, physical wellbeing, psychological wellbeing, associated factors.

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Creative Commons Attribution 4.0 and The Open Definition

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The swift increase of digital information in personal, educational, and business areas has generated an urgent demand for trustworthy, scalable, and easily accessible storage options. Conventional local and physical storage systems frequently fail to meet contemporary challenges like remote access, scalability, real-time collaboration, and data protection. This study details the creation and implementation of a Virtual Storage System (VSS) that provides a secure platform for file storage, management, and retrieval accessible via the cloud over the internet The system incorporates vital functions including secure user verification, role-based access management, file version control, and backup systems. It offers an intuitive web interface enabling users to carry out essential file tasks such as uploading, downloading, sharing, and deleting. Attention is focused on maintaining data integrity and confidentiality by utilizing encryption methods and resilient architecture. The proposed system underwent assessment for performance, usability, and security, showcasing its ability to act as a cost-efficient and effective substitute for conventional storage systems in both academic and industrial environments.

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Virtual Storage Framework; Cloud-Based Storage; Data Protection; File Administration; User Verification; Data Encryption; Role-Specific Access Control; Online Application; Data Archiving; File Revisioning.

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Creative Commons Attribution 4.0 and The Open Definition

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Tomato cultivation plays a crucial role in global agriculture and economy; however, it is often challenged by various diseases affecting plant health and yield. Timely detection and management of these diseases are vital for ensuring crop productivity and food security. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown promising results in automating disease detection tasks in plants. This study proposes a novel approach for detecting tomato leaf diseases utilizing CNNs. The proposed model leverages the power of deep learning to automatically learn discriminative features from input images, enabling accurate classification of healthy and diseased tomato leaves. The dataset used for training and evaluation comprises a diverse range of tomato leaf images representing different disease classes. Experimental results demonstrate the effectiveness of the proposed method in accurately identifying various tomato leaf diseases, outperforming traditional machine learning approaches and achieving competitive performance compared to existing deep learning-based methods. The developed model offers a valuable tool for early and accurate diagnosis of tomato leaf diseases, facilitating prompt intervention and effective management strategies for ensuring sustainable tomato production.

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Creative Commons Attribution 4.0 and The Open Definition

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Tomato cultivation, Convolutional Neural Networks (CNN), Deep Learning, leaf diseases, plant health

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Creative Commons Attribution 4.0 and The Open Definition

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Linear Programming was conceived in the period of the second world war to address defense needs. Dantzig, who developed LP modelling and simplex algorithms, tried to use LP for his personal diet plan (meeting his nutritional needs at minimum cost). He could not sustain the approach because of various factors. In the later history of LP modelling, diet problems have been addressed in various settings like hospitals, industries and with additional objectives like environmental impacts, cultural acceptance. One of UN Sustainability goals is meeting nutrition needs of children. In India programs like Akshaya Patra provide midday meals for school going children. Everyday millions of school kids are fed by such programs. Can we do what Dantzig did for himself (diet planning) for every school going kid? Such goals are now attainable and sustainable with modern developments. Quantum Computing and Artificial Intelligence will help us to realize this. Quantum computers using features of the quantum world (superposition and entanglement) allow us to solve problems which are not practically tractable for classical computers and provide exponential speedup in time. Quantum algorithms (like Quantum Approximation Quantum Algorithm) have been conceived for linear programming and tested. Artificial Intelligence can now provide user-friendly support for data extraction, model formulation, solution links and interpretation of results for action for various kinds of user settings. The paper develops a road map for Quantum AI Linear Programming decision support for personalized diet plans for members of massive organizations like national midday meals for school kids.

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Linear Programming, Diet Plan, Artificial Intelligence, Quantum Computing,Quantum Algorithms, National Midday Meals Scheme.D:Waves System.

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Solanum lycopersicum, commonly known as tomato, belongs to the Solanaceae family. It is a herbaceous, dicotyledonous plant found in tropical and temperate climates, including India, China, and the United States. This plant contains of carotenoids ,vitamins,phenolic compound and other bioactive compound like desmethyl-sterols and ?- and ?-tocopherols. Along with the tocopherols, delta-5-avenasterol, the desmethyl sterols, and citrostadienol that contribute to multiple biological activities, such as anti-inflammatory, antimicrobial, neuroprotective, anti-diabetic, antioxidant, cardioprotective, anti-cancer, and benefits for gut and skin health. This review aims to evaluate the hormonal potential of Solanum lycopersicum leaf extracts by examining their phytochemical composition.

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Solanum lycopersicum, bioactive compounds, tocopherols, antioxidant, anti-inflammatory, hormonal potential, phytochemical composition.

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Orally fast dissolving strip is a novel drug delivery dosage form design and develop as an alternative to orally conventional drug delivery system such as tablet, capsule , syrup . Recent trends are shifting towards developing an innovative drug delivery system to improve safety , efficacy and patient compliance by fast dissolving technologies , which dissolves within a minute in oral cavity .

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Fast dissolving flim , mouth ulcer , strip, pain relief

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract The widespread use of mobile phones has transformed digital engagement across various age groups, influencing both social and behavioral patterns. This cross-sectional study examines the relationship between age, gender, and screen time among residents of Jabalpur city. Data were collected through a structured survey, capturing mobile usage habits across different demographic groups. The findings reveal that younger individuals, particularly those aged 12-28, exhibit the highest screen time, with a significant proportion using smartphones for extended hours. Gender disparities are also evident, with young women showing higher mobile usage than a male counterparts, while middle-aged groups display an inverse trend. Excessive mobile phone use, particularly among adolescents and young adults, raises concerns about potential health risks, including digital addiction, anxiety, and sleep disturbances. The study emphasizes the need for awareness regarding responsible technology use and balanced digital habits. Future research could explore the long-term implications of excessive screen time and interventions for promoting healthier digital behaviors.

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Keywords Mobile phone, screen time, smartphone dependence, mental health, digital well-being, technology and behavior.

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This paper proposes the study of design and manufacturing of 20MW induction motor. This paper aims to design, analyze, and optimize the performance of a 20MW induction motor, focusing on its electrical and thermal characteristics. The study will investigate the motor's design parameters, operating conditions, and performance metrics to identify areas for improvement. Motor Design: Detailed design of the induction motor, including electromagnetic and thermal analysis. Performance Analysis: Analysis of the motor's performance under various operating conditions. Optimization: Application of optimization techniques to improve the motor's efficiency, power factor, and reliability. Thermal Management: Investigation of thermal management strategies to ensure reliable operation. The project is expected to provide: 1. A detailed design and analysis of a 20MW induction motor. 2. Optimized performance characteristics. 3. Effective thermal management strategies. This project will contribute to the field of electrical engineering, providing valuable insights into the design and operation of large-scale induction motors.

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Induction Motor, Efficiency, Power Factor.

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Abstract The digital era has revolutionized the creation, distribution, and consumption of intellectual property (IP). While this transformation has led to unparalleled access and innovation, it has also introduced significant challenges to existing Intellectual Property Rights (IPR) frameworks. This paper explores the evolving nature of digital content, legal complications, and the roles of technology and policy in safeguarding IP. It discusses case studies from industries such as music, film, and software, and evaluates how technologies like blockchain and AI are shaping the future of IP enforcement. Ultimately, this research underscores the need for dynamic and globally harmonized legal systems to balance protection with innovation in the digital economy.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract The digital era has revolutionized the creation, distribution, and consumption of intellectual property (IP). While this transformation has led to unparalleled access and innovation, it has also introduced significant challenges to existing Intellectual Property Rights (IPR) frameworks. This paper explores the evolving nature of digital content, legal complications, and the roles of technology and policy in safeguarding IP. It discusses case studies from industries such as music, film,

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This project presents a smart and efficient system for detecting faults in solar panels using deep learning techniques. Solar energy is a clean and sustainable power source, but faults in photovoltaic (PV) panels--such as short circuits, shading, or panel degradation--can reduce energy output and efficiency. The proposed system uses artificial neural networks (ANNs) trained on historical voltage, current, and temperature data, as well as thermal images, to automatically identify different types of faults in real-time. The model was tested using a dataset collected under varying environmental conditions and achieved high accuracy in classifying normal and faulty panel behavior. By enabling early fault detection, this system helps reduce maintenance time, minimize power losses, and improve the overall performance of solar installations. This work supports the move toward smarter, more reliable, and sustainable energy solutions.

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Solar Panel Fault Detection Photovoltaic System (PV) Deep Learning Artificial Neural Network (ANN) Thermal Imaging UAV Monitoring Machine Learning Power Loss Reduction Predictive Maintenance Current and Voltage Analysis Smart Energy System Renewable Energy

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