Volume 13 | Issue 5 |

 Abstract

Gender budgeting in India, introduced in 2005-06, aims to integrate a gender perspective into fiscal policy to address gender disparities and promote equitable resource allocation. This paper reviews the evolution, implementation, and impact of gender budgeting in India over the past two decades. It examines key milestones, such as the introduction of the Gender Budget Statement (GBS) and Gender Budgeting Cells (GBCs), alongside challenges like stagnant funding, lack of gender-disaggregated data, and limited sectoral coverage. Drawing from existing literature and policy documents, the study highlights achievements, such as increased awareness of gender issues in budgeting, and persistent gaps, including inadequate monitoring and evaluation. The review concludes with recommendations for enhancing the effectiveness of gender budgeting to align with India's commitments to gender equality, such as those under the Sustainable Development Goals (SDGs).

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 Keywords

Gender budgeting, Sustainable Development Goals, Sectoral, Fiscal Policy, Gender Equality

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?????? ????? ?????? ??? ????? ?? ???????? ?????? ?? ????? ??????? ?? ?????? ???????? ?? ?? ????-????? ?????? ?? ?????? ?? ?????? ?? ???? ??? ??? ?? ?????? ?? ???????? ?????? ????? ????? ????????? ?????? ???????? ????? ??????? ?? ??????? ????????? ?? ???????? ???? ??? ????? ?? ?????????? ??? ???? ??? ??, ???? ?????????? ???? ?? ????? ???????? ??? ????? ??????? ?? ?????? ?????? ??????? ???? ????????? ?? ????-????? ?????? ?? ???????? ?????? ??? ????????? ???? ????? ????? ?????? ??? ?????????? ????? ???? ????????? ??????? ???????, ??????, ?????, ????, ????????? ?? ?????? ??? ??? ???? ?????? ??? ????? ??????? 19??? ??? ?? ??? ??? ???? ???? ????????? ??????? ????? ????? ???? ?? ?????? ??? ?????? ????? ????????? ?? ???????? ?????? ?? ?????????? ?? ????????????? ????????? ?? ?????? ????? ?? ?????? ????? ???? ????????? ??? ?????????, ???, ?????, ????? ????? ?? ???????????? ?????? ?? ????? ?????? ????? ??? ?????? ?????????????, ??????? ?? ?? ???????? ?? ???????? ???? ???? ?????-????????, ???-???? ?? ???????? ???????? ??? ??? ??. ???????? ?????, ?????? ?????, ??. ???? ?????, ??. ????? ????, ??. ???????? ?????, ??. ???? ???? ?????, ??. ???? ???, ??. ????? ???, ??. ????? ???, ??. ??????? ???, ??. ?????? ???, ??. ???? ???, ??. ????? ??? ?? ???? ????????? ?? ?????? ?? ???? ?????? ??? ????? ?? ???????? ??????? ?? ?????? ?? ????? ?? ?????????? ?? ?????????? ??????????? ?? ???????? ????? ?? ??????? ??? ???, ???????, ?????, ????? ?????? ?? ???????-?????????? ???????? ??? ????? ?? ????? ?????? ???? ??? ??? ?????? ?? ?????? ???? ?? ?? ?????? ????? ?????-????????? ?? ? ???? ?????? ????? ??? ????????? ?? ?????? ?? ????? ???? ????? ?????? ?? ??? ?? ?????? ?? ????? ?? ??? ???? ???????? ?? ??? ???????? ?? ????????? ?????? ???? ??????? ?????????? ???????, ?????????? ?? ?????????? ????????? ?? ?????? ???????? ??? ?? ????? ?? ??????, ????????? ?? ??????? ?? ??? ?????????? ????? ????? ???? ???? ????? ????: ?????? ????? ????? ???????, ????? ???????, ??????? ?????????, ???????? ??????, ???????? ??????, ????????? ?? ??? ?????, ???????

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?????? ????? ????? ???????, ????? ???????, ??????? ?????????, ???????? ??????, ???????? ??????, ????????? ?? ??? ?????, ???????

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The aim of this study is to investigate the relationship between Teaching Effectiveness and Socio-Economic Status of secondary school teachers. The sample comprised 150 secondary school teachers working in secondary schools of Bengaluru Metro City selecting by simple random sampling technique. Research tools like Teacher Effectiveness Scale (2006) developed and standardized by Umme Kulsum was used to identify the teachers' teaching effectiveness and Modified Kuppuswamy's SES updated version of 2017 by Tulika Singh, Sanju Sharma and Seetharamiah Nagesh was to find out the socio-economic status of teachers. The Karl Pearson's Product Moment Coefficient of Correlation and independent 't' test was utilized to test the stated hypotheses. The correlation result shows that there was significant relationship between Teacher Effectiveness and Socio-Economic Status of secondary level teachers and the 't' test shows that sex was influenced on teaching effectiveness. The female teachers had higher teaching effectiveness than male teachers. The independent 't' test also confirmed that teachers who had higher socio-economic status had more effectiveness in teaching than moderate and low socio-economic status. The study recommends that the teachers' remuneration levels should be reviewed by the government with an aim of motivating their performance to enhance their effectiveness in efficiency. The government should invest in more infrastructures to cater for the growing numbers of learners nationally.

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Teaching Effectiveness, Socio-Economic Status, Secondary, School Teachers

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- In the digital age, the exponential growth of cybercrimes has emerged as a critical challenge to legal systems worldwide. Law enforcement authorities play a pivotal role in addressing this threat by investigating cyber offenses, enforcing cyber laws, and ensuring digital security. This paper examines the evolving role of law enforcement agencies in the prevention, detection, and prosecution of cybercrimes. It explores the legal and institutional frameworks supporting cybercrime enforcement, the adoption of cyber forensics, and the significance of inter-agency and international cooperation. The study also highlights key challenges faced by law enforcement, including jurisdictional issues, lack of technical expertise, and rapid technological changes. Emphasis is placed on the need for capacity-building, advanced training, and collaboration with private entities to enhance the effectiveness of cyber law enforcement. Strengthening these areas is essential to ensure a robust legal response to cyber threats and to uphold the rule of law in cyberspace.

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Cyber Crime, Law Enforcement, Cybersecurity, Digital Forensics, International Cooperation

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Legacy system migration and modernization are critical processes for organizations seeking to stay competitive in an increasingly digital world. This review introduces a novel model for legacy system migration that integrates predictive analytics, machine learning, and a multidimensional data approach, combining technical, operational, and organizational data. By comparing this model with traditional Waterfall and Agile methodologies, we demonstrate its enhanced predictive capabilities, adaptability to emerging technologies, and cost-effectiveness. The proposed framework offers significant improvements in risk management, scalability, and stakeholder engagement, addressing gaps in existing models. Furthermore, this paper explores the implications of these advancements for both practitioners and policymakers, providing actionable recommendations for successful legacy system modernization. Future research directions include the integration of additional emerging technologies, longitudinal migration outcomes, and a deeper understanding of human and organizational factors. This review aims to inform researchers, decision-makers, and industry professionals about the latest advancements and provide guidance for more reliable and efficient system migration strategies

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Legacy system migration, modernization, predictive analytics, machine learning, system transition, Agile migration model, Waterfall migration model, data integration, risk management, digital transformation, emerging technologies, stakeholder engagement.

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This review explores the integration of platform automation in e-commerce product management, focusing on its role in enhancing operational efficiency. As e-commerce businesses continue to evolve, the need for faster, more accurate, and cost-effective solutions has driven the adoption of advanced automation technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and cloud-based systems. The review proposes a new model for platform automation that combines these technologies to optimize key operational functions including inventory management, pricing strategies, supply chain coordination, and customer experience management. A comparative analysis with existing models reveals that the proposed framework improves predictive performance, scalability, and decision-making speed. Furthermore, the paper discusses the implications of these advancements for practitioners and policymakers, offering recommendations for future research. This review serves as a foundational resource for researchers, decision-makers, and industry professionals seeking to understand and apply platform automation in e-commerce product management.

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Platform Automation, Operational Efficiency, E-commerce, Artificial Intelligence, Machine Learning, Robotic Process Automation, Cloud Computing, Supply Chain Optimization, Demand Forecasting, Pricing Strategies, Inventory Management, Customer Experience Management, Predictive Models, E-commerce Product Management.

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As cloud adoption accelerates, optimizing operational costs in platforms like Amazon Web Services (AWS) has become a critical priority for organizations. This review paper synthesizes current research and practices surrounding cost optimization in AWS, focusing on three key components: EC2/RDS scheduling, AWS Compute Optimizer, and billing analysis. Despite the availability of individual tools and techniques, existing approaches often operate in silos, resulting in suboptimal outcomes. We propose a Unified Cost-Aware Scheduling and Forecasting Model (U-CSFM) that integrates performance telemetry, usage patterns, and cost data to enable predictive, automated, and business-aligned optimization. Through a comparative analysis of the proposed model with existing frameworks, we demonstrate superior performance in cost savings, resource efficiency, and anomaly response times. This review provides actionable insights for researchers, practitioners, and policymakers, while also outlining future directions for cross-cloud interoperability, compliance-aware optimization, and intelligent feedback systems. The findings aim to advance the development of more robust and proactive cost governance strategies in cloud environments.

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AWS Cost Optimization, EC2 Scheduling, RDS Scheduling, Compute Optimizer, Billing Analysis, Cloud Governance, Predictive Modeling, Resource Management, Anomaly Detection, Unified Framework

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Artificial Intelligence (AI) has emerged as a transformative force in Research and Development (R&D), reshaping traditional approaches to innovation, productivity, and creativity. This paper examines how AI-driven technologies--such as machine learning, natural language processing, and generative algorithms--are redefining the R&D landscape across industries. The study adopts a mixed-method approach, combining an extensive review of scholarly literature, industry case studies, and secondary data from leading organizations. Findings indicate that AI significantly accelerates innovation by enabling rapid data analysis, predictive modeling, and automated design processes, thereby shortening product development cycles and enhancing decision-making accuracy. In terms of productivity, AI reduces costs and research timelines through automation of repetitive tasks, real-time simulations, and optimized resource allocation. Moreover, AI tools have shown the potential to stimulate creativity by generating novel ideas, fostering cross-disciplinary collaboration, and supporting complex problem-solving. However, the research also highlights challenges such as overreliance on algorithms, ethical concerns, data privacy issues, and the risk of diminishing human originality. The paper concludes that while AI is not a replacement for human ingenuity, it serves as a powerful collaborator that augments researchers' capabilities. Recommendations emphasize the need for balanced integration of AI, ethical guidelines, and continuous human oversight to maximize its potential in advancing R&D outcomes.

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Artificial Intelligence, Research and Development, Innovation, Productivity, Creativity, Machine Learning, Generative AI, Human-AI Collaboration, Automation, Ethical AI, Scientific Discovery, Digital Transformation, Technological Advancement.

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Web content mining is a core component of web mining that focuses on extracting meaningful information from the unstructured or semi-structured content of web pages. As the volume of web data continues to grow, effective classification and information retrieval techniques have become essential. This paper presents a comparative study of three widely used machine learning algorithms in web content mining: Naive Bayes (NB), Support Vector Machine (SVM), and Neural Networks (NN). These algorithms are evaluated based on key factors such as classification accuracy, scalability, training time, and suitability for high-dimensional web data. The findings of this study aim to guide researchers and practitioners in selecting appropriate models for tasks such as web page classification, sentiment analysis, and content filtering.

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Creative Commons Attribution 4.0 and The Open Definition

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Pain is a complex sensory and emotional experience influenced by biological, psychological, and cultural factors. It represents a significant clinical challenge due to its varied etiology and individual response. In Ayurveda, pain (Shoola) is predominantly linked to Vata dosha, and Basti (medicated enema) is acknowledged as the most effective therapy for Vata vitiation. This article provides an in-depth analysis of the classical understanding and modern applicability of Basti therapy in managing pain conditions like dysmenorrhea, post-surgical pain, and chronic musculoskeletal discomfort. It highlights the types, formulations, and protocols of Basti, supported by classical references and recent clinical data, along with extensive tabular presentation of mechanisms, ingredients, and outcomes.

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Basti, Shoola, Pain, Ayurveda, Vata, Panchakarma, Dysmenorrhea, Post-operative Pain

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Creative Commons Attribution 4.0 and The Open Definition