Volume 14 | Issue 5 |

 Abstract

Compression ratio in engines refers to the ratio of the total cylinder volume when the piston is at the bottom of its stroke (known as the "bottom dead center" or BDC) to the total cylinder volume when the piston is at the top of its stroke (known as the "top dead center" or TDC). In other words, it is the ratio of the maximum to the minimum volume within a cylinder during the engine's operation. Compression ratios of 10, 11, 11.5, 12, 12.5 & 13 and engine speeds of 1000 to 5000 rpm, in increments of 100 rpm, were utilized. In the present study, the effect of different compression ratios was studied and optimum compression ratio was established for a dedicated CNG engine. The results show that with the increase in compression ratio, the performance characteristics viz. power, torque, brake thermal efficiency and brake specific fuel consumption are improved. The CNG engine has established itself as a viable alternative to diesel engines in both heavy commercial and passenger transport applications. The expansion of CNG distribution networks and the implementation of strict emission regulations have prompted Original Equipment Manufacturers (OEMs) to focus on the development of CNG vehicles across various market segments. As the global automotive industry continues its relentless pursuit of sustainable and environmentally friendly transportation solutions, compressed natural gas (CNG) has emerged as a promising alternative to traditional gasoline and diesel fuels. CNG, composed mainly of methane, offers several advantages, including lower greenhouse gas emissions and reduced reliance on fossil fuels. In this context, dedicated CNG engines have gained significant attention as they are purpose-built to maximize the potential benefits of this alternative fuel source. Among the myriad factors that influence the performance of CNG engines, the compression ratio stands out as a pivotal parameter deserving comprehensive investigation. The compression ratio, defined as the ratio of the cylinder's maximum and minimum volumes, plays a fundamental role in determining an engine's efficiency, power output, and emissions characteristics. It is a parameter of paramount importance in internal combustion engines, irrespective of the fuel type. However, its influence on dedicated CNG engines, specifically tailored to harness the unique properties of natural gas, has not been explored in depth. Understanding the intricate relationship between compression ratio and CNG engine performance is essential for optimizing engine design and achieving the desired balance between efficiency, power, and emissions.

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 Keywords

CNG engine, Thermal Efficiency, Compression Ratio, emission control, CO-NO?-THC-CH? emissions, ECU calibration.

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 Abstract

Abstract: Education has been considered instrumental in fostering economic development of a country. How well a State promotes and handles the issue of education is a subject of deep study, as the present and future of generations depend largely upon it. Educational policy research is a dynamic and complex field that play a pivotal role in policy analysis, evidence-based policy reforms, identifying gaps, prioritizing issues and deciding government funding for research and development. Policy research in education is intended to generate knowledge about underlying values that shape various educational processes and to develop a better insight of educational practices and their outcomes.

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 Keywords

Policy Research, Higher Education, Legislation, Challenges in formulation

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 Abstract

Economic infrastructure plays a critical role in fostering regional development and reducing spatial inequalities. The Kalyana Karnataka region of Karnataka has historically remained underdeveloped due to structural and institutional constraints. This study evaluates the status and progress of economic infrastructure development in the region, focusing on transportation, energy, irrigation, and industrial infrastructure. The study adopts a descriptive and analytical research design using secondary data sourced from the Economic Survey of Karnataka (2023-24), KKRDB reports, and government publications. The findings indicate that while infrastructure expansion has occurred across sectors, development remains uneven, with transportation and energy sectors performing relatively better than irrigation and industrial infrastructure. The study identifies significant inter-district disparities and a gap between infrastructure creation and utilization. Policy recommendations are provided to enhance balanced and inclusive regional development.

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 Keywords

Economic infrastructure, regional disparity, Kalyana Karnataka, inclusive development, public investment

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he Sutlej-Yamuna Link (SYL) Canal dispute represents one of the most complex and prolonged interstate river water conflicts in India. Originating after the reorganization of Punjab in 1966 and the creation of Haryana, the dispute revolves around the allocation and sharing of Ravi-Beas river waters between Punjab and Haryana. The proposed 214-kilometer SYL Canal was intended to facilitate the transfer of water from the Sutlej River to the Yamuna basin, enabling Haryana to access its allocated share. However, political resistance, regional identity, environmental concerns, constitutional debates, and judicial intervention transformed the issue into a major challenge to Indian federalism. This research paper critically examines the historical evolution, constitutional framework, judicial pronouncements, political dimensions, and environmental implications of the dispute. The paper also evaluates the role of the Supreme Court, the Union Government, and interstate tribunals in resolving river water conflicts. Further, it analyzes contemporary developments and suggests policy measures for sustainable water governance and cooperative federalism. The study concludes that the SYL dispute is not merely a legal disagreement over water sharing but a broader reflection of India's evolving federal structure, ecological crisis, and regional politics.

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 Keywords

: Sutlej-Yamuna Link Canal, interstate water dispute, Punjab, Haryana, federalism, river water sharing, Supreme Court, Ravi-Beas waters, constitutional law, cooperative federalism.

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The rapid emergence of Generative Artificial Intelligence (GenAI) marks a paradigm shift in Human Resource Management (HRM), transitioning the function from traditional administrative support to a data-driven strategic partnership. This research systematically synthesizes contemporary academic literature and industry case analyses to investigate the impact of GenAI integration on recruitment efficiency and employee engagement. Findings indicate that GenAI-enabled workflows significantly optimize the talent acquisition lifecycle; documented evidence shows that automating sourcing, screening, and job description generation can reduce recruitment cycles by up to 90% while cutting operating costs by 20% to 40%. Furthermore, the study examines the technology's influence on the internal employee experience, revealing that intelligent conversational agents and adaptive learning pathways facilitate hyper-personalization, resulting in a 105.5% increase in feedback frequency and a 35.5% improvement in mental well-being scores. However, the integration of Large Language Models (LLMs) introduces critical organizational challenges, including persistent risks of algorithmic bias, the "black box" transparency-opacity paradox, and heightened data privacy concerns under frameworks such as the EU AI Act. Drawing on the Resource-Based View (RBV) and Ability-Motivation-Opportunity (AMO) theory, this paper argues that while GenAI serves as a transformative strategic enabler, its success is contingent upon robust ethical governance and a "human-in-the-loop" mandate to preserve relational integrity. The research concludes by proposing a structured implementation roadmap that balances technological efficiency with the human-centric values essential to modern workforce management.

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 Keywords

Generative AI, HR Automation, Recruitment Efficiency, Employee Engagement, HR Analytics, Talent Acquisition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Currently, smartphones are used for nearly all activities, including chatting, watching videos, studying, and using various applications. To conserve battery power, phones use power-saving features like sleep mode, but these methods are mostly reactive. This means they only start working when the battery begins to drain, which can slow down apps and delay background tasks. Because of this, users may experience lag or reduced performance. To solve this, this paper presents an Intelligent Predictive Framework for Dynamic Mobile Energy Optimization. The proposed framework adopts a lightweight predictive approach for intelligent and efficient mobile energy optimization. Instead of simply turning apps on or off, the system analyzes user behavior, app usage, system load, and phone temperature in real time. Based on this, it predicts what needs to be done and manages apps smartly. Applications are divided into three states: Active, Throttled, and Deep Hibernate. Important apps run smoothly, while unnecessary apps are controlled to save battery. The system also manages device temperature to avoid overheating. Results show improved battery savings, smoother performance, and a more comfortable user experience.

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 Keywords

Artificial Intelligence, Machine Learning, Mobile Energy Optimization, Task Scheduling, Thermal Management, Predictive Systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The electric vehicle (EV) industry in India has experienced remarkable growth during the last few years due to increasing environmental awareness, rising fuel prices, technological advancements, and supportive government policies. Among the companies operating in the Indian EV sector, OLA Electric emerged as one of the fastest-growing electric two-wheeler manufacturers. The company gained significant market share in 2022 and became one of the leading brands in the Indian electric scooter market. However, between 2023 and 2026, OLA Electric faced several challenges, including rising competition, customer service issues, product quality concerns, and operational difficulties, which affected its market share and business performance. This research paper examines the rise, decline, and recovery strategies of OLA Electric within the Indian EV industry. The study is based on secondary data collected from industry reports, business articles, company websites, and market analysis reports. Statistical analysis has also been used to understand market trends and competitive positioning. The study concludes that although OLA Electric experienced a decline in market share after rapid expansion, the company continues to possess strong growth potential because of technological innovation, government support, and increasing demand for sustainable mobility solutions.

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Ola, EV, EV Market

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This paper presents an intelligent Optical Character Recognition (OCR) system for automated bill data extraction using Qwen VL2, a state-of-the-art vision-language model (VLM). Unlike traditional OCR pipelines that rely on Tesseract or rule-based pattern matching, the proposed system leverages the multimodal capabilities of Qwen VL2 to process bill images and directly return structured key-value pairs corresponding to fields such as consumer number, billing amount, due date, and service period. The system integrates a Node.js backend with a MongoDB database for scalable storage and retrieval. Experimental evaluation on 200 diverse real-world electricity and utility bills demonstrates superior extraction accuracy compared to conventional OCR approaches, achieving 94.6% overall field-level accuracy versus 73.0% for Tesseract. The architecture is lightweight, extensible, and deployable in cloud or on-premise environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Qwen VL2, Vision-Language Model, OCR, Bill Processing, Key-Value Extraction, Multimodal AI, Document Automation, Node.js, MongoDB.

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Creative Commons Attribution 4.0 and The Open Definition

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The present study aims to comparatively evaluate the phytochemical composition and pharmacological activities of Tridax procumbens and Tinospora cordifolia, two widely used medicinal plants. Plant extracts were prepared using standard solvent extraction methods and subjected to qualitative and quantitative phytochemical screening. Pharmacological activities, including antioxidant (DPPH assay), antimicrobial (agar well diffusion), anti-inflammatory (protein denaturation), and antidiabetic (?-amylase inhibition) assays, were evaluated. Phytochemical analysis revealed the presence of key bioactive compounds such as alkaloids, flavonoids, tannins, saponins, glycosides, and terpenoids in both plants. Quantitative estimation showed higher total phenolic and flavonoid content in Tinospora cordifolia compared to Tridax procumbens. In pharmacological studies, both extracts exhibited dose-dependent activity; however, Tinospora cordifolia demonstrated significantly stronger antioxidant activity (lower IC??), greater antimicrobial effects, higher inhibition of protein denaturation, and more pronounced ?-amylase inhibitory activity. The findings indicate a positive correlation between phytochemical content and pharmacological efficacy. Overall, Tinospora cordifolia showed superior therapeutic potential, although Tridax procumbens also exhibited considerable biological activity. This study supports the traditional use of both plants and highlights their potential as sources of bioactive compounds for the development of natural therapeutic agents. Further studies are recommended to isolate active constituents and validate their clinical applications.

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 Keywords

Tridax procumbens, Tinospora cordifolia, phytochemical screening, antioxidant activity, pharmacological evaluation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture is very important for the growth of the economy and ensuring that people have enough food, especially in countries that are still developing, like India. Even though it's important, the productivity of farming is often affected by things like changes in the weather that are hard to predict, choosing the wrong crops, not using water efficiently, and not having enough help from experts. In recent years, Artificial Intelligence (AI) has become a good solution for these problems by helping make decisions based on data and smart technology. Now, Generative AI has become popular because it can not only look at data but also explain and suggest things in a way that's easy for people to understand, like how humans would talk. This review paper looks at how Generative AI is being used in modern farming, especially for helping farmers decide which crops to grow, predicting how much they will harvest, and making farming more sustainable. It looks at the different machine learning methods currently used in agriculture, points out their weaknesses, and explains how Generative AI can make these tools easier to use, more clear, and build trust among farmers. The paper ends by saying that combining tools that predict the future with Generative AI can change traditional farming into a smarter, more explainable, and more sustainable way of growing food

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 Keywords

Generative Artificial Intelligence, Smart Agriculture, Crop Yield Prediction, Crop Recommendation, Decision Support Systems, Sustainable Farming

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Creative Commons Attribution 4.0 and The Open Definition