Volume 13 | Issue 5 |

 Abstract

: Effective allocation of government resources is essential to ensure equity, transparency, and accountability in public service delivery. This paper presents a blockchain-driven approach to enhance the management and distribution of governmental resources using Ethereum, Ganache, and an open-permissioned model incorporating Keyless Signature Infrastructure (KSI). Through the implementation of Merkle tree-based hashing and smart contracts, the system ensures immutability, traceability, and real-time verification of transactions. The proposed model promises not only enhanced trust and auditability but also quick and secure access to data, aiming to reduce corruption and optimize resource utility.

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 Keywords

1. Overview of current issues in resource allocation 2. Importance of data integrity and transparency 3. Potential of blockchain to address inefficiencies 4. Use of KSI and Merkle-tree for validation 5. Integration of smart contracts for automation

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Benzimidazole and its derivatives has been feasibly synthesized in a prominent way using Co- doped-Zn nano catalyst with easily available o-phenyl diamine in an eco-friendly manner. This leading approach is captivating due to its easy set up and quick response to excellent yield. Melting point determination and TLC have been performed for all the products. In this study all product have been characterized by 1H NMR and 13C NMR.

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Benzimidazole, Co-doped-Zn, o-phenyl di-amine, eco-friendly, In-situ recrystallized.

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Creative Commons Attribution 4.0 and The Open Definition

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In the medical field, the ward round procedure is essential for managing patient care and making decisions. However, because of the unstructured and diverse nature of the information shared during these cycles, it frequently suffers from inefficiencies. This procedure can be streamlined by using an ontology-based model, which offers a formal framework for obtaining and organising medical knowledge. In order to improve the ward round procedure in healthcare systems, this study proposes an ontology-based approach. The suggested model creates a coherent framework by combining many healthcare data sources, such as clinical guidelines, treatment regimens, and patient records. The approach guarantees consistent and reliable information retrieval by utilising standardised medical ontologies and semantic web technologies, which improves communication between medical experts. In the end, the ontology-based method enhances patient outcomes by supporting evidence-based decision-making and increasing the effectiveness of ward rounds. evidence-based decision-making, cohesive organisation, patient records, and treatment regimens.

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 Keywords

Healthcare, Decision Making, Patient Records, Semantic web Technologies

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Abstract: A simple, precise, and Development and Validation of RP-HPLC Method for Estimation of Nicardipine (Anti-Hypertensive) Drug Reverse Phase High Performance Liquid Chromatography (RP-HPLC) was developed and validated for the estimation of Nicardipine, an anti-hypertensive drug, in pharmaceutical dosage forms. The method utilized a C18 column with a mobile phase consisting of a mixture of phosphate buffer and acetonitrile. The chromatographic conditions were optimized to achieve good separation and resolution of Nicardipine from its degradation products. The method was validated according to ICH guidelines for specificity, linearity, accuracy, precision, robustness, and stability-indicating capability. The results showed good linearity (r2 > 0.999) over a concentration range of 10-50 ?g/mL, with a limit of detection (LOD) of 0.5 ?g/mL and a limit of quantification (LOQ) of 1.5 ?g/ml. The precision and accuracy of the method were demonstrated by low %RSD (< 2%) and high recovery (> 98%), respectively. The method was successfully applied for the estimation of Nicardipine in commercial tablets, demonstrating its suitability for routine analysis, quality control, and stability studies. The developed RP-HPLC method can be used to ensure the quality and efficacy of Nicardipine pharmaceutical products. This project focuses on the development and validation of a High-Performance Liquid Chromatography (HPLC) method for the determination of Nicardipine in tablet dosage form. The method was validated according to ICH guidelines for specificity, linearity, accuracy, precision, and robustness. The validated method was applied to determine the content uniformity and stability of Nicardipine tablets. The results demonstrated that the developed HPLC method is accurate, reliable, and suitable for quality control of Nicardipine tablets.

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Keywords : Nicardipine drug ,Assay method ,Method of validation ,Metthod of estimation

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Creative Commons Attribution 4.0 and The Open Definition

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The integration of Electric Vehicles (EVs) as sustainable energy systems offers dual benefits of enhanced grid stability as well as optimized energy utilization. This research paper examines the bidirectional relationship between EVs and microgrids, focusing on charging strategies, grid impact analysis, and optimization techniques. This paper presents the design, implementation, and testing of a hardware prototype for integrating electric vehicles with microgrids using rooftop solar power. The proposed system utilizes a microcontroller-based control architecture with an inverter and step-up transformer to manage bidirectional power flow between EVs and the microgrid. The prototype demonstrates effective charging management, solar integration, and grid support capabilities through a Battery Management System (BMS) and bidirectional energy conversion. This study presents innovative solutions for seamless EV-microgrid integration while maintaining grid stability and maximizing renewable energy utilization. Experimental results show successful implementation of controlled charging strategies and solar power utilization, achieving improved grid stability and efficient energy management.

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 Keywords

Electric vehicle (EV), Vehicle to grid (V2G), Grid integration, grid stability, Grid to vehicle (G2V), Bidirectional Charging, Battery Management System (BMS)

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Creative Commons Attribution 4.0 and The Open Definition

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The IEEE Transactions on Knowledge and Data Engineering is an interdisciplinary journal dedicated to research and advancements in knowledge and data engineering across computer science, artificial intelligence, and related fields. It features studies on expert systems, AI-driven data management, distributed databases, query languages, system architectures, performance algorithms, and real-time databases. The journal also emphasizes integrity, security, and fault tolerance in knowledge systems, with applications spanning diverse technological domains. It offers a platform for innovative approaches, fostering collaboration among academic and industry researchers.

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Knowledge Engineering, Data Engineering, Artificial Intelligence, Expert Systems, Data Management, Distributed databases, Query Languages, System architecture

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This paper explores the lived experiences, struggles, and triumphs of the Hijra community in India through the autobiographical narratives of A. Revathi and Laxmi Narayan Tripathi. These memoirs--The Truth About Me and Me Laxmi, Me Hijra--offer firsthand insights into the lives of individuals who identify outside the binary gender framework. Through their personal journeys, both authors address themes of gender identity, social exclusion, trauma, and empowerment. The study emphasizes the intersectionality of caste, gender, and socioeconomic status in shaping the marginalization experienced by Hijras and highlights their resistance through activism and self-representation in literature and public life.

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Hijra community, gender identity, social exclusion, autobiography, marginalization, activism, intersectionality, transgender rights, Indian society

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Stock market forecasting is a challenging task due to its volatile nature and dependence on multiple factors, including historical trends, economic indicators, and market sentiment. Traditional models, such as statistical and machine learning approaches, primarily focus on historical price data, often neglecting the crucial role of public sentiment in influencing stock prices. This research introduces a hybrid model that integrates Long Short-Term Memory (LSTM) neural networks with Natural Language Processing (NLP)-based sentiment analysis to enhance the accuracy of stock market predictions. The proposed framework collects historical stock prices from financial platforms and textual data from news articles, financial reports, and social media. Sentiment analysis is performed using NLP techniques such as VADER, Text Blob, and BERT to classify market sentiment as positive, neutral, or negative. These sentiment scores are then incorporated into an LSTM-based deep learning model, which processes both numerical stock data and qualitative sentiment trends to generate more reliable stock price forecasts. Experimental results demonstrate that integrating sentiment analysis improves the model's predictive accuracy by reducing Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) compared to standalone LSTM models. Additionally, a strong correlation is observed between public sentiment trends and stock price movements, confirming the significance of sentiment-aware forecasting in financial markets. This hybrid approach provides investors and financial analysts with a more comprehensive decision-making tool, allowing them to anticipate stock price fluctuations more effectively.

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 Keywords

Stock Market Predication, LSTM, ARIMA, Sentiment Analysis, Hybrid Model, Machine Learning, Natural Language Processing(NLP).

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The evolution of artificial intelligence has significantly transformed the education sector, ushering in the era of smart education systems. At the forefront of this transformation is generative AI, which has proven to be a powerful tool in automating and enhancing various aspects of teaching and learning. This study explores the integration of GROQ's Lemma API, large language models (LLMs), and natural language processing (NLP) into a smart education framework that dynamically supports the generation of educational content and assessments. By leveraging GROQ's Lemma API, which provides ultra-fast inference capabilities for LLMs, the application ensures real- time content generation with high efficiency. The integration of advanced NLP techniques allows the system to understand the semantic structure of input material and generate questions that test comprehension, critical thinking, and problem-solving skills. This leads to more interactive, engaging, and effective learning experiences. In conclusion, this research demonstrates how a smart education system powered by GROQ's Lemma and generative AI can redefine modern learning environments. By automating educational content generation, the system enhances productivity for educators, enables scalable personalized learning, and provides students with a more tailored and effective educational experience. This paves the way for the widespread adoption of intelligent educational technologies in schools, colleges, and online learning platforms worldwide.

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 Keywords

LLM(Large Language Model), BERT(Bidirectional Encoder Representation from Transformers), NLP

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Abstract: This research paper offers a comprehensive analysis of gender quotas in Indian politics, focusing on their role in enhancing women's political representation and participation. It explores the historical evolution of gender-based reservations, beginning with the 73rd and 74th Constitutional Amendments, which mandated one-third reservation for women in local governance, and culminating in the landmark 2023 Women's Reservation Bill. This bill, now a constitutional amendment, reserves one-third of seats in the Lok Sabha and state legislative assemblies for women, aiming to bridge the gender gap in political leadership. The paper evaluates the outcomes of existing quotas, highlighting successes such as increased female visibility and improved governance in local bodies. It also addresses persistent challenges, including delayed implementation, proxy representation, and the marginalization of lower caste women. A comparative lens is used to assess how India's approach aligns with international practices in countries like Rwanda and Sweden. The paper concludes with policy recommendations for ensuring that gender quotas not only increase numbers but also empower women substantively and inclusively in the political domain.

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 Keywords

Gender quotas, women's political representation, India, Women's Reservation Bill, political participation, comparative analysis etc.

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Creative Commons Attribution 4.0 and The Open Definition