Volume 13 | Issue 4

 Abstract

Medicinal plants and their secondary metabolites are increasingly used as complementary medicines for the treatment of various diseases. Many synthetic anti-inflammatory drugs are associated with significant side effects and toxicity. In contrast, plant-derived anti-inflammatory agents have been traditionally used to manage inflammation with minimal adverse effects. Inflammation is a biological response in which the body's white blood cells and their mediators protect against infections caused by external pathogens such as bacteria and viruses. This review highlights various medicinal plants, their active constituents, and their role in combating inflammation. Additionally, it discusses previous research studies supporting their anti-inflammatory potential. The review concludes that herbal remedies are natural, safe, eco-friendly, and generally free from side effects. As modern lifestyles become increasingly detached from nature and technology-driven, there is a pressing need to promote the use of herbal medicine for better health and well-being.

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 Keywords

Anti-inflammatory agents, Inflammation, Medicinal plants, Secondary metabolites, Herbal medicine, Synthetic drugs.

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 Abstract

Samajik Parivartan Ke Liye Siksha Me Naitik Mulyo Ka Samavesh

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Samajik Parivartan Ke Liye Siksha Me Naitik Mulyo Ka Samavesh

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The concept of a supreme creator or almighty being in tribal societies, particularly among the Adivasis of India, differs significantly from the monotheistic or organized religious structures seen in Brahmanism, Christianity and Islam. Adivasi spiritual traditions are deeply rooted in animism, nature worship and ancestral reverence, emphasizing a decentralized belief system that does not conform to the notion of a singular "Parmeshwar" or universal god. Instead, these indigenous communities recognize and venerate local deities, spirits and sacred natural elements such as the sun, moon, rivers, forests, and mountains. Adivasi religious traditions are diverse and vary across different tribal groups. The Santhal tribe worships Thakur Jiu, while the Munda tribe believes in Sing Bonga and the Gond tribe venerates Bara Deo. These deities are often associated with natural forces and ancestral spirits, reflecting a holistic worldview where the environment is considered sacred. Rituals, festivals, and ceremonies such as Sarhul, Karam and Sohrai play a crucial role in maintaining the spiritual and ecological balance within Adivasi societies. Shamans, known as Pahans, Dehuris and Ojhas, act as intermediaries between the human and spiritual realms, conducting rituals to appease spirits and ensure community well-being. Adivasi spiritual traditions contrast sharply with organized religions, as they lack a formal priesthood, religious scriptures, and centralized religious institutions. However, due to colonial influences, religious conversions, urbanization and government policies, many Adivasis have been compelled to adopt mainstream religious practices, often at the cost of their indigenous faiths. The Sarna Dharma Recognition Movement is an ongoing effort to obtain legal recognition for tribal religious practices, ensuring their preservation and protection. Despite challenges posed by industrialization, deforestation and migration, tribal communities continue to resist cultural assimilation and reclaim their spiritual heritage through grassroots movements and legal activism. Efforts to document and revive indigenous oral traditions, protect sacred groves and promote cultural awareness have gained momentum in recent years. Recognizing and respecting Adivasi spirituality is crucial not only for cultural preservation but also for ecological sustainability and social justice. This study explores the unique aspects of Adivasi religious traditions, the impact of external forces and the ongoing struggle for recognition and revival of indigenous belief systems in India.

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 Keywords

Adivasi Spirituality, Animism, Nature Worship, Tribal Deities, Sarna Dharma, Indigenous Religions, Ancestral Worship, Sacred Groves, Rituals and Festivals, Religious Conversions, Legal Recognition, Cultural Preservation.

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E-commerce businesses increasingly rely on AI to enhance customer interactions and provide real-time support. This project focuses on developing a Live Chat-Bot that enables seamless communication between customers and an e-commerce clothing website. Utilizing Natural Language Processing (NLP) and AI, the chatbot is designed to provide instant assistance, personalized recommendations, and 24/7 customer support, reducing the need for human intervention while improving the shopping experience. It integrates with the website's backend to analyze user preferences and guide them toward relevant products, ensuring an interactive and efficient shopping process. The chatbot offers key advantages such as instant query resolution, increased conversion rates, and enhanced user engagement. Built with Flask and Django, and leveraging cloud-based infrastructure, it can handle multiple inquiries simultaneously. However, challenges such as data privacy concerns and miscommunication risks must be addressed. Overall, the implementation of this chatbot aims to revolutionize e-commerce customer support, making it more efficient, scalable, and user-friendly.

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E-commerce,Chatbot,AI,NLP,Real-time support,Customer engagement Personalized recommendations,Flask,Query resolution,Scalability.

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Studies of gender and its relations to resources, cultural and environmental milieu has been an important theme in the social and cultural Geography. Bodos have patriarchal society which guide social norms and cultural conditions that have resulted into a distinct subordination of relationship on the basis of gender among the Bodos. This is reflected in the heritance of property between the male and female members in the family or in the community. Heritance of property by Bodo women is confined within tangible material goods like handloom items, share in agricultural products, dresses, gold-silver ornaments, domestic animals, etc. Bodo women are deprived of attaining the intangible and valuable properties like quality higher education, land ownership, right and freedom to economic activities, right and liberty for political standouts. Such practices of the society is an outcome of patriarchy and acceptance of women as paraya dhan i.e. (else's property) leading to gender disparity and poverty in the community as a whole. Overall sustainable development can only prevail in a community if the women are privileged to equally participate in the dynamics of property inheritance.

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Gender, Property, Rights, Bodo, Community.

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Creative Commons Attribution 4.0 and The Open Definition

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ABSTACRT: forest algorithm and the Adaboost algorithm. The results of the two algorithms are based on accuracy, precision, recall, and F1-score. The ROC curve is plotted based on the confusion matrix. The Random Forest and the Adaboost algorithms are compared and the algorithm that has the greatest accuracy, precision, recall, and F1-score is considered as the best algorithm that is used to detect the fraud. UPI fraud detection is presently the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. UPIfraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the UPI information for his use. In the present world, we are facing a lot of UPI problems. To detect the fraudulent activities the UPIfraud detection system was introduced. This project aims to focus mainly on machine learning algorithms. The algorithms used are random.

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Keywords UPI, Fraud Detection, Machine Learning, Anomaly Detection, Financial Security, Real-time Analysis

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Creative Commons Attribution 4.0 and The Open Definition

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In today's data-driven retail environment, accurately forecasting profits and understanding customer behavior are vital for strategic decision-making. This project presents an integrated AI-powered framework for profit prediction and behavioral clustering in retail, leveraging the capabilities of deep learning models. A neural network-based regression model is developed to predict retail profit using historical sales, customer, and product data. To enhance the interpretability of the model, SHAP (SHapley Additive exPlanations) values are used to explain feature contributions, enabling actionable business insights. For customer segmentation, the project utilizes autoencoders to extract latent features from high-dimensional behavioral data, followed by clustering techniques to identify distinct customer profiles. This dual approach not only improves forecasting accuracy but also aids in personalized marketing and inventory optimization. The system is deployed using Streamlit, offering an interactive and user-friendly interface for stakeholders. The proposed solution demonstrates the potential of deep learning in transforming retail analytics through predictive modeling and intelligent customer segmentation.

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Retail Analytics, Profit Prediction, Neural Networks, Customer Segmentation, Autoencoders, Deep Learning, Behavioral Clustering, SHAP Explainability, Predictive Modeling, Streamlit Deployment, Data-Driven Decision Making, Machine Learning, Feature Importance, Sales Forecasting, Unsupervised Learning.

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In recent years, social media has emerged as a dominant force in shaping communication, socialization, and even education, particularly among students. With platforms like Instagram, Facebook, YouTube, and WhatsApp widely used by high school and college students, questions arise about the implications of this digital shift on learning outcomes. This study investigates the relationship between social media use and academic performance. A mixed-methods approach was adopted, involving 500 students from both secondary and tertiary institutions. The findings indicate a nuanced impact--while social media can support collaborative learning and resource sharing, excessive and unregulated use is strongly correlated with distraction, procrastination, and lower academic performance. This paper explores these dimensions in detail, offering recommendations for students, educators, and institutions on how to harness social media as a tool for academic enhancement rather than a source of disruption.

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Creative Commons Attribution 4.0 and The Open Definition

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social media, academic performance, learning, digital distraction, education, students, productivity

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Creative Commons Attribution 4.0 and The Open Definition

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Landmines, while effective in securing territories during warfare, pose a significant threat to military personnel even after deployment, often resulting in accidental injuries or fatalities among friendly forces. This research presents the design and implementation of an intelligent [1] RF-based landmine deactivation system aimed at preventing unintended detonations for friendly soldiers while maintaining full functionality against enemy intrusions. The system is divided into two units: a transmitter embedded in the soldier's footwear and a receiver integrated within the landmine. When a friendly soldier, equipped with the RF transmitter, enters the proximity of the landmine, the receiver detects the signal and activates a relay to disconnect the detonation mechanism. In contrast, if an enemy without a valid transmitter approaches and steps on the landmine, the system remains armed and triggers upon pressure activation. The RF communication is facilitated using 434 MHz modules along with HT12E and HT12D encoder-decoder pairs for signal transmission and reception. This low-cost, efficient system demonstrates a selective activation mechanism that significantly enhances battlefield safety for [2] friendly troops. The project lays the groundwork for future integration of encrypted communication, [3] GPS tracking, and centralized control, contributing toward the development of smarter, more humane defense technologies.

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 Keywords

Landmine Deactivation, RF Communication, HT12E Encoder, HT12D Decoder, Soldier Safety, Wireless Transmitter-Receiver System, Relay-Based Switching, Selective Triggering, Embedded Defense Systems, Friendly Force Identification.

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This paper puts forward DecentRaise, an innovative decentralized crowdfunding platform, built on blockchain technology and aimed at overcoming the very basic limitations inherent in centralized crowdfunding. Raising the seed round affords a very unique opportunity to earn money running a project whilst allowing that person to maintain a majority stake in his own company. Our implementation builds with Solidity for smart contracts, React.js for the front-end interface, and IPFS for decentralized file storage. Web3.js has been incorporated in the architecture for blockchain interaction, MetaMask for wallet integration, and ERC-20 Token Standard for the creation of campaign tokens. Key features included an automated milestone-based release mechanism for funds, decentralized voting systems for project governance, and cryptographically verified authenticity for campaigns. Performance analysis shows a shortened cost down of 62 times less than regular ones, higher than other platforms, with a seriously thought secure-level guaranteed by the immutable transaction log, as well as the distributed consensus algorithm. System 99.9% is operationally a highly decentralized unrelated system of hosting, and this hit a meaningfully higher rank of transparency and trust metrics through empirical evaluations.

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Blockchain technology, Ethereum, smart contracts, decentralized applications (dApps), Web3, IPFS, crowdfunding platforms, distributed systems, consensus mechanisms, cryptocurrency

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Music through its auditory nature merges specific structured auditory elements and pauses into arrangements which induce mental wellness in people. Preferred music acts as a mood enhancer before vital occasions and provides soothing relief when emotions run high. It achieves this and more throughout the middle range too. People have long studied and respected music since it functions as a source of healing as well as entertainment. Modern scientific studies demonstrate that music possesses major capabilities which successfully decrease anxiety but also lower stress while enhancing cognitive focus. People commonly observe that slow-tempo music used in meditation helps decrease heart rate and both minimizes stress and anxiety. During Indian yoga meditation practitioners experience musical guidance through speakers who assist them in reaching their flow objectives and defining targets. Listening to music enhances attention while carrying out complex tasks. Listening to music can enhance brain functions for information storage such as memories and mental relationship formation. In this context the main purpose of this study to find out the attitude of higher secondary students towards music on the reduce of stress in West Bengal. Researcher framed Five null hypotheses, 200 students of higher secondary level in Nadia and kolkata district including urban and rural area of west Bengal was considered as sample. In this study, the purposive sampling technique was adopted for sample selection. Researcher developed a tool to measure attitude towards music on the reduce of stress.The validity of the questionnaire was estimated by the expert and content validity has been maintained properly in this study. For reliability measure, test retest method was applied. After collection of data 't' test was applied for testing hypotheses.

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Music , Stress , School Education , Mental Health

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Creative Commons Attribution 4.0 and The Open Definition

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Lung cancer is one of the most prevalent and deadly forms of cancer worldwide, primarily due to its late detection and the complexity involved in accurately diagnosing its various subtypes. Early detection and precise classification of lung cancer types are vital for effective treatment and improved patient survival. This research introduces a deep learning-based approach for the automated detection and classification of lung cancer using computed tomography (CT) scan images. Specifically, we utilize the VGG16 Convolutional Neural Network (CNN) architecture, known for its strong image feature extraction capabilities. The proposed model is trained to classify lung CT images into four categories: adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and normal (non-cancerous) tissue. We employed transfer learning to adapt the pre-trained VGG16 model to our medical imaging dataset. The data underwent pre-processing steps such as normalization and augmentation to enhance model performance and reduce overfitting. The results demonstrate that our deep learning model achieves high accuracy and strong performance across multiple evaluation metrics including precision, recall, and F1-score. This validates the effectiveness of CNN-based architectures in supporting radiologists and improving the diagnostic process. The approach shows significant potential in clinical applications for early and automated lung cancer detection, laying the foundation for future enhancements involving larger datasets and model interpretability.

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Lung Cancer, Deep Learning, VGG 16 CNN, CT Scans, Image Processing, Feature Extraction.

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Adhatoda vasica (Adulsa) is the main active ingredient of a herbal cough syrup that is being developed and evaluated in this study. Because of its well-established bronchodilatory, expectorant, and anti-inflammatory qualities, Adhatoda vasica is a useful treatment for respiratory conditions. The syrup was made with appropriate additions to improve palatability, stability, and medicinal efficacy, following standard pharmaceutical methods. The prepared syrup was assessed for a number of physicochemical characteristics, such as stability over time, microbiological load, viscosity, pH, and organoleptic qualities. The adulsa-based syrup passed acceptable pharmaceutical standards, according to preliminary data, and showed promise as a natural, efficient remedy for cough and related respiratory conditions.

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Adult formulation, cough syrup, antitussive, expectorant, Guaifenesin, dextromethorphan, and syrup base

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Creative Commons Attribution 4.0 and The Open Definition

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The paper titled Echoes of Silence: A Critical Examination of Women's Struggles in Mahesh Dattani's Dramatic Landscape delves into the complex portrayal of women's issues in the plays of Mahesh Dattani, a prominent Indian playwright. Dattani's works provide a profound exploration of the psychological, social, and emotional struggles faced by women, particularly in the context of contemporary Indian society. Through plays such as Tara, Bravely Fought the Queen, and Final Solutions, Dattani highlights themes of gender inequality, patriarchal control, and the silencing of women's voices. This paper examines how Dattani employs various dramatic techniques, including non-linear narrative structures, symbolic representations, and complex characterisation, to reveal the inner conflicts of female characters. The study also focuses on the intersectionality of gender, caste, and sexuality as explored in his works, illustrating how these factors shape the lives of women. Dattani's portrayal of women is not merely a reflection of their oppression but also a critique of the societal norms that perpetuate their marginalisation. By analysing the various dimensions of women's struggles in Dattani's plays, this paper aims to shed light on the broader socio-cultural implications of his work, offering a critical perspective on the quest for identity, autonomy, and empowerment in the face of silence and repression.

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Women's Struggles, Mahesh Dattani, Gender Inequality, Patriarchy, Empowerment

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The pursuit of equality, as envisioned by Ram Manohar Lohia, is a foundational aspect of his socio-political philosophy. Lohia, an influential Indian freedom fighter, political thinker, and social reformer, emphasized the necessity of economic, social, and cultural equality in shaping a just society. His principles advocated for the empowerment of marginalized groups, particularly those at the intersection of caste, class, and gender disparities. Lohia's critique of the prevailing social structures emphasized the need to address systemic inequalities through affirmative action, decentralization of power, and the promotion of social justice. His belief in the eradication of untouchability, his advocacy for linguistic equality, and his demand for the decolonization of the mind positioned him as a revolutionary thinker in post-independence India. This abstract explores the core tenets of Lohia's thought, analyzing his unique contributions to the discourse on equality, as well as his emphasis on a multi-dimensional approach to achieving social transformation. By synthesizing his ideas on economic democracy, social mobility, and cultural inclusiveness, Lohia's work continues to inspire movements aimed at realizing a truly egalitarian society.

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Equality, Empowerment, Social justice, Untouchability, Democracy, Social mobility.

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Hyperautomation is transforming the way businesses operate by integrating advanced technologies such as robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), and advanced analytics. Unlike traditional automation, which focuses on automating individual tasks or processes, hyperautomation adopts a more comprehensive approach, enabling the automation of complex workflows from start to finish. This integration of technologies allows organizations to streamline operations, minimize manual intervention, and enhance decision-making by leveraging data-driven insights. The holistic nature of hyperautomation lies in its ability to combine multiple tools and technologies to address both structured and unstructured processes. By doing so, it not only optimizes efficiency but also creates scalable systems that adapt to evolving business needs. Moreover, hyperautomation enhances agility by enabling faster responses to change in the market or internal processes, making it a critical asset for organizations striving to stay competitive in the digital age. This approach ensures that businesses can maximize operational efficiency while minimizing costs and resource usage.

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Hyperautomation, Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), Advanced Analytics, Workflow Automation, Digital Transformation, Process Optimization, Operational Efficiency, Data-Driven Decision Making, Business Agility, Unstructured Data Automation, End-to-End Automation

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In financial engineering, stock market prediction is a crucial subject, particularly given the ongoing advancement of innovative methods and strategies in this area. In this study, we look into how sentiment exhibited in Twitter tweets affects the ability to predict stock prices. Twitter is the social networking site that gives everyone a free forum to publicly share their opinions. In specifically, we use the API to retrieve the company's live Twitter tweets. Every special character and stop word in the dataset is taken out. The Random Forest classifier is used to analyze sentiment in the filtered data. As a result, the tweets are categorized as neutral, negative, and positive. To get the outcome, the machine learning model receives the stock data and the tweet data as input. The stock market price is predicted using the ML classifier as a model. The real stock market value is contrasted with the predicted value that was obtained. Experiments employing real-time Twitter data and daily stock data from a number of businesses. The project's objective is to forecast the future price of a stock of interest by combining sentiment analysis of news headlines and Twitter tweets with previous stock data.

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 Keywords

Stock Market, Data Analysis, Machine Learning, Stock price Prediction, Social Media.

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Creative Commons Attribution 4.0 and The Open Definition

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Adverse Drug Reactions (ADRs) resulting from drug-drug interactions are a major healthcare concern. While Graph Neural Networks (GNNs) effectively model these interactions, their one-dimensional processing limits complex feature extraction. This research introduces a novel extension by integrating a two-dimensional Convolutional Neural Network (CNN2D) to enhance ADR prediction. By converting drug interaction data into 2D matrices, CNN2D captures intricate spatial relationships, complementing the GNN's graph-based insights. This hybrid model achieves a superior prediction accuracy of 99.87%, significantly outperforming traditional methods like KNN and Decision Trees. The extension showcases the power of deep learning in advancing drug safety evaluation.

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Adverse Drug Reactions, Drug-Drug Interactions, Graph Neural Networks, Convolutional Neural Networks, Self-Supervised Learning, SMILES Representation, Deep Learning, Side Effect Prediction, Drug Safety, TF-IDF Vectorization.

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Objective: The present research work deals with the development and evaluation of herbal anti-acne gel containing the fruit extract of Cinnamomum tamala & Curcuma longa. Conclusion: The idea that natural medicines are safer and have fewer side effects than synthetic ones makes them more acceptable. The demand for herbal formulations is rising on the global market. Establishing the herbal anti-acne gel with C. tamala & C. longa extract is a trending approach. However further in vivo studies are highly recommended to prove the efficacy of our developed anti-acne formulation.

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Cinnamomum tamala, Curcuma longa, Propionibacterium acne, MIC, Carbopol 980, in vitro antimicrobial activity.

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Creative Commons Attribution 4.0 and The Open Definition

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The pedal powered compost shredder is a sustainable and cost-effective machine designed for organic waste management, especially in rural and urban garden environments. It operates without the need for electricity or fuel, relying on human power to shred biodegradable waste into smaller pieces to accelerate composting. This paper explores the design, applications, advantages, and limitations of this eco-friendly machine while highlighting its relevance in promoting green technology and reducing carbon emissions.

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Sustainability, Composting, Waste Management, Pedal Power, Shredder,Eco-friendly Technology, Ergonomics, Organic Waste,

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Creative Commons Attribution 4.0 and The Open Definition