Terna Engineering College

This study investigates the flow physics of tomato puree through a sudden expansion pipe joint in a processing plant. Tomato is a widely cultivated vegetable crop. The food processing industry uses tomato juice and tomato paste to produce finished food products such as sauce, ketchup, and pulp. In terms of fluid flow physics, tomato puree exhibits non-Newtonian, shear-thinning behavior. Viscosity of tomato puree is modeled using non-Newtonian power-law model available in ANSYS FLUENT Computational Fluid Dynamics (CFD) modeling software. An important engineering result for this type of problem is major loss or friction power loss. Results are presented for friction power loss inside sudden expansion pipe flow geometrical configuration for a range of Reynolds numbers using the steady-state analysis. Zones of possible tomato puree mass accumulation are identified. The larger the accumulation zone, the worse is the engineering design. The rheological response of tomato puree following an accidental shutdown of the pumping mechanism is investigated through transient CFD simulations. Based on the findings of the analysis, an alternate geometrical configuration that may potentially avoid puree mass accumulation and reduce energy loss is suggested. The effects of key input parameters, including Reynolds number and rheological parameters governing viscosity, on the friction factor are quantified through empirical correlations, constituting a valuable outcome of the analysis.

This study presents a retrieval augmented generation (RAG) based system designed to provide farmers with expert agricultural advisory services. The framework delivers context aware guidance on critical practices such as crop cultivation, pest and disease management, fertilizer application, and other agronomic practices, and compares the performance of four large language models (LLMs) in generating these recommendations. The system processes package of practices (PoP) documents for five major crops maize, ragi, sweet potato, cotton, and groundnut through semantic chunking and embedding using Amazon Titan via BedrockEmbeddings. Vector representations are indexed in ChromaDB to enable efficient similarity search for query-relevant content retrieval. Upon receiving user queries, the system retrieves the most semantically similar document chunks and incorporates them into structured prompts. Four LLMs such as Llama3.1, Mistral, Phi3, and Qwen2.5 were evaluated for their effectiveness in generating accurate agricultural recommendations. Performance was evaluated across multiple dimensions. Relevance and retrieval were assessed using precision@K, recall@K, mean reciprocal rank (MRR), and normalized discounted cumulative gain (NDCG). Lexical overlap was measured with the bilingual evaluation understudy (BLEU) and recall-oriented understudy for gisting evaluation (ROUGE-1, ROUGE-2, ROUGE-L) metrics. Semantic quality was analyzed using Bidirectional Encoder Representations from transformers score (BERTScore) precision, recall, F1, semantic similarity and faithfulness to capture contextual alignment between generated and reference responses. Source attribution was assessed through the attribution score, while efficiency was measured using retrieval time, generation time, and total time. Overall, mistral and Qwen2.5 achieved the highest performance, demonstrating superior relevance, semantic quality, and efficiency. This evaluation highlights which LLMs perform best for the agricultural domain and illustrates the potential of knowledge-grounded AI systems to democratize agricultural expertise, particularly in regions with limited access to traditional advisory services.

This paper addresses a decentralized platform designed to revolutionize open-source contributions by leveraging ERC20 tokens, Decentralized Finance (DeFi), and a Proof-of-Stake (PoS) mechanism for pull request management. The platform allows contributors to stake tokens on issues, with users setting prizes for successful resolutions. The PoS mechanism prioritizes pull requests based on stake amounts, ensuring critical contributions receive the necessary focus. GitStake integrates DeFi to create a transparent, trustless incentivization system, promoting a fair and efficient reward distribution model for open-source development. Additionally, GitStake employs Artificial Intelligence (AI) based filtering to identify and prioritize optimal contributions while avoiding redundancy by detecting and filtering out duplicate issues. It also features an initial token distribution model, where contributors receive free tokens initially (airdrop), followed by an Intitial Coin Offering (ICO) system for user engagement. This approach fosters global, decentralized collaboration while ensuring the sustainability and growth of open-source projects.

The advancement of research is highly dependent on the secure and transparent sharing of data among researchers. Traditional systems often face challenges such as improper checks, inefficiencies in collaboration, and lack of trust. This paper proposes a decentralized platform that uses blockchain technology and artificial intelligence to address these issues. Using ERC20 tokens to publish and access research data, the platform ensures that contributions are traceable and incentivized. AI-generated summaries provide a glimpse into each research publication, allowing users to request full access by paying a fee to the data owner. The system uses smart contracts to facilitate secure transactions and manage access, while a star-based reputation system fosters trust among users. Researchers can also stake and collaborate and contribute to existing data, with all interactions recorded on the blockchain. This platform offers a secure, transparent, and efficient solution for research collaboration, ensuring data integrity and accelerating scientific advancements.

In chemical industries, corrosion poses significant risks to the assets, people, environment, and safety. The implemented robust RPMS framework detects pipeline leakage and corrosion on a real-time basis, thus enhancing industrial safety. The system proposes a proactive detection method that works on chemical industries’ historical and real-time datasets through various sensors like temperature, flow, total dissolved solid, etc., to analyze the immediate leakage point in the pipeline system. A secured environment using blockchain stores the data, and changes are notified to the users with the help of smart contracts. Data captured through sensors is used in blockchain for further analysis and detection of leakage and corrosion. The ML SVM algorithm is used to detect the life and stage of the corrosion-causing factor and leakage point helping user to solve these issues fast and efficiently. This paper provides realistic RPMS testing and validation in the chemical industry that has demonstrated acceptable performance.