Authors: Dr. Himanshu Sharma, Dr. Ankur Goel, Dr. Sapna Deshwal

Abstract: Present-day tourism research has increasingly focused on food tourism. Food is always together with human’s life and will always have a hope to grow in the tourism industry. Food experiences both inside and outside the country is always a part of food tourism. Varanasi is expanding in all directions, and as a result, its tourism industry is expanding as well. This will open up a lot of new opportunities for the locals of this tourist destination to improve their food experiences and share them with others. The fundamental ideas surrounding food tourism are identified as a major research concern in this paper, which focuses on tourism research. In addition, this study reveals that employment generation and the development of local culture are directly linked to the expansion of local food tourism. The researcher also finds food tourism research from a cultural perspective. Most people agree that eating local food is an important part of what tourists do. Food that is both original and one-of-a-kind to the area can be important as a tourist attraction in and of itself as well as in shaping a destination's image. Experiences with local food have the potential to significantly support agricultural diversification, maintain regional recognition, and contribute to sustainable development.

Published by: vikaspatanker

Authors: Adefila Adewale James, Onosemuode Christopher

Abstract: Effective environmental regulation is critical for minimizing the air quality impacts of energy infrastructure, particularly in regions with dense industrial activity. In Nigeria’s Niger Delta, gas-fired power plants form the backbone of electricity generation, yet concerns persist regarding their environmental compliance and regulatory oversight. This study evaluates the effectiveness of air quality regulation and environmental compliance mechanisms governing gas-fired power plants in selected Niger Delta states. Using a mixed-methods approach, the study integrates ambient air quality measurements, regulatory document review, institutional analysis, and stakeholder interviews to assess compliance with national air quality standards and the enforcement capacity of regulatory agencies. Findings reveal persistent exceedances of regulatory limits for particulate matter and nitrogen dioxide in host communities, alongside systemic gaps in monitoring, enforcement, and inter-agency coordination. While regulatory frameworks exist on paper, weak implementation, limited technical capacity, and poor data transparency undermine their effectiveness. The study provides policy-relevant insights and proposes actionable reforms to strengthen air quality governance and protect public health in Nigeria’s energy-producing regions.

DOI: http://doi.org/10.5281/zenodo.18139392

Published by: vikaspatanker

Authors: K. Nagarathna, Darshan C D, Chetan Shanawad, Channu Anand Honnammanavar, Vijay Musaguri

Abstract: This project presents an IoT-based system for real-time vehicle tracking, fuel monitoring, and theft detection. The system integrates an ESP32 microcontroller, GPSmodule, GSMmodule, andfuel -levelsensorstomonitor vehicle conditions and transmit data to the cloud. A comprehensive alert mechanism notifies the user during unauthorizedvehiclemovement, fueltheft, orcaptampering. The system is designed to be cost-effective, accurate, and reliable, making it suitable for fleet management and personal vehicle security.

DOI: http://doi.org/10.5281/zenodo.18137733

Published by: vikaspatanker

Authors: Ashok Kumar Kanagala

Abstract: The proliferation of AI-native architectures has introduced autonomous, model-driven systems with unprecedented capabilities and complex security challenges. These systems, often deployed across multi-agent pipelines and edge environments, expand the attack surface and exhibit dynamic, unpredictable behaviors that traditional security frameworks fail to address. Despite emerging research on AI robustness and alignment, comprehensive strategies for proactively securing agentic AI remain underdeveloped. This paper investigates the operationalization of Zero Trust principles in AI-native architectures, aiming to provide a forward-looking framework for resilient and accountable systems. The proposed approach integrates continuous model verification, alignment assurance with transparency tooling, lifecycle-integrated security validation, and autonomous red-teaming to proactively identify and mitigate vulnerabilities. Key findings indicate that embedding self-assessing mechanisms, standardizing behavioral benchmarks, and applying cross-layer defenses significantly enhance system resilience and reduce dependency on reactive interventions. This research contributes a structured methodology for securing autonomous AI, advancing both practical and theoretical understanding of AI-native security in complex, adaptive environments.

DOI: http://doi.org/10.5281/zenodo.18137101

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Authors: Mrs.S. Dhivya,, Kanika N, Kavipriya A, Madhumitha S, Preethi S

Abstract: This This report presents the project titled “Donar Hub,” a web-based platform developed to connect individuals in need with donors and volunteers through a structured and accessible system. The platform enables the sharing of essential resources such as food, clothes, books, and medical assistance. Users can create Request Help and Offer Help posts, which are categorized and searchable to ensure efficient matching of needs and available support. Donar Hub aims to reduce resource wastage while promoting social responsibility and community collaboration. To ensure authenticity and prevent misuse, the system incorporates Aadhaar-based identity verification for users. Requests related to medical assistance require verification of medical reports or hospital-issued documents before approval. This validation mechanism enhances trust, transparency, and security within the platform. The application is designed with intuitive forms and a user-friendly interface, making it accessible to users with varying levels of digital literacy.

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Authors: Shaun Paul Moses, Vignesh. S

Abstract: Another emerging danger in the world of cybersecurity is the term steganography, which means concealing secret data in digital form, because concealed messages can be easily transferred to a different information exchange format. Other modalities such as audio steganography possess unique features that make it difficult to detect such signals, such as the temporal-frequency properties and audio signals are high dimensional. This project offers a DLDA, Deep Learning Based Detection System Stegware in Audio Files, that will inform whether the audio sample is a real cover or it is a stegware, i.e. it has embedded data in it. The system employs improved methods of feature extraction like Spectrogram Analysis and Mel-Frequency Cepstral Coefficients (MFCCs) to identify requisite frequency, amplitude and temporal indications to identify stegmodifications. The CNNs and LSTMs process subsequently learn a discriminative feature (CNNs) and temporal patterns (LSTM) that occurs between normal and manipulated audio. Training and testing are done using a dataset of clean audiofiles and audiofiles with various modifications done using steganography. The performance is measured by the accuracy, precision, recall and F1-score and the system has been found to be very reliable with accuracy of 97.8 and very few false detections. In the experimental results, it is seen that the model works fairly well when noise and compression is introduced, indicating its strength in the real world. Overall, the framework that is created due to the research effectively applies deep learning to offer a scalable, automated and accurate method of audio steganalysis, which is an outstanding achievement that can provide cybersecurity, digital forensics and secure communications as the number of illegal data transmission via audio channels decreases.

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Authors: K. Nagarathna, Mallikarjun Aralimard

Abstract: This paper presents the design and implementation of a simple Convolutional Neural Network (CNN) using Tensor Flow for binary image classification. The proposed model classifies 5×5 pixel images into two categories: images containing the pattern of an 'X' and images that do not. The study demonstrates dataset generation, model architecture, training, and evaluation, highlighting the effectiveness of CNNs for pattern recognition tasks.

DOI: http://doi.org/10.5281/zenodo.18129745

Published by: vikaspatanker

Authors: Dr. Osman Zenk

Abstract: As is known, memcapacitors, which are memory elements, are electrical circuit elements ex-pected to revolutionize various research and many engineering fields thanks to their variable capacitance and non-volatile memory effects. Although there is not yet enough scientific research on memcapacitors in power electronics systems, they have serious scientific discovery potential, especially in terms of reducing output voltage ripple, increasing voltage stability, and improving energy efficiency. In this study, theoretical and simulation results performed in the Matlab/Simulink environment are presented, showing that adding a memcapacitor to the output of a commonly used buck-boost converter, a type of DC-DC converter, significantly reduces the output voltage ripple. In the study, a memcapacitor emulator that can be implemented using commercial components is first proposed and validated. Then, this emulator was used to exam-ine the steady-state output voltage ripple and transient response of the buck-boost converter. The results show that the use of a load-controlled memcapacitor can reduce the output voltage ripple by up to 96%.

DOI: https://doi.org/10.5281/zenodo.18127037

Published by: vikaspatanker

Authors: Maram Mounika, Rakshitha L, Ramya R, Ramya Shree D, Dr Manasa P

Abstract: Inertial Navigation Systems (INS) are mission- critical subsystems in naval platforms, providing continuous nav- igation information independent of Global Navigation Satellite Systems (GNSS). In contested or GNSS-denied environments, the reliability of INS data directly impacts navigation accuracy, weapon control, and overall mission effectiveness. This paper presents the design and implementation of a real-time INS monitoring platform developed for Indian Naval shipboard appli- cations. The proposed system acquires high-speed navigation data from dual Ring Laser Gyro (RLG) based INS units operating at 10 Hz and 100 Hz through RS-422 interfaces. Using industrial- grade USB-to-serial hardware and a LabVIEW-based software framework, the system performs real-time data acquisition, frame validation, parameter extraction, visualization, logging, and replay. Experimental results demonstrate reliable, lossless data capture and synchronized monitoring of heading, roll, and pitch from forward and aft INS units, validating the effectiveness of the platform for onboard monitoring, testing, and post-mission analysis.

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Authors: Girish Kishor Ingavale

Abstract: The exponential growth in computational power and industrial processes has led to an increased demand for efficient cooling solutions in data centers. Traditional air-cooling systems are becoming inadequate due to their limitations in managing high thermal loads and their high energy consumption. In response to these challenges, Direct-to-Chip Liquid Cooling Systems (D2C LCS) have emerged as a promising alternative for thermal management in high-density computing environments. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) technologies to optimize the performance of D2C LCS in rack-mounted data center setups. The primary objective of this research is to develop and implement AI-driven models that can predict temperature and fluid flow within D2C LCS, thereby enabling the optimization of cooling strategies. By leveraging advanced algorithms such as Linear Regression and Support Vector Machine, the study aims to enhance thermal efficiency and reduce the energy consumption of data centers. Experimental data was collected from a simulated data center environment equipped with D2C LCS. The data was used to train and validate ML models, ensuring their accuracy and reliability in real-world applications. The results demonstrate that AI-optimized cooling strategies can achieve a 15% reduction in temperature and a 20% decrease in energy consumption compared to traditional air-cooling systems. The findings of this study highlight the significant benefits of integrating AI and ML technologies with D2C LCS for thermal management in data centers. The predictive models and optimized cooling strategies presented herein provide a robust framework for improving the efficiency and sustainability of data center operations. Future research directions include the development of more advanced AI models and the implementation of real-time monitoring systems to further enhance the performance of D2C LCS.

DOI: http://doi.org/10.5281/zenodo.18125704

Published by: vikaspatanker