Authors: Ketaki Nanaware, Vedant Dhopate, Atharv Hapse

Abstract: This research paper details the creation of a versatile smart IoT device using the ESP32 microcontroller, MIT App Inventor, and Google Firebase. The device integrates multiple interfaces for real-time data collection, processing, and control, making it ideal for applications in smart homes, industrial automation, and environmental monitoring. The ESP32 handles sensor data (e.g., temperature and humidity) and communicates with Google Firebase for real-time data storage. Users interact through a mobile app developed with MIT App Inventor, which allows monitoring, configuration, and control of connected devices, such as motors. The system also employs MDNS and HTTP servers for efficient local network communication. Key features include dynamic data collection, real-time visualization on the mobile app, and remote device control. The integration with Firebase ensures scalability and secure data handling. This IoT solution demonstrates flexibility and potential for diverse applications, with future enhancements possible through additional sensors and advanced analytics.

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Published by: vikaspatanker

Authors: Khodke Pawan Rajendra, Prof. Dr.U.S.Ansari

Abstract: Offshore steel fixed jacket platforms are essential structural systems that support exploration and production operations in harsh marine environments. These platforms are subjected to complex and fluctuating environmental loads, including wind, waves, currents, and seismic forces, which significantly influence their stability and long-term performance. Bracing systems play a crucial role in enhancing lateral stiffness, improving load distribution, and reducing structural deformations in offshore jackets. This study investigates the structural behaviour of offshore steel fixed jacket platforms equipped with different bracing configurations using SAP 2000. A detailed finite element model was developed, incorporating realistic material properties, geometric parameters, and environmental load combinations based on standard design codes. Static and dynamic analyses were performed to evaluate displacement, base shear, stress distribution, natural frequency, and overall stiffness under combined environmental loading. The comparative assessment revealed notable differences in performance among various bracing layouts, with some configurations demonstrating superior resistance to deformation and improved stability. The study emphasizes the importance of selecting an optimal bracing system to enhance the safety, reliability, and efficiency of jacket platforms. The findings provide valuable insights for design optimization and contribute to the advancement of offshore structural engineering practices.

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Published by: vikaspatanker

Authors: Dr. Priyanka B. Shingade

Abstract: Positivity questions occupy a central place in algebraic combinatory: given a naturally occurring symmetric or quasisymmetric, or polynomial function, when does it expand with nonnegative coefficients in a preferred basis monomial, elementary, Schurz, etc. This survey/research-style paper organizes classical and recent positivity problems, summarizes principal techniques, records key breakthroughs, and lists open conjectures and directions. We emphasize (i) classical positivity phenomena Littlewoods–Richardson, Schurz- and e- positivity, (ii) structural conjectures such as the Stanley–Stem bridge and Macdonald positivity problems and their recent status, (iii) positivity for representation-theoretic multiplicities Kroenke, platysma, and (iv) modern tools that have proved or advanced these questions. We close with a curated bibliography of key references and suggested research directions.

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Published by: vikaspatanker

Authors: Gowri A, Mohamed Sathik Z, Moses Saveriyar A, Sanjay P

Abstract: Construction sites are among the most hazardous work environments due to unsafe practices and the improper use of Personal Protective Equipment (PPE). To address these safety challenges, this project proposes an AI-based Construction Site Safety Violation Detection System that automatically identifies unsafe behaviors and PPE violations in real-time video streams. The system utilizes computer vision and deep learning techniques to detect workers, safety gear such as helmets and vests, and hazardous actions including entry into restricted zones and working at heights without protection. A tracking-by-detection approach is employed to monitor individuals across video frames, while pose estimation and action recognition models analyze human posture and movements to classify unsafe activities. When a safety violation persists beyond a predefined duration, the system generates instant alerts to enable timely intervention. This automated approach enhances workplace safety, reduces human supervision effort, and helps construction organizations proactively prevent accidents and injuries.

DOI: https://doi.org/10.5281/zenodo.18766472

Published by: vikaspatanker

Authors: Dr. Amudha G, Porselvi B, Radha A, Usha Mahi Pon

Abstract: Rising electricity demand and the need for sustainable energy solutions have encouraged exploration of alternative micro-generation technologies. This study investigates the use of piezoelectric powered flooring systems to harvest mechanical energy from human footsteps in high-footfall areas such as railway stations, commercial buildings, and educational institutions in India. By converting mechanical stress into electrical energy, piezoelectric materials offer a method of generating supplementary power without requiring additional land or fuel resources. The proposed work presents the system design framework, working principle, and feasibility of large-scale implementation within public infrastructure. Performance estimations indicate that although piezoelectric flooring cannot replace conventional energy sources, it can contribute to localized power needs and support smart, energy-efficient urban environments. The study highlights the potential of integrating energy harvesting technologies into everyday infrastructure to promote sustainable development.

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Published by: vikaspatanker

Authors: Pranabesh Ghosh, Abhishek Konar, Tahsina Tabia

Abstract: The microbial ecosystem within a wastewater treatment facility serves multiple functions-removal of contaminants, recycling nutrients, and recovering resources. However, conventional process monitoring focuses on bulk physicochemical data, limiting information on the microbial processes driving system function. Recent advancements in multi-omics technologies—metagenomics, metatranscriptomics, metaproteomics, and other omics domains—have significantly advanced the study of the microbial communities in wastewater treatment by allowing researchers to comprehensively describe a microbial community's composition, functional capacity, and metabolic activity. Omics technologies have improved knowledge of the biological processes governing the nitrogen, phosphorus, and carbon cycles in wastewater, and the technologies can identify new contaminants and antibiotic resistance genes. When paired with predictive bioprocess modelling, multi-omic data enhances operational control and system stability and promotes energy-efficient process design. Omics data have revealed opportunities for the modernized wastewater treatment plant (WWTP) to transition to a water resource recovery facility (WRRF), wherein the plant can produce methane, recover nutrients, generate biopolymers, and contribute to the carbon economy. Despite advancements utilizing multi-omics technologies, there remain obstacles. Ongoing and emerging challenges include the high cost of sequencing, data integration difficulties, limited applicability to real-time processes, and a lack of infrastructure in developing regions. Closing the gap on process-scale implementation of multi-omics technologies will require standardized testing, multi-disciplinary collaboration, and the use of artificial intelligence control systems. Multi-omics techniques signify a changing of the guard in precision ecological engineering and the development of sustainable, climate-resilient, and resource-efficient bioprocess systems for wastewater treatment. Sustained technological advancement and integration at the systems level will be essential for the future of energy-positive and carbon-neutral wastewater treatment.

DOI: https://doi.org/10.5281/zenodo.18755909

 

Published by: vikaspatanker

Authors: Dr. Bhanu Prakash S V, Dr. Sowmya R

Abstract: Modern crime scenes increasingly operate as cyber–physical systems in which physical evidence is tightly coupled with digital infrastructure, embedded devices, and networked environments. Conventional crime scene investigation methods are insufficient to address threats arising from remote access, data manipulation, and system-level vulnerabilities. This review presents a step-by-step, technology-driven framework that integrates cyber security engineering principles into crime scene investigation to enhance the integrity, reliability, and admissibility of physical evidence. The proposed approach models the crime scene as a cyber–physical system and applies threat modeling, secure system isolation, forensic data acquisition pipelines, cryptographic integrity verification, and cyber–physical correlation mechanisms. Emphasis is placed on reproducibility, system verification, and automated chain-of-custody controls from an engineering perspective.

DOI: https://doi.org/10.5281/zenodo.18755493

 

Published by: vikaspatanker

Authors: Mrs.T.Dheepa, Pradhakshna S, Vigiyalakshmi Muthu, Srividhya K

Abstract: Timely identification of bone tumors plays a vital role in improving diagnosis accuracy and treatment effectiveness. Although deep learning techniques have achieved remarkable success in medical image analysis, their lack of interpretability often restricts their acceptance in clinical environments. This study proposes an Explainable Artificial Intelligence (XAI)–driven framework for bone tumor detection using deep learning models. A convolutional neural network (CNN) is utilized to automatically extract relevant features from medical images such as X-rays or MRI scans to classify tumors into benign and malignant categories. To address the transparency issue, explainability techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) are incorporated to highlight critical image regions that influence the model’s predictions. These visual explanations assist clinicians in understanding and validating the system’s decisions. The experimental evaluation shows that the proposed model delivers reliable classification performance along with interpretable outputs, thereby supporting clinical decision-making and enhancing trust in AI-assisted diagnostic systems.

DOI: https://doi.org/10.5281/zenodo.18755185

Published by: vikaspatanker

Authors: Mr.C.Radhakrishnan, Suryaprakash S, Thayumanavan A

Abstract: Farmers face the complex challenge of managing multiple, dynamic variables from soil health and weather volatility to pest control and fluctuating market demands all while adhering to strict organic principles that prohibit synthetic chemicals. This project introduces a software platform designed to function as a comprehensive smart assistant specifically for the modern organic farmer. The primary objective is to bridge the gap between complex data and actionable, on-the-ground decisions, empowering farmers to adopt a proactive, data-driven management style. This system is engineered to automatically pull together, or "integrate," multiple crucial data streams. These include detailed soil characteristics from real-time weather conditions and forecasts, and data related to crop health. The platform moves beyond the mere presentation of raw data. It utilizes Artificial Intelligence (AI) to analyze this consolidated information and generate "intelligence"-smart, actionable recommendations. Its intelligence-driven recommendations cover strategic decisions such as what crops to grow (based on soil suitability), in-season farm management (such as organic pest and disease control), and post-harvest strategy (such as identifying the optimal time to sell for maximum profit).The platform is explicitly tailored to the principles of organic farming, ensuring all advice promotes sustainability, enhances soil health naturally, and strictly avoids synthetic inputs. By equipping the modern farmer with accessible technology for data driven decisions, Project aims to significantly improve the efficiency, sustainability, and profitability of organic farming operations.

DOI: https://doi.org/10.5281/zenodo.18755082

Published by: vikaspatanker

Authors: Rakesh Manilal H. Patel, Dr. Anand K. Acharya, Dr. Pankaj Kumar B. Solanki, Dr. Dhinesha Ruwanthi Perera, Dr. Nayanesh A. Gadhavi, Dr. Haresh D. Chaudhari

Abstract: The recent global economic slowdown has led to significant cuts in funding for the humanities and social sciences (HSS). At the same time, students increasingly opt for courses that provide a direct path way to professional careers. Yet, HSS plays a vital role in shaping the economic, social, and cultural well-being of society. Unlike the sciences, whose contributions to industry are more immediately visible, HSS sustains a complex knowledge ecosystem, linking producers and beneficiaries of knowledge in diverse ways. Beyond providing a skilled workforce in business and finance and fostering cultural understanding, HSS is indispensable for (1) Informing public policy and (2) Enhancing social well-being. This role has become even more urgent in the context of rapid advances in information technology (IT). The exponential growth of the Web and mobile communication is reshaping social life across industrialized and developing nations. Emerging technologies such as Google Glass signal a shift toward pervasive and largely invisible augmentation, raising challenges of regulation, monitoring, and ethics. Oxford philosopher Luciano Floridi has described this as a “fourth revolution” in knowledge, where human interaction with reality generates unprecedented ethical concerns. Despite pressures from techno-society and liberal economic values, HSS has advanced a vision of human well-being not reducible to GDP. The contributions of Amartya Sen and Martha Nussbaum on human development highlight broader dimensions of freedom and capability essential for improving the human condition. This paper explores the challenges HSS faces in an IT-driven society and argues that it remains critical for shaping public policy, enhancing social well-being, and guiding the types of technological development that truly advance human flourishing. Key Flow: Crisis & Funding Cuts → Pressure on HSS. HSS responds by providing public policy insights, social well-being frameworks, and critiques of GDP-focused progress. Theoretical foundations from Sen & Nussbaum reinforce human development beyond economics. IT revolution & new technologies raise ethical and regulatory issues. Conclusion: HSS is indispensable for guiding technological and social progress toward human flourishing.

Published by: vikaspatanker