Authors: N. Gokul Krishnan, M. Gokulnath, S.Manoj, Mrs.P.G.Gayathri

Abstract: In modern manufacturing, moving from an Engineering Bill of Materials (eBOM) to a Manufacturing Bill of Materials (mBOM) is still a manual, slow, and error-prone task. This problem often results in data inconsistencies, production delays, and higher manufacturing costs. To address these issues, we propose an AI-powered BOM Converter that automatically converts eBOM into improved mBOM for production workflows. The system uses a mix of machine learning and rule-based logic to examine eBOM structures, identify component connections, and produce an accurate mBOM, complete with manufacturing details like process steps, work centers, tooling, and procurement information. It integrates with existing ERP/PLM systems to ensure smooth data exchange and real-time updates with production planning. By automating the conversion from eBOM to mBOM, this system reduces manual labor, improves data consistency, cuts conversion time, and lowers operational costs. This intelligent converter seeks to transform the digital manufacturing workflow, allowing for quicker product launches and better overall production efficiency.

DOI: https://doi.org/10.5281/zenodo.18831453

 

Published by: vikaspatanker

Authors: Punit Chauhan, Aakash Chouhan, Sunny Maurya, Siddhesh Mundhe, Prof. Shilpa Doke

Abstract: In today's highly competitive job market, candidates often struggle to optimize their resumes for Applicant Tracking Systems (ATS) and lack access to realistic interview preparation environments. This paper presents ResuMentor, a full-stack, AI-driven web platform designed to bridge this gap by providing intelligent resume analysis and real-time mock interview simulation. The system accepts user-uploaded resumes in PDF or DOCX format alongside a specified job role or description, and leverages the OpenAI GPT-4o API via Spring AI to generate ATS compatibility scores, keyword gap analysis, and actionable improvement suggestions tailored to the target job profile. For interview preparation, ResuMentor deploys an AI voice agent that conducts a structured, 30-minute mock interview session, dynamically generating questions ranging from beginner to advanced level based on the parsed resume content. The platform employs the Web Speech API for real-time speech-to-text transcription, providing a live transcript visible to the user during the session. Post-session, a detailed feedback report evaluates the clarity, conciseness, and relevance of the candidate's responses with specific examples drawn from the transcript. The backend is developed using Java Spring Boot 3.3 with Spring Security and OAuth2 for Google-authenticated login, MySQL as the relational database, and Apache Tika for resume parsing. The frontend is built with plain HTML, CSS, and JavaScript, featuring a responsive dark/light theme toggle. A personalized dashboard tracks historical ATS scores and interview performance trends using Chart.js visualizations, enabling users to monitor their growth over time. ResuMentor demonstrates that integrating large language models into career development tools can significantly improve candidate preparedness and resume quality.

 

 

Published by: vikaspatanker

Authors: Victor Petrov, Kenji Nakamura, Thomas Bauer, Elena Garcia, Ananya Kulkarni

Abstract: Healthcare systems operate in highly dynamic environments where patient demand, workforce availability, and clinical resource utilization fluctuate continuously, creating significant challenges for effective talent coordination and resource planning. Traditional workforce management approaches within enterprise resource planning (ERP) systems often rely on historical reporting and static scheduling mechanisms that struggle to respond to real-time operational changes. The growing adoption of Internet of Things (IoT)–enabled medical devices and hospital telemetry infrastructure has created opportunities to capture continuous streams of operational data across healthcare environments. This study proposes a real-time healthcare talent orchestration framework that integrates IoT-driven telemetry, scalable big data pipelines, and artificial intelligence–based forecasting models within enterprise ERP architectures. Telemetry data generated from clinical monitoring systems, hospital infrastructure sensors, and workforce management platforms are processed through distributed big data pipelines capable of handling high-velocity operational information. Machine learning algorithms analyze these data streams to forecast patient inflow, anticipate staffing requirements, and identify potential operational bottlenecks before they impact service delivery. By embedding predictive insights directly into ERP-driven workforce coordination systems, healthcare organizations can dynamically adjust staffing allocations, optimize resource utilization, and support proactive decision-making. The proposed approach demonstrates how combining IoT telemetry, big data engineering, and AI-based forecasting can significantly improve workforce agility, operational efficiency, and service continuity in modern healthcare environments.

DOI: https://doi.org/10.5281/zenodo.18813979

 

Published by: vikaspatanker

Authors: S.Sathiyavathi, B.Keerthika, T.Saranya, V.Pavithra Vedhavalli

Abstract: Maize (Zea mays L.) is one of the most important cereal crops cultivated worldwide for food, feed, and industrial purposes. However, its production is significantly affected by various insect pests at different growth stages. Major pests of maize include the Fall armyworm, Corn earworm, corn leafhopper, stem borers such as leafhoppers. These pests cause damage by feeding on leaves, stems, tassels, and ears, leading to reduced yield and poor grain quality.

DOI: https://doi.org/10.5281/zenodo.18813089

 

Published by: vikaspatanker

Authors: Sanchali Sarkar, Dr. Anand Sur

Abstract: Fishes are often introduced as archetypal water breathers, yet a substantial fraction of extant species routinely combine branchial gas exchange with air breathing through lungs, modified swim bladders, buccopharyngeal surfaces, gut epithelia, or accessory organs. This dual (bimodal) respiratory strategy is widely recognized, but its functional significance is still unevenly explained across evolutionary history, comparative physiology, and ecology. A key gap in the literature is the tendency to treat air breathing as a simple hypoxia response rather than as a multi-trait adaptive complex that restructures ventilatory control, cardiovascular function, ionoregulation, and life history. This review synthesizes evidence on why dual respiration evolved repeatedly, how it operates mechanistically, and what tradeoffs it imposes. Using a comparative, concept-driven review framework, I integrate studies on (i) selective pressures that favor aerial supplementation (environmental hypoxia, hypercapnia, temperature, drought, and episodic habitat instability), (ii) physiological partitioning of oxygen uptake and carbon dioxide excretion between gills and air breathing organs, (iii) cardiovascular and blood oxygen transport adjustments that enable effective bimodal exchange, and (iv) ecological consequences including niche expansion, resilience to climate-driven deoxygenation, and evolutionary stepping stones toward amphibious lifestyles. The central argument is that dual respiration is best understood as an evolutionary solution to variable oxygen landscapes that is maintained by conditional benefits and constrained by costs such as surface predation risk, energetic demands of ventilation, and tensions between gill reduction and ionoregulatory capacity. The review proposes a conceptual model linking environment, organ design, control systems, and performance outcomes, and identifies priorities for future research in a rapidly warming and deoxygenating world.

DOI: https://doi.org/10.5281/zenodo.18812132

 

Published by: vikaspatanker

Authors: Matthew Aaron Salvador, Deejay Mark Cardinas, Utada Aaliyah Maclay, Jemaica Bayale, Engr. Carlo G. Quitos

Abstract: The study focused on the design and development of Oil Sentry, an environmentally friendly, fully automated device that uses Arduino Uno technology to improve oil spill detection and collection. It was intended as a preventive measure against the harmful effects of oil spills on water- based ecosystems, specifically the coastal areas of the Davao Gulf. Oil Sentry integrates a motorized suction system for oil removal, an infrared sensor for oil detection, and a GPS-based navigation system for surface movement. An obstacle detection mechanism was also incorporated to ensure safe and efficient operation during cleanup. The device underwent laboratory testing to evaluate suction capacity, performance efficiency, detection accuracy, and navigation stability. Results showed that Oil Sentry accurately detected and collected oil while maintaining stable movement on the water surface. The system demonstrated effective navigation with minimal water disturbance, highlighting the potential of robotic solutions for marine environmental sustainability applications.

DOI: https://doi.org/10.5281/zenodo.18811894

Published by: vikaspatanker

Prof. Elif Altürk
Affiliation	Scenior Scientist TÜBİTAK MAM – The Scientific and Technological Research Council of Turkey, Kocaeli
Email-Id:	elif.alturk@okan.edu.tr
Publication:  High stability of perovskite solar cells under ambient conditions; Emre Aslan, Tülin Ateş Türkmen, Elif Altürk, IET Renewable Power Generation, 2020, 1-5, doi: 10.1049/iet-rpg.2020.0098. Seda Dogan, Nesrin Töre, Adem Karsli, Parlak,E.A., Figen Türksoy, and Serap Günes, Photophysical and Photovoltaic Characterization of Flourene-AnthraceneBenzothiadiazole Based Donor–Acceptor Type Copolymers for Bulk Heterojunction Polymer Solar Cells, J. Nanoelectron. Optoelectron. 14, 8–18 (2019). Canımkurbey, B., Unay H., Çakırlar Ç., Büyükköse S., Çırpan A, Berber S. and Parlak E.A, ‘’Medium band gap polymer based solution-processed high-κ composite gate dielectrics for ambipolar OFET’’, Journal of Physics D:Applied Physics, 51(12) (2018) 125104. Kavak P, Parlak E.A., Investigation of Indoor Stability Testing of Polymer Solar Cell,International journal of polymer science, 7268197, (2016) Parlak E.A, S.O. Sarioglan, Investigation of the effect of diiodooctane on the morphology and performance of PTB7/PC71BM solar cells, Physica Status solidi (c), 1-5, (2015).

Published by: vikaspatanker

Authors: Narinder Sharma

Abstract: Mathematics education frequently confronts significant cognitive, emotional, and motivational barriers that impede student learning. Recent research suggests that mindfulness practice—the cultivation of intentional, nonjudgmental awareness of present-moment experience—can enhance learners’ academic engagement and performance. This article explores the theoretical foundations and empirical evidence connecting mindfulness to mathematics learning, examining how mindfulness influences attention, anxiety regulation, metacognition, motivation, and classroom climate. Drawing from cognitive science, educational psychology, and pedagogical practice, the article outlines practical strategies for integrating mindfulness into mathematics instruction, highlights measurable benefits, and discusses future research directions. The findings suggest that mindfulness may contribute to more resilient, reflective, and motivated mathematical learners.

 

 

Published by: vikaspatanker

Authors: Mrs. Thorat J.B, Saste Ghanshyam, Atharv Saste, Varun Salunkhe, Dnyaneshwar Shinde

Abstract: The current digital health landscape is saturated with generic fitness applications that fail to address the unique physiological and lifestyle constraints of individual users. This project, "FitAI Professional," addresses this challenge by developing a full-stack web application that leverages Generative AI for hyper-personalized coaching. Built using a React (Vite + TypeScript) frontend and a Node.js (Express) backend, the system integrates the Google Gemini model to act as an intelligent, context-aware planning engine. Unlike traditional rule-based systems, FitAI Professional interprets complex user profiles—including biometrics, equipment availability, and injuries—to generate structured, scientifically sound regimens. Key innovations include strict JSON schema enforcement for AI outputs and multimodal food analysis for seamless nutrition logging.

 

 

Published by: vikaspatanker

Authors: Melody Krystel Limboy, Jesson H. Cinto

Abstract: This study utilized cotton textile waste strips and broken glass as partial replacement for fine aggregates and coarse aggregates for development of eco-concretes. Through mixing, casting, and curing, three mixtures of eco-concretes and commercially produced concretes were produced. Areal density, bulk density, density, and compressive strength were tested for the three mixtures and the control group. Based on the findings, all mixtures showed successful results in compressive strength test with Mixture 3 achieving the highest average compressive strength (2.1 MPa). Among the mixtures, F- test of Independent Means showed no significant difference in the mean compressive strength among concrete mixes containing varying proportions of cotton waste textile strips and broken glass, and commercially produced concrete. This implied that the concrete with cotton textile waste strips and broken bottles can be a good substitute for commercial concrete and can also enhance more strength to the concrete made. To help improve compressive strength, use different drying days of curing days for concrete production

DOI: https://doi.org/10.5281/zenodo.18801235

 

Published by: vikaspatanker