Daily Archives: March 2, 2026

Authors: Thenmozhi P, Abarna M, Mahalakshmi D, Malini S

Abstract: Hybrid and nearshore outsourcing paradigms are becoming more popular in order to strike a balance between cost-effectiveness availability of talent and flexibility in operations nevertheless the problem of time- zone lack can affect geographically distributed teams and some of the issues include an uneven distribution of workload and infrequent monitoring of performance on the team the traditional project management tools use a static method of coordination and are not smart in terms of decision making in this project a smart platform to manage nearshore and hybrid outsourcing teams with an agentic ai based multi-agent architecture is introduced the platform automatically breaks down project goals into tasks and allocates them based on the knowledge availability time-zone coverage and historical outcomes specialized ai agents are involved in the organization of tasks time management forecasting performance and assessing risks the system developed based on an event-driven architecture with real time synchronization and continuous learning provides better accuracy in task allocation early risk identification and productivity in a distributed outsourcing setting.

 

 

Published by: vikaspatanker

Authors: Abhinav Pandey, CO. Dr. Preeti Devi

Abstract: This research report provides an exhaustive analysis ofi the intersection between digital governance and financial transparency within the municipal administrations ofi the BRICS nations—Brazil, Russia, India, China, and South Afirica. Utilizing a robust comparative firamework, the study evaluates how digital platforms, legal mandates, and institutional capacity influence the disclosure ofi fiscal infiormation to the public. The findings demonstrate a complex landscape: while national-level digital maturity is high across the bloc (evidenced by Group A and B rankings in the World Bank’s GovTech Maturity Index 2025), the actual translation into municipal transparency is hindered by over-centralization in Brazil, restricted access in Russia, localized “refiorm islands” in India, selective disclosure in China, and severe capacity constraints in South Afirica. Through an examination ofi the Open Budget Survey 2023 data, the report identifies that while transparency has increased globally by 24% since 2008, significant gaps remain in public participation and legislative oversight. Recommendations fiocus on decentralizing digital implementation, institutionalizing public engagement modules, and bridging the skill gap at the local level to ensure that technological advancements yield tangible improvements in fiscal accountability.

DOI: https://doi.org/10.5281/zenodo.18835347

Published by: vikaspatanker

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