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

Authors: Ashish Siddharth Kokare, Ayushraj Ashok Ankush, Aadarsh Rajesh Kamble, Punit Krishna Fasage

Abstract: Women Visitors arrive unannounced. Sometimes welcome, sometimes not. We built a doormat that watches. Raspberry Pi 4B+ waits quietly. Pressure sensor under mat feels weight, sends signal, system wakes. Telegram fires to owner: "Someone at door." Servo rotates camera, captures face, transmits image. Remote eyes where none existed. No subscription, no cloud lock-in, no monthly fees. Telegram free, Pi owned, code open. Cost under ₹5,000, works on any door, alerts any phone. Continuous monitoring eliminated—mat sleeps, Pi dozes, power sips. Only presence triggers, only relevance notifies. Smart because selective, not because complex.

DOI: https://doi.org/10.5281/zenodo.18786973

Published by: vikaspatanker

Authors: Dr. Suman Dhawan

Abstract: Faculty development programs (FDPs) have emerged as a strategic intervention in the scenario of higher learning in India. In fact, FDPs and their nexus with career advancement would be less meaningful in the absence of their continued relevance to the academic requirements of the faculty. This study is aimed at re-examining faculty development programs in the framework of their ‘relevance’ to the challenges in the arena of higher learning. In the research, the authors carried out an in-depth analysis of the alignment of FDPs with the needs of the faculty in the domain of teaching, research, the use of technologies, professional growth, and the need for national development. For the paper, the authors used empirical data obtained from Orientation and Refresher programmes conducted by the Academic Staff Colleges in Delhi. In the concluding part of the paper, the authors propose suggestions that can improve the relevance of FDPs in the changing scenario of higher education in the country.

 

 

Published by: vikaspatanker

Authors: G. Lavanya, B. Balaki, Dr. Bhuvana. R

Abstract: Skin diseases are among the most common health problems worldwide, affecting individuals regardless of age, gender, or geographical location. Early detection and appropriate treatment are essential to prevent complications and psychological distress. However, limited access to dermatologists, especially in rural and underserved areas, delays timely diagnosis. This paper presents SkinGuard AI, a deep learning-based dermatology assistant that utilizes a Convolutional Neural Network (CNN) for image-based skin disease classification. The system enables users to upload images of affected skin areas through a web interface, where the images are preprocessed and analyzed using a trained CNN model. The system predicts the disease category along with a confidence score and provides personalized treatment recommendations. Additionally, it integrates an intelligent chatbot for interactive assistance and an automated email notification module to send diagnostic reports to registered guardians. The proposed solution enhances accessibility, reduces dependency on immediate hospital visits, and provides cost-effective preliminary dermatology support. Experimental results demonstrate high classification accuracy and reliable real-time performance.

DOI:

Published by: vikaspatanker

Authors: Dave A. Camuta, Bryan B. Ortouste, Jen Rose A. Limentang, Krisha Nicole G. Bacolod, Jesson H. Cinto

Abstract: This study focused on developing a biodegradable food wrapper CalaSSAG, made from bio-based materials such as calamansi peel powder, starch, sodium alginate, and glycerol. The research aimed on making an alternative to plastic packaging that causes pollution. Calamansi is responsible for antimicrobial and antifungal properties. An experimental design was used to test how durable and thick the CalaSSAG bioplastic wrapper was, using three different concentrations of calamansi peel powder, 50%, 75%, and 100%. Testing its thickness and tensile strength. The results showed that the 100% calamansi mixture made the wrapper (1.26 mm), while the 50% mixture had the highest tensile strength (0.165 MPa). However, the control group was stronger than the alternative experimental group in terms of tensile strength with a tensile strength of 3.33 MPa. Statistical analysis using ANOVA confirmed that there is a significant difference in mean tensive strength among CalaSSAG bioplastic wrapper containing varying proportions of calamansi powder and commercially produced plastic wrapper. CalaSSAG was not as strong as the traditional control group, it showed to be flexible, biodegradable, and made from natural materials. It offers a safer, eco-friendly choice for food packaging. Future research is advised to test the antifungal properties of calamansi in the wrapper and its real-time application. CalaSSAG shows potential in reducing plastic pollution and waste.

DOI: https://doi.org/10.5281/zenodo.18781357

 

Published by: vikaspatanker

Authors: Divya Ravindra kshirsagar, Dr. Ansari U.S

Abstract: Eanhquakes pose a significant threat to the safety and stability of built structures. making seismic performance evaluation an essential aspect of modern structural engineering. Steel buildings, known for their high ductility, strength-to-weight ratio, and superior energy dissipation capacity, have emerged as a preferred solution in seismic regions. This study investigates the seismic behavior of a multi-storey steel building using advanced analytical tools and codal provisions, with the objective of understanding its dynamic response under earthquake loading. A derailed 3D structural model was developed using ETABS/STAAD Pro, and seismic forces were evaluated in accordance with IS 1893:2016, while member design considerations followed IS 800:2007. Response Spectrum Analysis (RSA) and Time—History Analysis (THA) were performed to assess critical parameters such as base shear, storey drift, lateral displacement, and structural stability. The results highlight the significance of structural configuration, bracing system selection, and stiffness distribution in determining overall seismic performance. Observed trends indicate that proper detailing and optimized member design substantially improve ductility and reduce seismic demand. The study provides valuable insights into the dynamic characteristics of steel buildings and offers recommendations for enhancing seismic resilience through performance-based design. The findings contribute to safer, more efficient, and code-compliant steel construction in earthquake-prone areas.

 

 

Published by: vikaspatanker

Authors: Nagender Yamsani

Abstract: This study examines the growing need to embed intelligence directly into master data platforms to improve regulatory compliance, operational transparency, and data quality monitoring in complex enterprise environments. Organizations increasingly struggle to maintain consistent governance and audit readiness as data volumes expand across distributed systems, creating a research problem centered on how artificial intelligence and structured evidence mapping can enhance visibility and decision support. The purpose of this research is to develop and evaluate a framework that integrates AI enabled dashboards with master data governance workflows to support continuous compliance and quality assurance. A mixed methodological approach was applied, combining evidence mapping of prior governance and analytics models with architectural synthesis and scenario based evaluation in enterprise data management contexts. The findings demonstrate that intelligent dashboards, when supported by automated metadata analysis, anomaly detection, and lineage driven monitoring, significantly improve detection of data quality risks, accelerate audit preparation, and enhance stewardship effectiveness. The study introduces an architectural model that aligns monitoring, policy enforcement, and predictive analytics within a unified governance layer. These contributions extend existing research in data governance and enterprise analytics by providing a practical and scalable design framework. The results highlight the strategic importance of integrating intelligence into master data platforms, offering guidance for industry practitioners and establishing a foundation for further academic research on autonomous governance and continuous data assurance.

DOI: https://doi.org/10.5281/zenodo.18770933

 

Published by: vikaspatanker

Authors: Tanvi Ramkumar Sunewad, Shahin Tanvir Shaikh, Divya Hemant Dhende, Diksha Datta Jethe

Abstract: Paralysis is a debilitating condition that significantly impairs a patient’s ability to move and communicate, often resulting from neurological disorders, spinal cord injuries, or diseases such as stroke. According to the World Health Organization, stroke and related neurological conditions are among the leading causes of long-term disability worldwide, emphasizing the need for continuous and reliable patient monitoring systems. The Paralysis Patient Health Care Monitoring System is an advanced, technology-driven solution designed to continuously monitor the vital parameters and safety of paralyzed patients in hospitals or home environments. The system integrates biomedical sensors to track essential health indicators such as heart rate, body temperature, blood pressure, oxygen saturation (SpO₂), and movement detection. These parameters are processed through a microcontroller-based unit and transmitted via wireless communication modules to caregivers or medical professionals in real time. The proposed system also incorporates emergency alert mechanisms, enabling patients to communicate distress signals through minimal physical input, such as eye blink detection or slight finger movement sensors. In critical situations, automated notifications are sent to caregivers or healthcare providers to ensure immediate medical intervention. Data collected by the system can be stored in a cloud-based database for continuous analysis, enabling long-term health trend monitoring and improved clinical decision-making. The system enhances patient safety, reduces the burden on caregivers, and supports timely medical response, ultimately improving the quality of life for paralysis patients. In conclusion, the Paralysis Patient Health Care Monitoring System provides an efficient, reliable, and cost-effective approach to continuous health monitoring, promoting better patient outcomes through real-time data tracking, remote accessibility, and intelligent alert mechanisms.

DOI: https://doi.org/10.5281/zenodo.18769831

 

Published by: vikaspatanker