Daily Archives: August 20, 2026

Authors: Manju Rathore

Abstract: Effective waste management practices are critical for mitigating environmental impact and promoting sustainability. This paper explores the role of biodegradable wastes in addressing these challenges and presents comparative data on the reduction of air pollutants associated with various waste materials. The analysis reveals that biodegradable wastes can significantly lower emissions compared to traditional materials. Specifically, glass waste results in 18% to 30% less air pollution, while plastic waste can reduce emissions by up to 66%. In contrast, iron cans contribute to 70% to 86% less air pollution, and aluminum cans are associated with a dramatic 95% reduction in air pollutants. These findings underscore the environmental benefits of transitioning towards more sustainable waste management practices and highlight the potential for biodegradable and recyclable materials to play a crucial role in minimizing air pollution and enhancing ecological sustainability.

DOI: https://doi.org/10.5281/zenodo.22026478

Published by: vikaspatanker

Authors: Ms. Gauri J. Sutar, Associate Professor Dr. Sarita V. Balshetwar, Assistant Professor Mrs. Rajani M. Mandhare

Abstract: This paper presents a low-cost pediatric wearable wristband for continuous fever-risk monitoring using temperature, heart-rate, and motion sensing. The device integrates an ESP32 controller, a DS18B20 temperature sensor, a pulse sensor, an MPU6050 accelerometer, a GSM module, a local buzzer/LED alert, and a Blynk cloud dashboard. Because the wrist skin temperature differs from core body temperature, the temperature channel is calibrated against a clinical digital thermometer and reported as a calibrated body-equivalent temperature. A lightweight, transparent Fever Risk Score (FRS) combines physiological and motion parameters, and a supervised machine-learning classifier (Random Forest) trained on a labeled dataset of 2,400 pediatric monitoring instances classifies the child’s condition into Normal, Warning, and High-Risk states. On a held-out test set the classifier achieved 91.4% accuracy, 94.5% sensitivity, and 98.1% specificity for High-Risk detection (macro F1 = 0.90). Bench validation gave a mean absolute temperature error of 0.18 °C against a clinical thermometer and 2.45 bpm against a pulse oximeter, with 95.5% fall-detection accuracy. Dual-channel alerting delivered GSM SMS in 6.5 ± 1.2 s and Blynk cloud updates in 1.9 ± 0.6 s, and the prototype operated continuously for 18.6 h on a 3.7 V Li-ion battery. The results indicate a validated, deployable early-warning and caregiver-alerting tool suitable for homes, schools, and rural healthcare settings, positioned as decision support rather than a replacement for clinical diagnosis.

Published by: vikaspatanker