Search

Air pollution is a big concern in developing countries due to its negative effects on both human wellbeing and the environment. Collecting real-time values of air quality is challenging using traditional methods. Because they have limited coverage and may not accurately reflect pollution levels in a specific location. However, Advances in Internet of Things (IoT) technology and Machine Learning (ML) algorithms, can play a vital role in collecting and analyzing large amounts of air quality data, resulting in a more complete and exact knowledge of pollution levels. Throughout this work, based on several air pollutants including sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), particulate matter (PM) 2.5, particulate matter (PM) 10 and carbon monoxide (CO) across a large urban region, we establish an IoT-based framework to collect real-time data. After collecting the real-time values, we applied two types of machine learning algorithms named regression and classification models including linear regression, decision trees (DT), random forest (RF), K-Nearest Neighbours (KNN), Naive Bayes (NB), and gradient boosting (GB), to analyze the gathered data and estimate pollution levels into good, satisfactory, moderate, poor and very poor. Among the machine learning models, RF outperforms the result. This work and the dataset will be helpful for researchers, environmental practitioners and agencies. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases engender serious public health concerns in developing nations since access to specialized medical equipment is often limited and standard treatment expenses can be prohibitive. This study proposes an efficient and relatively affordable electrocardiogram (ECG) monitoring system that reads and analyzes a person's electrocardiogram data to provide affordable and quality healthcare solutions. The device initially extracts features from electrocardiogram records by reading electrical signals in the heart. Extracted data are then analyzed by a trained deep learning model to determine precisely if the heart is in a healthy state or undergoing complexities. Experimental results showed that the fine-tuned ANN architecture outperformed the state-of-the-art architectures in this field with an accuracy of 98.95%. The data can also be sent to specialists through an MQTT server if necessary, allowing for remote diagnosis and treatment. The system is intended to be deployed in countries where rural regions lack access to specialized healthcare equipment and professionals. Additionally, the device is inexpensive and, hence can be made accessible to people with limited affordability. [ABSTRACT FROM AUTHOR]

Global plastic production in increasing rapidly making a big threat to our health and climate. Growing production of petroleum-based plastic is raising issue regarding environment due to their undisposable properties. These issues are encouraging researchers to engage themselves in bioplastic research to invent eco-friendly disposable plastic using natural ingredients. This work aimed to develop environment friendly plastic from low cost natural resource such as tamarind and Arjuna with compatible mechanical, thermal and antimicrobial properties. Bioplastic samples have been synthesized keeping tamarind as base material and changing the percentage of Arjuna. The synthesized plastics have been subjected to biodegradable test, mechanical test, FTIR test, SEM test, XRD test, thermal test and antimicrobial test. Biodegradable test reveals that the samples are biodegradable and the process of biodegradation increases with the increase of time with 73% biodegradation in only 30 days. 5.23 N/mm2 stress is produced from the synthesized bioplastic by tensile test. Presence of functional groups is revealed by FTIR analysis. Only 5% weight loss is observed at above the room temperature by the thermal analysis. Antibacterial tests showed the efficiency of the plastic against virus.

The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals. [ABSTRACT FROM AUTHOR]

Robotics is a crucial technology of Industry 4.0 that offers a diverse array of applications in the industrial sector. However, the quality of a robot's manipulator is contingent on its stability, which is a function of the manipulator's parameters. In previous studies, stability has been evaluated based on a small number of manipulator parameters; as a result, there is not much information about the integration/optimal arrangement/combination of manipulator parameters toward stability. Through Lagrangian mechanics and the consideration of multiple parameters, a mathematical model of a modern manipulator is developed in this study. In this mathematical model, motor acceleration, moment of inertia, and deflection are considered in order to assess the level of stability of the ABB Robot manipulator of six degrees of freedom. A novel mathematical approach to stability is developed in which stability is correlated with motor acceleration, moment of inertia, and deflection. In addition to this, fuzzy logic inference principles are employed to determine the status of stability. The numerical data of different manipulator parameters are verified using mathematical approaches. Results indicated that as motor acceleration increases, stability increases, while stability decreases as moment of inertia and deflection increase. It is anticipated that the implementation of these findings will increase industrial output. [ABSTRACT FROM AUTHOR]

In Bangladesh's rural regions, almost 30% of the population lives in poverty. Rural residents also have restricted access to nursing and diagnostic services due to obsolete healthcare infrastructure. Consequently, as cardiac failure occurs, they usually fail to call the services and adopt the facilities. The internet of things (IoT) offers a massive advantage in addressing cardiac problems. This study proposed a smart IoT-based electrocardiogram (ECG) monitoring system for heart patients. The system is divided into several parts: ECG sensing network (data acquisition), IoT cloud (data transmission), result analysis (data prediction) and monetization. P, Q, R, S, and T are ECG signal properties fetched, pre-processed, analyzed and predicted to age level for future health management. ECG data are saved in the cloud and accessible via message queuing telemetry transport (MQTT) and hypertext transfer protocol (HTTP) servers. The linear regression method is utilized to determine the impact of electrocardiogram signal characteristics and error rate. The prediction was made to see how much variation there was in PQRST regularity and its sufficiency to be utilized in an ECG monitoring device. Recognizing the quality parameter values, acceptable outcomes are achieved. The proposed system will diminish future medical costs and difficulties for heart patients. [ABSTRACT FROM AUTHOR]

Aquaculture is the farming of aquatic organisms in natural, controlled marine and freshwater environments. The real-time monitoring of aquatic environmental parameters is very important in fish farming. Internet of things (IoT) can play a vital role in the real-time monitoring. This paper presents an IoT framework for the efficient monitoring and effective control of different aquatic environmental parameters related to the water. The proposed system is implemented as an embedded system using sensors and an Arduino. Different sensors including pH, temperature, and turbidity, ultrasonic are placed in cultivating pond water and each of them is connected to a common microcontroller board built on an Arduino Uno. The sensors read the data from the water and store it as a comma-separated values (CSV) file in an IoT cloud named ThingSpeak through the Arduino microcontroller. To validate the experiment, we collected data from 5 ponds of various sizes and environments. After experimental evaluation, it was observed among 5 ponds, only three ponds were perfect for fish farming, where these 3 ponds only satisfied the standard reference values of pH (6.5-8.5), temperature (16-24 °C), turbidity (below 10 ntu), conductivity (970-1825 µS/cm), and depth (1-4) meter. At the end of this paper, a complete hardware implementation of this proposed IoT framework for a real-time aquatic environment monitoring system is presented. [ABSTRACT FROM AUTHOR]