The post-Moore's era has boosted the progress in carbon nanotube-based transistors. Indeed, the 5G communication and cloud computing stimulate the research in applications of carbon nanotubes in electronic devices. In this perspective, we deliver the readers with the latest trends in carbon nanotube research, including high-frequency transistors, biomedical sensors and actuators, brain–machine interfaces, and flexible logic devices and energy storages. Future opportunities are given for calling on scientists and engineers into the emerging topics.

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Applications of Carbon Nanotubes in the Internet of Things Era

This paper presents an example-based demonstration of our initial results on the modelling of health conditions to support the control and monitoring of (clinically) personalized healthcare services. The main goal of this work is to model health conditions at the interface, mechanical, biological, and environmental levels to support rigorous and reliable personalised healthcare services for unreliable IoT-based Healthcare 5.0 using different abstractions like rules, processes, and rates. Current approaches support either fine-grained (i.e., at DNA or protein level considering the effect of health conditions on cellular components, biological processes, and molecular functions) or coarse-grained (i.e., only parameters check ignoring the internal details and causes) modelling of various health conditions that hindered their usability to automatically control and monitor healthcare conditions in real-time due to lack of missing dependent information at different levels of health condition. Modelling health conditions is a challenging task because health condition is a complex process that involves various inter-dependent cyber, physical, mechanical, and biological sub-processes. We have developed a technique for modelling health conditions at multiple levels that supports various cyber, physical, and biological characteristics of health-condition and operate at different levels of abstraction including both coarse-grained and fine-grained levels that are related. We demonstrate the modelling technique through its application to a typical healthcare condition that includes Diabetes and Heart conditions

Towards Multi-Level Modelling and Monitoring of Real-time Personalised Health Conditions

Nowadays, health monitoring is crucial, especially monitoring the temperature and heartbeat of the patient in Covid / non-Covid situations. Continued monitoring of the patient is not a possible and tedious job. IoT plays a critical role in Hospitals where patients are in Intensive Care Units (ICU), and patients are treated at home (isolation points). The devices receive data continuously and monitor by the doctors remotely. This paper presents temperature and heartbeat monitoring using Internet Of Things (IoT) devices with an algorithm to capture data from devices and sends it to computer devices at a reasonable cost. Proof Of Concept has been created with the help of an Arduino board, Pulse Sensor, Temperature Sensor, Breadboard, ESP8266 Wi-Fi module, and Liquid Crystal Display (LCD). The IoT devices capture data from different devices (patient health data) in real time. The Health Care Monitoring (HCM) Application builds using microservices architecture, runs on top of the Thingspeak data, and sends notifications to the doctors if there is an emergency. The doctors can act according to rather than monitor continuously. This model eliminates manual intervention for taking the reading from time to time.

Health Care Monitoring Application using IoT Devices

Machining plays a crucial role in modern manufacturing, relying on automated processes to efficiently create complex parts through subtractive like lathe turning and cutting. However, a major concern in this manufacturing process is tool wear, necessitating a robust system for proactive malfunction detection. To keep up with advancements and meet the increasing demands of speed and precision, artificial intelligence (AI) emerges as a promising solution. However, AI algorithms often require fine-tuning of hyperparameters, which poses a challenge. Swarm intelligence algorithms, inspired by collaborative behaviors observed in nature, offer a potential solution. By applying swarm intelligence to hyperparameter optimization, AI algorithms can achieve optimized models that address time and hardware constraints. This work proposes a methodology based on Extreme Gradient Boosting (XGBoost) for forecasting malfunctions. Additionally, a modified optimization metaheuristic is introduced to specifically enhance the performance of this methodology. To evaluate the proposed approach, it has been applied to a real-world dataset and compared to several well-known optimizers. The results demonstrate admirable performance, highlighting the potential of swarm intelligence in achieving efficient and effective machining processes. 

Metaheuristic Optimized Extreme Gradient Boosting Milling Maintenance Prediction

Nowadays, health monitoring is crucial, especially monitoring the temperature and heartbeat of the patient in Covid / non-Covid situations. Continued monitoring of the patient is not a possible and tedious job. IoT plays a critical role in Hospitals where patients are in Intensive Care Units (ICU), and patients are treated at home (isolation points). The devices receive data continuously and monitor by the doctors remotely. This paper presents temperature and heartbeat monitoring using Internet Of Things (IoT) devices with an algorithm to capture data from devices and sends it to computer devices at a reasonable cost. Proof Of Concept has been created with the help of an Arduino board, Pulse Sensor, Temperature Sensor, Breadboard, ESP8266 Wi-Fi module, and Liquid Crystal Display (LCD). The IoT devices capture data from different devices (patient health data) in real time. The Health Care Monitoring (HCM) Application builds using microservices architecture, runs on top of the Thingspeak data, and sends notifications to the doctors if there is an emergency. The doctors can act according to rather than monitor continuously. This model eliminates manual intervention for taking the reading from time to time. 

In many ways, monitoring the patient's health with appropriate sensors to monitor the hospital’s connection, as if the patient is lying in bed. The previous method of monitoring can be uncomfortable for a variety of reasons. Tracking a growing number of patients requires many trained health professionals and doctors. The proposed predictive data regression technique (PDRT) based carbon nanotubes (CNT) biosensor system has been introduced in the fields like Biomedical. In this analysis technique, the sensor value is compared to the threshold value; if any changes occur, the controller sends the user alert via IoT. The patients are present in this area so that their physicians and many physiological measurements and applications can be seen. Carbon nanotubes (CNT) have been used as electronic mediators and adsorption substrates in biosensors due to their extraordinary electrochemical properties. Explore its potential biosensing applications, platinum, carbon nanomaterials, electrochemical electrode preparation and features. The purpose of this proposal is to maintain a system of inpatient physiological parameters and activity. The physician can see a graphical view of the patient's parameters and a computer system worn by the patient, the Wi-Fi network, and the display. Abnormalities inpatient data are messages sent to the doctor via an IoT system to remind them of the situation. The Proposed Predictive Data Regression Technology (PDRT) system is given a better performance to the user, and it provides the output accuracy.

Predictive data regression technique based carbon nanotube biosensor for efficient patient health monitoring system

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Predictive data regression technique based carbon nanotube biosensor for efficient patient health monitoring system