Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the number of aging people. Technologies with 5G in wearables and healthcare can intensely reduce the cost of diagnosing and preventing diseases and saving patient lives. This paper reviewed the benefits of 5G technologies, which are implemented in healthcare and wearable devices such as patient health monitoring using 5G, continuous monitoring of chronic diseases using 5G, management of preventing infectious diseases using 5G, robotic surgery using 5G, and 5G with future of wearables. It has the potential to have a direct effect on clinical decision making. This technology could improve patient rehabilitation outside of hospitals and monitor human physical activity continuously. This paper draws the conclusion that the widespread adoption of 5G technology by healthcare systems enables sick people to access specialists who would be unavailable and receive correct care more conveniently.

Thermal energy harvesting has seen a rise in popularity in recent years due to its potential to generate renewable energy from the sun. One of the key components of this process is the solar absorber, which is responsible for converting solar radiation into thermal energy. In this paper, a smart performance optimization of energy efficient solar absorber for thermal energy harvesting is proposed for modern industrial environments using solar deep learning model. In this model, data is collected from multiple sensors over time that measure various environmental factors such as temperature, humidity, wind speed, atmospheric pressure, and solar radiation. This data is then used to train a machine learning algorithm to make predictions on how much thermal energy can be harvested from a particular panel or system. In a computational range, the proposed solar deep learning model (SDLM) reached 83.22 % of testing and 91.72 % of training results of false positive absorption rate, 69.88 % of testing and 81.48 % of training results of false absorption discovery rate, 81.40 % of testing and 72.08 % of training results of false absorption omission rate, 75.04 % of testing and 73.19 % of training results of absorbance prevalence threshold, and 90.81 % of testing and 78.09 % of training results of critical success index. The model also incorporates components such as insulation and orientation to further improve its accuracy in predicting the amount of thermal energy that can be harvested. Solar absorbers are used in industrial environments to absorb the sun's radiation and turn it into thermal energy. This thermal energy can then be used to power things such as heating and cooling systems, air compressors, and even some types of manufacturing operations. By using a solar deep learning model, businesses can accurately predict how much thermal energy can be harvested from a particular solar absorber before making an investment in a system.

In many fields, such as environmental monitoring, food safety, and medical diagnostics, the identification of organic compounds is essential. It is crucial to create exceptionally sensitive and selective sensors for the detection of organic compounds in order to safeguard the environment and human health. Due to its outstanding electrical, mechanical, and chemical characteristics, the two-dimensional carbon substance graphene has recently attracted much attention for use in sensing applications. The purpose of this research is to create an organic material sensor made from graphene for the detection of organic substances like phenol, ethanol, methanol, chloroform, etc. Due to its high surface-to-volume ratio and potent interactions with organic molecules, graphene improves the sensor’s performance while the metasurface structure enables the design of highly sensitive and selective sensing elements. The suggested sensor is highly sensitive and accurate at detecting a broad spectrum of organic molecules, making it appropriate for a number of applications. The creation of this sensor has the potential to have a substantial impact on the field of organic sensing and increase the safety of food, medicine, and the environment. The graphene metasurface organic material sensor (GMOMS) was categorized into three types denoted as GMOMS1, GMOMS2, and GMOMS3 based on the specific application of the graphene chemical potential (GCP). In GMOMS1, GCP was applied on both the CSRR and CS surfaces. In GMOMS2, GCP was applied to the CS surface and the surrounding outer region of the CSRR. In GMOMS3, GCP was applied to the CSRR and the surrounding outer region of the CSRR surface. The results show that all three designs exhibit high relative sensitivity, with the maximum values ranging from 227 GHz/RIU achieved by GMOMS1 to 4318 GHz/RIU achieved by GMOMS3. The FOM values achieved for all the designs range from 2.038 RIU⁻¹ achieved by GMOMS2 to 31.52 RIU⁻¹ achieved by GMOMS3, which is considered ideal in this paper.

We proposed a novel approach based on a complementary split-ring resonator metamaterial in a two-port MIMO antenna, giving high gain, multiband results with miniature size. We have also analyzed a circular disk metasurface design. The designs are also defected using ground structure by reducing the width of the ground plane to 8 mm and etching all other parts of the ground plane. The electric length of the proposed design is 0.5λ × 0.35λ × 0.02λ. The design results are also investigated for a different variation of complementary split-ring resonator ring sizes. The inner and outer ring diameters are varied to find the optimized solution for enhanced output performance parameters. Good isolation is also achieved for both bands. The gain and directivity results are also presented. The results are compared for isolation, gain, structure size, and the number of ports. The compact, multiband, high gain and high isolation design can apply to WiMAX, WLAN, and satellite communication applications.

This article provides a summary of recent microwave antenna sensor technologies published in peer-reviewed journals. This study is primarily focuses on the resonator-based microwave sensor, microwave wearable textile antenna sensors, and resonator-based flexible metamaterial devices along with their possible industrial applications. The survey emphasizes on both the possible disadvantages and advantages of employing this technology. Furthermore, it provides an overview of the key issues addressed in the context of antenna sensors, starting with material selection and finishing with the antenna’s frame, as well as the numerous case applications. This article, in particular, offers a description of the various types of textile materials used in the creation of wearable antenna sensors. This article provides various instances of antenna sensor applications in the human body, including the detection of NaCl and sugar solutions, blood, and other biological variables like strain, finger positions, and temperature. In addition, suggestions are made regarding the potential industry and scientific obstructions, as well as future directions for the study.