The technology acceptance model is a widely used model to investigate whether users will accept or refuse a new technology. The Metaverse is a 3D world based on virtual reality simulation to express real life. It can be considered the next generation of using the internet. In this paper, we are going to investigate variables that may affect users’ acceptance of Metaverse technology and the relationships between those variables by applying the extended technology acceptance model to investigate many factors (namely self-efficiency, social norm, perceived curiosity, perceived pleasure, and price). The goal of understanding these factors is to know how Metaverse developers might enhance this technology to meet users’ expectations and let the users interact with this technology better. To this end, a sample of 302 educated participants of different ages was chosen to answer an online Likert scale survey ranging from 1 (strongly disagree) to 5 (strongly agree). The study found that, first, self-efficiency, perceived curiosity, and perceived pleasure positively influence perceived ease of use. Secondly, social norms, perceived pleasure, and perceived ease of use positively influences perceived usefulness. Third, perceived ease of use and perceived usefulness positively influence attitude towards Metaverse technology use, which overall will influence behavioral intention. Fourth, the relationship between price and behavioral intention was significant and negative. Finally, the study found that participants with an age of less than 20 years were the most positively accepting of Metaverse technology.

This paper describes the experimental setup and measurements of the emissivity of porcine skin samples over the band of 80–100 GHz. Measurements were conducted on samples with and without dressing materials and before and after the application of localized heat treatments. Experimental measurements indicate that the differences in the mean emissivity values between unburned skin and burned damaged skin was up to ~0.28, with an experimental measurement uncertainty of ±0.005. Measured differences in the mean emissivity values between unburned and burn damaged skin increases with the depth of the burn, indicating a possible non-contact technique for assessing the degree of a burn. The mean emissivity of the dressed burned skin was found to be slightly higher than the undressed burned skin, typically ~0.01 to ~0.02 higher. This indicates that the signature of the burn caused by the application of localized heat treatments is observable through dressing materials. These findings reveal that radiometry, as a non-contact method, is capable of distinguishing between normal and burn-damaged skin under dressing materials without their often-painful removal. This indicates the potential of using millimeter wave (MMW) radiometry as a new type of medical diagnostic to monitor burn wounds.

Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating costs. The main aim of this paper is to make forecasting models to accurately estimate the electrical load based on the measurements of current electrical loads of the electricity company. The importance of having forecasting models is in predicting the future electrical loads, which will lead to reducing costs and resources, as well as better electric load distribution for electric companies. In this paper, deep learning algorithms are used to forecast the electrical loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated Recurrent Units (GRU), and (3) Recurrent Neural Networks (RNN). The models were tested, and the GRU model achieved the best performance in terms of accuracy and the lowest error. Results show that the GRU model achieved an R-squared of 90.228%, Mean Square Error (MSE) of 0.00215, and Mean Absolute Error (MAE) of 0.03266.

The millimeter-wave band is an ideal part of the electromagnetic radiation to diagnose human skin conditions because this radiation interacts only with tissue down to a depth of a millimetre or less over the band range from 30 GHz to 300 GHz. In this paper, radiometry is used as a non-contact sensor for measuring the human skin reflectance under normal and wet skin conditions. The mean reflectance of the skin of a sample of 50 healthy participants over the (80–100) GHz band was found to be ~0.615 with a standard deviation of ~0.088, and an experimental measurement uncertainty of ±0.005. The thinner skin regions of the back of the hand, the volar forearms and the inner wrist had reflectances 0.068, 0.068 and 0.062 higher than the thicker skin regions of the palm of the hand, the dorsal forearm and the outer wrist skin. Experimental measurements of human skin reflectance in a normal and a wet state on the back of the hand and the palm of the hand regions indicated that the mean differences in the reflectance before and after the application of water were ~0.078 and ~0.152, respectively. These differences were found to be statistically significant as assessed using t-tests (34 paired t-tests and six independent t-tests were performed to assess the significance level of the mean differences in the reflectance of the skin). Radiometric measurements in this paper show the quantitative variations in the skin reflectance between locations, sexes, and individuals. The study reveals that these variations are related to the skin thickness and water content, a capability that has the potential to allow radiometry to be used as a non-contact sensor to detect and monitor skin conditions such as eczema, psoriasis, malignancy, and burn wounds.

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