Investigating gender differences based on emotional changes becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (anger, happiness, sadness, and neutral) of audiovisual stimuli. In this study, conventional filter and wavelet (WT) denoising techniques were applied as a preprocessing stage and Hurst exponent $\left({}\right)$

The aim of the present study is to identify the level of prosoical behavior of Baghdad University's students and to recognize the differences between male and female students. Moreover, it also aims to identify the level of openness to experience for these students. A random sample of (123) students has been selected; 77 males and 46 females. Two scales have been used in the study. The Prosocialness scale for adults by Caprara. Et al (2005) has been translated into the Arabic language and relies on four types of actions (Helping, Sharing, Taking care, and feeling Empathetic with others) and the other scale is the Openness to Experience Scale, which is one of the Big Five Inventory by John and Srivastava (1999). The main results showed a

Traditional healthcare for chronic wounds and Cold Atmospheric Plasma (CAP) treatments relies on passive dressings and large-volume stationary equipment operating with open-loop systems, which severely limits their use and confines it to specialized clinical environments. To address the lack of active thermal safety mechanisms in mobile devices, this research proposes a wearable smart plasma patch equipped with a closed-loop adaptive electronic control system to ensure safe patient care and treatment at home. The smart patch integrates real-time analog biosensors to continuously monitor skin temperature and relative humidity. An algorithm running on a microcontroller dynamically adjusts the high-voltage plasma parameters using Pulse

This study employs wavelet transforms to address the issue of boundary effects. Additionally, it utilizes probit transform techniques, which are based on probit functions, to estimate the copula density function. This estimation is dependent on the empirical distribution function of the variables. The density is estimated within a transformed domain. Recent research indicates that the early implementations of this strategy may have been more efficient. Nevertheless, in this work, we implemented two novel methodologies utilizing probit transform and wavelet transform. We then proceeded to evaluate and contrast these methodologies using three specific criteria: root mean square error (RMSE), Akaike information criterion (AIC), and log