Wireless power transmission (WPT) has been attracting a wide range of subjects in various fields and also become a highly active research area because of their potential in providing high technology to our daily lives. The wireless power transmission will be mandatory to use in the near future because this technology enables the transmission of electrical energy from a power source to an electrical load across an air gap without interconnecting wires. In this paper, we carry out a pilot study to present the existing technologies of wireless power transmission, their recent technology as well as its future trends. Furthermore, we also describe plenty of applications in wireless transmission.

University students are especially susceptible to the negative effects of mental stress in today's environment, which is a serious issue overall. A great deal of pressure is now being placed on a period of life that was traditionally considered to be the most carefree. People in today's culture are exposed to increasingly high levels of mental stress, which has been related to a broad variety of health problems, such as depression, suicide, heart attacks, and strokes. Because of this, in order to primarily extract, for the purposes of this research, the mental stress ratings of university students, we applied a total of six distinct machine learning methods. The Decision Tree Classifier, the Random Forest Classifier, the SVC, the KNN Classifier, the Multinomial NB, and the K-Nearest Neighbors Regressor are only some of the machine learning algorithms that are available. This investigation's principal objective is to determine the percentage of students who are struggling to deal with emotional pressure in their lives. The dataset was put together by hand with paper and manual information obtained from a survey. Out of the six distinct classification strategies, the Decision Tree Classifier and the Random Forest Classifier both achieved a test result of 0.99, which is the maximum score that can be achieved.

In recent years, there has been a rapid rise in new product innovations in robots, robotic devices, human-robot interfaces, and social robots across the globe. This has largely been due to several inter-disciplinary research & development output from industries as well as a few robotics research institutions who have successfully embraced the convergence of braintech, robotics engineering, biomedical engineering, artificial intelligence, machine learning, and novel platform in human-machine interactions. However, the rate and scale of innovation in human-robot interaction (HRI) and their impact in creating social goods is still much slower compared to similar developments in other fields of research. We surmise that HRI innovation and commercialization of HRI products present unique challenges that are typically not encountered in other industries. Fragmented research, lack of supporting infrastructure and the gaps between lab research and user needs; as well as lack of venture capital investment in hardware products have led to sluggish innovation and longer duration of bringing those innovation into the real-world market. In this paper, we discuss the unique set of challenges faced by current HRI research/innovation and propose some methodology to direct the academic intellectual resources towards faster commercialization of their research.