It is possible to levitate and manipulate objects without contact by utilizing the radiation force of ultrasound. In this paper, I introduce the concept of a noncontact manipulation device utilizing ultrasound. The generation of resonant modes in solids and enclosed media enables the efficient levitation of objects. The acoustic fields, the acoustic radiation forces acting on objects, and the positions at which the objects are trapped can be predicted by numerical simulations using the finite element method, thereby providing crucial insights for the design of the device. The positions of trapped objects can be controlled on various trajectories by adjusting the acoustic fields in a timely and spatially precise manner.

This paper introduces the analysis and control methods of the epidemic spreading model, which is denoted by three states: Susceptible, infected, and Recovered individuals. Based on these states, the SIR, SIS, and SIRS models are introduced and categorized into population- and agent-based models. The theoretical results of these models, which determine the behavior of the epidemic models, are explained, and various control methods, including the authors' research on epidemic spreading models, are introduced. Finally, we discuss future perspectives.

The number of lives lost in rural areas could have been reduced had the same care been provided in urban areas, given that patients in rural areas require long-distance and time-consuming transportation to specialized facilities. to receive advanced care. Extracorporeal cardiopulmonary resuscitation（ECPR）has been demonstrated to be an effective intervention when initiated within 60 minutes of cardiac arrest. Only large hospitals in urban areas are equipped to provide such advanced treatment to patients. The implementation of ECPR outside a hospital setting has the potential to significantly enhance the survival rates of patients who have experienced cardiac arrest in rural areas, where the likelihood of survival was previously limited. The mobile operating room（Doctor Car V3）for emergency surgery represents a novel vehicle that can be utilized to initiate ECPR in a prehospital setting. This vehicle has the potential to be an effective means of saving the lives of cardiac arrests and improving their prognoses.

In this paper, we present a study on road obstacle detection using an autoencoder with vehicle driving information. We describe a method for detecting the occurrence and location of a road obstacle using the autoencoder, a machine learning algorithm that aggregates vehicle driving information measured by the electronic toll collection system 2.0 on-board units installed in vehicles as probe data via intelligent transport systems spots. The autoencoder continuously builds a model by learning information on vehicle behavior in a normal traffic flow before the occurrence of a road obstacle, and it detects the ofstacle of it when the output from the model shows a poor fit. This approach is highly applicable to ever-changing traffic flows and to a variety of roadway environments. By computer simulations, we show that the detection method using the autoencoder outperforms the supervised learning method using a support vector classifier.