To assess the effectiveness of response strategies of avoiding large gatherings or crowded areas and to predict the spread of COVID-19 infections in Japan, we developed a stochastic transmission model by extending the Susceptible-Infected-Removed (SIR) epidemiological model with an additional modeling of the individual action on whether to stay away from the crowded areas. The population were divided into three compartments: Susceptible, Infected, Removed. Susceptible transitions to Infected every hour with a probability determined by the ratio of Infected and the congestion of area. The total area consists of three zones crowded zone, mid zone and uncrowded zone, with different infection probabilities characterized by the number of people gathered there. The time for each people to spend in the crowded zone is curtailed by 0, 2, 4, 6, 7, and 8 hours, and the time spent in mid zone is extended accordingly. This simulation showed that the number of Infected and Removed will increase rapidly if there is no reduction of the time spent in crowded zone. On the other hand, the stagnant growth of Infected can be observed when the time spent in the crowded zone is reduced to 4 hours, and the growth number of Infected will decrease and the spread of the infection will subside gradually if the time spent in the crowded zone is further cut to 2 hours. In conclusions The infection spread in Japan will be gradually contained by reducing the time spent in the crowded zone to less than 4 hours.

Fifth Generation (5G) mobile communications technology became available in Japan as of the end of March 2020. The Ministry of Internal Affairs and Communications (MIC) is proceeding with a plan to use 5G for a doctor-to-doctor remote diagnosis system. This remote diagnosis offers patients the benefit of receiving advanced medical care without having to travel long distances. The provision of a remote diagnosis will provide elderly patients in rural areas with an earlier diagnosis without burdening patients in Japan where the aging population and the uneven distribution of doctors are increasing. However, the system will increase the burden on specialists by expanding the doctor's catchment area. As a solution to that problem, deep learning-based artificial intelligence (AI) is expected to reduce the burden on doctors. In order to realize 5G- and AI-based real-time diagnostic support, diagnostic imaging using AI and an AI model that provides instructions are required. This is because ultrasonography and endoscopy, which can be used for remote diagnosis, do not acquire data on fixed areas like a CT or MRI scan. The AI model needs to instruct the doctor at the patient's home in order to collect appropriate information in accordance with the patient's symptoms and status. In order to build an interactive AI model, the interactions between doctors who are making a remote diagnosis should be recorded as training data and a 5G-based remote diagnosis system should be created. A remote diagnostic support system incorporating 5G and interactive diagnostic imaging incorporating AI will result in a system that places less of a burden on patients and doctors.

Background: Instead of the complete lockdown, since the outbreak of coronavirus disease 2019 (COVID-19), Japan has been trying to control the infection by self-restraint request policy. It seems that the number of infected people has subsided, however, the increasing human activities again in the resumption of economy may lead to the second wave of infections. Here, we analyzed the major factors behind the success control of the first outbreak in Japan and the potential risk of the second wave. Methods: Employing a localized stochastic transition model, we analyze the real data and the results of simulation in Tokyo from March 1 to July 31. In the model, population is divided into three compartments: susceptible, infected, and removed; and area into three zones: crowded, mid and uncrowded. Different zones have different infection probabilities characterized by the number of people gathered there. The flow of the infection simulation in one day consists of three steps: (I) intercity movement of population, (II) isolating infected people, and (III) zone shifting following group behavioral patterns. Results: The major cause for the success of controlling the first outbreak in Tokyo is demonstrated through our simulation to be the early request of self-restraint as well as the early detection of infected people. Meanwhile, the observation that the increasing human activities again in the resumption of economy will lead to the second wave of infections is also found in the simulation with an extended period. Based on the analysis of intercity movement and behavioral pattern on Tokyo where normally about 2.9 million people come from the surrounding cities to the central area by using the public railway system every day, results showed that turning the workstyle of 55% of working people ranging in age from 20 to 64 years old into teleworking (remote work) may control the spread of infection without significant economic damage. Meanwhile, to keep about 75% of the normal activity level and to advocate the shift to telework are indispensable because a sudden resumption of activity from the lockdown sate can rapidly spread infection. Conclusions: As a new normal in face of COVID-19 for Tokyo and other cities that with a high population density, shifting the workstyle of 55% of working people to teleworking and to reduce 25% time staying in the high infection risk area could be an effective measure to control the spread of infection while maintaining a certain level of economic activity.

Automated liver tumor detection in abdominal ultrasonography with a modified faster region-based convolutional neural networks (Faster R-CNN) architecture Kenji Karako, Yuichiro Mihara, Junichi Arita, Akihiko Ichida, Sung Kwan Bae, Yoshikuni Kawaguchi, Takeaki Ishizawa, Nobuhisa Akamatsu, Junichi Kaneko, Kiyoshi Hasegawa, Yu Chen

In Japan, there is a growing initiative to construct centralized databases and platforms that can aggregate and manage a vast range of medical, health, and caregiving data for research and analysis. Recent advancements in artificial intelligence (AI), particularly in general-purpose models like the Segment Anything model and Chat GPT, promise significant progress towards utilizing such data-rich platforms effectively for healthcare. Traditionally, AI has displayed superior performance by learning specific images or languages, but now it is advancing towards creating models capable of learning universal traits from images and languages by training on extensive datasets. The challenge lies in the fact that these general-purpose models are trained on data that does not sufficiently incorporate medical information, making their direct application to healthcare difficult. However, the introduction of data platforms can potentially solve this problem. This would lead to the development of universally applicable models to process medical images and AI assistants that can support both doctors and patients. These medical AI assistants can function as a "sub-doctor" with extensive knowledge, assisting in comprehensive analysis of symptoms, early detection of rare diseases, and more. They can also serve as an intermediary between the doctor and the patient, helping to simplify consultations and enhance patient understanding of medical conditions and treatments. By bridging this gap, the AI assistant can help to reduce doctors' workload, improve the quality of healthcare, and facilitate early detection and prevention in the elderly population.

Low-carbon transitions are long-term complex processes that are driven by multiple factors. To provide a theoretical and practical framework of this process, we argue that the combination of the multi-level perspective (MLP) and agent-based modeling (ABM) enables us to reach a deeper and detailed analysis of low-carbon transitions. As an extensively applied theoretical form, MLP conceptualizes low-carbon transitions as a nonlinear process and allows a system to be analyzed and organized into multiple dimensions (landscape, regime, and niche). However, MLP cannot explain the many details of complex transitions, whereas ABM can estimate the influence of interacting behaviors in a complex system. Therefore, the main advantages of the combined approach for the analysis of low-carbon transition are verified: the MLP can contribute to the overall design of ABM, and ABM can provide a dynamic, continuous, and quantitative description of the MLP. To construct this combination framework, this paper offers a guiding principle that combines the two perspectives under a low-carbon transitional background to create an integrated strategy using three procedures: defining the common concepts, their interaction, and their combination. Through the proposed framework, the goal of this work was to reach a better understanding of social system evolution from the present high-carbon state to a low-carbon state under the pressure of ambitious climate goals, providing specific policy recommendations.