There is an increasing demand for high-reality video systems. The ITU-R has standardized video parameters for ultra-high-definition TV (UHDTV), and the full-specification video signal stated in this international standard is prescribed as having a 7,680 (H) × 4,320 (V) pixel count, 120Hz frame frequency with progressive scanning, 12b tone reproduction, and wide color gamut. A 33Mpixel 120fps CMOS image sensor with a 12b column-parallel analog-to-digital converter (ADC) is reported in [1]. In addition to standard operation, higher sensitivity, smaller pixels, and a higher frame rate of 240fps or more are required for CMOS image sensors. Backside-illuminated stacked CMOS image sensors [2] are effective for simultaneously achieving both high sensitivity for small pixels and high operation speed. However, these stacked structures are still insufficient for high-frame-rate UHDTV image sensors since the pixel wafer and the ASIC wafer are connected by through-silicon vias in the peripheral area.

(2018). Evaluation of tsunami evacuation planning considering vehicle usage and start timing of evacuation. Transportmetrica A: Transport Science: Vol. 14, Measuring, Analysing and Evaluating Transport Network Reliability, pp. 50-65.

We used a Bayesian structural time series (BSTS) model to evaluate the short- and long-term impacts of the coronavirus disease 2019 (COVID-19) pandemic on transit ridership. We accessed smart-card data from Miyazaki City, Japan. We defined attributes based on card types (commuters, students and elders) and aggregated attributes (high-frequency users and “frequently used bus-stop pairs”) and analyzed the differences between all users and the extracted groups. Among card types, the short-term impact on elders was almost identical to that of all users, however, the short-term impact of the pandemic on commuters was much smaller and that of students was much larger than that of all users. The long-term trend of commuters was less fluctuated than that of all users. The long-term ridership recovery of students was higher than that of all users. Among aggregated attributes, the short-term impact was smaller on “high-frequency users” than on all users: the decrease in ridership immediately after the appearance of COVID-19 was smaller among “high-frequency users” than among all users. The long-term recoveries in the riderships of the extracted subsets were slower than the recoveries of riderships of all users.

It is well known that under the condition of high traffic volume lane-flow distribution becomes unbalanced; more traffic tends to use a median lane rather than a middle and outer lane, which causes the deterioration of traffic capacity at bottleneck sections. As intensive development of ITS, active and dynamic lane management has been practically implemented. By employing the technology of ITS, balancing lane-flow distribution is one of the feasible solutions to increase the throughput of bottleneck flow. However, the mechanism of the unbalanced usage among lanes is still unclear and traffic flow models enabling online and network-wide evaluation of dynamic and strategic lane management have not been proposed due to the lack of the method to computing lane-based traffic flow including lane-changing dynamics. This paper developed the multi-lane first order traffic flow model, which depicts the dynamics of lane-changing. It is assumed that each vehicle changes the lane to improve its utility or decrease its disutility, and also that the equilibrium of lane flow distribution is achieved as the condition of stochastic user equilibrium (SUE), where all drivers believe that they cannot improve their utility by changing the lanes. Thus, in the model, lane-changes are represented as the dynamics towards lane-flow equilibrium. As a result of simulating multilane traffic on freeway without any merging and diverging, it is revealed that the proposed model can represent the equilibrium curve of lane-flow distribution, and depict the propagation of traffic congestion at a lane-drop bottleneck section

High counts of bacteria are present in beach sand, and human health threats attributable to contact with sand have been reported. In this study, we investigated fecal indicator bacteria in the top surface sand of coastal beaches. Monitoring investigations were performed during a monsoon when rainfall occurs randomly, and the composition of the coliforms was analyzed. The coliform count in the top surface sand (depth < 1 cm) increased by approximately 100 fold (26–2.23 × 10³ CFU/100 g) with increasing water content because of precipitation. The composition of the coliforms in the top surface sand changed within 24 h of rainfall, with Enterobacter comprising more than 40% of the coliforms. Estimation of factors that changed the bacterial counts and composition revealed that coliform counts tended to increase with increasing water content in the top surface sand. However, the abundance of Enterobacter was independent of the sand surface temperature and water content. Coliform counts in the top surface sand rapidly increased and the composition showed remarkable variations because of the supply of water to the beach following rainfall. Among them, some bacteria with suspected pathogenicity were present. Controlling bacteria in coastal beaches is important for improving public health for beachgoers.