Information on congestion of buses, which are one of the major public transportation modes, can be very useful in light of the current COVID-19 pandemic. Because it is unrealistic to manually monitor the number of riders on all buses in operation, a system that can automatically monitor congestion is necessary. The main goal of this paper’s work is to automatically estimate the congestion level on a bus route with acceptable performance. For practical operation, it is necessary to design a system that does not infringe on the privacy of passengers and ensures the safety of passengers and the installation sites. In this paper, we propose a congestion estimation system that protects passengers’ privacy and reduces the installation cost by using Bluetooth low-energy (BLE) signals as sensing data. The proposed system consists of (1) a sensing mechanism that acquires BLE signals emitted from passengers’ mobile terminals in the bus and (2) a mechanism that estimates the degree of congestion in the bus from the data obtained by the sensing mechanism. To evaluate the effectiveness of the proposed system, we conducted a data collection experiment on an actual bus route in cooperation with Nara Kotsu Co., Ltd. The results showed that the proposed system could estimate the number of passengers with a mean absolute error of 2.49 passengers (error rate of 38.8%)

For the COVID-19 Contact Confirming Application (COCOA) to be fully effective, it is important that many people install COCOA on their smartphones. However, because the current COCOA implements only a minimum number of functions, it is difficult to motivate people to install it. Therefore, we focus on an extrinsic approach that motivates the installation of COCOA by adding fun functions through interaction with a ubiquitous system on the environment side. In this paper, we introduce an interactive system that nudges users to install COCOA by reframing the act of installing COCOA as getting a ticket to participate in fun experiences. CCS CONCEPTS• Human-centered computing → Interaction design.

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Imaging complex vascular structures by X‐ray microcomputed tomography (μ‐CT) is becoming vital for research purposes in pathology of vascular diseases. Acrylic‐based polymerizable resins are widely adopted for the contrast agent to prepare pathological specimens for imaging of vascular structures. For imaging of vascular structures at higher resolution, it is promising to develop inorganic‐type contrast agents with higher X‐ray attenuation coefficient as well as low viscosity, homogeneity, minimum shrinkage, curable (gellable) for replication, and low cost. Herein, a novel inorganic sol–gel system based on concentrated colloidal dispersion of NiAl layered double hydroxide (LDH) nanoparticles is described, allowing imaging of vascular structures at high resolution. NiAl LDH acts as nanofiller and alkaline catalyst to form a silica/LDH monolithic material with homogeneity from the nanoscale. Moreover, NiAl LDH nanoparticles contribute to the strong enhancement of the X‐ray attenuation. As a proof‐of‐concept, X‐ray μ‐CT imaging of the developed contrast agent in glass capillaries and of blood vessels of a human placenta and murine liver is demonstrated.

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