We present a new simple Internet of Things (IoT) device that we call “Smart Bottle Cap”, which enables a standard bottle to become a user-controllable liquid pouring system. It consists of a mini vacuum pump to start the liquid flowing, a microcontroller to control the liquid flow, a BLE module to connect it to a smartphone, an accelerometer to detect the tilt angle of the bottle, an LED for drawing the attention of users, and a 3.7 V LiPo battery. The device's novel point is that a flow control mechanism built into a standard bottle cap makes the system suitable for general use and enables it to be easily extended.

A readout technique using single-flux-quantum (SFQ) circuits enables superconducting single photon detectors (SSPDs) to operate at further high-speed, where a mutually-coupled dc-to-SFQ (MC-dc/SFQ) converter is used as an interface between SSPDs and SFQ circuits. In this work, we investigated pulse response of the MC-dc/SFQ converter. We employed on-chip pulse generators to evaluate pulse response of the MC-dc/SFQ converter for various pulses. The MC-dc/SFQ converter correctly operated for the pulse current with the amplitude of 52,$mu$A and the width of 179,ps. In addition, we examined influence of the pulse amplitude and width to operation of the MC-dc/SFQ converter by numerical simulation. The simulation results indicated that the MC-dc/SFQ converter had wide operation margins for pulse current with amplitudes of 30--60,$mu$A irrespective of the pulse widths.

In this paper, we propose a low-invasive framework to predict changes in cognitive performance using only heart rate variability (HRV). Although a lot of studies have tried to estimate cognitive performance using multiple vital data or electroencephalogram data, these methods are invasive for users because they force users to attach a lot of sensor units or electrodes to their bodies. To address this problem, we proposed a method to estimate cognitive performance using only HRV, which can be measured with as few as two electrodes. However, this can't prevent loss of worker productivity because the workers' productivity had already decreased even if their current cognitive performance had been estimated as being at a low level. In this paper, we propose a framework to predict changes in cognitive performance in the near future. We obtained three principal contributions in this paper: (1) An experiment with 45 healthy male participants clarified that changes in cognitive performance caused by mental workload can be predicted using only HRV. (2) The proposed framework, which includes a support vector machine and principal component analysis, predicts changes in cognitive performance caused by mental workload with 84.4 % accuracy. (3) Significant differences were found in some HRV features for test participants, depending on whether or not their cognitive performance changes had been predicted accurately. These results lead us to conclude that the framework has the potential to help both workers and managerial personnel predict what their performances will be in the near future. This will make it possible to proactively suggest rest periods or changes in work duties to prevent losses in productivity caused by decreases of cognitive work performance.