THE LESSON FROM DEMAND RESPONSE IN JAPAN

This study aims to contribute to Japanese policy recommendations with using the concept of ‘Nudge’ aimed at encouraging DR adoption that is scheduled for the wholesale electricity market starting in April 2017. Because consumers have bounded rationality, if the concept of Nudge is added to DR, there is a possibility of expanding the participation in DR more widely. In this paper, we reviewed Japanese electricity demand reduction efforts (the voluntary program) and demonstrated projects with using the nudge concept. The study found that the concept of nudge was useful in both DR trials. However, there was also the problems in these projects. The problem with the voluntary program was that when the consumer believed there was sufficient electricity, they were less willing to cooperate. The problem with the demonstration projects was the relatively low participation rate. Therefore, what the government needs to do is to assist consumers in understanding the importance and the necessity of DR, and in this education process, the Japanese government could use the nudge concept by reflecting on lessons from experiences in Japan and abroad

Functional modulation of AMP-activated protein kinase by cereblon

AbstractMutations in cereblon (CRBN), a substrate binding component of the E3 ubiquitin ligase complex, cause a form of mental retardation in humans. However, the cellular proteins that interact with CRBN remain largely unknown. Here, we report that CRBN directly interacts with the α1 subunit of AMP-activated protein kinase (AMPK α1) and inhibits the activation of AMPK activation. The ectopic expression of CRBN reduces phosphorylation of AMPK α1 and, thus, inhibits the enzyme in a nutrient-independent manner. Moreover, AMPK α1 can be potently activated by suppressing endogenous CRBN using CRBN-specific small hairpin RNAs. Thus, CRBN may act as a negative modulator of the AMPK signaling pathway in vivo

Computational Complexity of Web Service Composition Based on Behavioral Descriptions

The Web Service Composition (WSC) problem on behav-ioral descriptions deals with the automatic construction of a coordinator web service to control a set of web services to reach the goal states. As such, WSC is one of the fundamen-tal techniques to enable the Service Oriented Architecture on the Web. Despite its importance and implications, how-ever, very few studies exist on the computational complexi-ties of the WSC problem. In this paper, we present two novel theoretical findings on WSC problems: (1) Solving the WSC problem with “complete ” information is EXP-hard, and (2) Solving the WSC problem with “incomplete ” information is 2-EXP-hard. These findings imply that more efforts to de-vise efficient approximate solutions to the WSC problem be needed. 1

A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning

Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs—e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, “within-paradigm transfer learning,” aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, “cross-paradigm transfer learning,” involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments