武藤 佳恭

It has been revealed that the cause of senescence and diseases is associated with the reactive oxygen species "hydroxyl radicals" (center dot OH). Senescence and diseases may be overcome as long as we can scavenge center dot OH mostly produced in mitochondria. It is one and only one "molecular hydrogen" (H-2) that can both penetrate into the mitochondria and scavenge the center dot OH. The H-2 in the body can function in disease prevention and recovery. H-2 gas is explosive so that a safe hydrogen inhaler has to be developed for home use. We would like to advocate the great use of H-2.

This paper demonstrates that constrained pseudorandom number can solve classic coin-weighing puzzles faster than human-devised algorithms. Deep learning and ensemble machine learning lie in inductive methods while automated reasoning implemented in deductive computer languages is based on deductive methods. In the inductive methods, intelligence is inferred by pseudorandom number for creating the sophisticated decision trees. In machine learning, the goal is to learn a function that minimizes an error or one that maximizes reward over punishment. This paper shows a new challenge that every solution candidate is solely generated by the constrained pseudorandom number, while a simple deductive rule is applied for selecting solutions among the generated candidates. In this paper, the computation performance of the proposed method was measured by comparing with the existing open source codes by solving classic 12-coin and 24-coin puzzles, respectively. The proposed method can be used for solving quantitative group testing in biology and medicine.

Recent indoor air quality studies show that even 1000 parts per million (ppm) concentration of Carbon Dioxide (CO2) has an adverse effect on human intellectual activities. Therefore, it is required to keep the CO2concentration below a certain value in a room. In this study, in order to analyze the diffusion tendency of carbon dioxide by breathing, we constructed a simultaneous multi-point sensing system equipped with a carbon dioxide concentration sensor to measure indoor environment. Furthermore, it was evaluated whether the prediction model can be effectively used by comparing the prediction value by the model and the actually measured value from the sensor. The experimental results showed that CO2by exhaled breathing diffuses evenly throughout the room regardless of the sensor's relative positions to the human test subjects. The existing model is sufficiently accurate in a room which has above at least a 0.67 cycle/h ventilation cycle. However, there is a large gap between the measured and the model's predicted values in a room with a low ventilation cycle, and that suggests a measurement with a sensor still is necessary to precisely monitor the indoor air quality.

Production and excretion of hydrogen (H-2) gas in human was reported in 1969, since then it has been regarded as non-toxic molecule. For preventive and therapeutic medical uses, a possible treatment for cancer was reported and another article was published on how H(2)acts as a therapeutic antioxidant by selectively reducing cytotoxic oxygen radicals. A variety of H-2 gas inhalers have been available in the market for hospital and home uses. However, H-2 is odorless and flammable or explosive ignited by static electricity. We have examined the safety of a variety of H(2 )gas concentrations from the viewpoint of flammability and explosion. We have also measured concentrations of H-2 gas inhalers in the market respectively. This paper also details how to control H-2 gas concentration for preventing explosions.

The study presented in this paper aims to show the impact of introducing a priority-based internal power flow management system on the local energy market of prosumers with solar panels, fuels cells and batteries. The community used for the study is based on the Open Energy Systems (OES), in which 19 autonomous subsystems, equipped with batteries, solar panels and AC grid connection are interconnected via a DC power bus. For the simulation purposes, fuel cell, modeled after Ene-Farm, is added to the original configuration. Each standalone system has internal, priority-based, agent, capable of scheduling the operating hours of the fuels cell to maximize the utilization of the generation and minimize curtailment. The energy market is based on a simplified version of the Zaraba, a continuous double auction algorithm used by the Japanese Stock Exchange, in which prosumer can submit a bid for a future timeslot. The preliminary numerical evaluation is based on the results from several simulations using different versions of the internal management system.

Peer-to-peer (P2P) energy trading is a mechanism that allows people to share locally the energy they have generated from distributed renewable resources (DER), to generate profit form the unused resources and to reduce the cost for electricity for the household and in the community itself. This calls for the design of new energy markets, accompanied with the development of comprehensive exchange strategies, which reflect both the consumer?s preferences and the heterogeneity of the renewable sources. The paper presents a study of the behavior patterns of individual prosumers using bidding strategies based on the State-of-Charge of the battery and two different pricing algorithm, one with fixed prices and one based on the battery price of each standalone system as well as the effect such patterns have on a local energy market with solar panels, fuels cells and batteries. The evaluation is achieved by the use of a prototype based on the Open Energy Systems (OES), a community in Okinawa, Japan, made of 19 interconnected houses with residential storage, photovoltaic cells and AC grid connection. In order to simulate heterogeneity, a fuel cell, modeled after Ene-Farm, is added to the original configuration. Each house has a power flow management system, which uses a priority-based algorithm to maintain demand-response efficiency, capable of scheduling the operating hours of the available fuels cells. The energy market is based on Zaraba, a continuous double auction algorithm used by the Japanese Stock Exchange, in which prosumer can bid for a desired amount and price of electricity for a 30-minutes time slot in the future The preliminary numerical evaluation is based on the results from several simulations using different versions of the bidding agent.

The embeddings of entities in a large knowledge base (e.g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge. In this paper, we present Wikipedia2Vec, a Python-based open-source tool for learning the embeddings of words and entities from Wikipedia. The proposed tool enables users to learn the embeddings efficiently by issuing a single command with a Wikipedia dump file as an argument. We also introduce a web-based demonstration of our tool that allows users to visualize and explore the learned embeddings. In our experiments, our tool achieved a state-of-the-art result on the KORE entity relatedness dataset, and competitive results on various standard benchmark datasets. Furthermore, our tool has been used as a key component in various recent studies. We publicize the source code, demonstration, and the pretrained embeddings for 12 languages at https://wikipedia2vec.github.io.

In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves state-of-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.

In this chapter, we describe our question answering system, which was the winning system at the Human-Computer Question Answering (HCQA) Competition at the Thirty-first Annual Conference on Neural Information Processing Systems (NIPS). The competition requires participants to address a factoid question answering task referred to as quiz bowl. To address this task, we use two novel neural network models and combine these models with conventional information retrieval models using a supervised machine learning model. Our system achieved the best performance among the systems submitted in the competition and won a match against six top human quiz experts by a wide margin.

Accidentally a new method of water pipe repairs using liquid nitrogen freezing was discovered in 2008. Freezing water creates an ice plug to stop water flow. The new method uses double-ice-plug freezing instead of the world widely used single-ice-plug freezing. We have investigated the static sliding (shear) friction between the water pipe and the frozen ice-plug(s). The proposed method significantly increases water pressure in order to stop water flow. The experimented result shows that the ice adhesive strength of double-ice-plug freezing is roughly four times higher than that of single-ice-plug freezing. The unfrozen part of the water pipe plays a key role for achieving the high ice adhesive strength by taking advantage of water expansion upon freezing. The brief results were published in Science Advances. Further results are detailed in this note.

The study, presented in this paper, aims to show the impact of installation of fuel cells in a local energy market with renewables and batteries. The market is in the form of double auction where a prosumer can submit a bid for future timeslot with a given amount and price for electricity, determined by prosumer?s Exchange Strategy, based on the State of Charge (SoC) of the battery. In addition, each prosumer is also equipped with a priority-based agent for internal power management. The proposed simulation is based on the Open Energy Systems (OES), in which 19 autonomous subsystems are interconnected via a DC power bus. Each subsystem is equipped with batteries, solar panels and AC grid connection. For the simulation purposes, fuel cells are added to the configuration. The preliminary numerical evaluation is based on the results from several simulations with various configurations for the subsystems' power sources.

The latest study shows that various substances indoor have an adverse effect on the quality of human life. It was reported that a high concentration of Carbon Dioxide (CO2) has an adverse effect on human's intellectual activity. In order to improve our intellectual activities, we must clarify the diffusion of CO2 concentration from human breath by CO2 measurement units. In this study, we have measured and analyzed the diffusion of CO2 depending on human positions, the distance and heights in the room by simultaneous multi-point sensing. In order to control the amount of CO2 emitted from human breath, we examined using respiration induction that human breath frequency is controlled by music. The results show that CO2 from human breath diffuses throughout the room regardless of the position of persons, distance and height difference. We also have found that it is possible to influence CO2 emissions by respiration induction using monotonous music.