Piezoresistivity in Polymer-Ceramic Composites

Composite materials composed of randomly dispersed semiconducting ceramic particles in an insulating polymer matrix show a pronounced change in resistivity with pressure. Different amounts of iron oxide (Fe₃O₄) powder and antimony-doped tin oxide (SnO₂:Sb) powder were dispersed in an epoxy polymer matrix to form pressure-sensitive composites. In each family of materials, an insulator-to-semiconductor transition is observed in agreement with percolation theory. Composites within a certain range of filler content showed substantial piezoresistive effect under both uniaxial and hydrostatic pressure in which sensitivity is controlled by the choice of filler material and the volume fraction. The effect of temperature on the piezoresistance effect was also examined. Piezoresistors made from Fe₃O₄ composites showed larger temperature changes than those filled with Sb-doped SnO₂.     

Continual conversion of free fatty acid in rice bran oil to triacylglycerol by immobilized lipase

Rice bran oil containing 30–50% free fatty acid was continually converted to an oil containing more than 75% of triacylglycerol (TG) by means of immobilized lipase. The reaction was carried out at 60°C for 24 h with dehydration and reactant mixing by dry nitrogen flow under a positive nitrogen atmosphere. Enzymatic TG synthesis with evaporation by heating was not suitable because of the increasing peroxide value of the oil. Part of this article was presented at the annual meeting of the Japan Oil Chemists' Society at Sendai, Japan, October, 16, 1990.     

Predicting viewer-perceived activity/dominance in soccer games with stick-breaking HMM using data from a fixed set of cameras

We attempted to predict activity/dominance for soccer games, where activity is defined as the degree of activity of the game as perceived by the viewer, whereas dominance is the degree at which the viewer perceives a particular team to dominate over the other team. Such activity/dominance information would help a layman viewer understand the game. It would also enable construction of an automatic digest creation system that extracts scenes having high activity/dominance. There are two facets of this study: 1. The main part of the underlying prediction model consists of a Stick-Breaking Hidden Markov Model, where the data automatically estimates the number of states of the Markov process behind the data. 2. The data used in this paper is vector time-series data consisting of player, referee, and ball positions, together with team information, acquired by a set of fixed cameras. The problem was approached with a Bayesian framework where learning and prediction were implemented by three different methods: Markov Chain Monte Carlo, Expectation Maximization, and Variational Bayes. The proposed method was tested using a dataset consisting of 10 professional soccer games and was compared against standard regression methods.     

Generalized measuring-worm algorithm: high-accuracy mapping and movement via cooperating swarm robots

Recently, many extensive studies have been conducted on robot control via self-positioning estimation techniques. In the simultaneous localization and mapping (SLAM) method, which is one approach to self-positioning estimation, robots generally use both autonomous position information from internal sensors and observed information on external landmarks. SLAM can yield higher accuracy positioning estimations depending on the number of landmarks; however, this technique involves a degree of uncertainty and has a high computational cost, because it utilizes image processing to detect and recognize landmarks. To overcome this problem, we propose a state-of-the-art method called a generalized measuring-worm (GMW) algorithm for map creation and position estimation, which uses multiple cooperating robots that serve as moving landmarks for each other. This approach allows problems of uncertainty and computational cost to be overcome, because a robot must find only a simple two-dimensional marker rather than feature-point landmarks. In the GMW method, the robots are given a two-dimensional marker of known shape and size and use a front-positioned camera to determine the marker distance and direction. The robots use this information to estimate each other’s positions and to calibrate their movement. To evaluate the proposed method experimentally, we fabricated two real robots and observed their behavior in an indoor environment. The experimental results revealed that the distance measurement and control error could be reduced to less than 3 %.     

An incentive game based evolutionary model for crowd sensing networks

Crowd sensing networks can be used for large scale sensing of the physical world or other information service by leveraging the available sensors on the phones. The collector hopes to collect as much as sensed data at relatively low cost. However, the sensing participants want to earn much money at low cost. This paper examines the evolutionary process among participants sensing networks and proposes an evolutionary game model to depict collaborative game phenomenon in the crowd sensing networks based on the principles of game theory in economics. A effectively incentive mechanism is established through corrected the penalty function of the game model accordance with the cooperation rates of the participant, and corrected the game times in accordance with it’s payoff. The collector controls the process of game by adjusting the price function. We find that the proposed incentive game based evolutionary model can help decision makers simulate evolutionary process under various scenarios. The crowd sensing networks structure significantly influence cooperation ratio and the total number of participant involved in the game, and the distribution of population with different game strategy. Through evolutionary game model, the manager can select an optimal price to facilitate the system reach equilibrium state quickly, and get the number of participants involved in the game. The incentive game based evolutionary model in crowd sensing networks provides valuable decision-making support to managers.     

Generating function of color information detection using genetic programming

This article proposes a function for color information detection using genetic programming (GP). In image-processing, object detection is one of the important processes. In cases where the object has a complex color domain, detection becomes more difficult. We generated a detection function for a complex color domain by using GP. The detection function deals with one pixel of an input image, and it obtains an output image by processing for all pixels. We aimed at a reduction in the time taken by a human to consider an image-processing system design. We applied the generation of GP to detect a target color region in actual images. The results show that the detection function has sufficient ability for these detections.     

Ligand density-dependent influence of arginine–glycine–aspartate functionalized gold nanoparticles on osteogenic and adipogenic differentiation of mesenchymal stem cells

Extracellular matrix (ECM) plays a very important role in regulating cell function and fate. It is highly desirable to fabricate biomimetic models to investigate the role of ECM in stem cell differentiation. In this study, arginine–glycine–aspartate (RGD)-modified gold nanoparticles (Au NPs) with tunable surface ligand density were prepared to mimic the ECM microenvironment. Their effect on osteogenic and adipogenic differentiation of human mesenchymal stem cells (MSCs) was investigated. The biomimetic Au NPs were taken up by MSCs in a ligand density-dependent manner. The biomimetic NPs with a high RGD density had an inhibitive effect on the alkaline phosphatase (ALP) activity, calcium deposition, and osteogenic marker gene expression of MSCs. Their effect on oil droplet formation and adipogenic marker gene expression was negative when RGD density was low, while their effect was promotive when RGD density was high. The biomimetic Au NPs regulated the osteogenic and adipogenic differentiation of MSCs mainly through affecting the focal adhesion and cytoskeleton. This study highlights the roles of biomimetic NPs on stem cell differentiation that could provide a meaningful strategy in fabricating functional biomaterials for tissue engineering and biomedical applications.

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