Fat Bloom Caused by Partial De-Oiling on Chocolate Surfaces after High-Temperature Exposure

Fat bloom in chocolate is a substantial problem that affects its sensory properties, such as texture and appearance. This phenomenon is because of diffuse light reflection on a roughened surface of chocolate, caused by structural changes of fat crystals subjected to various temperature conditions. The purpose of this study is to characterize the fat bloom formed through gradual two-step cooling after exposure to temperatures (35–37 °C) slightly above the cocoa butter Form βV melting point (33.8 °C). To clarify the fat bloom formation process, the structural changes in cocoa butter and on the chocolate surface, at the dynamic thermal condition for bloom formation, was investigated using X-ray diffraction (XRD), fluorescence light microscopy, and scanning electron microscopy (SEM). The results revealed that an entirely light brown fat bloom occurred, even in the absence of the Form βVI or other polymorphic transformation. Microscopic observation showed that the light brown appearance was because of the porous structure on the chocolate surface. This porous structure was formed by liquid oil moving inside of chocolate from the surface. The formation of a coarse network and the subsequent de-oiling, because of movement of unsolidified liquid fat into the chocolate, appeared to be the main causes of bloom formation. Therefore, a coarsened fat network and oil movement besides the conventional principles of polymorphic transformation of cocoa butter should be considered.     

Extrasynaptic-GABA-mediated neuromodulation in a sensory cortical neural network

Simulating a neural network model of an early sensory cortical area, we investigated how gamma-aminobutyric acid (GABA) accumulated in extracellular space (ambient GABA), which depends on the synaptic activity of GABAergic interneurons, acts on the GABAa-receptors located on extrasynaptic membrane regions of principal cells (P), feedback inhibitory cells (F) and lateral inhibitory cells (L). The ambient GABA enhanced the selective responsiveness of P-cells to a target feature stimulus, if it acted on the extrasynaptic GABAa-receptors of P-cells. The ambient GABA led to depolarizing P-cells during ongoing (spontaneous) neuronal-activity periods, if it acted on the extrasynaptic GABAa-receptors of F or L cells. This membrane depolarization contributed to establishing an ongoing subthreshold neuronal state, by which the P-cells could respond quickly to the target stimulus. We suggest that the combinatorial inhibition of P, F, and L cells, meditated by extrasynaptic GABAa-receptors recognizing ambient GABA, is crucial for processing the information of relevant sensory features and for establishing an ongoing subthreshold cortical state that prepares as a ready state for subsequent sensory input. A failure in neuronal-activity-dependent regulation of ambient GABA, stemming largely from the depletion of GABA in extracellular space during senescence, may cause the degeneration of intracortical inhibition that leads to cognitive dysfunction in old animals.     

Three-dimensional medical image recognition of cancer of the liver by a revised radial basis function (RBF) neural network algorithm

In this study, we propose a revised radial basis function (RBF) neural network algorithm and apply this algorithm to computer-aided diagnosis (CAD) of the liver. First, the revised RBF neural network algorithm is applied to recognition of the liver regions, and the recognition results are compared with those obtained using the conventional RBF neural network and the conventional multilayered neural network trained using the back-propagation algorithm. It is shown that the revised RBF neural network is accurate, and is a useful method because the parameters are automatically determined. Then, the revised RBF neural network is applied to CAD of the liver cancer called hepatocellular carcinoma (HCC).     

Robust control method for the inverted pendulum system with structured uncertainty caused by measurement error

In this article, we propose a design method for an inverted pendulum system with a structured uncertainty. We consider that such an uncertainty is caused by a measurement error in the rotation angle of the pendulum and effects on the system structure that cannot be included in the nominal elements. For the uncertain system obtained, we apply an integral tracking control and the guaranteed cost control to design a robust, stable, tracking control system. Finally, we show the effectiveness of our method through a numerical example.     

Vehicle inductive signatures recognition using a Madaline neural network

In this paper, we report results obtained with a Madaline neural network trained to classify inductive signatures of two vehicles classes: trucks with one rear axle and trucks with double rear axle. In order to train the Madaline, the inductive signatures were pre-processed and both classes, named C2 and C3, were subdivided into four subclasses. Thus, the initial classification task was split into four smaller tasks (theoretically) easier to be performed. The heuristic adopted in the training attempts to minimize the effects of the input space non-linearity on the classifier performance by uncoupling the learning of the classes and, for this, we induce output Adalines to specialize in learning one of the classes. The percentages of correct classifications presented concern patterns which were not submitted to the neural network in the training process, and, therefore, they indicate the neural network generalization ability. The results are good and stimulate the maintenance of this research on the use of Madaline networks in vehicle classification tasks using not linearly separable inductive signatures.     

Scale free interval graphs

Scale free graphs have attracted attention by their non-uniform structure that can be used as a model for various social and physical networks. In this paper, we propose a natural and simple random model for generating scale free interval graphs. The model generates a set of intervals randomly under a certain distribution, which defines a random interval graph. The main advantage of the model is its simpleness. The structure/properties of generated graphs are analyzable by relatively simple probabilistic and/or combinatorial arguments, which is different from many other models. Based on such arguments, we show for our random interval graph that its degree distribution follows a power law, and that it has a large average clustering coefficient.     

Fabrication of sub-micron structures for MEMS using deep X-ray lithography

Fabrication techniques of microstructures with high resolution and high aspect ratio are necessary for practical microelectromechanical systems (MEMS) that have high performance and integration. In order to fabricate microstructures with sub-micron resolution and high aspect ratio, deep X-ray lithography has been investigated using the compact synchrotron radiation (SR) light source called “AURORA”. An X-ray mask for sub-micron deep X-ray lithography, which is composed of 1 μm thick Au as absorbers, 2 μm thick SiC as a membrane and 625 μm thick Si as a frame, was designed. In preliminary experiments, the following results were achieved: EB resist microstructures with an aspect ratio of 22 corresponding with 0.07 μm width and 1.3 μm height were formed; a 10 μm thick PMMA resist containing no warp was formed by direct polymerization, enabling more precise gap control.     

Interactive learning and management of visual information via human-like software robot

To achieve smooth real-world interaction between people and computers, we developed a system that displays a three-dimensional computer-graphic human-like image from the waist up (anthropomorphic software robot: hereinafter “robot”) on the display, that interactively sees and hears, and that has fine and detailed control functions such as facial expressions, line of sight, and pointing at targets with its finger. The robot visually searches and identifies persons and objects in real space that it has learned in advance (registered space, which was our office in this case), manages the history information of the places and times it found objects and/or persons, and tells the user, indicating their three-dimensional positions with line of sight and its finger. It interactively learns new objects and persons with line of with their names and owners. By using this function, the robot can engage in simple dialogue (do a task) with the user. Osamu Hasegawa, Ph.D.: He received the B.E. and M.E. degrees from the Science University of Tokyo, in 1988, 1990 respectively. He received Ph.D. degree from the University of Tokyo, in 1993. Currently, he is a senior research scientist at the Electrotechnical Laboratory (ETL), Tsukuba, Japan. His research interests include Computer Vision and Multi-modal Human Interface. Dr. Hasegawa is a member of the AAAI, the Institute of Electronics, Information and Communication Engineers, Japan (IEICE), Information Processing Society of Japan and others. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from the University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information Processing Society of Japan. He is a member of IEICE, IEEE, IPSJ, ITE. Satoru Hayamizu, Ph.D.: He is a leader of Interactive Intermodal Integration Lab. at Electrotechnical Laboratory. He received the B.E., M.E., Ph.D. degrees from Tokyo University. Since 1981, he has been working on speech recognition, spoken dialogue, and communication with artifacts. From 1989 to 1990, he was a visiting scholar in Carnegie Mellon University and in 1994 a visiting scientist in LIMSI/CNRS.