Local disturbances preceding a running crack front in a viscoelastic solid

The local disturbances ahead of a running crack front in a viscoelastic solid were measured through noncontact electro-optical tools. It was observed that the very high local strain rate up to 200/sec exists even in the present quasistatic loading case. It may be concluded that the running crack propagation velocities, not the conventional average strain rates ranging from about 10^?4/sec to 10^?2/sec, govern the local disturbances, as the crack propagation velocities always exceed 200m/sec for both average strain rates, while the local strain rates observed do not show much differences between both average strain rate cases.     

Aggregation arrestant pheromone of the German cockroach,Blattella germanica (L.) (Dictyoptera: Blattellidae): Isolation and structure elucidation of blattellastanoside-A and -B

The aggregation pheromone of the German cockroach,Blattella germanica, consists of attractant and arrestant, which can be detected by olfactometer and choice-chamber assay, respectively. Both were extracted from the frass-contaminated filter paper being used as a shelter. They were separated by solvent partition withn-butanol and water. The arrestant from then-butanol phase was purified by open column chromatography and then successive HPLC isolated two major arrestant components. Spectral evidence from SI-MS, HR-EI-MS, and NMR experiments with pulse techniques provided possible structures as 1-(6-chloro-4,5-epoxy-5-stigmast-3-yl)--d-glucopyranoside and 1-(6-chloro-5-hydroxy-5-stigmast-3-yl)--d-glu-copyranoside, denoted as blattellastanoside-A and blattellastanoside-B, respectively. They represented arrestant activity as median effective doses (ED₅₀) at 0.044 (A) and 3.2 (B) nmol on 1.0 cm² of Whatman No. 1 filter paper.     

Molecular motions of non-crystalline poly(aryl ether-ether-ketone) PEEK and influence of elecron beam irradiation

The dynamic mechanical relaxation of non-crystalline poly(aryl ether-ether-ketone) PEEK and the one irradiated with electron beam were studied. The three distinct γ, β, α′ relaxation maxima were observed in unirradiated PEEK from low to high temperature. It was revealed from the study on the irradiation effects that three different molecular processes are overlapped in γ relaxation peak, i.e., molecular motion of water bound to main chain (peak temperature; at ?100°C), local motion of main chain (at ?80°C), and local mode of the aligned and/or oriented moiety (at ?40°C). The β relaxation connected with the glass transition occurred at 150°C and it shifted to higher temperature by irradiation. The α′ relaxation which can be attributed to rearrangement of molecular chain due to crystallization was observed in unirradiated PEEK ～ 180°C and its magnitude decreased with the increase in irradiation dose. This effect indicates the formation of structures inhibiting crystallization such as crosslinking and/or short branching during irradiation. A new relaxation, β′, appeared in the temperature range of 40° to 100°C by irradiation and its magnitude increased with dose. This relaxation was attributed to rearrangement of molecular chain from loosened packing around chain ends, which were introduced into the non-crystalline region by chain scission under irradiation, to more rigid molecular packing, From these observations, we proposed that deterioration in mechanical properties of non-crystalline PEEK by high energy electron beam was brought about not only by chain scission but structural changes such as crosslinking and/or branching in the main chain.     

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 %.     

Microscopic image restoration based on tensor factorization of rotated patches

In microscopic image processing for analyzing biological objects, structural characters of objects such as symmetry and orientation can be used as a prior knowledge to improve the results. In this study, we incorporated filamentous local structures of neurons into a statistical model of image patches and then devised an image processing method based on tensor factorization with image patch rotation. Tensor factorization enabled us to incorporate correlation structure between neighboring pixels, and patch rotation helped us obtain image bases that well reproduce filamentous structures of neurons. We applied the proposed model to a microscopic image and found significant improvement in image restoration performance over existing methods, even with smaller number of bases.     

Efficient Leave-<Emphasis Type="Italic">m</Emphasis>-out Cross-Validation of Support Vector Regression by Generalizing Decremental Algorithm

We propose a computationally efficient method for cross-validation of the Support Vector Regression (SVR) by generalizing the decremental algorithm of SVR. Incremental and decremental algorithm of Support Vector Machines (SVM) ^{2, 8, 9)} efficiently update the trained SVM model when a single data point is added to or removed from the training set. The computational cost of leave-one-out cross-validation can be reduced using the decremental algorithm. However, when we perform leave-m-out cross-validation (m > 1), we have to repeatedly apply the decremental algorithm for each data point. In this paper, we extend the decremental algorithm of SVR^{8, 9)} in such a way that several data points can be removed more efficiently. Experimental results indicate that the proposed approach can reduce the computational cost. In particular, we observed that the number of breakpoints, which is the main computational cost of the involved path-following, were reduced from \({\mathcal O}(m)\) to \({\mathcal O}(\sqrt{m})\).     

Accurate finite element method for atomic calculations based on density functional theory and Hartree–Fock method

An accurate finite element method is developed for atomic calculations based on density functional theory (DFT) within local density approximation (LDA) and Hartree–Fock (HF) method. The radial wave functions are expanded by cubic Hermite spline functions on a uniform mesh for , and all the associated integrals are analytically evaluated in conjunction with fitting procedures of the Hartree and the exchange–correlation potentials to the same cubic Hermite spline functions using a set of recurrence formulas. The total energy of atoms systematically converges from above, and the error algebraically decays as the mesh spacing decreases. When the mesh spacing d is taken to be , the total energy for an atom of atomic number Z can be calculated within error of ¹⁰−7 hartree for both the LDA and HF methods. The equal applicability of the method to DFT and the HF method with a similar degree of high accuracy enables the method to be a reliable platform for development of new functionals in DFT such as hybrid functionals.     

Using selective precipitant for uranyl ions — Fundamental studies for evaluating the precipitant performance

We have developed a simple reprocessing process for spent FBR fuels using N-cyclohexyl-2-pyrrolidone (NCP) which has selective precipitation ability for UO₂²⁺ ions. It was confirmed that NCP has sufficient precipitation ability for UO₂²⁺ ions, decontamination capability (separation of UO₂²⁺ from simulated fission products), and resistance to γ-ray radiation in nitric acid solutions. These findings indicate that NCP is applicable to our reprocessing process. We have also evaluated performances of other precipitants such as N-n-propyl-2-pyrrolidone (NProP), N-n-butyl-2-pyrrolidone (NBP), and N-n-butyl-2-pyridone (NBPyr). It was found that higher decontamination factors (DFs) are obtained by using NProP and NBP. This can be interpreted that the hydrophobicity of NProP and NBP is lower than that of NCP. Furthermore, we have obtained an experimental result that the resistance of NBPyr to γ-ray radiation is superior to that of NCP.     

A Knowledge Discovery System with Support for Model Selection and Visualization

The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.     

Adaptive fuzzy inference neural network

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.