Voltammetric study of electrocatalysis for dioxygen reduction by trinuclear rutheniumammine complex confined in a polymer membrane

Electrocatalytic O₂ reduction was studied using a modified electrode coated with a Nafion membrane (Nf) dispersing a trinuclear ruthenium ammine complex ([(NH₃)₅Ru^IIIORu^IV(NH₃)₄ORu^III(NH₃)₅]Cl₆, Ru-red). When measuring cyclic voltammogram under O₂ atmosphere (at 0.5 mV s⁻¹), catalytic currents due to O₂ reduction were found to develop below −0.2 V (vs. Ag/AgCl). Since Ru-red undergoes irreversible decomposition into the mononuclear complexes via the reduced state (Ru^III-Ru^III-Ru^III) (∼−0.1 V), it is suggested that the electrocatalysis originates from the decomposed species (initial active species: Ru^II(NH₃)₅(OH₂) and Ru^II(NH₃)₄(OH₂)₂) rather than from the Ru-red. Although the present electrocatalyst was also applied to H₂O₂ reduction system, the catalytic activity was found to be poor from the voltammetric behavior. It appeared that the kinetics of the electrocatalysis is much faster in the O₂ reduction than in the H₂O₂ one. A selective and direct catalysis for O₂ reduction into H₂O was suggested from a ring-disk voltammogram to take place by an aggregate of the mononuclear ruthenium complexes in the polymer matrix. In addition, it was found that electrocatalytic O₂ reduction involves a slow kinetic process, so that factors affecting the overall kinetics were discussed in terms of the catalysis mechanism.     

Significant prognostic factors in breast cancer of Japanese women: special reference to nuclear grade and sinus histiocytosis

Progress of psychotherapy and of related behaviour sciences makes evident the importance of a better understanding of human relations. But psychoanalysis finds it hard to describe interpersonal processes without transference. In order to remain within the conceptional frame of metapsychology it has to see interaction between individuals as the oral, aggressive or sexual cathexis of an object or as satisfaction or denial of the subject by the object. The structure of the "ego", which--in analogy to medical thinking--is conceived as an organ with its functions, is considered to have no interpersonal activities. The "ego" of the classic psychoanalytic theory is chiefly occupied with itself. It has to care for its egoistical interests and to guarantee its self-preservation. As an auxiliary and meanwhile popular concept the "self" has been introduced to describe object-relations. This concept is not sharply defined. Due to its metapsychological implications it produces additional theoretical difficulties. Linguistic studies show that every inventory of words implies a certain insight into reality. For this reason the metapsychological machine-like concept of psychic structures does not permit new ideas about interpersonal relations. If we leave metapsychology and base on colloquial speech we see that the experience of "I" is much more related to persons than the rather autistic concept of the "ego" shows. Further we learn that self-preservation cannot be an egoistical interest; it depends on the attachment to others. All feelings of self-esteem depend much more on interpersonal relations than on "narcissistic regulations". From these experiences three conclusions are derived: a) One of the main qualities of the ego is the relatedness to persons. b) The concept of narcissistic regulation as a successor of primary narcissism is no longer useful. Narcissistic traits develop as the secundary compensations if the individual failed to build up satisfactory interpersonal relations. c) The revision of (a) ego-psychology and (b) theory of narcissism asks for modifications of the therapeutic technique, where now the interest is especially concentrated on interpersonal problems instead on the pathology of the ego.     

SiO2 thin film deposition on the inner surface of a poly(tetra-fluoroethylene) narrow tube by atmospheric-pressure glow microplasma

SiO₂ thin films were deposited on the inner surfaces of a commercial poly(tetrafluoroethylene) narrow tube with an inner diameter of 0.5 mm using tetraethoxysilane/O₂ feedstock gases and He carrier gas by atmospheric-pressure microplasma-enhanced chemical vapor deposition. A glow microplasma was generated inside the tube by radio frequency (RF) capacitively coupled discharge. X-ray photoelectron spectroscopy spectra showed that the tube inner surface was covered by a SiO₂ thin film. Transparent SiO₂ thin films were obtained with a deposition rate of 230 nm/min at an RF power of 6 W and substrate temperature of 100 °C. The wettability of the SiO₂-coated tube was about 3 times as large as that of an untreated sample tube.     

Calculations of impurity doping effects in CrO/sub 2/

The effects of impurity doping in CrO/sub 2/ have been calculated based on the Cr/sub 9/O/sub 40/ cluster using the method of DV-X /spl alpha/ molecular orbital calculation. Elements (Mo, W) belonging to the same family as Cr and the ones (Zr, Ru, Sn) whose dioxides have the same crystalline structure as the rutile were selected for impurity doping. The calculated results show the large enhancements of the magnetic moment and the Curie temperature by the doping of Zr and Ru, respectively.     

Dynamic cluster generation for a fuzzy classifier with ellipsoidalregions

In this paper, we discuss a fuzzy classifier with ellipsoidal regions that dynamically generates clusters. First, for the data belonging to a class we define a fuzzy rule with an ellipsoidal region. Namely, using the training data for each class, we calculate the center and the covariance matrix of the ellipsoidal region for the class. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. Then if the number of the data belonging to a class that are misclassified into another class exceeds a prescribed number, we define a new cluster to which those data belong and the associated fuzzy rule. Then we tune the newly defined fuzzy rules in the similar way as stated above, fixing the already obtained fuzzy rules. We iterate generation of clusters and tuning of the newly generated fuzzy rules until the number of the data belonging to a class that are misclassified into another class does not exceed the prescribed number. We evaluate our method using thyroid data, Japanese Hiragana data of vehicle license plates, and blood cell data. By dynamic cluster generation, the generalization ability of the classifier is improved and the recognition rate of the fuzzy classifier for the test data is the best among the neural network classifiers and other fuzzy classifiers if there are no discrete input variables     

Efficient distribution-free population learning of simple concepts

We consider a variant of the ‘population learning model’ proposed by Kearns and Seung [8], in which the learner is required to be ‘distribution-free’ as well as computationally efficient. A population learner receives as input hypotheses from a large population of agents and produces as output its final hypothesis. Each agent is assumed to independently obtain labeled sample for the target concept and output a hypothesis. A polynomial time population learner is said to PAC-learn a concept class, if its hypothesis is probably approximately correct whenever the population size exceeds a certain bound which is polynomial, even if the sample size for each agent is fixed at some constant. We exhibit some general population learning strategies, and some simple concept classes that can be learned by them. These strategies include the ‘supremum hypothesis finder’, the ‘minimum superset finder’ (a special case of the ‘supremum hypothesis finder’), and various voting schemes. When coupled with appropriate agent algorithms, these strategies can learn a variety of simple concept classes, such as the ‘high–low game’, conjunctions, axis-parallel rectangles and others. We give upper bounds on the required population size for each of these cases, and show that these systems can be used to obtain a speed up from the ordinary PAC-learning model [11], with appropriate choices of sample and population sizes. With the population learner restricted to be a voting scheme, what we have is effectively a model of ‘population prediction’, in which the learner is to predict the value of the target concept at an arbitrarily drawn point, as a threshold function of the predictions made by its agents on the same point. We show that the population learning model is strictly more powerful than the population prediction model. Finally, we consider a variant of this model with classification noise, and exhibit a population learner for the class of conjunctions in this model. This revised version was published online in June 2006 with corrections to the Cover Date.     

Voltage-balancing method for IGBTs connected in series

This paper presents a new method for balancing voltages of series-connected insulated gate bipolar transistors (IGBTs). This method can be implemented only by adding simple circuits to the gate drive system of the IGBTs, and its effect of balancing the IGBT's collector-emitter voltages during the switching transients is remarkable. This principal strategy and experimental results with series-connected IGBTs are first described. After that, further experimental results are shown from the switching tests of four 2.5-kV flat-packaged IGBTs connected in series. Through the switching tests, superior characteristics of the proposed method have been confirmed.     

Meshless analysis of potential problems in three dimensions with the hybrid boundary node method

Combining a modified functional with the moving least‐squares (MLS) approximation, the hybrid boundary node method (Hybrid BNM) is a truly meshless, boundary‐only method. The method may have advantages from the meshless local boundary integral equation (MLBIE) method and also the boundary node method (BNM). In fact, the Hybrid BNN requires only the discrete nodes located on the surface of the domain. The Hybrid BNM has been applied to solve 2D potential problems. In this paper, the Hybrid BNM is extended to solve potential problems in three dimensions. Formulations of the Hybrid BNM for 3D potential problems and the MLS approximation on a generic surface are developed. A general computer code of the Hybrid BNM is implemented in C++. The main drawback of the ‘boundary layer effect’ in the Hybrid BNM in the 2D case is circumvented by an adaptive face integration scheme. The parameters that influence the performance of this method are studied through three different geometries and known analytical fields. Numerical results for the solution of the 3D Laplace's equation show that high convergence rates with mesh refinement and high accuracy are achievable. Copyright © 2004 John Wiley & Sons, Ltd.