Photoelectrochemical studies of passivating films on tin in slightly alkaline solutions

The photoresponse of passivating films grown on tin in slightly alkaline solutions has been studied under both galvanostatic and potentiostatic conditions. The response of films growing under galvanostatic conditions was used to determine the nature and magnitude of their deviation from stoichiometry, as a function of potential. The photoresponse of films formed at potentials between 0.5 and 1.6 V can be described by Gartner's theory of depletion layer photoeffects. For thicker films the presence of localized levels acting as traps is postulated to explain the slow response and potential dependence of the photocurrents. The spectral response is independent of film thickness. Under sub-bandgap illumination indirect transitions with an absorption edge of 2.55eV are predominant. No evidence of impurity band transitions was found     

Home bedding modifies ultrasonic vocalization of infant bank voles

Bank vole,Clethrionomys glareolus, pups, similar to many other infant rodents, emit ultrasonic vocalizations when they are removed from the nest and cooled. Infants exposed to bedding from their home cage produced fewer and shorter calls than infants tested without bedding. Sound frequencies were significantly higher in infants exposed to home bedding than in other experimental groups. These results provide evidence that infant bank voles are able to identify the presence of home bedding, probably on the basis of odor. This appears to be the first report of shifts in frequency of pup ultrasonic calls in response to olfactory cues.     

Prototype selection algorithms for distributed learning

Distributed learning from data is one of the typical tasks solved by distributed data-mining techniques and is seen as a fundamental computational problem. One of the approaches suitable for distributed learning is to select, by data reduction, relevant local patterns, called also prototypes, from geographically distributed databases. Next, locally selected prototypes can be moved to other sites and merged into the global knowledge model. The paper presents three agent-based population learning algorithms for distributed learning. The proposed algorithms are based on agent collaborations in distributed prototype selection processes and on agent collaborations when the learning global model is created. The basic property of the presented algorithms is that the prototypes are selected by agent-based population learning algorithm from data clusters induced at distributed sites. The main goal of the paper is to empirically compare how the way of inducing such clusters can influence the distributed learning performance. The paper investigates the agent-based population learning algorithms used to solve distributed data reduction and gives a brief discussion of the procedures for clusters initialization. Finally, computational experiment results are shown.     

Training of an artificial neural network in the diagnostic system of a technical object

The present paper covers the issue of training of an artificial neural network in an intelligent diagnostic system whose purpose is to evaluate repairable technical objects. The structure of the diagnostic system was characterized, and the measurement and diagnostic subsystems were described. An artificial neural network is an important element in an intelligent diagnostic subsystem. The structure, the algorithm, the organization of a neural network and the basic relations that describe its work were presented. The information presented in the form of the vectors of diagnostic signals, and their standard vectors constitute the primary information base used in “DIAG” computer program. Training of an artificial neural network is an important aspect that is presented in the paper. The issue concerning these problems is not presented in the literature. Training of a network was presented on the grounds of teaching vectors, which are determined in a diagnostic system in the process of a simulation of a specific state in the object examined. An example of training of a network was presented in a diagnostic system which evaluates a control system of the operation of a car engine. Appropriate connections were presented for the purpose of a qualitative assessment of the training process of a neural network.     

Preliminary study on the accuracy of respiratory input impedance measurement using the interrupter technique

Respiratory input impedance contains information about the state of pulmonary mechanics in the frequency domain. In this paper the possibility of respiratory impedance measurement by interrupter technique as well as the accuracy of this approach are assessed. Transient states of flow and pressure recorded during expiratory flow interruption are simulated with a complex, linear model for the respiratory system and then used to calculate the impedance, including three states of respiratory mechanics and the influence of the measurement noise. The results of computations are compared to the known, theoretical impedance of the model. At 1 kHz sampling rate, the optimal time window lays between 100 and 200 ms and is centred around the pressure jump caused by the flow interruption. The proposed algorithm yields satisfactory accuracy in the range from 10 to 400 Hz, particularly to 150 Hz. Depending on the simulated respiratory system state, the error of calculated impedance (relative Euclidean distance between the vectors of computed and theoretical values), for the window of 190 ms, varies between 5.0% and 7.1%.     

Voltage preamplifier for extensional quartz sensors used in scanning force microscopy

Extensional-mode quartz resonators are being increasingly used as force sensors in dynamic scanning force microscopy or atomic force microscopy (AFM). We propose a voltage preamplifier in order to amplify the charge induced on quartz electrodes. The proposed solution has some advantages over the typically used current-to-voltage converters. First, the gain does not depend on the inner parameters of the quartz resonator, which are usually unknown for the specific resonator and may even vary during the measurement. Second, with such an amplifier a better signal-to-noise ratio can be achieved. Finally, we present AFM images of the Si(111) and the SiO(2) surfaces obtained by the voltage preamplifier with simultaneously recorded tunneling current.     

Au/Ce1−xZrxO2 as effective catalysts for low-temperature CO oxidation

The physico-chemical properties and activity of Ce-Zr mixed oxides, CeO₂ and ZrO₂ in CO oxidation have been studied considering both their usefulness as supports for Au nanoparticles and their contribution to the reaction. A series of Ce_1−xZr_xO₂ (x = 0, 0.25, 0.5, 0.75, 1) oxides has been prepared by sol–gel like method and tested in CO oxidation. Highly uniform, nanosized, Ce-Zr solid solutions were obtained. The activity of mixed oxides in CO oxidation was found to be dependent on Ce/Zr molar ratio and related to their reducibility and/or oxygen mobility. CeO₂ and Ce_0.75Zr_0.25O₂, characterized by the cubic crystalline phase show the highest activity in CO oxidation. It suggests that the presence of a cubic crystalline phase in Ce-Zr solid solution improves its catalytic activity in CO oxidation. The relation between the physico-chemical properties of the supports and the catalytic performance of Au/Ce_1−xZr_xO₂ catalysts in CO oxidation reaction has been investigated. Gold was deposited by the direct anionic exchange (DAE) method. The role of the support in the creation of catalytic performance of supported Au nanoparticles in CO oxidation was significant. A direct correlation between activity and catalysts reducibility was observed. Ceria, which is susceptible to the reduction at the lowest temperature, in the presence of highly dispersed Au nanoparticles, appears to be responsible for the activity of the studied catalysts. CeO₂-ZrO₂ mixed oxides are promising supports for Au nanoparticles in CO oxidation whose activity is found to be dependent on Ce/Zr molar ratio.