Performance of four-seed caching corvid species in operant tests of nonspatial and spatial memory.

The performance of 4 seed-caching corvid species was tested using 2 different operant nonmatching tasks. These species differ in their dependence on stored food, and differences in spatial memory tests have been correlated with better performance by the more cache-dependent species. Acquisition and retention of a color non-matching-to-sample task was tested in Experiment 1. Acquisition of the color task was not correlated with cache dependence, and no differences between species in performance during memory testing were found. Acquisition and retention of an operant spatial non-matching-to-sample task was tested in Experiment 2. Species differences in the spatial task were found for acquisition and during retention testing. The influence of natural history on the evolution of memory is discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Magnetic sensor with prism coupler

The physical principle of proposed sensor is based on the combination of two effects: the modulation of permittivity tensor elements in materials with induced anisotropy by external magnetic field and the creation of sharp minima in reflectance on prism base at guided mode resonance. As result we can observe pointed up the polarization states of reflected light beam influenced by external magnetic field.

Procedures based on the Yeh’s 4×4 matrix formalism are described for the treatment of the electromagnetic interactions in prism-layered or -bulk structure with magnetic ordering. The tunneling phenomenon between coupling prism and inspected object is analyzed for ultrathin approximation and for the case of a gap spacing wide enough so that the prism could be considered as weak perturbation to the modes of the free structure. The value of Kerr rotation at resonant states achieves to one radian. Its dependence on in-plane modulation of magnetization direction offers the possibility to realize the magnetic vector sensor with angle distinguishing better than one degree.     

Selective oxidation of methane with air over silica catalysts

Partial oxidation of methane by oxygen to form formaldehyde, carbon oxides, and C₂ products (ethane and ethene) has been studied over silica catalyst supports (fumed Cabosil and Grace 636 silica gel) in the 630–780 °C temperature range under ambient pressure. The silica catalysts exhibit high space time yields (at low conversions) for methane partial oxidation to formaldehyde, and the C₂ hydrocarbons were found to be parallel products with formaldehyde. Short residence times enhanced both the C₂ hydrocarbons and formaldehyde selectivities over the carbon oxides even within the differential reactor regime at 780 °C. This suggests that the formaldehyde did not originate from methyl radicals, but rather from methoxy complexes formed upon the direct chemisorption of methane at the silica surface at high temperature. Very high formaldehyde space time yields (e.g., 812 g/kg cat h at the gas hourly space velocity = 560 000 (NTP)/kg cat h) could be obtained over the silica gel catalyst at 780 °C with a methane/air mixture of 1.5/1. These yields greatly surpass those reported for silicas earlier, as well as those over many other catalysts. Low CO₂ yields were observed under these reaction conditions, and the selectivities to formaldehyde and C₂ hydrocarbons were 28.0 and 38.8%, respectively, at a methane conversion of 0.7%. A reaction mechanism was proposed for the methane activation over the silica surface based on the present studies, which can explain the product distribution patterns (specifically the parallel formation of formaldehyde and C₂ hydrocarbons).     

Experimental Determination of the Length-Scale Parameter for the Phase-Field Modeling of Macroscale Fracture in Cr–Al2O3 Composites Fabricated by Powder Metallurgy

Metallurgical and Materials Transactions A - A novel approach is proposed to determine a physically meaningful length-scale parameter for the phase-field modeling of macroscale fracture in...     

Automatic prostate cancer detection model based on ensemble VGGNet feature generation and NCA feature selection using magnetic resonance images

Multimedia Tools and Applications - Prostate cancer is one of the most common types of cancer in men and its frequency is 28 per hundred thousand in the world. This cancer is detected using...     

Adaptable image segmentation via simple pixel classification

We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well‐known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors; (ii) the approach suffers from very little overfitting and hence generalizes well to unseen images; (iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel‐centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.     

Multiobjective capital allocation for supplier development under risk

Supplier development is increasingly important due to the complexity of today’s supply chains and the globalisation of businesses. Since manufacturers have only limited resources, they need to make an informed decision about which suppliers to develop. Moreover, the returns from investment in supplier development are uncertain, so manufacturers have to take this risk into account when choosing their suppliers for development programmes. In this paper, we propose a multi-objective model for capital allocation for supplier development under risk. We apply it to an example of a global car manufacturer and support the decision-making process with data downloaded from the Bloomberg database. We use stock market returns and cost of capital of suppliers to assess their performance. Our model supports an informed decision, which is that tradeoffs exist between risk and cost of supplier development programme. Depending on the risk aversion of the manufacturer, we demonstrate different allocation schemes for supplier development.     

Thermal degradation studies on rigid polyurethane foams blown with pentane

The effect of sodium dihydrogenphosphate, trisodium pyrophosphate, and sodium aluminocarbonate on the thermal decomposition of rigid polyurethane (PUR) foams, based on diphenylmethane‐4,4‐diisocyanate, diphenyl‐2,2‐propane‐4,4‐dioxyoligo(ethylene oxide), and oxyalkylenated toluene‐2,6‐diamine, blown with pentane, was studied. Thermogravimetric (TG) data have shown that there is a stabilization effect of additives in the initial stage of degradation, both in nitrogen and air atmosphere, and the decomposition proceeded in two steps up to 600°C. Results of the kinetic analysis by the isoconversional methods of Ozawa–Flynn–Wall and Friedman yielded values of (apparent) activation energy (E_a) and preexponential factor (A). For phosphate‐stabilized PUR samples, E_a remained stable over a broad area of the degree of conversion, while for carbonate‐containing sample two regions of E_a were observed. Further advanced kinetic analysis by a nonlinear regression method revealed the form of kinetic function that was the best approximation for experimental data—for a two‐stage consecutive reaction the first step was the Avrami–Erofeev nucleation‐dependent model, and the second step was a chemical reaction (1st or nth order) model. The integrated thermogravimetric (TG)/Fourier transform infrared (FTIR) technique probed the thermal degradation of modified PURs by analyzing the evolved gases. The solid residue remaining at different temperatures was identified by diffuse reflection FTIR (Kubelka–Munk format). The complex thermal behavior was discussed on the basis of the obtained results—it can be shown that the global stabilization effect is a multistage process whose initial conditions are of critical importance in governing the nature of the entire process. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 2319–2330, 2003     

Damage detection in Timoshenko beam structures by multilayer perceptron and radial basis function networks

This study investigates the efficiency of artificial neural networks (ANNs) in health monitoring of pristine and damaged beam-like structures. Beam modeling is based on Timoshenko theory. Two commonly used network models, multilayer perceptron (MLP) and radial basis neural network (RBNN), are used. Beam material and geometrical properties, beam end conditions and dynamically obtained data are used as input to the neural networks. The combinations of these parameters yield umpteenth input data. Therefore, to examine the effectiveness of ANNs, the frequency of intact beams is first tried to be determined by the network models, given the material and geometrical characteristics of beam elements and support conditions. The methodology to compute the vibrational data utilized in training the networks is provided. Showing the robustness of network models, the second stage of the study is carried out. At this stage, the crack parameters (e.g. the location and severity of crack) are estimated by the ANNs using the beam properties, beam end conditions and vibrational data, which consist of natural frequencies and mode shape rotation values. Despite the multiplexed input data, no data reduction schemes or multistage computations are executed in training and validation of neural network models. As a result of analysis runs, the optimal MLP and RBNN models are determined. Comparison of these models shows that the optimal RBNN algorithm performs better. The effectiveness of optimal ANN models in the presence of noise is also presented. As a conclusion, the trained network can be used as a diagnosis method in structural health monitoring of beam-like structures.