Structural characteristics of oxide nanosphere supported metal nanoparticles

Structural characterization by transmission electron microscopy of metal nanoparticle coatings on oxide nanospheres has been employed to point out the potential of such materials for use as porous support model catalysts. On silica and titania nanospheres of various origins surface-mediated metal deposition at mild temperature conditions has been utilized to fabricate Pt, Pd, Ag, and Au nanoparticle coatings on the oxide supports. Promising coating characteristics have been achieved for Ag and Au nanoparticles by direct reduction on terminating hydroxyl-rich St?ber silica. A high-resolution electron microscopy analysis directed to surface stress, lattice contraction and planar lattice defects of the latter particles revealed no strong metal-support interaction.     

Nondestructive mechanical release of ordered polymer microfiber arrays from porous templates

The fabrication of one-dimensional (1D) nanostructures and microstructures inside the pores of porous templates is intensively investigated. The release of these structures is commonly accomplished by etching and destroying the templates. The 1D nanostructures and microstructures tend to condense because of the occurrence of capillary forces during drying of the specimens. It is shown that highly ordered arrays of polymer microfibers can be easily detached from silanized porous templates by mechanical lift-off. This procedure leaves the templates intact, thus allowing their recycling, and does not involve the use of solutions or solvents, thus circumventing condensation. Therefore, mechanical lift-off may enable the up-scaling of template-based approaches to the fabrication of highly ordered assemblies of 1D nanostructures and microstructures.     

Emotion regulation and decision making under risk and uncertainty.

It is well established that emotion plays a key role in human social and economic decision making. The recent literature on emotion regulation (ER), however, highlights that humans typically make efforts to control emotion experiences. This leaves open the possibility that decision effects previously attributed to acute emotion may be a consequence of acute ER strategies such as cognitive reappraisal and expressive suppression. In Study 1, we manipulated ER of laboratory-induced fear and disgust, and found that the cognitive reappraisal of these negative emotions promotes risky decisions (reduces risk aversion) in the Balloon Analogue Risk Task and is associated with increased performance in the prehunch/hunch period of the Iowa Gambling Task. In Study 2, we found that naturally occurring negative emotions also increase risk aversion in Balloon Analogue Risk Task, but the incidental use of cognitive reappraisal of emotions impedes this effect. We offer evidence that the increased effectiveness of cognitive reappraisal in reducing the experience of emotions underlies its beneficial effects on decision making. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods. The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems. Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data. The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers. Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.     

The possible role of arterial radial vibration in heart rate and blood pressure matching

MOTIVATION: This study tests if the 'oscillating spring' analogy for the radial vibration of the arterial wall can help to describe the relationship between the heart rate (HR), the blood pressure (BP), and properties of the arterial wall when different types of stimulation are applied on the cardiovascular system. It may help to suggest a possible role for arterial radial vibration in the association between the arterial stiffening and hypertension. METHODS: Either mechanical stimulation was applied (0.5-mmHg pressure variation) to Wistar rats by at near-HR frequency (group A) or administered Propranolol (2mg/kg i.p.; group B), and measured HR and BP simultaneously. RESULTS: In both groups, HR and BP were noted to change in the same direction (r2 = 0.72 and 0.62, respectively; p both < 0.05 by F-test). ANCOVA was performed on these two regression lines, and it was found that there was no significant difference between them (p > 0.3). CONCLUSION: In both groups, changes in haemodynamic parameters can be explained by the 'oscillating spring' analogy for the radial vibration of the arterial wall. This illustrates that, when facing various stimulations, it may be an important regulatory mechanism for the heart and the arteries to restore their frequency-matching condition in order to improve the arterial transmission efficiency. Paying more attention to the radial movement of the wall may therefore help to suggest a novel explanation of the mechanism that underlies the bidirectional relation between hypertension and arterial stiffening.     

Emotion-Induced Retrograde Amnesia and Trait Anxiety.

Emotional arousal can both enhance and impair memory. Considering that both emotional memory and trait anxiety (TA) have been associated with adrenergic activity, the authors investigated whether there is an association between 2 opposite emotional memory biases and the TA. The authors used a procedure recently put forward by B. A. Strange, R. Hurlemann, and R. J. Dolan (2003) to elicit an emotion-induced retrograde amnesia (ERA) coupled to an emotional memory enhancement (EME). The authors contrasted the association between these emotional memory biases and the TA in several conditions involving different levels of encoding and types of recall. The results presented here indicated a significant interaction of the TA with EME and ERA and the dependency of these biases on the consciously controlled use of memory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Effectiveness of cognitive-load based adaptive instruction in genetics education

Research addressing the issue of instructional control in computer-assisted instruction has revealed mixed results. Prior knowledge level seems to play a mediating role in the student’s ability to effectively use given instructional control. This study examined the effects of three types of instructional control (non-adaptive program control, learner control, adaptive program control) and prior knowledge (high school, 1st year and 2nd year college students) on effectiveness and efficiency of learning in a genetics training program. The results revealed that adaptive program control led to highest training performance but not to superior post-test or far-transfer performance. Furthermore, adaptive program control proved to be more efficient in terms of learning outcomes of the test phase than the other two instructional control types. College students outperformed the high school students on all aspects of the study thereby strengthening the importance of prior knowledge in learning effectiveness and efficiency. Lastly, the interaction effects showed that for each prior knowledge level different levels of support were beneficial to learning.