Book review: Media violence and its effects on aggression: Assessing the scientific evidence.

Review of book: Media violence and its effects on aggression: Assessing the scientific evidence, by Jonathan L. Freedman. Toronto: ON: University of Toronto Press, 2002, 227 pp. Hardcover. ISBN 0-8020-3553-1. Reviewed by Claire Crooks. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Exercise as a buffer of life stress: A prospective study of adolescent health.

Investigated stress and well-being in adolescence to examine the ability of exercise to buffer stress-induced deteriorations in physical health. 384 female 7th–8th graders completed a life events survey, the Seriousness of Illness Rating Scale, and an activity questionnaire at the beginning and end of the academic year. Analyses revealed that the negative impact of stressful life events on health declined as exercise levels increased. Findings suggest that exercise may be a valuable resource for combating life stress. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Practical scalable image analysis and indexing using Hadoop

The ability to handle very large amounts of image data is important for image analysis, indexing and retrieval applications. Sadly, in the literature, scalability aspects are often ignored or glanced over, especially with respect to the intricacies of actual implementation details. In this paper we present a case-study showing how a standard bag-of-visual-words image indexing pipeline can be scaled across a distributed cluster of machines. In order to achieve scalability, we investigate the optimal combination of hybridisations of the MapReduce distributed computational framework which allows the components of the analysis and indexing pipeline to be effectively mapped and run on modern server hardware. We then demonstrate the scalability of the approach practically with a set of image analysis and indexing tools built on top of the Apache Hadoop MapReduce framework. The tools used for our experiments are freely available as open-source software, and the paper fully describes the nuances of their implementation.     

Sensitivity analysis for biometric systems: A methodology based on orthogonal experiment designs

The purpose of this paper is to introduce an effective and structured methodology for carrying out a biometric system sensitivity analysis. The goal of sensitivity analysis is to provide the researcher/developer with insight and understanding of the key factors—algorithmic, subject-based, procedural, image quality, environmental, among others—that affect the matching performance of the biometric system under study. This proposed methodology consists of two steps: (1) the design and execution of orthogonal fractional factorial experiment designs which allow the scientist to efficiently investigate the effect of a large number of factors—and interactions—simultaneously, and (2) the use of a select set of statistical data analysis graphical procedures which are fine-tuned to unambiguously highlight important factors, important interactions, and locally-optimal settings. We illustrate this methodology by application to a study of VASIR (Video-based Automated System for Iris Recognition)—NIST iris-based biometric system. In particular, we investigated k = 8 algorithmic factors from the VASIR system by constructing a (2^6?1 × 3¹  × 4¹) orthogonal fractional factorial design, generating the corresponding performance data, and applying an appropriate set of analysis graphics to determine the relative importance of the eight factors, the relative importance of the 28 two-term interactions, and the local best settings of the eight algorithms. The results showed that VASIR’s performance was primarily driven by six factors out of the eight, along with four two-term interactions. A virtue of our two-step methodology is that it is systematic and general, and hence may be applied with equal rigor and effectiveness to other biometric systems, such as fingerprints, face, voice, and DNA.     

Towards the Optimal Learning Rate for Backpropagation

A backpropagation learning algorithm for feedforward neural networks withan adaptive learning rate is derived. The algorithm is based uponminimising the instantaneous output error and does not include anysimplifications encountered in the corresponding Least Mean Square (LMS)algorithms for linear adaptive filters. The backpropagation algorithmwith an adaptive learning rate, which is derived based upon the Taylorseries expansion of the instantaneous output error, is shown to exhibitbehaviour similar to that of the Normalised LMS (NLMS) algorithm. Indeed,the derived optimal adaptive learning rate of a neural network trainedby backpropagation degenerates to the learning rate of the NLMS for a linear activation function of a neuron. By continuity, the optimal adaptive learning rate for neural networks imposes additional stabilisationeffects to the traditional backpropagation learning algorithm.     

Transient analysis of diffusion least‐mean squares adaptive networks with noisy channels

In this paper, we study the effect of noisy channels on the transient performance of diffusion adaptive network with least‐mean squares (LMS) learning rule. We first drive the update equation of diffusion LMS which incorporates the effects of noisy channels. Then, using the framework of fundamental weighted energy conservation relation, we derive closed‐form expressions for learning curves in terms of mean‐square deviation and excess mean‐square error. We also find the mean and mean‐square stability bounds of step‐size for diffusion LMS with noisy channels. We show that although noisy channels affect the performance of the diffusion LMS network, the stability bounds of the step‐size are the same form as in the ideal channels case. The derived closed‐form expressions are shown to provide a good match with values found by simulation. Copyright © 2011 John Wiley & Sons, Ltd.     

Durability of CFRP cables exposed to simulated concrete environments

One of the main causes of structural deficiency in concrete bridges is the deterioration of the constituent materials. Some transportation agencies have started exploring the use of carbon fiber reinforced polymers (CFRP) as a corrosion-resistant alternative to steel prestressing materials for longer lasting concrete bridge structures. To implement CFRP in prestressed concrete bridge structures with more confidence, an understanding of the challenges pertaining to durability under in-service environmental conditions is essential. This paper explores the effects of temperature with alkaline and alkaline/chloride solutions on material properties over time, in the context of reinforced and prestressed concrete structures for transportation applications. The durability testing yielded several key findings: (1) higher temperatures accelerate degradation, (2) moisture sorption was the primary process responsible for the observed degradation, with plasticization and microcracking as the controlling mechanisms leading to fiber–matrix interfacial debonding, and (3) a relaxation-based analytical model was implemented to predict residual properties after environmental exposure, showing promising agreement with experimental data.     

Functionalization of hybrid poly(n‐isopropylacrylamide) hydrogels for Escherichia coli cell capture via adsorbed intermediate dye molecule

Poly(n‐isopropylacrylamide) Laponite (PNIPAM‐Lap) hybrid hydrogels, which use the synthetic clay Laponite as a crosslinker, permanently adsorb cationic laser dyes out of solution. This proof‐of‐concept expounds on this capability by adsorbing an intermediate dye molecule and using it as the foundation for successfully conjugating microbial antibodies to the surface of a PNIPAM hydrogel. The study involves using acriflavinium chloride molecules, adsorbed by a PNIPAM‐Lap hydrogel from an acriflavine laser dye solution, as an intermediate molecule to attach antibodies raised against E. coli to the hydrogel and demonstrate cell capture. Furthermore, this system exemplifies a novel biotechnological platform for greatly expanding PNIPAM hydrogels' capabilities and applicability through conjugation chemistry to surface‐bound molecules. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41557.     

Development and Validation of an Artificial Mechanical Skin Model for the Study of Interactions between Skin and Microneedles

Microneedles are small needle‐like structures that are almost invisible to the naked eye. They have an immense potential to serve as a valuable tool in many medical applications, such as painless vaccination. Microneedles work by breaking through the stratum corneum, the outermost barrier layer of the skin, and providing a direct path for drug delivery into the skin. A lot of research has been presented over the past two decades on the applications of microneedles, yet the fundamental mechanism of how they interact, pressure, and penetrate the skin in its native state is worth examining further. As such, a major difficulty with understanding the mechanism of microneedle–skin interaction is the lack of an artificial mechanical human skin model to use as a standardized substrate. In this research news, the development of an artificial mechanical skin model based on a thorough mechanical study of fresh human and porcine skin samples is presented. The artificial mechanical skin model can be used to study the mechanical interactions between microneedles and skin, but not diffusion of molecules across skin. This model can assist in improving the performance of microneedles by enhancing the reproducibility of microneedle depth insertions for optimal drug delivery and biosensing.

     

Survey of acceptor dopants in SrTiO3: Factors limiting room temperature hole concentration

Unintentional impurities often found in strontium titanate (doped or undoped) have hindered efforts to study individual impurities experimentally. To fill this gap, a computational survey of acceptor-type point defects of common intentional or unintentional impurities (Al, Cu, Fe, K, Mg, Mn, N, Na, Ni, and Zn) is presented. Utilizing defect formation energies from density functional theory using hybrid exchange correlation functionals in a grand canonical model of the defect chemistry, the equilibrium Fermi level (μ_e) was calculated as a function of processing conditions for pure SrTiO₃, SrTiO₃ individually doped with each impurity, and SrTiO₃ co-doped with Al and N. Above a certain concentration, each impurity reduced the maximum predicted hole concentration relative to the intrinsic case. Al, Mg, Zn, K, and Na exhibited similar trends and behaved more like ideal acceptors while N, Ni, Fe, Mn, and Cu were all unique and pinned μ_e near or above the mid-gap in most conditions. Al/N:SrTiO₃ also exhibited similar trends at 800°C for all Al/N ratios, but more variation at 25°C. Additionally, the behavior of Al:SrTiO₃ was not recovered until Al/N = 10⁴. This suggests that to achieve SrTiO₃ with free holes at room temperature, the concentration of most impurities must be controlled.