The effects of attrition on baseline comparability in randomized experiments in education: A meta-analysis.

Using meta-analysis, randomized experiments in education that either clearly did or clearly did not experience student attrition were examined for the baseline comparability of groups. Results from 35 studies suggested that after attrition, the observed measures of baseline comparability of groups did not differ more than would be expected given sampling error. The degree of either overall or differential attrition did not relate to baseline comparability, a finding that held under sensitivity analyses. Also, both overall and differential attrition rates were unrelated to posttest effect sizes. All of these analyses, however, lacked sufficient statistical power to detect small but potentially meaningful effects. Results suggest caution is warranted when applying quality scales and other blanket rules pertaining to attrition that are meant to either serve as inclusion-exclusion criteria or in scoring study quality. Much greater attention is needed to both the reporting of attrition in primary studies and to the development of conceptual and empirical models of the attrition process. These developments would aid further investigation of the relation between attrition and study outcomes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Pile Response Adjacent to Braced Excavation

The excavation of soil for the construction of basements or cut-and-cover tunnels results in ground movements. One particular concern is that the excavation-induced lateral soil movements may adversely affect any nearby pile foundation. The lateral loads imposed by the soil movements induce bending moments and deflections in the pile, which may lead to structural distress and failure. This paper presents the results of an actual full-scale instrumented study that was carried to examine the behavior of an existing pile due to nearby excavation activities resulting from the construction of a 16 m deep cut-and-cover tunnel. The pile was located 3 m behind a 0.8 m thick diaphragm wall. Excavation to the formation level that was 16 m below the ground surface resulted in a maximum lateral pile movement of 28 mm. A simplified numerical procedure based on the finite-element method was used to analyze the pile response. Generally, the theoretical predictions were in reasonable agreement with the measured results.     

Accelerated wear testing with a microfabricated surface to evaluate the lubrication ability of biomolecules on polyethylene

Wear of ultrahigh‐molecular‐weight polyethylene (UHMWPE) and wear‐particle‐induced osteolysis and bone resorption are the major factors causing the failure of total joint replacements. It is feasible to improve the lubrication and reduce the wear of artificial joints. We need further understanding of the lubrication mechanism of the synovial fluid. The objective of this study is to evaluate the lubricating ability of three major components in the synovial fluid: albumin, globulin, and phospholipids. An accelerated wear testing procedure in which UHMWPE is rubbed against a microfabricated surface with controlled asperities has been developed to evaluate the lubrication behavior. An analysis of the wear particle dimensions and wear amount of the tests has provided insights for comparing their lubrication performance. It is concluded that the presence of biomolecules at the articulating interface may reduce friction. A higher concentration of a biological lubricant leads to a decrease in the wear particle width. In addition, in combination with the wear results and mechanical analysis, the roles of individual biomolecules contributing to friction and wear at the articulating interface are discussed. These results can help us to identify the role of the biomolecules in the boundary lubrication of artificial joints, and further development of lubricating additives for artificial joints may be feasible. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Performance study of linear and nonlinear diversity-combining techniques in synchronous FFH/MA communication systems over fading channels

Based on the Fourier-Bessel series and exponential characteristic function approach, the paper presents analytical bit-error-rate expressions for synchronous fast frequency-hopped spread-spectrum multiple-access systems over Rician fading channels with M-ary frequency-shift-keying modulation. Linear-combining, product-combining and clipper receivers are studied. Numerical results show that both the clipper and product-combining receivers perform significantly better than the linear-combining receiver. The clipper receiver with an optimum threshold level slightly outperforms the product-combining receiver. However, the product-combining receiver is the preferred diversity-combining method because it is completely nonparametric. In addition, the optimum diversity levels for various diversity-combining receivers over Rayleigh fading channels are higher than that of the Rician fading channels     

Conditional Data Mapping Flip-Flops for Low-Power and High-Performance Systems

This paper introduces a new family of low-power and high-performance flip-flops, namely conditional data mapping flip-flops (CDMFFs), which reduce their dynamic power by mapping their inputs to a configuration that eliminates redundant internal transitions. We present two CDMFFs, having differential and single-ended structures, respectively, and compare them to the state-of-the-art flip-flops. The results indicate that both CDMFFs have the best power-delay product in their groups, respectively. In the aspect of power dissipation, the single-ended and differential CDMFFs consume the least power at data activity less than 50%, and are 31% and 26% less power than the conditional capture flip-flops at 25% data activity, respectively. In the aspect of performance, CDMFFs achieve small data-to-output delays, comparable to those of the transmission-gate pulsed latch and the modified-sense-amplifier flip-flop. In the aspect of timing reliability, CDMFFs have the best internal race immunity among pulse-triggered flip-flops. A post-layout case study is demonstrated with comparison to a transmission-gate flip-flop. The results indicate the single-ended CDMFF has 34% less in data-to-output delay and 28% less in power at 25% data activity, in spite of the 34% increase in size     

Solvent fractionation of petroleum pitch for mesophase formation

Petroleum pitch (Ashland A240) has been subjected to thermal treatment and solvent fractionation to produce refined pitches to be evaluated as impregnants for C-C composites. The solvent fractions were obtained by sequential Soxhlet extraction with solvents such as hexane, benzene, and pyridine. Thermal treatment to 408 °C produced a mesophase pitch (≈50% mesophase); an appreciable portion of the mesophase was soluble in strong solvents. There were substantial differences in chemical composition and in pyrolysis behaviour of the fractions. As the depth of fraction increased, the pyrolysis yield and bloating increased, and the microstructure of the coke became finer until glassy microconstituents were formed in the deepest fractions.     

Deep learning approaches for speech emotion recognition: state of the art and research challenges

Speech emotion recognition (SER) systems identify emotions from the human voice in the areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and human-machine interaction. In the classical SER process, discriminative acoustic feature extraction is the most important and challenging step because discriminative features influence the classifier performance and decrease the computational time. Nonetheless, current handcrafted acoustic features suffer from limited capability and accuracy in constructing a SER system for real-time implementation. Therefore, to overcome the limitations of handcrafted features, in recent years, variety of deep learning techniques have been proposed and employed for automatic feature extraction in the field of emotion prediction from speech signals. However, to the best of our knowledge, there is no in-depth review study is available that critically appraises and summarizes the existing deep learning techniques with their strengths and weaknesses for SER. Hence, this study aims to present a comprehensive review of deep learning techniques, uniqueness, benefits and their limitations for SER. Moreover, this review study also presents speech processing techniques, performance measures and publicly available emotional speech databases. Furthermore, this review also discusses the significance of the findings of the primary studies. Finally, it also presents open research issues and challenges that need significant research efforts and enhancements in the field of SER systems.

     

Initial stages of plasma electrolytic oxidation of titanium

The initial stages of oxide growth on titanium are examined in a recently developed commercial alkaline pyrophosphate/aluminate electrolyte of interest for plasma electrolytic oxidation of light metal alloys. Constant current anodizing was employed, with resultant films examined by scanning and transmission electron microscopies and Rutherford backscattering spectroscopy. The initial film is relatively uniform and composed of TiO₂, with low concentrations of aluminium and phosphorus species incorporated from the electrolyte. With increase in voltage the film breaks down locally, and regions of original and modified film develop simultaneously, with the latter occupying more of the surface as the voltage rises. Porous regions due to dielectric breakdown also become increasingly evident. At 240 V, sparking commences, and the surface reveals extensive, relatively uniform porosity, with the coating now containing much enhanced concentrations of aluminium and phosphorus species compared with the coating at lower voltages. The films develop at low efficiency due to generation of oxygen. The oxygen is produced within the original film material and at sites of dielectric breakdown. The former type of film develops a two-layered morphology, with an outer layer of amorphous TiO₂ and an inner layer with numerous fine and course cavities. The cavities are due to the generation of oxygen that may be associated with the formation of anatase in the inner layer.     

Redox and pH Dual Responsive Polymer Based Nanoparticles for In Vivo Drug Delivery

Responsive nanomaterials have emerged as promising candidates as drug delivery vehicles in order to address biomedical diseases such as cancer. In this work, polymer‐based responsive nanoparticles prepared by a supramolecular approach are loaded with doxorubicin (DOX) for the cancer therapy. The nanoparticles contain disulfide bonds within the polymer network, allowing the release of the DOX payload in a reducing environment within the endoplasm of cancer cells. In addition, the loaded drug can also be released under acidic environment. In vitro anticancer studies using redox and pH dual responsive nanoparticles show excellent performance in inducing cell death and apoptosis. Zebrafish larvae treated with DOX‐loaded nanoparticles exhibit an improved viability as compared with the cases treated with free DOX by the end of a 3 d treatment. Confocal imaging is utilized to provide the daily assessment of tumor size on zebrafish larva models treated with DOX‐loaded nanoparticles, presenting sustainable reduction of tumor. This work demonstrates the development of functional nanoparticles with dual responsive properties for both in vitro and in vivo drug delivery in the cancer therapy.     

An Artificial Neural Network Classifier Design Based-on Variable Kernel and Non-Parametric Density Estimation

This paper proposes a probabilistic variant of the SOM-kMER (Self Organising Map-kernel-based Maximum Entropy learning Rule) model for data classification. The classifier, known as pSOM-kMER (probabilistic SOM-kMER), is able to operate in a probabilistic environment and to implement the principles of statistical decision theory in undertaking classification problems. A distinctive feature of pSOM-kMER is its ability in revealing the underlying structure of data. In addition, the Receptive Field (RF) regions generated can be used for variable kernel and non-parametric density estimation. Empirical evaluation using benchmark datasets shows that pSOM-kMER is able to achieve good performance as compared with those from a number of machine learning systems. The applicability of the proposed model as a useful data classifier is also demonstrated with a real-world medical data classification problem.