Comprehensive meta-analysis of integrity test validities: Findings and implications for personnel selection and theories of job performance.

Conducted a comprehensive meta-analysis based on 665 validity coefficients across 576,460 data points to investigate whether integrity test validities are generalizable and to estimate differences in validity due to potential moderating influences. Results indicate that integrity test validities are substantial for predicting job performance and counterproductive behaviors on the job, such as theft, disciplinary problems, and absenteeism. The estimated mean operational predictive validity of integrity test for predicting supervisory ratings of job performance is .41. Results from predictive validity studies conducted on applicants and using external criterion measures (i.e., excluding self-reports) indicate that integrity tests predict the broad criterion of organizationally disruptive behaviors better than they predict employee theft alone. Despite the influence of moderators, integrity test validities are positive across situations and settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Information cut for clustering using a gradient descent approach

We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density functions. We propose to optimize the Information Cut using a gradient descent-based approach. Our algorithm has several advantages compared to many other graph-based methods in terms of determining an appropriate affinity measure, computational complexity, memory requirements and coping with different data scales. We show that our method may produce clustering and image segmentation results comparable or better than the state-of-the art graph-based methods.     

Classification of semantic relations between nominals

The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of semantic relations in text. We present the development and evaluation of a semantic analysis task: automatic recognition of relations between pairs of nominals in a sentence. The task was part of SemEval-2007, the fourth edition of the semantic evaluation event previously known as SensEval. Apart from the observations we have made, the long-lasting effect of this task may be a framework for comparing approaches to the task. We introduce the problem of recognizing relations between nominals, and in particular the process of drafting and refining the definitions of the semantic relations. We show how we created the training and test data, list and briefly describe the 15 participating systems, discuss the results, and conclude with the lessons learned in the course of this exercise.     

Subnanosecond switching, plasma electrode pockels cell for high-contrast imaging with partially coherent light

A high-contrast optical switch for imaging partially coherent light (~150 times the diffraction limit) requires that it have a large angular acceptance. We describe the development of a high-speed Pockels cell that uses a thin crystal to simultaneously achieve high-contrast (greater than 1800:1) and large-angular acceptance (greater than 7 mrad for a 5-mm aperture). A KD*P crystal was used in a longitudinal-mode configuration with plasma discharges forming low-resistance, high optical transmission electrodes to couple the switching voltage. Rise times of the switched optical pulse of the order of 500 ps were observed. Characterization of the device in the near and far fields was also performed.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.     

Electrospun polymeric nanofibrous composites containing TiO2 short nanofibers

In this study, polymeric nanofibrous composites containing anatase TiO₂ short nanofibers (TiO₂-SNF) were successfully produced via electrospinning. The fabrication of the nanofibrous composite structure includes two steps. First, anatase TiO₂ nanofibers were obtained by calcination of electrospun PVP/TiO₂ nanofibers and then crushed into short nanofibers ranging from few microns in length. Second, these TiO₂-SNF were dispersed into polymer solutions and then electrospun into nanofibrous composites. We obtained nanofibers containing TiO₂-SNF from different polymer types including PMMA, PAN, PET and PC. The SEM and TEM imaging indicated that some of the TiO₂-SNF were fully covered by the polymeric matrix whereas some TiO₂-SNF were partially covered and/or stick on the surface of the fibers. The photocatalytic activity of nanofibrous composites containing TiO₂-SNF was evaluated by monitoring the photocatalytic decomposition of a model dye (rhodamine-6G) under UV irradiation.     

A new size distribution model by t-family curves for comminution of limestones in an impact crusher

In the evaluation of crushers, t-family curves obtained from single particle test methods are frequently used. It is known that there are many difficulties and problems in these tests. In this study the breakage behaviour of three different limestones in an impact crusher was investigated. A new size distribution model was developed by t-family value evaluation and Bond work index approach. As a result, the validity of the equation was proved by a high regression value (r² = 0.88).     

Effect of gamma irradiation on the properties of tyre cords

Gamma irradiation of high tenacity Nylon 6.6 (Ny 66) and polyester (PET) tyre cords was investigated. The untreated and treated tyre cords with different twist levels were irradiated at different dose rates in air. The effects of irradiation on both Ny 66 and PET cords were not found to be depending on the twist levels of the cords. The changes in the mechanical and thermal properties with absorbed dose at two different dose rates were measured. The mechanical properties were observed to deteriorate with increasing dose for Ny 66 cords, whereas remained almost unchanged for PET cords both in greige and dipped forms. Hot shrinkage value for the greige Ny 66 cords was found to be improved, i.e. decreased. This decrease was much lower for greige PET than Ny 66 cords. It is concluded that PET cord has higher radiation resistance than Ny 66 cord and the effects of high energy irradiation on tyre cords have to be taken into consideration during tyre design if pre-vulcanization with high energy radiation is to be applied.     

Polymers for medical and tissue engineering applications

Recent decades have seen great advancements in medical research into materials, both natural and synthetic, that facilitate the repair and regeneration of compromised tissues through the delivery and support of cells and/or biomolecules. Biocompatible polymeric materials have become the most heavily investigated materials used for such purposes. Naturally‐occurring and synthetic polymers, including their various composites and blends, have been successful in a range of medical applications, proving to be particularly suitable for tissue engineering (TE) approaches. The increasing advances in polymeric biomaterial research combined with the developments in manufacturing techniques have expanded capabilities in tissue engineering and other medical applications of these materials. This review will present an overview of the major classes of polymeric biomaterials, highlight their key properties, advantages, limitations and discuss their applications. © 2014 Society of Chemical Industry