Right Hemisphere Sensitivity to Word- and Sentence-Level Context: Evidence From Event-Related Brain Potentials.

Researchers using lateralized stimuli have suggested that the left hemisphere is sensitive to sentence-level context, whereas the right hemisphere (RH) primarily processes word-level meaning. The authors investigated this message-blind RH model by measuring associative priming with event-related brain potentials (ERPs). For word pairs in isolation, associated words elicited more positive ERPs than unassociated words with similar magnitudes and onset latencies in both visual fields. Embedded in sentences, these same pairs showed large sentential context effects in both fields. Small effects of association were observed, confined to incongruous sentences after right visual hemifield presentation but present for both congruous and incongruous sentences after left visual hemifield presentation. Results do not support the message-blind RH model but do suggest hemispheric asymmetries in the use of word and sentence context during real-time processing. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Depressive symptoms among amphetamine and cocaine users before and after substance abuse treatment.

This study examined whether higher rates of depressive symptoms among amphetamine compared with cocaine users result from amphetamine use itself, polydrug use, or experiencing a major lifetime depressive episode and whether depressive symptoms among amphetamine users are more likely to persist 12 months after treatment. The association between amphetamine use and depressive symptoms disappears when controlling for polydrug use and lifetime major depressive episode. Polydrug use and lifetime depressive episode are significantly related to depressive symptoms in the year preceding treatment. Amphetamine use at intake does not predict depressive symptoms among individuals who are abstinent at follow-up, and amphetamine users are no more likely than cocaine users to report depression at a 12-month follow-up. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Evaluation of potential human health risk associated with consumption of edible products from livestock fed ration supplemented with Red Lake Diatomaceous Earth

ABSTRACT

Red Lake Diatomaceous Earth (DE) is a naturally occurring blend of diatomaceous earth and calcium bentonite that can be used as an anti-caking agent in animal feed and contains naturally occurring dioxins. A quantitative risk assessment was conducted to assess potential human health risk associated with consumption of edible tissues from livestock exposed to dioxins via feed containing Red Lake DE. Empirical data characterising the transfer of dioxins to eggs and other tissues in chickens demonstrate that resulting concentrations in eggs are lower than those found in the general food supply. These data also provided product-specific input for a risk assessment conducted both with default parameters and with media-specific input from the feed study. Results demonstrate that exposure to dioxins in edible tissue from livestock that consumed Red Lake DE in feed would not be associated with an increased risk to humans. Findings from this assessment highlight the utility and importance of accounting for bioavailability as part of health-based risk assessment and provide information critical to risk managers in determining the safe use of Red Lake DE as an anticaking agent in livestock and pet feed.     

Prediction-based Deadbeat Control for Grid-connected Inverter with L-filter and LCL-filter

Abstract—In this article, an improved deadbeat control algorithm suitable for digital signal processor-based circuit implementation is proposed. The control algorithm allows the derivation of a nearly sine wave output current with a fixed switching frequency of a current-controlled voltage source inverter. Two low-pass output filters configurations are considered in this study: a simple inductance filter and an LCL-filter. By taking advantage of prior knowledge of the state variables’ shape, the improved deadbeat control algorithm is based on a simple prediction model to derive the expected duty cycle needed to switch on and off the power switches. The control study of the grid-connected inverter with L and LCL output filters has been considered using a co-simulation approach with (Powersim Inc., Rockville, Maryland, USA) and MATLAB software (The MathWorks, Natick, Massachusetts, USA). The obtained results show the improvement of both shape quality and tracking accuracy of the output current quantified by low ripple content and a nearly unity power factor.     

Digital image analysis of gunshot residue dimensional dispersion by computer vision method

Detection and identification of gunshot residues (GSR) have been used as base evidence in elucidating forensic cases. GSR particles consist of burnt and partially unburned material and contaminate the hands, face, hair, and clothes of the shooter when coming out of the gun. Nowadays, GSR samples are collected from the hands of the suspect and are analyzed routinely in forensic laboratories by the scanning electron microscope/energy dispersive spectroscopy (SEM/EDS) method. GSR particles are comprised of a morphological and specific structure (generally spherical and have a diameter between 0 and 100 μm [occasionally even larger]). In addition, the present studies in the field have claimed that GSR particles during formation are formed under equilibrium surface distribution and are unrelated to morphological dimensional classification. Our contribution to this study is two-folded. First, this study offers a new approach to identify images of GSR particles by computer vision gathered by SEM/EDS method from the hand of the shooter. Second, it presents open access to the SEM/EDS image data set of the analyzed GSR. During the study, a new data set consisting of 22,408 samples from three different types of MKEK (Mechanical and Chemical Industries Corporation) brand ammunition has been used. It is seen in the results that the computer vision method has been successful in the dimensional classification of GSR.     

Energy oriented simulation of manufacturing systems – Concept and application

Energy efficiency has become a key concern in industry due to the increased energy costs and the associated environmental impacts. Besides considering single processes/machines, the perspective on process chains and factories as a whole bears further potentials for improvement. In this context, dynamic interactions of different processes as well as auxiliary equipments (e.g. compressed air generation) need to be taken into account when planning and controlling manufacturing systems. This paper presents an innovative energy oriented simulation model for planning of manufacturing systems. A number of industry cases are used to demonstrate the potentials and applicability of the proposed methodology.     

连接工业--连接器依靠其节省劳力的设计使工人能安全、快速装配,取得了成本效益

为使连接器保持竞争力,工程人员们都在努力提高其使用简易程度和成本效益以及工效学.连接器仅在气密和阻漏方面优越是不够的.在今天市场上,作为连接器工业还必须具其它卓越性能.     

Application of cognitive robotics in disassembly of products

Disassembly is a critical step to increase the value of end-of-life (EOL) products and to reduce the environmental footprint. Despite worldwide efforts, disassembly is still performed manually due to the uncertainty associated with the quality and the quantity of the returned EOL products. In this paper, a cognitive robotics based system is proposed to address this problem. The system is equipped with four cognitive functions: reasoning, execution monitoring, learning and revision. The proposed system is tested using LCD screens. The results show that the system is flexible enough to deal with any product models without prior information.     

From engineering diagrams to engineering models: Visual recognition and applications

We present a computational recognition approach to convert network-like, image-based engineering diagrams into engineering models with which computations of interests, such as CAD modeling, simulation, information retrieval and semantic-aware editing, are enabled. The proposed approach is designed to work on diagrams produced using computer-aided drawing tools or hand sketches, and does not rely on temporal information for recognition. Our approach leverages a Convolutional Neural Network (CNN) as a trainable engineering symbol recognizer. The CNN is capable of learning the visual features of the defined symbol categories from a few user-supplied prototypical diagrams and a set of synthetically generated training samples. When deployed, the trained CNN is applied either to the entire input diagram using a multi-scale sliding window or, where applicable, to each isolated pixel cluster obtained through Connected Component Analysis (CCA). Then the connectivity between the detected symbols are analyzed to obtain an attributed graph representing the engineering model conveyed by the diagram. We evaluate the performance of the approach with benchmark datasets and demonstrate its utility in different application scenarios, including the construction and simulation of control system or mechanical vibratory system models from hand-sketched or camera-captured images, content-based image retrieval for resonant circuits and sematic-aware image editing for floor plans.