Piezoresistivity in Polymer-Ceramic Composites

Composite materials composed of randomly dispersed semiconducting ceramic particles in an insulating polymer matrix show a pronounced change in resistivity with pressure. Different amounts of iron oxide (Fe₃O₄) powder and antimony-doped tin oxide (SnO₂:Sb) powder were dispersed in an epoxy polymer matrix to form pressure-sensitive composites. In each family of materials, an insulator-to-semiconductor transition is observed in agreement with percolation theory. Composites within a certain range of filler content showed substantial piezoresistive effect under both uniaxial and hydrostatic pressure in which sensitivity is controlled by the choice of filler material and the volume fraction. The effect of temperature on the piezoresistance effect was also examined. Piezoresistors made from Fe₃O₄ composites showed larger temperature changes than those filled with Sb-doped SnO₂.     

Effect of Dyebath Additives on Decolorization Efficiency in the Ozonation of Dyehouse Effluent

It is necessary to study the effect of dyebath additives on decolorization efficiency in order to optimize ozone-based decolorization processes as the consumption of ozone can be reduced through selecting ozone favorable additives. The effect of 5 dyebath additives viz. electrolytes (sodium chloride and sodium sulfate), chelating agent (ethylene diamine tetra acetic acid or EDTA), reducing agent (sodium dithionite), optical brightener (Uvitex BHT), and dispersing agent (Zetex DNVL) was investigated. All of the additives showed synergistic effect as addition of sodium chloride, sodium dithionite and Zetex DN-VL markedly improved decolorization efficiency, but EDTA and optical brightener showed negative effect. Sodium sulfate did not show any positive or negative effect on decolorization efficiency.     

Using a virtual environment to assess cognition in the elderly

Early diagnosis of Alzheimer’s disease (AD) is essential if treatments are to be administered at an earlier point in time before neurons degenerate to a stage beyond repair. In order for early detection to occur tools used to detect the disorder must be sensitive to the earliest of cognitive impairments. Virtual reality technology offers opportunities to provide products which attempt to mimic daily life situations, as much as is possible, within the computational environment. This may be useful for the detection of cognitive difficulties. We develop a virtual simulation designed to assess visuospatial memory in order to investigate cognitive function in a group of healthy elderly participants and those with a mild cognitive impairment (MCI). Participants were required to guide themselves along a virtual path to reach a virtual destination which they were required to remember. The preliminary results indicate that this virtual simulation has the potential to be used for detection of early AD since significant correlations of scores on the virtual environment with existing neuropsychological tests were found. Furthermore, the test discriminated between healthy elderly participants and those with a MCI.     

Buoyancy effects on gaseous slip flow in a vertical rectangular microchannel

Consideration is given to the buoyancy effects on the fully developed gaseous slip flow in a vertical rectangular microduct. Two different cases of the thermal boundary conditions are considered, namely uniform temperature at two facing duct walls with different temperatures and adiabatic other walls (case A) and uniform heat flux at two walls and uniform temperature at other walls (case B). The rarefaction effects are treated using the first-order slip boundary conditions. By means of finite Fourier transform method, analytical solutions are obtained for the velocity and temperature distributions as well as the Poiseuille number. Furthermore, the threshold value of the mixed convection parameter to start the flow reversal is evaluated. The results show that the Poiseuille number of case A is an increasing function of the mixed convection parameter and a decreasing function of the channel aspect ratio, whereas its functionality on the Knudsen number is not monotonic. For case B, the Poiseuille number is decreased by increasing each of the mixed convection parameter, the Knudsen number, and the channel aspect ratio.     

Exploiting Ontology for Software License Agreements

Software end-users need to sign licenses to seal an agreement with the product providers. Habitually, users agree with the license (i.e. terms and conditions) without fully understanding the agreement. To address this issue, an ontological model is developed to formulate the user requirements and license agreements formally. This paper, introduces ontological model that includes the abstract license ontology of common features found in di?erent license agreements. The abstract license ontology is then extended to a few real world license agreements. The resulting model can be used for di?erent purposes such as querying the appropriate licenses for a speciˉc requirement or checking the license terms and conditions with user requirements.     

Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach

Diagnosis, detection and classification of tumors, in the brain MRI images, are important because misdiagnosis can lead to death. This paper proposes a method that can diagnose brain tumors in the MRI images and classify them into 5 categories using a Convolutional Neural Network (CNN). The proposed network uses a Convolutional Auto-Encoder Neural Network (CANN) to extract and learn deep features of input images. Extracted deep features from each level are combined to make desirable features and improve results. To classify brain tumor into three categories (Meningioma, Glioma, and Pituitary) the proposed method was applied on Cheng dataset and has reached a considerable performance accuracy of 99.3%. To diagnosis and grading Glioma tumors, the proposed method was applied on IXI and BraTS 2017 datasets, and to classify brain images into six classes including Meningioma, Pituitary, Astrocytoma, High-Grade Glioma, Low-Grade Glioma and Normal images (No tumor), the all datasets including IXI, BraTS2017, Cheng and Hazrat-e-Rassol, was used by the proposed network, and it has reached desirable performance accuracy of 99.1% and 98.5%, respectively.

     

Infrared thermography for inspecting the adhesion integrity of plastic welded joints

This work aim at developing a non-destructive tool for the evaluation of bonded plastic joints. The paper examines infrared thermographic transmission and reflection mode imaging and validates the feasibility of the thermal NDT approach for this application. Results demonstrate good estimation performance for adhesion integrity, uniformity and bond strength using a transmission mode application of infrared thermography. In addition, results from a pulsed infrared thermographic application using a modified dynamic infrared tomography scheme show good performance for estimating adhesion layer thickness mapping and detecting delaminations.     

Studying the needed effort for identifying duplicate issues

Many recent software engineering papers have examined duplicate issue reports. Thus far, duplicate reports have been considered a hindrance to developers and a drain on their resources. As a result, prior research in this area focuses on proposing automated approaches to accurately identify duplicate reports. However, there exists no studies that attempt to quantify the actual effort that is spent on identifying duplicate issue reports. In this paper, we empirically examine the effort that is needed for manually identifying duplicate reports in four open source projects, i.e., Firefox, SeaMonkey, Bugzilla and Eclipse-Platform. Our results show that: (i) More than 50 % of the duplicate reports are identified within half a day. Most of the duplicate reports are identified without any discussion and with the involvement of very few people; (ii) A classification model built using a set of factors that are extracted from duplicate issue reports classifies duplicates according to the effort that is needed to identify them with a precision of 0.60 to 0.77, a recall of 0.23 to 0.96, and an ROC area of 0.68 to 0.80; and (iii) Factors that capture the developer awareness of the duplicate issue’s peers (i.e., other duplicates of that issue) and textual similarity of a new report to prior reports are the most influential factors in our models. Our findings highlight the need for effort-aware evaluation of approaches that identify duplicate issue reports, since the identification of a considerable amount of duplicate reports (over 50 %) appear to be a relatively trivial task for developers. To better assist developers, research on identifying duplicate issue reports should put greater emphasis on assisting developers in identifying effort-consuming duplicate issues.