Defect study of Zn-doped p-type gallium antimonide using positron lifetime spectroscopy

Defects in p-type Zn-doped liquid-encapsulated Czochralski - grown GaSb were studied by the positron lifetime technique. The lifetime measurements were performed on the as-grown sample at temperature varying from 15 K to 297 K. A positron trapping center having a characteristic lifetime of 317 ps was identified as the neutral VGa-related defect. Its concentration in the as-grown sample was found to be in the range of 1017-1018 cm-3. At an annealing temperature of 300 °C, the VGa-related defect began annealing out and a new defect capable of trapping positrons was formed. This newly formed defect, having a lifetime value of 379 ps, is attributed to a vacancy - Zn-defect complex. This defect started annealing out at a temperature of 580 °C. A positron shallow trap having binding energy and concentration of 75 meV and 1018 cm-3, respectively, was also observed in the as-grown sample. This shallow trap is attributed to positrons forming hydrogenlike Rydberg states with the ionized dopant acceptor Zn.published_or_final_versio

Different contributions of the hippocampus and perirhinal cortex to recognition memory

Brain regions involved in visual recognition memory, including the hippocampus, have been investigated by measuring differential neuronal activation produced by novel and familiar pictures. Novel and familiar pictures were presented simultaneously, one to each eye, using a paired viewing procedure. Differential neuronal activation was determined using immunohistochemistry for the protein products of c-fos as an imaging technique. The results establish that the regions of the rat brain associated with discriminating the novelty or familiarity of an individual item (such as a single object) differ from those responding to a spatial array of items (such as a scene). Perirhinal cortex and area TE of the temporal lobe are activated significantly more by pictures of novel than of familiar individual objects, but the hippocampus is not differentially activated. In contrast, pictures of novel arrangements of familiar items produce significantly greater activation than familiar arrangements of these items in postrhinal cortex and subfield CA1 of the hippocampus but significantly less activation in the dentate gyrus and subiculum; perirhinal cortex and area TE are not differentially activated. Thus, the hippocampus is importantly involved in processing information essential to recognition memory concerning the relative familiarity of arrangements of items, as needed for episodic memory of scenes, whereas the perirhinal cortex processes such information for individual items

Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging

Although singular spectrum analysis (SSA) has been successfully applied for data classification in hyperspectral remote sensing, it suffers from extremely high computational cost, especially for 2D-SSA. As a result, a fast implementation of 2D-SSA namely F-2D-SSA is presented in this paper, where the computational complexity has been significantly reduced with a rate up to 60%. From comprehensive experiments undertaken, the effectiveness of F-2D-SSA is validated producing a similar high-level of accuracy in pixel classification using support vector machine (SVM) classifier, yet with a much reduced complexity in comparison to conventional 2D-SSA. Therefore, the introduction and evaluation of F-2D-SSA completes a series of studies focused on SSA, where in this particular research, the reduction in computational complexity leads to potential applications in mobile and embedded devices such as airborne or satellite platforms

The strengthening effect of inter-layer cold working and post-deposition heat treatment on the additively manufactured Al-6.3Cu alloy

Wire + Arc Additive Manufacture (WAAM) attracts great interest from the aerospace industry for producing components with aluminum alloys, particularly Al-Cu alloy of the 2000 series such as 2219 alloy. However the application is restricted by the low strength properties of the as-deposited WAAM metal. In this study two strengthening methods were investigated - inter-layer cold working and post-deposition heat treatment. Straight wall samples were prepared with 2319 aluminum alloy wire. Inter-layer rolling with loads of 15 kN, 30 kN and 45 kN were employed during deposition. The ultimate tensile strength (UTS) and yield strength (YS) of the inter-layer rolled alloy with 45 kN load can achieve 314. MPa and 244. MPa respectively. The influence of post-deposition T6 heat treatment was investigated on the WAAM alloy with or without rolling. Compared with inter-layer rolling, post-deposition heat treatment can provide much greater enhancement of the strength. After T6 treatment, the UTS and YS of both of the as-deposited and 45 kN rolled alloys exceeded 450. MPa and 305. MPa respectively, which are higher than the properties of the wrought 2219-T6 alloy. The strengthening mechanisms of this additively manufactured Al-6.3Cu alloy were investigated through microstructure analysis

MIMR-DGSA: unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm

Band selection plays an important role in hyperspectral data analysis as it can improve the performance of data analysis without losing information about the constitution of the underlying data. We propose a MIMR-DGSA algorithm for band selection by following the Maximum-Information-Minimum-Redundancy (MIMR) criterion that maximises the information carried by individual features of a subset and minimises redundant information between them. Subsets are generated with a modified Discrete Gravitational Search Algorithm (DGSA) where we definine a neighbourhood concept for feature subsets. A fast algorithm for pairwise mutual information calculation that incorporates variable bandwidths of hyperspectral bands called VarBWFastMI is also developed. Classification results on three hyperspectral remote sensing datasets show that the proposed MIMR-DGSA performs similar to the original MIMR with Clonal Selection Algorithm (CSA) but is computationally more efficient and easier to handle as it has fewer parameters for tuning

A methodology of integrating affective design with defining engineering specifications for product design

Affective design and the determination of engineering specifications are commonly conducted separately in early product design stage. Generally, designers and engineers are required to determine the settings of design attributes (for affective design) and engineering requirements (for engineering design), respectively, for new products. Some design attributes and some engineering requirements could be common. However, the settings of the design attributes and engineering requirements could be different because of the separation of the two processes. In previous studies, a methodology that considers the determination of the settings of the design attributes and engineering requirements simultaneously was not found. To bridge this gap, a methodology for considering affective design and the determination of engineering specifications of a new product simultaneously is proposed. The proposed methodology mainly involves generation of customer satisfaction models, formulation of a multi-objective optimisation model and its solving using a chaos-based NSGA-II. To illustrate and validate the proposed methodology, a case study of mobile phone design was conducted. A validation test was conducted and the test results showed that the customer satisfaction values obtained based on the proposed methodology were higher than those obtained based on the combined standalone quality function deployment and standalone affective design approach

Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design

Facing fierce competition in marketplaces, companies try to determine the optimal settings of design attribute of new products from which the best customer satisfaction can be obtained. To determine the settings, customer satisfaction models relating affective responses of customers to design attributes have to be first developed. Adaptive neuro-fuzzy inference systems (ANFIS) was attempted in previous research and shown to be an effective approach to address the fuzziness of survey data and nonlinearity in modeling customer satisfaction for affective design. However, ANFIS is incapable of modeling the relationships that involve a number of inputs which may cause the failure of the training process of ANFIS and lead to the 'out of memory' error. To overcome the limitation, in this paper, rough set (RS) and particle swarm optimization (PSO) based-ANFIS approaches are proposed to model customer satisfaction for affective design and further improve the modeling accuracy. In the approaches, the RS theory is adopted to extract significant design attributes as the inputs of ANFIS and PSO is employed to determine the parameter settings of an ANFIS from which explicit customer satisfaction models with better modeling accuracy can be generated. A case study of affective design of mobile phones is used to illustrate the proposed approaches. The modeling results based on the proposed approaches are compared with those based on ANFIS, fuzzy least-squares regression (FLSR), fuzzy regression (FR), and genetic programming-based fuzzy regression (GP-FR). Results of the training and validation tests show that the proposed approaches perform better than the others in terms of training and validation errors.School of DesignDepartment of Industrial and Systems Engineerin

Intranasal delivery of full-length anti-Nogo-A antibody: A potential alternative route for therapeutic antibodies to central nervous system targets

Antibody delivery to the CNS remains a huge hurdle for the clinical application of antibodies targeting a CNS antigen. The blood-brain barrier and blood-CSF barrier restrict access of therapeutic antibodies to their CNS targets in a major way. The very high amounts of therapeutic antibodies that are administered systemically in recent clinical trials to reach CNS targets are barely viable cost-wise for broad, routine applications. Though global CNS delivery of antibodies can be achieved by intrathecal application, these procedures are invasive. A non-invasive method to bring antibodies into the CNS reliably and reproducibly remains an important unmet need in neurology. In the present study, we show that intranasal application of a mouse monoclonal antibody against the neurite growth-inhibiting and plasticity-restricting membrane protein Nogo-A leads to a rapid transfer of significant amounts of antibody to the brain and spinal cord in intact adult rats. Daily intranasal application for 2 wk of anti-Nogo-A antibody enhanced growth and compensatory sprouting of corticofugal projections and functional recovery in rats after large unilateral cortical strokes. These findings are a starting point for clinical translation for a less invasive route of application of therapeutic antibodies to CNS targets for many neurological indications