Neural activity associated with semantic versus phonological anomia treatments in aphasia

Naming impairments in aphasia are typically targeted using semantic and/or phonologically based tasks. However, it is not known whether these treatments have different neural mechanisms. Eight participants with aphasia received twelve treatment sessions using an alternating treatment design, with fMRI scans pre- and post-treatment. Half the sessions employed Phonological Components Analysis (PCA), and half the sessions employed Semantic Feature Analysis (SFA). Pre-treatment activity in the left caudate correlated with greater immediate treatment success for items treated with SFA, whereas recruitment of the left supramarginal gyrus and right precuneus post-treatment correlated with greater immediate treatment success for items treated with PCA. The results support previous studies that have found greater treatment outcome to be associated with activity in predominantly left hemisphere regions, and suggest that different mechanisms may be engaged dependent on the type of treatment employed.

Computer analysis of impact behavior of concrete filled steel tube columns

This paper presents a numerical study of the response of axially loaded concrete filled steel tube (CFST) columns under lateral impact loading using explicit non-linear finite element techniques. The aims of this paper are to evaluate the vulnerability of existing columns to credible impact events as well as to contribute new information towards the safe design of such vulnerable columns. The model incorporates concrete confinement, strain rate effects of steel and concrete, contact between the steel tube and concrete and dynamic relaxation for pre-loading, which is a relatively recent method for applying a pre-loading in the explicit solver. The finite element model was first verified by comparing results with existing experimental results and then employed to conduct a parametric sensitivity analysis. The effects of various structural and load parameters on the impact response of the CFST column were evaluated to identify the key controlling factors. Overall, the major parameters which influence the impact response of the column are the steel tube thickness to diameter ratio, the slenderness ratio and the impact velocity. The findings of this study will enhance the current state of knowledge in this area and can serve as a benchmark reference for future analysis and design of CFST columns under lateral impact.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

Performance assessment in a heat exchanger tube fitted with double counter twisted tape inserts

The present study explored the effects of the double counter twisted tapes on heat transfer and fluid friction characteristics in a heat exchanger tube. The double counter twisted tapes were used as counter-swirl flow generators in the test section. The experiments were performed with double counter twisted tapes of four different twist ratios (y = 1.95, 3.85, 5.92 and 7.75) using air as the testing fluid in a circular tube turbulent flow regime where the Reynolds number was varied from 6950 to 50,050. The experimental results demonstrated that the Nusselt number, friction factor and thermal enhancement efficiency were increased with decreasing twist ratio. The results also revealed that the heat transfer rate in the tube fitted with double counter twisted tape was significantly increased with corresponding increase in pressure drop. In the range of the present work, heat transfer rate and friction factor were obtained to be around 60 to 240% and 91 to 286% higher than those of the plain tube values, respectively. The maximum thermal enhancement efficiency of 1.34 was achieved by the use of double counter twisted tapes at constant blower power. In addition, the empirical correlations for the Nusselt number, friction factor and thermal enhancement efficiency were also developed, based on the experimental data.

Implementation of Art1 and Art2 Artificial Neural Networks on Ring and Mesh Architectures

The Artificial Neural Networks (ANNs) are being used to solve a variety of problems in pattern recognition, robotic control, VLSI CAD and other areas. In most of these applications, a speedy response from the ANNs is imperative. However, ANNs comprise a large number of artificial neurons, and a massive interconnection network among them. Hence, implementation of these ANNs involves execution of computer-intensive operations. The usage of multiprocessor systems therefore becomes necessary. In this article, we have presented the implementation of ART1 and ART2 ANNs on ring and mesh architectures. The overall system design and implementation aspects are presented. The performance of the algorithm on ring, 2-dimensional mesh and n-dimensional mesh topologies is presented. The parallel algorithm presented for implementation of ART1 is not specific to any particular architecture. The parallel algorithm for ARTE is more suitable for a ring architecture.

A direct adaptive neural command controller design for an unstable helicopter

This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC).

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Denoising neural data with state-space smoothing: Method and application

Neural data are inevitably contaminated by noise. When such noisy data are subjected to statistical analysis, misleading conclusions can be reached. Here we attempt to address this problem by applying a state-space smoothing method, based on the combined use of the Kalman filter theory and the Expectation–Maximization algorithm, to denoise two datasets of local field potentials recorded from monkeys performing a visuomotor task. For the first dataset, it was found that the analysis of the high gamma band (60–90 Hz) neural activity in the prefrontal cortex is highly susceptible to the effect of noise, and denoising leads to markedly improved results that were physiologically interpretable. For the second dataset, Granger causality between primary motor and primary somatosensory cortices was not consistent across two monkeys and the effect of noise was suspected. After denoising, the discrepancy between the two subjects was significantly reduced.

Unsteady flow with attenuation in a fluid filled elastic tube with a stenosis

An equation governing the excess pressure has been derived, for an axially tethered and stenosed elastic tube filled with viscous liquid, by introducing the elasticity of the tube through pressure-area relation. This equation is solved numerically for large Womersley parameter and the results are presented for different types of pressure-radius relations and geometries by prescribing an outgoing wave suffering attenuation at some axial point of the tube. For a locally constricted tube it is observed that the pressure oscillates more and generates sound on the down stream side of the constriction.

Flow of micropolar fluid through a tube with stenosis

mathematical model for the steady flow of non-Newtonian fluid through a stenotic region is presented. The results indicate that the general shape and size of the stenosis together with rheological properties of blood are important in understanding the flow characteristics and the presence of flow separation.

Impedance tube technology for flow acoustics

Acoustic impedance of a termination, or of a passive subsystem, needs to be measured not only for acoustic lining materials but also in the exhaust systems of flow machinery, where mean flow introduces peculiar problems. Out of the various methods of measurement of acoustic impedance, the discrete frequency, steady state, impedance tube method [1] is most reliable, though time consuming, and requires no special instrumentation.

The development of a guideline of care for patients with a Sengstaken-Blakemore Tube insitu in a general Intensive Care Unit using translational change theory

Aims & Objectives - identify and diagnose the current problems associated with patient care with regard to the nursing management of patients with Sengstaken-Blakemore tubes insitu; - Identify current nursing practice currently in place within the ICU and the hospital; identify the method by which the assessment and provision of nursing care is delivered in the ICU

A one-tube fluorescent assay for the quarantine detection and identification of Tilletia indica and other grass bunts in wheat

A molecular assay with enhanced specificity and sensitivity has been developed to assist in the surveillance of Karnal bunt, a quarantineable disease with a significant impact on international trade. The protocol involves the release of DNA from spores, PCR amplification to enrich Tilletia-specific templates from released DNA and a five-plex, real-time PCR assay to detect, identify and distinguish T. indica and other Tilletia species (T. walkeri, T. ehrhartae, T. horrida and a group comprising T. caries, T. laevis, T. contraversa, T. bromi and T. fusca) in wheat grains. This fluorescent molecular tool has a detection sensitivity of one spore and thus bypasses the germination step, which in the current protocol is required for confirmation when only a few spores have been found in grain samples. The assay contains five dual-labelled, species-specific probes and associated species-specific primer pairs in a PCR mix in one tube. The different amplification products are detected simultaneously by five different fluorescence spectra. This specific and sensitive assay with reduced labour and reagent requirements makes it an effective and economically sustainable tool to be used in a Karnal bunt surveillance program. This protocol will also be valuable for the identification of some contaminant Tilletia sp. in wheat grains.