Vasculature Segmentation in MRA Images Using Gradient Compensated Geodesic Active Contours

Precise segmentation of vasculature from three-dimensional (3D) magnetic resonance angiography (MRA) images is playing an important role in image-guided neurosurgery, pre-operation planning and clinical analysis. Active Contour based evolution algorithms are being widely applied to MRA data sets, however existing approaches exhibit some difficulties in extracting tiny parts of the vessels. Our objective is to develop an automated segmentation scheme to accurately extract vasculature of the brain, especially tiny vessels. Inspired by the intrinsic properties of MRA, we have proposed a scheme called the gradient compensated geodesic active contours (GCGAC), which compensates for low gradients near edges of thin vessels. The GCGAC, which is implemented based on level set, has been tested on both synthetic volumetric image and real 3D MRA images. Our experiments show that the introduced gradient compensation can facilitate more accurate segmentation of tiny blood vessels.     

Realizations of mutative 4-ports and their applications to memstor simulations

In this paper, in addition to the universal 4-port mutator circuit introduced earlier with an adder and a subtractor block, two more 4-port mutator circuits, one with plus type (CCII+) and minus type current conveyors (CCII?), the other with a plus type current conveyor (CCII+) and one minus type current follower (CF?) are presented, their port relation matrix and their realization of different memstors are tabulated. How the transfer characteristics of the ideal mutative 4-ports with respect to frequency hold is verified using their transistor level simulations. By terminating properly two ports of the mutative 4-port simulations of a memristor with three different mutators, of a meminductor and of a memcapacitor are presented and compared also with some mutators existing in the literature.     

High strength steels,stiffness of vehicle front-end structure,and risk of injury to rear seat occupants

Previous research has shown that rear seat occupant protection has decreased over model years, and front-end stiffness is a possible factor causing this trend. In this research, the effects of a change in stiffness on protection of rear seat occupants in frontal crashes were investigated. The stiffness was adjusted by using higher strength steels (DP and TRIP), or thicker metal sheets. Finite element simulations were performed, using an LS Dyna vehicle model coupled with a MADYMO dummy. Simulation results showed that an increase in stiffness, to the extent it happened in recent model years, can increase the risk of AIS3+ head injuries from 4.8% in the original model (with a stiffness of 1000 N/mm) to 24.2% in a modified model (with a stiffness of 2356 N/mm). The simulations also showed an increased risk of chest injury from 9.1% in the original model to 11.8% in the modified model. Distribution of injuries from real world accident data confirms the findings of the simulations.     

Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance

This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise. The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system. Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering, we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points, reduces distant...     

Evaluation of suspended load transport rate using transport formulas and artificial neural network models （Case study： Chelchay Catchment）＊

Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r = 0.69) and lower Root Mean Square Error (RMSE = 0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.     

Development of an efficient stabiliser mixture for physical stability of nonfat unfizzy doogh

The effects of locust bean, carboxymethyl cellulose, a mixture of locust bean and carboxymethyl cellulose and Persian gum on different properties of nonfat doogh were studied over a period of 28 days. The results showed that samples containing a mixture of locust bean and carboxymethyl cellulose had the highest stability. Furthermore, the rheological behaviour of the doogh changed from Newtonian to pseudoplastic. Better sensory acceptability was shown for the treatments containing a mixture of locust bean gum and carboxymethyl cellulose. In general, it was shown that a mixture of locust bean and carboxymethyl cellulose could be of practical use in the industrial production of nonfat doogh.     

Expansion behaviour of a binary solid-liquid fluidised bed with different solid mass ratio

This study reports the effect of particle mass compositions on the bed expansion behaviour of a binary solid liquid fluidised bed (SLFB) system. Experiments were performed comprising equal density (2230 kg m^?3) spherical glass beads particles of diameter 3, 5 and 8 mm and water as fluidising medium with different particle mass ratios varying from 0.17 to 6.0. In the expanded bed, both segregated and intermixed zones were observed depending on the different particle diameter combinations. In a completely segregated SLFB, the bottom monosized layer exhibited a negative deviation ～23% whereas a positive deviation ～25% was found in the top monosized layer when compared with the corresponding pure monosized system. A small mixing zone spanning approximately two particle diameters thick was observed to exist even in a completely segregated SLFB for higher diameter ratio cases. A slight decrease in the mixing zone height was noted with increasing liquid superficial velocity. For lower diameter ratio cases, a relatively lager mixing zone height was observed which increased with increasing liquid superficial velocity. The bed expansion ratio was noted to decrease with increasing solid mass ratio however it increased with increase in the fluidising velocity ratio following a reasonable power law trend. The expanded bed height of the binary mixture was not entirely additive of its corresponding mono-component bed heights and both positive and negative deviations were observed. Finally, a two-dimensional (2D) Eulerian-Eulerian (E-E) model incorporating the kinetic theory of granular flow (KTGF) was used to quantify the binary system hydrodynamics. The model predicted expanded bed height agreed with experimental measurements within ±6% deviation. Presence of a mixing zone was also confirmed by the CFD model and simulated particle phase volume fraction distribution qualitatively agreed with the experimental visualisations.     

Oxygen adsorption and desorption characteristics of LSCF5582 membranes for oxygen separation applications

This study examines the oxygen adsorption and desorption characteristics of La_0.5Sr_0.5Co_0.8Fe_0.2O_3?δ (LSCF5582) membranes prepared at sintering temperatures of 1200–1300 °C, with the aim of gaining an insight into their performance in the surface reaction limited regime for oxygen separation applications. The findings of this work are then compared with our experimental data on Ba_0.5Sr_0.5Co_0.8Fe_0.2O_3?δ (BSCF5582) membranes. It is demonstrated that the adsorption rate constants of both membranes are two orders of magnitude greater than their respective oxygen desorption rate constants as the oxygen adsorption occurs in less than 24 s whilst the oxygen desorption takes approximately one hour to reach equilibrium. The activation energy for oxygen adsorption of LSCF5582 reduced to a quarter of its value with increasing the sintering temperature from 1200 °C to 1300 °C. This is attributed to the oxygen exchange occurring more rapidly along the grain boundaries resulting in a lower activation energy. The LSCF5582 grain sintered at 1200 °C is the optimum selection for oxygen separation applications at an operating temperature of 850 °C and oxygen partial pressure of 0.21 bar.     

An ensemble tree-based machine learning model for predicting the uniaxial compressive strength of travertine rocks

Neural Computing and Applications - Estimating the uniaxial compressive strength (UCS) of travertine rocks with an indirect modeling approach and machine learning algorithms is useful as models can...     

Competency‐based IT personnel selection using a hybrid SWARA and ARAS‐G methodology

In the knowledge economy, human capital is a key factor in any organization to achieve a sustainable competitive advantage. Thus, selection of competent personnel is the most important function of human resource managers. However, because of a wide range of criteria and organizational factors that affect the process, personnel selection is often regarded as a complex problem that can be answered through multicriteria decision‐making (MCDM) procedures. Despite the great importance of determining a comprehensive set of criteria, it has not gained enough attention in the literature. This study presents a competency framework with five criteria for choosing the best information technology (IT) expert from five alternatives. The stepwise weight assessment ratio analysis (SWARA) and grey additive ratio assessment (ARAS‐G) methods are also used to derive the criteria weights and provide the final alternative, respectively. The results reveal that subject competency is the major criteria in IT personnel selection.