Hemodynamic evaluation using four-dimensional flow magnetic resonance imaging for a patient with multichanneled aortic dissection

The hemodynamic function of multichanneled aortic dissection (MCAD) requires close monitoring and effective management to avoid potentially catastrophic sequelae. This report describes a 47-year-old man who underwent endovascular repair based on findings from four-dimensional (4D) flow magnetic resonance imaging of an MCAD. The acquired 4D flow data revealed complex, bidirectional flow patterns in the false lumens and accelerated blood flow in the compressed true lumen. The collapsed abdominal true lumen expanded unsatisfactorily after primary tear repair, which required further remodeling with bare stents. This case study demonstrates that hemodynamic analysis using 4D flow magnetic resonance imaging can help understand the complex pathologic changes of MCAD

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A comparison between soil loss evaluation index and the C-factor of RUSLE: a case study in the Loess Plateau of China

Land use and land cover are most important in quantifying soil erosion. Based on the C-factor of the popular soil erosion model, Revised Universal Soil Loss Equation (RUSLE) and a scale-pattern-process theory in landscape ecology, we proposed a multi-scale soil loss evaluation index (SL) to evaluate the effects of land use patterns on soil erosion. We examined the advantages and shortcomings of SL for small watershed (SL<sub>sw</sub>) by comparing to the C-factor used in RUSLE. We used the Yanhe watershed located on China's Loess Plateau as a case study to demonstrate the utilities of SL<sub>sw</sub>. The SL<sub>sw</sub> calculation involves the delineations of the drainage network and sub-watershed boundaries, the calculations of soil loss horizontal distance index, the soil loss vertical distance index, slope steepness, rainfall-runoff erosivity, soil erodibility, and cover and management practice. We used several extensions within the geographic information system (GIS), and AVSWAT2000 hydrological model to derive all the required GIS layers. We compared the SL<sub>sw</sub> with the C-factor to identify spatial patterns to understand the causes for the differences. The SL<sub>sw</sub> values for the Yanhe watershed are in the range of 0.15 to 0.45, and there are 593 sub-watersheds with SL<sub>sw</sub> values that are lower than the C-factor values (LOW) and 227 sub-watersheds with SL<sub>sw</sub> values higher than the C-factor values (HIGH). The HIGH area have greater rainfall-runoff erosivity than LOW area for all land use types. The cultivated land is located on the steeper slope or is closer to the drainage network in the horizontal direction in HIGH area in comparison to LOW area. The results imply that SL<sub>sw</sub> can be used to identify the effect of land use distribution on soil loss, whereas the C-factor has less power to do it. Both HIGH and LOW areas have similar soil erodibility values for all land use types. The average vertical distances of forest land and sparse forest land to the drainage network are shorter in LOW area than that in HIGH area. Other land use types have shorter average vertical distances in HIGH area than that LOW area. SL<sub>sw</sub> has advantages over C-factor in its ability to specify the subwatersheds that require the land use patterns optimization by adjusting the locations of land uses to minimize soil loss

Study of intermixing in a GaAs/AlGaAs quantum-well structure using doped spin-on silica layers

The effect of two different dopants, P and Ga, in spin-on glass (SOG) films on impurity-free vacancy disordering (IFVD) in GaAs/AlGaAs quantum-well structures has been investigated. It is observed that by varying the annealing and baking temperatures, P-doped SOG films created a similar amount of intermixing as the undoped SOG films. This is different from the results of other studies of P-doped SiO₂ and is ascribed to the low doping concentration of P, indicating that the doping concentration of P in the SiO₂ layer is one of the key parameters that may control intermixing. On the other hand, for all the samples encapsulated with Ga-doped SOG layers, significant suppression of the intermixing was observed, making them very promising candidates with which to achieve the selective-area defect engineering that is required for any successful application of IFVD.One of the authors (H.H.T.) acknowledges a fellowship awarded to him by the Australian Research Council