Improved resolution of the multiple inverse method by eliminating erroneous solutions

The multiple inverse method is a numerical technique designed to separate stresses from heterogeneous fault–slip data. The method is one of the resampling methods based on the pattern recognition. Plotting solutions determined from k-fault subset into the parameter space, we have clusters representing significant stresses for the dataset. This paper presents a technique to improve the resolution of stress for the method. The regularity/singularity test of the subsets taken from the fault–slip data is the key for this purpose. The resolution and accuracy of the method are improved by eliminating erroneous stresses or artifacts that were yielded by the method. The performances of the method are demonstrated with a variety of artificial datasets.     

Neural network-based failure rate prediction for De Havilland Dash-8 tires

An artificial neural network (ANN) model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the two-layered feed-forward back-propagation algorithm as a learning rule is developed. The inputs to the neural network are independent variables and the output is the failure rate of the tires. Six years of data are used for model building and validation. Model validation, which reflects the suitability of the model for future prediction is performed by comparing the predictions of the model with that of Weibull regression model. The results show that the failure rate predicted by the ANN is closer in agreement with the actual data than the failure rate predicted by the Weibull model.     

Current approach to the management of gastrinoma and insulinoma in adults with multiple endocrine neoplasia type I

Although there is a recent increase in the use of the isolated pancreatic islets of the rat in the transplantation and functional studies, there has been no detailed quantitative assessment on the size and cellular constituents of islets after the isolation procedure. The present work was undertaken to study the size classes of the isolated islets and the morphometry of their cellular populations. Islets of the rat pancreas were isolated by using the intraductal collagenase digestion technique, the most commonly used procedure for the isolation of pancreatic islets. Different endocrine cells of the isolated islets were stained by immunoperoxidase staining techniques. The distribution of the cellular constituents of the isolated islets was similar to that of the intact islets of the normal pancreas; A, D, and PP cells were peripherally arranged around the centrally located B cells. However, morphometric quantitative study showed that the percent volume and percent number of A, D, and PP cells of the isolated islets were lower than those of the corresponding intact ones. Further, the mean true diameter of the isolated islets was lower than that of the intact ones. These data indicate loss of islet cells during the process of isolation. Most of the lost cells were from the periphery of islets. This may provide an explanation for the incomplete metabolic control and recurrence of hyperglycemia encountered after isolated islet transplantation in the treatment of diabetes mellitus. It seems that further refinements of the isolation techniques are necessary to obtain islet tissue with total cellular integrity, before a complete success in transplantation could be achieved.     

DynamicCloudSim: Simulating heterogeneity in computational clouds

Simulation has become a commonly employed first step in evaluating novel approaches towards resource allocation and task scheduling on distributed architectures. However, existing simulators fall short in their modeling of the instability common to shared computational infrastructure, such as public clouds. In this work, we present DynamicCloudSim which extends the popular simulation toolkit CloudSim with several factors of instability, including inhomogeneity and dynamic changes of performance at runtime as well as failures during task execution. As a validation of the introduced functionality, we simulate the impact of instability on scientific workflow scheduling by assessing and comparing the performance of four schedulers in the course of several experiments both in simulation and on real cloud infrastructure. Results indicate that our model seems to adequately capture the most important aspects of cloud performance instability. The source code of DynamicCloudSim and the examined schedulers is available at https://code.google.com/p/dynamiccloudsim/.     

Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises

The information fusion estimation problems are investigated for multi-sensor stochastic uncertain systems with correlated noises. The stochastic uncertainties caused by correlated multiplicative noises exist in the state and observation matrices. The process noise and the observation noises are one-step auto-correlated and two-step cross-correlated, respectively. While the observation noises of different sensors are one-step cross-correlated. The optimal centralized fusion filter, predictor and smoother are proposed in the linear minimum variance sense via an innovative analysis approach. To enhance the robustness and flexibility, a distributed fusion filter is put forward, which requires the calculation of filtering error cross-covariance matrices between any two local filters. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection (CI) fusion algorithm, which can reduce the computational cost. A simulation example is given to show the effectiveness of the proposed algorithms.     

Evolutionary circuit design for fast FPGA-based classification of network application protocols

The evolutionary design can produce fast and efficient implementations of digital circuits. It is shown in this paper how evolved circuits, optimized for the latency and area, can increase the throughput of a manually designed classifier of application protocols. The classifier is intended for high speed networks operating at 100 Gbps. Because a very low latency is the main design constraint, the classifier is constructed as a combinational circuit in a field programmable gate array (FPGA). The classification is performed using the first packet carrying the application payload. The improvements in latency (and area) obtained by Cartesian genetic programming are validated using a professional FPGA design tool. The quality of classification is evaluated by means of real network data. All results are compared with commonly used classifiers based on regular expressions describing application protocols.