From the viewpoint of service level agreements, data transmission accuracy is one of the critical performances for assessing Internet by service providers and enterprise customers. The stochastic computer network (SCN), in which each edge has several capacities and the accuracy rate, has multiple terminals. This paper is aimed mainly to evaluate the system reliability for an SCN, where system reliability is the probability that the demand can be fulfilled under the total accuracy rate. A minimal capacity vector allows the system to transmit demand to each terminal under the total accuracy rate. This study proposes an efficient algorithm to find all minimal capacity vectors by minimal paths. The system reliability can then be computed in terms of all minimal capacity vectors by the recursive sum of disjoint products (RSDP) algorithm.

It is secure for customers to store and share their sensitive data in the cryptographic cloud storage. However, the revocation operation is a sure performance killer in the cryptographic access control system. To optimize the revocation procedure, we present a new efficient revocation scheme which is efficient, secure, and unassisted. In this scheme, the original data are first divided into a number of slices, and then published to the cloud storage. When a revocation occurs, the data owner needs only to retrieve one slice, and re-encrypt and re-publish it. Thus, the revocation process is accelerated by affecting only one slice instead of the whole data. We have applied the efficient revocation scheme to the ciphertext-policy attribute-based encryption (CP-ABE) based cryptographic cloud storage. The security analysis shows that our scheme is computationally secure. The theoretically evaluated and experimentally measured performance results show that the efficient revocation scheme can reduce the data owner’s workload if the revocation occurs frequently.

For a long time, trouble detection and maintenance of freight cars have been completed manually by inspectors. To realize the transition from manual to computer-based detection and maintenance, we focus on dust collector localization under complex conditions in the trouble of moving freight car detection system. Using mid-level features which are also named flexible edge arrangement (FEA) features, we first build the edge-based 2D model of the dust collectors, and then match target objects by a weighted Hausdorff distance method. The difference is that the constructed weighting function is generated by the FEA features other than specified subjectively, which can truly reflect the most basic property regions of the 3D object. Experimental results indicate that the proposed algorithm has better robustness to variable lighting, different viewing angle, and complex texture, and it shows a stronger adaptive performance. The localization correct rate of the target object is over 90%, which completely meets the need of practical applications.

A new procedure of learning in Gaussian graphical models is proposed under the assumption that samples are possibly dependent. This assumption, which is pragmatically applied in various areas of multivariate analysis ranging from bioinformatics to finance, makes standard Gaussian graphical models (GGMs) unsuitable. We demonstrate that the advantage of modeling dependence among samples is that the true discovery rate and positive predictive value are improved substantially than if standard GGMs are applied and the dependence among samples is ignored. The new method, called matrix-variate Gaussian graphical models (MGGMs), involves simultaneously modeling variable and sample dependencies with the matrix-normal distribution. The computation is carried out using a Markov chain Monte Carlo (MCMC) sampling scheme for graphical model determination and parameter estimation. Simulation studies and two real-world examples in biology and finance further illustrate the benefits of the new models.

Various molecular docking software packages are available for modeling interactions between small molecules and proteins. However, there have been few reports of modeling the interactions between metal ions and metalloproteins. In this study, the AutoDock package was employed to example docking into a di-iron binding protein, bacterioferritin. Each binding site of this protein was tested for docking with iron ions. Blind docking experiments showed that all docking conformations converged into two clusters, one for internal iron binding in sites within the metalloprotein and the other for external iron binding on the protein surface. Local docking experiments showed that there were significant differences between two internal iron binding sites. Docking at one site gave a reasonable root-mean-square deviation (RMSD) distribution with relatively low binding energy. Analysis of the binding mode quality for this site revealed that more than half of the docking conformations were categorized as having good binding geometry, while no good conformations were found for the other site. Further investigations indicated that coordinating water molecules contributed to the stability of binding geometries. This study provides an empirical approach towards the study of molecular docking in metalloproteins.

A path-following method for fixed-wing unmanned aerial vehicles (UAVs) is presented in this paper. This method consists of an outer guidance loop and an inner control loop. The guidance law relies on the idea of tracking a virtual target. The motion of the virtual target is explicitly specified. The main advantage of this guidance law is that it considers the maneuvering ability of the aircraft. The aircraft can asymptotically approach the defined path with smooth movements. Meanwhile, the aircraft can anticipate the upcoming transition of the flight path. Moreover, the inner adaptive flight control loop based on attractive manifolds can follow the command generated by the outer guidance loop. This adaptive control law introduces a first-order filter to avoid solving the partial differential equation in the immersion and invariance adaptive control. The performance of the proposed path-following method is validated by the numerical simulation.

Pulsars are rapidly rotating neutron stars that generate pulsed electromagnetic radiation. A new method for inter-satellite relative position determination between a global navigation satellite system (GNSS) and spacecraft using X-ray pulsars is proposed in this paper. The geometric model of this method is formulated, and two different resolution algorithms are introduced and analyzed. The phase cycle ambiguity resolution is investigated, and a new strategy is proposed and formulated. Using the direct vector parameters of the pulsar, geometric dilution of precision (GDOP) is studied. It is shown that this method has advantages of simplicity and efficiency, and is able to eliminate the clock errors. The analytical results are verified numerically via computer simulations.

With the help of relative entropy theory, norm theory, and bootstrap methodology, a new hypothesis testing method is proposed to verify reliability with a three-parameter Weibull distribution. Based on the relative difference information of the experimental value vector to the theoretical value vector of reliability, six criteria of the minimum weighted relative entropy norm are established to extract the optimal information vector of the Weibull parameters in the reliability experiment of product lifetime. The rejection region used in the hypothesis testing is deduced via the area of intersection set of the estimated truth-value function and its confidence interval function of the three-parameter Weibull distribution. The case studies of simulation lifetime, helicopter component failure, and ceramic material failure indicate that the proposed method is able to reflect the practical situation of the reliability experiment.