Delegating revocations and authorizations in collaborative business environments

Efficient collaboration allows organizations and individuals to improve the efficiency and quality of their business activities. Delegations, as a significant approach, may occur as workflow collaborations, supply chain collaborations, or collaborative commerce. Role-based delegation models have been used as flexible and efficient access management for collaborative business environments. Delegation revocations can provide significant functionalities for the models in business environments when the delegated roles or permissions are required to get back. However, problems may arise in the revocation process when one user delegates user U a role and another user delegates U a negative authorization of the role. This paper aims to analyse various role-based delegation revocation features through examples. Revocations are categorized in four dimensions: Dependency, Resilience, Propagation and Dominance. According to these dimensions, sixteen types of revocations exist for specific requests in collaborative business environments: DependentWeakLocalDelete, Dependent WeakLocalNegative, DependentWeakGlobalDelete, DependentWeakGlobalNegative, IndependentWeak LocalDelete, IndependentWeakLocalNegative, Inde pendentWeakGlobalDelete, IndependentWeakGlobal Negative, and so on. We present revocation delegating models, and then discuss user delegation authorization and the impact of revocation operations. Finally, comparisons with other related work are discussed

Deformation modes and ideal strengths of ternary layered Ti2AlC and Ti2AlN from first-principles calculations

Deformation and failure modes were studied for Ti2 AlC and Ti2 AlN by deforming the materials from elasticity to structural instability using the first-principles density functional calculations. We found that the Ti C0.5 Ti N0.5 slabs remain structurally stable under deformations, whereas the weak Ti-Al bonds accommodate deformation by softening and breaking at large strains. The structural stability of the ternary compound is determined by the strength of Ti-Al bond, which is demonstrated to be less resistive to shear deformation than to tension. The ideal stress-strain relationships of ternary compounds are presented and compared with those of the binary materials, TiC and TiN, respectively. For Ti2 AlC and Ti2 AlN, their ideal tensile strengths are comparable to those of the binary counterparts, while the ideal shear strengths yield much smaller values. Based on electronic structure analyses, the low shear deformation resistance is well interpreted by the response of weak Ti-Al bonds to shear deformations. We propose that the low shear strengths of Ti2 AlC and Ti2 AlN originate from low slip resistance of Al atomic planes along the basal plane, and furthermore suggest that this is the mechanism for low hardness, damage tolerance, and intrinsic toughness of ternary layered carbides and nitrides

Record Setting Scores Open 2016-17 Rifle Season

Record Setting Scores Open 2016-17 Rifle Season Eagles win their opening event of the seaso

PUEPro : A Computational Pipeline for Prediction of Urine Excretory Proteins

This work is supported by the National Natural Science Foundation of China (Grant Nos. 81320108025, 61402194, 61572227), Development Project of Jilin Province of China (20140101180JC) and China Postdoctoral Science Foundation (2014T70291).Postprin

Clustering Single-cell RNA-sequencing Data based on Matching Clusters Structures

Single-cell sequencing technology can generate RNA-sequencing data at the single cell level, and one important single-cell RNA-sequencing data analysis method is to identify their cell types without supervised information. Clustering is an unsupervised approach that can help find new insights into biology especially for exploring the biological functions of specific cell type. However, it is challenging for traditional clustering methods to obtain high-quality cell type recognition results. In this research, we propose a novel Clustering method based on Matching Clusters Structures (MCSC) for identifying cell types among single-cell RNA-sequencing data. Firstly, MCSC obtains two different groups of clustering results from the same K-means algorithm because its initial centroids are randomly selected. Then, for one group, MCSC uses shared nearest neighbour information to calculate a label transition matrix, which denotes label transition probability between any two initial clusters. Each initial cluster may be reassigned if merging results after label transition satisfy a consensus function that maximizes structural matching degree of two different groups of clustering results. In essence, the MCSC may be interpreted as a label training process. We evaluate the proposed MCSC with five commonly used datasets and compare MCSC with several classical and state-of-the-art algorithms. The experimental results show that MCSC outperform other algorithms

The Australian PCEHR System: Ensuring Privacy and Security through an Improved Access Control Mechanism

An Electronic Health Record (EHR) is designed to store diverse data accurately from a range of health care providers and to capture the status of a patient by a range of health care providers across time. Realising the numerous benefits of the system, EHR adoption is growing globally and many countries invest heavily in electronic health systems. In Australia, the Government invested $467 million to build key components of the Personally Controlled Electronic Health Record (PCEHR) system in July 2012. However, in the last three years, the uptake from individuals and health care providers has not been satisfactory. Unauthorised access of the PCEHR was one of the major barriers. We propose an improved access control model for the PCEHR system to resolve the unauthorised access issue. We discuss the unauthorised access issue with real examples and present a potential solution to overcome the issue to make the PCEHR system a success in Australia

Linear transformation models for censored data under truncation

In many observational cohort studies, a pair of correlated event times are usually observed for each individual. This paper develops a new approach for the semiparametric linear transformation model to handle the bivariate survival data under both truncation and censoring. By incorporating truncation, the potential referral bias in practice is taken into account. A class of generalised estimating equations are proposed to obtain unbiased estimates of the regression parameters. Large sample properties of the proposed estimator are provided. Simulation studies under different scenarios and analyses of real-world datasets are conducted to assess the performance of the proposed estimator