Target reliability index for serviceability limit state of single piles

The objective is to develop an approach for the determination of the target reliability index for serviceability limit state(SLS) of single piles. This contributes to conducting the SLS reliability-based design(RBD) of piles. Based on a two-parameter,hyperbolic curve-fitting equation describing the load-settlement relation of piles, the SLS model factor is defined. Then, taking into account the uncertainties of load-settlement model, load and bearing capacity of piles, the formula for computing the SLS reliability index(βsls) is obtained using the mean value first order second moment(MVFOSM) method. Meanwhile, the limit state function for conducting the SLS reliability analysis by the Monte Carlo simulation(MCS) method is established. These two methods are finally applied to determine the SLS target reliability index. Herein, the limiting tolerable settlement(slt) is treated as a random variable. For illustration, four load test databases from South Africa are compiled again to conduct reliability analysis and present the recommended target reliability indices. The results indicate that the MVFOSM method overestimates βsls compared to that computed by the MCS method. Besides, both factor of safety(FS) and slt are key factors influencing βsls, so the combination of FS and βsls is welcome to be used for the SLS reliability analysis of piles when slt is determined. For smaller slt, pile types and soils conditions have significant influence on the SLS target reliability indices; for larger slt, slt is the major factor having influence on the SLS target reliability indices. This proves that slt is the most key parameter for the determination of the SLS target reliability index.

Shared Resource Quality Monitoring and Dynamic Trust Management in a Community Cloud

The collaboration tools offered by Cloud Computing have increased the need to share data and services within companies or between autonomous organizations. This has led to the deployment of community cloud infrastructures. However, several challenges will arise from this grouping of heterogeneous organizations. One of the main challenges is the management of trust between the actors of the community. Trust issues arise from the uncertainty about the quality of the resources and entities involved. The quality of a resource can be examined from a security or functional perspective. Therefore, ensuring security and monitoring the quality of resources is to ensure a high level of trust. Therefore, we propose in this paper a technique for dynamic trust management and quality monitoring of resources shared between organizations. Our approach consists, on the one hand, in evaluating the quality of resources based on quality of service measurement attributes and, on the other hand, in updating the trust values according to the information deduced from these measurements. The proposed framework is evaluated in terms of resource sharing success rate and execution time. Experimental results and comparison with TNA-SL and InterTrust models show that the framework can identify and track the behavior of malicious organizations with relatively low execution time.

An Optimization Algorithm for Service Composition Based on an Improved FOA

Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.

The Equalization Gap of Basic Public Services Supply in China:An Empirical Study on Education,Health Care and Social Security

The equalization of basic public services which helps to meet people’s ever-growing needs for a better life is an important component of the supply-side structural reform under the new normal.This paper constructs the mechanism model and index of basic public services equalization,measures the equalization of education,health care and social security in China during the period of 2005-2016,and empirically tests the contributory factors with regional panel data.It is found that the equalization of basic public services has not been significantly improved by the last 12 years of urbanization;people tend to seek high-end consumption beyond basic public services with the increasing per capita disposable income;while the population structure and the gap between urban and rural areas constitute the main impediments,local governments’revenues and fiscal efficiency can significantly improve the equalization of basic public services.Therefore,the key to the supply-side structural reform of public services lies in enhancing the matching capability of public services in urbanization,reducing the consumption gap between urban and rural public services,and improving the efficiency of government’s public services expenditure.