Cit-CSP及其应用

介绍了Cit-CSP--Cit/E-commerce信息安全保障平台子系统，并阐述了它所提供的消息摘要、块加密、加密(公钥)、签名和MAC等功能和服务。     

Influence of the silicon surface treatment by plasma etching and scratching on the nucleation of diamond grown in HFCVD - a comparative study

A comparative study for the nucleation of diamond was carried out using surface treatment like (i) surface scratching with 1 μm diamond paste and (ii) surface etching using chlorine plasma at different RF powers (50, 100 and 150 W). Atomic force microscopic study shows variation in roughness from 31 nm to 110 nm. Scratching results in random scratches, whereas plasma etches a surface uniformly. Scanning electron microscopic observations show well faceted crystallites with a predominance of angular shaped grains corresponding to 〈100〉 and 〈110〉 crystallite surfaces for the scratched as well as plasma etched substrate. Surface etching at 150 W plasma power results in a better growth in comparison with 50 and 100 W plasma powers. Chlorine-radical is found responsible for the changes in the growth morphology. Raman spectroscopy shows a sharp peak at 1,332 cm⁻¹ and a peak at ∼1,580 cm⁻¹ for both samples.     

The influence of chloride-ion concentration on the Pitting of steel in simulated concrete environment

The influence of chloride-ion concentration on the pitting behaviour of steel in saturated Ca(OH)₂ solution was studied using the D.C. polarization technique. Results seem to indicate a threshold chloride level for the on-set of severe pitting around 3000 ppm, the pitting potential E_p exhibits dramatic drop and large hysteresis loops develop. The results provide insight into the pitting performance of steel in concrete and highlight the poor repassivation of this metal/environment system.     

Rate sensitivity of high birefringent fiber optic sensors underlarge dynamic loads

New experimental studies on the effects of large dynamic compressive loads on highly birefringent optical fibers are presented. The intensity rate of applied dynamic loads ranged between 2.18-6719 KN/m-sec. A Fibercore (previously York) HB600 Bow-Tie fiber with an initial beat length of 1.16 mm was employed in the present study. Sensor response is discussed in terms of the effects of force amplitudes and velocities on the calibration parameter T_f. T_f is a function of the amplitude as well as the velocity of applied loading. Therefore, calibration of the sensor should be achieved through regression analysis of the load-fringe data for the entire spectrum of the desired force velocities. The ramification of this finding is that a single experiment for the measurement of T_f cannot provide the required parameters for calibration. This further implies that the change in beat length is also dependent on the amplitude and velocity of loading. The suggested method takes both effects into account through empirical analysis of experimental data     

A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach.

     

Large deformation analysis in the context of 3D compressible nonlinear elasticity using the VDQ method

In this article, a new solution approach is developed to numerically compute large deformations of 3D hyperelastic solids based on the compressible nonlinear elasticity. The governing equations are derived by the minimum total potential energy principle, and the Neo-Hookean model is used for the hyperelastic character of material. One of the key novelties of the work is its formulation in which the tensor form of equations is replaced by an efficient matrix–vector form that can be readily utilized in the coding process. Moreover, the variational differential quadrature technique is adopted to arrive at the discretized governing equations in a direct way. Simple implementation, fast convergence rate, and computational efficiency are the main advantages of present approach. Through some examples, the accuracy and effectiveness of the proposed numerical approach are revealed.

     

Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS

This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang–Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R²) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R² of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.

     

Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm

Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.

     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.