Design,Modeling and Control of a Simulator of an Aircraft Maneuver in the Wind Tunnel Using Cable Robot

International Journal of Control, Automation and Systems - In this paper, a new controllable simulator is proposed and modeled by which, experimental tests of the aircraft’s models can be...     

An optimized skin texture model using gray-level co-occurrence matrix

Neural Computing and Applications - Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group...     

A survey and taxonomy on virtual data center embedding

The Journal of Supercomputing - Data center network virtualization is being considered as a promising technology to provide a performance guarantee for cloud computing applications. One important...     

Scarring trachoma is associated with polymorphism in the tumor necrosis factor alpha (TNF-alpha) gene promoter and with elevated TNF-alpha levels in tear fluid

Tumor necrosis factor alpha (TNF-alpha) may play a central role in the disease pathogenesis which occurs as a consequence of chlamydial infection. To investigate the importance of TNF-alpha gene promoter polymorphisms and TNF-alpha levels in tear fluid in scarring trachoma, a large matched-pair case-control study was performed in The Gambia. The -308A allele was present in a higher proportion of patients (28.4%) than controls (18.4%), with an increasing association for homozygotes (chi2 for trend, P = 0.032; allele frequency, 0.163 in patients and 0.099 in controls; chi2, P = 0.025). For the -238A allele, the association was similar but not significant. The disease association was highly significant when the number of either -308A or -238A sites in an individual was considered (P = 0.003). TNF-alpha promoter alleles are tightly linked to some HLA class I and II alleles, but multivariate analysis confirmed that the disease associations were independent of HLA, although a class I allele, A*6802, is also associated with disease. TNF-alpha was more frequently detected in tear samples from patients (27.6%) than from controls (15.9%), increasingly so for higher levels of detectable TNF-alpha (P = 0.015). Among patients, detectable TNF-alpha in tears was highly associated with the presence of ocular chlamydial infection (P < 0.001). The results indicate that TNF-alpha plays a major role in the tissue damage and scarring which occurs as a consequence of Chlamydia trachomatis infection.     

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。     

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.