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For several decades, the disadvantages of traditional metallic rock and soil support structures have become increasingly prominent. As a result, it is urgent to seek lightweight, high-strength, durable and green economic substitute products made from the basalt fibre reinforced composites. Combining the two-phase material summing principle of composite material mechanics and the extension and application of Mohr–Coulomb strength criterion, the uniaxial tensile computing model of a cylindrical polymer specimen containing randomly distributed and discontinuous basalt fibres was established in accordance with the numerical simulation software Flac3D. Furthermore, the influence laws and intrinsic mechanism of the number of discrete fibres and different fibre distribution angles on the integral axial tensile properties of a cylindrical specimen with chopped basalt fibre thermosetting resin matrix composites were investigated and elucidated, and the stress–strain curve of basalt fibre thermosetting resin matrix composites were obtained. Consequently, the results have indicated that: i) the axial tensile strength of the polymer system can be enhanced through the incorporation of a moderate quantity of chopped fibres, with the resultant improvement typically not surpassing a 10% increment; ii) when the number of fibres has increased to the quantitative state, namely, the fibre density reaches 1.02 pieces/cm3 or more, the amplification of axial tensile strength of composite material will be controlled; iii) the fibre distribution directions, are maintaining a 0° parallel state to the axial direction of the specimen and the ones of load application, will ensure that the tensile strength of the composite can be sufficiently utilized. As the production process within the composite industry have become increasingly adept, the research results hold substantial value for further promoting the development, testing and optimizing application of various types of high-performance basalt fibre reinforced composite products. [ABSTRACT FROM AUTHOR]

This paper conducts an energy efficiency assessment of the steam system in a cigarette factory. By analyzing different heat-consuming units, a heat utilization model is established to calculate the thermal efficiency and exergy efficiency of each unit and to identify factors affecting the efficiency of units with low heat utilization. The results indicate that the exergy efficiency of the steam system is only 7.13%, with significant exergy losses. Among these, the exergy efficiency of the production system is 4.20%, followed by the exergy efficiency of the heat source at 34.80%. Based on these findings, an energy-saving retrofit plan utilizing flue gas waste heat to provide hot water is proposed. This approach can improve boiler efficiency by 11.5%, demonstrating effective results. [ABSTRACT FROM AUTHOR]

Drug targets discovery is one of the most important elements in new drug development, and a variety of methods have been developed recently from this point of view. This paper proposed a network-based local and global consistency for cardiovascular genes identification. Results were evaluated through the widely used database HPRD and DrugBank. Results showed that our algorithm can give reasonable candidate targets set. The method in this paper could be an impressive solution for targets searching.

Network pharmacology, as a new developmental direction of drug discovery, was generating attention of more and more researchers. The key problem in drug discovery was how to identify the new interactions between drugs and target proteins. Prediction of new interaction was made to find potential targets based on the predicting model constructed by the known drug-protein interactions. According to the deficiencies of existing predicting algorithm based bipartite graph, a supervised learning integration method of bipartite graph was proposed in this paper. Firstly, the bipartite graph network was constructed based on the known interactions between drugs and target proteins. Secondly, the evaluation model for association between drugs and target proteins was created. Thirdly, the model was used to predict the new interactions between drugs and target proteins and confirm the new predicted targets. On the testing dataset, our method performed much better than three other predicting methods. The proposed method integrated chemical space, therapeutic space and genomic space, constructed the interaction network of drugs and target proteins, created the evaluation model and predicted the new interactions with good performance.

Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM.