Search Results (4)

Pouring semi-flexible pavement material (PSFM) is widely used as a wearing layer material or below pavement due to its excellent resistance to deformation at high temperatures and under heavy loads. However, in cold regions, the material exhibits severe cracking issues. The primary objective of this study is to enhance the resistance of pouring semi-flexible pavements (SFPs) to low-temperature cracking in cold regions by strategically designing pavement structures that incorporate PSFM. To achieve this goal, we conducted indoor tests to determine the relaxation modulus and temperature shrinkage coefficient of PSFM and simulated a pavement structure using COMSOL finite element simulation. The impacts of different application layers and layer thicknesses on low-temperature stresses were investigated based on these findings. The research findings indicate that when PSFM is used as the wearing layer material, the low-temperature stress is 4.7% lower than that of typical materials used in the pavement-wearing layer. When used as the binder layer material, the low-temperature stress on the wearing layer material increases by 3.5%. As the thickness of the wearing layer increases, the low-temperature stress within the pavement structure decreases, but the low-temperature stress on the pavement surface increases. Therefore, it is recommended to use PSFM as the binder layer material and appropriately increase the thickness of the wearing layer to enhance the pavement’s resistance to low-temperature cracking. Full article

As the size and complexity of software systems grow, knowing how to effectively judge whether there are defects in the programs has attracted extensive attention in research. However, current software defect prediction methods only extract semantic information at the syntactic level and lack features to mine defect manifestations at the semantic level of code, because defective software is incomplete or defective in semantic representation. Defective software exhibits incomplete or flawed semantic behavior. This paper proposes a software defect prediction method based on the program semantics feature mining (PSFM) method. Specifically, the semantic information is first extracted from the code grammatical structure information and code text information. Then, the defect feature is mined through the semantic information. Finally, software defects are predicted by using the mined defect features. The experimental results show that, compared with the existing software defect prediction methods, the method in this paper (PSFM method) obtained a higher F-measure value. Full article

For engineering structures, strain flexibility-based approaches have been widely used for structural health monitoring purposes with prominent advantages. However, the applicability and robustness of the method need to be further improved. In this paper, a novel damage index based on differences in uniform load strain field (ULSF) is developed for plate-like structures. When estimating ULSF, the strain flexibility matrix (SFM) based on mass-normalized strain mode shapes (SMSs) is needed. However, the mass-normalized strain mode shapes (SMSs) are complicated and difficult to obtain when the input, i.e., the excitation, is unknown. To address this issue, the proportional strain flexibility matrix (PSFM) and its simplified construction procedure are proposed and integrated into the frames of ULSF, which can be easily obtained when the input is unknown. The identification accuracy of the method under the damage with different locations and degrees is validated by the numerical examples and experimental examples. Both the numerical and experimental results demonstrate that the proposed method provides a reliable tool for output-only damage detection of plate-like structures without estimating the mass-normalized strain mode shapes (SMSs). Full article

DNA-binding proteins play crucial roles in various biological processes, such as DNA replication and repair, transcriptional regulation and many other biological activities associated with DNA. Experimental recognition techniques for DNA-binding proteins identification are both time consuming and expensive. Effective methods for identifying these proteins only based on protein sequences are highly required. The key for sequence-based methods is to effectively represent protein sequences. It has been reported by various previous studies that evolutionary information is crucial for DNA-binding protein identification. In this study, we employed four methods to extract the evolutionary information from Position Specific Frequency Matrix (PSFM), including Residue Probing Transformation (RPT), Evolutionary Difference Transformation (EDT), Distance-Bigram Transformation (DBT), and Trigram Transformation (TT). The PSFMs were converted into fixed length feature vectors by these four methods, and then respectively combined with Support Vector Machines (SVMs); four predictors for identifying these proteins were constructed, including PSFM-RPT, PSFM-EDT, PSFM-DBT, and PSFM-TT. Experimental results on a widely used benchmark dataset PDB1075 and an independent dataset PDB186 showed that these four methods achieved state-of-the-art-performance, and PSFM-DBT outperformed other existing methods in this field. For practical applications, a user-friendly webserver of PSFM-DBT was established, which is available at http://bioinformatics.hitsz.edu.cn/PSFM-DBT/. Full article