A systematic review of the utilization of waste materials as aggregate replacement in stone matrix asphalt mixes.

The daily utilization of a large amount of raw materials is causing a rapid depletion of natural resources. The growth of the human population is accompanied by higher activities in the agricultural and manufacturing sectors that resulted in a larger volume of waste materials being disposed of in landfills each year. Researchers are seeking ways to reduce the adverse impact of waste materials on the environment. One method for managing waste materials is using them as a substitute for natural materials, for example, as aggregate replacement in the construction of road pavements. This paper reviews the previous studies that explored the use of waste materials as aggregate replacement in stone matrix asphalt (SMA) mix and the performance of asphalt pavements constructed using these materials. A systematic literature search of four databases revealed that waste materials could be used as an alternative to the natural aggregates. Future studies on the SMA mixes should investigate using other waste materials that could improve mix design and enhance pavement performance. There is a need to establish a standard code of practice and train material technologists to use different types of waste in SMA pavement construction. In summary, it is essential to perform a life cycle cost analysis (LCCA) and life cycle assessment (LCA) to quantify the economic and environmental impacts of the different waste materials used as aggregates in SMA.

iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters.

MOTIVATION: Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra- and interclass variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge. RESULTS: We present iPromoter-BnCNN for identification and accurate classification of six types of promoters-σ24,σ28,σ32,σ38,σ54,σ70. It is a CNN-based classifier which combines local features related to monomer nucleotide sequence, trimer nucleotide sequence, dimer structural properties and trimer structural properties through the use of parallel branching. We conducted experiments on a benchmark dataset and compared with six state-of-the-art tools to show our supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an independent test dataset. AVAILABILITY AND IMPLEMENTATION: Our proposed tool iPromoter-BnCNN web server is freely available at http://103.109.52.8/iPromoter-BnCNN. The runnable source code can be found https://colab.research.google.com/drive/1yWWh7BXhsm8U4PODgPqlQRy23QGjF2DZ. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.