Prediction of runoff characteristics in permeable pavements using experimental data and intelligent models.

Considering the growing use of permeable pavements, the prediction of runoff passing through this pavement model is of considerable importance. The prediction of rainfall-runoff relationships can be a challenge because of several factors including data uncertainty, non-linear relationships, and high temporal and spatial variability. To deal with these challenges, intelligent algorithms are often used to predict such complex phenomena. In this research, runoff control parameters were investigated in two types of permeable pavements (permeable interlocking concrete pavement (PICP) and high strength clogging resistant permeable pavement (CRP)) using support vector machine (SVM), support vector machine-bat (SVM-BA) and support vector machine-grasshopper (SVM-GOA). Variables used in the models included percentage of coverage by permeable pavement (A), rainfall intensity (I), slope (S), and pavement type coefficient (K) as input data, and runoff coefficient (C), time to runoff (Tr), and peak discharge (Qp) as output data. In this research, from the total of 108 data extracted from the experimental results, 86 data were used in the training period, and 22 data were used in the test period. The results of the test period show that the SVM-BA model has the best performance with values of MAE = 0.010 in predicting C, MAE = 1.330 min in predicting Tr, and MAE = 0.029 lit/min in predicting Qp. The SVM-GOA model is ranked second with values of MAE = 0.051 in predicting C, MAE = 3.285 min in predicting Tr, and MAE = 0.097 lit/min in predicting Qp. Also, the SVM model is ranked third with values of MAE = 0.063 in predicting C, MAE = 4.470 min in predicting Tr, and MAE = 0.121 lit/min in predicting Qp. In summary, the SVM-BA algorithm showed the best performance and the SVM algorithm showed the weakest performance in predicting runoff characteristics in permeable pavements.

Nanomaterial strategies in wound healing: A comprehensive review of nanoparticles, nanofibres and nanosheets.

Wound healing is a complex process that orchestrates the coordinated action of various cells, cytokines and growth factors. Nanotechnology offers exciting new possibilities for enhancing the healing process by providing novel materials and approaches to deliver bioactive molecules to the wound site. This article elucidates recent advancements in utilizing nanoparticles, nanofibres and nanosheets for wound healing. It comprehensively discusses the advantages and limitations of each of these materials, as well as their potential applications in various types of wounds. Each of these materials, despite sharing common properties, can exhibit distinct practical characteristics that render them particularly valuable for healing various types of wounds. In this review, our primary focus is to provide a comprehensive overview of the current state-of-the-art in applying nanoparticles, nanofibres, nanosheets and their combinations to wound healing, serving as a valuable resource to guide researchers in their appropriate utilization of these nanomaterials in wound-healing research. Further studies are necessary to gain insight into the application of this type of nanomaterials in clinical settings.

Experimental study of the effect of high-strength clogging-resistant permeable pavement (CRP) on the runoff using a rainfall simulator.

Permeable pavements play an effective role in reducing runoff by decreasing the impermeable area. But, conventional permeable pavements suffer disadvantages such as low resistance. To address this, the 'high-strength clogging-resistant permeable pavement (CRP)' has been developed. The present study aimed to evaluate the performance of the CRP model with varying percentages of coverage (A) of 25, 50, and 100%, slopes (S) of 1, 3, and 5%, as well as rainfall intensities (I) of 45, 55, 70, 90, 170, and 200 mm/h. Based on the results, there was an increase in A from 50 to 100% at I = 90 mm/h, decreased runoff coefficient (C) of 18, 15, and 13% at S of 1, 3, and 5%, respectively. At the same I, increasing S from 1 to 5% increased the C coefficient in A of 0, 25, 50, and 100% by 3, 31, 32, and 39%, respectively. Due to the ever-increasing urbanization and the subsequent increase in impervious areas, the risk of severe floods has greatly increased. Therefore, providing solutions such as the CRP model can help reduce flood risks in urban areas. The findings of this research can be used as a guide in the design of high-strength clogging-resistant permeable pavements in urban areas.