A numerical study of reactive pollutant dispersion in street canyons with green roofs

Roof greening is a new technique for improvement of outdoor thermal environment which influences air quality through its impacts on thermal and flow field. In order to examine effects of green roofs on reactive pollutant dispersion within urban street canyons, a computational fluid dynamics (CFD) model was employed which contained NO-NO₂-O₃ photochemistry and energy balance models. Simulations were performed for street canyons with different aspect ratios (H/W) of 0.5, 1.0, and 2.0 such that leaf area density (LAD) of green roofs changed. It was found that roof greening led distribution of pollutants to alter for H/W = 0.5 and 1.0 cases in such a manner that their averaged concentrations had small variations as LAD changed. However, by increasing LAD in H/W = 2.0, ventilation efficiency of nitrogen oxides increased since the flow was enhanced within the canyon. Additionally, averaged concentration of ozone in H/W = 2.0 increased with increasing LAD, owing to downward flux of ozone at roof level. Results show that roof greening is a good strategy which can be used in order to improve air quality and thermal environment, especially within deep street canyons.     

Parametric Analysis of Dry Hydrocarbons on the Performance of Organic Rankine Cycle Improved by Working Fluid Reheating

In this paper, the performance of organic Rankine cycle with a two‐stage turbine and internal heat exchanger, considering different dry hydrocarbons as working fluid, has been analyzed. This thermodynamic analysis is done using Engineering Equation Solver version 8.379 software. The influence of working fluid reheating has been studied and the critical temperatures for the thermal and exergy efficiencies are determined. Results show that thermal and exergy efficiencies increase with working fluid reheating and also through a two‐stage turbine. RC‐318 is a good replacement for R‐236fa, R‐113 has a better efficiency than R‐236fa, R‐245fa, and iso‐butane and finally cyclohexane can achieve the highest efficiency. Although the maximum value of efficiencies for each one of working fluids are different, but all of these maximum values almost happen at a unique value of relative pressure of the cycle. The same result has been presented for variation of turbine inlet temperature.     

Cone location and correction of keratoconus with rigid gas-permeable contact lenses

Purpose

To evaluate the influence of cone location and corneal cylinder on RGP corrected visual acuities and residual astigmatism in patients with keratoconus.

Methods

In this prospective study, 156 eyes from 134 patients were enrolled. Complete ophthalmologic examination including manifest refraction, Best spectacle visual acuity (BSCVA), slit-lamp biomicroscopy was performed and corneal topography analysis was done. According to the cone location on the topographic map, the patients were divided into central and paracentral cone groups. Trial RGP lenses were selected based on the flat Sim K readings and a ‘three-point touch’ fitting approach was used. Over contact lens refraction was performed, residual astigmatism (RA) was measured and best-corrected RGP visual acuities (RGPVA) were recorded.

Results

The mean age (±SD) was 22.1 ± 5.3 years. 76 eyes (48.6%) had central and 80 eyes (51.4%) had paracentral cone. Prior to RGP lenses fitting mean (±SD) subjective refraction spherical equivalent (SRSE), subjective refraction astigmatism (SRAST) and BSCVA (logMAR) were −5.04 ± 2.27 D, −3.51 ± 1.68 D and 0.34 ± 0.14, respectively. There were statistically significant differences between central and paracentral cone groups in mean values of SRSE, SRAST, flat meridian (Sim K1), steep meridian (Sim K2), mean K and corneal cylinder (p-values < 0.05). Comparison of BSCVA to RGPVA shows that vision has improved 0.3 logMAR by RGP lenses (p < 0.0001). Mean (±SD) RA was −0.72 ± 0.39 D. There were no statistically significant differences between RGPVAs and RAs of central and paracentral cone groups (p = 0.22) and (p = 0.42), respectively. Pearson's correlation analysis shows that there is a statistically significant relationship between corneal cylinder and BSCVA and RGPVA, However, the relationship between corneal cylinder and residual astigmatism was not significant.

Conclusions

Cone location has no effect on the RGP corrected visual acuities and residual astigmatism in patients with keratoconus. Corneal cylinder and Sim K values influence RGP-corrected visual acuities but do not influence residual astigmatism.     

Predicting acidification recovery at the Hubbard Brook Experimental Forest, New Hampshire: evaluation of four models

The performance and prediction uncertainty (owing to parameter and structural uncertainties) of four dynamic watershed acidification models (MAGIC, PnET-BGC, SAFE, and VSD) were assessed by systematically applying them to data from the Hubbard Brook Experimental Forest (HBEF), New Hampshire, where long-term records of precipitation and stream chemistry were available. In order to facilitate systematic evaluation, Monte Carlo simulation was used to randomly generate common model input data sets (n = 10,000) from parameter distributions; input data were subsequently translated among models to retain consistency. The model simulations were objectively calibrated against observed data (streamwater: 1963-2004, soil: 1983). The ensemble of calibrated models was used to assess future response of soil and stream chemistry to reduced sulfur deposition at the HBEF. Although both hindcast (1850-1962) and forecast (2005-2100) predictions were qualitatively similar across the four models, the temporal pattern of key indicators of acidification recovery (stream acid neutralizing capacity and soil base saturation) differed substantially. The range in predictions resulted from differences in model structure and their associated posterior parameter distributions. These differences can be accommodated by employing multiple models (ensemble analysis) but have implications for individual model applications.     

Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis

Noise corrupts ultrasound images and degrades spatial and contrast resolutions. Hence, it is challenging to characterize the lesions precisely using ultrasound images. The present study aims to evaluate 67 denoising filters and select the best one for ultrasound image denoising. Seven test images were synthesized to evaluate the performance of filters at three different noise levels. Eleven full-reference quantitative image quality metrics (IQMs) were employed to evaluate the performance of the filters. A new filter evaluation method, Rank Analysis, was introduced and utilized at each noise level. The ten best filters with the smallest mean rank in all noise levels were defined for further analysis on real ultrasound images. The Rank Analysis was also employed for real ultrasound images, and filters were evaluated based on 14 IQMs (11 full-reference and three no-reference). Finally, the best filter was defined using the repeated measures analysis statistical test. According to the Rank Analysis results, the Spatial correlation (SCorr) filter obtained the best results with the mean rank scores±SD of 1 ± 0, which was significantly better than the other nine filters (p < 0.001). The second-best results were achieved by three filters, Bitonic, most homogeneous neighborhood, and Lee diffusion (p < 0.05). We concluded that SCorr is the best filter for ultrasound image denoising. It can be used in the pre-processing step before segmentation and diagnostic procedures. In addition, a new filter evaluation method, Rank Analysis, was introduced in this study, which is easy to use, fast, and provides reliable results. So, it can be used to evaluate newly developed filters in the future studies.     

Prediction of asphaltene adsorption capacity of clay minerals using machine learning

A thorough understanding of asphaltene adsorption on clay minerals is particularly important in oil production and contaminated soil remediation using clay-based adsorbents. In this paper, we introduced a machine learning approach as a reliable alternative for commonly used adsorption isotherms that suffer from inherent limitations in the prediction of asphaltene adsorption onto clay minerals. Machine learning (ML) models, namely multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), random forest (RF), and committee machine intelligent system (CMIS) combined with two optimizers were used. Experimental data (142 data points for six different clay minerals) was used for the modelling. To improve the accuracy of the smart models, a comprehensive data preparation such as outlier removal and feature selection was carried out. The results showed that relatively all the proposed models predict asphaltene adsorption on clay minerals with acceptable precision. Nevertheless, the MLP model showed superior performance compared with other models in which the overall root mean square error (RMSE) and coefficient of determination (R²) values of 6.72 and 0.93 were obtained, respectively. Finally, the developed MLP model was compared with the well-known adsorption isotherms of Langmuir and Freundlich and exhibited superior performance.