Marshall Stability Prediction with Glass and Carbon Fiber Modified Asphalt Mix Using Machine Learning Techniques.

Pavement design is a long-term structural analysis that is required to distribute traffic loads throughout all road levels. To construct roads for rising traffic volumes while preserving natural resources and materials, a better knowledge of road paving materials is required. The current study focused on the prediction of Marshall stability of asphalt mixes constituted of glass, carbon, and glass-carbon combination fibers to exploit the best potential of the hybrid asphalt mix by applying five machine learning models, i.e., artificial neural networks, Gaussian processes, M5P, random tree, and multiple linear regression model and further determined the optimum model suitable for prediction of the Marshall stability in hybrid asphalt mixes. It was equally important to determine the suitability of each mix for flexible pavements. Five types of asphalt mixes, i.e., glass fiber asphalt mix, carbon fiber asphalt mix, and three modified asphalt mixes of glass-carbon fiber combination in the proportions of 75:25, 50:50, and 25:75 were utilized in the investigation. To measure the efficiency of the applied models, five statistical indices, i.e., coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error were used in machine learning models. The results indicated that the artificial neural network outperformed other models in predicting the Marshall stability of modified asphalt mix with a higher value of the coefficient of correlation (0.8392), R2 (0.7042), a lower mean absolute error value (1.4996), and root mean square error value (1.8315) in the testing stage with small error band and provided the best optimal fit. Results of the feature importance analysis showed that the first five input variables, i.e., carbon fiber diameter, bitumen content, hybrid asphalt mix of glass-carbon fiber at 75:25 percent, carbon fiber content, and hybrid asphalt mix of glass-carbon fiber at 50:50 percent, are highly sensitive parameters which influence the Marshall strength of the modified asphalt mixes to a greater extent.

Factors affecting bowel preparation adequacy and procedural time.

BACKGROUND AND AIM: Poor bowel preparation results in difficult colonoscopies, missed lesions, and repeat procedures. Identifying patient risk factors for poor bowel preparation, such as prolonged runway time and prolonged cecal intubation, will aid in interventions prior to a procedure. METHODS: This was a retrospective, single-center analysis of 3 295 colonoscopies performed between May 2012 and November 2014. Indications for colonoscopy included gastrointestinal bleed and anemia, change in bowel habits, for screening, and others (including planning re-anastomoses, abdominal distension, family history and angioectasias). Data were collected from medical charts and endoscopy reports. Comparisons between patient factors and runway time were made with adequacy of bowel preparation as the primary outcomes. RESULTS: Male and diabetic patients had statistically higher rates of inadequate bowel preparation and prolonged cecal intubation times. A previous history of abdominal surgery also demonstrated prolonged cecal intubation. A runway time of ≤7.63 h was associated with higher rates of adequate bowel preparation by multivariate analysis. The optimal time frame is 3-6 h for the highest success rates. CONCLUSION: Patient risk factors for inadequate bowel preparation or prolonged cecal intubation should signal clinicians to intervene prior to colonoscopy. A runway time between 3 and 6 h is optimal for adequate bowel preparation. This may involve further patient education, along with work flow optimization, to facilitate ideal runway times. Future studies should explore how to avoid repeat endoscopies using protocols enforcing this timeframe.

Additive Effect on Soybean Peroxidase-Catalyzed Removal of Anilines from Water.

Soybean peroxidase has been shown to be effective in removal of aromatic compounds from wastewater, while the use of additives effectively reduces enzyme concentration requirement, hence overall treatment cost. Enzymatic treatment, an oxidative polymerization, was successful in removal of over 95% of both aniline and o-anisidine. The originality of this study lies in the findings that the additives, sodium dodecyl sulfate (SDS), sodium dodecylbenzenesulfonate (SDBS), Triton X-100, and sodium dodecanoate (SDOD), reduced enzyme concentration requirement, while polyethylene glycol (PEG, average molar mass of 3350 g/mol) had no effect on the required enzyme concentration. In addition, the presence of SDS also enhanced treatment by improving precipitation and color removal. These results are enabling advancement of soybean peroxidase-catalyzed treatment of anilines found in wastewaters as a new sustainable method.