Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R² value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.     

Long-term external pitting and corrosion of buried cast iron water pipes

Improved understanding of the development with time of pitting and corrosion of the external surfaces of buried cast iron water pipes is crucial for the management of water supply distribution networks. The present study reports observations of surface topography of cast iron pipes exposed between 34 and 129 years in clay soils. Pit depths, surface areas and widths associated with deeper pits are reported, including as functions of exposure period. Pitting appears to progress in a step-wise manner as also earlier reported for the corrosion of steels. While there is a relation between pit depth and lateral size of pits through the amalgamation of pits to form plateaus into which new pits tend to form, the predominant progression of material loss through corrosion and pitting is into the metal rather than laterally. The practical implications of these observations are discussed.     

Thermodynamic and Electrochemical Investigations of Poly(Methyl Methacrylate–Maleic Anhydride) as Corrosion Inhibitors for Mild Steel in 0.5 M HCl

Protection of Metals and Physical Chemistry of Surfaces - In this research, poly(methyl methacrylate-maleic anhydride)P(MMA-MAH)s with different percentages methyl methacrylate and maleic anhydride...     

Recent development on recovering bioactive peptides and phenolic compounds from under-utilised by-products during production of certain edible oil plants: current situation and future perspectives

During the edible oil production process, massive amounts of by-products are generated each year that are not efficiently utilised, posing economic and environmental challenges. Under-utilised products derived from valuable oil-plants such as oil-extracted wheat germ, oil-extracted moringa seed, oil-extracted walnut and flaxseed meal will be increased, and new integration concepts will be required as soon as possible. These underutilised products can be rich sources of phenolic compounds and bioactive peptides with appropriate health-promoting properties such as anti-diabetic, antioxidant, anti-hypertensive, anti-inflammatory, anticancer and antibacterial properties, according to this review. The recovery of bioactive compounds from these by-products can go a long way toward ensuring a sustainable food supply. Current challenges and requirements for recovering bioactive peptides and phenolic compounds from underutilised products derived from valuable oil-plants as well as the relationship between their biological activity and structure are discussed in this study.     

Incorporation of Iron Nanoparticles into Silicon Carbide Nanoparticles as Novel Antimicrobial Bimetallic Nanoparticles

Silicon - Zero valent iron nanoparticles have an attracting and ever growing interest in various research fields due to the fascinating potential. In the present work, antimicrobial activity of...     

Prediction of boundary shear stress in circular and trapezoidal channels with entropy concept

Relying on the power law to develop new relationships, the present study developed the Shannon entropy concept to predict shear stress distribution. The presented method was evaluated and compared with valid laboratory results and the shear stress distribution surveyed through this concept in circular, circular with flat bed and trapezoidal channels. By increasing the flow depth in circular channels, the model presented herein gives an improved prediction of shear stress distribution, while in circular channels with flat bed it is better in predicting shear stress both at lower flow depth and subcritical flow conditions. In comparison, in trapezoidal channels the model is much better in predicting the shear stress distribution at lower flow depths. Accordingly, with a mean error percentage of 1.99% in circular channels, 3.69% in circular channels with flat bed and 4.1% in trapezoidal channels, this novel model shows good ability in predicting shear stress distribution.     

A Hybrid GDM-SERVQUAL-QFD Approach for Service Quality Assessment in Hospitals

Quality is one of the most critical factors in health systems. To improve service quality in hospitals, we propose a hybrid approach incorporating Group decision-making (GDM), Service Quality measurement (SERVQUAL), and Quality function deployment (QFD). QFD is used to translate patient needs into quality characteristics (derived from SERVQUAL), while taking into account interrelationships between design requirements and quality characteristics. One of the most important steps in the QFD process is to prioritize quality characteristics from the patients’ viewpoint, which is inherently affected by two types of uncertainties: human mental perception and patient heterogeneity. In this research, a novel two-stage GDM approach is employed to cope with these uncertainties in an integrated manner. Furthermore, the proposed methodology is applied in a public hospital to effectively enhance quality characteristics, which will be considered in the QFD. Accordingly, the proposed methodology provides a framework for engineering professionals and managers in hospitals to assess and manage quality characteristics.     

Investigation on the effect of process parameter on physical properties of RF-sputtered Mo–Ni thin films as a back contact thin-film solar cell

Journal of Materials Science: Materials in Electronics - In this work, the influence of RF power and deposition time on the structural, chemical, morphological, optical, and electrical properties...     

European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation

Regarding the ability of data mining algorithms for post-processing the output of climate models, and on the other hand, the successful application of multi-model ensemble approaches in climate forecasts, in this paper, some important data mining algorithms are evaluated for the monthly forecast of precipitation over Iran. For this purpose, four European climate models, from DWD, ECMWF, CMCC and Meteo-France, with six lead times, are used to be post-processed by applying four different algorithms including artificial neural networks, support vector regression, decision tree and random forests. Based on the proposed approach, 72 different models are provided for 12 months, each month with six lead times. The approach is applied for the monthly forecast of precipitation over Iran. According to the results, the neural network and random forest methods performed better than the decision tree and the support vector machine. This advantage preserved for all months of the year. Also, the proposed multi-model approach outperformed any of the individual European models.

     

Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads

Water Resources Management - An important issue in water engineering is predicting suspended sediment load (SSL). For the Telar River and its tributaries, this study employs an inclusive multiple...