Examination of Various Feature Selection Approaches for Daily Precipitation Downscaling in Different Climates

To turn General Circulation Models (GCMs) projection toward better assessment, it is crucial to employ a downscaling process to get more reliability of their outputs. The data-driven based downscaling techniques recently have been used widely, and predictor selection is usually considered as the main challenge in these methods. Hence, this study aims to examine the most common approaches of feature selection in the downscaling of daily rainfall in two different climates in Iran. So, the measured daily rainfall and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) predictors were collected, and Support Vector Machine (SVM) was considered as downscaling methods. Also, a complete set of comparative tests considering all dimensions was employed to identify the best subset of predictors. Results indicated that the skill of various selection methods in different tests is significantly different. Despite a few partial superiorities viewed between selection models, they not presented an obvious distinction. However, regarding all related factors, it may be deduced that the Stepwise Regression Analysis (SRA) and Bayesian Model Averaging (BMA) are better than others. Also, the finding of this study showed that there are some weaknesses in the interpretation of SRA, so concerning this issue, it may be concluded that BMA has more reliable performance. Furthermore, results indicated that generally, the downscaling procedure has more accuracy in arid climate than cold-semi arid climate.

     

A Methodology for the Breakdown of NRW into Real and Administrative Losses

The estimation of Non Revenue Water (NRW) is simple and easy for water suppliers who keep records of the system input volume and the billed authorized consumption. However, the breakdown of NRW into its two main components real and administrative which refers to the unbilled authorized consumption plus apparent losses is not an easy or straight forward task. Methods reported in the literature for the breakdown of NRW into its components are top down approach and bottom up approach. Both approaches suffer from certain limitations and shortcomings that limits their use and reduce our confidence in the results obtained by them. This paper presents a methodology that can be used to draw a line between the real and the administrative losses with an acceptable level of accuracy. This methodology is based on the fact that the administrative losses are delivered to the demand site and consequently reach the wastewater collection system whereas the real losses are lost from the system and consequently do not reach the wastewater collection system. The methodology applies water balance from the water treatment plant outlet till the inlet of the wastewater treatment plant (WWTP). The mass balance approach of the Water Evaluation And Planning (WEAP) system was implemented for this purpose. In this methodology, the breakdown of NRW into its two main components is adjusted iteratively so that the difference between WEAP calculated and measured inflow to the WWTP is minimal. The presented methodology was applied to Amman and Zarqa cities in Jordan which return their wastewater to As Samra WWTP. The results showed that this methodology is capable of dividing NRW water into its two main components with an acceptable level of accuracy.     

Multi Objective Simulation-Optimization Approach in Pollution Spill Response Management Model in Reservoirs

In this study, a Pollution Spill Response Management Model (PSRMM) is developed to provide an emergency response on reservoir operation during accidental injection of hazardous material to reservoirs. PSRMM consist of spatial system analyzing (SSA) model, 2D hydrodynamic and water quality simulation model (CE-QUAL-W2), and multi-objective particle swarm optimization (MOPSO) algorithm. CE-QUAL-W2 model is applied for spatial and temporal analysis of water body in simulation routine of PSRMM. Also, in an advanced modeling framework, CE-QUAL-W2 is coupled with MOPSO algorithm to obtain desirable near optimal reservoir operation strategy and/or emergency planning in selective withdrawal framework. The simulation-optimization (SO) routine of PSRMM provides pareto optimum reservoir operation strategy in selective scheme to minimize reservoir cleanup time and to reduce the magnitude and frequency of water quality standard violations. The proposed tool is applied in Ilam reservoir in Iran, as a multipurpose hydraulic project providing water for drinking, irrigation, and flood control during an accident spill of conservative hazardous material. Different scenarios are defined and tested employing the proposed PSRMM for managing accidental spill of conservative pollutant into the reservoir.     

Webometric analysis of Iranian universities of medical sciences

Introduction  There are many researches have been conducted on webometrics, especially the impacts of websites on each other and the web impact factor. However, there are few studies focusing on the websites of Iranian universities. This study analyzed the websites of Iranian universities of medical sciences according to the webometric indicators. Method and materials  In a cross-sectional study, the number of web pages, inlinks, external inlinks and also the overall and absolute web impact factors for Iranian universities of medical sciences with active exclusive websites were calculated and compared using AltaVista search engine. Finally, the websites were ranked based on these webometric indicators. Results  The results showed that the website of Tehran university of medical sciences with 49,300 web pages and 9860 inlinks was ranked first for the size and number of inlinks, while its impact factor was ranked 38th. Rafsanjan UMS with 15 web pages and 211 links had the highest rank for the web impact factor among Iranian universities of medical sciences. Discussions and conclusions  The study revealed that Iranian universities of medical sciences did not have much impact on the web and were not well known internationally. The major reason relies on linguistic barriers. Some of them also suffer from technical problems in their web design.     

Comparison of 2 year carbonation depths of common cement concretes using the modified draft CEN test

This paper reports the results of a 2 year study of the development of carbonation of hardened concrete using a modified version of the draft CEN test method. Ten different common cement mixes, at an equal grade of 37 N/mm² have been tested and compared to a benchmark PC/30% PFA mix. The effect of storage condition and the type of cement on carbonation are determined.

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Résumé Cet exposé est le compte-rendu des résultats de deux années d'étude à propos du développement de la carbonatation du béton durci en se servant d'une version modifiée du processus CEN préliminaire. Dix types de béton d'un usage courant, chacun d'un niveau constant de 37 N/mm², ont été analysés puis comparés à un mélange de référence ciment Portland/30% de cendres volantes. Tout cela nous permet de déterminer l'effet aussi bien du genre du béton que des conditions de stockage, sur la carbonatation.

     

Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network

The unconfined compressive strength (UCS) of rocks is an important design parameter in rock engineering and geotechnics, which is required and determined for rock mechanical studies in mining and civil projects. This parameter is usually determined through a laboratory UCS test. Since the preparation of high-quality samples is difficult, expensive and time consuming for laboratory tests, development of predictive models for determining the mechanical properties of rocks seems to be essential in rock engineering. In this study, an attempt was made to develop an artificial neural network (ANN) and multivariable regression analysis (MVRA) models in order to predict UCS of rock surrounding a roadway. For this, a database of laboratory tests was prepared, which includes rock type, Schmidt hardness, density and porosity as input parameters and UCS as output parameter. To make a database (including 93 datasets), different rock samples, ranging from weak to very strong types, are used. To compare the performance of developed models, determination coefficient (R ²), variance account for (VAF), mean absolute error (E _a) and mean relative error (E _r) indices between predicted and measured values were calculated. Based on this comparison, it was concluded that performance of the ANN model is considerably better than the MVRA model. Further, a sensitivity analysis shows that rock density and Schmidt hardness were recognized as the most effective parameters, whereas porosity was considered as the least effective input parameter on the ANN model output (UCS) in this study.     

Generating different profiles of gradient concentrations inside a gel-filled chamber: design and simulation

Spatial and temporal in vivo variations of biochemical cues affect the cellular behavior and responses in live systems. To study the biological phenomena, an appropriate environment that could facilitate generation of gradients within extracellular spaces is highly desirable. Microfluidic platforms have been widely used in cellular biology research because of their ability to mimic in vivo environments. This paper discusses the design and simulation of a microfluidic device to generate predictable profiles of various stable gradient concentrations in a hydrogel-filled chamber. Simulations have been carried out by using Coventorware for steady state, transient mode and overlapping gradients to fine-tune the design parameters. The design will have applications in three-dimensional biological cell cultures and studies.