Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

The application of numerical weather prediction （NWP） products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous （yes/no）, and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCER UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.

QoS Based and Energy Aware Multi-Path Hierarchical Routing Algorithm in WSNs

In hierarchical networks, nodes are separated to play different roles such as CHs and cluster members. Each CH collects data from the cluster members within its cluster, aggregates the data and then transmits the data to the sink. Each algorithm that is used for packet routing in quality of service (QoS) based applications should be able to establish a tradeoffs between end to end delay parameter and energy consumption. Therefore, enabling QoS applications in sensor networks requires energy and QoS awareness in different layers of the protocol stack. We propose a QoS based and Energy aware Multi-path Hierarchical Routing Algorithm in wireless sensor networks namely QEMH. In this protocol, we try to satisfy the QoS requirements with the minimum energy via hierarchical methods. Our routing protocol includes two phase. In first phase, performs cluster heads election based on two parameters: node residual energy and node distance to sink. In second phase, accomplishes routes discovery using multiple criteria such as residual energy, remaining buffer size, signal-to-noise ratio and distance to sink. When each node detect an event can send data to the CH as single hop and CH to the sink along the paths. We use a weighted traffic allocation strategy to distribute the traffic amongst the available paths to improve the end to end delay and throughput. In this strategy, the CH distributes the traffic between the paths according to the end to end delay of each path. The end to end delay of each path is obtained during the paths discovery phase. QEMH maximizes the network lifetime as load balancing that causes energy consume uniformly throughout the network. Furthermore employs a queuing model to handle both real-time and non-real-time traffic. By means of simulations, we evaluate and compare the performance of our routing protocol with the MCMP and EAP protocols. Simulation results show that our proposed protocol is more efficient than those protocols in providing QoS requirements and minimizing energy consumption.

Computer-aided design of the effects of Cr_2O_3 nanoparticles on split tensile strength and water permeability of high strength concrete

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of 8 input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Cr2O3 nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network was gained, the best results were obtained and in genetic programming model, when 4 genes were selected to construct the model, the best results were acquired. Although neural network has predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.

Effects of CuO Nanoparticles on Microstructure,Physical,Mechanical and Thermal Properties of Self-Compacting Cementitious Composites

In the present study,split tensile strength of self-compacting concrete with different amount of CuO nanoparticles has been investigated.CuO nanoparticles with the average particle size of 15 nm were added partially to self compacting concrete and split tensile strength of the specimens has been measured.The results indicate that CuO nanoparticles are able to improve the split tensile strength of self compacting concrete and recover the negative effects of polycarboxylate superplasticizer on split tensile strength.CuO nanoparticle as a partial replacement of cement up to 4 wt% could accelerate C-S-H gel formation as a result of increased crystalline Ca（OH）2 amount at the early ages of hydration.The increase of the CuO nanoparticles more than 4 wt% causes the decrease of the split tensile strength because of unsuitable dispersion of nanoparticles in the concrete matrix.Accelerated peak appearance in conduction calorimetry tests,more weight loss in thermogravimetric analysis and more rapid appearance of related peaks to hydrated products in X-ray diffraction（XRD） results all also indicate that CuO nanoparticles up to4 wt% could improve the mechanical and physical properties of the specimens.Finally,CuO nanoparticles could improve the pore structure of concrete and shift the distributed pores to harmless and few-harm pores.

Retraction Note:Computer-aided design of the effects of Cr_(2)O_(3)nanoparticles on split tensile strength and water permeability of high strength concrete

The Journal Editorial Officehas retracted this articlebecause it shows significantoverlapwith a numberofarticles including those that were under consideration at the same time[1-4].Additionally,the article shows evidence of peer reviewmanipulation.The Publisher has not been able to obtain a current email address for both authors.

Effect of Layers Position on Fracture Toughness of Functionally Graded Steels in Crack Divider Configuration

In the present study, fracture toughness of functionally graded steels in crack divider configuration has been modeled. By utilizing plain carbon and austenitic stainless steels slices with various thicknesses and arrangements as electroslag remelting electrodes, functionally graded steels were produced. The fracture toughness of the functionally graded steels in crack divider configuration has been found to depend on the composites＇ type together with the volume fraction and the position of the containing phases. According to the area under stress-strain curve of each layer in the functionally graded steels, a mathematical model has been presented for predicting fracture toughness of composites by using the rule of mixtures. The fracture toughness of each layer has been modified according to the position of that layer where for the edge layers, net plane stress condition was supposed and for the central layers, net plane strain condition was presumed. There is a good agreement between experimental results and those acquired from the analytical model.