The generalized hyers-ulam stability of sextic functional equation in various matrix spaces

Using the fixed point method, we prove the generalized Hyers-Ulam stability of the followinggeneralized sextic functional equation Df (x, y):= f (mx+ y) +f (mx− y) +f(x+ my) +f(x− my)− (m4+ m2) [f(x+ y+f(x− y)] −2(m6− m4− m2+ 1) [f(x) + f(y)]in matrix fuzzy normed spaces. Furthermore, using the fixed point method, we also prove theHyers-Ulam stability of the above functional equation in matrix random normed spaces.Keywords: Hyers-Ulam stability; fixed point method; matrix fuzzy normed space; matrixrandom normed spaces; sextic functional equation

Optimization of Reinforced Concrete Retaining Walls Using Ant Colony Method

Risk and Reliability in Geotechnical Engineerin

Experimental and numerical study of the behaviour of shallow rectangular tunnels

This is the final version of the article. Available from JVE International via the DOI in this record.The behavior of an underground structure under dynamic loading is affected by many factors such as shape, depth and stiffness of the structure as well as the frequency content of the input motion. Scarcity of experimental/field investigations precludes proper understanding of these parameters’ effects on the seismic behavior of aforementioned structures. In this study, the effects of input motion along with structural stiffness properties on seismic behavior of rectangular tunnels are investigated. Three reduced-scale 1 g shaking table models were constructed in 1/48 scale. Tests were carried out in the shaking table facility at the University of Tabriz on model tunnels of the rectangular section of the shallow Tabriz subway tunnel, using input motions of different amplitudes and frequencies. In addition, a numerical study was done using the coupled scaled boundary finite element-finite element (SBFE-FE) method. A good agreement between the numerical model and the results of the experimental test was achieved. Using the shaking table test, the accelerations and bending moments of the tunnel lining were measured. The results show that tunnel lining stiffness affects the acceleration response of the ground. A parametric study by the numerical approach was presented and effects of the variation of elastic modulus and mass density of the soil were evaluated

Immunomodulatory effects of human umbilical cord wharton's Jelly-Derived mesenchymal stem cells on differentiation, maturation and endocytosis of monocyte-derived dendritic cells

The Wharton's jelly of the umbilical cord is believed to be a source of mesenchymal stem cells (MSCs) which can be therapeutically applied in degenerative diseases. In this study, we investigated the immunomodulatory effect of umbilical cord derivedmesenchymal stem cells (UC-MSCs) and bone marrow-derived-mesenchymal stem cells (BM-MSCs) on differentiation, maturation, and endocytosis of monocyte-derived dendritic cells in a transwell culture system under laboratory conditions. Monocytes were differentiated into immature dendritic cells (iDCs) in the presence of GM-CSF and IL-4 for 6 days and then differentiated into mature dendritic cells (mDCs) in the presence of TNF-for 2 days. In every stage of differentiation, immature and mature dendritic cells were separately cocultured with UC-MSCs and BM-MSCs. The findings showed that UC-MSCs and BM-MSCs inhibited strongly differentiation and maturation of dendritic cells at higher dilution ratios (1:1). The BM-MSCs and UC-MSCs showed more inhibitory effect on CD1a, CD83, CD86 expression, and dendritic cell endocytic activity, respectively. On the other hand, these cells severely up-regulated CD14 marker expression. We concluded that UC-MSCs and BM-MSCs could inhibit differentiation, maturation and endocytosis in monocyte-derived DCs through the secreted factors and free of any cellcell contacts under laboratory conditions. As DCs are believed to be the main antigen presenting cells for naive T cells in triggering immune responses, it would be logical that their inhibitory effect on differentiation, maturation and function can decrease or modulate immune and inflammatory responses. Copyright © Spring 2013, Iran J Allergy Asthma Immunol. All rights reserved

Seismic velocity deviation log: An effective method for evaluating spatial distribution of reservoir pore types

Velocity deviation log (VDL) is a synthetic log used to determine pore types in reservoir rocks based on a combination of the sonic log with neutron-density logs. The current study proposes a two step approach to create a map of porosity and pore types by integrating the results of petrographic studies, well logs and seismic data. In the first step, velocity deviation log was created from the combination of the sonic log with the neutron-density log. The results allowed identifying negative, zero and positive deviations based on the created synthetic velocity log. Negative velocity deviations (below − 500 m/s) indicate connected or interconnected pores and fractures, while positive deviations (above + 500 m/s) are related to isolated pores. Zero deviations in the range of [− 500 m/s, + 500 m/s] are in good agreement with intercrystalline and microporosities. The results of petrographic studies were used to validate the main pore type derived from velocity deviation log. In the next step, velocity deviation log was estimated from seismic data by using a probabilistic neural network model. For this purpose, the inverted acoustic impedance along with the amplitude based seismic attributes were formulated to VDL. The methodology is illustrated by performing a case study from the Hendijan oilfield, northwestern Persian Gulf. The results of this study show that integration of petrographic, well logs and seismic attributes is an instrumental way for understanding the spatial distribution of main reservoir pore types

Scaled boundary point interpolation method for seismic soil-tunnel interaction analysis

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordThe scaled boundary method (SBM) is an effective numerical approach for analyzing elasto-statics of bounded and unbounded media. To enhance abilities of the scaled boundary approach, this method can be coupled with mesh free technology. In this paper a point interpolation based SBM is proposed to analyze seismic soil-tunnel interaction problems. In the proposed approach, boundary of the domain is modelled with the scaled boundary point interpolation method while the interior domain is modelled by the conventional finite element method (FEM). This is the first time that a mesh-free scaled boundary method is used to analyze seismic problems. The presented method has some advantages over previously presented mesh-free SBMs. In the scaled boundary point interpolation method, the shape functions have the Kronecker delta function property and do not require radial basis functions to discretize the boundary of 2D problems. A shaking table test is designed and used to verify the proposed method. It is shown that the proposed numerical approach leads to results that are in a good agreement with those of the designed shaking table tests

Pore-Facies as a tool for incorporation of small scale dynamic information in integrated reservoir studies

In this study, the quantification and incorporation of pore geometry, a qualitative parameter, and a source of dynamic information, will be demonstrated in the integrated reservoir studies. To quantify pore geometry, mercury injection capillary pressure (MICP) curves have been exploited. For each MICP curve, 20 parameters were derived and multi-resolution graph-based clustering was applied to classify the curves into nine representative distinct clusters. The number of clusters was determined based on petrography and cluster analysis. The quantified pore geometry in terms of discrete variable has been called pore-facies, and like electro-facies and litho-facies could be used in facies modelling and rock typing phases of an integrated study. The dependence of dynamic reservoir rock properties on pore geometry makes the pore-facies an interesting and powerful approach for incorporation of small-scale dynamic data into a reservoir model. A comparison among various facies definitions proved that neither litho-facies nor electro-facies is capable of characterizing dynamic rock properties, and the best results were achieved by the pore-facies method. Based on this study, it is recommended that for facies analysis in reservoir modelling, methods based on pore characteristics such as pore-facies, introduced in this paper, be used rather than traditional facies that rely on matrix properties. The next generation of the reservoir models which incorporate pore-facies-based rock types will provide a basis for more accurate static and dynamic models, a narrower range of uncertainty in the models, and a better prediction of reservoir performance

A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf

Normalized oil content (NOC) is an important geochemical factor for identifyingpotential pay zones in hydrocarbon source rocks. The present study proposes an optimaland improved model to make a quantitative and qualitative correlation between NOC andwell log responses by integration of neural network training algorithms and thecommittee machine concept. This committee machine with training algorithms (CMTA)combines Levenberg-Marquardt (LM), Bayesian regularization (BR), gradient descent(GD), one step secant (OSS), and resilient back-propagation (RP) algorithms. Each ofthese algorithms has a weight factor showing its contribution in overall prediction. Theoptimal combination of the weights is derived by a genetic algorithm. The method isillustrated using a case study. For this purpose, 231 data composed of well log data andmeasured NOC from three wells of South Pars Gas Field were clustered into 194modeling dataset and 37 testing samples for evaluating reliability of the models. Theresults of this study show that the CMTA provides more reliable and acceptable resultsthan each of the individual neural networks differing in training algorithms. Also CMTAcan accurately identify production pay zones (PPZs) from well logs

A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran

Total Organic Carbon (TOC) content present in reservoir rocks is one of the important parameters which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon bearing units. In general, organic rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher gamma-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into eighty-seven training sets to build the CMIS model and thirty-seven testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC