Sharif Digital Repository

Wettability alteration as the main mechanism of improved oil recovery in carbonates during low salinity/engineered water flooding (LS/EWF) is a complex phenomenon due to high heterogeneity of rock. During LS/EWF, wettability changes when electrochemical interactions at carbonate-brine interface happen. Anhydrite impurity in carbonates is one of the most important parameters affecting the electrochemical interactions at the rock-brine interface and the wettability alteration process. Therefore, the success of LS/EWF in carbonate reservoirs lies in perceiving the role of impurities such as anhydrite, from a geochemical and dissolution point of view. Modified flotation tests (MFT) were... 

Organ-on-a-chip technology tries to mimic the complexity of native tissues in vitro. Important progress has recently been made in using this technology to study the gut with and without microbiota. These in vitro models can serve as an alternative to animal models for studying physiology, pathology, and pharmacology. While these models have greater physiological relevance than two-dimensional (2D) cell systems in vitro, endocrine and immunological functions in gut-on-a-chip models are still poorly represented. Furthermore, the construction of complex models, in which different cell types and structures interact, remains a challenge. Generally, gut-on-a-chip models have the potential to... 

Evaluating the statistical dimension is a common tool to determine the asymptotic phase transition in compressed sensing problems with Gaussian ensemble. Unfortunately, the exact evaluation of the statistical dimension is very difficult and it has become standard to replace it with an upper-bound. To ensure that this technique is suitable, [1] has introduced an upper-bound on the gap between the statistical dimension and its approximation. In this work, we first show that the error bound in [1] in some low-dimensional models such as total variation and ell _{1} analysis minimization becomes poorly large. Next, we develop a new error bound which significantly improves the estimation gap... 

Observatory-scale data collection efforts allow unprecedented opportunities for integrative, multidisciplinary investigations in large, complex watersheds, which can affect management decisions and policy. Through the National Science Foundation-funded REACH (REsilience under Accelerated CHange) project, in collaboration with the Intensively Managed Landscapes-Critical Zone Observatory, we have collected a series of multidisciplinary data sets throughout the Minnesota River Basin in south-central Minnesota, USA, a 43,400-km2 tributary to the Upper Mississippi River. Postglacial incision within the Minnesota River valley created an erosional landscape highly responsive to hydrologic change,... 

Visual attributes are great means of describing images or scenes, in a way both humans and computers understand. In order to establish a correspondence between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand-crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the applicability of those methods for more realistic cases. We introduce a deep neural network architecture for the task of relative attribute prediction. A convolutional neural network (ConvNet) is adopted to learn the features by including an... 

An integrated approach is presented for predicting granule particle size using Partial Correlation (PC) analysis and Artificial Neural Networks (ANNs). In this approach, the proposed model is an abstract form from the ANN model, which intends to reduce model complexity via reducing the dimension of the input set and consequently improving the generalization capability of the model. This study involves comparing the capability of the proposed model in predicting granule particle size with those obtained from ANN and Multi Linear Regression models, with respect to some indicators. The numerical results confirm the superiority of the proposed model over the others in the prediction of granule... 

The need for simplified physical models representing frequency dependent soil impedances has been the motivation behind many researches throughout history. Generally, such models are generated to capture impedance functions in a wide range of excitation frequencies, which leads to relatively complex models. That is while there is just a limited range of frequencies that really influence the response of the structure. Here, a new methodology based on the response-matching concept is proposed, which can lead to the development of simpler discrete models. The idea is then used to upgrade an existing simple model of surface foundations to the case of embedded foundations. The applicability of... 

A new methodology is presented with which the likely impact of accident in one process unit of an industry on other process units can be forecast and assessed. The methodology is based on Monte Carlo Simulation and overcomes the limitations of analytical methods, used hitherto, which were inherently limited in their ability to handle the uncertainty and the complexity associated with domino effect phenomena. The methodology has been validated and its applicability has been demonstrated with two case studies  

Thermal cracking of hydrocarbons converts them into valuable materials in the petrochemical industries. Multiplicity of the reaction routes and complexity of the mathematical approach has led us use a kind of black-box modelingsartificial neural networks. Reactor feed type plays an essential role on the product qualities. Feed type is a qualitative character. In this paper, a method is presented to introduce a range of petroleum fractions to the neural network. To introduce petroleum cuts with final boiling points of 865 °F maximum to the neural network, a real component substitute mixture is made from the original mixture. Such substitute mixture is fully defined, it has a chemical...