Sharif Digital Repository

We propose a self-adaptive process (SAP) that maintains the software architecture quality using the MAPE-K standard model. The proposed process can be plugged into various software development processes and service-oriented methodologies due to its explicitly defined inputs and outputs. To this aim, the proposed SAP is integrated with the service-oriented modeling and application (SOMA) methodology in a two-layered structure to create a novel methodology, named self-adaptive service-oriented architecture methodology (SASOAM), which provides a semi-automatic self-aware method by the composition of architectural tactics. Moreover, the maintenance activity of SOMA is improved using... 

We propose a self-adaptive process (SAP) that maintains the software architecture quality using the MAPE-K standard model. The proposed process can be plugged into various software development processes and service-oriented methodologies due to its explicitly defined inputs and outputs. To this aim, the proposed SAP is integrated with the service-oriented modeling and application (SOMA) methodology in a two-layered structure to create a novel methodology, named self-adaptive service-oriented architecture methodology (SASOAM), which provides a semi-automatic self-aware method by the composition of architectural tactics. Moreover, the maintenance activity of SOMA is improved using... 

We propose a self-adaptive process (SAP) that maintains the software architecture quality using the MAPE-K standard model. The proposed process can be plugged into various software development processes and service-oriented methodologies due to its explicitly defined inputs and outputs. To this aim, the proposed SAP is integrated with the service-oriented modeling and application (SOMA) methodology in a two-layered structure to create a novel methodology, named self-adaptive service-oriented architecture methodology (SASOAM), which provides a semi-automatic self-aware method by the composition of architectural tactics. Moreover, the maintenance activity of SOMA is improved using... 

Named Data Networking (NDN) is a new information-centric networking architecture in which data or content is identified by a unique name and saved pieces of the content are used in the cache of routers. Certainly, routing is one of the major challenges in these networks. In NDN, to achieve the required data for users, interest messages containing the names of data are sent. Because the source and destination addresses are not included in this package, routers forward them using the names that carried in packages. This forward will continue until the interest package is served. In this paper, we propose a routing algorithm for NDN. The purpose of this protocol is to choose a path with the... 

Named data networking (NDN) is a new information-centric networking architecture in which data or content is identified by a unique name and saved pieces of the content are used in the cache of routers. Certainly, routing is one of the major challenges in these networks. In NDN, to achieve the required data for users, interest messages containing the names of data are sent. Because the source and destination addresses are not included in this package, routers forward them using the names that carried in packages. This forward will continue until the interest package is served. In this paper, we propose a routing algorithm for NDN. The purpose of this protocol is to choose a path with the... 

Information-centric network (ICN) has become a basic research topic with regard to the future architecture of the Internet. Storage and routing data is an important feature of ICN, as it improves network performance and content distribution. One of the challenges in this type of network is the routing problem. The objective of this paper is to propose a routing algorithm in the named data network. Owing to the impressive results presented by the enhanced heuristic methods, tabu search has been selected to develop the proposed algorithm. This work has been carried out in five major steps, Creating the initial solution, The mechanisms of generating a neighborhood, Evaluating the neighbor... 

We propose a graph-based data clustering algorithm which is based on exact clustering of a minimum spanning tree in terms of a minimum isoperimetry criteria. We show that our basic clustering algorithm runs in O(nlogn) and with post-processing in almost O(nlogn) (average case) and O(n2) (worst case) time where n is the size of the data-set. It is also shown that our generalized graph model, which also allows the use of potentials at vertices, can be used to extract an extra piece of information related to anomalous data patterns and outliers. In this regard, we propose an algorithm that extracts outliers in parallel to data clustering. We also provide a comparative performance analysis of... 

In this study, the mechanisms of passing water and salt ions through nanoporous single-layer graphene membrane are simulated using classical molecular dynamics. The effects of functional groups placed in nanopores and feed water's salt concentration on water desalination are investigated. In order to understand the role of functional groups in desalination process, Methyl, Ethyl and a combination of Fluorine and Hydrogen molecules are distributed around the nanopores. In all cases, different number of functional molecules is employed in order to find an optimum distribution of the groups at hand. The results show that an appropriate distribution of Alkyl groups can properly stop the salt... 

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, few reports have been published on their properties and characteristics of analytical figures of merit. Currently, most performance measures for these types of networks only incorporate some function of prediction error. While useful, these measures are incomplete and cannot be used as an objective comparison among different models. In this report, a new method for calculating the sensitivity of any type of neural network is proposed and studied on both simulated and real... 

In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, few reports have been published on their properties and characteristics of analytical figures of merit. Currently, most performance measures for these types of networks only incorporate some function of prediction error. While useful, these measures are incomplete and cannot be used as an objective comparison among different models. In this report, a new method for calculating the sensitivity of any type of neural network is proposed and studied on both simulated and real... 

The water desalination process using nanoporous single-layer graphene membranes is simulated through classical molecular dynamics. The effect of nanopores shapes on the capacity of the membrane for filtration of water is investigated. According to the results, the geometry of the nanopores considerably affects the performance of the membrane and can completely change the water flow rate and salt rejection. The results reveal that the effective area of the nanopores plays a critical role and for a better understanding of the impact of this parameter, aspect ratio and the equal diameter of noncircular pores based on different methods such as equal area, equal perimeter, and hydraulic diameter...