Sharif Digital Repository

Nanoscratch testing is a highly reliable method used to extract a variety of film properties. It is proven that many of the experimental factors can influence the obtained results, such as the probe tilt, the scratch depth, etc. On the other hand, the surface roughness of the samples is an important parameter in nanoscratch and other similar tests, including the nanoindentation test. Thus, the effect of surface roughness on both the nanoscratch experiments and finite element simulations has been investigated. By performing scratch tests on gold and copper films and carrying out the finite element (FE) simulations on the rough and smooth surfaces, the importance of surface morphology was... 

Gold and copper thin films are widely used in microelectromechanical system (MEMS) and nanoelectromechanical system (NEMS) devices. Nanoindentation has been developed in mechanical characterization of thin films in recent years. Several researchers have examined the effect of surface roughness on nanoindentation results. It is proved that the surface roughness has great importance in nanoindentation of thin films. In this paper, the surface topography of thin films is simulated using the extracted data from the atomic force microscopy (AFM) images. Nanoindentation on a rough surface is simulated using a three-dimensional finite-element model. The results are compared with the results of... 

In this paper, motivated by network inference, we introduce a general framework, called LSR-Weighted, to efficiently recover sparse characteristic of links in weighted networks. The links in many real-world networks are not only binary entities, either present or not, but rather have associated weights that record their strengths relative to one another. Such models are generally described in terms of weighted networks. The LSR-Weighted framework uses a newly emerged paradigm in sparse signal recovery named compressive sensing. We study the problem of recovering sparse link vectors with network topological constraints over weighted networks. We evaluate performance of the proposed framework... 

This paper addresses the problem of identifying central nodes from the information flow standpoint in a social network. Betweenness centrality is the most prominent measure that shows the node importance from the information flow standpoint in the network. High betweenness centrality nodes play crucial roles in the spread of propaganda, ideologies, or gossips in social networks, the bottlenecks in communication networks, and the connector hubs in biological systems. In this paper, we introduce DICeNod, a new approach to efficiently identify central nodes in social networks without direct measurement of each individual node using compressive sensing, which is a well-known paradigm in sparse... 

Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k-highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top-k betweenness centrality nodes in networks, using compressive sensing.... 

One of the most relevant characteristics of social networks is community structure, in which network nodes are joined together in densely connected groups between which there are only sparser links. Uncovering these sparse links (i.e. intercommunity links) has a significant role in community detection problem which has been of great importance in sociology, biology, and computer science. In this paper, we propose a novel approach, called CS-ComDet, to efficiently detect the inter-community links based on a newly emerged paradigm in sparse signal recovery, called compressive sensing. We test our method on real-world networks of various kinds whose community structures are already known, and... 

In this paper, we consider a dual-sourcing model with constant demand and stochastic lead times. Two suppliers may be different in terms of purchasing prices and lead-time parameters. The ordering takes place when the inventory level depletes to a reorder level, and the order is split among two suppliers. Unlike previous works in the order splitting literature, the supply lead time between vendor and buyer as well as unit purchasing prices is considered to be order quantity dependent. The proposed model finds out the optimal reorder point, order quantity and splitting proportion, using a solution procedure. Numerical results show that neglecting the relationship between ordering batch size... 

Adsorption of human serum proteins (Albumin and total protein) onto high purity synthesis nano-hydroxyapatite (HA), Ca 10(PO 4) 6(OH) 2, has been studied in a wide temperature range by UV-visible spectrophotometer. Adsorption isotherm is basically important to describe how solutes interact with adsorbent, andiscriticalinoptimizing the use of adsorbent. Inthe present study, the experimental results werefitted tothe Langmuir, Freundlich, Temkin and Dubinin-Radushkevich (DR) models to obtain the characteristic parameters of each model and square of the correlation coefficients (R 2). According to the results, the DR isotherm model had the best agreement with the experimental data. The effect of...