A new bracing system for improvement of seismic performance of steel jacket type offshore platforms with float-over-deck

In this paper, the seismic response of a newly designed steel jacket offshore platform with a float over deck (FOD) system in the Persian Gulf was investigated through incremental dynamic analysis. Comparison of incremental dynamic analysis results for both directions of the platform shows that the lateral strength of the platform in the float over direction is less than its lateral strength in other direction. Dynamic characteristics measurement of a scale model of platform was also performed using forced vibration tests. From experimental measurement of the scaled model, it was observed that dynamic characteristic of the platform is different in the float over direction compared to the other direction. Therefore, a new offshore installed bracing system for the float over direction was proposed for improvement of seismic performance of this type of platform. Finally, the structure with the modified system was assessed using the probabilistic seismic assessment method as well as experimental measurement of its dynamic characteristics. It was observed that the proposed offshore installed bracing system improves the performance of platforms subjected to strong ground motion.     

Sonochemical Synthesis and Structural Characterization and DFT Calculations of a Novel Nano Flower Pb(II) Coordination Compound [Pb(phen)2(4-abs)2]n

Nano-structures of a new coordination polymer of divalent Pb with the ligands 1,10-phenanthroline (phen) and 4-aminobenzene sulfonic acid (4-abs), [Pb(phen)₂(4-abs)₂]_n (1), were synthesized by a sonochemical method that produced a coordination polymer of nanometer size. The new nano-structure was characterized by scanning electron microscopy, X-ray powder diffraction and IR spectroscopy and elemental analysis. Compound 1 was structurally characterized by single crystal X-ray diffraction and showed that the coordination number of the Pb(II) ions is six; i.e. (PbN₄O₂). Compound 1 also has a stereochemically active lone pair of electrons and a hemi-directed coordination sphere. The chains interact with each other through π–π stacking to create a 3D framework. The structure of 1 was optimized by density functional theory calculations. The calculated structural parameters and IR spectra of 1 are consistent with the crystal structure.     

The application of homotopy perturbation method for MHD flows of UCM fluids above porous stretching sheets

In this study, by means of homotopy perturbation method (HPM) an approximate analytical solution of the magnetohydrodynamic (MHD) boundary layer flow of an upper-convected Maxwell (UCM) fluid over a porous stretching sheet is obtained. The main feature of the HPM is that it deforms a difficult problem into a set of problems which are easier to solve. HPM produces analytical expressions for the solution of nonlinear differential equations. The obtained analytic solution is in the form of an infinite power series. In this work, the analytical solution obtained by using only two terms from HPM solution. The results reveal that the proposed method is very effective and simple and can be applied to other nonlinear problems. Also it is shown that this method coincides with homotopy analysis method (HAM) for the studied problem.     

Deriving support threshold values and membership functions using the multiple-level cluster-based master–slave IFG approach

Today, development of e-commerce has provided many transaction databases with useful information for investigators exploring dependencies among the items. In data mining, the dependencies among different items can be shown using an association rule. The new fuzzy-genetic (FG) approach is designed to mine fuzzy association rules from a quantitative transaction database. Three important advantages are associated with using the FG approach: (1) the association rules can be extracted from the transaction database with a quantitative value; (2) extracting proper membership functions and support threshold values with the genetic algorithm will exert a positive effect on the mining process results; (3) expressing the association rules in a fuzzy representation is more understandable for humans. In this paper, we design a comprehensive and fast algorithm that mines level-crossing fuzzy association rules on multiple concept levels with learning support threshold values and membership functions using the cluster-based master–slave integrated FG approach. Mining the fuzzy association rules on multiple concept levels helps find more important, useful, accurate, and practical information.     

Reducing channel zapping time in live TV broadcasting over content centric networks

Channel zapping delay is a big challenge in delivering TV service over the Internet infrastructure. Previous research works have studied this delay, its components, and solutions to decrease it. Unfortunately, the best proposed solutions reduce the delay at the expense of increasing bandwidth usage or decreasing the received video quality. After channel switching, the Set Top Box (STB) or player application should buffer sufficient frames before starting to play the received video. However, the buffering process takes place at the playback rate and leads to a delay which is inversely related to the buffer duration. Regarding Information Centric Networking (ICN) paradigm, this paper introduces a new channel zapping protocol that aims to remove the synchronization and buffering delays while maintaining the bandwidth utilization and also the received video quality. The general idea of the proposed solution is to exploit the in-network caching feature of the ICN to retrieve the frames from the network at the network speed. Although the analyses show that the proposed zapping protocol eliminates the delay dependency to the buffer duration, network throughput becomes the bottleneck instead. So, novel solutions have been proposed to reduce the queuing delay as the main component of network delay. These solutions include two new caching algorithms, a new cache replacement algorithm, and applying scheduling methods to the forwarding queues. Simulation results show that increasing link rates, using the proposed caching and cache replacement algorithms, and applying an appropriate scheduling method will greatly reduce the zapping delay without sacrificing the bandwidth or video quality.     

Ultrahigh on/off-current ratio γ-graphyne-1 nanotube-based sub-10-nm TFET modeling and simulation

Journal of Computational Electronics - The use of γ-graphyne-1 nanotubes (GyNTs) in tunneling field effect transistors (TFETs) suppresses ambipolarity and enhances the subthreshold...     

A high-performance algorithm for finding influential nodes in large-scale social networks

As one of the significant issues in social networks analysis, the influence maximization problem aims to fetch a minimal set of the most influential individuals in the network to maximize the number of influenced nodes under a diffusion model. Several approaches have been proposed to tackle this NP-hard problem. The traditional approaches failed to develop an efficient and effective solution due to the exponential growth of the size of social networks (due to massive computational overhead). In this paper, a three-stage framework based on the community detection approach is devised, namely LGFIM. In the first stage, the search space was controlled by partitioning the network into communities. Simultaneously, three heuristic methods were presented for modifying the community detection algorithm to extract the optimal communities: core nodes selection, capacity constraint on communities, and communities combination. These extracted communities were highly compatible with the information propagation mechanism. The next stages apply a scalable and robust algorithm at two different levels of the network: 1. Exploring the local scope of communities to select the most influential nodes of each community and construct the potential influential nodes set 2. Exploring the global scope of the network to select the target influential nodes among potential influential nodes set. Experimental results on various real datasets proved that LGFIM could achieve remarkable results compared with the state-of-the-art algorithms, especially acceptable influence spread, much better running time, and more applicable to massive social networks.

     

Towards efficient data collection mechanisms in the vehicular ad hoc networks

Recently, the fast growth of communication technology has led to the design and implementation of different types of networks in different environments. One of these remarkable networks is vehicular ad hoc network (VANET). The ubiquitous connectivity among vehicles is possible through VANET in the absence of fixed infrastructure. Moreover, it provides safety and comfort to people sitting in the vehicles. In this regard, collecting information from vehicles that are moved constantly is an essential challenge. To develop an efficient method for data collection in the VANET, some parameters must be considered such as data aggregation, latency, packet delivery ratio, packet loss, scalability, security, transmission overhead, and vehicle density. Since data collection has a significant importance in the VANET, the aim of this study is to investigate the existing methods and describe the types of important issues and challenging problems that can be addressed in data collection in the VANET. The data collection techniques are investigated in four primary groups, namely, topology‐based, cluster‐based, geocast‐based, and fog‐based. Also, the mentioned parameters are important to compare all of the presented techniques.     

VAnDroid: A framework for vulnerability analysis of Android applications using a model-driven reverse engineering technique

Android is extensively used worldwide by mobile application developers. Android provides applications with a message passing system to communicate within and between them. Due to the risks associated with this system, it is vital to detect its unsafe operations and potential vulnerabilities. To achieve this goal, a new framework, called VAnDroid, based on Model Driven Reverse Engineering (MDRE), is presented that identifies security risks and vulnerabilities related to the Android application communication model. In the proposed framework, some security-related information included in an Android app is automatically extracted and represented as a domain-specific model. Then, it is used for analyzing security configurations and identifying vulnerabilities in the corresponding application. The proposed framework is implemented as an Eclipse-based tool, which automatically identifies the Intent Spoofing and Unauthorized Intent Receipt as two attacks related to the Android application communication model. To evaluate the tool, it has been applied to several real-world Android applications, including 20 apps from Google Play and 110 apps from the F-Droid repository. VAnDroid is also compared with several existing analysis tools, and it is shown that it has a number of key advantages over those tools specifically regarding its high correctness, scalability, and usability in discovering vulnerabilities. The results well indicate the effectiveness and capacity of the VAnDroid as a promising approach in the field of Android security.