Determining trust in media-rich websites using semantic similarity

Significant growth of multimedia content on the World Wide Web (or simply ??Web??) has made it an essential part of peoples lives. The web provides enormous amount of information, however, it is very important for the users to be able to gauge the trustworthiness of web information. Users normally access content from the first few links provided to them by search engines such as Google or Yahoo!. This is assuming that these search engines provide factual information, which may be popular due to criteria such as page rank but may not always be trustworthy from the factual aspects. This paper presents a mechanism to determine trust of websites based on the semantic similarity of their multimedia content with already established and trusted websites. The proposed method allows for dynamic computation of the trust level of websites of different domains and hence overcomes the dependency on traditional user feedback methods for determining trust. In fact, our method attempts to emulate the evolving process of trust that takes place in a user??s mind. The experimental results have been provided to demonstrate the utility and practicality of the proposed method.     

Supplier selection using a novel intuitionist fuzzy clustering approach

Supplier selection is a complicated decision-making problem involving multicriteria, alternative and decision makers (DMs). The main purpose of this paper is to demonstrate the use of a clustering-based method to solve a group decision making (GDM) problem and, also to achieve more realistic and homogeneous results. Intuitionistic fuzzy value (IFV) is used to show the decision makers’ preferences and IFN clustering method is utilized to cluster around DM's preferences. Intuitionistic fuzzy weighted geometric (IFWG) is applied to aggregate the obtained clusters. Ranking process is used based on the two indices, score function and accuracy function, to rank the alternatives. Lastly, to demonstrate the efficiency of our proposed method, it is implemented to choose suppliers in a car factory.The strength of the propose approach is considering the group agreement on proposed DMs’ preferences for giving different effect on their judgment. Besides, encountering the qualitative judgment of DMs using IFV concept with score function and the accuracy function for modeling the DMs’ knowledge is the other contribution of this paper.     

Solving coupled matrix equations over generalized bisymmetric matrices

In this paper, an iterative algorithm is established for finding the generalized bisymmetric solution group to the coupled matrix equations (including the generalized (coupled) Lyapunov and Sylvester matrix equations as special cases). It is proved that proposed algorithm consistently converges to the generalized bisymmetric solution group for any initial generalized bisymmetric matrix group. Finally a numerical example indicates that proposed algorithm works quite effectively in practice.     

An isogeometrical approach to structural topology optimization by optimality criteria

The Isogeometric Analysis (IA) method is applied for structural topology optimization instead of finite elements. For this purpose, a control point based Solid Isotropic Material with Penalization (SIMP) method is employed and the material density is considered as a continuous function throughout the design domain and approximated by the Non-Uniform Rational B-Spline (NURBS) basis functions. To prevent the formation of layouts with porous media, a penalization technique similar to the SIMP method is used. For optimization an optimality criteria is derived and implemented. A few examples are presented to demonstrate the performance of the method. It is shown that, dissimilar to the element based SIMP topology optimization, the resulted layouts by this method are independent of the number of the discretizing control points and checkerboard free.     

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Theoretical and experimental analyses of rheological,compatibility and mechanical properties of PVMQ/XNBR-g GMA/XNBR/GO ternary hybrid nanocomposites

Iranian Polymer Journal - Effects of graphene oxide (GO) on various properties of rubber hybrid nanocomposites based on PVMQ/XNBR-g-GMA/XNBR (phenyl-vinyl-methyl-polysiloxane/carboxylated nitrile...     

Toward A Mechanistic Understanding of Re-Infiltration in Naturally Fractured Reservoirs with Surfactant-Aided Gravity Drainage Process

Surfactant-aided gravity drainage is an improved oil recovery technique for water-invaded zone in fractured carbonate reservoirs, which are mostly oil-wet or mixed-wet rocks. The re-infiltration mechanism in water-invaded zone has a considerable effect on oil vertical movement in gravity drainage processes. In this work, a mechanistic understanding of re-infiltration in surfactant-aided gravity drainage, in comparison to oil–water gravity drainage is presented using an experimentally and numerically validated model. A column model is constructed from three matrix blocks. These blocks are separated from each other by horizontal fractures. A storage tank is considered on top of the model to store depleted oil from matrix blocks. The stacked-blocks model for re-infiltration is validated and verified to simulate water and chemical flooding using a mesh independency study and experimental flooding data in a composite core experiment. Using this model, several analyses are performed to investigate effects of rock and fluid properties, rock saturation functions, wettability alteration, surfactant adsorption, and capillary continuity on re-infiltration.     

Noncertainty equivalent adaptive control of robot manipulators without velocity measurements

This paper presents a noncertainty equivalent adaptive motion control scheme for robot manipulators in the absence of link velocity measurements. A new output feedback adaptation algorithm, based on the attractive manifold design approach, is developed. A proportional-integral adaptation is selected for the adaptive parameter estimator to strengthen the passivity of the system. In order to relieve velocity measurements, an observer is designed to estimate the velocities. The controller guarantees semiglobal asymptotic motion tracking and velocity estimation, as well as L_∞ and L₂ bounded parameter estimation error. The effectiveness of the proposed controller is verified by simulations for a two-link robot manipulator and a four-bar linkage. The results are further compared with the earlier certainty-equivalent adaptive partial and full state feedback controller to highlight potential closed-loop performance improvements.     

Precautious-RM: a predictable non-preemptive scheduling algorithm for harmonic tasks

A major requirement of many real-time embedded systems is to have time-predictable interaction with the environment. More specifically, they need fixed or small sampling and I/O delays, and they cannot cope with large delay jitters. Non-preemptive execution is a known method to reduce the latter delay; however, the corresponding scheduling problem is NP-Hard for periodic tasks. In this paper, we present Precautious-RM as a predictable linear-time online non-preemptive scheduling algorithm for harmonic tasks which can also deal with the former delay, namely sampling delay. We derive conditions of optimality of Precautious-RM and show that satisfying those conditions, tight bounds for best- and worst-case response times of the tasks can be calculated in polynomial-time. More importantly, response time jitter of the tasks is analyzed and it is proven that under specific conditions, each task has either one or two values for response time, which leads to improving the predictability of the system interaction with the environment. Simulation results demonstrate efficiency of Precautious-RM in increasing accuracy of control applications.     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.