Small-scale crack blunting at a bimaterial interface with Coulomb friction

The deformation field near the tip of a tensional crack impinging upon a normally loaded bimaterial frictional interface is studied. By allowing the two materials to slide with respect to each other along the interface the high stresses around the crack tip are relaxed. The region of slip as well as the stress distribution inside the slip zone is obtained as a function of the far field loading parameter.     

Approximate analysis of serial flow lines with multiple parallel-machine stations

This paper studies serial flow lines, in which each station consists of multiple identical reliable parallel machines. The parallel machines of different work stations are not necessarily identical, viz., station processing times are assumed to be exponentially distributed with non-identical mean service rates. Initially, a model consisting of two stations with multiple parallel machines and an intermediate buffer is solved analytically, by developing a recursive algorithm that generates the transition matrix for any value of the intermediate buffer capacity. This model is used as a decomposition block for solving larger lines. More specifically, the decomposition block is solved via exact Markovian analysis and then the decomposition equations and an algorithm that simultaneously solves them are derived in order to evaluate the performance measures of large production systems with multiple parallel-machine stations. Numerical results are provided for large production lines with up to 1000 workstations. These results are compared against simulation and the average percentage error is found to be very small.     

CRITICAL PLANE APPROACHES IN HIGH-CYCLE FATIGUE: ON THE DEFINITION OF THE AMPLITUDE AND MEAN VALUE OF THE SHEAR STRESS ACTING ON THE CRITICAL PLANE

Critical plane approaches are useful methods when designing against long-term fatigue of machine components made from metals. Somewhat surprisingly, the very basic problem of the evaluation of the amplitude and mean value of the shear stress acting on the critical plane is still not resolved satisfactorily for non-proportional cyclic loading conditions. In the present paper, existing proposals for solving this problem are briefly reviewed and their weaknesses highlighted. Then it is shown, through particular examples, that application of these proposals can lead to ambiguous results. Therefore, new definitions of the amplitude and mean value of the shear stress acting on the critical plane are formulated here. These new definitions are free from any ambiguity because they are based on the construction of the unique minimum-circumscribed circle to the path described by the shear stress on the critical plane. The centre of this circle defines the mean shear stress, whereas its radius provides the corresponding shear stress amplitude. The algorithm yielding this minimum-circumscribed circle is presented in some detail.     

Intelligent search in social communities of smartphone users

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain, however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P (http://smartp2p.cs.ucy.ac.cy/) system deployed over our SmartLab (http://smartlab.cs.ucy.ac.cy/) testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95 %, with one order of magnitude less time and two orders of magnitude less energy than its competitors.     

Graphs of linear clique-width at most 3

A graph has linear clique-width at most k if it has a clique-width expression using at most k labels such that every disjoint union operation has an operand which is a single vertex graph. We give the first characterisation of graphs of linear clique-width at most 3, and we give the first polynomial-time recognition algorithm for graphs of linear clique-width at most 3. In addition, we present new characterisations of graphs of linear clique-width at most 2. We also give a layout characterisation of graphs of bounded linear clique-width; a similar characterisation was independently shown by Gurski and by Lozin and Rautenbach.     

Community detection in Social Media

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e.g. biology, social sciences, bibliometrics. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles. The survey places special emphasis on the performance of existing methods in terms of computational complexity and memory requirements. It presents both a theoretical and an experimental comparative discussion of several popular methods. In addition, it discusses the possibility for incremental application of the methods and proposes five strategies for scaling community detection to real-world networks of huge scales. Finally, the survey deals with the interpretation and exploitation of community detection results in the context of intelligent web applications and services.     

Power efficiency through tuple ranking in?wireless sensor network monitoring

In this paper, we present an innovative framework for efficiently monitoring Wireless Sensor Networks (WSNs). Our framework, coined KSpot, utilizes a novel top-k query processing algorithm we developed, in conjunction with the concept of in-network views, in order to minimize the cost of query execution. For ease of exposition, consider a set of sensors acquiring data from their environment at a given time instance. The generated information can conceptually be thought as a horizontally fragmented base relation R. Furthermore, the results to a user-defined query Q, registered at some sink point, can conceptually be thought as a view V. Maintaining consistency between V and R is very expensive in terms of communication and energy. Thus, KSpot focuses on a subset V??(?V) that unveils only the k highest-ranked answers at the sink, for some user defined parameter k. To illustrate the efficiency of our framework, we have implemented a real system in nesC, which combines the traditional advantages of declarative acquisition frameworks, like TinyDB, with the ideas presented in this work. Extensive real-world testing and experimentation with traces from UC-Berkeley, the University of Washington and Intel Research Berkeley, show that KSpot provides an up to 66% of energy savings compared to TinyDB, minimizes both the size and number of packets transmitted over the network (up to 77%), and prolongs the longevity of a WSN deployment to new scales.