Abstract: In healthcare systems there is a requirement to provide the best possible medical treatment for the patient, and that involves application of different procedures conducted by various experts in the field of medicine and other connected disciplines (engineers, software developers, managers, etc). For the medical treatment of the bone fractures various types of internal and external fixation techniques are used. The important components of these techniques are the fixators which enable fixation of broken bone parts. In this paper a new method for the customization of the modified cloverleaf plate fixator for the proximal part of the humerus bone is introduced. Furthermore, the application of Model-Based System Engineering (MBSE) for the modeling of the customization process is also presented. The main goal of this and future researches is to develop a complete system for the medical treatment of the humerus bone (and possible other human bones) which can improve patient general health and recovery process, and which will help doctors to improve their diagnostic and treatment skills in the field of orthopedic surgery.

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Abstract: Selection of the most suitable material for a given biomedical application is very complex, important and responsible task. A decision support system based on the use of method of multi-criteria decision making (MCDM) methods, named MCDM Solver, was developed in order to facilitate the selection process of biomedical materials and increase selection confidence and objectivity. In this paper, a MCDM problem which referes to the selection of the most suitable material for the compensation of the missing parts of the long bones was solved by using the developed MCDM Solver.

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Abstract: This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions.

Abstract: In this paper we propose a method for determining measure of influence of Facebook pages. Three characteristics of a Facebook page are measured: total number of fans of Facebook page, activity of Facebook page (frequency of posting) and post engagement of fans (weighted sum of number of likes, comments and shares) per post. Fuzzy approach is used to describe and determine measure of influence of Facebook pages. Finally, the paper presents results (influential and non-influential Facebook pages refined based on page category) obtained by combining these three measures and querying the 1.3 million Facebook pages large database.

Abstract: Consumer load profiles play an important role in distribution network analysis. Determining typical consumers can be based on a data mining algorithm called clustering or cluster analysis. Given the multitude of clustering algorithms available today and the disparity of data sets, algorithm selection becomes a non trivial task. One approach to this task is to use multi-criteria decision making algorithms to rank data mining algorithms based on performance and other relevant metrics. In order to move focus from algorithm ranking to selection and tuning, one needs a framework that offers performance data manipulation as well as flexible and customizable ranking algorithms. This paper proposes one such framework that will support research of consumer clusterisation algorithms for power distribution networks.

Abstract: This paper focuses on exploring the possibilities of applying graph drawing algorithms to lay out custom diagrams, with emphasis put on UML class diagrams. Implementing even the simplest of layout algorithms that would lead to acceptable results requires excessive knowledge of graph theory. For this reason, many developers have to rely on existing solutions. There are several open source Java libraries for graph drawing and analysis, but most of them come with certain problems and limitations making their integration with a separately developed graphical editor overly complex. To deal with those issues, we are developing another graph drawing and analysis library, called Grad.

Abstract: This paper presents administration subsystem that was developed to enable dynamic customization of enterprise applications specified by our Kroki tool. Kroki is a tool for participative development of enterprise applications based on executable mockups. The administration subsystem is based on standard RBAC model for access control. It enables users to perform previously authorized tasks by dynamically adjusting their actions. Available actions are determined based on operations, relationships and constraints, according to RBAC. Every user role can access only specific parts of the application that its role is entitled to, with the use of menu adjusted especially for its needs. Dynamic adjustments of available actions are implemented by our runtime engines based on aspect-oriented approach.

Abstract: The paper analysis the performances of different RDF stores on servers with average hardware specification. For this purpose, various tests have been performed on three RDF stores, namely Jena-Fuseki, Sesam-OWLIM and Virtuoso. Using different data sets and queries, the tests have measured CPU usage, heap memory consumption and execution time. Based on the results, for different application scenarios, an appropriate RDF store has been suggested.

Abstract: Motivated by the facts that there is no one-fits-all sustainability assessment framework in mining and that support of information and communications technologies (ICT) to mining is focusing on specific mining process aspects we propose a cutting-edge ICT supported integral framework. The framework relays on multi-objective optimization theory, adaptive control theory, the mining process itself and modern ICT technologies, which position it in line with the concept of the Factory-of-Future. The framework should be able to provide data to all interested parties, e.g. to help top management in mining industry to make optimal decisions, to make available public data, to generate alarms in critical situations.

Abstract: Power supply companies require IT support and infrastructure at heterogeneous levels that vary from supervisory control to low level management of field devices. On the other side quality of software for power supply systems is important characteristic to consider, but it is difficult to be measured and reason about in formal way. This is mostly due to the fact, that for such systems testing is not always applicable into real-world environment. A big power supply company in Serbia is Jugoistok. In order to overcome the aforesaid problem there, the paper presents architecture of a platform that will collect data to be used for calculation of software quality of the information systems in Jugoistok. To overcome the problem with testing, data will be gathered in various ways – by simulation, user feedback and expert opinion.

Abstract: Systemic view in Strategic Environmental Management is gaining even more attention. The paper proposes the case of a Territorial Environmental and Social Assessment (TESA), based on a Life-cycle approach, applied to set-up the Matera Provincial Plan for a strategic environmental management. Information for performing LCA is here integrated through an Energy-Social Planning model as a decisional support tool. The city-owned buildings are critical facilities for Territorial energy management: the focus here is mainly on energy efficiency. The results obtained of the study contribute to reach EU targets according to the new Directive of the European Parliament and of the Council on the energy performance of buildings.

Abstract: The ekoNET system is developed for a real-time monitoring of air pollution and other atmospheric condition parameters such as temperature, air pressure and humidity. The ekoNET service is based on EB800 device integrating low-cost gas, Particluate Matters (PM) and meteorological sensors providing cost-efficient, simple to deploy, use and maintain solution targeted for the usage within the IoT domain of smart cities and smart enterprises. This paper gives an overview of the overall system architecture, ekoNET device, back-end cloud IoT infrastructure mapped to the IoT-A Architecture Reference Model (ARM), data handling and visualization engine as well as the applicationlevel components and modules.

Abstract: Continuous increase of global energy demand combined with the lack of conventional energy sources resulted in rise of operational costs for energy infrastructures. This calls for systematic and comprehensive Energy Management (EM) approaches that are able to take the advantage of increasingly used Renewable Energy Sources (RES) as well as to provide for more efficient operation of existing multi-carrier energy infrastructures. The presented work focuses on the development of an extensive, flexible and modular simulation tool for the analysis and optimisation of such energy systems based on extended Energy Hub concept. The tool was developed as a software module which allows for both energy planning and operation scenarios within multi-carrier environment. The prototype of the analysis and optimisation tool was first developed in MATLAB® and subsequently a corresponding software module was implemented in Java® environment. Technical details and challenges associated with the implementation of software module were elaborated in detail after which the applicability of developed software is highlighted and finally an example of use-case is given.

Abstract: In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBFGP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF.

Abstract: High performance tire design calls for multiobjective optimization of tire design parameters. This paper discusses the application of artificial neural networks (ANNs) for determination of optimal tire design parameters for simultaneous minimization of strain energy density at belt edge and chafer. Based on finite element (FE) simulation experimental trials, conducted according to full factorial design where three tire design parameters were arranged (belt angle, belt cord spacing and elasticity of tread compound), two ANN models of the same topology were developed. The set of optimal tire design parameter values was obtained by graphical optimization method. The quality of multi-objective optimization solutions was validated by performing additional FE experimental trials.

Abstract: The process of railcars accumulation is one of the most important operations carried out at marshalling yards as it is the part of train forming process. Accumulation parameter is used for the evaluation as the indicator that measures the efficiency of the processes in the classification yard. It is used to determine the average collection train time per average number of wagons. This paper presents a model for reducing of collection times by genetic algorithm. Model uses genetic algorithm to search for the initial railcar groups from which to start the collection process and thus minimize the accumulation parameter. The model was tested for the Belgrade marshalling yard to calculate the optimal schedule of collection process for direct trains.

Abstract: The analysis of the railway traffic with all complex processes involved can be effectively performed by computer simulation. Simulation modeling is an efficient tool for analyzing railway systems on an operational, tactical and strategic level. We present a discrete model for simulation of railway traffic on a single track railway line. Model uses hierarchy to connect levels and subsystems. State machines and flow charts are used to model how system reacts to events such as train route conflicts. Model was tested on a single track line for different train traffic scenarios.

Abstract: This paper aims to introduce open access satellite data for the area of Serbia. We describe different satellite platforms which collect images with different spatial and spectral resolution, access and practical utility of historical data and the data that are being collected today. Radar platforms with different bands are introduced as well. We also discuss applicability of these data in different governmental institutions. The paper also indentifies the need to semantically describe such data and proposes introduction of semantic metadata.