Making a decision in everyday life always comes with uncertainty and responsibility. To reduce the risk to a minumum and in order to make a right decision a person can use methods of multicriteria analysis in combination in fuzzy logic. A married couple, representing decision makers in this case study, have purchased an apartment and it needs to be completely refurbished including outside carpentry. Aim of this study is to select the most suitable manufacturer of PVC carpentry for apartment refurbishing. A total pool of 14 quantitative and qualitative criteria is used as a base for selection of the most suitable of seven available manufacturers. For this case study we will use one of newer methods of multicriteria analysis of fuzzy Evaluation Based on Distance from Average Solution (fuzzy EDAS) method. Having reached the results, an analysis of sensitivity has been conducted showing stability of results where manufacturer number 4 represents an optimal solution in 13 experimental sets out of 14 total. DOI: http://dx.doi.org/10.5755/j01.ee.29.3.16818

Phase-Shifting Transformers (PST) are installed in order to block or facilitate certain power flows. The installation of a single device affects power flows in a wider geographical area. Therefore, determining the optimal location for the Phase Shift transformer is of the utmost importance. Firstly, this paper presents summarizes the key factors for the installation of Phase Shift Transformers. Secondly, it give the prerequisites for the installation and finally, and thirdly it determines the deduced critical factors which have to be analyzed from the local (substation level), and global (system-wise) perspective. Given the necessity for regional approach when determining optimal PST location, this paper focuses on selected results of possibilities for active power flow control using PSTs in the South Eastern Europe (SEE) transmission grid. At the end, in the form a a power flow anaylsis, such an example is presented.

Data mining and clustering are important elements of various applications in different fields. One of the areas were clustering is rather frequently used is web intelligence, which nowadays represents an important research area. Data collected from the web are usually very complex, dynamic, without structure and rather large. Traditional clustering techniques are not efficient enough and need to be improved. In this paper, we propose combination of recent swarm intelligence algorithm, bare bones fireworks algorithm, and k-means for clustering web intelligence data. The proposed method was compared with other approaches from literature. Based on the experimental results, it can be concluded that the proposed method has very promising characteristics in terms of the quality of clustering, as well as the execution time.

Financial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation.

Incidence of breast cancer ranges from 27 per 100,000 in Middle Africa and Eastern Asia to 92 per 100,000 in Northern America. It is the fifth most common cause of death from cancer in women, with an estimated 522,000 deaths per year (6.4% of the total). Autosomal dominant inheritance of these cancers is characterized by transmission of cancer predisposition from generation to generation, with around 5-10% of all breast cancers being associated with inherited mutations in BRCA1, BRCA2 and other genes.  Breast and ovarian cancers are strongly associated with BRCA1 and BRCA2 mutations. In this study, we genotyped BRCA1 gene for large genomic rearrangements in breast and ovarian cancer patients from Bosnia and Herzegovina, with aim to assess frequency of large BRCA1 mutations (exon deletions/duplications) in this group. We collected 59 breast cancer samples, as well as other data concerning patients’ histopathological parameters of tumor, like age at diagnosis, cancer type, TNM class, cancer grade, as well as estrogen, progesterone and Her2/neu expression. Following DNA extraction from breast cancer samples (tissue after biopsy), BRCA1 mutations were identified by Multiplex Ligase - Dependent Probe Amplification (MLPA) analysis. Biostatistical analyses were conducted using MedCalc v.9.2.0.0 software. In all statistical tests p<0.05 was considered significant. Mean age at diagnosis was 54±1.75 (range 17 – 80). BRCA1 genomic rearrangements were found in 22% of breast and ovarian cancer patients. Statistically significant associations and correlations were found between BRCA1 genomic rearrangements and cancer type, estrogen, progesterone and Her2/neu expression, but not cancer grade, size, invasiveness or patients’ age

OBJECTIVE This study sought to obtain a comprehensive, reliable and valid instrument for evaluation of the presence of dental fear and anxiety (DFA) in children, through evaluation of the reliability and validity of three modified versions of the Children's Fear Survey Schedule-Dental Subscale (CFSS-DS). MATERIALS AND METHODS The study sample comprised children aged 9, 10, 11 and 12 years. The first sample group (200 patients) filled in a modified version of the CFSSDS scale, the second sample group (100 patients) filled in a short form of the Dental Fear Survey Schedule, and the third sample group (100 patients) filled in a short version of the CFSS-DS scale, prior to dental treatment, respectively. In order to determine test-retest reliability, the 184 patients from the first sample group filled in the modified version of the CFSS-DS scale again, prior to their next scheduled dental appointment. RESULTS The modified version of the CFSS-DS scale had the best internal consistency reliability (α=0.907), as well as validity results, compared to the other two instruments used. Test-retest reliability was moderate (Intraclass correlation coefficient: 0.58). CONCLUSIONS Of the three psychometric instruments used for evaluation of DFA presence in children, the modified version of the CFSS-DS scale showed the most clinically adequate reliability and validity values. This study thus provides a new psychometric instrument that should be considered for clinical use in evaluation of DFA presence in 9-12 year-olds, in a clinical setting sample type of children.

Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused on specific learning tasks and typically gives pessimistic bounds which are at odds with the empirical results. We tackle these problems and provide the first unified risk analysis of learning with random Fourier features using the squared error and Lipschitz continuous loss functions. In our bounds, the trade-off between the computational cost and the expected risk convergence rate is problem specific and expressed in terms of the regularization parameter and the \emph{number of effective degrees of freedom}. We study both the standard random Fourier features method for which we improve the existing bounds on the number of features required to guarantee the corresponding minimax risk convergence rate of kernel ridge regression, as well as a data-dependent modification which samples features proportional to \emph{ridge leverage scores} and further reduces the required number of features. As ridge leverage scores are expensive to compute, we devise a simple approximation scheme which provably reduces the computational cost without loss of statistical efficiency.

Extensive research confirms the benefits of group work in various educational and business domains. There has, however, been little consideration to rigorous formation of groups, especially project teams, in software engineering disciplines to improve the outcomes of these groups. Previous studies show that the outcome of groups will be affected by a number of different factors, such as the context in which these groups interact, the characteristics and the behaviour of each individual and the group composition. This research evaluates the extent to which it is possible to enhance the group outcomes by systematically reconstructing the groups of students and hence improve the performances and raise the overall outcome level of a software engineering lecture at two universities, the Alpen-Adria University of Klagenfurt and the Technical University of Kosice. An empirical experiment has been carried out involving 69 groups and 140 individuals. The results of this experiment were then compared with historical data of 961 groups (approximately 2,400 students) on group outcomes over a period of 12 years. The findings show statistically significant improvements of the outcomes for those groups that were systematically constructed. These results could enable business leaders and educators to systematically form their groups for improving the outcomes of these groups.

In this paper, we investigate asynchronous coded caching in fog radio access networks (F-RAN). To minimize the fronthaul load, the encoding set collapsing rule and encoding set partition method are proposed to establish the relationship between the coded-multicasting contents in asynchronous and synchronous coded caching. Furthermore, a decentralized asynchronous coded caching scheme is proposed, which provides asynchronous and synchronous transmission methods for different delay requirements. The simulation results show that our proposed scheme creates considerable coded-multicasting opportunities in asynchronous request scenarios.