36 research outputs found
Transparency effect in the emergence of monopolies in social networks
Power law degree distribution was shown in many complex networks. However, in
most real systems, deviation from power-law behavior is observed in social and
economical networks and emergence of giant hubs is obvious in real network
structures far from the tail of power law. We propose a model based on the
information transparency (transparency means how much the information is
obvious to others). This model can explain power structure in societies with
non-transparency in information delivery. The emergence of ultra powerful nodes
is explained as a direct result of censorship. Based on these assumptions, we
define four distinct transparency regions: perfect non-transparent, low
transparent, perfect transparent and exaggerated regions. We observe the
emergence of some ultra powerful (very high degree) nodes in low transparent
networks, in accordance with the economical and social systems. We show that
the low transparent networks are more vulnerable to attacks and the
controllability of low transparent networks is harder than the others. Also,
the ultra powerful nodes in the low transparent networks have a smaller mean
length and higher clustering coefficients than the other regions.Comment: 14 Pages, 3 figure
Coupled criticality analysis of inflation and unemployment
In this paper, we are interested to focus on the critical periods in the
economy which are characterized by large fluctuations in macroeconomic
indicators.
To capture unusual and large fluctuations of inflation and unemployment, we
concentrate on the non-Gaussianity of their distributions.
To this aim, by using the coupled multifractal approach, we analyze US data
for a period of 70 years from 1948 until 2018 and measure the non-Gausianity of
the distributions. Then, we investigate how the non-Gaussianity of the
variables affects the coupling structure of them. By applying the multifractal
method, one can see that the non-Gaussianity depends on the scales. While the
non-Gaussianity of unemployment is noticeable only for periods smaller than 1
year and for longer periods tends to Gaussian behavior, the non-Gaussianities
of inflation persist for all time scales. Also, it is observed that the
coupling structure of these variables tends to a Gaussian behavior after
years.Comment: 13 pages, 3 figure
Molecular Identification of Dermatophyte species causing cutaneus infections in West Azarbayjan of Iran
Enhancement of Robust Tracking Performance via Switching Supervisory Adaptive Control
When the process is highly uncertain, even linear minimum phase systems must sacrifice desirable feedback control benefits to avoid an excessive ‘cost of feedback’, while preserving the robust stability. In this paper, the problem of supervisory based switching Quantitative Feedback Theory (QFT) control is proposed for the control of highly uncertain plants. According to this strategy, the uncertainty region is suitably divided into smaller regions. It is assumed that a QFT controller-prefilter exits for robust stability and performance of the individual uncertain sets. The proposed control architecture is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the candidate local model behavior with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching for stability reasons. Besides, each controller is designed to be stable in the whole uncertainty domain, and as accurate in command tracking as desired in its uncertainty subset to preserve the robust stability from any failure in the switching
PP-069 Development of RFLP-PCR assay to identify Aspergillus species isolated from clinical and environmental specimens
Learning to Speed Up Query Planning in Graph Databases
Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing — Query Planning — is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries. We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including dbpedia, yago, and freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy
A national school-based screening program for mental health problems among children aged 6 to 12 years in Iran: scale development and psychometric evaluation
Schools are an ideal setting in which to measure and promote mental health difficulties. The aim of present study was to develop the Nemad Electronic Mental-Health Assessment Devices for Children (NEMAD-C) aged 6 to 12 years in Iran. A sample of parents and teachers (N = 10,163) were recruited to complete the parent and teacher reports. Totally, explorative and confirmatory factor analyses showed that the eight-factor model provides a better fit for both parental report and teacher report versions. Results revealed a screening tool consisting of eight dimensions: child abuse risk, self-harm, anxiety, depression, disruptive behavior disorders, attention deficit/hyperactivity disorders, academic achievement deficit, and self-regulation. Findings showed that the internal consistency coefficients of the subscales were high, and convergent validity was evidenced by significant correlations with theoretically related constructs. Therefore, the NEMAD-C has adequate reliability and validity and could be used for multi-dimensional assessment of mental health problems in Iran