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The extraction of neural information from the surface EMG for the control of upper-limb prostheses
Dario Farina, Ning Jiang, Hubertus Rehbaum, Aleš Holobar, Bernhard Graimann, Hans Dietl, Oskar Aszmann, 2014, original scientific article

Abstract: Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.
Keywords: neural drive to muscle, high-density EMG, motor neuron, motor unit, myoelectronic control, pattern recognition, regression
Published: 25.05.2015; Views: 440; Downloads: 0

6.
Regression analysis of variables describing poultry meat supply in European countries
Miro Simonič, Ksenija Dumičić, Gabrijel Devetak, 2012, original scientific article

Abstract: In this paper, based on the analysis of official FAOSTAT and EUROSTAT data on poultry meat for 38 European countries for years 2007 and 2009, two hypotheses were examined. Firstly, considering four clustering variables on poultry meat, i.e. production, export and import in kg/capita, as well as the producer price in US $/t, using descriptive exploratory and cluster analysis, the hypothesis that the clusters of countries may be recognized was confirmed. As a result six clusters of similar countries were distinguished. Secondly, based on multiple regression analysis, this paper proofs that there exists the statistically significant relationship of poultry meat production on export and import of that kind of meat, all measured in kg/capita. There is also a high correlation between production, as a dependent, and each of two independent variables.
Keywords: poultry meat, marketing strategy, cluster analysis, correlation, multiple regression
Published: 10.07.2015; Views: 462; Downloads: 113
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7.
The impact of economic growth on the dynamics of enterprises
Dijana Močnik, 2010, original scientific article

Abstract: The aim of this paper was to test the hypothesized U-shaped relationship between economic development and dynamics of enterprises. The dynamics of enterprises is influenced by the achieved economic development. This paper first analyzed the association between the regional gross value added (GVA) growth rate and different measures of enterprises dynamics from Slovenian data from 2000 to 2005. Our graphical analyses indicated that 1) the rate of gross entry and GVA growth rate were linearly and negatively associated; 2) the association between the rate of gross exit and GVA growth rate is best represented by the downward U-shape function (Ç); and 3) a U-shaped association exists between the rate of net entry and GVA growth rate. The size of the impact was estimated using the regression analysis between the net entries as dependent variable and GVA growth as independent variable that showed the best fit. According to the results, 1) economic growth significantly impacts net entries; 2) the hypothesized U-shaped relationship between net entries and economic growth was confirmed as the Slovenian net entries decrease until the GVA growth rate reaches 10% yet increase when the growth in GVA is higher than 10%; and 3) a ‘natural rate’ of entrepreneurship is to some extent governed by ‘laws’ related to the economic growth rate. The results further indicate that the average net entry rate should be increased by 0.787 units (%) as a result of a region's specific environmental factors. This research confirms the theoretical assumptions that have previously been sparsely tested empirically and even rarely supported by results. Therefore, our results represent a contribution to the robustness of the theoretical as well as empirical clarification of the relationship between entrepreneurship and economic development.
Keywords: dynamics of enterprises, firm entries, net entries, economic growth, regression analysis
Published: 10.07.2015; Views: 517; Downloads: 25
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Comprehensible predictive modeling using regularized logistic regression and comorbidity based features
Gregor Štiglic, Petra Povalej Bržan, Nino Fijačko, Fei Wang, Alexandros Kalousis, Boris Delibašić, Zoran Obradović, 2015, original scientific article

Abstract: Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755%0.771) to 0.769 (95% CI: 0.761%0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.
Keywords: predictive models, logistic regression, readmission classification, comorbidities
Published: 19.06.2017; Views: 267; Downloads: 106
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Is trust in banks in Slovenia put to the test?
Sabina Taškar Beloglavec, Urban Šebjan, 2015, original scientific article

Abstract: The question of the banking system´s stability in connection to trust since the 2008 crisis has been the subject of many debates seeking to find permanent solutions to banking system problems, as the current situation affects bank customers´ behavior. This article examined trust in banks during the financial crisis and offers, via demographic variables, explanations as tow whether or not customers tend to withdraw their deposits during a crisis. The results contribute to banks´ decision-making regarding deposits management and understanding customers´ behavior, especially during a crisis. The results show a negative relationship between trust and deposit withdrawal intention, where gender and education level play an important role.
Keywords: trust, financial institution, bank, Slovenia, logistic regression
Published: 03.04.2017; Views: 267; Downloads: 111
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Characterization of Slovenian coal and estimation of coal heating value based on proximate analysis using regression and artificial neural networks
Darja Kavšek, Adriána Bednárová, Miša Biro, Roman Kranvogl, Darinka Brodnjak-Vončina, Ernest Beinrohr, 2013, original scientific article

Abstract: Chemical composition of Slovenian coal has been characterised in terms of proximate and ultimate analyses and the relations among the chemical descriptors and the higher heating value (HHV) examined using correlation analysis and multivariate data analysis methods. The proximate analysis descriptors were used to predict HHV using multiple linear regression (MLR) and artificial neural network (ANN) methods. An attempt has been made to select the model with the optimal number of predictor variables. According to the adjusted multiple coefficient of determination in the MLR model, and alternatively, according to sensitivity analysis in ANN developing, two descriptors were evaluated by both methods as optimal predictors: fixed carbonand volatile matter. The performances of MLR and ANN when modelling HHV were comparable; the mean relative difference between the actual and calculated HHV values in the training data was 1.11% for MLR and 0.91% for ANN. The predictive ability of the models was evaluated by an external validation data set; the mean relative difference between the actual and predicted HHV values was 1.39% in MLR and 1.47% in ANN. Thus, the developed models could be appropriately used to calculate HHV.
Keywords: Slovenian coal, higher heating value, HHV, regression, artificial neural network
Published: 03.04.2017; Views: 484; Downloads: 110
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