Using the Fisher Vector Approach for Cold Identification

In this paper, we present a computational paralinguistic method for assessing whether a person has an upper respiratory tract infection (i.e. cold) using their speech. Having a system that can accurately assess a cold can be helpful for predicting its propagation. For this purpose, we utilize Mel-frequency Cepstral Coefficients (MFCC) as audio-signal representations, extracted from the utterances, which allowed us to fit a generative Gaussian Mixture Model (GMM) that serves to produce an encoding based on the Fisher Vector (FV) approach. Here, we use the URTIC dataset provided by the organizers of the ComParE Challenge 2017 of the Interspeech Conference. The classification is done by a linear kernel Support Vector Machines (SVM); owing to the high imbalance of classes on the training dataset, we opt for undersampling the majority class, that is, to reduce the number of samples to those of the minority class. We find that applying Power Normalization (PN) and Principal Component Analysis (PCA) on the Fisher vector features is an effective strategy for the classification performance. We get better performance than that of the Bag-of-Audio-Words approach reported in the paper of the challenge.

Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter

The proportionate affine projection sign subband adaptive filter (PAP-SSAF) has a better performance than the affine projection sign subband adaptive filter (AP-SSAF) when we eliminate the echoes. Still, the robustness of the PAP-SSAF algorithm is insufficient under unknown environmental conditions. Besides, the best balance remains to be found between low steady-state misalignment and fast convergence rate. In order to solve this problem, we propose a normalized combination of PAP-SSAF (NCPAP-SSAF) based on the normalized adaption schema. In this paper, a power normalization adaptive rule for mixing parameters is proposed to further improve the performance of the NCPAP-SSAF algorithm. By using Nesterov’s accelerated gradient (NAG) method, the mixing parameter of the control combination can be obtained with less time consumed when we take the l1-norm of the subband error as the cost function. We also test the algorithmic complexity and memory requirements to illustrate the rationality of our method. In brief, our study contributes a novel adaptive filter algorithm, accelerating the convergence speed, reducing the steady-state error, and improving the robustness. Thus, the proposed method can be utilized to improve the performance of echo cancellation. We will optimize the combination structure and simplify unnecessary calculations to reduce the algorithm’s computational complexity in future research.

Neurofeedback Effects on EEG Connectivity in Children with Reading Disorder: I. Coherence

Electroencephalograms (EEGs) of children with reading disorder (RD) are characterized by higher power and coherence in slow frequencies (delta and theta bands) and lower power and coherence in the alpha band compared to EEGs of typically developed children. Neurofeedback (NFB) is useful for treating learning disorders by reinforcing the reduction of the theta/alpha ratio. This efficacy is supported by EEG power normalization and cognitive-behavioral improvement. To further explore brain changes in isolated areas, this study aimed to explore the effects of this NFB protocol on functional connectivity (coherence) in children with RD. Twenty children with RD and an abnormally high theta/alpha ratio underwent 30 sessions of NFB. After treatment, 90% of par-ticipants learned to decrease their theta/alpha ratio, and on average, children increased their reading accuracy and comprehension scores. Interhemispheric coherence diminished in the delta, theta and beta bands, mainly between frontopolar regions. Intrahemispheric coherence decreased in delta, theta and beta bands and increased in alpha band. The reduction in theta coherence be-tween the left frontal area and other regions of the left hemisphere had particular relevance for reading. These results suggest that this NFB treatment could positively impact reading-related functions in the brain networks of children with RD.

Respiratory Parameters for the Classification of Dysfunctional Insulin Secretion by Pancreatic Islets

The development of obesity and type 2 diabetes (T2D) has been associated with impaired mitochondrial function. In pancreatic beta (β) cells, mitochondrial energy metabolism plays a central role in triggering and controlling glucose-stimulated insulin secretion (GSIS). Here, we have explored whether mitochondrial bioenergetic parameters assessed with Seahorse extracellular flux technology can quantitatively predict insulin secretion. We metabolically stressed male C57BL/6 mice by high-fat feeding (HFD) and measured the glucose sensitivity of islet respiration and insulin secretion. The diet-induced obese (DIO) mice developed hyperinsulinemia, but no pathological secretory differences were apparent between isolated DIO and chow islets. Real-time extracellular flux analysis, however, revealed a lower respiratory sensitivity to glucose in DIO islets. Correlation of insulin secretion with respiratory parameters uncovers compromised insulin secretion in DIO islets by oxidative power. Normalization to increased insulin contents during DIO improves the quantitative relation between GSIS and respiration, allowing to classify dysfunctional properties of pancreatic insulin secretion, and thereby serving as valuable biomarker for pancreatic islet glucose responsiveness and health.

The basic stages of improving the power grid complex functioning of the Chechen Republic

The article considers the state of the electric power industry functioning of the Chechen Republic. Comparative data on the reception in the network and useful electricity supply of the Chechen Republic in recent years are presented. The total transformer capacity structure of all substations, as well as the power lines total length of Chechenenergo JSC, is considered. The main electric power industry problems of the Chechen power system, which are the lack of own generating capacities, the transformer capacities shortage of substations 110 and 35 kV and the main production assets high level wear of the electric grid economy, are studied. The tasks to reduce the Chechen energy system electric energy losses are considered. The values and causes of the electric energy losses in different district electric networks (RES) of JSC "Chechenenergo" are determined. A comparative analysis of the electrical energy losses in recent years has been performed. Issues related to reactive power in electrical networks are considered. An analysis was carried out to determine the reactive power values in different district electric networks of Chechenenergo JSC. Measures are proposed that contribute to the reactive power normalization and the improvement of technical and economic indicators in electric networks. The networks electrical circuits improvement analysis was carried out to ensure the power supply stability and reliability to consumers in the republic. The production process control system and diagnostics is proposed, which will allow to regulate and coordinate the activities to manage the technical condition, as well as the power system reliability.

Modeling of Extreme Values via Exponential Normalization Compared with Linear and Power Normalization

Several new asymmetric distributions have arisen naturally in the modeling extreme values are uncovered and elucidated. The present paper deals with the extreme value theorem (EVT) under exponential normalization. An estimate of the shape parameter of the asymmetric generalized value distributions that related to this new extension of the EVT is obtained. Moreover, we develop the mathematical modeling of the extreme values by using this new extension of the EVT. We analyze the extreme values by modeling the occurrence of the exceedances over high thresholds. The natural distributions of such exceedances, new four generalized Pareto families of asymmetric distributions under exponential normalization (GPDEs), are described and their properties revealed. There is an evident symmetry between the new obtained GPDEs and those generalized Pareto distributions arisen from EVT under linear and power normalization. Estimates for the extreme value index of the four GPDEs are obtained. In addition, simulation studies are conducted in order to illustrate and validate the theoretical results. Finally, a comparison study between the different extreme models is done throughout real data sets.

On Burnup Modelling Issues Associated with VVER–440 Fuels

The paper investigates various computational modelling issues associated with VVER-440 fuel depletion, relevant to burnup credit. The SCALE system and the TRITON sequence are used for the calculations. The effects of variations in depletion parameters and used calculation methods on the isotopic vectors are investigated. The burnup behaviour of Gadolinium is quite important in actual core analysis, but its behaviour is somewhat complicated, requiring special treatment in numerical modelling and calculations. Therefore, a special part of the paper is devoted to the treatment of Gadolinium-bearing fuels. Moreover, some discussions on power normalization are also included. To assess the acquired modelling experience used to predict the VVER-440 spent fuel nuclide composition, the measured compositions of Novovoronezh NPP irradiated fuel assembly are compared to data calculated by TRITON sequence. The samples of fuel assembly with 3.6 wt. % U-235 enrichment underwent 4-cycle campaign of totally 1109 effective full power days in the core and cooling period of 1-13 years. Calculated concentrations are compared to measured values burdened with their experimental uncertainties for totally 47 nuclides. The calculated results show overall a good agreement for all nuclides, differences from measured are pointed out and discussed in the paper.