Analysis of the Dynamics of Cardiovascular Health in the Population of Ivano-Frankivsk Region over the Past Seventeen Years

The key to increase the level of life expectancy is good health. To study the indicators of cardiovascular health in the population of the Carpathian region, the analysis of the indicators of cardiovascular disease prevalence over the period 1998-2014 was made. The analysis was conducted based on statistical data of the Regional Information-Analytical Center of Medical Statistics and medical records of the Ivano-Frankivsk Regional Clinical Cardiology Dispensary over the period 1998-2014.To identify the population structure in Ivano-Frankivsk region, the analysis of the main demographic indices over the period 1998-2014 was made. The analysis revealed that in 2007, the total population of the Carpathian region was 1,386,000 people while in 2014, it was 1,379,400 people that was 1.05% and 5.76% lower compared to the total population in 1998 (1,463,600 people). Similar tendency was observed across the whole country. During the studied period, the indicators of the overall prevalence of hypertension (all forms) increased by 2.89 times while the indicators of primary disease incidence increased by 1.89 times. The indicator of the overall prevalence of ischemic heart disease among the adult population of Ivano-Frankivsk region during the studied period increased by 2.11 times ranging from 9780.3 to 20629.1 cases per 100,000 population. It should be noted that since 2012 a reduction in the prevalence of angina pectoris from 6545.7 to 6126.2 cases per 100,000 population (by 1.07 times) was observed. The increase in the incidence of acute myocardial infarction from 81 to 108.2 cases per 100,000 population (by 1.34 times) was detected as well. Cardiovascular diseases are known to be the most urgent problem of modern health care system having no geographical, socioeconomic and sexual preferences. They remain to be the major cause of mortality accounting for about 17,300,000 cases per year.Conclusions. Thus, important factors affecting life expectancy of Ivano-Frankivsk region residents include morbidity and mortality due to cardiovascular diseases which have increased recently

Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem

In this paper, we develop a Bayesian evidence maximization framework to solve the sparse non-negative least squares (S-NNLS) problem. We introduce a family of probability densities referred to as the Rectified Gaussian Scale Mixture (R- GSM) to model the sparsity enforcing prior distribution for the solution. The R-GSM prior encompasses a variety of heavy-tailed densities such as the rectified Laplacian and rectified Student- t distributions with a proper choice of the mixing density. We utilize the hierarchical representation induced by the R-GSM prior and develop an evidence maximization framework based on the Expectation-Maximization (EM) algorithm. Using the EM based method, we estimate the hyper-parameters and obtain a point estimate for the solution. We refer to the proposed method as rectified sparse Bayesian learning (R-SBL). We provide four R- SBL variants that offer a range of options for computational complexity and the quality of the E-step computation. These methods include the Markov chain Monte Carlo EM, linear minimum mean-square-error estimation, approximate message passing and a diagonal approximation. Using numerical experiments, we show that the proposed R-SBL method outperforms existing S-NNLS solvers in terms of both signal and support recovery performance, and is also very robust against the structure of the design matrix.Comment: Under Review by IEEE Transactions on Signal Processin

A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem

We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide variety of applications where an unknown, non-negative quantity must be recovered from linear measurements. We present a unified framework for S-NNLS based on a rectified power exponential scale mixture prior on the sparse codes. We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules. Such update rules are convenient for solving large sets of S-NNLS problems simultaneously, which is required in contexts like sparse non-negative matrix factorization (S-NMF). We provide theoretical justification for the proposed approach by showing that the local minima of the objective function being optimized are sparse and the S-NNLS algorithms presented are guaranteed to converge to a set of stationary points of the objective function. We then extend our framework to S-NMF, showing that our framework leads to many well known S-NMF algorithms under specific choices of prior and providing a guarantee that a popular subclass of the proposed algorithms converges to a set of stationary points of the objective function. Finally, we study the performance of the proposed approaches on synthetic and real-world data.Comment: To appear in Signal Processin

INDUCTIVE LINEAR DISPLACEMENT SENSOR IN ACTIVE MAGNETIC BEARING

Active magnetic bearings are increasingly used in various fields of industry. The absence of mechanical contact makes it possible to use them in ultra-high-speed electric drives. The main trend of active magnetic bearings development is the improvement of the control system. The main problem of the control system is the displacement sensor (most of them has low accuracy and large interference). The sensor must have the following properties: simple in realization, high linearity of the characteristic, high sensitivity and noise immunity, high reliability. At the present time there is no sensor that satisfies all these conditions. Most manufacturers use various kinds of filters to get an accurate position signal. This increases the response time of the control system. Thus, problem of designing and modeling the position sensor, considered in the article is topical

An observation-based assessment of the influences of air temperature and snow depth on soil temperature in Russia

This study assessed trends in the variability of soil temperature (T-SOIL) using spatially averaged observation records from Russian meteorological land stations. The contributions of surface air temperature (SAT) and snow depth (SND) to T-SOIL variation were quantitatively evaluated. Composite time series of these data revealed positive trends during the period of 1921-2011, with accelerated increases since the 1970s. The T-SOIL warming rate over the entire period was faster than the SAT warming rate in both permafrost and non-permafrost regions, suggesting that SND contributes to T-SOIL warming. Statistical analysis revealed that the highest correlation between SND and T-SOIL was in eastern Siberia, which is underlain by permafrost. SND in this region accounted for 50% or more of the observed variation in T-SOIL. T-SOIL in the non-permafrost region of western Siberia was significantly correlated with changes in SAT. Thus, the main factors associated with T-SOIL variation differed between permafrost and non-permafrost regions. This finding underscores the importance of including SND data when assessing historical and future variations and trends of permafrost in the Northern Hemisphere