Separation of geophysical signals in the LAGEOS geocentre motion based on singular spectrum analysis

SUMMARY We recompute the 26-yr weekly Geocentre Motion (GCM) time-series from 1994 to 2020 through the network shift approach using Satellite Laser Ranging (SLR) observations to LAGEOS1/2. Then the Singular Spectrum Analysis (SSA) is applied for the first time to separate and investigate the geophysical signals from the GCM time-series. The Principal Components (PCs) of the embedded covariance matrix of SSA from the GCM time-series are determined based on the w-correlation criterion and two PCs with large w-correlation are regarded as one periodic signal pair. The results indicate that the annual signal in all three coordinate components and semi-annual signal in both X and Z components are detected. The annual signal from this study agrees well in both amplitude and phase with those derived by the Astronomical Institute of the University of Bern and the Center for Space Research, especially for the Y and Z components. Besides, the other periodic signals with the periods of (1043.6, 85, 28), (570, 280, 222.7) and (14.1, 15.3) days are also quantitatively explored for the first time from the GCM time-series by using SSA, interpreting the corresponding geophysical and astrodynamic sources of aliasing effects of K1/O1, T2 and Mm tides, draconitic effects, and overlapping effects of the ground-track repeatability of LAGEOS1/2.

Features of psychosomatization of medical professionals with occupational deformations

Introduction. The stressful conditions of the occupational activity of medical workers lead to the appearance of symptoms of occupational deformations and psychosomatization in them. Purpose. The purpose of identifying the features of medical workers with occupational deformations was achieved by interviewing 6830 medical workers, including 816 cases with pronounced deformations of a professional plan, and establishing links between the components of occupational deformations and psychosomatization. Material and methods. There were used Maslach Burnout Inventory and its modification, Mental Burnout Definition Questionnaire, Questionnaire for Identifying Problems and Their Impact on Health, Psychosomatics of Our Lives, Giessen somatic complaint questionnaire. Mathematical and statistical processing included descriptive statistics, C. Pearson’s correlation criterion, and Cheddock’s table. Results. Medical workers with a high level of exhaustion of emotional and psychoemotional species have a higher average level with a tendency to a high level of deformations of the occupational occupational plan, a higher average level of reduction changes in personal and professional achievements and motivation professional species and an average level of depersonalization processes and estrangement of personal and professional nature dominates exhaustion emotional and psychoemotional species, which determines a high level of complaints about the activity of the cardiovascular system (heart complaints) and intensity of complaints, above average complaints about the activity of the digestive and respiratory systems, gastric complaints, the average level of complaints about the activity of the musculoskeletal system, complaints negative skin reactions, pain in various parts of the body. Discussion. The results of studies by domestic and foreign doctors and psychologists confirm the need for diagnostics, prevention, and correction of professional deformation and leveling of psychosomatic symptoms of medical workers in the context of health care reform. Conclusion. Psychosomatization of medical workers with professional deformations develops according to the coronary type, which gives reason to recommend to medical workers systematic diagnosis, prevention, and treatment of disorders of the cardiovascular system.

ОЦІНКА ПОТУЖНОСТІ КРИТЕРІЇВ ТРЕНДУ

An approach to the selection and comparison of the criteria that are used in the analysis of time series of registration parameters of the technical camp of power and power plants based on gas turbine engines is proposed. The approach is based on established important characteristics of trending criteria, namely the power of such criteria, which are considered as criteria for distinguishing complex hypotheses. For analysis, we propose a statistical model for generating data in the form of a combination of deterministic trends and random components. A deterministic component is considered as a linear approximation of its expansion in a Taylor series. This assumption is justified by the need to show a trend in the shortest period of time at which the trend component allows a linear approximation. A random compound is taken as a sample of a general population of independent random variables that have a normal distribution. For analysis, the most common trend criteria were selected: Student's criterion for equality of means; Fisher dispersion ratio criterion; correlation criterion and its varieties. The supporting hypothesis has the form of belonging of a time series to a sample from the general set of independent random variables, and an alternative is belonging to a sample with a linear trend. Trend statistics of the relevant criteria generated on a moving or sectional disjoint analysis window of a given dimension. The trend development parameter was selected as the ratio of the trend growth during the analysis to the standard deviation of the random component. For the considered trend criteria, the obtained dependences of their power on the trend development parameter and the probability of an error of the first kind (erroneous alarm), as well as the operational characteristics of the criteria. The analysis was performed by the methods of analytical estimates and statistical modeling. It has been established that in the case of an alternative, the statistics of the correlation criterion and the Fisher criterion are quickly normalized, and student statistics do not change their type. A comparison of trending power criteria with equal values of the probability of an error of the first kind allows us to establish the advantage of the Student criterion, and the correlation criterion has the worst performance. Obtaining indicators of the power of trend criteria are important for applied applications since it allows you to establish the probability of the second kind of error (skipping a trend).

Geocenter motion determination and analysis from SLR observations to Lageos1/2

<p>Accurate quantification and analysis of geocenter motion are of great significance to the construction and maintenance of the international terrestrial reference frame and its geodetic and geophysical applications. Here, the time series of 13-year geocenter motion coordinates (from 2006 to 2019) is determined by using the network shift approach from Satellite Laser Ranging (SLR) observations to Lageos1 / 2. Then, the geocenter motion time series is analyzed by using singular spectrum analysis. The principal components of geocenter motion are determined with the w-correlation criterion and two principal components with large w-correlation are regarded as the periodic signals. The results show that the annual periodic terms are clearly detectable in all out of three coordinate components, whereas the semi-annual term is only detected in the X-component. Moreover, weak periodic oscillations of 3 to 4 months exist in the X- and Y-components. Besides weak periodic signals with periods of about 8 months and 1 month for the X- and Y-components, respectively, a significant periodic signal of about 2.8 years exists in the  Z-component. Compared to the geocenter motion signals derived by the Center for Space Research (CSR) and Wrocław University of Environmental and Life Sciences (WUELS), both amplitude and phase agree well, with a better consistency with those from CSR, especially for the X- and Y-components.</p>

Functional optimality of personal the meaning of the profession

The purpose of this study was to determine the functional potential of various types of personal meaning of the profession. The article describes the results of an empirical study that involved a sample of 647 subjects. As methods of mathematical-statistical processing were used: the Pearson correlation criterion, one-way analysis of variance, followed by post hoc comparison of the mean values of groups by the Scheffe method, the criterion GT-Vegeliusa. The analysis made it possible to identify the optimal, suboptimal and pessimistic personal meaning of the profession from the point of view of its regulatory functions.

An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.