Late Cretaceous-Cenozoic history of the Lake Baikal depression and formation of its unique biodiversity

Independent methods of geological and molecular-biological chronologies have made it possible to define generally corresponding stages in the geological and biological evolution of the environments and communities of Lake Baikal since the Late Cretaceous, i.e., during the last 70 myr. All the abiotic elements drastically changed during geological evolution, with destruction of existing and formation of new natural complexes. Nevertheless, some specific zones retained relicts of former settings. The resulting present-day natural complex includes elements of different ages and geneses. Similar to different natural zones of the present-day Earth, which are populated by different biocoenoses, stages in the development of abiotic elements are also characterized by different faunal and floral assemblages. Some taxa were replaced by others, and the resulting aqueous biota of Lake Baikal includes different-age and ecologically different elements. The oldest groups of Baikal organisms appeared approximately 70 Ma ago, although the largest proportion of the lake biota started forming 4–3 Ma ago in response to the most drastic changes in the abiotic elements of the environment. The youngest taxa appeared 1.8 to 0.15 Ma ago, i.e., during the period when superdeep lake environments and mountainous glaciations were developing. The chronological coincidence of main stages in development of abiotic and biotic elements of the nature indicates their relationships. Particular transformations of abiotic elements and the probable mechanism of their influence on the evolution of living communities are also considered.     

Symplectic adaptive algorithm for solving nonlinear two-point boundary value problems in Astrodynamics

In this paper, from a Hamiltonian point of view, the nonlinear optimal control problems are transformed into nonlinear two-point boundary value problems, and a symplectic adaptive algorithm based on the dual variational principle is proposed for solving the nonlinear two-point boundary value problem. The state and the costate variables within a time interval are approximated by using the Lagrange polynomial and the costate variables at two ends of the time interval are taken as independent variables. Then, based on the dual variational principle, the nonlinear two-point boundary value problems are replaced by a system of nonlinear equations which can preserve the symplectic structure of the nonlinear optimal control problem. Furthermore, the computational efficiency of the proposed symplectic algorithm is improved by using the adaptive multi-level iteration idea. The performance of the proposed algorithm is tested by the problems of Astrodynamics, such as the optimal orbital rendezvous problem and the optimal orbit transfer between halo orbits.     

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.     

Effectiveness of fast electron retention in the magnetic cavern during disturbance of the magnetic field azimuthal symmetry

The loss rate of fast electrons (with an energy much higher than the energy of the plasma electron thermal motion), when they leave a magnetic cavern during a disturbance of the magnetic field azimuthal symmetry, is studied. The cases of point and volume sources of fast particles have been considered. The plasma density in the cavern is supposed to be low, so that collisions of fast electrons with plasma particles can be neglected. The effect of the electric field on particle motion is neglected because it is assumed that the electric charge particles outgoing from the cavern is compensated by the counter current of plasma conductivity electrons. The dependence of the loss value on the harmonic number and the amplitude of the cavern boundary radius disturbance has been obtained.     

Oil pollution of the anabranches of the Ob river on the territory of Khanty-Mansi Autonomous Okrug–Yugra

An analysis is made of the oil pollution of the Ob river waters using a long-term (1993–2013) hydrochemical monitoring of oil fields in Khanty-Mansi Autonomous Okrug. Contents of oil hydrocarbons (OHC) were determined in 4277 samples from the main Ob channel, and in 7076 samples from its anabranches by using infrared spectrometry. An increase in MAC (0.05 mg/dm³) was observed in 28% of the samples from the main channel, and in 32% from the anabranches. The total percentage of samples with an extremely high (> 50 MAC) and high (30–50 MAC) pollution level made up 0.3% of the samples for the anabranches, and 0.1% for the main channel. Maximum pollution was revealed in the eastern part of Okrug, from its eastern boundary to the mouth of the Trom’egan river which is associated with a considerable number of accidents on the pipelines within the Nizhnevartovskii district. The upper and lower quantiles in the most polluted anabranches, Pasl and Bagras, are 0.03–01.7 and 0.032–0.16 mg/dm³, respectively, whereas in the main Ob channel they vary from 0.022 to 0.065 mg/dm³ (0.4–1.2 MAC). The methods of geoinformatics and mathematical statistics were used to assess a dependence of OHC on the number of pollution sources (well clusters, and the area of oil spills) in zones at different distances from the river channels. By calculating the Spearman correlation coefficients, it was shown that OHC contents are dependent mainly on the number of wells, and on the area of oil spills located at less than 2 km from the channels.     

Singularly perturbed solution of coupled model in atmosphere-ocean for global climate

A box model of the interhemispheric thermohaline circulation （THC） in atmosphere-ocean for global climate is considered. By using the multi-scales method, the asymptotic solution of a simplified weakly nonlinear model is discussed. Firstly, by introducing first scale, the zeroth order approximate solution of the model is obtained. Secondly, by using the multi-scales, the first order approximate equation of the model is found. Finally, second order approximate equation is formed to eliminate the secular terms, and a uniformly valid asymptotic expansion of solution is decided. The multi-scales solving method is an analytic method which can be used to analyze operation sequentially. And then we can also study the diversified qualitative and quantitative behaviors for corresponding physical quantities. This paper aims at providing a valid method for solving a box model of the nonlinear equation.     

Peculiarities of surface air temperature variations over the Far East regions in 1976–2005

Peculiarities are investigated of the air temperature variation tendencies at some stations of the Far East in 1976–2005. The estimate of linear trend equation coefficients is computed according to the air temperature observation data using the least squares method. It is demonstrated that the air temperature trend in northern regions possesses a small probability at small values of residual variability. In the southern regions, the trend significance increases for almost all seasons at small values of residual variability. At midlatitude stations, the trend significance in January and February decreases considerably due to the large values of residual variability.     

Global-Mode Analysis of Full-Disk Data from the <Emphasis Type="Italic">Michelson Doppler Imager</Emphasis> and the <Emphasis Type="Italic">Helioseismic and Magnetic Imager</Emphasis>

Building upon our previous work, in which we analyzed smoothed and subsampled velocity data from the Michelson Doppler Imager (MDI), we extend our analysis to unsmoothed, full-resolution MDI data. We also present results from the Helioseismic and Magnetic Imager (HMI), in both full resolution and processed to be a proxy for the low-resolution MDI data. We find that the systematic errors that we saw previously, namely peaks in both the high-latitude rotation rate and the normalized residuals of odd \(a\)-coefficients, are almost entirely absent in the two full-resolution analyses. Furthermore, we find that both systematic errors seem to depend almost entirely on how the input images are apodized, rather than on resolution or smoothing. Using the full-resolution HMI data, we confirm our previous findings regarding the effect of using asymmetric profiles on mode parameters, and also find that they occasionally result in more stable fits. We also confirm our previous findings regarding discrepancies between 360-day and 72-day analyses. We further investigate a six-month period previously seen in \(f\)-mode frequency shifts using the low-resolution datasets, this time accounting for solar-cycle dependence using magnetic-field data. Both HMI and MDI saw prominent six-month signals in the frequency shifts, but we were surprised to discover that the strongest signal at that frequency occurred in the mode coverage for the low-resolution proxy. Finally, a comparison of mode parameters from HMI and MDI shows that the frequencies and \(a\)-coefficients agree closely, encouraging the concatenation of the two datasets.     

Mineral waters of the projected specially protected natural territory “Istoki Amura”

We examine the various types of mineral waters occurring on the projected specially protected transboundary natural territory “Istoki Amura”, on the Hentei-Dauria neotectonic high. We suggest the pattern of rational utilization of the mineral springs according to the nature conservation status of the terrain (nature reserves, national parks, and public access areas).     

A Stochastic Approach to Estimate Distribution of Built-Up Area in Regions with Thick Tree Cover

Buildings and other human-made constructions have been accepted as an indicator of human habitation and are identified as built-up area. Identification of built-up area in a region and its subsequent measurement is a key step in many fields of studies like urban planning, environmental studies, and population demography. Remote sensing techniques utilising medium resolution images (e.g. LISS III, Landsat) are extensively used for the extraction of the built-up area as high-resolution images are expensive, and its processing is difficult. Extraction of built land use from medium resolution images poses a challenge in regions like Western-Ghats, North-East regions of India, and countries in tropical region, due to the thick evergreen tree cover. The spectral signature of individual houses with a small footprint are easily overpowered by the overlapping tree canopy in a medium resolution image when the buildings are not clustered. Kerala is a typical case for this scenario. The research presented here proposes a stochastic-dasymetric process to aid in the built-up area recognition process by taking Kerala as a case study. The method utilises a set of ancillary information to derive a probability surface. The ancillary information used here includes distance from road junctions, distance from road network, population density, built-up space visible in the LISS III image, the population of the region, and the household size. The methodology employs logistic regression and Monte Carlo simulation in two sub processes. The algorithm estimates the built-up area expected in the region and distributes the estimated built-up area among pixels according to the probability estimated from the ancillary information. The output of the algorithm has two components. The first component is an example scenario of the built-up area distribution. The second component is a probability surface, where the value of each pixel denotes the probability of that pixel to have a significant built-up area within it. The algorithm is validated for regions in Kerala and found to be significant. The model correctly predicted the built-up pixel count count over a validation grid of 900 m in 95.2% of the cases. The algorithm is implemented using Python and ArcGIS.