Dynamics of change of lipid and monoamine metabolisms and the blood coagulation system during experimental atherosclerosis caused by restriction of movement

Shifts in lipid, catecholamine, and blood coagulation systems following various periods (1, 2, 3, and 4 months) of experimentally induced atherosclerosis were studied. The same indices were studied in the tissues of the myocardium, liver, and brain stem-reticular formation after decapitation of the animals at the end of the experiment. Periodic motion restriction caused an increase in blood beta-lipoproteins in the rabbits at the beginning of the experiment. An increase in general cholesterol content and a decrease in the lecithincholesterol index were established at the end of the experiment. Myocardial beta-lipoprotein and brain stem reticular formation general cholesterol contents were elevated; catecholamine content was increased at the end of the experiment. In the initial months, free adrenaline basically increased, while in later months blood adrenaline decreased and blood noradrenaline increased

The world's largest oil and gas hydrocarbon deposits: ROSA database and GIS project development

This article proposes the use of Big Data principles to support the future extraction of hydrocarbon resources. It starts out by assessing the possible energy-system transformations in order to shed some light on the future need for hydrocarbon resource extraction and corresponding drilling needs. The core contribution of this work is the development of a new database and the corresponding GIS (geographic information system) visualization project as basis for an analytical study of worldwide hydrocarbon occurrences and development of extraction methods. The historical period for the analytical study is from 1900 to 2000. A number of tasks had to be implemented to develop the database and include information about data collection, processing, and development of geospatial data on hydrocarbon deposits. Collecting relevant information made it possible to compile a list of hydrocarbon fields, which have served as the basis for the attribute database tables and its further filling. To develop an attribute table, the authors took into account that all accumulated data features on hydrocarbon deposits and divided them into two types: static and dynamic. Static data included the deposit parameters that do not change over time. On the other hand, dynamic data are constantly changing. Creation of a web service with advanced functionality based on the Esri Geoportal Server software platform included search by parameter presets, viewing and filtering of selected data layers using online mapping application, sorting of metadata, corresponding bibliographic information for each field and keywords accordingly. The collected and processed information by ROSA database and GIS visualization project includes more than 100 hydrocarbon fields across different countries

Application of artificial intelligence for Euler solutions clustering

International audienceResults of Euler deconvolution strongly depend on the selection of viable solutions. Synthetic calculations using multiple causative sources show that Euler solutions cluster in the vicinity of causative bodies even when they do not group densely about the perimeter of the bodies. We have developed a clustering technique to serve as a tool for selecting appropriate solutions. The clustering technique uses a methodology based on artificial intelligence, and it was originally designed to classify large data sets. It is based on a geometrical approach to study object concentration in a finite metric space of any dimension. The method uses a formal definition of cluster and includes free parameters that search for clusters of given properties. Tests on synthetic and real data showed that the clustering technique successfully outlines causative bodies more accurately than other methods used to discriminate Euler solutions. In complex field cases, such as the magnetic field in the Gulf of Saint Malo region (Brittany, France), the method provides dense clusters, which more clearly outline possible causative sources. In particular, it allows one to trace offshore the main inland tectonic structures and to study their interrelationships in the Gulf of Saint Malo. The clusters provide solutions associated with particular bodies, or parts of bodies, allowing the analysis of different clusters of Euler solutions separately. This may allow computation of average parameters for individual causative bodies. Those measurements of the anomalous field that yield clusters also form dense clusters themselves. Application of this clustering technique thus outlines areas where the influence of different causative sources is more prominent. This allows one to focus on these areas for more detailed study, using different window sizes, structural indices, etc

Геофизические процессы в Арктике и системный анализ их воздействия на функционирование и развитие транспортной инфраструктуры

The scientific research that has become the subject of consideration in this article is related to assessment of the influence of geophysical factors on sustainable functioning of transport systems and the system analysis of their impact on the transport infrastructure at the Arctic latitudes. The research is a new direction in the field of study of operational reliability of transport systems and scientific support for development of transport infrastructure in the Russian Arctic.The paper touches upon the issues of reliability and possible failures of technical equipment under the influence of space weather, and also discusses multifaceted problems of safety and efficiency of development of transport systems considering new data on the structure and properties of the lithosphere referring to thawing of permafrost and mineral deposits. A separate section is devoted to new information on seismic activity and seismic hazard assessment in areas of operation and promising development of the transport infrastructure of the Arctic zone of the Russian Federation (AZRF).Intellectual accounting and generalisation of the obtained interdisciplinary results together with their visualisation are provided by geoinformatics methods. The paper presents also the results of adoption of modern geodatabase management systems, of the application of modern technologies of geoportals and interactive spherical visualisations for qualitative presentation of new geophysical knowledge obtained in the course of research.Научные исследования, ставшие предметом рассмотрения в этой статье, связаны с оценкой влияния геофизических факторов на устойчивое функционирование транспортных систем и системным анализом их воздействия на транспортную инфраструктуру в арктических широтах. Они являются новым направлением в области изучения эксплуатационной надёжности транспортных систем и научного сопровождения развития транспортной инфраструктуры в российской Арктике.В работе затронуты вопросы надёжности и возможных отказов технических средств под влиянием космической погоды. Также обсуждаются комплексные проблемы безопасности и эффективности развития транспортных систем с учётом новых данных о строении и свойствах литосферы, связанных с растеплением многолетнемёрзлых пород и месторождений полезных ископаемых. Отдельный раздел посвящён новым сведениям о сейсмической активности и оценке сейсмической опасности в районах эксплуатации и перспективного развития транспортной инфраструктуры Арктической зоны Российской Федерации (АЗРФ).Интеллектуальный учёт, обобщение получаемых междисциплинарных результатов и их визуализация обеспечиваются методами геоинформатики. В работе также представлены результаты внедрения современных систем управления базами геоданных, применения современных технологий геопорталов и интерактивных сферических визуализаций для качественного представления новых геофизических знаний, полученных в ходе исследований

Do logarithmic proximity measures outperform plain ones in graph clustering?

We consider a number of graph kernels and proximity measures including commute time kernel, regularized Laplacian kernel, heat kernel, exponential diffusion kernel (also called "communicability"), etc., and the corresponding distances as applied to clustering nodes in random graphs and several well-known datasets. The model of generating random graphs involves edge probabilities for the pairs of nodes that belong to the same class or different predefined classes of nodes. It turns out that in most cases, logarithmic measures (i.e., measures resulting after taking logarithm of the proximities) perform better while distinguishing underlying classes than the "plain" measures. A comparison in terms of reject curves of inter-class and intra-class distances confirms this conclusion. A similar conclusion can be made for several well-known datasets. A possible origin of this effect is that most kernels have a multiplicative nature, while the nature of distances used in cluster algorithms is an additive one (cf. the triangle inequality). The logarithmic transformation is a tool to transform the first nature to the second one. Moreover, some distances corresponding to the logarithmic measures possess a meaningful cutpoint additivity property. In our experiments, the leader is usually the logarithmic Communicability measure. However, we indicate some more complicated cases in which other measures, typically, Communicability and plain Walk, can be the winners.Comment: 11 pages, 5 tables, 9 figures. Accepted for publication in the Proceedings of 6th International Conference on Network Analysis, May 26-28, 2016, Nizhny Novgorod, Russi

georgia higher education system dynamics and institutional diversity

The evolution of Georgian higher education system in recent decades almost perfectly mirrors the political and socio-economic developments in the country. Having emerged from the uniform Soviet system, it has been undergoing radical changes and has transformed into a diverse institutional setup, which, for all its similarities with various higher education systems existing in other countries, cannot be categorised as a typical representative of one

The Walk Distances in Graphs

The walk distances in graphs are defined as the result of appropriate transformations of the $\sum_{k=0}^\infty(tA)^k$ proximity measures, where $A$ is the weighted adjacency matrix of a graph and $t$ is a sufficiently small positive parameter. The walk distances are graph-geodetic; moreover, they converge to the shortest path distance and to the so-called long walk distance as the parameter $t$ approaches its limiting values. We also show that the logarithmic forest distances which are known to generalize the resistance distance and the shortest path distance are a subclass of walk distances. On the other hand, the long walk distance is equal to the resistance distance in a transformed graph.Comment: Accepted for publication in Discrete Applied Mathematics. 26 pages, 3 figure

A Class of Graph-Geodetic Distances Generalizing the Shortest-Path and the Resistance Distances

A new class of distances for graph vertices is proposed. This class contains parametric families of distances which reduce to the shortest-path, weighted shortest-path, and the resistance distances at the limiting values of the family parameters. The main property of the class is that all distances it comprises are graph-geodetic: $d(i,j)+d(j,k)=d(i,k)$ if and only if every path from $i$ to $k$ passes through $j$. The construction of the class is based on the matrix forest theorem and the transition inequality.Comment: 14 pages. Discrete Applied Mathematic

Early diagnostics of geomagnetic storms based on observations of space monitoring systems

We address the problem of early diagnostics of geomagnetic storms based on the use of models of coordinates of movements of centers of solar coronal mass ejections (CME) and observations of their angular positions obtained from space monitoring systems. We propose a method for early diagnostics of geomagnetic storms, introduce a function to predict the distance between Earth and CME centers, and establish a decision-making procedure. We give an example of calculating the distance prediction function and implement the diagnostic decision-making procedure based on coordinate models and model observations of angular positions of CME centers. We determine the efficiency of the decision-making procedure for the algorithm for early diagnostics of geomagnetic storms