Search for short lived particles in high multiplicity environment

A method of statistical selection of short lived particles in high multiplicity nucleus-nucleus collisions is discussed

SPORT EXERCISE CAPACITY OF SOCCER PLAYERS AT DIFFERENT LEVELS OF PERFORMANCE

The aim of the study is to compare the level of exercise capacities to the loads occurring at the lactate threshold among soccer players representing different levels of sport mastery. The research included 51 soccer players representing different levels of sport mastery. The research was conducted at the beginning of the preparatory period for the spring season. A field exercise test of increasing intensity was performed to check the players’ exercise capacities on the soccer pitch. The test enabled us to determine the 4 millimolar lactate threshold (TLA 4 mmol · l-1) on the basis of lactate concentration in blood (LA), and to define the threshold running speed and the threshold heart rate (HR). The lactate level in blood was measured using a Lactate Scout photometer with the enzyme-amperometric method from capillary blood for 20 seconds after each load. The threshold running speed at the level of the 4 millimolar lactate threshold was marked using the two-point form of the equation of a straight line. The conducted tests showed significant differentiation of the threshold running speed among individual teams. The soccer players of a leading first league club were expected to achieve the best result. The conducted tests did not confirm this assumption. Juniors reached the highest threshold running speed of 3.61 m · s-1. Lower values of the analysed indicator were acquired by players of the first league team (3.50 m · s-1) and the lowest by players of the second league team (3.28 m · s-1). Statistically significant differences were noted between the junior group and second league team (p≤ 0.01) and between the first and the second league soccer players (p<0.05)

Małopłytkowość — najczęstsze zaburzenie hemostazy na OIT

Thrombocytopenia is the most common haemostatic disorder in patients admitted to Intensive Care Units (ICUs). The mechanisms contributing to a decrease in the platelet count in critically ill patients are multifactorial, among which sepsis and trauma are the most frequent. A differential diagnosis of profound thrombocytopenia is crucial for effective treatment. A low platelet count is a strong independent predictor of morbidity and mortality because it is associated with life-threatening bleeding or thrombosis. This article aims to outline the definition and pathophysiology of thrombocytopenia and present a three-step algorithm of the clinical management of this haemostatic disorder

SciGRID_gas: The raw NO data set

The goal of SciGRID_gas is to develop methods to create an automated process that can generate a gas transmission network data set for Europe. Gas transmission networks are fundamental for simulations by the gas transmission modelling community, to derive major dynamic characteristics. Such simulations have a large scope of application, for example, they can be used to perform case scenarios, to model the gas consumption, to minimize leakages and to optimize overall gas distribution strategies. The focus of SciGRID_gas will be on the European transmission gas network, but the principal methods will also be applicable to other geographic regions. Data required for such models are the gas facilities, such as compressor stations, LNG terminals, pipelines, etc. One needs to know their locations, in addition to a large range of attributes, such as pipeline diameter and capacity, compressor capacity, configuration, etc. Most of this data is not freely available. However throughout the SciGRID_gas project it was determined, that data can be found and grouped into two fundamental different groups: a) OSM data, and b) non-OSM data. The OSM data consists of geo-referenced facility data that is stored in the OpenStreetMap (OSM) data base, and is freely available. However, the OSM data set currently contains hardly any other information than the location of the facilities. The Non-OSM data set can fill some of those gaps, by supplying information such as pipeline diameter, compressor capacity and more. Part of the SciGRID_gas project is to mine and collate such data, and combine it with the OSM data set. Here, this document describes one of the non-OSM data set, called the "NO" data set, which originated from the norwegian "Gassco" entity :cite:`NO_Gassco`. This document explains the origin and structure of this single data sets. In this document, the chapter "Introduction" will supply some background information on the SciGRID_gas project, followed by the chapter "Data structure", that gives a detailed description of the data structure that is being used in the SciGRID_gas project. Chapter "Data sources" describes the NO data set. The appendix contains a glossary, references, location name alterations convention and finishes with the table of country abbreviation

SciGRID_gas: The filled INET gas transmission network data set

The goal of SciGRID_gas is to develop methods to create an automated process that can generate a gas transmission network data set for Europe. Gas transmission networks are fundamental for simulations by the gas transmission modelling community, to derive major dynamic characteristics. Such simulations have a large scope of application, for example, they can be used to perform case scenarios, to model the gas consumption, to minimize leakages and to optimize overall gas distribution strategies. The focus of SciGRID_gas will be on the European transmission gas network, but the principal methods will also be applicable to other geographic regions. Data required for such models are the gas facilities, such as compressor stations, LNG terminals, pipelines, etc. One needs to know their locations, in addition to a large range of attributes, such as pipeline diameter and capacity, compressor capacity, configuration, etc. Most of this data is not freely available. However throughout the SciGRID_gas project it was determined, that data can be found and grouped into two fundamental different groups: a) OSM data, and b) non-OSM data. The OSM data consists of geo-referenced facility data that is stored in the OpenStreetMap (OSM) data base, and is freely available. However, the OSM data set currently contains hardly any other information than the location of the facilities. The Non-OSM data set can fill some of those gaps, by supplying information such as pipeline diameter, compressor capacity and more. Part of the SciGRID_gas project is to mine and collate such data, and combine it with the OSM data set. In addition heuristic tools are required to fill data gaps, so that a complete gas network data set can be generated. Here, this document describes a non-OSM data set, called the “INET” data set, which is one of the fundamental building blocks of the SciGRID_gas project. The INET data set is test data sets, and a previous version of the INET data set has been published. However here, it is attempted to publish a data set where all missing values have been estimated. Hence this this is the filled INET data set. This document explains the origin and structure of the data set, and the processes undertaken to fill the missing values. In this document, the chapter “Introduction” will supply some background information on the SciGRID_gas project, followed by the chapter “Data structure“, which gives a detailed description of the data structure that is being used in the SciGRID_gas project. Chapter “Data sources” describes the raw INET data set. This is followed by chapter “Heuristic”, which describes the steps that were implemented to fill the missing values of the INET data set, resulting in a gas transmission network data set. The appendix contains a glossary, references, location name alterations convention and finishes with the table of country abbreviation

SciGRID_gas: The raw GIE data set

https://orcid.org/0000-0002-0156-9720The goal of SciGRID_gas is to develop methods to create an automated process that can generate a gas transmission network data set for Europe. Gas transmission networks are fundamental for simulations by the gas transmission modelling community, to derive major dynamic characteristics. Such simulations have a large scope of application, for example, they can be used to perform case scenarios, to model the gas consumption, to minimize leakages and to optimize overall gas distribution strategies. The focus of SciGRID_gas will be on the European transmission gas network, but the principal methods will also be applicable to other geographic regions. Data required for such models are the gas facilities, such as compressor stations, LNG terminals, pipelines, etc. One needs to know their locations, in addition to a large range of attributes, such as pipeline diameter and capacity, compressor capacity, configuration, etc. Most of this data is not freely available. However throughout the SciGRID_gas project it was determined, that data can be found and grouped into two fundamental different groups: a) OSM data, and b) non-OSM data. The OSM data consists of geo-referenced facility data that is stored in the OpenStreetMap (OSM) data base, and is freely available. However, the OSM data set currently contains hardly any other information than the location of the facilities. The Non-OSM data set can fill some of those gaps, by supplying information such as pipeline diameter, compressor capacity and more. Part of the SciGRID_gas project is to mine and collate such data, and combine it with the OSM data set. In addition heuristic tools are required to fill data gaps, so that a complete gas network data set can be generated. Here, this document describes one of the non-OSM data set, called the “GIE” data set, which originated from “Gas Infrastructure Europe” [GasIEurop20]. This document explains the origin and structure of this single data sets. In this document, the chapter “Introduction” will supply some background information on the SciGRID_gas project, followed by the chapter “Data structure”, that gives a detailed description of the data structure that is being used in the SciGRID_gas project. Chapter “Data sources” describes the GIE data set. The appendix contains a glossary, references, location name alterations convention and finishes with the table of country abbreviation

SCiGRID_gas

Vortrag ueber den SciGRID_gas Datensat

SciGRID_gas: The raw INET data set

The goal of SciGRID_gas is to develop methods to create an automated process that can generate a gas transmission network data set for Europe. Gas transmission networks are fundamental for simulations by the gas transmission modelling community, to derive major dynamic characteristics. Such simulations have a large scope of application, for example, they can be used to perform case scenarios, to model the gas consumption, to minimize leakages and to optimize overall gas distribution strategies. The focus of SciGRID_gas will be on the European transmission gas network, but the principal methods will also be applicable to other geographic regions. Data required for such models are the gas facilities, such as compressor stations, LNG terminals, pipelines, etc. One needs to know their locations, in addition to a large range of attributes, such as pipeline diameter and capacity, compressor capacity, configuration, etc. Most of this data is not freely available. However throughout the SciGRID_gas project it was determined, that data can be found and grouped into two fundamental different groups: a) OSM data, and b) non-OSM data. The OSM data consists of geo-referenced facility data that is stored in the OpenStreetMap (OSM) data base, and is freely available. However, the OSM data set currently contains hardly any other information than the location of the facilities. The Non-OSM data set can fill some of those gaps, by supplying information such as pipeline diameter, compressor capacity and more. Part of the SciGRID_gas project is to mine and collate such data, and combine it with the OSM data set. In addition heuristic tools are required to fill data gaps, so that a complete gas network data set can be generated. This document here describes the so called “InternetData” data set (INET), which is one of the fundamental building blocks of the SciGRID_gas project. The INET data set is the first data set to be published as part of the SciGRID_gas project. This document explains the origin and structure of the data set. In this document, the chapter “Introduction” will supply some background information on the SciGRID_gas project, followed by the chapter “Data structure”, which gives a detailed description of the data structure that is being used in the SciGRID_gas project. Chapter “Data sources” describes the INET data sets

SciGRID_gas: The raw EMAP data set

The goal of SciGRID_gas is to develop methods to create an automated process that can generate a gas transmission network data set for Europe. Gas transmission networks are fundamental for simulations by the gas transmission modelling community, to derive major dynamic characteristics. Such simulations have a large scope of application, for example, they can be used to perform case scenarios, to model the gas consumption, to minimize leakages and to optimize overall gas distribution strategies. The focus of SciGRID_gas will be on the European transmission gas network, but the principal methods will also be applicable to other geographic regions. Data required for such models are the gas facilities, such as compressor stations, LNG terminals, pipelines, etc. One needs to know their locations, in addition to a large range of attributes, such as pipeline diameter and capacity, compressor capacity, configuration, etc. Most of this data is not freely available. However throughout the SciGRID_gas project it was determined, that data can be found and grouped into two fundamental different groups: a) OSM data, and b) non-OSM data. The OSM data consists of geo-referenced facility data that is stored in the OpenStreetMap (OSM) data base, and is freely available. However, the OSM data set currently contains hardly any other information than the location of the facilities. The Non-OSM data set can fill some of those gaps, by supplying information such as pipeline diameter, compressor capacity and more. Part of the SciGRID_gas project is to mine and collate such data, and combine it with the OSM data set. In addition heuristic tools are required to fill data gaps, so that a complete gas network data set can be generated. Here, this document describes one of the non-OSM data set, called the “EMAP” data set, which was generated from an EntsoG PDF map [EntsoG20]. This document here explains the origin and structure of this single data sets, and how the data set was generated. In this document, the chapter “Introduction” will supply some background information on the SciGRID_gas project, followed by the chapter “Data structure”, that gives a detailed description of the data structure that is being used in the SciGRID_gas project. Chapter “Data sources” describes the EMap data set. The appendix contains a glossary, references, location name alterations convention and finishes with the table of country abbreviatio