Ladungsanregungen im ungeordneten t-t’-t”-J-Modell

Für die theoretische Beschreibung verschiedener Substanzen, so z.B. für diverse Kuprate die Anwendungen als Hochtemperatur-Supraleiter finden, spielt das t-J-Modell eine wichtige Rolle. In vielen Fällen kann man Abweichungen der Verbindungen vom idealen translationsinvarianten Festkörper vernachlässigen, für bestimmte Eigenschaften ist jedoch der Einfluß von Störstellen,z.B. Dotieratomen, bedeutsam. Um solche Verunreinigungen einzubeziehen, behandelt die vorliegende Arbeit das t-J-Modell mit einer zusätzlichen on-site-Energie mit über die Gitterpläte zufallsverteilten Werten. Um für dieses Modell die Einteilchen-Greensfunktion zu bestimmen, wird ein Verfahren entwickelt, welches auf der Projektionstechnik basiert und die Einbeziehung des Unordnungsterms ermöglicht. Die notwendige Mittelung über die möglichen Unordnungskonfigurationen erfolgt näherungsweise durch Faktorisierung und ist verwandt mit der sogenannten average T-matrix approximation, wird hier jedoch auf ein stark korreliertes System erweitert. Zur Illustration wird der Grundzustand von La2−xSrxCuO4 und Nd2−xCexCuO4 bei einem zusätzlichen Ladungsträger über Halbfüllung untersucht. Wie Bandstrukturrechnungen zeigen, ruft die Dotierung der elektronendotierten Substanz gerade einen solchen Zufallsterm hervor. Dies wurde in der bisherigen Literatur meist vernachlässigt. Bei der Übertragung der Bandstrukturergebnisse in die Modellparameter des t-t′-t′′-J-Modells zeigt sich, daß der Einfluß der Dotieratome bei La2−xSrxCuO4 um etwa eine Größenordnung geringer ist als in Nd2−xCexCuO4 . Als wichtige Ursache hierfür wird der Einfluß der Apex-Sauerstoffatome angesehen, die im Fall von La2−xSrxCuO4 die Seltenerd- Dotieratome gegenüber der Kupferoxidebene abschirmen. Für das mit diesen Parametern belegte Modell wird anschließend die Einteilchen- Greensfunktion berechnet, die Ausgangspunkt der Berechnung verschiedener Observablen ist. Die für die elektronendotierte Substanz auftretende lokale Mode gibt zu dem Vorschlag Anlaß, daß die unterschiedliche Stabilität der antiferromagnetischen Phase für die beiden betrachteten Substanzen nicht nur auf die Art der Ladungsträger zurückzuführen ist, sondern auch auf die Struktur der Elementarzelle.The t-J-Modell can be applied to several classes of materials, e.g. high-Tc cuprate superconductors. Often translational invariance can be assumed, but sometimes it is necessary to take into account the effects of the doping atoms at randomly distributed sites. Therefore a t-J-Modell with an additional randomly distributed on-site energy is investigated. To calculate the one-particle Green’s function considering this term of disorder, a method is developed which bases on projection technique. The average over the possible configurations of the dopand atoms is approximated by factorization and is similar to the so-called average T-matrix approximation. Here it is extended to a model with strong correlations. In order to illustrate the methode the single-particle ground state of La2−xSrxCuO4 and Nd2−xCexCuO4 is analyzed. Band-structure calculations exhibit that for the electron-doped case the doping atoms (in first approximation) induce indeed a term of disordered on-site energies. The transformation of the values of this energies at the copper sites into the parameters in the t − t′ − t′′ − J-model shows that the influence of doping in La2−xSrxCuO4 is by about an order of magnitude smaller than in Nd2−xCexCuO4 . The existence of apex oxygen atoms between the rare-earth plane and the copper-oxygen plane in La2−xSrxCuO4 is one important reason for that effect. The single-particle Greens function for the t-t′-t′′-J-model with these parameters is calculated. A local mode appears in the electron-doped case, which suggests that the differences of the stability of the antiferromagnetic phases in both compounds are not only due to the type of charge carriers but also due to the structure of the unit cell

Data-Driven Methods for the Detection of Causal Structures in Process Technology

In modern industrial plants, process units are strongly cross-linked with eachother, and disturbances occurring in one unit potentially become plant-wide. This can leadto a flood of alarms at the supervisory control and data acquisition system, hiding the originalfault causing the disturbance. Hence, one major aim in fault diagnosis is to backtrackthe disturbance propagation path of the disturbance and to localize the root cause of thefault. Since detecting correlation in the data is not sufficient to describe the direction of thepropagation path, cause-effect dependencies among process variables need to be detected.Process variables that show a strong causal impact on other variables in the process comeinto consideration as being the root cause. In this paper, different data-driven methods areproposed, compared and combined that can detect causal relationships in data while solelyrelying on process data. The information of causal dependencies is used for localization ofthe root cause of a fault. All proposed methods consist of a statistical part, which determineswhether the disturbance traveling from one process variable to a second is significant, and aquantitative part, which calculates the causal information the first process variable has aboutthe second. The methods are tested on simulated data from a chemical stirred-tank reactorand on a laboratory plant

A New Alarm Generation Concept For Water Distribution Networks Based On Machine Learning Algorithms

Water Distribution Networks (WDNs) are critical infrastructures that are exposed to deliberate or accidental chemical, biological or radioactive contamination. A monitoring system capable of protecting a WDN against contamination events in real time is a big challenge needed to be accomplished. Powerful online sensor systems are currently developed and the prototypes are able to detect a small change in water quality. Consequently, the main objective of the project SMaRT-OnlineWDN is the development of an online security management toolkit for WDNs that is based on sensor measurements of water quality as well as water quantity. A new approach for the fast and reliable detection of abnormal events in the WDNs by an alarm generation module is presented in this paper. Although in the past several approaches have been investigated and implemented (e.g. CANARI of EPA), so far these alarm generation concepts haven\u27t been widely applied in real WDNs. Two reasons for that are: (1) The parameterization of existing alarm generation software products is too complex and time consuming, (2) a lot of abnormalities in the data appear due to special operational actions (e.g. sensor calibrations, flushing of pipes, rapid changes of water quality due to mixing of different water resources). To cope with this difficulties, in our approach the alarm generation module is trained both by historical data and in online mod using OPC technologies. Multi-variate statistical methods which need only a few parameters (e.g. Principal Component) are used. A fingerprint database is built up by the water utility experts and it is used to label known events. Results based on real WDN data of Berlin, Strasbourg and Paris are presented

SMaRT-OnlineWDN: A Franco-German Project For The Online Security Management Of Water Distribution Networks

Water Distribution Networks (WDNs) are critical infrastructures that are exposed to deliberate or accidental chemical, biological or radioactive contamination which need to be detected in due time. However, until now, no monitoring system is capable of protecting a WDN in real time. Powerful online sensor systems are currently developed and the prototypes are able to detect a small change in water quality. In the immediate future, water service utilities will install their networks with water quantity and water quality sensors. For taking appropriate decisions and countermeasures, WDN operators will need to dispose of: 1) a fast and reliable detection of abnormal events in the WDNs; 2) reliable online models both for the hydraulics and water quality predictions; 3) methods for contaminant source identification backtracking from the data history. Actually, in general none of these issues (1) – (3) are available at the water suppliers. Consequently, the main objective of the project SMaRT-OnlineWDN is the development of an online security management toolkit for WDNs that is based on sensor measurements of water quality as well as water quantity. Its main innovations are the detection of abnormal events with a binary classifier of high accuracy and the generation of real-time, reliable (i) flow and pressure predictions, (ii) water quality indicator predictions of the whole water network. Detailed information regarding contamination sources (localization and intensity) will be explored by means of the online running model, which is automatically calibrated to the measured sensor data. Its field of application ranges from the detection of deliberate contamination including source identification and decision support for effective countermeasures to improved operation and control of a WDN under normal and abnormal conditions (dual benefit).In this project, the technical research work is completed with a sociological, economical and management analysis

Assessing Interaction Networks with Applications to Catastrophe Dynamics and Disaster Management

In this paper we present a versatile method for the investigation of interaction networks and show how to use it to assess effects of indirect interactions and feedback loops. The method allows to evaluate the impact of optimization measures or failures on the system. Here, we will apply it to the investigation of catastrophes, in particular to the temporal development of disasters (catastrophe dynamics). The mathematical methods are related to the master equation, which allows the application of well-known solution methods. We will also indicate connections of disaster management with excitable media and supply networks. This facilitates to study the effects of measures taken by the emergency management or the local operation units. With a fictious, but more or less realistic example of a spreading epidemic disease or a wave of influenza, we illustrate how this method can, in principle, provide decision support to the emergency management during such a disaster. Similar considerations may help to assess measures to fight the SARS epidemics, although immunization is presently not possible

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causesthe infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformaticstools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection,understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to getinsight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for theroutine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemicand evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets anddevelopment of therapeutic strategies. For each tool, we briefly describe its use case and how it advances researchspecifically for SARS-CoV-2.Fil: Hufsky, Franziska. Friedrich Schiller University Jena; AlemaniaFil: Lamkiewicz, Kevin. Friedrich Schiller University Jena; AlemaniaFil: Almeida, Alexandre. the Wellcome Sanger Institute; Reino UnidoFil: Aouacheria, Abdel. Centre National de la Recherche Scientifique; FranciaFil: Arighi, Cecilia. Biocuration and Literature Access at PIR; Estados UnidosFil: Bateman, Alex. European Bioinformatics Institute. Head of Protein Sequence Resources; Reino UnidoFil: Baumbach, Jan. Universitat Technical Zu Munich; AlemaniaFil: Beerenwinkel, Niko. Universitat Technical Zu Munich; AlemaniaFil: Brandt, Christian. Jena University Hospital; AlemaniaFil: Cacciabue, Marco Polo Domingo. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chuguransky, Sara Rocío. European Bioinformatics Institute; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Drechsel, Oliver. Robert Koch-Institute; AlemaniaFil: Finn, Robert D.. Biocurator for Pfam and InterPro databases; Reino UnidoFil: Fritz, Adrian. Helmholtz Centre for Infection Research; AlemaniaFil: Fuchs, Stephan. Robert Koch-Institute; AlemaniaFil: Hattab, Georges. University Marburg; AlemaniaFil: Hauschild, Anne Christin. University Marburg; AlemaniaFil: Heider, Dominik. University Marburg; AlemaniaFil: Hoffmann, Marie. Freie Universität Berlin; AlemaniaFil: Hölzer, Martin. Friedrich Schiller University Jena; AlemaniaFil: Hoops, Stefan. University of Virginia; Estados UnidosFil: Kaderali, Lars. University Medicine Greifswald; AlemaniaFil: Kalvari, Ioanna. European Bioinformatics Institute; Reino UnidoFil: von Kleist, Max. Robert Koch-Institute; AlemaniaFil: Kmiecinski, Renó. Robert Koch-Institute; AlemaniaFil: Kühnert, Denise. Max Planck Institute for the Science of Human History; AlemaniaFil: Lasso, Gorka. Albert Einstein College of Medicine; Estados UnidosFil: Libin, Pieter. Hasselt University; BélgicaFil: List, Markus. Universitat Technical Zu Munich; AlemaniaFil: Löchel, Hannah F.. University Marburg; Alemani

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories.Peer Reviewe

Ladungsanregungen im ungeordneten t-t’-t”-J-Modell

Für die theoretische Beschreibung verschiedener Substanzen, so z.B. für diverse Kuprate die Anwendungen als Hochtemperatur-Supraleiter finden, spielt das t-J-Modell eine wichtige Rolle. In vielen Fällen kann man Abweichungen der Verbindungen vom idealen translationsinvarianten Festkörper vernachlässigen, für bestimmte Eigenschaften ist jedoch der Einfluß von Störstellen,z.B. Dotieratomen, bedeutsam. Um solche Verunreinigungen einzubeziehen, behandelt die vorliegende Arbeit das t-J-Modell mit einer zusätzlichen on-site-Energie mit über die Gitterpläte zufallsverteilten Werten. Um für dieses Modell die Einteilchen-Greensfunktion zu bestimmen, wird ein Verfahren entwickelt, welches auf der Projektionstechnik basiert und die Einbeziehung des Unordnungsterms ermöglicht. Die notwendige Mittelung über die möglichen Unordnungskonfigurationen erfolgt näherungsweise durch Faktorisierung und ist verwandt mit der sogenannten average T-matrix approximation, wird hier jedoch auf ein stark korreliertes System erweitert. Zur Illustration wird der Grundzustand von La2−xSrxCuO4 und Nd2−xCexCuO4 bei einem zusätzlichen Ladungsträger über Halbfüllung untersucht. Wie Bandstrukturrechnungen zeigen, ruft die Dotierung der elektronendotierten Substanz gerade einen solchen Zufallsterm hervor. Dies wurde in der bisherigen Literatur meist vernachlässigt. Bei der Übertragung der Bandstrukturergebnisse in die Modellparameter des t-t′-t′′-J-Modells zeigt sich, daß der Einfluß der Dotieratome bei La2−xSrxCuO4 um etwa eine Größenordnung geringer ist als in Nd2−xCexCuO4 . Als wichtige Ursache hierfür wird der Einfluß der Apex-Sauerstoffatome angesehen, die im Fall von La2−xSrxCuO4 die Seltenerd- Dotieratome gegenüber der Kupferoxidebene abschirmen. Für das mit diesen Parametern belegte Modell wird anschließend die Einteilchen- Greensfunktion berechnet, die Ausgangspunkt der Berechnung verschiedener Observablen ist. Die für die elektronendotierte Substanz auftretende lokale Mode gibt zu dem Vorschlag Anlaß, daß die unterschiedliche Stabilität der antiferromagnetischen Phase für die beiden betrachteten Substanzen nicht nur auf die Art der Ladungsträger zurückzuführen ist, sondern auch auf die Struktur der Elementarzelle.The t-J-Modell can be applied to several classes of materials, e.g. high-Tc cuprate superconductors. Often translational invariance can be assumed, but sometimes it is necessary to take into account the effects of the doping atoms at randomly distributed sites. Therefore a t-J-Modell with an additional randomly distributed on-site energy is investigated. To calculate the one-particle Green’s function considering this term of disorder, a method is developed which bases on projection technique. The average over the possible configurations of the dopand atoms is approximated by factorization and is similar to the so-called average T-matrix approximation. Here it is extended to a model with strong correlations. In order to illustrate the methode the single-particle ground state of La2−xSrxCuO4 and Nd2−xCexCuO4 is analyzed. Band-structure calculations exhibit that for the electron-doped case the doping atoms (in first approximation) induce indeed a term of disordered on-site energies. The transformation of the values of this energies at the copper sites into the parameters in the t − t′ − t′′ − J-model shows that the influence of doping in La2−xSrxCuO4 is by about an order of magnitude smaller than in Nd2−xCexCuO4 . The existence of apex oxygen atoms between the rare-earth plane and the copper-oxygen plane in La2−xSrxCuO4 is one important reason for that effect. The single-particle Greens function for the t-t′-t′′-J-model with these parameters is calculated. A local mode appears in the electron-doped case, which suggests that the differences of the stability of the antiferromagnetic phases in both compounds are not only due to the type of charge carriers but also due to the structure of the unit cell

Data-driven Methods for Fault Localization in Process Technology

Control systems at production plants consist of a large number of process variables. When detecting abnormal behavior, these variables generate an alarm. Due to the interconnection of the plant\u27s devices the fault can lead to an alarm flood. This again hides the original location of the causing device. In this work several data-driven approaches for root cause localization are proposed, compared and combined. All methods analyze disturbed process data for backtracking the propagation path