Numerische Simulationen von Metalloxiden

Oxides like silicates, alumina or periclase, are materials with significant properties and are therefore investigated extensively in experiment and in theory. The aim of this PhD thesis was to propose and further to develop methods, which make molecular dynamic simulations of oxides with large particle numbers and for long simulation times possible. The work consists of three parts. In the first one the already existing methods for simulating oxides will be discussed, while in the second one their methodological progress will be presented. The third chapter is solely reserved for the phenomenon of flexoelectricity, which has been discovered during the visualization of the crack propagation in alumina.Oxide, wie z.B. Silikate, Korund oder Periklas, sind bedeutende Funktionswerkstoffe und werden daher experimentell wie theoretisch intensiv untersucht. Ziel dieser Dissertation war es, Verfahren vorzustellen und derart zu optimieren, dass sie Molekulardynamiksimulationen von Oxiden mit großen Teilchenzahlen und über lange Zeiten ermöglichen. Die Arbeit gliedert sich dabei in drei Bereiche. Im ersten Teil wird auf die einzelnen bereits vorhandenen Methoden zur Simulation von Oxiden eingegangen, im zweiten Kapitel deren Verbesserung vorgestellt. Der dritte Bereich widmet sich ausschließlich dem Phänomen der Flexoelektrizität, welche durch die geschickte Visualisierung der Rissausbreitung in Korund entdeckt wurde

Direct Wolf summation of a polarizable force field for silica

We extend the Wolf direct, pairwise r^(-1) summation method with spherical truncation to dipolar interactions in silica. The Tangney-Scandolo interatomic force field for silica takes regard of polarizable oxygen atoms whose dipole moments are determined by iteration to a self-consistent solution. With Wolf summation, the computational effort scales linearly in the system size and can easily be distributed among many processors, thus making large-scale simulations of dipoles possible. The details of the implementation are explained. The approach is validated by estimations of the error term and simulations of microstructural and thermodynamic properties of silica.Comment: See http://link.aip.org/link/?JCP/132/194109 - 8 pages, 6 figures. Changes in v3: Copyright notice added, minor typographical changes. Changes in v2: 1. Inserted Paragraph in Sec. IV B describing the limitations of the TS potential. 2. We corrected transcription errors in Tab. II, and adjusted the deviation percentages mentioned in Sec. IV B, first paragraph, accordingl

Technical Quality and Associated Iatrogenic Errors of Endodontic Treatments Performed in Extracted Anterior Single-Rooted Teeth by Preclinical Undergraduate Students

Aim: To evaluate technical quality of root canal treatments performed in extracted anterior single-rooted teeth in an undergraduate dental clinic, and to record the associated iatrogenic errors

Complexity of Recent Earthquake Swarms in Greece in Terms of Non-Extensive Statistical Physics

Greece exhibits the highest seismic activity in Europe, manifested in intense seismicity with large magnitude events and frequent earthquake swarms. In the present work, we analyzed the spatiotemporal properties of recent earthquake swarms that occurred in the broader area of Greece using the Non-Extensive Statistical Physics (NESP) framework, which appears suitable for studying complex systems. The behavior of complex systems, where multifractality and strong correlations among the elements of the system exist, as in tectonic and volcanic environments, can adequately be described by Tsallis entropy (Sq), introducing the Q-exponential function and the entropic parameter q that expresses the degree of non-additivity of the system. Herein, we focus the analysis on the 2007 Trichonis Lake, the 2016 Western Crete, the 2021–2022 Nisyros, the 2021–2022 Thiva and the 2022 Pagasetic Gulf earthquake swarms. Using the seismicity catalogs for each swarm, we investigate the inter-event time (T) and distance (D) distributions with the Q-exponential function, providing the qT and qD entropic parameters. The results show that qT varies from 1.44 to 1.58, whereas qD ranges from 0.46 to 0.75 for the inter-event time and distance distributions, respectively. Furthermore, we describe the frequency–magnitude distributions with the Gutenberg–Richter scaling relation and the fragment–asperity model of earthquake interactions derived within the NESP framework. The results of the analysis indicate that the statistical properties of earthquake swarms can be successfully reproduced by means of NESP and confirm the complexity and non-additivity of the spatiotemporal evolution of seismicity. Finally, the superstatistics approach, which is closely connected to NESP and is based on a superposition of ordinary local equilibrium statistical mechanics, is further used to discuss the temporal patterns of the earthquake evolution during the swarms

GUS Reporter-Aided Promoter Deletion Analysis of A. thaliana POLYAMINE OXIDASE 3

Polyamine oxidases (PAOs) have been correlated with numerous physiological and developmental processes, as well as responses to biotic and abiotic stress conditions. Their transcriptional regulation is driven by signals generated by various developmental and environmental cues, including phytohormones. However, the inductive mechanism(s) of the corresponding genes remains elusive. Out of the five previously characterized Arabidopsis PAO genes, none of their regulatory sequences have been analyzed to date. In this study, a GUS reporter-aided promoter deletion approach was used to investigate the transcriptional regulation of AtPAO3 during normal growth and development as well as under various inductive environments. AtPAO3 contains an upstream open reading frame (uORF) and a short inter-cistronic sequence, while the integrity of both appears to be crucial for the proper regulation of gene expression. The full-length promoter contains several cis-acting elements that regulate the tissue-specific expression of AtPAO3 during normal growth and development. Furthermore, a number of TFBS that are involved in gene induction under various abiotic stress conditions display an additive effect on gene expression. Taken together, our data indicate that the transcription of AtPAO3 is regulated by multiple environmental factors, which probably work alongside hormonal signals and shed light on the fine-tuning mechanisms of PAO regulation

GUS Reporter-Aided Promoter Deletion Analysis of <i>A. thaliana POLYAMINE OXIDASE 3</i>

Polyamine oxidases (PAOs) have been correlated with numerous physiological and developmental processes, as well as responses to biotic and abiotic stress conditions. Their transcriptional regulation is driven by signals generated by various developmental and environmental cues, including phytohormones. However, the inductive mechanism(s) of the corresponding genes remains elusive. Out of the five previously characterized Arabidopsis PAO genes, none of their regulatory sequences have been analyzed to date. In this study, a GUS reporter-aided promoter deletion approach was used to investigate the transcriptional regulation of AtPAO3 during normal growth and development as well as under various inductive environments. AtPAO3 contains an upstream open reading frame (uORF) and a short inter-cistronic sequence, while the integrity of both appears to be crucial for the proper regulation of gene expression. The full-length promoter contains several cis-acting elements that regulate the tissue-specific expression of AtPAO3 during normal growth and development. Furthermore, a number of TFBS that are involved in gene induction under various abiotic stress conditions display an additive effect on gene expression. Taken together, our data indicate that the transcription of AtPAO3 is regulated by multiple environmental factors, which probably work alongside hormonal signals and shed light on the fine-tuning mechanisms of PAO regulation

OpenAIRE Graph Beginner's Kit

Baglioni M, Atzori C, Bardi A, et al. OpenAIRE Graph Beginner's Kit. OpenAIRE Nexus; 2022.The OpenAIRE Graph is an Open Access dataset containing metadata about research products (literature, datasets, software, etc.) linked to other entities of the research ecosystem like organisations, project grants, and data sources.&nbsp; The large size of the OpenAIRE Graph is a major impediment for beginners to familiarise with the underlying data model and explore its contents. Working with the Graph in its full size typically requires access to a huge distributed computing infrastructure which cannot be easily accessible to everyone.&nbsp;&nbsp; The OpenAIRE Beginner&rsquo;s Kit aims to address this issue. It consists of two components: A subset of the OpenAIRE Graph composed of the research products published between 2022-06-29 and 2022-12-29, all the entities connected to them and the respective relationships. The subset is composed of the following parts: publication.tar: metadata records about research literature (includes types of publications listed here) dataset.tar: metadata records about research data (includes the subtypes listed here) software.tar: metadata records about research software (includes the subtypes listed here) otherresearchproduct.tar: metadata records about research products that cannot be classified as research literature, data or software (includes types of products listed here) organization.tar: metadata records about organizations involved in the research life-cycle, such as universities, research organizations, funders. datasource.tar: metadata records about data sources&nbsp;whose content is available in the OpenAIRE Graph. They include&nbsp;institutional and thematic repositories, journals, aggregators, funders&#39; databases. project.tar: metadata records about project grants. relation.tar: metadata records about relations between entities in the graph. communities_infrastructures.tar: metadata records about research communities and research infrastructures Each file is a tar archive containing gz files, each with one json per line. Each json is compliant to the schema available at http://doi.org/10.5281/zenodo.7492151. A Zeppelin notebook that demonstrates how you can use PySpark to analyse the Graph and get answers to some interesting research questions:&nbsp;beginners_kit_zeppelin_notebook.json. Here&nbsp;a guide to Apache Zeppelin. </ul