Supramolecular complexes containing pyridine N-oxides.

In the first section, the coordination chemistry of a new, divergent, N,O ligand was investigated. Several metal complexes of 4,4 \u27-bipyridine N-monoxide, 2a, were characterised by X-ray crystallography, including CuI, Cu II, PdII, CdII, HgII, and EuIII. The observed non-covalent interactions and coordinative preferences of 2a were contrasted with those of 4,4\u27 -bipyridine and 4,4\u27-bipyridine N,N \u27-dioxide. Each of the compounds has a unique molecular topology that can be applied to the generation of ordered, pre-designed solids. Examples of this methodology are given with respect to the CuI, Cu II, and HgII metal complexes. Chapter three is concerned with the design and synthesis of multi-dimensional, polyrotaxane architectures. A new [2]pseudorotaxane, 3b ⊂ DB24C8, was used as a divergent ligand to connect metal nodes into extended coordination frameworks. Three distinct polyrotaxane networks were synthesised, one two-dimensional net containing CdII in which one-dimensional polyrotaxane strands are pillared in the second dimension by 3b. Two different three-dimensional topologies were generated using five different lanthanide cations. Structures containing SmIII, Eu III, GdIII, TbIII are isomorphous and adopt an alpha-polonium type lattice. The slightly smaller lanthanide, Yb III, generates the previously unreported 3 4,6 6 three-dimensional net. The final chapter describes the integration of an electrostatic component into the formation of [2]pseudorotaxanes. A derivative of DB24C8, 4d , was synthesised in which there are pendant-SO3 - groups on each benzo ring. The association constants of several threads were measured with 4d. It was shown that through the introduction of an electrostatic contribution to the recognition process that [2]pseudorotaxanes could be formed in a competitive solvent such as acetic acid. The X-ray crystal structure of 3b ⊂ 4d is presented and confirms the interpenetrated nature of the [2]pseudorotaxane.Dept. of Chemistry and Biochemistry. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .H64. Source: Masters Abstracts International, Volume: 43-03, page: 0851. Adviser: S. J. Loeb. Thesis (M.Sc.)--University of Windsor (Canada), 2004

KBSET -- Knowledge-Based Support for Scholarly Editing and Text Processing with Declarative LaTeX Markup and a Core Written in SWI-Prolog

KBSET is an environment that provides support for scholarly editing in two flavors: First, as a practical tool KBSET/Letters that accompanies the development of editions of correspondences (in particular from the 18th and 19th century), completely from source documents to PDF and HTML presentations. Second, as a prototypical tool KBSET/NER for experimentally investigating novel forms of working on editions that are centered around automated named entity recognition. KBSET can process declarative application-specific markup that is expressed in LaTeX notation and incorporate large external fact bases that are typically provided in RDF. KBSET includes specially developed LaTeX styles and a core system that is written in SWI-Prolog, which is used there in many roles, utilizing that it realizes the potential of Prolog as a unifying language.Comment: To appear in DECLARE 2019 Revised Selected Paper

{YAGO}2: A Spatially and Temporally Enhanced Knowledge Base from {Wikipedia}

We present YAGO2, an extension of the YAGO knowledge base, in which entities, facts, and events are anchored in both time and space. YAGO2 is built automatically from Wikipedia, GeoNames, and WordNet. It contains 80 million facts about 9.8 million entities. Human evaluation confirmed an accuracy of 95\% of the facts in YAGO2. In this paper, we present the extraction methodology, the integration of the spatio-temporal dimension, and our knowledge representation SPOTL, an extension of the original SPO-triple model to time and space

Depressive Symptom Change Patterns during the COVID-19 Pandemic and Their Impact on Psychiatric Treatment Seeking: A 24-Month Observational Study of the Adult Population

Despite the presence of individual differences in the depressive symptom change in adults during the COVID‐19 pandemic, most studies have investigated population‐level changes in depression during the first year of the pandemic. This longitudinal repeated‐measurement study obtained 39,259 observations from 4,361 adults assessed nine times over a 24‐month period in Norway (March 2020 to March 2022). Using a Latent Change Score Mixture Model to investigate differential change patterns in depressive symptoms, five profiles were identified. Most adults revealed a consistently resilient (42.52%) or predominantly resilient pattern differentiated by an initial shock in symptomatology (13.17%). Another group exhibited consistently high depressive adversities (8.5%). One group showed mild deterioration with small increases in depressive symptomatology compared to onset levels (29.04%), and a second strong deterioration group exhibited clinically severe levels of gained symptoms over time (6.77%). Both deteriorating depressive symptom change patterns predicted the presence of a psychiatric diagnosis and treatment seeking at the end of the study period. Together, the absence of a preexisting psychiatric diagnosis at the onset of the pandemic and severe symptom increases during, combined with reports of psychiatric treatment seeking and diagnosis at the end of the study period, indicated that the strongly deteriorating subgroup represents an additional and newly emerged group of adults struggling with depressive problems. Factors related to general adverse change (lower education levels, lone residence), initial shocks prior to recovery (frequent information seeking, financial and occupational concerns), and resilience and recovery (older age, being in a relationship, physical activity) were identified. Binge drinking and belonging to an ethnic minority were influential predictors of the strongly deteriorating group. All major change patterns in depressive symptoms occurred during the first 3 months of the pandemic, suggesting this period represents a window of sensitivity for the development of long‐lasting depressive states versus patterns of recovery and resilience. These findings call for increased vigilance of psychiatric symptoms during the initial phases of infectious disease outbreaks and highlight a specific target period for the implementation of preventive measures

Die Idee dahinter ... : Aspekte zur Gestaltung lernreicher Lehre

Der Band umfasst zahlreiche Beispiele von Lehrenden, die ihre Veranstaltungen in mehreren Aspekten ‚lernreich(er)’ gestaltet haben. Die Konzepte wurden alle im Rahmen des Vertiefungsmoduls des Programms „Professionelle Lehrkompetenz für die Hochschule“ des Netzwerks "hochschuldidaktik nrw" an der Universität Siegen entwickelt oder weiterentwickelt. Die elf Beiträge umfassen ein breites Spektrum an Veranstaltungsformaten und Fächern: Natur- und Ingenieurwissenschaften sind ebenso vertreten wie Architektur, Pädagogik, Soziale Arbeit und Literaturwissenschaft. Bei den Veranstaltungen handelt es sich um Praktika, Seminare, Übungen usw., oft mit Projektcharakter bzw. -elementen, häufig auch mit wechselnden Lernorten, semester-begleitend oder kompakt

Knowledge Questions from Knowledge Graphs

We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose an end-to-end approach. The approach first selects a named entity from the knowledge graph as an answer. It then generates a structured triple-pattern query, which yields the answer as its sole result. If a multiple-choice question is desired, the approach selects alternative answer options. Finally, our approach uses a template-based method to verbalize the structured query and yield a natural language question. A key challenge is estimating how difficult the generated question is to human users. To do this, we make use of historical data from the Jeopardy! quiz show and a semantically annotated Web-scale document collection, engineer suitable features, and train a logistic regression classifier to predict question difficulty. Experiments demonstrate the viability of our overall approach

SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles

SocialLink is a project designed to match social media profiles on Twitter to corresponding entities in DBpedia. Built to bridge the vibrant Twitter social media world and the Linked Open Data cloud, SocialLink enables knowledge transfer between the two, both assisting Semantic Web practitioners in better harvesting the vast amounts of information available on Twitter and allowing leveraging of DBpedia data for social media analysis tasks. In this paper, we further extend the original SocialLink approach by exploiting graph-based features based on both DBpedia and Twitter, represented as graph embeddings learned from vast amounts of unlabeled data. The introduction of such new features required to redesign our deep neural network-based candidate selection algorithm and, as a result, we experimentally demonstrate a significant improvement of the performances of SocialLink