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Coat color and pattern are a distinguished feature in mammalian carnivores, shaped by climatic cycles and habitat type. It can be expressed in various ways, such as gradients, polymorphisms, and rare color variants. Although natural selection explains much of the phenotypic variation found in the wild, genetic drift and heterozygote deficiency, as prominent in small and fragmented populations, may also affect phenotypic variability through the fixation of recessive alleles. The aim of this study was to test whether rare color variants in the wild could relate to a deficiency of heterozygotes, resulting from habitat fragmentation and small population size. We present an overview of all rare color variants in the order Carnivora, and compiled demographic and genetic data of the populations where they did and did not occur, to test for significant correlations. We also tested how phylogeny and body weight influenced the presence of color variants with phylogenetic generalized linear mixed models (PGLMMs). We found 40 color-variable species and 59 rare color variants. In 17 variable phenotypic populations for which genetic diversity was available, the average AR was 4.18, HO = 0.59, and HE= 0.66, and FIS= 0.086. We found that variable populations displayed a significant reduction in heterozygosity and allelic richness compared to non-variable populations across species. We also found a significant negative correlation between population size and inbreeding coefficients. Therefore, it is possible that small effective size had phenotypic consequences on the extant populations. The high frequency of the rare color variants (averaging 20%) also implies that genetic drift is locally overruling natural selection in small effective populations. As such, rare color variants could be added to the list of phenotypic consequences of inbreeding in the wild.
The production of isolated metallic nanoparticles with multifunctionalized properties, such as size and shape, is crucial for biomedical, photocatalytic, and energy storage or remediation applications. This study investigates the initial particle formations of gold nanoparticles (AuNPs) bioproduced in the cyanobacteria Anabaena sp. using high-resolution transmission electron microscopy images for digital image analysis. The developed method enabled the discovery of cerium nanoparticles (CeNPs), which were biosynthesized in the cyanobacteria Calothrix desertica. The particle size distributions for AuNPs and CeNPs were analyzed. After 10 h, the average equivalent circular diameter for AuNPs was 4.8 nm, while for CeNPs, it was approximately 5.2 nm after 25 h. The initial shape of AuNPs was sub-round to round, while the shape of CeNPs was more roundish due to their amorphous structure and formation restricted to heterocysts. The local PSDs indicate that the maturation of AuNPs begins in the middle of vegetative cells and near the cell membrane, compared to the other regions of the cell.
In contemporary decision-making systems, the integration of machine learning (ML) models such as CatBoost, Random Forest, and Decision Tree has become ubiquitous, exerting substantial influence on societal dynamics. This pervasive adoption accentuates the critical necessity for efficacious fairness interventions to mitigate inherent biases and discrimination. However, prevailing approaches predominantly address binary classifications and frequently draw upon limited, region-specific datasets, thereby constraining their relevance and applicability. To address these shortcomings, we propose an extension to the fairness projection model that uses ensemble learning tree-based classifiers as the base classifying model. The proposed model is named Fairness Projection with Ensemble Trees (FPET), an innovative post-processing intervention specifically designed for multiclass classification tasks. Fairness Projection with Ensemble Trees is uniquely designed to accommodate multiple and overlapping protected groups, rendering it versatile and inclusive. A distinguishing feature of FPET lies in its model-agnostic nature and scalability to large datasets, facilitated by an information-theoretic framework centered around information projection. This approach furnishes robust theoretical assurances regarding convergence and sample complexity, thereby ensuring its practical viability. Furthermore, FPET’s design is augmented by its support for parallel processing, further enhancing its suitability for large-scale applications. Comprehensive evaluation against diverse datasets, including Brazil’s ENEM exam dataset, HSLS, and COMPAS, demonstrates the superior performance of our proposed model, Fairness Projection with Ensemble Trees (FPET), which uses the Cat-Boost classifier for both binary and multi-class classification tasks. In all datasets, CatBoost performed exceptionally well. Our fairness method also outperformed other benchmark models, such as Equality of Odds (EqOdds), Level Equal Opportunity (LevEqOpp), reduction method, and rejection methods. The results were compared using two metrics: Mean Equal Opportunity and Statistical Parity. These findings highlight the effectiveness of FPET across various contexts and introduce a novel approach to fairness in machine learning, ensuring equitable and inclusive decision-makings.
X-ray computed tomography (XRT) is a three-dimensional (3D), non-destructive, and reproducible investigation method capable of visualizing and examining internal and external structures of components independent of the material and geometry. In this work, XRT with its unique abilities complements conventionally utilized examination methods for the investigation of microstructure weakening induced by hydrogen corrosion and furthermore provides a new approach to corrosion research. The motivation for this is the current inevitable transformation to hydrogen-based steel production. Refractories of the system Al2O3-SiO2 are significant as lining materials. Two exemplary material types A and B, which differ mainly in their Al2O3:SiO2 ratio, are examined here using XRT. Identical samples of the two materials are measured, analyzed, and then compared before and after hydrogen attack. In this context, hydrogen corrosion-induced porosity and its spatial distribution and morphology are investigated. The results show that sample B has an higher resistance to hydrogen-induced attack than sample A. Furthermore, the 3D-representation revealed a differential porosity increase within the microstructure.
This thesis explores and examines the effectiveness and efficacy of traditional machine learning (ML), advanced neural networks (NN) and state-of-the-art deep learning (DL) models for identifying mental distress indicators from the social media discourses based on Reddit and Twitter as they are immensely used by teenagers. Different NLP vectorization techniques like TF-IDF, Word2Vec, GloVe, and BERT embeddings are employed with ML models such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) followed by NN models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to methodically analyse their impact as feature representation of models. DL models such as BERT, DistilBERT, MentalRoBERTa and MentalBERT are end-to-end fine tuned for classification task. This thesis also compares different text preprocessing techniques such as tokenization, stopword removal and lemmatization to assess their impact on model performance. Systematic experiments with different configuration of vectorization and preprocessing techniques in accordance with different model types and categories have been implemented to find the most effective configurations and to gauge the strengths, limitations, and capability to detect and interpret the mental distress indicators from the text. The results analysis reveals that MentalBERT DL model significantly outperformed all other model types and categories due to its specific pretraining on mental data as well as rigorous end-to-end fine tuning gave it an edge for detecting nuanced linguistic mental distress indicators from the complex contextual textual corpus. This insights from the results acknowledges the ML and NLP technologies high potential for developing complex AI systems for its intervention in the domain of mental health analysis. This thesis lays the foundation and directs the future work demonstrating the need for collaborative approach of different domain experts as well as to explore next generational large language models to develop robust and clinically approved mental health AI systems.
Nanoparticles are sensitive and robust systems; they are particularly reactive due to their large surface area and have properties that the bulk material does not have. At the same time, the production of nanoparticles is challenging, because even with the same parameters and conditions, the parameters can vary slightly from run to run. In order to avoid this, this work aims to develop a continuous synthesis in the microjet reactor for nanoceria. The aim is to obtain monodisperse nanoparticles that can be used in biosensors.
This work focuses on two precipitation syntheses with the intermediate steps of cerium carbonate and cerium hydroxide, as well as a microemulsion synthesis for the production of nanoceria. The cerium oxide nanoparticles are compared using different characterisation and application methods. The synthesised nanoparticles will be characterised with respect to their size, stability, chemical composition and catalytic capabilities, by electron microscopy, X-ray diffraction, Raman spectroscopy and photoelectron spectroscopy.
The biosensor systems to evaluate the nanoceria are designed to detect histamine and glucose or hydrogen peroxide, which are resulting from the oxidation of histamine and glucose. Hydrogen peroxide and glucose are detected by an electrochemical sensor and histamine by a colorimetric sensor system.
In dieser wiederkehrenden Zeitschriftenreihe wollen wir die Arbeit junger Wissenschaftlerinnen und Wissenschaftler an der Universität Koblenz kommunizieren und Studierenden Austausch- und Publikationsmöglichkeiten für den wissenschaftlichen Werdegang eröffnen.
In dieser Ausgabe:
Christin Robrecht: Helfen kommt nach der Flut: Über die Ambivalenz situativ-nichtalltäglicher Dynamiken im Kontext der Flutkatastrophe im Ahrtal
Theresa Josephine Fischbach: Kontrollüberzeugung und Job Strain: Eine empirische Analyse verschiedener Ausprägungen von Job Strain aufgrund individueller Unterschiede der Kontrollüberzeugung
Rebekka Jachmig: Der Ukraine-Krieg im rechtspopulistischen Diskurs: Eine Analyse der Deutungsmuster von AfD-Politiker*innen
Hanna Schroer: Am Rande Galliens, inmitten der Welt: Eine Comicanalyse interkultureller Fremderfahrung am Beispiel der Comicserie Asterix
Lisa-Marie Schwab: Koloniale Spuren des Handels in Koblenz: Eine kritische Aufarbeitung
Jana Semrau: Okey-Doke: Political Critique in Spike Lee’s "BlacKkKlansman" (2018)
Anna Mira Olszewski: Of Wardrobes and Closets. A Lesson Plan on "Simon vs. Homo Sapiens Agenda" (2015) by Becky Albertalli
Marina Neuwert: Projektive Kommunikation von (Berufs-)Identität: Promotionsinteresse und Zukunftskarrieren bei Lehramtsstudierenden
Jan-Lukas Löwen: Zur Möglichkeit und Notwendigkeit der ästhetischen Erfahrung im Literaturunterricht
Malin Britz: Wearable Devices: Ein systemisches Review
This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems—as required for calibration—is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 cm was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring.
In der vorliegenden Dissertation mit dem Titel "Blickanalysen bei mentalen Rotationsaufgaben" wird eine Analyse der visuellen Verarbeitungsprozesse bei mentalen Rotationsaufgaben mittels Eye-Tracking-Technologie durchgeführt, um die zugrundeliegenden kognitiven Prozesse und Strategien, die bei der Lösung dieser Aufgaben angewandt werden, zu untersuchen. Ein Anliegen dieser Arbeit ist es, die Problemstellung zu adressieren, wie individuelle Unterschiede, insbesondere geschlechtsspezifische Differenzen in den Blickmustern, die visuelle Verarbeitung und Leistung bei mentalen Rotationsaufgaben beeinflussen. Hierzu wurden drei Studien durchgeführt, die nicht nur die Identifikation von Blickmustern und die Analyse der Leistungsunterschiede in Bezug auf Geschlecht umfassen, sondern auch die Korrelation zwischen Blickverhalten und Leistung untersuchen. Die Ergebnisse dieser Forschung bieten Einblicke in die Mechanismen der visuellen und kognitiven Verarbeitung bei mentalen Rotationsaufgaben und heben die Bedeutung des Eye-Tracking als Forschungsinstrument in der kognitiven Psychologie hervor, um ein umfassendes Verständnis der Einflussfaktoren auf räumliches Denken und Problemlösungsstrategien zu erlangen.
Classical music has played a central role in German music education since at least the second half of the 20th century. However, in more recent music pedagogical discourse, classical music remains a controversial topic. But what do music teachers think about classical music as a subject for music education? This topic has not yet been systematically researched in German-speaking music education.
In this qualitative-empirical study, eight semi-structured interviews were conducted to address the question of how music teachers perceive classical music in music education. The data was evaluated using the Grounded Theory Methodology. The theory developed from the study indicates that music teachers have varying objectives when using classical music in music education. However, they generally consider it unfamiliar to their students. To address this situation, music teachers develop various methods and strategies. These can be categorized into three approaches for dealing with the unfamiliarity of classical music: avoidance, reduction/relativization, and utilization.
The study's findings are contextualized within the framework of foreignness theory, music didactics, and transformational educational theory. This dissertation contributes to the field of music education in classical music, laying the groundwork for further theoretical, empirical, and didactic research.
Diese Dissertation widmet sich der inhaltsanalytischen, quantitativen Analyse der Kompilation Disney Princess durch die Anwendung der Theorie des male gaze von Laura Mulvey, welche sie in Visual Pleasure and Narrative Cinema (1975) sowie Afterthoughts on `Visual Pleasure and Narrative Cinema‘ inspired by King Vidor´s Duel in the Sun (1946) (1981) darstellte.
Die Autorin der Dissertation nutzt die quantitative Inhaltsanalyse nach Patrick Rössler, um die Filme der Kompilation Disney Princess aus den Jahren 1937 bis 2016 sowie den Film Die Eiskönigin (2013) auf die Darstellung der weiblich und männlich gelesenen Filmfiguren im Hinblick auf die Körperproportionen, den Grad ihrer Aktivität und den Umfang ihrer Präsenz sowie das Geschlecht der Filmmitarbeiter:innen zu untersuchen.
Focusing on the triangulation of detective fiction, masculinity studies and disability studies, "Investigating the Disabled Detective – Disabled Masculinity and Masculine Disability in Contemporary Detective Fiction" shows that disability challenges common ideals of (hegemonic) masculinity as represented in detective fiction. After a theoretical introduction to the relevant focal points of the three research fields, the dissertation demonstrates that even the archetypal detectives Dupin and Holmes undermine certain nineteenth-century masculine ideals with their peculiarities. Shifting to contemporary detective fiction and adopting a literary disability studies perspective, the dissertation investigates how male detectives with a form of neurodiversity or a physical impairment negotiate their masculine identity in light of their disability in private and professional contexts. It argues that the occupation as a detective supports the disabled investigator to achieve ‘masculine disability’. Inversing the term ‘disabled masculinity’, predominantly used in research, ‘masculine disability’ introduces a decisively gendered reading of neurodiversity and (acquired) physical impairment in contemporary detective fiction. The term implies that the disabled detective (re)negotiates his masculine identity by implementing the disability in his professional investigations and accepting it as an important, yet not defining, characteristic of his (gender) identity. By applying this approach to five novels from contemporary British and American detective fiction, the dissertation demonstrates that masculinity and disability do not negate each other, as commonly assumed. Instead, it emphasises that disability allows the detective, as much as the reader, to rethink masculinity.
Im Rahmen der Masterthesis „Analyse des Managements invasiver gebietsfremder Arten am Beispiel des Roten Amerikanischen Sumpfkrebses (Procambarus clarkii) während und im Anschluss an notwendige Sanierungsarbeiten am Hochwasserrückhaltebecken Breitenauer See östlich von Heilbronn“ wurde das Vorkommen des invasiven Roten Amerikanischen Sumpfkrebses am Breitenauer See umfangreich kartiert. Auch die nahegelegene Sulm mit bekanntem Vorkommen des Signalkrebses sowie das Nonnenbachsystem mit bekanntem Vorkommen des Steinkrebses wurden erfasst. Der Fokus lag auf der Beantwortung dreier Kernfragen. Zunächst wurde untersucht, ob und wie ein dauerhaftes IAS-Management (invasive alien species) des Roten Amerikanischen Sumpfkrebses am Breitenauer See nachhaltig durchgeführt werden kann, um inakzeptable ökologische Effekte zu vermeiden. Die zweite Fragestellung bezog sich auf die Wirksamkeit ergriffener Risikomanagementmaßnahmen während der Ablassaktion des Breitenauer Sees. Abschließend war fraglich, wie sich der Rote Amerikanische Sumpfkrebs verhält, wenn sein besiedeltes Gewässer trockenfällt.
Empirical studies in software engineering use software repositories as data sources to understand software development. Repository data is either used to answer questions that guide the decision-making in the software development, or to provide tools that help with practical aspects of developers’ everyday work. Studies are classified into the field of Empirical Software Engineering (ESE), and more specifically into Mining Software Repositories (MSR). Studies working with repository data often focus on their results. Results are statements or tools, derived from the data, that help with practical aspects of software development. This thesis focuses on the methods and high order methods used to produce such results. In particular, we focus on incremental methods to scale the processing of repositories, declarative methods to compose a heterogeneous analysis, and high order methods used to reason about threats to methods operating on repositories. We summarize this as technical and methodological improvements. We contribute the improvements to methods and high-order methods in the context of MSR/ESE to produce future empirical results more effectively. We contribute the following improvements. We propose a method to improve the scalability of functions that abstract over repositories with high revision count in a theoretically founded way. We use insights on abstract algebra and program incrementalization to define a core interface of highorder functions that compute scalable static abstractions of a repository with many revisions. We evaluate the scalability of our method by benchmarks, comparing a prototype with available competitors in MSR/ESE. We propose a method to improve the definition of functions that abstract over a repository with a heterogeneous technology stack, by using concepts from declarative logic programming and combining them with ideas on megamodeling and linguistic architecture. We reproduce existing ideas on declarative logic programming with languages close to Datalog, coming from architecture recovery, source code querying, and static program analysis, and transfer them from the analysis of a homogeneous to a heterogeneous technology stack. We provide a prove-of-concept of such method in a case study. We propose a high-order method to improve the disambiguation of threats to methods used in MSR/ESE. We focus on a better disambiguation of threats, operationalizing reasoning about them, and making the implications to a valid data analysis methodology explicit, by using simulations. We encourage researchers to accomplish their work by implementing ‘fake’ simulations of their MSR/ESE scenarios, to operationalize relevant insights about alternative plausible results, negative results, potential threats and the used data analysis methodologies. We prove that such way of simulation based testing contributes to the disambiguation of threats in published MSR/ESE research.
Sind Menschen von einer Pflegebedürftigkeit in Deutschland betroffen, so regelt der durch § 14 SGB XI festgeschriebene Pflegebedürftigkeitsbegriff den Zugang zu Leistungen der Pflegeversicherung. Der Pflegebedürftigkeitsbegriff ist dabei ein normativ gesetzter und basiert bislang nicht auf empirischen Studien aus dem Bereich der Pflege und der Pflegewissenschaft. Durch seine gesetzliche Fundierung lenkt er die Bedingungen und Strukturen, unter welchen Pflegeleistungen in Deutschland von Pflegefachpersonen erbracht werden. Weiterhin ist davon auszugehen, dass die Pflegefachpersonen durch ihre professionelle Sozialisierung einen fachlichen Fokus auf das Konstrukt der Pflegebedürftigkeit legen, welcher sich vom Pflegebedürftigkeitsbegriff unterscheidet und strukturell nicht in die Leistungsbemessung einfließt. Daraus ergeben sich Aspekte einer pflegerischen Unter- und Überversorgung.
Die vorliegende Ph.D.-Thesis verfolgt das Anliegen, die Herausforderungen des Pflegebedürftigkeitsbegriffs in Deutschland aufzuzeigen, indem die Aspekte der Pflegebedürftigkeit von Pflegefachpersonen im ambulanten Setting im Hinblick auf deren Interaktion mit pflegebedürftigen Menschen empirisch erfasst und zu einem theoretischen Konzept ausgearbeitet werden. Zur methodischen Bearbeitung des Forschungsinteresses werden problemzzentrierte Interviews mit ambulanten Pflegefachpersonen geführt, die mit Rückbezug auf den Symbolischen Interaktionismus nach Herbert Blumer unter methodologischen und methodischen Gesichtspunkten mittels einer Grounded Theory nach Kathy Charmaz sowie Juliet Corbin und Anselm Strauss erhoben und ausgewertet werden. Dabei kommt ein reflexives-konstruktivistisches Forschen und Schreiben als Konsequenz der epistemologisch-methodologischen Fundierung der Autorin zur Anwendung.
Die erarbeitete Theorie beschreibt die Herausforderungen der Pflegebedürftigkeit aus Sicht der befragten Pflegefachpersonen. So werden in der Kernkategorie Aushandlungsprozesse in den Bereichen Nähe und Distanz, Anwaltschaft und Verantwortungsüberlassung sowie Ethos und Technokratie beschrieben. Sämtliche Aspekte zeigen auf, inwiefern der gesetzliche Pflegebedürftigkeitsbegriff zu Herausforderungen innerhalb der pflegerischen Arbeit führt. Die Ph.D.-Thesis liefert mit ihren Ergebnissen einen Beitrag zur Einordnung und Relevanz pflegerischer Beziehungsarbeit im Hinblick auf herrschende Rahmenbedingungen der Pflegebedürftigkeit und zeigt auf, inwiefern sich Interaktion und Kommunikation der Akteur*innen vor dem Anspruch individueller Pflege und dem deutschen ambulanten Pflegesystem wechselseitig bedingen. Sie liefert damit einen professionell und empirisch begründeten Ansatz für die Einschätzung und Bearbeitung von pflegefachlich erlebter Pflegebedürftigkeit.
In the last years, the public interest in epidemiology and mathematical modeling of disease spread has increased - mainly caused by the COVID-19 pandemic, which has emphasized the urgent need for accurate and timely modelling of disease transmission. However, even prior to that, mathematical modelling has been used for describing the dynamics and spread of infectious diseases, which is vital for developing effective interventions and controls, e.g., for vaccination campaigns and social restrictions like lockdowns. The forecasts and evaluations provided by these models influence political actions and shape the measures implemented to contain the virus.
This research contributes to the understanding and control of disease spread, specifically for Dengue fever and COVID-19, making use of mathematical models and various data analysis techniques. The mathematical foundations of epidemiological modelling, as well as several concepts for spatio-temporal diffusion like ordinary differential equation (ODE) models, are presented, as well as an originally human-vector model for Dengue fever, and the standard (SEIR)-model (with the potential inclusion of an equation for deceased persons), which are suited for the description of COVID-19. Additionally, multi-compartment models, fractional diffusion models, partial differential equations (PDE) models, and integro-differential models are used to describe spatial propagation of the diseases.
We will make use of different optimization techniques to adapt the models to medical data and estimate the relevant parameters or finding optimal control techniques for containing diseases using both Metropolis and Lagrangian methods. Reasonable estimates for the unknown parameters are found, especially in initial stages of pandemics, when little to no information is available and the majority of the population has not got in contact with the disease. The longer a disease is present, the more complex the modelling gets and more things (vaccination, different types, etc.) appear and reduce the estimation and prediction quality of the mathematical models.
While it is possible to create highly complex models with numerous equations and parameters, such an approach presents several challenges, including difficulties in comparing and evaluating data, increased risk of overfitting, and reduced generalizability. Therefore, we will also consider criteria for model selection based on fit and complexity as well as the sensitivity of the model with respect to specific parameters. This also gives valuable information on which political interventions should be more emphasized for possible variations of parameter values.
Furthermore, the presented models, particularly the optimization using the Metropolis algorithm for parameter estimation, are compared with other established methods. The quality of model calculation, as well as computational effort and applicability, play a role in this comparison. Additionally, the spatial integro-differential model is compared with an established agent-based model. Since the macroscopic results align very well, the computationally faster integro-differential model can now be used as a proxy for the slower and non-traditionally optimizable agent-based model, e.g., in order to find an apt control strategy.
Artificial neural networks is a popular field of research in artificial intelli-
gence. The increasing size and complexity of huge models entail certain
problems. The lack of transparency of the inner workings of a neural net-
work makes it difficult to choose efficient architectures for different tasks.
It proves to be challenging to solve these problems, and with a lack of in-
sightful representations of neural networks, this state of affairs becomes
entrenched. With these difficulties in mind a novel 3D visualization tech-
nique is introduced. Attributes for trained neural networks are estimated
by utilizing established methods from the area of neural network optimiza-
tion. Batch normalization is used with fine-tuning and feature extraction to
estimate the importance of different parts of the neural network. A combi-
nation of the importance values with various methods like edge bundling,
ray tracing, 3D impostor and a special transparency technique results in a
3D model representing a neural network. The validity of the extracted im-
portance estimations is demonstrated and the potential of the developed
visualization is explored.