Refine
Year of publication
Document Type
- Doctoral Thesis (3195) (remove)
Language
- English (3195) (remove)
Keywords
- climate change (52)
- Klimawandel (51)
- Modellierung (27)
- Nanopartikel (22)
- machine learning (22)
- Arabidopsis thaliana (17)
- Blickbewegungen (17)
- Fernerkundung (17)
- Arktis (15)
- Synchronisation (15)
Institute
- Institut für Biochemie und Biologie (746)
- Institut für Physik und Astronomie (554)
- Institut für Geowissenschaften (409)
- Institut für Chemie (348)
- Extern (146)
- Institut für Informatik und Computational Science (129)
- Institut für Mathematik (126)
- Institut für Umweltwissenschaften und Geographie (118)
- Institut für Ernährungswissenschaft (116)
- Department Linguistik (98)
- Hasso-Plattner-Institut für Digital Engineering GmbH (95)
- Wirtschaftswissenschaften (86)
- Department Psychologie (72)
- Sozialwissenschaften (45)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (42)
- Institut für Anglistik und Amerikanistik (39)
- Department Sport- und Gesundheitswissenschaften (31)
- Fachgruppe Politik- & Verwaltungswissenschaft (17)
- Fachgruppe Betriebswirtschaftslehre (15)
- Strukturbereich Kognitionswissenschaften (15)
- Philosophische Fakultät (13)
- Öffentliches Recht (11)
- Department Erziehungswissenschaft (10)
- Institut für Philosophie (10)
- Digital Engineering Fakultät (9)
- Fachgruppe Volkswirtschaftslehre (7)
- Institut für Germanistik (7)
- Potsdam Institute for Climate Impact Research (PIK) e. V. (7)
- Institut für Jüdische Studien und Religionswissenschaft (6)
- Historisches Institut (5)
- Institut für Künste und Medien (5)
- Mathematisch-Naturwissenschaftliche Fakultät (5)
- Fachgruppe Soziologie (3)
- Psycholinguistics and Neurolinguistics (3)
- Institut für Jüdische Theologie (2)
- Institut für Romanistik (2)
- Multilingualism (2)
- Patholinguistics/Neurocognition of Language (2)
- Applied Computational Linguistics (1)
- Bürgerliches Recht (1)
- Department Grundschulpädagogik (1)
- Department für Inklusionspädagogik (1)
- Deutsches GeoForschungsZentrum GFZ (1)
- Fakultät für Gesundheitswissenschaften (1)
- Foundations of Computational Linguistics (1)
- Institut für Religionswissenschaft (1)
- Institut für Slavistik (1)
- Interdisziplinäres Zentrum für Dynamik komplexer Systeme (1)
- Interdisziplinäres Zentrum für Kognitive Studien (1)
- Lehreinheit für Wirtschafts-Arbeit-Technik (1)
- Phonology & Phonetics (1)
- Potsdam Research Institute for Multilingualism (PRIM) (1)
- Syntax, Morphology & Variability (1)
This thesis focuses on the molecular evolution of Macroscelidea, commonly referred to as sengis. Sengis are a mammalian order belonging to the Afrotherians, one of the four major clades of placental mammals. Sengis currently consist of twenty extant species, all of which are endemic to the African continent. They can be separated in two families, the soft-furred sengis (Macroscelididae) and the giant sengis (Rhynchocyonidae). While giant sengis can be exclusively found in forest habitats, the different soft-furred sengi species dwell in a broad range of habitats, from tropical rain-forests to rocky deserts.
Our knowledge on the evolutionary history of sengis is largely incomplete. The high level of superficial morphological resemblance among different sengi species (especially the soft-furred sengis) has for example led to misinterpretations of phylogenetic relationships, based on morphological characters. With the rise of DNA based taxonomic inferences, multiple new genera were defined and new species described. Yet, no full taxon molecular phylogeny exists, hampering the answering of basic taxonomic questions. This lack of knowledge can be to some extent attributed to the limited availability of fresh-tissue samples for DNA extraction. The broad African distribution, partly in political unstable regions and low population densities complicate contemporary sampling approaches. Furthermore, the DNA information available usually covers only short stretches of the mitochondrial genome and thus a single genetic locus with limited informational content.
Developments in DNA extraction and library protocols nowadays offer the opportunity to access DNA from museum specimens, collected over the past centuries and stored in natural history museums throughout the world. Thus, the difficulties in fresh-sample acquisition for molecular biological studies can be overcome by the application of museomics, the research field which emerged from those laboratory developments.
This thesis uses fresh-tissue samples as well as a vast collection museum specimens to investigate multiple aspects about the macroscelidean evolutionary history. Chapter 4 of this thesis focuses on the phylogenetic relationships of all currently known sengi species. By accessing DNA information from museum specimens in combination of fresh tissue samples and publicly available genetic resources it produces the first full taxon molecular phylogeny of sengis. It confirms the monophyly of the genus Elephantulus and discovers multiple deeply divergent lineages within different species, highlighting the need for species specific approaches. The study furthermore focuses on the evolutionary time frame of sengis by evaluating the impact of commonly varied parameters on tree dating. The results of the study show, that the mitochondrial information used in previous studies to temporal calibrate the Macroscelidean phylogeny led to an overestimation of node ages within sengis. Especially soft-furred sengis are thus much younger than previously assumed. The refined knowledge of nodes ages within sengis offer the opportunity to link e.g. speciation events to environmental changes.
Chapter 5 focuses on the genus Petrodromus with its single representative Petrodromus tetradactylus. It again exploits the opportunities of museomics and gathers a comprehensive, multi-locus genetic dataset of P. tetradactylus individuals, distributed across most the known range of this species. It reveals multiple deeply divergent lineages within Petrodromus, whereby some could possibly be associated to previously described sub-species, at least one was formerly unknown. It underscores the necessity for a revision of the genus Petrodromus through the integration of both molecular and morphological evidence. The study, furthermore identifies changing forest distributions through climatic oscillations as main factor shaping the genetic structure of Petrodromus.
Chapter 6 uses fresh tissue samples to extent the genomic resources of sengis by thirteen new nuclear genomes, of which two were de-novo assembled. An extensive dataset of more than 8000 protein coding one-to-one orthologs allows to further refine and confirm the temporal time frame of sengi evolution found in Chapter 4. This study moreover investigates the role of gene-flow and incomplete lineage sorting (ILS) in sengi evolution. In addition it identifies clade specific genes of possible outstanding evolutionary importance and links them to potential phenotypic traits affected. A closer investigation of olfactory receptor proteins reveals clade specific differences. A comparison of the demographic past of sengis to other small African mammals does not reveal a sengi specific pattern.
This thesis presents an attempt to use source code synthesised from Coq formalisations of device drivers for existing (micro)kernel operating systems, with a particular focus on the Linux Kernel.
In the first part, the technical background and related work are described. The focus is here on the possible approaches to synthesising certified software with Coq, namely the extraction to functional languages using the Coq extraction plugin and the extraction to Clight code using the CertiCoq plugin. It is noted that the implementation of CertiCoq is verified, whereas this is not the case for the Coq extraction plugin. Consequently, there is a correctness guarantee for the generated Clight code which does not hold for the code being generated by the Coq extraction plugin. Furthermore, the differences between user space and kernel space software are discussed in relation to Linux device drivers. It is elaborated that it is not possible to generate working Linux kernel module components using the Coq extraction plugin without significant modifications. In contrast, it is possible to produce working user space drivers both with the Coq extraction plugin and CertiCoq. The subsequent parts describe the main contributions of the thesis.
In the second part, it is demonstrated how to extend the Coq extraction plugin to synthesise foreign function calls between the functional language OCaml and the imperative language C. This approach has the potential to improve the type-safety of user space drivers. Furthermore, it is shown that the code being synthesised by CertiCoq cannot be used in kernel space without modifications to the necessary runtime. Consequently, the necessary modifications to the runtimes of CertiCoq and VeriFFI are introduced, resulting in the runtimes becoming compatible components of a Linux kernel module. Furthermore, justifications for the transformations are provided and possible further extensions to both plugins and solutions to failing garbage collection calls in kernel space are discussed.
The third part presents a proof of concept device driver for the Linux Kernel. To achieve this, the event handler of the original PC Speaker driver is partially formalised in Coq. Furthermore, some relevant formal properties of the formalised functionality are discussed. Subsequently, a kernel module is defined, utilising the modified variants of CertiCoq and VeriFFI to compile a working device driver. It is furthermore shown that it is possible to compile the synthesised code with CompCert, thereby extending the guarantee of correctness to the assembly layer. This is followed by a performance evaluation that compares a naive formalisation of the PC speaker functionality with the original PC Speaker driver pointing out the weaknesses in the formalisation and possible improvements. The part closes with a summary of the results, their implications and open questions being raised.
The last part lists all used sources, separated into scientific literature, documentations or reference manuals and artifacts, i.e. source code.
Deep learning has seen widespread application in many domains, mainly for its ability to learn data representations from raw input data. Nevertheless, its success has so far been coupled with the availability of large annotated (labelled) datasets. This is a requirement that is difficult to fulfil in several domains, such as in medical imaging. Annotation costs form a barrier in extending deep learning to clinically-relevant use cases. The labels associated with medical images are scarce, since the generation of expert annotations of multimodal patient data at scale is non-trivial, expensive, and time-consuming. This substantiates the need for algorithms that learn from the increasing amounts of unlabeled data. Self-supervised representation learning algorithms offer a pertinent solution, as they allow solving real-world (downstream) deep learning tasks with fewer annotations. Self-supervised approaches leverage unlabeled samples to acquire generic features about different concepts, enabling annotation-efficient downstream task solving subsequently.
Nevertheless, medical images present multiple unique and inherent challenges for existing self-supervised learning approaches, which we seek to address in this thesis: (i) medical images are multimodal, and their multiple modalities are heterogeneous in nature and imbalanced in quantities, e.g. MRI and CT; (ii) medical scans are multi-dimensional, often in 3D instead of 2D; (iii) disease patterns in medical scans are numerous and their incidence exhibits a long-tail distribution, so it is oftentimes essential to fuse knowledge from different data modalities, e.g. genomics or clinical data, to capture disease traits more comprehensively; (iv) Medical scans usually exhibit more uniform color density distributions, e.g. in dental X-Rays, than natural images. Our proposed self-supervised methods meet these challenges, besides significantly reducing the amounts of required annotations.
We evaluate our self-supervised methods on a wide array of medical imaging applications and tasks. Our experimental results demonstrate the obtained gains in both annotation-efficiency and performance; our proposed methods outperform many approaches from related literature. Additionally, in case of fusion with genetic modalities, our methods also allow for cross-modal interpretability. In this thesis, not only we show that self-supervised learning is capable of mitigating manual annotation costs, but also our proposed solutions demonstrate how to better utilize it in the medical imaging domain. Progress in self-supervised learning has the potential to extend deep learning algorithms application to clinical scenarios.
Today, near-surface investigations are frequently conducted using non-destructive or minimally invasive methods of applied geophysics, particularly in the fields of civil engineering, archaeology, geology, and hydrology. One field that plays an increasingly central role in research and engineering is the examination of sedimentary environments, for example, for characterizing near-surface groundwater systems. A commonly employed method in this context is ground-penetrating radar (GPR). In this technique, short electromagnetic pulses are emitted into the subsurface by an antenna, which are then reflected, refracted, or scattered at contrasts in electromagnetic properties (such as the water table). A receiving antenna records these signals in terms of their amplitudes and travel times. Analysis of the recorded signals allows for inferences about the subsurface, such as the depth of the groundwater table or the composition and characteristics of near-surface sediment layers. Due to the high resolution of the GPR method and continuous technological advancements, GPR data acquisition is increasingly performed in three-dimensional (3D) fashion today.
Despite the considerable temporal and technical efforts involved in data acquisition and processing, the resulting 3D data sets (providing high-resolution images of the subsurface) are typically interpreted manually. This is generally an extremely time-consuming analysis step. Therefore, representative 2D sections highlighting distinctive reflection structures are often selected from the 3D data set. Regions showing similar structures are then grouped into so-called radar facies. The results obtained from 2D sections are considered representative of the entire investigated area. Interpretations conducted in this manner are often incomplete and highly dependent on the expertise of the interpreters, making them generally non-reproducible.
A promising alternative or complement to manual interpretation is the use of GPR attributes. Instead of using the recorded data directly, derived quantities characterizing distinctive reflection structures in 3D are applied for interpretation. Using various field and synthetic data sets, this thesis investigates which attributes are particularly suitable for this purpose. Additionally, the study demonstrates how selected attributes can be utilized through specific processing and classification methods to create 3D facies models. The ability to generate attribute-based 3D GPR facies models allows for partially automated and more efficient interpretations in the future. Furthermore, the results obtained in this manner describe the subsurface in a reproducible and more comprehensive manner than what has typically been achievable through manual interpretation methods.
Improving permafrost dynamics in land surface models: insights from dual sensitivity experiments
(2024)
The thawing of permafrost and the subsequent release of greenhouse gases constitute one of the most significant and uncertain positive feedback loops in the context of climate change, making predictions regarding changes in permafrost coverage of paramount importance. To address these critical questions, climate scientists have developed Land Surface Models (LSMs) that encompass a multitude of physical soil processes. This thesis is committed to advancing our understanding and refining precise representations of permafrost dynamics within LSMs, with a specific focus on the accurate modeling of heat fluxes, an essential component for simulating permafrost physics.
The first research question overviews fundamental model prerequisites for the representation of permafrost soils within land surface modeling. It includes a first-of-its-kind comparison between LSMs in CMIP6 to reveal their differences and shortcomings in key permafrost physics parameters. Overall, each of these LSMs represents a unique approach to simulating soil processes and their interactions with the climate system. Choosing the most appropriate model for a particular application depends on factors such as the spatial and temporal scale of the simulation, the specific research question, and available computational resources.
The second research question evaluates the performance of the state-of-the-art Community Land Model (CLM5) in simulating Arctic permafrost regions. Our approach overcomes traditional evaluation limitations by individually addressing depth, seasonality, and regional variations, providing a comprehensive assessment of permafrost and soil temperature dynamics. I compare CLM5's results with three extensive datasets: (1) soil temperatures from 295 borehole stations, (2) active layer thickness (ALT) data from the Circumpolar Active Layer Monitoring Network (CALM), and (3) soil temperatures, ALT, and permafrost extent from the ESA Climate Change Initiative (ESA-CCI). The results show that CLM5 aligns well with ESA-CCI and CALM for permafrost extent and ALT but reveals a significant global cold temperature bias, notably over Siberia. These results echo a persistent challenge identified in numerous studies: the existence of a systematic 'cold bias' in soil temperature over permafrost regions. To address this challenge, the following research questions propose dual sensitivity experiments.
The third research question represents the first study to apply a Plant Functional Type (PFT)-based approach to derive soil texture and soil organic matter (SOM), departing from the conventional use of coarse-resolution global data in LSMs. This novel method results in a more uniform distribution of soil organic matter density (OMD) across the domain, characterized by reduced OMD values in most regions. However, changes in soil texture exhibit a more intricate spatial pattern. Comparing the results to observations reveals a significant reduction in the cold bias observed in the control run. This method shows noticeable improvements in permafrost extent, but at the cost of an overestimation in ALT. These findings emphasize the model's high sensitivity to variations in soil texture and SOM content, highlighting the crucial role of soil composition in governing heat transfer processes and shaping the seasonal variation of soil temperatures in permafrost regions.
Expanding upon a site experiment conducted in Trail Valley Creek by \citet{dutch_impact_2022}, the fourth research question extends the application of the snow scheme proposed by \citet{sturm_thermal_1997} to cover the entire Arctic domain. By employing a snow scheme better suited to the snow density profile observed over permafrost regions, this thesis seeks to assess its influence on simulated soil temperatures. Comparing this method to observational datasets reveals a significant reduction in the cold bias that was present in the control run. In most regions, the Sturm run exhibits a substantial decrease in the cold bias. However, there is a distinctive overshoot with a warm bias observed in mountainous areas. The Sturm experiment effectively addressed the overestimation of permafrost extent in the control run, albeit resulting in a substantial reduction in permafrost extent over mountainous areas. ALT results remain relatively consistent compared to the control run. These outcomes align with our initial hypothesis, which anticipated that the reduced snow insulation in the Sturm run would lead to higher winter soil temperatures and a more accurate representation of permafrost physics.
In summary, this thesis demonstrates significant advancements in understanding permafrost dynamics and its integration into LSMs. It has meticulously unraveled the intricacies involved in the interplay between heat transfer, soil properties, and snow dynamics in permafrost regions. These insights offer novel perspectives on model representation and performance.
Homomorphisms are a fundamental concept in mathematics expressing the similarity of structures. They provide a framework that captures many of the central problems of computer science with close ties to various other fields of science. Thus, many studies over the last four decades have been devoted to the algorithmic complexity of homomorphism problems. Despite their generality, it has been found that non-uniform homomorphism problems, where the target structure is fixed, frequently feature complexity dichotomies. Exploring the limits of these dichotomies represents the common goal of this line of research.
We investigate the problem of counting homomorphisms to a fixed structure over a finite field of prime order and its algorithmic complexity. Our emphasis is on graph homomorphisms and the resulting problem #_{p}Hom[H] for a graph H and a prime p. The main research question is how counting over a finite field of prime order affects the complexity.
In the first part of this thesis, we tackle the research question in its generality and develop a framework for studying the complexity of counting problems based on category theory. In the absence of problem-specific details, results in the language of category theory provide a clear picture of the properties needed and highlight common ground between different branches of science. The proposed problem #Mor^{C}[B] of counting the number of morphisms to a fixed object B of C is abstract in nature and encompasses important problems like constraint satisfaction problems, which serve as a leading example for all our results. We find explanations and generalizations for a plethora of results in counting complexity. Our main technical result is that specific matrices of morphism counts are non-singular. The strength of this result lies in its algebraic nature. First, our proofs rely on carefully constructed systems of linear equations, which we know to be uniquely solvable. Second, by exchanging the field that the matrix is defined by to a finite field of order p, we obtain analogous results for modular counting. For the latter, cancellations are implied by automorphisms of order p, but intriguingly we find that these present the only obstacle to translating our results from exact counting to modular counting. If we restrict our attention to reduced objects without automorphisms of order p, we obtain results analogue to those for exact counting. This is underscored by a confluent reduction that allows this restriction by constructing a reduced object for any given object. We emphasize the strength of the categorial perspective by applying the duality principle, which yields immediate consequences for the dual problem of counting the number of morphisms from a fixed object.
In the second part of this thesis, we focus on graphs and the problem #_{p}Hom[H]. We conjecture that automorphisms of order p capture all possible cancellations and that, for a reduced graph H, the problem #_{p}Hom[H] features the complexity dichotomy analogue to the one given for exact counting by Dyer and Greenhill. This serves as a generalization of the conjecture by Faben and Jerrum for the modulus 2. The criterion for tractability is that H is a collection of complete bipartite and reflexive complete graphs. From the findings of part one, we show that the conjectured dichotomy implies dichotomies for all quantum homomorphism problems, in particular counting vertex surjective homomorphisms and compactions modulo p. Since the tractable cases in the dichotomy are solved by trivial computations, the study of the intractable cases remains. As an initial problem in a series of reductions capable of implying hardness, we employ the problem of counting weighted independent sets in a bipartite graph modulo prime p. A dichotomy for this problem is shown, stating that the trivial cases occurring when a weight is congruent modulo p to 0 are the only tractable cases. We reduce the possible structure of H to the bipartite case by a reduction to the restricted homomorphism problem #_{p}Hom^{bip}[H] of counting modulo p the number of homomorphisms between bipartite graphs that maintain a given order of bipartition. This reduction does not have an impact on the accessibility of the technical results, thanks to the generality of the findings of part one. In order to prove the conjecture, it suffices to show that for a connected bipartite graph that is not complete, #_{p}Hom^{bip}[H] is #_{p}P-hard. Through a rigorous structural study of bipartite graphs, we establish this result for the rich class of bipartite graphs that are (K_{3,3}\{e}, domino)-free. This overcomes in particular the substantial hurdle imposed by squares, which leads us to explore the global structure of H and prove the existence of explicit structures that imply hardness.
The experience of premenstrual syndrome (PMS) affects up to 90% of individuals with an active menstrual cycle and involves a spectrum of aversive physiological and psychological symptoms in the days leading up to menstruation (Tschudin et al., 2010). Despite its high prevalence, the precise origins of PMS remain elusive, with influences ranging from hormonal fluctuations to cognitive, social, and cultural factors (Hunter, 2007; Matsumoto et al., 2013).
Biologically, hormonal fluctuations, particularly in gonadal steroids, are commonly believed to be implicated in PMS, with the central factor being varying susceptibilities to the fluctuations between individuals and cycles (Rapkin & Akopians, 2012). Allopregnanolone (ALLO), a neuroactive steroid and progesterone metabolite, has emerged as a potential link to PMS symptoms (Hantsoo & Epperson, 2020). ALLO is a positive allosteric modulator of the GABAA receptor, influencing inhibitory communication (Rupprecht, 2003; Andréen et al., 2006). Different susceptibility to ALLO fluctuations throughout the cycle may lead to reduced GABAergic signal transmission during the luteal phase of the menstrual cycle.
The GABAergic system's broad influence leads to a number of affected physiological systems, including a consistent reduction in vagally mediated heart rate variability (vmHRV) during the luteal phase (Schmalenberger et al., 2019). This reduction in vmHRV is more pronounced in individuals with high PMS symptoms (Baker et al., 2008; Matsumoto et al., 2007). Fear conditioning studies have shown inconsistent associations with cycle phases, suggesting a complex interplay between physiological parameters and PMS-related symptoms (Carpenter et al., 2022; Epperson et al., 2007; Milad et al., 2006).
The neurovisceral integration model posits that vmHRV reflects the capacity of the central autonomous network (CAN), which is responsible for regulatory processes on behavioral, cognitive, and autonomous levels (Thayer & Lane, 2000, 2009). Fear learning, mediated within the CAN, is suggested to be indicative of vmHRV's capacity for successful
VI
regulation (Battaglia & Thayer, 2022). Given the GABAergic mediation of central inhibitory functional connectivity in the CAN, which may be affected by ALLO fluctuations, this thesis proposes that fluctuating CAN activity in the luteal phase contributes to diverse aversive symptoms in PMS.
A research program was designed to empirically test these propositions. Study 1 investigated fear discrimination during different menstrual cycle phases and its interaction with vmHRV, revealing nuanced effects on acoustic startle response and skin conductance response. While there was heightened fear discrimination in acoustic startle responses in participants in the luteal phase, there was an interaction between menstrual cycle phase and vmHRV in skin conductance responses. In this measure, heightened fear discrimination during the luteal phase was only visible in individuals with high resting vmHRV; those with low vmHRV showed reduced fear discrimination and higher overall responses.
Despite affecting the vast majority of menstruating people, there are very limited tools available to reliably assess these symptoms in the German speaking area. Study 2 aimed at closing this gap, by translating and validating a German version of the short version of the Premenstrual Assessment Form (Allen et al., 1991), providing a reliable tool for future investigations, which closes the gap in PMS questionnaires in the German-speaking research area.
Study 3 employed a diary study paradigm to explore daily associations between vmHRV and PMS symptoms. The results showed clear simultaneous fluctuations between the two constructs with a peak in PMS and a low point in vmHRV a few days before menstruation onset. The association between vmHRV and PMS was driven by psychological PMS symptoms.
Based on the theoretical considerations regarding the neurovisceral perspective on PMS, another interesting construct to consider is attentional control, as it is closely related to functions of the CAN. Study 4 delved into attentional control and vmHRV differences between menstrual cycle phases, demonstrating an interaction between cycle phase and PMS symptoms. In a pilot, we found reduced vmHRV and attentional control during the luteal phase only in participants who reported strong PMS.
While Studies 1-4 provided evidence for the mechanisms underlying PMS, Studies 5 and 6 investigated short- and long-term intervention protocols to ameliorate PMS symptomatology. Study 5 explored the potential of heart rate variability biofeedback (HRVB) in alleviating PMS symptoms and a number of other outcome measures. In a waitlist-control design, participants underwent a 4-week smartphone-based HRVB intervention. The results revealed positive effects on PMS, with larger effect sizes on psychological symptoms, as well as on depressive symptoms, anxiety/stress and attentional control.
Finally, Study 6 examined the acute effects of HRVB on attentional control. The study found positive impact but only in highly stressed individuals.
The thesis, based on this comprehensive research program, expands our understanding of PMS as an outcome of CAN fluctuations mediated by GABAA receptor reactivity. The results largely support the model. These findings not only deepen our understanding of PMS but also offer potential avenues for therapeutic interventions. The promising results of smartphone-based HRVB training suggest a non-pharmacological approach to managing PMS symptoms, although further research is needed to confirm its efficacy.
In conclusion, this thesis illuminates the complex web of factors contributing to PMS, providing valuable insights into its etiological underpinnings and potential interventions. By elucidating the relationships between hormonal fluctuations, CAN activity, and psychological responses, this research contributes to more effective treatments for individuals grappling with the challenges of PMS. The findings hold promise for improving the quality of life for those affected by this prevalent and often debilitating condition.
The automotive industry is a prime example of digital technologies reshaping mobility. Connected, autonomous, shared, and electric (CASE) trends lead to new emerging players that threaten existing industrial-aged companies. To respond, incumbents need to bridge the gap between contrasting product architecture and organizational principles in the physical and digital realms. Over-the-air (OTA) technology, that enables seamless software updates and on-demand feature additions for customers, is an example of CASE-driven digital product innovation. Through an extensive longitudinal case study of an OTA initiative by an industrial- aged automaker, this dissertation explores how incumbents accomplish digital product innovation. Building on modularity, liminality, and the mirroring hypothesis, it presents a process model that explains the triggers, mechanisms, and outcomes of this process. In contrast to the literature, the findings emphasize the primacy of addressing product architecture challenges over organizational ones and highlight the managerial implications for success.
Human activities modify nature worldwide via changes in the environment, biodiversity and the functioning of ecosystems, which in turn disrupt ecosystem services and feed back negatively on humans. A pressing challenge is thus to limit our impact on nature, and this requires detailed understanding of the interconnections between the environment, biodiversity and ecosystem functioning. These three components of ecosystems each include multiple dimensions, which interact with each other in different ways, but we lack a comprehensive picture of their interconnections and underlying mechanisms. Notably, diversity is often viewed as a single facet, namely species diversity, while many more facets exist at different levels of biological organisation (e.g. genetic, phenotypic, functional, multitrophic diversity), and multiple diversity facets together constitute the raw material for adaptation to environmental changes and shape ecosystem functioning. Consequently, investigating the multidimensionality of ecosystems, and in particular the links between multifaceted diversity, environmental changes and ecosystem functions, is crucial for ecological research, management and conservation. This thesis aims to explore several aspects of this question theoretically.
I investigate three broad topics in this thesis. First, I focus on how food webs with varying levels of functional diversity across three trophic levels buffer environmental changes, such as a sudden addition of nutrients or long-term changes (e.g. warming or eutrophication). I observed that functional diversity generally enhanced ecological stability (i.e. the buffering capacity of the food web) by increasing trophic coupling. More precisely, two aspects of ecological stability (resistance and resilience) increased even though a third aspect (the inverse of the time required for the system to reach its post-perturbation state) decreased with increasing functional diversity. Second, I explore how several diversity facets served as a raw material for different sources of adaptation and how these sources affected multiple ecosystem functions across two trophic levels. Considering several sources of adaptation enabled the interplay between ecological and evolutionary processes, which affected trophic coupling and thereby ecosystem functioning. Third, I reflect further on the multifaceted nature of diversity by developing an index K able to quantify the facet of functional diversity, which is itself multifaceted. K can provide a comprehensive picture of functional diversity and is a rather good predictor of ecosystem functioning. Finally I synthesise the interdependent mechanisms (complementarity and selection effects, trophic coupling and adaptation) underlying the relationships between multifaceted diversity, ecosystem functioning and the environment, and discuss the generalisation of my findings across ecosystems and further perspectives towards elaborating an operational biodiversity-ecosystem functioning framework for research and conservation.
Knowledge about causal structures is crucial for decision support in various domains. For example, in discrete manufacturing, identifying the root causes of failures and quality deviations that interrupt the highly automated production process requires causal structural knowledge. However, in practice, root cause analysis is usually built upon individual expert knowledge about associative relationships. But, "correlation does not imply causation", and misinterpreting associations often leads to incorrect conclusions. Recent developments in methods for causal discovery from observational data have opened the opportunity for a data-driven examination. Despite its potential for data-driven decision support, omnipresent challenges impede causal discovery in real-world scenarios. In this thesis, we make a threefold contribution to improving causal discovery in practice.
(1) The growing interest in causal discovery has led to a broad spectrum of methods with specific assumptions on the data and various implementations. Hence, application in practice requires careful consideration of existing methods, which becomes laborious when dealing with various parameters, assumptions, and implementations in different programming languages. Additionally, evaluation is challenging due to the lack of ground truth in practice and limited benchmark data that reflect real-world data characteristics.
To address these issues, we present a platform-independent modular pipeline for causal discovery and a ground truth framework for synthetic data generation that provides comprehensive evaluation opportunities, e.g., to examine the accuracy of causal discovery methods in case of inappropriate assumptions.
(2) Applying constraint-based methods for causal discovery requires selecting a conditional independence (CI) test, which is particularly challenging in mixed discrete-continuous data omnipresent in many real-world scenarios. In this context, inappropriate assumptions on the data or the commonly applied discretization of continuous variables reduce the accuracy of CI decisions, leading to incorrect causal structures.
Therefore, we contribute a non-parametric CI test leveraging k-nearest neighbors methods and prove its statistical validity and power in mixed discrete-continuous data, as well as the asymptotic consistency when used in constraint-based causal discovery. An extensive evaluation of synthetic and real-world data shows that the proposed CI test outperforms state-of-the-art approaches in the accuracy of CI testing and causal discovery, particularly in settings with low sample sizes.
(3) To show the applicability and opportunities of causal discovery in practice, we examine our contributions in real-world discrete manufacturing use cases. For example, we showcase how causal structural knowledge helps to understand unforeseen production downtimes or adds decision support in case of failures and quality deviations in automotive body shop assembly lines.