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Towards Consistent Subgrid Momentum Closures
No full textThis thesis addresses the challenge of accurately representing oceanic dynamics characterized by a multitude of interacting processes in numerical models. Specifically, it focuses on the simulation of oceanic circular patterns ranging from 10 to 100 km in diameter, known as mesoscale eddies. These eddies play a critical role in transporting energy, water properties, and nutrients across the ocean. This research uses grid resolutions that directly capture some larger mesoscale eddies (resolved) while employing advanced mathematical techniques to represent the effects of smaller, unresolved eddies. The primary aim of the thesis is to develop and incorporate novel mathematical and numerical approaches into the Finite Volume Sea Ice-Ocean Model (FESOM2) to improve the representation of mesoscale eddies while maintaining manageable computational costs.
To bridge the gap between low-resolution and high-resolution simulations, the study enhances the mesoscale eddy modeling framework formulated by Juricke et al. (2019) through the implementation of new components that address unresolved dynamics. This includes the addition of an advection-based component to capture nonlinear interactions between resolved and unresolved eddies, which demonstrates positive performance. Furthermore, stochastic elements are introduced into the governing equations to better represent small-scale variability missing from deterministic formulations. In parallel, the thesis explores alternative and complementary parameterization strategies, offering fresh perspectives on modeling at partially resolved scales. Each enhancement is rigorously evaluated using a suite of diagnostic tools — many developed as part of this work — with a particular focus on spectral analysis and energy pathways. Overall, the thesis proposes an integrated approach to mesoscale eddy modeling, advancing the accuracy and consistency of ocean simulations across eddy-permitting resolutions
Modelling plankton dynamics and community compositions in temperate lakes
No full textIn recent years, lakes have faced rising pressure from anthropogenic activities and climate warming, and the aquatic communities of some lake ecosystems are reshaping in ways that can form harmful algal blooms. It is crucial to understand how lake phytoplankton communities respond to environmental stressors under varying environmental conditions. The cell size of phytoplankton has multiple important implications for the dynamics, diversity, and productivity of a phytoplankton community. Empirical investigations in lakes showed that the size composition of phytoplankton communities differs with inorganic nutrient conditions, grazing pressure (usually quantified by zooplankton abundance), and water temperature. However, it is not clear how these three factors interact to shape the size composition of lake phytoplankton. In this thesis, I use size-based plankton modelling to elucidate how a trade-off mechanism, dependent on inorganic nutrient availabilities and zooplankton size-specific grazing strategies, shapes the dynamics, the size composition, and the exclusion pattern of phytoplankton in a generic temperate lake. Lastly, I recast the model to a specific Swiss lake, Greifensee, by using high-frequency data comprising phytoplankton cell size (biovolume) and plankton abundances. In summary, this thesis investigates the interactive effects of inorganic nutrient regimes and zooplankton grazing strategies on the community dynamics and compositions of lake phytoplankton and offers a glimpse into the future size compositions of phytoplankton and nutrient and plankton dynamics of Greifensee. The results not only advance our understanding of plankton communities in temperate lakes, but they also identify hypotheses related to zooplankton grazing strategies that can be further tested experimentally. The data-driven modelling approach presented here can contribute to strategic conservation and management plans for mitigating the effects of ongoing environmental change
Bridging the Gap: A Semantic Approach to Industry 4.0 Maturity Models for Enhanced Adoption of Industry 4.0
No full textThe existing Industry 4.0 maturity models (I4.0 MM) have mostly been built and tested in developed nations, making them less effective in developing countries with unique issues. Additionally, flexible updated models are needed to support the smooth integration of I4.0 adoption in rapid technological advancements and organizational matters. The research addresses the challenges by developing an adaptable I4.0 MM using approaches starting with structured literature reviews (SLRs), investigating the causal relationship and prioritization of I4.0 MMs key driving factors, aligning it with reputable reference architecture model (RAMs), and developing an ontology, named Ontomat 4.0, to facilitate interoperability of I4.0 MMs.
The research's general findings highlight the core gaps in existing I4.0 MMs, the need to prioritize and the interdependence of the key driving factor in I4.0 transformation, the importance of strategically enhancing I4.0 adoption by aligning the key factor of I4.0 MMs with RAMs, and the necessity of a framework with an approach that can bridge the theoretical foundation of I4.0 MMs with practical application.
The research concludes by describing contributions to the issues and challenges in the findings. However, while the research acknowledges the significant progress in its accomplishment, there are limitations to the study that need to be addressed in future research directions, including integrating sustainability metrics and increasingly essential factors, such as Customers, the potential integration of artificial intelligence (AI) within Ontomat 4.0, future exploration equipped with longitudinal studies, and the expansion of Ontomat 4.0 into a collaborative ecosystem where knowledge sharing and best practices can grow
The Decision to Start a Business: Determinants of Business Formation and Differences between Entrepreneurs and Employees
No full textEntrepreneurship is vital in driving innovation, economic growth, and societal transformation. Due to its growing relevance, this dissertation examines the factors that motivate individuals to engage in entrepreneurial endeavors, grounded in the Theory of Planned Behavior (TPB) and enriched by supplementary theoretical perspectives. The research combines qualitative and quantitative methods to explore how attitudes, subjective norms, and perceived behavioral control interact with these diverse aspects. The first study identifies key influences on entrepreneurial intention through qualitative interviews with entrepreneurs, thereby revealing ten critical determinants. The second study expands upon these findings by employing a quantitative approach to validate these parameters through a survey among entrepreneurs in Germany. It demonstrates the central importance of economics-oriented education, materialistic values, and resilience while refining the theoretical assumptions of the TPB in entrepreneurial contexts. Lastly, the third study extends the analysis by comparing the personality traits of entrepreneurs and employees utilizing the OCEAN model. This research delineates substantial differences across all five traits, underscoring the psychological dimensions of entrepreneurial behavior. By amalgamating the three studies and their respective findings, this dissertation contributes to the theoretical understanding of entrepreneurship by enhancing the predictive validity of the TPB for research on entrepreneurship and highlighting differences in personality traits among entrepreneurs and employees. Through the integration of qualitative insights and quantitative validation, this dissertation provides a comprehensive and nuanced perspective on the complex nature of entrepreneurship
The Structure and Dynamics of Groups in Open Source Software Development: A Computational Social Science Approach to Understanding Online Collaboration
No full textThis dissertation examines Free/Libre Open Source Software (FLOSS) development groups through three interconnected studies, each applying computational social science methods to understand different aspects of online collaboration. The first study analyzes group interactions via pull requests, identifying five organizational structures ranging from hierarchical to collaboratively governed networks. This typology moves beyond the traditional "bazaar-cathedral" dichotomy and reveals how group structure impacts outcomes such as popularity, stability, and productivity. The second study employs an agent-based model informed by Affect Control Theory to explore how cultural dynamics shape roles, status, power distribution, and gender biases within FLOSS communities. Findings illustrate the interplay between cultural norms and social structures, highlighting pathways toward gender equality through cultural norm shifts. The third study investigates macro-level project dynamics, examining how repository fitness, preferential attachment, and aging influence project popularity. It compares the meritocratic nature of scientific research and FLOSS development, employing generative probabilistic models based on stochastic processes to understand the mechanisms driving popularity. Together, these studies demonstrate how platform design, cultural norms, and social structures collectively shape FLOSS projects, advancing our understanding of digital collaborative systems
An interoperable knowledge enabler for smart energy management systems in the sustainability paradigm using Web 3 technologies
No full textEnergy management and sustainability have become critical global priorities in response to growing environmental concerns and the need to optimize resource consumption. As industries expand and technological advancements continue to shape modern societies, energy demands are rapidly rising. This leads to escalating levels of carbon emissions and resource depletion, negatively impacting the environment and the economy.
The thesis proposes an innovative approach to addressing energy management and sustainability complexities. The research focuses on developing a framework for smart energy systems that can autonomously improve their performance through knowledge sharing and semantic interoperability. The core idea behind this thesis is to enable smart energy systems to self-develop their knowledge models through decentralized technologies, particularly blockchain while ensuring peer-to-peer semantic interaction and collaboration across different environments and supply chains.
A key innovation of this thesis is the use of blockchain technology as the underlying platform for achieving semantic interoperability and knowledge exchange among smart systems. By leveraging blockchain’s decentralized nature, a peer-to-peer semantic interaction framework is established. The research introduces a smart contract mechanism and a token-based economic model to incentivize stakeholders within the blockchain network, ensuring that participants align with the sustainability goals of the network.
The thesis presents a novel approach to storing and exchanging knowledge models on the blockchain using the InterPlanetary File System (IPFS). This enables real-time updates to smart systems' knowledge models, allowing them to adapt and respond dynamically to changing environmental conditions and data inputs. Through the proposed blockchain ecosystem, the research provides a comprehensive solution for enhancing the interoperability, autonomy, and sustainability of smart energy systems
Sustainable Wastewater Phycoremediation, Resource Recovery and Bioproduct Development towards a circular economy.
No full textRapidly expanding anthropogenic activities are generating increasing volumes of wastewater globally each year, the majority of which remains inadequately treated before being released into aquatic ecosystems. Microalgal technologies offer a promising alternative for nutrient recovery in wastewater treatment, demonstrating significant advantages over conventional methods that are often energy-intensive and costly. Inadequate treatment not only leads to environmental pollution but also results in the irreversible loss of valuable nutrients, thereby disrupting the nutrient cycle. In recent years, the extraction of bioactive compounds from microalgae has attracted substantial attention. However, much of the research has remained confined to laboratory-scale studies with a focus on either energy efficiency or bioproduct synthesis, limiting their practical applicability. A major bottleneck in the scalability of algal-based systems is the energy- and cost-intensive nature of biomass harvesting, which can contribute up to 20–30% of total downstream processing costs. Additionally, the dependence on sunlight and large land areas further restricts the feasibility of microalgae-based wastewater treatment technologies in diverse environments. This study addresses three critical challenges associated with algae-based wastewater treatment. First, an innovative cultivation approach was developed to enable continuous wastewater treatment across two contrasting seasonal conditions—summer and winter. Second, the characteristics of wastewater post-treatment were analysed to identify fouling factors affecting the harvesting process. Third, a novel strategy was implemented to induce “hyper compensation” and “luxury uptake” of inorganic phosphorus by microalgae, achieving an exceptional phosphorus recovery rate of nearly 96%. To fully capitalize on the treated biomass, a novel bioplastic/bio-composite was developed by combining polylactic acid with phosphorus-enriched microalgae
Advancing Environmental, Social, and Governance (ESG) Assessment and Reporting: A Hybrid Framework of Semantic Modeling, Multi-Criteria Analysis, and Maturity Models
No full textEnvironmental, Social, and Governance (ESG) reporting is increasingly critical for corporate transparency and accountability as demands from investors, regulators, and the public grow. Despite its importance, ESG reporting faces persistent challenges, including fragmented standards, inconsistent metrics, misalignment with global goals like the UN SDGs, and limited relevance for stakeholders. These issues weaken benchmarking, data reliability, and decision-making, raising the risk of greenwashing. Although previous studies have explored drivers of ESG performance, there is still a clear need for technical solutions that enhance the quality and usefulness of ESG disclosures through integrated approaches.
This dissertation addresses these critical gaps by developing and proposing a novel, integrated framework that leverages the strength of semantic technologies, MCDM methods, and ESG maturity models. The research is structured through several interconnected studies, exploring specific industry applications using multi-criteria analysis techniques to identify and prioritize relevant ESG KPIs. A systematic literature review (SLR) provides a foundational understanding of existing ontology-driven solutions and their limitations in addressing reporting challenges and integrating quantitative methods. Based on these insights, the core contribution is the design of an ontology-based framework called ESGOnt. This framework utilizes a modular ESG ontology to standardize terminology, integrate fragmented data sources, and explicitly map ESG metrics to SDG targets.
This research contributes by addressing critical challenges in ESG reporting through a novel, integrated framework. It advances the understanding of how semantic models can be combined with quantitative methods to create robust, transparent, and actionable sustainability reporting systems, strengthening corporate accountability and supporting more effective contributions to global sustainable development
Unveiling Retail Dynamics: "Mining Predictive Insights and Customer Segmentation from Online Retail Data"
No full textAs e-commerce continues to grow at a rapid pace, the ability to comprehend, segment, and predict customer behavior has become the core of business success. The objective of this project report is to study customer segmentation and predictive modeling using data mining methods on real-life online retail datasets. Exploiting Recency-Frequency-Monetary (RFM), K-Means Clustering, and Predictive Modeling (Logistic Regression, RandomForest, XGBoost, Deep Learning), the investigation explores novel customer segments. It assesses model performance demanding high value from customers. Findings indicate useful implications for segment behavior with the Deep Learning model giving outstanding performance in this case (accuracy up to 87.4% and ROC AUC of 0.932). The RFM-KMeans segmentation approach exposes tactical marketing opportunities in different customer groups, such as Champions, At-Risk Customers, and Big Spenders. This paper proposes a pipeline for scalable and interpretable analyses that combine unsupervised/supervised learning methods to support targeted marketing, retention forecasting, and long-tail customer value maximization in digital retail environments
Physical aspects of symmetry breaking in Bose gases at thermal equilibrium
No full textThe theory of non-interacting Bose gases is supplemented by a numerical quantum field description with a two-dimensional non-local order parameter that allows the modeling of wave-like atomic correlations and interference effects in the limit of low atomic densities. From the present model, it is possible to explain symmetry aspects of non-interacting and very weakly interacting Bose gases in the limit of fluctuating particle numbers, like the forward propagation of time and the relation to the breaking and preservation of phase gauge symmetry in solids. In the present formalism, the propagation of one-directional time arises from the pre-defined and equivalent convergence of independent quantum fields towards the Boltzmann equilibrium, and it is shown that Glauber coherent states are related to the definition of the quantized field. Coherently coupling condensate and non-condensate parts as a direct consequence of the increasing quantum coherence time between the different quantum field components in the Bose gas from cooling to below the critical temperature, the present model describes symmetry breaking, which is originally known from the definition of a specific gauge field from Elitzur’s theorem for local gauge fields, as a global physical rather than a purely formal mathematical process