Open Access

Photovoltaics are a key technology in the transition toward renewable energy. Among emerging materials, poly(3-hexylthiophene) (P3HT) offers an attractive alternative to silicon owing to its solution processability, mechanical flexibility, and tunable optoelectronic properties. This thesis investigates how electric fields, mechanical deformation, and processing conditions influence the structural and photophysical properties of P3HT and the flexible substrate poly(ethylene terephthalate) (PET) using complementary time and frequency domain spectroscopic techniques. Femtosecond transient absorption pump-probe spectroscopy demonstrates that below-band-gap excitation directly generates polaron pairs (PPs) in P3HT. The transient response comprises fast PP and slower delocalized polaron pair (DPP) contributions. External electric fields modify the competition between DPP recombination and bimolecular annihilation, while uniaxial stretching up to 7% redistributes PP and DPP populations through polymer chain alignment. Above this strain, the transient response becomes increasingly complex owing to efficient strain transfer and microcrack formation. Raman and ultraviolet–visible spectroscopy correlate mechanical deformation with structural and optical changes. P3HT and PEDOT:PSS/P3HT films retain an essentially constant optical band gap up to approximately 7% strain, followed by a slight widening at 10%, indicating the onset of electronically significant deformation. Analysis of the Raman C=C and C–C stretching modes shows that variations in band position and full width at half maximum distinguish temperature-induced morphological changes from mechanically induced chain alignment. PET exhibits increased optical absorption and irreversible Raman spectral changes above approximately 5% strain, indicating molecular reorganization.

This dissertation examines how digital technologies can support the integration of discipline-specific language in accounting and taxation. Across three essays, it analyzes the acquisition, application, and analysis of specialized terminology. Essay I investigates a wiki-based collaborative glossary in introductory accounting education and shows that it improves students' academic performance and engagement with accounting terminology, while also revealing challenges related to coordination and content reliability. Essay II uses dictionary-based text analysis to create a cross-country dataset on corporate environmental tax legislation and finds that such taxes are shaped more by institutional and political factors than by environmental risks. Essay III develops and applies a domain-specific large language model to qualitative corporate tax disclosures, demonstrating that specialized LLMs outperform general-purpose models and traditional text analysis methods. Overall, the dissertation shows that digital technologies can enhance the acquisition, application, and analysis of specialized language in accounting and taxation. It contributes to accounting education, tax policy research, and methodological debates on the use of natural language processing in discipline-specific contexts.

(In)security in Africa influences African societies, geopolitics, and geoeconomics. Yet African perspectives on (in)security are rarely sought, and how African states shape their security policies remains unclear: conventional internal/external, domestic/foreign, and secure/insecure distinctions are outdated, while Western/Northern-centric social sciences and African Studies/IR literatures remain trapped in essentialisms, stereotypes, and mono-causal explanations. This thesis argues for new analytical devices to study African security policies in less essentialist and more context-sensitive ways by understanding the practice turn and appropriating Pierre Bourdieu's reflexive, practice-theoretical, and field-analytical sociology through an in-depth sociospatial reading. This helps interpret his concepts as global, interrelated thinking tools for African social formations, against a superficial Bourdieusian International Studies literature. Taking Nigeria as exploratory case study, the thesis traces how field and habitus intersect, develops a Field of Nigerian Security Policy (FNSP) as object and tool, and treats habitus and capital primarily as outcomes of the analysis. It asks how Nigeria shapes its security policy towards Boko Haram as a contemporary space of (in)security, drawing on almost four months of self-reflexive, ethnographic fieldwork. It combines sociological discourse analysis of textual sources with interviews with leading Nigerian experts and practitioners. It reveals how a FNSP is constituted by diverse agents, relations of (informal) cooperation, competition, domination, and transversality, a dual habitus of problems/scarcities and aspirations/necessities, and multiple (including negative) kinds of capital—and how agents' classificatory practices ultimately produce Nigeria's security policy towards Boko Haram. The thesis thereby advances African International Relations theoretically and offers deep practical insights into Nigeria's political space.

In intelligent and autonomous systems, users must often share private data with external platforms to enable advanced functionalities. A key challenge in this process arises when the data to be protected is not a single, precise value, but is instead inherently uncertain or represents a range of possible values. This thesis addresses the privacy of such uncertain data, which is formally represented and processed using set-based methods. We review existing privacy-preserving techniques and introduce new mechanisms to safeguard this data during processing on untrusted platforms, exploring both cryptographic and non-cryptographic approaches. Specifically, the thesis presents novel privacy-preserving mechanisms for set-based data processing, categorized by the data type within the sets. For sets of real-valued data, we introduce a differential privacy mechanism for set-based estimation in linear and non-linear dynamical systems. This approach protects the sensitive information contained within sets, such as zonotopes that model system uncertainties, while minimizing the loss of utility for the estimation process. For sets of discrete, binary data, we propose a mechanism that uses Fast Fully Homomorphic Encryption to ensure privacy. This method allows for the secure processing of sets represented as logical zonotopes in untrusted environments, maintaining computational practicality. The evaluations demonstrate the effectiveness and practicality of the proposed mechanisms across various applications in autonomous and intelligent systems.

Rare earth elements (REE, or REY including yttrium) are widely used in modern technologies and are increasingly released into aquatic environments. Their environmental behaviour and bioaccumulation in aquatic ecosystems remain poorly understood. This thesis investigates the bioavailability, bioaccumulation, and trophic transfer of both geogenic and anthropogenic REY using aquatic organisms and environmental samples from European freshwater and marine systems. Shells of three invasive freshwater bivalves (Corbicula fluminea, Dreissena polymorpha, and Dreissena bugensis) collected from seven major European rivers show strong REY bioaccumulation, with concentrations up to five orders of magnitude higher than in ambient water. Anthropogenic lanthanum contamination from the Rhine River was recorded in mussel shells, whereas no enrichment of anthropogenic gadolinium from MRI contrast agents was observed, suggesting its stability in freshwater systems. Further analyses of freshwater (Anodonta anatina) and marine (Mytilus edulis) mussels reveal higher REY concentrations in internal organs than in muscle tissues and shells, while biological processes exert only minor influence on REY fractionation. A trophic-level study along the Rhine River shows a general biodilution trend from primary producers to fish, while shale-normalised REY patterns remain consistent across trophic levels. These results indicate that mussels can serve as effective biomonitors for environmental REY contamination.

This thesis investigates the integration of sustainability assessment considering Industry 4.0 technologies and the use of Markov Decision Process capabilities. The manufacturing industry is facing increasing pressure to improve sustainability assessment performance, and Industry 4.0 technologies like Digital Twins, Internet of Things, Big Data Analytics, Cloud Computing, Machine Learning, and Artificial Intelligence have the potential to support these efforts. However, effectively integrating sustainability assessment goals and Industry 4.0 technologies within manufacturing systems can be challenging. The research addresses this challenge by developing a framework for optimizing the flow of operations in a manufacturing system while incorporating sustainability assessment and Industry 4.0 technologies effectively. The framework utilizes the Markov Decision Process to model the decision-making process of the manufacturing system and its decision-makers. From the other side, it includes sustainability assessment goals as constraints or objectives in the Markov Decision Process model. The use of Industry 4.0 technologies is integrated into the framework to gather data and optimize the decision-making process based on that data. The thesis begins by reviewing the literature on sustainability assessment, Industry 4.0 technologies, and their impacts with regard to manufacturing systems. The proposed framework is then presented, and its capabilities are demonstrated through case studies of single and multiple agents on a shop floor. The trend in pioneer manufacturing firms is to implement new technological applications on their shop floor to agile their Manufacturing Execution System. The findings from the case study indicate that the proposed framework can effectively support decision-making at the top-tier level of the enterprise by integrating sustainability assessment and the Industry 4.0 paradigm.

Modern online courses often replicate traditional instruction as static artifacts, failing to reveal the cognitive causes of learner errors. This paper proposes a self-improving educational ecosystem integrating interactive modules, diagnostic assessments, and AI-driven analytics in a closed feedback loop. The model is implemented on a real platform using WordPress as a flexible application framework. Each module combines theory, interactive practice (H5P), and diagnostic assessment. Natural-language queries to an AI assistant serve as diagnostic signals, revealing hidden cognitive barriers. A three-level management model separates operational support (AI tutor), pedagogical quality assurance, and strategic product development. Continuous improvement follows a four-stage cycle: signal collection, pattern analysis, targeted instructional adjustments, and impact verification. This approach demonstrates that intelligent, evidence-based learning management can transform courses into self-correcting systems, where each cohort improves the experience for the next.

Digital tools are increasingly used in inclusive early childhood education and care (ECEC) to support children’s social skills — especially communication, emotion recognition, self-regulation, prosocial behavior, and peer interaction. Across Europe, policy momentum for inclusive digital education is accelerating, while research is expanding from general “screen time” debates toward evidence-based, developmentally appropriate, educator-mediated designs. This paper synthesizes current trends, highlights the European and German context, and proposes a feasible mixed-method study plan to evaluate digital social-skills interventions in inclusive ECEC settings. The review maps tool categories (tablet apps, serious games, social robots, multimodal platforms, and digital assessment/screening), describe equity and accessibility design principles, and identify evidence gaps (long-term outcomes, implementation fidelity, child-led vs. adult-guided interaction, and inclusion of multilingual/migrant families). We propose a pragmatic evaluation framework aligned with European priorities for accessibility, quality, and teacher capacity-building.

The research essay examines the opportunities and barriers to implementing artificial intelligence (AI) in the education system of the Republic of Kazakhstan based on a comparative analysis of the experience of Germany. The aim of the work is to identify key factors influencing the successful integration of AI tools into educational practice, including regulatory, methodological, technological and personnel aspects. The paper analyzes international and national strategies for regulating AI, features of the legal framework of Kazakhstan and the European Union, as well as models of teacher training. Special attention is paid to the issues of academic integrity, ethics, personal data protection and overcoming the digital divide. Based on the German experience, we formulate systematic recommendations for Kazakhstan aimed at developing institutional teacher training, reforming the assessment system and forming national AI sovereignty in education. The forecast of AI development in the educational system of Kazakhstan until 2029 is made, emphasizing the need to move from fragmented technology implementation to a sustainable, ethically verified and inclusive model of digital education.

This paper examines the integration of artificial intelligence (AI) into the education system, focusing on its key opportunities and challenges in the context of the Fourth Industrial Revolution. Artificial intelligence has become an important tool for modernizing education, enhancing learning quality, supporting personalized instruction, and improving educational management processes. The study is based on a comparative analysis of the experiences of Germany and Kazakhstan in implementing AI in education. The German model emphasizes strategic planning, teacher training, and ethical and legal regulation, while Kazakhstan’s approach focuses on accessibility and rapid implementation through widely used EdTech platforms. The findings indicate that the effective use of artificial intelligence depends not only on technological infrastructure but also on teacher readiness, data security, and ethical responsibility. The paper highlights the potential of AI to transform education and identifies the key conditions for its sustainable and balanced integration.

This technical report provides a comprehensive analysis of the process of digital transformation in the education system of Kazakhstan, particularly in the context of the implementation gap between the standards of digital competence and the current state of the education system. The research is based on the application of two main methodological models for the evaluation of the level of educator competence, in which the European Framework for the Digital Competence of Educators is considered the scientific basis for the analysis of the areas of professional growth, while the TPACK model is applied to evaluate the efficiency of the educational process in the context of the intersection of technology, pedagogy, and subject matter content. The analysis of the empirical data of the TALIS-2024 study allows for the qualitative comparison of the current state of the education system in Kazakhstan with the global trends in the context of OECD countries. It is stated that the educators of Kazakhstan exhibit a high level of "Digital Optimism," in which the level of teacher confidence in the level of technological knowledge is recorded at 75.12 points, which is significantly higher than the OECD average of 70.1. However, it is important to note that, in spite of the high level of self-assessment, there is a significant need for professional growth, in which 46.59% of educators require the acquisition of basic ICT skills. The progress in the development of the education system is limited by the "infrastructure ceiling," in which there is a lack of digital resources in 22.83% of the schools and unstable internet access in 22.69% of the educational environment. The research also further highlights how Kazakhstan’s most important asset in this process of change is its already existing culture of professionalism and mutual support. It has also been recommended that, while systemic changes are necessary at a national level, teachers themselves need to take an active role in developing their own personal Digital Roadmap. This involves a systematic process of self-assessment through the DigCompEdu SELFIE tool, with a further need for teachers to develop their Technological Knowledge (TK) in order to more effectively integrate this into TPACK. In conclusion, it has been established how Kazakhstan already has a robust foundation of digital optimism, with a world-class culture of teacher support. By closing the already existing infrastructure gap, it is considered that the potential for digital excellence within schools throughout Kazakhstan is limitless.

This longitudinal research paper investigates digital transformation of the traditional education sector of Kazakhstan as it pertains to Edtech (educational technologies) and progressive use of artificial intelligence (AI) in classrooms by developing a strategic approach to the creation of an eco-system of education on a national level. The focus of the study is to investigate the barriers that need to be addressed to enable Kazakhstan to achieve its goal of transitioning to an eco-system of education. Research Problem: The current situation in Kazakhstan is that billions of dollars have been invested in the creation of a digital infrastructure to support the implementation of digital initiatives throughout the country. However, the country has been unable to bring its diverse digital initiatives together into a cohesive programme due to the fact that there is still a gap between the ability of the country to implement technology and the improvement of educational outcomes of its students. The primary hindrance to bringing together the successful use of all of the technologies being implemented is due to a lack of a comprehensive strategy to integrate and govern the use of technology in education. Methodology: To develop the recommendations for transitioning Kazakhstan to the creation of an eco-system of education, the researchers utilised a comparative analysis of three strategic models of Edtech governance from leading educational systems of Singapore, Finland, and Germany. The rationale for choosing these three countries is based on the differences in their methods of implementation in education.

This dissertation focuses on the identification and characterization of efflux pump inhibitors targeting the main tripartite efflux pump in Escherichia coli, AcrAB-TolC. Tripartite efflux pumps are integral membrane complexes that confer antimicrobial resistance to Gram-negative bacteria by extruding antibiotics. Inhibiting efflux systems with small molecules represents a promising strategy for extending the spectrum of antibiotics, and restoring antibiotic susceptibility in multidrug-resistant bacteria. However, no efflux pump inhibitors have been approved for clinical use so far. Two substances, LP-115 and carmofur, that represent a basis for the development of novel efflux pump inhibitors were discovered, while postulated AcrA inhibitors were shown to be non-specific binders. LP-115 was identified employing an in silico repurposing screen targeting the outer membrane factor TolC followed by microbiological validation and deconstruction of a hit compound into fragments. Binding to TolC and AcrB was confirmed using MST, and a ligand-induced destabilization of the efflux pump complex assembly was observed using dynamic light scattering. Cryo-EM provided detailed molecular insights into the binding site at the AcrA-TolC interface. Our results suggest that LP-115 is an efflux pump inhibitor with a novel mechanism of action that consists of disrupting the AcrAB-TolC efflux pump assembly. Carmofur was identified employing a microbiological repurposing screen focusing on antimicrobial potentiating effects, followed by microbiological and biophysical characterization of the interaction with the isolated efflux pump subunits using microscale thermophoresis, nano differential scanning fluorimetry, and dynamic light scattering. The synergistic activity of carmofur in combination with an AcrAB-TolC substrate was TolC-dependent and specific binding to TolC was observed. Thus, carmofur could be used as starting point for the development of novel efflux pump inhibitors.

Climate change driven by anthropogenic CO₂ emissions requires effective mitigation strategies. Negative emission technologies (NETs), particularly ocean alkalinity enhancement (OAE), are promising because they increase ocean alkalinity and promote CO₂ sequestration. This dissertation examines how marine molecular biology can help assess ecological risks and the overall efficacy of OAE. It presents two risk assessments on bacterial community responses to alkalinity exposure and develops a framework for a novel biological proxy for monitoring, reporting, and verification (MRV) in OAE. Chapter 1 provides a general introduction. Chapter 2 investigates how gradually increased alkalinity affects pelagic bacterial communities using a mesocosm experiment with 16S rRNA gene sequencing and flow cytometry. Results show high structural resilience, but quantitative shifts in bacterial abundance linked to phytoplankton dynamics indicate indirect ecological effects of OAE. Chapter 3 expands this work by comparing two OAE strategies: olivine dissolution and direct dissolved alkalinity addition. A mesocosm experiment assessed microbial responses in seawater and oyster gills (Ostrea edulis). Olivine increased pollution-tolerant and biofilm-forming taxa, while dissolved alkalinity caused minimal change. These findings suggest that dissolved alkalinity below 500 µmol L⁻¹ is a relatively safe OAE approach. Chapter 4 proposes carbonic anhydrase (CA), a key enzyme in marine carbon cycling, as a biological proxy for evaluating OAE performance. Structured hypotheses outline how CA expression and activity assays could support future OAE MRV systems. The chapter recommends shifting resources from broad bacterial community assessments toward investigating how alkalization affects CA.

Industrial Demand Response (IDR) systems have emerged as a key enabler for enhancing grid flexibility, particularly as industries face increasing pressure to optimize energy consumption and integrate with renewable energy sources. However, despite their potential, the adoption and scalability of IDR solutions are limited by a range of technical, infrastructural, and organizational challenges. This dissertation investigates how emerging digital technologies—namely, Artificial Intelligence/Machine Learning (AI/ML) and the computing continuum (edge, fog, and cloud computing)—can be leveraged to overcome these limitations and enable scalable, intelligent, and interoperable IDR architectures. The study addresses three core research questions. First, it develops a taxonomy of barriers to IDR adoption, distinguishing between technological and non-technological constraints. Second, it explores how distributed computing paradigms can mitigate these challenges by enabling real-time, privacy-aware, and latency-sensitive decision-making across industrial sites. Third, it examines the synergistic integration of AI/ML within the computing continuum, emphasizing methods such as federated learning, transfer learning, and multi-agent reinforcement learning to overcome issues related to data sparsity, system complexity, and semantic heterogeneity. A reference architecture for IDR aggregators is proposed, combining layered intelligence, semantic interoperability, and orchestration mechanisms. This architecture is mapped to real-world cloud and open-source platforms to demonstrate its practical applicability. The findings confirm that the integration of AI/ML and distributed computing is not only feasible but essential for advancing the resilience, autonomy, and responsiveness of future industrial energy systems.

This dissertation presents the synthesis of novel reduced POMs functionalized with dimethylarsinate groups. Chapter 1 introduces POMs, emphasizing polyoxomolybdates. Chapter 2 reviews organofunctionalized POMs and the rationale for this study, building on prior work with dimethylarsinate-functionalized molybdenum POMs. Chapter 3 covers experimental methods, characterization techniques, and synthesis of heterophosphonic and arsonic acid ligands. Chapter 4 describes the synthesis and characterization of eight dimethylarsinate-functionalized phosphomolybdates(V), [RPMoV6O15(OH)3{AsO2(CH3)2}3]2− (R = H, HO, CH3, HO2CCH2, HO2CC2H4, C6H5, 4-FC6H4, 4-F3COC6H4), the monoanionic mixed-valent heptamolybdate [HOMoVIMoV6O15(OH)3{AsO2(CH3)2}3]−, and d-block metal-substituted analogues [MPMoV6O15(OH)3{AsO2(CH3)2}3]2− (M = Fe2+, Ni2+, Mn2+). Chapter 5 focuses on the synthesis, structural features, and antibacterial properties of eleven dimethylarsinate-functionalized arsenomolybdates(V), [RAsMoV6O15(OH)3{AsO2(CH3)2}3]2− (R = HO, CH3, C2H5, C6H5, 3,5-(HOOC)2C6H3, 4-FC6H4, 4-F3CC6H4, 4-F3COC6H4, 4-BrC6H4 and 4-N3C6H4) and [AsIIIMoV6O15(OH)3{AsO2(CH3)2}3]3−. All compounds were synthesized in aqueous media and characterized in the solid state by single-crystal X-ray diffraction, TGA, elemental analysis, FT-IR, and PXRD, while their stability in solution and the gas phase was probed using multinuclear NMR (1H, 31P, 19F, 13C), ESI-MS, ion mobility MS, and MS/MS. Chapter 6 describes the synthesis and characterization of a novel heterometallic, dimethylarsinate-capped wheel-type POM, MoV12WVI18O84{AsO2(CH3)2}18]18− (Mo12W18), prepared under mildly acidic aqueous conditions, with alternating MoV2 and WVI3 units forming a ring with a ~1.5 nm central cavity. Its solid-state and solution behavior were probed using single-crystal XRD, IR, TGA, elemental analysis, MAS and CPMAS NMR, multinuclear solution NMR (1H, 13C, 183W, DOSY), UV-Vis, Raman spectroscopy, and SAXS.

This thesis investigates consistency challenges in distributed stream processing systems. Prior work on this topic has made significant progress, with many ideas being implemented in state-of-the-art Stream Processing Engines (SPEs). In this thesis, we focus on formal modeling to better characterize existing problems and explore potential improvements. We introduce a formal model of delivery guarantees and show that deterministic SPEs can theoretically achieve lower latency than non-deterministic ones for exactly-once guarantee. This is supported by experimental results demonstrating that a novel deterministic implementation performs better than current alternatives. The thesis also presents a formal model for substream management, identifying a lower bound on the additional network traffic required for detecting substream termination. A corresponding framework is implemented that meets this bound and demonstrates improved performance over existing approaches. These results contribute formal foundations and practical techniques for improving the performance and predictability of distributed stream processing systems.

The relationship between network structure (structural connectivity, SC) and network representations of dynamics (functional connectivity, FC) is a topic of high scientific interest both for advancing theoretical understanding of complex systems and for its relevance to a wide range of applications. In this thesis, correlations between structural and functional connectivity were investigated distinguishing between synchronous and sequential activity of the nodes. The primary analysis encompasses applying different dynamical models to network architectures to explore how SC/FC correlations are shaped by variations in network topology, coupling strength, and intrinsic system parameters across excitable, chaotic, and oscillatory dynamics. A more detailed investigation was conducted on regular graphs of coupled logistic maps. Symbolic encoding of the initial dynamics was used to construct equivalent cellular automaton models, followed by an analysis of the structure of their resulting attractors. The influence of noise on SC/FC correlations was also explored. Finally, SC and the two types of FC were conceptualized in a hydrological case study. Structural and functional networks were constructed from data collected in the Walnut Gulch Experimental Watershed (Arizona, USA). SC/FC correlations served as metrics to describe event-level hydrological responses of the watershed after various rainfall events, and their relationships to hydrological quantities were analyzed.

The innate immune response is the first line of defense against viral infection. Host pattern recognition receptors (PRRs) detect pathogen-associated molecular patterns (PAMPs) and trigger several signaling pathways following viral infections. One such pathway, the cGAS-STING pathway, detects cytosolic DNA to induce the production and secretion of type I interferons. The cGAS-STING pathway is activated by the presence of cytosolic DNA. cGAS (cGMP-AMP synthase), a cytosolic DNA detector, binds double-stranded DNA, dimerizes, and catalyzes the production of the second messenger cyclic GMP-AMP (cGAMP) from GTP and ATP. STING, an ER adaptor protein, binds to cGAMP and becomes activated through dimerization, which results in its trafficking from the ER to the Golgi. At the Golgi, STING recruits the kinase TBK1 and the transcription factor IRF3. TBK1 phosphorylates STING, itself, and IRF3. Phosphorylation activates IRF3, causing its translocation to the nucleus, where it induces the production of type I interferons. To counteract the host immune response, human cytomegalovirus (HCMV) encodes several immunoevasin proteins. HCMV glycoproteins such as US6 inhibit antigen presentation by blocking peptide transport via the transporter associated with antigen processing (TAP). The region spanning amino acids 89–108 of US6 was identified as responsible for TAP inhibition. Our studies have identified a novel interaction and function of US6. For the first time, we show that US6 interacts with the host p24 proteins TMED2 and TMED10. US6 inhibits the cytosolic DNA-triggered cGAS-STING pathway and reduces the production of IFNβ1. We have discovered a correlation between the binding of US6 to TMED2 and TMED10 and its ability to inhibit the production of type I interferons. Using sequential mutants, we show that US6 possesses two distinct and separable regions responsible for its functions. Microscopy reveals that US6 delays STING trafficking from the ER to the Golgi.

The transition toward sustainable urban mobility and industrial efficiency requires decision-making tools that go beyond correlation-based analysis to uncover true cause-and-effect relationships. Traditional machine learning models, while effective for prediction, often act as "black boxes," lacking interpretability and failing to reveal the mechanisms underlying complex systems. To address these limitations, this dissertation introduces a modular Causal AI framework for smart decision-making, integrating causal discovery and inference with structured domain knowledge to enhance sustainability outcomes. The framework is validated across three key domains: (1) urban CO2 emissions, (2) shared mobility demand, and (3) SME energy use. The first case study analyzes over 500,000 vehicles to uncover how engine performance and maintenance drive urban emissions. The second study examines shared bike systems, identifying causal impacts of weather patterns, station topology, and temporal demand fluctuations, supporting more adaptive fleet operations. The third applies the framework in a manufacturing SME, identifying the root causes of energy inefficiency and enabling targeted interventions to improve operational performance without compromising productivity. This research advances the interpretability and actionability of AI in sustainability contexts by replacing opaque predictive models with transparent, evidence-based causal reasoning. Algorithms such as PC, FCI, GES, and DirectLiNGAM are employed alongside domain ontologies to uncover valid causal relationships and support decision-making. A hybrid approach also addresses feature selection, dimensionality reduction, and model explainability, making the methodology broadly applicable across diverse sustainability challenges. While the framework demonstrates strong applicability, future work may focus on enhancing real-time scalability, adaptive ontology integration, and broader validation across domains such as electric mobility and smart energy systems. Overall, this thesis contributes a generalizable, interpretable Causal AI framework that enhances systemic understanding and supports sustainable transformation in policy, planning, and industrial decision-making.

Energy 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.

This project investigates customer behavior and pricing dynamics in the context of online apparel sales, using a real-world clickstream from a European e-commerce platform. Through a structured process of exploratory data analysis and predictive modeling, the study explores how product visuals, attributes, and browsing behavior influence both purchasing patterns and pricing outcomes. Random Forest models were used for both regression and classification tasks to predict product prices and classify items into budget or premium tiers. These model outputs were then combined to detect potential pricing–perception mismatches, where a product appears over- or underpriced relative to how it is perceived based on its features. This approach helps highlight products that may benefit from a review of their pricing or presentation strategy. Key insights from the analysis show that products with frontal model photography tend to perform better in both pricing and sales, while black and blue items generate the highest revenue. These findings lead to clear business recommendations in areas such as pricing, visual merchandising, and product positioning. The project demonstrates how machine learning can be applied not only to forecast outcomes but also to guide practical, data-driven decisions in e-commerce.

This study conducts an extensive sentiment analysis of 7,357 English Tesla-related tweets using an XGBoost classifier, addressing the critical need to understand public perception of innovative companies in the electric vehicle (EV) sector (Jain et al., 2019). The methodology involves advanced preprocessing with tweet-preprocessor and NLTK, feature engineering using TF-IDF (2,000 features) and weighted VADER sentiment scores, and model optimization via GridSearchCV with SMOTE balancing (Chawla et al., 2002). The model achieved an accuracy of 71.67% and a macro F1-score of 67.73% ± 5.97%, with a sentiment distribution of 37.31% negative, 30.58% neutral, and 32.11% positive. Theoretical assumptions explore the impact of social media on EV sentiment (Thelwall et al., 2010), while results and discussions highlight model performance and Tesla-specific insights (Chen & Guestrin, 2016). The study concludes with implications for EV marketing and future research directions in NLP.

Tests and proofs are two main techniques in modern software verification. To test a program means running the program to check if its execution yields an expected outcome. To prove a program is to build a mathematical proof, showing the correctness of the program against its desired properties. In the traditional view, however, tests and proofs are considered as two incompatible techniques. They are often treated as warring siblings and mostly applied in isolation. The complementarity of tests and proofs — though not immediately apparent — has been relatively underexplored. Can their combination mitigate each other’s weaknesses while harnessing their respective strengths? This thesis tries to straddle the border between tests and proofs and suggest a concrete answer. It explores how the two approaches can collaborate with and mutually benefit one another. Three key contributions arise from this exploration. The first contribution is Proof2Test, a framework that transforms failed proofs into useful test cases, allowing programmers to use tests to debug failed proofs effectively. The second contribution consists of several proof-based test generation strategies, which use proofs to enhance both the efficiency and effectiveness of test generation. This thesis also extends SC with “loop unrolling”, considering not just zero or one but any number of iterations, up to a set limit. It also includes an empirical study to examine how much (if anything) testing strategies miss when they limit themselves to standard branch coverage and, conversely, how many more bugs we can find if we unroll loops. The last contribution of this thesis is an automatic program repair approach, Proof2Fix, which takes advantages of the proposed test generation methods to produce meaningful corrections to faults revealed by proofs.

Entrepreneurship 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.

Small business lending is significantly influenced by the interplay between the information institution types, the information models, and the due diligence process undertaken by financial institutions. While previous research has examined the impact of hard and soft information on credit availability, limited attention has been given to how different combinations of these information types and various financial institution types influence the quality of loan applications and the loan application success rates. Additionally, the role of due diligence in assessing small business loan applications and the specific signals lenders rely on for decision-making remain underexplored. This study integrates insights from three research streams to provide a unique analysis of small business lending dynamics. First, based on primary data collected from 242 small firms in Ghana and considering different financial institutions, including non-banks, we examine the effect of three distinct combinations of hard and soft information on loan application success rates. Our findings challenge conventional wisdom, indicating that an increased emphasis on soft information does not necessarily enhance transparency or improve loan application success rates. Moreover, while small-sized banks positively influence credit availability, this effect does not extend to small-sized non-banks. Second, leveraging signal theory, we analyze the due diligence process undertaken by financial institutions through qualitative insights from 24 loan officer interviews. We identify 11 key hard and soft signals that influence credit risk assessments, including key person risk, change in leadership risk, articulation of company value, adaptation to change, data accuracy, and completeness. These insights highlight the multifaceted nature of credit decision-making.

This dissertation contributes to three distinct focuses of BF design as: BF for flexible connectivity, BF for enhanced connectivity, and BF for over-the-air-computating (AirComp). The first objective is the “BF for flexible connectivity”. In order to realize flexibility for preserving connectivity regardless of the user position in the coverage area, the recent cell-free MIMO (CF-MIMO) system is considered. For the BF design, a flexible design is proposed, which is directly adaptable not only for both uplink (UL) and downlink (DL) communication modes but also for both under-loaded and over-loaded scenarios. As for the second focus, termed “BF for enhanced connectivity,” a novel BF design compatible with three distinct power allocation schemes is proposed. For the sake of connectivity enhancement, a DL MIMO-rate splitting multiple access (RSMA) system is considered. Even though the proposed BF design is assumed to be used only for under-loaded or fully-loaded scenarios, the computational complexity required to design BF is significantly less than that of the state-of-the-art (SotA) alternative. Finally, the ”BF for Over the Air Computing” is considered for envisioning the realization of integrated AI and communication. In this focus, novel receiver (RX) BF designs compatible with uniform-forcing (UF) precoding for a multi-user UL multiple-input single-output (MISO)-AirComp system are considered for higher performance or lower complexity. Toward higher performance design, while the proposed design sacrifices computational complexity in the BF, the resulting AirComp has a lower mean square error(MSE) performance. On the other hand, the proposed BF design for lower complexity realizes equivalent MSE performance achieved by the high-performance BF design with significantly lower complexity thanks to the combination of recent convex optimization and Bayesian optimization (BO) methods.

In this research, I explored the geology of igneous intrusive domes in the Moon and Mars. These structures have not been widely investigated outside Earth, mainly due to the difficulty in locating them. I decided to do a detailed analysis of two systems: the Valentine Domes on the Moon, and the Utopia Planitia Dome field on Mars, focusing on their properties at the surface. I followed a cartographic approach in this research, using geostratigraphic units to characterize the locations and define their geological evolution. While analyzing the Valentine Domes, I noticed the lack of an open-source tool to work with the spectral data of the Moon, this led to the creation of the MoonIndex library, a tool to process spectral cubes and generate spectral indexes for the Moon. With the aid of MoonIndex, I performed the geological analysis of the Valentine Domes. The first result was the discovery of a new dome, which was detected by using the aspect parameter. I also found that several smaller structures such as rilles, dykes, and secondary domes are associated with the main domes. The dome field in Utopia Planitia is different from the lunar location, hundreds of domes were emplaced in a large area. The study of the domes showed they originated from an intrusive-to-extrusive system, since their shapes range from cryptodomes to volcanic domes. The lunar and Martian domes show some similarities, they are basaltic, have a small incidence in the surface morphology, and their parental magmas took advantage of structural features to reach the surface. However, the genesis of the system is different. The Valentine Domes formed under a polygenetic style, while the dome field in Utopia Planitia originated in a monogenetic system. This research will open the door to discovering new intrusive systems and to better understand the ones already known.

Utilizing agro-industrial waste as a raw material for the production of chemicals, fuels and materials could support a circular bioeconomy, helping to minimize carbon and energy loss. Pectin-rich biomass is a generally under-utilized feedstock, and includes residues such as citrus peel, apple pomace and sugar beet pulp. It offers itself as a feedstock for biotechnological production processes using microorganisms as a cell factory. Unlike other biomass residues, this waste is advantageous due to its high sugar and low lignin content. One key component of pectin is D-galacturonic acid (D-GalUA), an oxidized substrate that S. cerevisiae cannot metabolize naturally. To enable its consumption, S. cerevisiae was equipped with the catabolic pathway naturally present in filamentous fungi. However, this did not allow D-GalUA to serve as sole carbon and energy source. The inability to grow on D-GalUA was thought to be due to a lack of electrons, as the pathway requires NAD(P)H. In this work, this problem was addressed by providing a co-substrate - glycerol - a by-product of the biodiesel industry. The electrons provided in this way were supposed to not only enable D-GalUA utilization but also support its fermentation to ethanol. Therefore, S. cerevisiae was equipped with both the fungal pathway for D-GalUA catabolism and the ‘DHA pathway’ for glycerol utilization – the latter channeling electrons from glycerol oxidation into cytosolic NADH. The resulting strain not only consumed D-GalUA at a high specific rate, but also co-fermented the substrates into ethanol, achieving a maximum yield of 71% of the theoretical maximum. Additionally, the native Gcy1, a non-specific aldo-keto reductase, was found to convert D-GalUA into L-galactonate, an intermediate of the D-GalUA catabolic pathway. By providing valuable insights into the co-utilization of glycerol and D-GalUA, this study lays the foundation for future endeavors towards the valorization of these two industrial by-products.

Wireless communications technology has gone through a significant evolution since its inception in the late 19th century, integrating itself as a critical pillar of modern society and the functions of the world. Successive generations of the mobile network systems since the first generation (1G) until the present have seen an ever-increasing demand of the key performance indicators (KPIs), such as data rate, reliability, spectral effciency, device connectivity, and more – outlined by the scope of the current fifth generation (5G) systems as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine- type communications (mMTC), to support the emergent use cases such as autonomous and intelligent networks, extended reality (XR) applications, and Internet-of-Things (IoT), under the enabling technologies such as millimeter-wave (mmWave)/Terahertz (THz) bands, massive multiple-input multiple-output (mMIMO), cell-free MIMO (CF-MIMO), reconfigurable intelligent surface (RIS), and more. Furthermore, the imminent beyond fifth generation (B5G) and sixth-generation (6G) systems anticipate even higher requirements and ambitious paradigms in wireless technologies, aiming to improve upon the performance of 5G but also addressing other challenges which have assumed greater importance for the future, such as resource effciency (spectrum, energy, and hardware), physical layer security, system scalability, and high-mobility. In summary, this thesis entails a comprehensive investigation into the realisation of beyond fifth generation (B5G), underpinned by the two major topics of resource-effciency and low-complexity within the frameworks of the two selected enabling technologies, index modulation (IM) and integrated sensing and communications (ISAC), thereby proposing novel methods and analyses from unique yet complementary perspectives of the addressed problem of achieving scalable and high-performance next-generation wireless systems.

Since the invention of the first synthetic polymer in 1907, plastics have revolutionized industries but have also caused significant environmental challenges. Over 170 trillion plastic particles are estimated to float in the world's oceans. Once in the marine environment, these plastics fragment into smaller particles (microplastics, MPs, <5 mm) under environmental forces, contaminating ecosystems worldwide, including the remote Arctic. This thesis investigates the Norwegian Coastal Current (NCC), a key transport route for MPs from northern Europe to the Arctic Ocean. Using novel sampling devices and advanced micro-Fourier transform infrared microscopy (μFTIR), small MPs (SMPs, 11–300 μm) were analyzed in seawater and sediments, providing the first detailed assessment of their spatial and temporal distribution in the NCC. The results reveal the prevalence of SMPs from surface seawater to deep sediments, including layers deposited before the advent of plastics. Key findings include a relatively homogeneous horizontal distribution of SMPs in surface and subsurface seawater and significant variability in sediment concentrations (54–12491 MP kg⁻¹) across cores. SMP accumulation trends in post-1950 sediment layers varied, challenging their reliability as markers of the Anthropocene. A total of 21 polymer types were identified, with smaller size classes dominating, highlighting their ecological significance. Further analysis of data representativeness revealed significant variability in MP concentrations and polymer diversity across stations, emphasizing the need for standardized protocols to ensure reliable data. Despite being based on a single research cruise, this study provides a valuable snapshot of SMP distribution in the NCC. The findings critically evaluate current MP research practices and highlight the need for robust methodologies to improve the reliability of future studies.

Steel manufacturing is characterized by its high energy consumption and the production of high-value-added products. In real-world steel production, unforeseen events frequently disrupt schedules, emphasizing the critical need for effective and adaptive planning to ensure continuous operations. This study makes significant contributions to the steel industry by offering new approaches to improve efficiency, streamline operations, and optimize production processes, ultimately driving advancements in steel manufacturing performance. To address the challenges inherent to EAF-based steelmaking, a seamless pipeline of algorithms has been developed. This pipeline works together to enhance the manufacturing planning process by providing an accurate chemical condensation of molten steel and classifying this outcome according to the most feasible steel grade, which can be obtained with a minimum of purification. Finally, the pipeline reschedules the planning process with the objective of maximizing machine utilization. To achieve these goals, the steelmaking stage key performance indicators (KPIs) that have the most impact on the quality of the final product were first identified. In the next step, a novel prediction algorithm utilizing a multilayer feedforward neural network was developed to estimate key quality parameters. Finally, to enhance the pipeline's resilience to disruptions, a Genetic Algorithm (GA) is employed to mitigate the impact of scenarios where processing times for jobs vary and do not align with the established schedule. This is a prevalent disruption event that renders the base schedule infeasible. The proposed algorithm aims to minimize total completion and waiting times, thereby enhancing operational efficiency and minimizing manufacturing costs.

Nitric oxide (NO) is a small gaseous molecule with important functions in cell biology and atmospheric chemistry owed to its unique physical and chemical properties. Since its relevance in biology was established, research on NO has focused primarily on its roles as signaling molecule, cytotoxin, and metabolic intermediate. Indeed, as a free radical and highly reactive compound, NO is as a potent toxin that can inhibit microbial growth, however it also has a central position in the microbial nitrogen cycle as a key intermediate in processes such as denitrification, aerobic ammonia oxidation, anaerobic ammonium oxidation, and nitrite-dependent anaerobic methane oxidation. Additionally, NO is a very energy-rich molecule with a high redox potential (NO/N2O; E0’ = +1.175 V) and it may have played a key role in the evolution of life on early Earth and the bioenergetic pathways related to modern denitrification and aerobic respiration. During recent years, we have been presented with new roles of NO in the nitrogen cycle. It appears as if the focus of NO research has slowly started to change its course as we begin to recognize its potential as direct substrate for microbial growth. Given its important roles in past and present microbial life, we believe that there must be a plethora of microorganisms that are capable of growing on NO conversions. Therefore, the main goal of my PhD project was to challenge our understanding of NO as mere toxin and intermediate, and investigate its potential as direct energy source for microbial life, whether it is through known or novel biochemical reactions, and the microorganisms that use it for this purpose, using a combination of continuous and batch incubations, physiological experiments, and multi-OMIC analyses.

The rapid rise of new technologies, such as mobile phones, social networks, and increased internet access, has created new opportunities for retailers to expand through omnichannel strategies, which aim to provide a seamless customer experience across different channels. While omnichannel can offer benefits and competitive advantages, it faces challenges like price inconsistencies and poor information sharing. Despite its growing recognition, its integration with Product Lifecycle Management (PLM) is underexplored. This thesis investigates the overlap and mutual effects of omnichannel and PLM, particularly emphasizing the importance of data and knowledge sharing between them. As research into data analytics in omnichannel evolves, tracking and tracing mechanisms in PLM become increasingly critical. The main objective of this study is to develop a bi-directional framework connecting omnichannel and PLM using a System Dynamics approach. This framework aims to incorporate omnichannel mechanisms into PLM and provide cause-and-effect analysis to better understand the operational role of omnichannel within PLM. Additionally, the research introduces an approach to integrate Business-to-Business (B2B) aspects from PLM with Business-to-Customer (B2C) elements through omnichannel strategies. Leveraging omnichannel's influence on touchpoints like influencers and social media, this approach helps companies align consumer behavior with strategic goals, boosting competitiveness. Ultimately, this thesis aims to bridge the gap between omnichannel and PLM, driving a shift in consumer behavior and strengthening the integration of B2B and B2C in retail.

Methane is the most abundant hydrocarbon on Earth, and plays a vital role in the global carbon cycle. In marine ecosystems, large quantities of produced methane are oxidized by methane-oxidizing microorganisms before it reaches the atmosphere. Aerobic methanotrophic bacteria, which consume methane in the upper oxic layers of marine sediments and the water column, represent the final oceanic methane filter. To date, the majority of marine aerobic methanotrophs remains uncultivated, with currently only nine formally described cultures. This hinders our understanding of their physiology that ultimately controls their activity and affects the dispersal of these methanotrophs in nature. In order to fill this gap in knowledge, the present work was focused on the isolation and characterization of marine methanotrophs from the North Sea and the Western Scheldt estuary sediments. The obtained methanotrophic cultures were investigated in physiological tests, and their metabolism was reconstructed based on high quality genomes. The isolation of four new methanotrophic species affiliated to the genera Methyloprofundus and Methylomarinum allowed to determine specific ecophysiological preferences and key conserved and distinct features within these genera. The isolate of Methylomarinum sarcina B3 exhibited a sarcina-like cell organization, which has not been previously reported for any marine methanotroph. The unusual Embden-Meyerhof-Parnas pathway identified in the new Methyloprofundus spp. could potentially serve as an alternative to the canonical glycolytic route. Finally, the discovery of a new putative nitrate reductase in Methyloprofundus spp. could have important implications for the understanding of the diversity of bacterial nitrate reductases and anaerobic respiration. Altogether, this work has advanced the characterization of the Methyloprofundus and Methylomarinum genera and laid the foundation for future research in microbial carbon and nitrogen metabolism.

The thesis comprises three main sections, in which the central theme revolves around the supramolecular reporter pairs. The first and second sections aim to design intricate coupled reactions, namely supramolecular tandem enzyme membrane assays and supramolecular membrane enzyme assays, achieving the simulation of continuous physiological processes. In the supramolecular tandem enzyme membrane assays section, we describe the usage of two supramolecular reporter pairs, with one set located inside the vesicles and the other set positioned outside the vesicles, to mimic the simple process of digestion and absorption. The second section, the supramolecular tandem membrane enzyme assays, focuses on simulating the processes of cellular uptake and metabolism using a single set of reporter pair located within the vesicles. In the uptake process, the transport of substrates across the vesicle membrane is monitored by the internal reporter pair calix[4]arene•luciginin (CX4•LCG) or cucurbit[7]uril•berberine (CB7•BE). Similarly, the metabolic process is monitored by the same reporter pair within the vesicles, monitoring the enzymatic conversion of the substrates. The third section represents an intriguing exploration of the effects of variations in the vesicle surface microenvironment on the interactions between supramolecular hosts and guests. The aim is to gain a deeper understanding of the mechanisms underlying the regulation of cell signaling transduction, transmembrane transport, and sensing processes, which are governed by the dynamic and complex presence of proteins, saccharides, and other molecules on the cell surface.

A novel composite fabric-based adsorbent is presented, in the form of an elastic and robust woven fabric belt following the general designs observed in known horizontal flat belt conveyors. The adsorbent was a strong cation-exchanger functionalized by sulphopropyl (SP) made of nylon 6 reaching a static binding capacity equal to 107.3 mg/g. Moreover, excellent dynamic binding capacity (DBC) values (54.5 mg/g) was tested even when operated at high flow rates (480 cm/h). In a subsequent step, a continuous protein purification system based on the true moving bed concept, namely moving adsorption belt system, is presented. The composite adsorbent was embedded into a bench-scale prototype, permitting performance studies with a model protein (lysozyme). Results indicated that the moving belt system could recover lysozyme with a productivity up to 0.5 mg/cm2/h. Subsequently, a monoclonal antibody (Humira) was recovered from unclarified CHO_K1 cell line culture with high purity as judged by reducing SDS-PAGE, high purification factor (5.8), and in a single step confirming the suitability and selectivity of the purification procedure. A dynamic decision-support tool, was utilized to evaluate how the potential of the moving belt system various across a range of scale (50 to 5000 kg/year) from an economic perspective. The comparison of cost of goods per gram (COG/g) demonstrates that moving belt system cannot offer a better manufacturing cost comparing to protein A chromatography when the production scale was smaller than 5000 kg/year. At production scale of 5000 kg/year, both capture processes offer similar COG/g values. The most effective way to narrow the difference in COG/g is to increase the DBC of the moving belt process 4 times. Certain adoption barriers were observed during processing and the possible optimization approaches were introduced. Inherently, scale-up is relatively easy to be fulfilled if moving adsorption system is utilized for purification.

The popularity of plant-based proteins, especially legumes has experienced a remarkable surge in recent years, due to the heightened awareness health benefits, concerns on animal welfare, environmental sustainability, and advancements in food technology. Apart from their nutritional value, many of the encrypted peptide sequences have shown remarkable bioactivities. However, most of the legume crops consumed worldwide are not evaluated for protein profile and bioactive potential, except for soya beans, peas, and lentils. The lack of scientific evidence has become a major barrier in successful utilization of these legumes. Also, there are many chemical and physiological barriers which limit the protein digestion such as protease inhibitors and complex structural diversity. So, this study was planned to evaluate the amino acid profile, presence of bioactive peptide sequences and improving the digestibility of proteins from common legumes cultivated in Sri Lanka, including pigeon pea-Cajanus cajan, green gram- Vigna radiata, cowpea - Vigna unguiculata, black gram - Vigna mungo and horse gram- Macrotyloma uniflorum. Extraction with acid, alkaline, salt, alcohol and TRIS buffers were compared for the yield and the diversity of protein profile. Digested protein extracts with trypsin, chymotrypsin and pepsin were analysed by LC-ESI-MS. Peptide sequencing was performed with PEAKS software to identify proteins, post-translational modifications, and amino acid profile. The peptide fragments identified from trypsin, pepsin and chymotrypsin hydrolysates were screened with different in-silico approaches and in vitro bioassays. Also, the effect of domestic processing methods including soaking, gemination, boiling and roasting on the digestibility of green gram proteins was evaluated. Results showed that the alkaline extraction is effective in extracting an optimum protein yield with most diverse and balanced protein profile, comprising storage, enzyme, and other functional proteins. Most of the cultivars tested show similar storage protein profiles, but there were significant differences in functional proteins important in abiotic stress management. Also, seed storage proteins show extensive post-translational modifications, which serves as another level of protection and functionalities such as dormancy release and metabolic resumption. They also have balanced essential amino acid profile, except for Methionine (M) and tryptophane (W). All legumes exhibited significant potential in antioxidant and antibacterial properties, and trypsin mediated digestion is more successful in generating bioactive peptides. Boiling and roasting enhanced the digestibility of legume proteins, which can be attributed to the removal of protease inhibitors, unfolding of globular storage proteins and alterations in secondary structures into less ordered conformations.

Germany’s national climate protection policy, the German Energy Transition, is oriented towards the reduction of energy-related greenhouse gas emissions by rapidly expanding the share of renewable energy systems. New challenges arise regarding technical and energy economical integration as installed wind and photovoltaic power capacities increase continuously. This thesis contributes to the topic by introducing a framework for online multi-criteria flexibility allocation in electrical distribution networks with transport capacity limitations. It considers technical aspects and market participant interests that may contradict each other. Local master data is preprocessed using geospatial information of buildings, photovoltaic systems, wind turbines, and the human population within a spatial domain of interest. By combining weather forecast data, this establishes the foundation for predicting power flow in the electrical distribution network and generating grid-beneficial criteria utilizing the smart grid traffic light concept. Furthermore, master data of the German wind and photovoltaic power plant portfolio is combined with weather forecast data to predict ationwide wind and photovoltaic power feed-in and to generate ecological- and market-beneficial criteria. In addition, metamodels are created and employed to forecast balancing group management data, i.e., EPEX SPOT Day-Ahead electricity prices in the bidding zone of Germany and Luxembourg, and imbalance energy demand of difference balancing groups. At last, non-scalarized schedule solutions are estimated to reach a multi-criteria flexibility allocation. The framework applicability is demonstrated by defined scenarios. Results show that publicly available data allows modelling, calibrating, and operating the introduced multi-criteria flexibility allocation framework in a real-world setting.

This thesis focuses on the role of drug repurposing in containment of an emerging disease, taking SARSCoV‐2 as a case study. The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) emerged in2019 causing a deadly respiratory disease: COVID‐19. The increasing knowledge about SARS‐CoV‐2allowed the expansion of multiple approaches to contain the spread of infection. Eventually the rapid development of anti‐SARS‐CoV‐2 vaccines allowed a control of the pandemic. Effective antiviral pharmacological treatments are still rare and viral evolution allowed a fast adaptation and escape from available containment methods. Since the beginning of the SARS‐CoV‐2 pandemic drug repurposing was considered as a valuable source for identification of new antivirals, due to the advantage of available clinical safety data and activity profiles. To interfere with SARS‐CoV‐2 infection and to identify new antiviral compounds, key steps of the virus replication cycle and their corresponding targets were selected for assay development. The screening approaches not only identified new molecules, that can act as starting points to develop new antiviral therapies, but also revealed critical steps and pitfalls in developing assays, that will help to optimize the translation of compound effects from biochemical to cell‐based state. The study also adresses the misconception that repurposed drug cannot interfere with assay, by showing examples of compound‐reactivity through generation of reactive oxygen species, and readout interference. In addition, a drug‐combination approach for entry‐inhibitors aiming at a synergistic response is shown as an option to overcome difficulties in reaching necessary intracellular target‐doses without increasing cytotoxicity. In conclusion, this research highlights the potential of drug repurposing in antiviral drug discovery. The generated results contribute to the publicly available data on drug repurposing against SARS‐CoV‐2, which may be used for research.

Our unintentional large-scale geoengineering project, characterized by a rapid in- crease in greenhouse gas concentrations, poses significant challenges in predicting and mitigating global and regional consequences. Climate researchers worldwide are constructing and refining climate models to understand and navigate the complex Earth system state and evolution. This thesis focuses on my contributions to this endeavor, specifically the construction, evaluation, and application of the AWI-CM3 coupled climate model. Additionally, I address the importance of improving the energetic consistency across the critical interface between the atmosphere and ocean. This research was conducted as part of the DFG collaborative Research Center Transregio (TRR) 181 ”Energy Transfers in Atmosphere and Ocean”, which aims to develop mathematically rigorous tools for climate analysis and modelling. By focusing on the interactions between the atmosphere and ocean, I strive to enhance our under- standing of the exchange of heat, momentum, and mass, while incorporating model components for sea ice and river runoff. I review the selection and method of computation for physical interface fluxes, introduce of stochastic remapping to conserve information across the coupling interface, and adapt vertical ocean mixing parameterizations to enhance the realism of the AWI-CM3 model. Through these efforts, I aim to contribute to the development of a comprehensive Earth System Model and advance our understanding of climate change and its societal implications.

Antibiotics enter the bacterial cells through the outer membrane diffusion channels called porins. The antibiotic permeation process through porins is of immediate interest and the understanding is expected to aid the development of antibacterial drugs with improved efficacy. The accurate estimation of free energy for translocation is a prerequisite for obtaining quantitative estimates from simulations which would enable a meaningful comparison of different antibiotic permeation mechanisms. This goal, however, has proved to be a significant challenge in the studies on bulky antibiotics, presumably due to a number of slow modes that govern the permeation process. Umbrella sampling and well-tempered metadynamics, that have been extensively used in the field, are limited in the number of degrees of freedom that can be simultaneously biased. In recent years, several methods have been developed that allow biasing simultaneously more degrees of freedom. The primary objective of the present thesis is to examine a few temperature acceleration-based sampling schemes for the enhanced exploration of antibiotic permeation pathways. Subsequently, the temperature accelerated sliced sampling method has been applied to the study of permeation pathways for a few antibiotics. The method, in combination with applied field simulations, is used to uncover the mechanistic aspects of L3 conformational dynamics in antibiotic permeation and voltage gating. The findings provide a strong rationale for the fast permeation of positive and zwitterionic antibiotics reported in experiments. Finally, the combination of a Brownian dynamics scheme with the temperature accelerated molecular dynamics method has been used for the fast and approximate estimation of antibiotic permeability constants.

Over the past 40 years, a modern welfare state has emerged in the People’s Republic of China (PRC). Social protection has thereby extended from a selected group of privileged state employees and civil servants to most of the population. This dissertation argues that the significant extension of social protection, and of pension schemes in particular, is not only a consequence of domestic economic development and political reform; international influences also manifested themselves during the course of the development and reforms of the Chinese social protection system. My key research question is: How have international influences impacted on Chinese social security and pension reform? While I do analyze the roles of “Western” influences and International Organizations, I particularly focus on one important gap in the literature – the scarcely studied role of “Eastern” or East Asian influences on Chinese social protection development. Theoretically, this dissertation builds on the policy learning literature as well as the concept of East Asian welfare productivism to explain how international influences impacted on Chinese social protection expansion and reforms and to better understand welfare features the PRC shares with its East Asian neighbors. Regarding methodology, this dissertation employs several methods in the tradition of qualitative research: process tracing, document analysis, and discourse analysis. The core data of this research was collected via expert interviews during fieldwork in China. Moreover, archive data, official policy reports in Chinese, English and Japanese, and other primary and secondary data were utilized. This cumulative dissertation consists of four interdependent articles focusing on the development and expansion of Chinese social protection under international influences, and especially on the two major reforms of Chinese pension schemes: the 1990s Urban Employee Pension Reforms, and the 2009 New Rural Pension Scheme.

The Amazon is the largest River on earth, accounting for 15–20% of the global river freshwater discharge, and making it an important source of trace metals, nutrients and organic matter to the Atlantic Ocean. The nearby Pará River is the 5th largest river and converges to mix in the Amazon Estuary. Trace metals in the ocean (e.g., Mn, Co, Fe, Ni, Cu, Zn, Cd and Pb) act as important nutrients and/or toxins to marine organisms. However, no data exists for these trace metals in the Amazon Estuary after 1976. Therefore, it is of urgent importance to establish a baseline for trace metals in the Amazon estuary. A GEOTRACES process study (cruise GApr11) was conducted in the Amazon estuary during the wet season (April–May) of 2018. Herein we present data for dissolved trace metals and organic matter from samples collected from this cruise. Chapter 1 focuses on copper (Cu), a micronutrient and potential toxin, and its complexation to organic ligands. Chapter 2 discusses two other micronutrients, cobalt (Co) and nickel (Ni), in surface and depth samples analyzed by two different methods. Chapter 3 brings together all trace metals from this study (Al, Mn, Co, Fe, Ni, Cu, Zn, Cd, Pb and U) to calculate the fluxes from the Amazon and Pará Rivers into the Atlantic Ocean. Finally, chapter 4 describes depth profiles of bioactive metals in different size fractions. Trace metal cycling in the estuary was influenced by complex biogeochemical processes, including ligand complexation, particle adsorption-desorption, colloidal flocculation, physical mixing and biological activity. In addition, we observed distinct influences from the Amazon and Pará Rivers, which draw from distinct catchment areas. Cu was mostly conservative with respect to salinity, while Fe and Pb were highly particle reactive during early mixing and experienced the greatest estuary removal. We estimated that the Amazon and Pará Rivers account for ~21% and 18% of the global riverine Cu and Ni to the oceans.

Increasingly decentralized collaboration drives the development of new creativity support systems (CSS) that offer virtual teams various means for communication, information exchange, and creative collaboration. As CSS aim to mitigate the limitations of virtual communication and collaboration, reason suggests that more functionalities yield better outcomes. Hence, a wide range of capabilities for interaction, knowledge sharing, or innovative collaboration processes is implemented into these CSS. However, if CSS offer functionalities beyond the required level, they can become confusing to use, overload users, or cause feature fatigue. Previous research shows that having more options or functionalities does not always lead to better results, especially in the context of creativity, and suggests a curvilinear relationship between constraints and creativity. While the effects of constraints on creative collaboration have been studied in analogue settings before, their design and application to support creative collaboration in virtual contexts, remains under-researched. This PhD thesis examines how constraints in CSS can be designed to foster creative collaboration and contribute to a better understanding of how limited functionalities and interactions during particular phases of creative collaboration can help individuals and teams access a CSS's instrumental potential and benefit from idea generation and exploration beyond routine performance, and encourage more radical forms of creativity. To support the evolution of knowledge about the theory and practice of CSS design, this research project follows a design science research (DSR) approach.

This PhD thesis focuses on the investigation of high-temperature hydrothermal vent fluids from multiple vent sites between the North-Eastern Lau Spreading Center and the Tofua arc. The sampling area is located in the northeastern part of the Lau Basin which is affected by some of earths’ highest subduction rate, a highly complex microplate tectonic, an influence of hot spot material from the Samoan mantle plume as well as the subduction of the Louisville Seamount Chain. This study extends our knowledge of high-temperature hydrothermal systems in the North-East Lau Basin, in that it reports for the first time on the chemical and isotopic composition of vent fluids from Maka volcano and Niuatahi volcano. This cumulative PhD thesis highlights the compositional variability of hydrothermal fluids associated with different geologic settings. The newly reported vent fluid data as well as systematic spatial distribution of trace metals and metalloids adds to our understanding of hydrothermal processes and in the future may help improve the estimates of element specific fluxes associated with seafloor hydrothermalism.

Microplastics (MP) have received increased scientific, political and societal attention due to their environmental omnipresence. This thesis aims to provide comprehensive data on aquatic MP pollution through the application of state-of-the-art analytical methods, and compares data outputs from two data pipelines. Within the River Weser–North Sea transitional system, small MP (<500 µm) predominated, with a notable abundance of suspected paint particles. Large MP (>500 µm) exhibited low abundances, mainly composed of the common plastic polymers polyethylene and polypropylene. The estuary’s turbidity maximum zone showed the highest MP concentrations, then declining towards the North Sea, possibly influenced by increased vertical and horizontal export or dilution in the larger marine water body. Additionally, this thesis evaluated two wastewater treatment plants as potential riverine MP point sources. Interference by post-processing residual material required an adaptation of the FTIR reference database by the inclusion of new reference material. Results showed that polyolefins were prevalent in the effluent, and that observed temporal patterns in MP concentrations could be partially explained by technical and environmental parameters. Input of MP into the River Weser via effluent is likely, necessitating more research to understand the full dynamics of MP pollution within this river system. Furthermore, the MP analysis pipeline comparison study showed discrepancies for certain polymer types, possibly due to different polymer grouping methods, or overestimation effects. By excluding these polymer types, both datasets generally were in accordance, suggesting a harmonization of both pipelines should be undertaken to improve comparability of MP data. In summary, this thesis provides a detailed foundation for understanding MP dynamics in the River Weser–North Sea system and highlights methodological challenges inherent in the field of MP pollution research.

Despite success, the use of therapeutic monoclonal antibodies (mAbs) in clinical settings has been complicated by the ability of the patient’s immune system to provoke an unwanted humoral immune response against the drug, generally through the formation of anti-drug antibodies (ADAs) – termed immunogenicity. ADA onset may compromise clinical efficacy and impact safety in patients. A fundamental area of immunogenicity research is investigating mAb-derived peptides processed by dendritic cells (DCs) and presented through major histocompatibility complex (MHC) class II receptors. These mAb-derived peptides, representing potential T cell engaging epitopes, orchestrate the immunogenicity cascade by directly influencing T cell activation leading to ADA production. The MHC-II-associated peptide proteomics (MAPPs) assay is a Roche-invented methodology to identify and quantify such potential T cell epitopes. As an integrated approach during preclinical drug development, MAPPs is used alongside other in vitro, in silico, and in vivo tools to address the risk of immunogenicity. This PhD work aims to extend the applications of MAPPs for the development of a tool for personalized healthcare (PHC) in the clinic with the purpose of identifying patients with a potential risk of immunogenicity prior to treatment in order to devise an ideal treatment plan (main aim 1). Moreover, since most MAPPs studies are currently restricted to HLA-DR as the dominant MHC-II genotype due to lack of satisfactory MHC-II receptor-precipitating reagents available, an immunoprecipitation strategy using the MAPPs assay alongside the advanced epitope–prediction algorithm NetMHCIIpan was developed to accommodate MHC-II pan receptors for improved predictability of potential T cell epitopes (main aim 2). Taken together, these reformed uses of the MAPPs assay will lead to an invaluable clinical tool for immunogenicity risk assessments that support personalized healthcare.

The field of logistics and supply chain management deals with various supply network problems on multiple levels starting from strategic years-long decisions regarding network topology, down to operational weekly-based decisions. Moreover, due to the interaction of customers and random accidents, the system becomes stochastic and difficult to control based only on logistic experience. To help with the problem of supply chain design and management, scientists try to approach the field with existing instruments including network science, mathematical modeling, control theory, machine learning, etc. In this thesis, a complex approach that addresses different aspects of supply network management is demonstrated. First, an automatization scheme for the daily management of logistic requirements is proposed. Second, an in-depth investigation of non-conventional usage of natural language processing and machine learning algorithms is presented. The developed approach can be used to enhance the process of requirement management by extracting additional knowledge about the supply network operation. Third, the strategic problem of designing robust supply networks is addressed by developing a minimalistic model of a supply network. The model is designed to simulate a scenario of supply-demand imbalance and generate networks that satisfy the imbalance in a robust way. The overall outcome of the work is a better understanding of separate supply network aspects and an attempt to holistically improve the way how supply networks are managed.

Diazo compounds are remarkable versatile building blocks and increasingly important molecules in organic synthesis. Even though the field of diazo chemistry was discovered more than hundred years ago it is still a subject of great research interest. Besides, the field of organofluorine chemistry has grown immensely in recent years, and fluorochemicals have permeated into nearly every aspect of our daily lives. Hence, the aim of this work was the synthesis of a bench-stable 2-diazo-1,1,3,3,3-pentafluoropropyl phosphonate, the first fluorinated diazo compound bearing trifluoromethyl and difluoromethyl phosphonate moiety, along with a demonstration of its synthetic applications in a series of chemical reactions. After the successful synthesis, the novel diazo compound was incorporated in cyclopropanation reaction of aromatic and aliphatic terminal alkenes using CuI as a catalyst. Consequently, sixteen new cyclopropanes were synthesized in good to very good yields and under mild reaction conditions. Furthermore, a new pathway for synthesizing fluorinated β-alkoxy vinyl phosphonates via O-H insertion reaction using Rh2(OAc)4 as a catalyst is presented in this thesis. The insertion reaction of benzyl and aliphatic alcohols using the diazo compound were carried out under mild conditions and nineteen new fluorinated β-alkoxy vinyl phosphonates were synthesized in good to very good yields. The following reaction was a catalyst- and solvent-free 1,3-dipolar cycloaddition of alkynes and alkene with the diazo compound. This green approach provided an efficient route for direct synthesis of fluorinated pyrazoles and pyrazoline in moderate to excellent yields. Lastly, a Cu(II) catalysed [2,3]-sigmatropic rearrangement reaction of propargyl and allyl sulfides with the diazo compound produced products bearing difluoromethylphosphonate and trifluoromethyl groups.