OPUS documents https://opus.constructor.university/index/index/ Mon, 30 Mar 2026 09:07:27 +0200 Mon, 30 Mar 2026 09:07:27 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1321 Understanding how collective patterns emerge on graphs is a fundamental challenge across disciplines, from biological and ecological networks to computational and physical systems. This thesis explores the interplay between network topology and emergent dynamics using minimal models and spectral graph techniques. A first investigation focuses on network inference, showing that Turing patterns encode structural information about the underlying graph, which we use to infer missing links. The second study investigates multistability in reaction-diffusion networks, showing how local spectral gaps influence the attractor landscape of Turing patterns using a heuristic binary classification algorithm. Finally, the third study applies the sandpile model to soil erosion processes, bridging concepts from self-organised criticality and connectivity-based geomorphology to investigate the role of minimal models in empirical research. This thesis combines theoretical analysis, computational modelling and empirical validation to highlight how structure shapes dynamics across different contexts and illustrate the potential of minimal models as predictive, explanatory and exploratory tools for complex systems. Selim Haj Ali doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1321 Mon, 30 Mar 2026 09:07:27 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1360 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. Keran ZHANG doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1360 Mon, 09 Mar 2026 14:19:09 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1356 The accelerating pace of digital transformation (DT) is profoundly reshaping the world of work, placing new demands on employees and affecting their well-being. As employee well-being is closely linked to engagement and performance, this PhD project investigates how organizations can engage employees during DT, with particular consideration of their well-being. The Self-Determination Theory (SDT) serves as the kernel theory in this research for understanding well-being, expanded to include physical health. Furthermore, both different working conditions and various life stages of employees are incorporated in order to capture the dynamic nature of well-being. However, promoting well-being requires a comprehensive understanding of its multifaceted effects, both positive and negative, on employees, a challenge further intensified by the ongoing DT. While many companies recognize the benefits of DT, they often struggle with its implementation and the associated impacts on the workforce. Maturity models are a common tool to provide guidance during DT by serving as frameworks for assessing and developing organizational capabilities. In practice, maturity models are often too strategic, inflexible, and insufficiently user-centered. Furthermore, social aspects such as employee well-being have so far been largely neglected. To close this gap, an adaptable human-centered maturity model focusing on well-being was designed and empirically validated within the framework of this cumulative dissertation consisting of six papers, following the Design Science Research (DSR) approach. The model uniquely integrates basic psychological needs, physical health, and life stage perspectives, dimensions largely absent in existing DT maturity models. Overall, this PhD project advances the human-centered discourse on well-being by providing a practice-oriented maturity model that supports organizations in identifying the effects of DT on well-being and deriving appropriate courses of action. Maximilian Helms doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1356 Tue, 17 Feb 2026 12:59:02 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1358 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. Majid Sodachi doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1358 Tue, 17 Feb 2026 10:44:39 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1357 This research paper investigates the advanced Artificial Intelligence (AI) architecture underpinning Germany's multimodal transportation ecosystem, drawing from the author's year-long immersive professional experience. The study shifts the focus from traditional physical logistics to the efficiency of data processing throughput, characterizing the transport network as a dynamic information organism. The author analyzes the integration of Edge Computing and IoT sensors through the MQTT protocol, facilitating resilient data transmission in unstable environments. Furthermore, the paper details the implementation of Real-time Stream Processing using Apache Kafka and Redis, alongside the application of LSTM (Long Short-Term Memory) neural networks for high-precision delay forecasting. A significant technical analysis is provided on Neural Text-to-Speech (NTTS) synthesis for passenger notifications, emphasizing its role in enhancing user experience (UX) through natural language generation. Beyond technical frameworks, the author addresses critical infrastructure security via TLS 1.3 and PKI, ensuring compliance with GDPR standards. The paper concludes that the success of modern transport lies in its "algorithmic soul"—a data-driven approach that prioritizes reliability and transparency. Ultimately, the author advocates for the strategic transfer of these AI-integrated architectures to Kazakhstan’s "Smart City" initiatives, suggesting that such digital transformation will serve as a catalyst for the broader technological evolution of the national economy. Aigul Yelikbayeva workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1357 Mon, 16 Feb 2026 14:41:30 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1355 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. Assel Bekmoldayeva workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1355 Fri, 06 Feb 2026 14:11:01 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1344 Digital transformation (DT) fundamentally reshapes organizational structures and work processes. Despite its strategic importance, up to 70% of DT initiatives fail, primarily due to insufficient consideration of human factors. This cumulative dissertation addresses this gap by developing and validating a human-centered Change Management Maturity Model that systematically integrates employee needs into digital transformation processes, with particular emphasis on the AI-driven third phase of DT. Existing DT maturity models predominantly focus on technological, strategic, and organizational aspects while neglecting human-centered dimensions such as employee motivation, psychological well-being, and change readiness. Likewise, established change management frameworks tend to operate either at the organizational level (e.g., McKinsey 7S) or the individual level (e.g., ADKAR), without systematically integrating both perspectives. To address this limitation, this dissertation proposes a comprehensive maturity model comprising nine dimensions across three categories: Motivation & Leadership Behavior, Dealing with Change, and Well-being & Health. The research follows an echeloned Design Science Research (eDSR) approach and is structured as a cumulative dissertation consisting of six research papers. The model is grounded in multiple kernel theories, including Self-Determination Theory, Herzberg’s Two-Factor Theory, Maslow’s Hierarchy of Needs, the Dynamic Capabilities Framework, and established change management models. Empirical validation was conducted in the skilled trades sector and across industries in the retail sector, demonstrating the model’s applicability across organizational contexts and its practical relevance for managing AI-driven transformation initiatives. Julia Bosbach doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1344 Fri, 06 Feb 2026 10:32:54 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1354 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. Sholpan Arzymbetova workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1354 Fri, 06 Feb 2026 09:45:51 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1352 This paper examines current mathematical modeling methods used for the development and optimization of composite polymer stabilizers. A review of key challenges related to the stability of polymer materials is provided, along with a discussion of modern computational approaches, including molecular dynamics, finite element analysis, thermodynamic modeling, and machine learning. The necessity of an interdisciplinary approach integrating chemistry, materials science, and computational technologies is justified. Perspectives on the further development of modeling methods to enhance the efficiency and stability of polymer stabilizers are presented. Zhadyra Artykova workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1352 Thu, 05 Feb 2026 13:04:20 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1351 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. Raviya Amdamova; Sholpan Aitmetova workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1351 Fri, 30 Jan 2026 09:22:59 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1349 This essay examines the role of artificial intelligence tutors in Kazakhstan's education system, addressing whether AI-based tools replace teachers or serve as supportive instruments that enhance teaching practice. Against the backdrop of ongoing digital transformation in education, the central thesis argues that AI tutors do not replace teachers but function as complementary tools that assist with specific tasks such as assignment evaluation, progress monitoring, and personalized feedback. While AI systems offer significant advantages — including personalized learning pathways, expanded access to educational resources in underserved rural areas, and reduced administrative burden for teachers — they cannot substitute essential pedagogical elements such as human interaction, emotional intelligence, cultural awareness, and moral guidance. The analysis draws on theoretical frameworks from UNESCO and OECD, examining both the potential and limitations of AI integration within Kazakhstan's educational context, where regional disparities in infrastructure and digital literacy create uneven implementation. Through comparative insights from Germany's education system, the essay demonstrates how AI tutors can be effectively integrated as supportive tools while preserving the teacher's central role in pedagogical decision-making and student development. The findings emphasize that successful AI integration requires maintaining human oversight, addressing the digital divide, and ensuring that teachers retain professional autonomy in shaping educational outcomes. Meruyert Sambetova; Zhanar Khudaibergenova workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1349 Thu, 29 Jan 2026 13:11:28 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1346 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. Gulzhan Bakhtiyarova; Darkhan Pshkeyeva workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1346 Thu, 29 Jan 2026 10:19:21 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1345 Underwater robots need reliable perception for navigation, mapping, diver interaction, and manipulation, yet vision is degraded by wavelength-dependent attenuation, scattering, and variable water optics. These effects reduce contrast, distort color, and destabilize visual cues, so perception must be tailored to underwater image formation and field reliability constraints. This thesis develops multimodal, 3D-aware perception for adverse marine and deep-sea conditions, based on experiments and integration within the EU projects MORPH, CADDY, and DexROV. By combining complementary sensors (2D imagery, stereo 3D structure, inertial and acoustic cues) with learning pipelines, the approaches compensate for individual sensor weaknesses. First, it enriches 2D perception with 3D context and underwater-specific enhancement. Contributions include terrain-complexity estimation from texture metrics and stereo geometry to adapt AUV speed during surveys, plus color restoration/image enhancement to improve detection and pose estimation. For human-robot interaction, it introduces diver detection and pose estimation that merge stereo point-cloud descriptors with recurrent neural networks to handle low-contrast imagery. Second, it presents end-to-end systems, including the CADDY underwater stereo-vision dataset for gesture-based communication and a gesture-recognition pipeline that blends classical learning, deep detectors, and a grammar-guided human-in-the-loop design for safer diver and AUV communication. Finally, for deep-sea intervention, it proposes a simulation-in-the-loop validation to reduce sim-to-real gaps and an adaptive localization framework fusing dense 3D reconstruction, planar geometry, image-quality cues, and visual odometry to maintain accurate navigation in low visibility. The methods are validated on real data and integrated into autonomous demonstrators for safety-critical missions during field trials. Arturo Gomez Chavez doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1345 Thu, 29 Jan 2026 09:24:14 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1348 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. Zhadyra Abatova; Gulnur Kenesbay; Nassima Bauzhanova workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1348 Wed, 28 Jan 2026 15:18:21 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1347 Artificial intelligence (AI) and gamification are becoming important tools in modern education. Gamification uses elements such as points, rewards, and challenges to increase student motivation and engagement. However, its effectiveness depends on learners’ interests, abilities, and the quality of game design. Personalized learning aims to adapt content and tasks to individual student needs, but teachers often struggle to do this in traditional classrooms due to limited time and large class sizes. AI can support personalization by analyzing student performance, participation, and learning difficulties. Based on this data, AI systems can recommend appropriate materials, adjust task difficulty, and create individual learning paths. This article compares educational platforms in Germany and Kazakhstan. Germany is well prepared to integrate AI into education and widely uses AI-based personalization and gamification. In contrast, many Kazakhstani platforms rely mainly on video lessons with limited interactive features. The article also presents the educational chatbot “Help YOU!” for schools in Kazakhstan. By combining AI and gamification, the chatbot provides personalized student support and reduces teachers’ workload, demonstrating the potential of these technologies to improve education. Gaziza Makhambetova; Makpal Duisenbekova workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1347 Tue, 27 Jan 2026 15:25:17 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1342 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. Assel Bekmoldayeva; Aigul Yelikbayeva workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1342 Fri, 23 Jan 2026 13:06:05 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1341 In this thesis, I examine the relationship between internal governance and university performance within the context of Russian higher education from 2012 to 2020, a period marked by the prominent application of New Public Management (NPM) instruments. This study investigates several dimensions of internal governance and its connection to university performance. First, how do internal governance characteristics - such as centralization, stakeholder involvement, external communication, and strategic orientation - relate to university performance? Second, is there a relationship between institutional strategy adoption and university performance from the perspective of university department heads? Additionally, I explore the institutional structures and governance arrangements in Russian higher education, with particular attention to two interrelated developments: the adoption of NPM instruments and system stratification. The study draws on data from a national-level survey of university leaders and administrators, complemented by statistical information. Depending on the data structure and variable characteristics, various quantitative methods - from simple difference tests to conditional efficiency estimations - will be applied to address the research questions. Daria Platonova doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1341 Wed, 21 Jan 2026 11:13:41 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1340 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. Tania Szal doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1340 Tue, 20 Jan 2026 13:25:37 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1339 Our modern society relies heavily on the availability and utilisation of rare earth elements and yttrium (REY) for high-tech products and processes, which provokes a growing release of these metals into the environment and draws attention to biological and ecotoxicological consequences of their increasing concentrations in the environment. However, research has long neglected the environmental behaviour of REY. Coupled with publications including incomplete REY sets or data of questionable analytical quality, many open questions remain. This dissertation investigates samples from the biosphere and from the hydrosphere to shed light on the REY transfer at their interface. Duckweeds, widely occurring small water plants, and Norwegian fjord waters together with Baltic Sea outflow samples were chosen as main study objects from the biosphere and the hydrosphere, respectively. The findings of the biosphere-focused part improve the characterisation of the duckweed reference material BCR-670 (Lemna minor) and highlight the necessity of comparable sample processing for validation of data quality. All naturally grown duckweeds investigated are REY quasi-hyperaccumulators and share similarly shaped, mildly fractionated shale-normalised REY patterns without positive anthropogenic Gd anomalies, regardless of whether they grew in waters with or without anomalous Gd enrichment. The hydrosphere-focused part presents the first evidence for constant anthropogenic Gd input into the Baltic Sea outflow. The data combined with literature data further suggest that this signal is transported to southern Norway. In future, it may reach fjord waters further north along the Norwegian coast. Overall, this dissertation provides important new information about the fate of geogenic and anthropogenic REY at the hydrosphere-biosphere interface and highlights the relevance of basic research as the basis for understanding the complex REY transfer mechanisms across environmental compartments. Anna-Lena Zocher doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1339 Wed, 17 Dec 2025 13:47:45 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1338 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. Dominik Antoni doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1338 Thu, 11 Dec 2025 12:52:01 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1337 Mapping challenging confined underwater environments pushes the boundaries of what is possible for state-of-the-art robotics. Current state-of-the-art high-performance equipment allows already for accurate mapping in such scenarios. However, these systems are often expensive. Affordable underwater robotic systems and sensors come with significantly reduced capabilities. Especially sonars are necessary for mapping unknown environments, due to cluttered water resulting in bad visibility for vision based sensors. Yet, affordable sonar sensors suffer from higher noise levels, reduced accuracy, and limited coverage. Consequently, developing methods to achieve reliable and accurate mapping of challenging environments with affordable hardware remains an open research question. This thesis presents a Fourier-SOFT in 2D (FS2D) registration method for robust matching of high-noise 2D sonar scans. A Simultaneous Localization and Mapping (SLAM) framework designed to the unique challenges of affordable Mechanical Scanning Sonars (MSS) is presented, integrating this FS2D registration method. In the context of the digitization of cultural heritage, the Bunker Valentin Memorial in Bremen is surveyed, and maps of its multiple basins are generated. Additionally, this thesis contributes an open dataset with accurate ground truth for development and benchmarking 2D sonar navigation, mapping, and SLAM algorithms. Overall, this thesis demonstrates that, when the unique characteristics of affordable hard ware are considered correctly, and the methods are designed accordingly, affordable underwater robots can effectively map and explore challenging, unknown environments. The BlueAUV design, the FS2D registration method, SLAM framework for affordable hardware, and an openly available dataset provide a foundation for advancing robust mapping of challenging underwater environments within the research community. Tim Hansen doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1337 Thu, 04 Dec 2025 14:34:50 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1326 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. Atit Bashyal doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1326 Mon, 10 Nov 2025 14:16:01 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1327 The rapid growth of large-scale software systems has led to the adoption of automatic error reporting platforms collecting millions of crash reports from real users. Central to these reports is the stack trace — a record of function calls leading to failure — which serves as a crucial diagnostic resource. However, the sheer volume, diversity, and redundancy of reports create bottlenecks: developers are overwhelmed by duplicates and highly variable submissions from the same defect, impeding efficient issue resolution. Existing deduplication and triage solutions in industry and academia mainly rely on string-matching, information retrieval, or graph-based heuristics. While efficient, string and IR methods often miss semantic and contextual nuances; graph-based models lose detail about individual reports, reducing accuracy. These limitations cause missed linkages between related errors and fragmentation of bug databases. The lack of scalable algorithms, real-world benchmarks, and advanced learning methods further restricts current tools. This dissertation advances automation of error report processing via stack trace analysis. It introduces (1) hybrid similarity metrics extending traditional techniques, (2) deep learning models for robust similarity estimation, (3) aggregation strategies leveraging group-level information, (4) scalable solutions for industrial use, (5) the first models for automated developer assignment in stack trace–centered triage, and (6) methods for interpreting and highlighting the most informative stack frames. The research is validated on multiple proprietary and open datasets, including new benchmarks released as part of this work. Together, these contributions provide a unified, reproducible foundation for scalable, accurate, and actionable error report deduplication, grouping, assignment, and tooling in real-world software engineering. Aleksandr Khvorov doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1327 Wed, 05 Nov 2025 15:23:27 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1336 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. Vinaya Siby doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1336 Tue, 04 Nov 2025 14:11:25 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1335 The essay explores the integration of digitalization and Lean management in the transport industry, emphasizing their combined role in enhancing efficiency, flexibility, and sustainability. It outlines the origins of Lean management in the Toyota Production System and its adaptation to logistics and transport through practices such as 5S, Kaizen, and Just-in-Time. The paper highlights successful examples from global companies like DHL, UPS, Delta Airlines, and DB Schenker, demonstrating measurable improvements in productivity and cost reduction. Digital technologies—including the Internet of Things, Artificial Intelligence, Big Data, and digital twins—strengthen Lean principles by enabling real-time data analysis, automation, and predictive decision-making. The essay also examines Germany’s leadership in transport digitalization and describes practical observations from the Mercedes-Benz plant in Bremen. Finally, it discusses challenges such as cybersecurity, integration complexity, and ethical concerns, concluding that the synergy between Lean and digitalization forms the foundation for the future of transport—making it smarter, greener, and more resilient in the global economy. Askar Saukhimov; Aliya Omarova workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1335 Tue, 21 Oct 2025 09:17:42 +0200