Publication Server of Constructor University Library
Not a member yet
858 research outputs found
Sort by
AI-Driven Real-Time Data and Neural Synthesis in German Transport
No full textThis 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
Collective patterns on graphs
No full textUnderstanding 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
Trends in the Development of Digital Tools for Inclusive Early Childhood Education with a Focus on Social Skills
No full textDigital 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
Robust Underwater Perception: Using Multimodal and 3D Visual Cues to Boost Machine Learning Frameworks in Marine Applications
No full textUnderwater 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
Digital Competence Framework for Teachers: Implementation Gap
No full textThis 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
Biogeochemical Fractionation of Rare Earth Elements within Aquatic Organisms and a Natural Freshwater Ecosystem
No full textRare 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
Identification and characterization of small molecules targeting the E. coli AcrAB-TolC efflux pump
No full textThis 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
AI Integration in Education: Opportunities and Challenges
No full textThe 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
An Intelligent learning management platform for Data-Driven course improvement
No full textModern 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
Using Markov Decision Process Model for Sustainable Assessment in Industry 4.0
No full textThis 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