Iskanje po katalogu digitalne knjižnice

Opis: In this thesis, we aimed to design, test, and implement a custom transmitter for electromagnetic compatibility (EMC) testing. After assembling a technological overview of all relevant technologies, we engineered the design of the first prototype. It was a battery powered, manually tunable, communication isolated, compact, portable transmitter that can operate without other control devices in a test setup. The central parameters of improvement were enhanced power output and EMC. The power output needed to be manually tunable and enable constant magnetic field strength over a select set of distances. We had to implement all enhancements to the design while simultaneously improving the electromagnetic compatibility as much as possible. We were focusing on the noise produced by the transmitter itself. According to the test plan, we performed tests in signal quality, output power over distance, and EMC in a GTEM cell. In the first prototype, we did not reach the goals of increasing power output, adding the ability to adjust the output power manually, and increasing electromagnetic compatibility. The amplification stage and output driver stage did not meet the full specifications. The initial stages of the transmitter were fully functional, and we can include them in the next design.
Ključne besede: transmitter, EMC testing, wireless transmission, signal synthesis.
Objavljeno v DKUM: 16.12.2025; Ogledov: 0; Prenosov: 0

Opis: Superparamagnetic iron oxide nanoparticles (SPIONs) with sizes below 10 nm are biocompatible and non-toxic, making them promising for biomedical applications. To prevent their agglomeration and enhance their functionality, the nanoparticles were coated with citric acid (CA), which modifies the surface charge, improves dispersion stability, and facilitates biomedical use. In this work, a modular flow-through microreactor system was employed to synthesize and coat the nanoparticles in a single, continuous two-step process. The system enables precise control over temperature and mixing, ensuring uniform reaction conditions and minimizing hot spots. The synthesized Fe3O4 nanoparticles exhibited an average crystallite size of ~5 nm (XRD) and particle sizes of 4–6 nm (TEM). FTIR analysis confirmed the successful surface functionalization with CA, while TGA indicated a coating mass fraction of approximately 4–20 wt%, increasing with higher CA concentration. Zeta potential measurements revealed strong colloidal stability, with values around −35 mV at pH 6.5. Among the tested CA concentrations, the sample with a molar ratio of Fe3O4:CA = 1:0.25 exhibited the most favorable properties, including narrow size distribution and improved dispersion stability. These findings demonstrate that the continuous modular flow approach enables the reproducible synthesis of highly stable, sub-10 nm CA-coated SPIONs, offering promising potential for biomedical applications, particularly as magnetic resonance imaging (MRI) contrast agents.
Ključne besede: superparamagnetic iron oxide nanoparticles, citric acid, modular flow microreactor system, continuous synthesis, zeta potential measurements
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (1,54 MB)

Opis: Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering problems. However, a comprehensive and holistic understanding of the current status needs to be included. To close the above gap, synthetic knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and 5 themes—namely, natural language processing in software engineering, use of artificial intelligence in the management of the software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934), Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded, showing the strong interest in this research topic. The convergence of AI and software engineering can significantly reduce the required resources, improve the quality, enhance the user experience, and improve the well-being of software developers.
Ključne besede: software engineering, artificial intelligence, machine learning, synthetic knowledge synthesis, AI
Objavljeno v DKUM: 30.07.2025; Ogledov: 0; Prenosov: 12
Celotno besedilo (1,39 MB)
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Opis: Background: Artificial intelligence and machine learning are playing crucial roles in digital transformation, aiming to improve the efficiency, effectiveness, equity, and responsiveness of primary health systems and their services. Method: Using synthetic knowledge synthesis and bibliometric and thematic analysis triangulation, we identified the most productive and prolific countries, institutions, funding sponsors, source titles, publications productivity trends, and principal research categories and themes. Results: The United States and the United Kingdom were the most productive countries; Plos One and BJM Open were the most prolific journals; and the National Institutes of Health, USA, and the National Natural Science Foundation of China were the most productive funding sponsors. The publication productivity trend is positive and exponential. The main themes are related to natural language processing in clinical decision-making, primary health care optimization focusing on early diagnosis and screening, improving health-based social determinants, and using chatbots to optimize communications with patients and between health professionals. Conclusions: The use of machine learning in primary health care aims to address the significant global burden of so-called “missed diagnostic opportunities” while minimizing possible adverse effects on patients.
Ključne besede: primary health care, machine learning, research landscape, synthetic knowledge synthesis
Objavljeno v DKUM: 24.07.2025; Ogledov: 0; Prenosov: 6
Celotno besedilo (887,18 KB)

Opis: Interconnected computers and software systems have become an indispensable part of people’s lives in the period of digital transformation. Consequently, software quality research is becoming more and more critical. There have been multiple attempts to synthesise knowledge gained in software quality research; however, they were focused mainly on single aspects of software quality and did not structure the knowledge holistically. To fill this gap, we harvested software quality publications indexed in the Scopus bibliographic database. We analysed them using synthetic content analysis which is a triangulation of bibliometrics and content analysis. The search resulted in 15,468 publications. The performance bibliometric analysis showed that the production of research publications relating to software quality is currently following an exponential growth trend and that the software quality research community is growing. The most productive country was the United States, followed by China. The synthetic content analysis revealed that the published knowledge could be structured into six themes, the most important being the themes regarding software quality improvement by enhancing software engineering, advanced software testing and improved defect and fault prediction with machine learning and data mining.
Ključne besede: software engineering, software quality, knowledge synthesis, bibliometrics, synthetic knowledge synthesis
Objavljeno v DKUM: 07.04.2025; Ogledov: 0; Prenosov: 6
Celotno besedilo (1,74 MB)
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