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Diese Arbeit untersucht die Charakterisierung einer Swept-Source VCSEL für Anwendungen der optischen Kohärenztomografie. Ziel war es, das spektrale Emissionsverhalten im Dauerstrich und gepulsten Betrieb zu erfassen, die linearen Abhängigkeiten der Emissionswellenlänge von Betriebsstrom und Betriebstemperatur (Wellenlängenkennlinie) zu bestimmen und die Eignung
der Lichtquelle für OCT-Messaufgaben zu bewerten.
Im Rahmen dieser Bachelorarbeit wurde ein kompaktes Gitterspektrometer entwickelt und aufgebaut, das ein zeitliches Auflösungsvermögen 20 ns bei einem spektralen Auflösungsvermögen von ≈ 1,112 · 10^4 erreicht. Auf dieser Grundlage wurden die Wellenlängenkennlinie einer Swept-Source VCSEL sowohl im cw- als auch im gepulsten Betrieb aufgenommen, die resultierenden Tuning-Koeffizienten bestimmt und die Eignung der Quelle für kostengünstige OCT-Anwendungen bewertet.
This work presents a comprehensive analysis of the variability and reliability of the resistive switching (RS) behavior in Prussian Blue (a mixed-valence iron(III/II) hexacyanoferrate compound) thin films, used as the active layer. These films are fabricated through a simple and scalable electrochemical process, and exhibit robust bipolar resistive switching, making them suitable both for neuromorphic computing applications and hardware cryptography. A detailed statistical evaluation was conducted over 100 consecutive switching cycles using multiple parameter extraction techniques to assess cycle-to-cycle (C2C) variability in key RS parameters, including set/reset voltages and corresponding currents. One and two-dimensional coefficients of variation (1DCV and 2DCV) were calculated to quantify variability and identify application potential. Results demonstrate moderate variability compatible with neuromorphic computing and cryptographic functionalities, including physical unclonable functions and true random number generation. These findings position Prussian Blue-based memristors as promising candidates for low-cost, stable, and multifunctional memory.
Additive Manufacturing (AM), also known as rapid prototyping or 3D printing, is widely used across various industries, including medical products and automotive spare parts. The COVID-19 pandemic has further accelerated its adoption to address supply chain disruptions caused by shortages in production resources and logistics constraints. However, as AM integrates into supply chains, structural changes in nodes and data flows create new challenges in information sharing and data standardization. Ontologies have proven effective in enhancing data interoperability and improving information quality through semantic modeling. Despite this, a comprehensive approach that combines AM and logistics ontologies to address cross-domain challenges remains underexplored. This study develops an ontology-based supply chain model for AM by integrating existing AM and logistics ontologies. Using the Design Science Research Methodology (DSRM), the proposed ontology is constructed and instantiated with a sample dataset for validation. The results provide a foundational framework for improving data management and coordination in AM supply chains.
Additive manufacturing (AM) revolutionises traditional manufacturing by enabling localised, on-demand production, reducing waste, and enhancing design flexibility. The adoption of the AM method also transforms supply chains (SCs) in several perspectives due to, removing and adding some nodes and arcs. While this transformation offers numerous benefits, it also presents significant challenges in configuring an optimal network for AM SCs, especially when a decentralization network is preferable. In this regard, this study investigates using the network optimisation modelling (NOM) method to optimise decentralised AM SCs. Utilising AnyLogistix software, the study models an AM SC to determine the optimal network configuration that minimises costs while ensuring timely deliveries. It explores the advantages of decentralised production, such as reduced lead times and costs. This study contributes to the growing body of literature by addressing gaps related to NOM in AM contexts, providing valuable insights for practical applications in SC management.
Smoking remains a prominent preventable health risk in Germany, creating a need for effective cessation interventions. Digital smoking cessation interventions (DSCIs) present promising support for individuals aiming to quit, yet their utilization and acceptance are not thoroughly understood. This study analyzes usage patterns and acceptance levels of DSCIs among smokers, occasional smokers, and former smokers in Germany, focusing on user behavior, acceptance determinants, and the influence of prescription and reimbursement status. An online questionnaire based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model was administered to participants. Data were collected through recruitment via social media, email lists, counseling groups, and public postings. The responses were analyzed using SPSS. The study included 173 participants (61.85% female, 37.57% male, 0.58% diverse) with an average age of 35.28 years. They reported smoking for an average of 18.21 years and attempting cessation 3.42 times. Among respondents, 41.62% had used DSCIs, predominantly former smokers (54.17%) and women (79.17%), with the “Smoke Free” app being the most utilized intervention. Although 73.05% expressed willingness to (re)use DSCIs, actual usage showed moderate acceptance levels. Significant predictors of acceptance included willingness to pay (p = 0.013), self-efficacy (p = 0.018), and physician prescription with clinical evidence (p = 0.019). The results highlight a rising demand for digital solutions focused on long-term smoking cessation, particularly among middle-aged women, emphasizing the need for a deeper understanding of acceptance drivers and model expansions to address healthcare dynamics.
This study investigates the antimicrobial potential of an indirect cold plasma method for the treatment of wounds. Indirect plasma methods differ from direct methods in that the cold plasma does not come into direct contact with the surface to be treated. The indirect plasma method described here has been implemented in the PLASMOHEAL device. The device generates an aerosol of liquid particles, which is conditioned with plasma reaction products and passed over the areas to be treated without contact. In vitro tests show a significant germ reduction of 3.4 to 4.5 log levels against various microorganisms. In vivo tests on volunteers demonstrate a reduction in E. coli contamination of 4.06 to 5.15 log levels. These results show that indirect plasma methods can achieve equivalent effects to direct methods. The highly effective, pain-free treatment at moderate costs make the indirect plasma method a promising option in modern wound care.
