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Auswirkungen einer möglichen Überlastung im Rettungsdienst auf die Patientenversorgung und die Gesundheit der Beschäftigten
No full textEinen wichtigen Bestandteil unserer Gesellschaft und der notfallmedizinischen Versorgungskette stellt der Rettungsdienst dar. Aktuell leidet er unter den Veränderungen in der Gesellschaft, wodurch die Herausforderungen an den Rettungsdienst stetig zu nehmen. In diesem Fall sind es die steigenden Einsatzzahlen und -zeiten. Diese können die Risikofaktoren des Berufes, welche die Mitarbeiter physisch und psychisch belasten können, verstärken. Daher ist es wichtig herauszufinden, wie überlastet der Rettungsdienst tatsächlich ist und ob sich die Belastung auf die Gesundheit der Rettungskräfte auswirkt und somit eine Patientengefährdung entsteht.
Ziel dieser Bachelorarbeit ist es, eine Übersicht der möglichen Faktoren, welche die Rettungskräfte gesundheitlich belasten und die zu Überlastungen im Rettungsdienst selbst führen, vorzustellen und diese mit Daten einer durchgeführten Umfrage über die tatsächliche Lage des Rettungsdienstes zu vergleichen.
Dafür wird eine umfassende Literaturrecherche zu den Themen Gesundheit der Rettungskräfte und gesellschaftlichen Veränderungen durchgeführt. Des Weiteren wird eine Umfrage, welche die Lage des Rettungsdienstes widerspiegelt, erstellt und an Rettungskräfte geschickt. Anschließend werden die Ergebnisse aufgeführt, ausgewertet und mit Hilfe der Literaturrecherche untersucht.
Die Ergebnisse der Ausarbeitung zeigen deutlich, dass der Rettungsdienst überlastet ist. Die Überlastung wirkt sich nur auf wenige Rettungskräfte aus, wodurch weiterhin von einer qualitativ hochwertigen notfallmedizinischen Patientenversorgung ausgegangen werden kann. Eine Voraussetzung dafür stellt allerdings die Bereitschaft der Eigenleistung für die Gesundheit der Rettungskräfte dar.
Wenn keine Veränderungen wie Umstrukturierung des Rettungswesens oder Aufklärung der Bürger erfolgen, so wird die Überlastung des Rettungsdienstes zunehmen, wodurch sich die Lage des Rettungsdienstes weiter verschlimmern würde. Es würde zunehmend die Gesundheit der Rettungskräfte negativ beeinflussen, wodurch das Risiko einer Patientengefährdung steigt
31/2023 Zweite Änderungssatzung zur Wahlordnung zu den Gremien und Organen der Hochschule Rhein-Waal vom 27.09.2023
No full textLong-term effects after a short learning event: Evaluation of the effectivity of an ESD (Education for Sustainable Development) module on the importance of pollinating insects
No full textIn order to design a fairer and more sustainable planet, the United Nations have agreed on the Agenda 2030 and the corresponding 17 sustainable development goals (SDGs) as well as the Education for Sustainable Development (ESD) campaign. In order to assess the effectivity of this widely funded campaign, evaluation is in order. While usually larger projects and programs are evaluated, this thesis focuses on whether a short one-time education event can also create long-term learning effects. For a two-hour module on insect and pollinator diversity and their economic importance and conservation, evaluation was performed using empirical questionnaires with statistical analysis as well as participatory evaluation methodology. Statistical analysis shows that knowledge, skills and motivation increase strongly directly after the event, but for some part lower significantly after three weeks’ time. Participatory interviews reflect very different developments in the participants, with some only having an increased awareness of the topic but others having implemented conservation measures and networking in their daily lives. Overall, the evaluation shows that long-term effects of short events are only partly observable and different depending on the participating person. There is, however, a potential for further education and activism whose importance should not be underestimated. Accordingly, even though longer programs are surely more effective in creating long-term learning effects and activism, short events can be very efficient as a gateway into many sustainability topics and should be part of ESD
Evaluating the potential of mRNA vaccines for human rabies
No full textTo this date, rabies is still one of the most fatal diseases worldwide and generates at least 59,000 deaths per year. Although current vaccines are safe and effective, RNA technology proposes improved vaccines that are potentially able to facilitate eradication of rabies until 2030. In this review, RNA vaccine candidates are evaluated for their potential to be effective against rabies and to be implemented in immunization programs. To understand the relevance of disease prevention, rabies virus and its function in the body is clarified. RNA technology is explained to comprehend its mode of action in the human immune system and its potential to be a vaccine platform. Comparison to existing vaccines showed advantages in several characteristics that are relevant for vaccine approval. RNA vaccines scored better in efficacy, stability, production, and dosing, but there is still room for improvements in terms of safety and tolerability. Theoretical potential is proven, but practical translation of RNA vaccines into clinical candidates requires more attention, as safety and tolerability are the leading limiting factor for approval of any new drug or vaccine. Producing a safe, stable and effective RNA vaccine does not necessarily ensure less rabies deaths, because rabies control strategies heavily rely on proper funding and availability in these rabies endemic region. Nonetheless, RNA vaccine technology offers potential solutions for the elimination of rabies
MESENCHYMAL STEM CELLS (MSC) IN BREAST CANCER TREATMENT: ADVANTAGES AND CHALLENGES OVER TRADITIONAL THERAPIES
No full textBreast cancer is one of the most prevalent and deadly malignancies in women globally, due to its aggressive behavior and poor response to traditional treatments. Despite the significant improvements in understanding of tumor therapies, efforts to treat patients with advanced cancer are still hampered by the size and heterogeneity of the tumor burden and the development of drug resistance. Therefore, the need for novel therapeutic strategies is essential.
Mesenchymal stem cells (MSCs) have been studied for a long time with encouraging results in the treatment of metastatic cancers, including breast cancer. The discovery of MSCs' therapeutic abilities to inhibit the growth of cancer, cause cell death, and trigger immune responses served as a catalyst for the development of bioengineered MSCs. The use of MSCs in cell therapy is the subject of numerous investigations now.
This thesis aimed to summarize the current knowledge on traditional breast cancer treatments and compare them to novel approaches based on MSCs usage. To this end, scientific publications retrieved from databases such as PubMed were selected considering specific inclusion/exclusion criteria and were extensively analyzed. Results obtained underline the potential applications of MSCs for breast cancer treatment, such as using MSCs as carriers of oncolytic viruses, suicide genes, and anticancer medicines to malignancies. Furthermore, challenges and limitations of several MSCs based treatments and MSCs control were evaluated and discussed based on ongoing clinical trials
15/2023 Zweite Änderungssatzung zur Prüfungsordnung für den Bachelorstudiengang Biomaterials Science B.Sc. der Fakultät Technologie und Bionik an der Hochschule Rhein-Waal vom 15.11.2022
No full text18/2023 Gleichstellungsquoten zur Gewährleistung der Chancengerechtigkeit von Frauen und Männern bei der Berufung von Professorinnen und Professoren der Fakultät Technologie und Bionik an der Hochschule Rhein-Waal
No full text19/2023 Gleichstellungsquoten zur Gewährleistung der Chancengerechtigkeit von Frauen und Männern bei der Berufung von Professorinnen und Professoren der Fakultät Life Sciences an der Hochschule Rhein-Waal
No full text21/2023 Gleichstellungsquoten zur Gewährleistung der Chancengerechtigkeit von Frauen und Männern bei der Berufung von Professorinnen und Professoren der Fakultät Kommunikation und Umwelt an der Hochschule Rhein-Waal
No full textEstimating the State of Charge of Lithium-ion Batteries using Deep Learning for Electric Vehicle Applications
No full textReliable and safe operation of Li-ion batteries in electric vehicles relies on an accurate estimation of their states, more specifically their state of charge (SOC), which is used for managing charging and discharging, for example. Deep learning is increasingly used for modeling complex systems, which generates more interest in battery state estimation.
The theoretical investigation of candidate neural networks for SOC estimation is preceded an experimental approach to evaluate the shortlisted neural networks performance. Publicly available testing datasets representing real driving cycles has been used to train the selected networks, taking into consideration different operating temperatures for the exact cycles. Testing the prediction capability of the models across a realistic range of operating conditions is the driving factor for including testing cycles with a temperature range of [0–40] °C in the training datasets. A demonstration of the full training cycle is conducted, along with the optimization problem of network parameters.
This work demonstrates the applicable neural network family used for training this kind of sequential data, followed by the shortlisting of two neural networks based on their performance using literature research. After selecting the networks, finer evaluation criteria is applied, including both their prediction capability and training requirements. The results show that both networks, long short-term memory (LSTM) and gate recurrent units (GRU), offer satisfactory prediction capability with mean absolute error (MAE) of (2.9%, 2.7%) respectively, when averaged across the tested temperatures. The GRU network on the other hand had significantly higher resource requirements and a more complex architecture. Finally, a more recent architecture is briefly discussed, borrowing the strength of a parallel application field. The scope of this work is to highlight both the capabilities and cost of training deep learning networks for SOC estimation