OPUS Publikationsserver der Hochschule Rhein-Waal
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IMPROVING HOCHSCHULE RHEIN-WAAL CAFETERIA THROUGH LEAN PRACTICES AND MATRIX PROCEDURE FOR LAYOUT PLANNING
No full textCafeterias, integral to the food and beverage industry, face challenges such as long queues and high waiting times, leading to customer dissatisfaction. While temporary measures like altering food serving strategies or eliminating stations may provide short-term relief, having sustainable solutions necessitates comprehensive layout planning. The thesis aims to propose an optimized cafeteria layout which will reduce the throughput distance and throughput time of the customers, and eventually, suggest recommendations for Hochschule Rhein-Waal Mensa. Through data collection on customer movements and preferences, service times, and waiting periods, the existing layout was analyzed, and it revealed inefficiencies. By applying Lean management tools and the Matrix Procedure, a proposed layout was formulated, aiming to reduce throughput distance and time. Evaluation of both current and proposed layouts showed that out of 52 customers, 69.23% of customers’ throughput distance was reduced, and 59.61% of customers’ throughput time was reduced compared to the existing layout. The thesis concludes by suggesting general recommendations and improvements, including an optimized process diagram and layout, and a proposed spaghetti diagram. Eventually, the research contributes to food and beverage field by providing insights into better layout planning for facility managers, logisticians, and entrepreneurs, offering tangible strategies to enhance cafeteria operations and customer satisfaction
13/2024 Ordnung zur Regelung des Verfahrens zur Verhängung von Ordnungsmaßnahmen bei Ordnungsverstößen durch Studierende an der Hochschule Rhein-Waal vom 10.09.2024
No full textImplementation of the Brain Imaging Data Structure (BIDS) with EEG Data for Machine Learning-Based applications in Schizophrenia patients
No full textSchizophrenia, a complex mental disorder characterized by delusions, hallucinations, and cognitive impairments, remains challenging to diagnose accurately due to the reliance on subjective clinical evaluations. This study aimed to develop a robust machine learning model for classifying schizophrenia patients using neurophysiological markers derived from Electroencephalography (EEG) data. The work explored multiple machine-learning architectures, including traditional Support Vector Machines (SVM) and deep learning models such as Long Short-Term Memory (LSTM) and CNN-LSTM hybrids.
EEG data was collected from schizophrenia patients and healthy controls, with preprocessing conducted using the Brain Imaging Data Structure (BIDS) framework to ensure consistency and standardization. Given the varying quality and structure of the EEG recordings—such as missing channels and differing recording lengths—the study employed several preprocessing techniques, including channel interpolation, zero-padding, and limiting data to the first three minutes of resting-state recordings for standardization. Data imbalance was addressed through the application of the Synthetic Minority Over-sampling Technique (SMOTE) to ensure balanced representation of both schizophrenia and control groups during model training.
The findings showed that the SVM model reached an accuracy of 88.8% in patient classification, whereas the LSTM model, after addressing data imbalances through SMOTE, achieved an improved classification accuracy of 99.3%. However, challenges arose when attempting to predict schizophrenia severity levels using MMN task data and a CNN-LSTM hybrid model. Despite efforts to standardize the number of epochs across subjects, the model achieved a more modest accuracy of 66.7%. This lower accuracy was largely attributed to the limited and unbalanced dataset, as some subjects had fewer than the target number of epochs.
Further challenges included BIDS conversion issues due to discrepancies in file naming conventions between .sdt and .set files, which required considerable time to resolve. The successful creation of a tailored BIDS conversion script ensured the data could be standardized and used effectively for analysis.
In conclusion, while the classification models achieved high accuracy, particularly the LSTM-based approach, predicting schizophrenia severity levels remains a challenge due to the limitations of the available dataset. Future research will focus on gathering larger and more balanced datasets, refining preprocessing methods, and optimizing model architectures to improve prediction accuracy. Ultimately, the goal is to apply these models in clinical settings to facilitate early diagnosis and intervention, providing timely medical care to schizophrenia patients before their symptoms worsen
Using the U-Net Model for Steel Microstructure Identification and Analysis
No full textThis thesis presents the development and training of a U-Net- a convolutional neural network (CNN) model for the segmentation and classification of 4 microstructural phases in C45 steel: retained austenite, martensite, pearlite, and ferrite. It also features the steps taken to prepare the metallographic samples. The U-Net was designed and developed for biomedical microscopy
imaging but has a wide array of uses for semantic segmentation in other fields as well. Utilizing Nital-etched microscopy images, the network was trained to identify and differentiate between these microstructures, which is critical for determining the mechanical properties of the steel.
The dataset is comprised of a set of 35 grayscale images, and its corresponding labels/masks annotated with 4 colours for the various phases. Moreover, this thesis is an attempt at using deep learning to segregate retained austenite from martensite through visual LOM methods, as opposed to EBSD and XRD which are the widely used methods for identifying and quantifying retained austenite in a sample. The trained network demonstrated reasonable performance in segmenting the microstructural components. This research highlights the potential of deep learning approaches in materials science, particularly for automating the analysis of metallographic images, contributing to more efficient and precise material characterization
18/2024 Ordnung über die Auslaufplanung für den Bachelorstudiengang „Industrial Engineering B.Sc.” der Fakultät Technologie & Bionik an der Hochschule Rhein-Waal vom 17.10.2024
No full textEntwicklung von Handlungsansätzen einer adressatenorientierten Förderung der psychischen Gesundheit jugendlicher Schülerinnen und Schüler in Ganztagsschulen
No full textNicht nur die Covid-19-Pandemie wirkte auf die allgemeine Verschlechterung der psychischen Gesundheit von Jugendlichen in Deutschland. Das Erleben vielseitiger Entwicklungen in der Jugendzeit machen diese Lebensphase zu einer psychisch vulnerablen Zeit. Gleichzeitig haben Schulen einen großen Einfluss auf das Leben der Schülerinnen und Schüler und bilden für die psychische Gesundheitsförderung einen zentralen Wirkungsort. Besonders Ganztagsschulen zeichnen sich durch erweiterte Betreuungszeiten und Ressourcen aus. Wünsche und Interessen der Schülerinnen und Schüler werden sowohl in der Gesundheitsförderung als auch in der Ganztagsschule selten erhoben und einbezogen (vgl. BMFSFJ, 2017).
Die vorliegende Arbeit geht der Frage nach, welche adressatenorientierten Handlungsansätze aus der aktuellen Forschungslage zur Förderung der psychischen Gesundheit im Setting Schule für jugendliche Schülerinnen und Schüler an Ganztagsschulen abgeleitet werden können. Ziel ist die Ableitung adressatenorientierter, auf Ganztagsschulen ausgerichteter Handlungsansätze.
Zur Beantwortung der Forschungsfrage wurde mithilfe einer systematischen Literaturrecherche der aktuelle Forschungsstand der psychischen Gesundheitsförderung im Setting Schule erhoben. Auf Grundlage der analysierten Ergebnisse wurden adressatenorientierte Handlungsansätze diskutiert.
Die Ergebnisse zeigen den Bedarf der Edukation von Lehrkräften in der Früherkennung psychischer Krankheiten, die zusammen mit ganztagsschulischen Unterstützungsnetzwerken das Potenzial tragen, Schülerinnen und Schüler frühzeitig zu unterstützen. Des Weiteren hebt sich unter Orientierung am Adressaten die Dringlichkeit einer geschlechtersensibleren psychischen Gesundheitsförderung ab. Des Weiteren werden fehlende entwicklungsorientierte und partizipative Formen der psychischen Gesundheitsförderung festgestellt. Ganztagsschulen, die sich als Teil der Lebenswelt ihrer Schülerinnen und Schüler verstehen, können schulische und psychische Faktoren stärken.
Die erarbeiteten Handlungsansätze heben Diskrepanzen und Dringlichkeiten hervor und ergänzen die bestehende psychische Gesundheitsförderung um eine jugendorientierte Perspektive
Medi: A Software System for Improving the Analysis and Interpretation of Medical Tests in Clinical Laboratories.
No full textThe rapid increase in patient numbers and the complexity of clinical information, coupled with a significant lack of assistive technologies for routine procedures, has emphasized the urgent need for innovative technological solutions in clinical laboratories. This need is amplified by the existence of over 238 blood tests, highlighting the challenge of managing and interpreting vast amounts of clinical information and data efficiently.
The primary objective of this research is to evaluate the effectiveness of Medi, a state-of-the-art software system designed to enhance the workflow of medical routine tasks in the pre-testing steps in clinical laboratories. By focusing on routine hematology, biochemistry, and microbiology tests, the study aims to revolutionize clinical laboratory processes, improve diagnostic accuracy, and enhance patient care.
Through an extensive approach regarding specimen collection of blood and the utilization of automated medical devices, this study sheds light on the advantage of Medi to streamline operations within clinical laboratories. The research methodically assesses the impact of Medi on laboratory workflows and patient outcomes, employing a user-centric design to ensure the software meets the practical needs of healthcare professionals.
Medi demonstrated significant improvements in laboratory efficiency, with streamlined workflow. Key findings include the standardization of blood specimen collection procedures, limiting blood-tube-selection errors, and a positive impact on laboratory operations. The software facilitated a more cohesive integration of clinical data, contributing to better-informed decision-making and improved patient care.
By comparing these outcomes with the current state of clinical diagnostics, Medi's introduction represents a transformative shift in laboratory operations. It not only addresses the pressing need for advanced assistive technologies in the clinical laboratory settings but also sets a new standard in patient care. This research underlines the potential of integrating software solutions with existing laboratory technologies, paving the way for a new era in clinical testing
Living Green Walls in the Workplace: Evaluating the Impact on Employee Well-being, Productivity, and Indoor Environmental Quality
No full textLiving Green Walls (LGWs) represent a promising pathway towards sustainable agriculture and have proven psychological benefits applicable to educational and workspace environments (Van den Berg et al., 2016). This bachelor thesis investigates the impact of LGWs on indoor environmental quality, employees' well-being, and performance in the offices of the Facility Management Department of Hochschule Rhein-Waal (HSRW) - Kleve campus. A hybrid methodology is adopted, combining sensor measurements of physical parameters and a self-developed questionnaire assessing perceived LGW impact. The sensors are set up in four offices, three of which featured LGWs, while the questionnaire is distributed online to workers before and after the LGW installation. Eight workers participated in the study, of which 62.5 % were female, and 37.5 % were male, with age range between 25 and above 45 years old.
The results indicate a positive overall impact of LGWs on office environmental quality and employee well-being. The CO2 concentration was reduced on average by 30 ppm, whereas the relative humidity was increased, on average, by approximately 6 %. Hence, the LGWs enhanced slightly the air quality in the offices. Moreover, the target group claimed that their motivation level, on average, was raised by 15% after exposure to the LGWs. Hence, half of them agreed that the LGWs impacted their performance positively. However, challenges emerged concerning LGW technical design and employee engagement with the research. Despite obstacles, the study suggests potential for future improvements in methodology and technical aspects, offering promising project ideas for further research. Furthermore, the overall positive impact of LGW in this study has proven that integrating such systems into work environment can boost the motivation level of the workers and improve the environmental quality in workspaces
AUTOMATION OF SOLAR WATER DISINFECTION (SODIS) ON A SMALL-SCALE USING ARDUINO DUE
No full textClean water is essential for human health and well-being. In many parts of the world, access to safe drinking water is limited, leading to various waterborne diseases. Solar water disinfection(SODIS) is a simple and cost-effective method used to purify water using sunlight, where itinactivates the pathogens and makes the water safe for consumption. This disinfection process involves filling polyethylene terephthalate (PET) bottles with water and exposing them to sunlight for a specified period. The aim of this thesis is to develop a working prototype that exploresthe automation of SODIS on a small-scale using Arduino Due, a microcontroller board that offers advanced features for automation. The prototype consists of the necessary parts to perform SODIS automatically. In conventional SODIS, it is assumed that the required SODIS criteria are met within 6 hours of a sunny day. In contrast, in the prototype microcontroller pumps the water into the PET bottle, monitors sunlight exposure for 6 hours, determines disinfection, and then pumps out the water for consumption only when the threshold values for irradiance are met. If the conditions are not met, the exposure is extended by an hour each time and checked again. The prototype automates the SODIS on a small-scale using cost-efficient components. However, the prototype lacks oxygenation capacity, and there is a possibility of entering an infinite loop if the irradiance values are not met for a long time. The prototype could be further developed with extra sensors and extensive research for advanced automation
Analyse der Entwicklung von Virtualisierung zu Containerisierung der IT-Infrastruktur in der öffentlichen Verwaltung unter besonderer Berücksichtigung von Cybersicherheit und Geschäftskontinuität
No full textThis thesis examines the development from virtualisation to containerisation of the IT infrastructure in public administration. In particular, the aspects of cyber security and business continuity are analysed. The study covers the basics of virtualisation and containerisation, including their architecture and isolation mechanisms. Furthermore, the security risks and benefits of both approaches are analysed, especially with regard to container outbreak scenarios and vulnerability lifecycle management. A particular focus is placed on the breakdown of containerisation into different groups, including container runtime, container image and orchestration, in order to enable a differentiated view of security and efficiency potentials. Based on these findings, a concrete architecture proposal is presented that includes recommendations for the secure orchestration and management of containers. The aim of the work is to show how modern container technologies such as Docker and Kubernetes can provide a secure, scalable and cost-efficient platform for public administrations.Die vorliegende Arbeit untersucht die Entwicklung von der Virtualisierung hin zur Containerisierung der IT"=Infrastruktur in der öffentlichen Verwaltung. Dabei werden insbesondere die Aspekte der Cybersicherheit und der Geschäftskontinuität beleuchtet. Die Untersuchung umfasst die Grundlagen der Virtualisierung und Containerisierung, inklusive ihrer Architektur und Isolationsmechanismen. Des Weiteren werden die Sicherheitsrisiken und "=vorteile beider Ansätze analysiert, insbesondere im Hinblick auf Container"=Ausbruchsszenarien und das Schwachstellen"=Lifecycle"=Management. Ein besonderer Schwerpunkt liegt auf der Aufteilung der Containerisierung in verschiedene Gruppen, einschließlich Container Runtime, Container Image und Orchestrierung, um eine differenzierte Betrachtung der Sicherheits"= und Effizienzpotenziale zu ermöglichen. Auf Basis dieser Erkenntnisse wird ein konkreter Architekturvorschlag präsentiert, der Empfehlungen für die sichere Orchestrierung und Verwaltung von Containern beinhaltet. Ziel der Arbeit ist es, aufzuzeigen, wie moderne Container"=Technologien wie Docker und Kubernetes eine sichere, skalierbare und kosteneffiziente Plattform für öffentliche Verwaltungen bieten können