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The growing threat posed by multidrug-resistant (MDR) pathogens, such as Klebsiella pneumoniae (Kp), represents a significant challenge in modern medicine. Traditional antibiotic therapies are often ineffective against these pathogens, leading to high mortality rates. MDR Kp infections pose a novel challenge in military medical contexts, particularly in Medical Biodefense, as they can be deliberately spread, leading to resource-intensive care in military centres. Recognizing this issue, the European Defence Agency initiated a prioritised research project in 2023 (EDF Resilience PHAGE- SGA 2023). To address this challenge, the Bundeswehr Institute of Microbiology (IMB) leads BMBF- (Federal Ministry of Education and Research) and EU-funded projects on the use of bacteriophages as adjuvant therapy alongside antibiotics. Since 2017, the IMB has isolated and characterised Kp phages, collecting over 600 isolates and optimizing their production for therapy, in compliance with the EMA (European Medicine Agency) guidelines. This involves in vitro phage genome packaging to minimize endotoxin load, reduce manufacturing costs, and shorten production times. The goal of this work was to establish MinION sequencing (Oxford Nanopore Technology) as a quick and reliable way for initial identification and characterisation of phage genomes. Especially as a quick screening method for isolated on Kp, prior to more precise but also more expensive and time consuming sequencing methods like Illumina. This characterisation is crucial for developing a personalized pipeline aimed at producing magistral or Good Manufacturing Practice (GMP) quality medicinal phage solutions tailored individually for each patient. DNA extraction methods were compared to identify suitable input DNA for sequencing purposes. Additionally, the quality of this DNA was as- sessed to determine its suitability for in vitro phage packaging, which was successfully done achieving a phage titer of 103, confirming that the DNA used for MinION sequencing could indeed be used for acellular packaging. The created genomes were annotated and compared with Illumina sequencing, revealing high similarity in all five individually tested cases. Between the generated sequences only a 4% maximal percentual difference in genome size was observed, while simultaneously showing high similarity in the actual sequence. Throughout the course of this study, a total of 645.15 GB of sequencing data were generated. In total, 38 phages were successfully characterised, with 21 phage genomes assembled and annotated, and saved in the IMB database.
Steroid hormones (SHs) are a rising concern due to their high bioactivity, ubiquitous nature, and prolonged existence as a micropollutants in water, they pose a potential risk to both human health and the environment, even at low concentrations. Estrogens, progesterone, and testosterone are the three important types of steroids essential for human development and maintaining multiorgan balance, are focus to this concern. These steroid hormones originate
from various sources, including human and livestock excretions, veterinary medications, agricultural runoff, and pharmaceuticals, contributing to their presence in the environment. According to the recommendation of WHO, the guidance value for estradiol (E2) is 1 ng/L. There are several methods been attempted to remove the SH micropollutant by conventional water and wastewater technologies which are still under research. Among the various methods, electrochemical membrane reactor (EMR) is one of the emerging technologies that can address the challenge of insufficient SHs removal from the aquatic environment by conventional treatment. The degradation of SHs can be significantly influenced by various factors when treated with EMR.
In this project, the removal of SH and the important mechanism for the removal using carbon nanotube CNT-EMR is studied and the efficiency of CNT-EMR in treating the SH micropollutant is identified. By varying different parameters this experiment is carried out with the (PES-CNTs) ultrafiltration membrane. The study is carried out depending upon the SH removal based on the limiting factor such as cell voltage, flux, temperature, concentration, and type of the SH.
Globale Ereignisse politischen, wirtschaftlichen oder kulturellen Ursprungs führen dazu, dass Unternehmen sich gezwungen sehen zu handeln, um wettbewerbsfähig zu bleiben. In vielen Fällen wird dabei so vorgegangen, dass versteckte Preiserhöhungen vollzogen werden. Auch Unternehmen, die mit ihrem Angebot einen gesellschaftlichen Zusatznutzen erbringen, erhöhen die Preise, da nur so der verfolgte ökologische oder soziale Purpose erhalten werden kann, kommunizieren dies jedoch offen und erhöhen dadurch ihre Kundenloyalität. Dieses Vorgehen ist bislang jedoch überwiegend in den USA zu beobachten. Da das Thema Purpose auch in Deutschland immer höhere Relevanz erfährt, stellt sich die Frage der Übertragbarkeit. Die Forschungsfrage lautet deshalb: Inwiefern beeinflusst die Kommunikation von purpose-getriebenen Preissteigerungen das Markenimage von B2C-Unternehmen in Deutschland? Um der Forschungsfrage nachzugehen, wurde ein empirischer Forschungsansatz gewählt, der in Form einer quantitativen Studie umgesetzt wurde, die die Meinung der Studierendenschaft der Gen Z und Y der Hochschule Offenburg abbildet. Die Ergebnisse der Umfrage zeigten, dass die Glaubwürdigkeit und Akzeptanz von purpose-getriebenen Preiserhöhungen vom gegenwärtigen Markenimage bzw. dem übergeordneten Image einer Branche abhängen. Purpose wird eine hohe Bedeutung zugesprochen, die sich jedoch aufgrund der Preissensibilität der Zielgruppe nicht immer in der Markenwahl widerspiegelt. Dies verdeutlicht, dass Purpose das Potenzial besitzt, das Markenimage nachhaltig zu beeinflussen und zu prägen und die Zielgruppe der Gen Z und Y zukünftig an sich zu binden. Ferner wurde deutlich, dass bei der Umsetzung von Purpose bestimmte Kriterien verfolgt werden sollten, die bei der Kommunikation zu berücksichtigen sind, um eine authentische Wirkung zu erzielen.
This thesis focuses on the development and implementation of a Datagram Transport Layer Security (DTLS) communication framework within the ns-3 network simulator, specifically targeting the LoRaWAN model network. The primary aim is to analyse the behaviour and performance of DTLS protocols across different network conditions within a LoRaWAN context. The key aspects of this work include the following.
Utilization of ns-3: This thesis leverages ns-3’s capabilities as a powerful discrete event network simulator. This platform enables the emulation of diverse network environments, characterized by varying levels of latency, packet loss, and bandwidth constraints.
Emulation of Network Challenges: The framework specifically addresses unique challenges posed by certain network configurations, such as duty cycle limitations. These constraints, which limit the time allocated for data transmission by each device, are crucial in understanding the real-world performance of DTLS protocols.
Testing in Multi-client-server Scenarios: A significant feature of this framework is its ability to test DTLS performance in complex scenarios involving multiple clients and servers. This is vital for assessing the behaviour of a protocol under realistic network conditions.
Realistic Environment Simulation: By simulating challenging network conditions, such as congestion, limited bandwidth, and resource constraints, the framework provides a realistic environment for thorough evaluation. This allows for a comprehensive analysis of DTLS in terms of security, performance, and scalability.
Overall, this thesis contributes to a deeper understanding of DTLS protocols by providing a robust tool for their evaluation under various and challenging network conditions.
The progress in machine learning has led to advanced deep neural networks. These networks are widely used in computer vision tasks and safety-critical applications. The automotive industry, in particular, has experienced a significant transformation with the integration of deep learning techniques and neural networks. This integration contributes to the realization of autonomous driving systems. Object detection is a crucial element in autonomous driving. It contributes to vehicular safety and operational efficiency. This technology allows vehicles to perceive and identify their surroundings. It detects objects like pedestrians, vehicles, road signs, and obstacles. Object detection has evolved from being a conceptual necessity to an integral part of advanced driver assistance systems (ADAS) and the foundation of autonomous driving technologies. These advancements enable vehicles to make real-time decisions based on their understanding of the environment, improving safety and driving experiences. However, the increasing reliance on deep neural networks for object detection and autonomous driving has brought attention to potential vulnerabilities within these systems. Recent research has highlighted the susceptibility of these systems to adversarial attacks. Adversarial attacks are well-designed inputs that exploit weaknesses in the deep learning models underlying object detection. Successful attacks can cause misclassifications and critical errors, posing a significant threat to the functionality and safety of autonomous vehicles. With the rapid development of object detection systems, the vulnerability to adversarial attacks has become a major concern. These attacks manipulate inputs to deceive the target system, significantly compromising the reliability and safety of autonomous vehicles. In this study, we focus on analyzing adversarial attacks on state-of-the-art object detection models. We create adversarial examples to test the models’ robustness. We also check if the attacks work on a different object detection model meant for similar tasks. Additionally, we extensively evaluate recent defense mechanisms to see how effective they are in protecting deep neural networks (DNNs) from adversarial attacks and provide a comprehensive overview of the most commonly used defense strategies against adversarial attacks, highlighting how they can be implemented practically in real-world situations.
Privacy is the capacity to keep some things private despite their social repercussions. It relates to a person’s capacity to control the amount, time, and circumstances under which they disclose sensitive personal information, such as a person’s physiology, psychology, or intelligence. In the age of data exploitation, privacy has become even more crucial. Our privacy is now more threatened than it was 20 years ago, outside of science and technology, due to the way data and technology highly used. Both the kinds and amounts of information about us and the methods for tracking and identifying us have grown a lot in recent years. It is a known security concern that human and machine systems face privacy threats. There are various disagreements over privacy and security; every person and group has a unique perspective on how the two are related. Even though 79% of the study’s results showed that legal or compliance issues were more important, 53% of the survey team thought that privacy and security were two separate things. Data security and privacy are interconnected, despite their distinctions. Data security and data privacy are linked with each other; both are necessary for the other to exist. Data may be physically kept anywhere, on our computers or in the cloud, but only humans have authority over it. Machine learning has been used to solve the problem for our easy solution. We are linked to our data. Protect against attackers by protecting data, which also protects privacy. Attackers commonly utilize both mechanical systems and social engineering techniques to enter a target network. The vulnerability of this form of attack rests not only in the technology but also in the human users, making it extremely difficult to fight against. The best option to secure privacy is to combine humans and machines in the form of a Human Firewall and a Machine Firewall. A cryptographic route like Tor is a superior choice for discouraging attackers from trying to access our system and protecting the privacy of our data There is a case study of privacy and security issues in this thesis. The problems and different kinds of attacks on people and machines will then be briefly talked about. We will explain how Human Firewalls and machine learning on the Tor network protect our privacy from attacks such as social engineering and attacks on mechanical systems. As a real-world test, we will use genomic data to try out a privacy attack called the Membership Inference Attack (MIA). We’ll show Machine Firewall as a way to protect ourselves, and then we’ll use Differential Privacy (DP), which has already been done. We applied the method of Lasso and convolutional neural networks (CNN), which are both popular machine learning models, as the target models. Our findings demonstrate a logarithmic link between the desired model accuracy and the privacy budget.
This study investigates the impact of global payroll outsourcing on organizational efficiency and cost reduction based on the analysis of diverse implications stemming from thirty one (31) survey results. The findings reveal multifaceted challenges and benefitsassociated with outsourcing global payroll processing.
The research also unveils the most benefits of global payroll outsourcing. Notably, there's a consensus on the reduction in time-to-process payroll, cost per payroll processed, and improved payroll accuracy rate. Outsourcing streamlines processes, enhances operational efficiency, and contributes to faster, more accurate financial reporting.
Despite these benefits and challenges, statistical analysis reveals weak correlations between outsourcing global payroll and cost reduction or improved efficiency in various parameters, indicating a lack of a significant relationship. Consequently, the results, suggest no substantial correlation between global payroll outsourcing and enhanced efficiency or cost reduction based on this study's data.
Decarbonisation Strategies in Energy Systems Modelling: APV and e-tractors as Flexibility Assets
(2023)
This work presents an analysis of the impact of introducing Agrophotovoltaic technologies and electric tractors into Germany’s energy system. Agrophotovoltaics involves installing photovoltaic systems in agricultural areas, allowing for dual usage of the land for both energy generation and food production. Electric tractors, which are agricultural machinery powered by electric motors, can also function as energy storage units, providing flexibility to the grid. The analysis includes a sensitivity study to understand how the availability of agricultural land influences Agrophotovoltaic investments, followed by the examination of various scenarios that involve converting diesel tractors to electric tractors. These scenarios are based on the current CO2 emission reduction targets set by the German Government, aiming for a 65% reduction below 1990 levels by 2030 and achieving zero emissions by 2045. The results indicate that approximately 3% of available agricultural land is necessary to establish a viable energy mix in Germany. Furthermore, the expansion of electric tractors tends to reduce the overall system costs and enhances the energy-cost-efficiency of Agrophotovoltaic investments.
In the past ten years, applications of artificial neural networks have changed dramatically. outperforming earlier predictions in domains like robotics, computer vision, natural language processing, healthcare, and finance. Future research and advancements in CNN architectures, Algorithms and applications are expected to revolutionize various industries and daily life further. Our task is to find current products that resemble the given product image and description. Deep learning-based automatic product identification is a multi-step process that starts with data collection and continues with model training, deployment, and continuous improvement. The caliber and variety of the dataset, the design selected, and ongoing testing and improvement all affect the model's effectiveness. We achieved 81.47% training accuracy and 72.43% validation accuracy for our combined text and image classification model. Additionally, we have discussed the outcomes from the other dataset and numerous methods for creating an appropriate model.
As the population grows, so does the amount of biowaste. As demand for energy grows, biogas is a promising solution to the problem. Lignocellulosic materials are challenged of slow degradability due to the presence of polymers such as cellulose, lignin and hemicellulose. There are several pretreatment methods available to enhance the degradability of such materials, including enzymatic pretreatment. In this pretreatment, there are few parameters that can influence the results, the most important being the enzyme to solid ratio and the solid to liquid ratio. During this project, experiments were conducted to determine the optimal conditions for those two factors. It was discovered that a solid to liquid ratio of 31 g of buffer per 1 gram of organic dry matter produced the highest reducing sugar release in flasks when combined with 34 mg of protein per 1 gram of organic dry mass. Additionally, another experiment was carried out to investigate the impact of enzymatic pretreatment on biogas production using artificial biowaste as a substrate. Artificial biowaste produced 577,9 NL/kg oDM, while enzymatically pretreated biowaste produced 639,3 NL/kg oDM. This resulted in a 10,6% rise in cumulative biogas production compared to its use without enzymatic pretreatment. By the conclusion of the investigation, specific cumulative dry methane yields of 364,7 NL/kg oDM and 426,3 NL/kg oDM were obtained from artificial biowaste without and with enzymatic pretreatment, respectively. This resulted in a methane production boost of 16,9%. Additionally in case of the reactors with enzymatically pretreated substrate kinetic constant was lower more than double, where maximum volume of biogas increased, comparing to the reactors without enzymatic pretreatment.
In dieser Arbeit werden Untersuchungen an einem neuartigen Sensorkonzept zur Erfassung von Winkelbeschleunigungen durchgeführt. Ziel dieser Arbeit war es, die Möglichkeit, eine Miniaturisierung des Prototyps zu untersuchen. Hierfür wurde eine analytische und experimentelle Untersuchung durchgeführt. Für die analytische Betrachtung erfolgte eine Fehlerfortpflanzung nach Gauß, welche die Fertigungstoleranzen, Dimensionsfehler des Accelerometers, Rauschen und Messabweichungen von Accelerometer und Gyroskop berücksichtigt. Die Ergebnisse zeigen, dass bei Verwendung der hochwertigen Inertial Measurment Units (IMUs) eine theoretische Verkleinerung bis auf 21mm eine höhere Genauigkeit bietet als die numerischen Differentiationen der Winkelgeschwindigkeit.
Für die Verifizierung unter realen Bedingungen wurden verschiedene Prüfkonzepte verglichen.
Dabei erwies sich ein Pendelprüfstand als vielversprechender Ansatz. Durch die Verwendung von Kugellagern kann ein breites Spektrum an Winkelbeschleunigungen abgebildet werden. Die kontinuierliche Erfassung reflektierender Marker auf der Pendelstange ermöglicht die Ermittlung der Winkel, die als Grundlage für ein Modell dienen, wodurch sich reale Winkelbeschleunigungen mit den Messdaten des Sensors vergleichen lassen. Dabei stellt die Modellierung der Verlustterme eine zukünftige Herausforderung dar.
Die Ergebnisse zeigen, dass eine Miniaturisierung des Sensorprototyps möglich ist und das der Pendelprüfstand eine Methode zur Verifizierung darstellt. Dies trägt dazu bei, die Anwendungsmöglichkeiten des Sensorkonzepts in der Praxis zu erweitern.
Im Rahmen dieser Arbeit wurde das elektrisch / elektronische System des Hocheffizienzfahrzeugs „Schluckspecht 6“ hinsichtlich seiner Übersichtlichkeit und Modularität optimiert. Essenziell war die Vernetzung der durch verschiedene Projektgruppen erstellten Teilsysteme mittels des neu integrierten CAN-Bus. Im Zuge der Überarbeitung des E/E-Systems wurde auch ein neuer Gesamtfahrzeugschaltplan angefertigt.
Im Rahmen der Optimierung des E-Antriebsstrangs wurde eine neue Motorsteuerung entwickelt, die aufgrund des verbauten Vierquadrantenstellers neben einem zuverlässigen Antrieb des Schluckspecht 6 zukünftig auch die Steuerung und Regelung von Lastmaschinen in – für den Schluckspecht 6 neu entwickelten – Testständen erlaubt. Für die Messdatenerfassung, während Test- und Rennläufen sowie in den Testständen, wurden diverse Messsysteme realisiert. Dazu gehören die Messung des Motorstroms, der Zwischenkreisspannung und der Motordrehzahl. Basierend auf der Motorstrommessung und Zwischenkreisspannungsmessung wurde eine Stromregelung implementiert, um die Bedienfreundlichkeit und Effizienz des S6 im Rennbetrieb zu erhöhen.
Die Visualisierung von Programmabläufen ist ein zentraler Aspekt für Programmieranfänger, um das Verständnis von Codeabläufen zu erleichtern und den Einstieg in der Softwareentwicklung zu unterstützen. In dieser Masterthesis wird ein speziell auf die Bedürfnisse von Einsteigern zugeschnittenes generisches Framework vorgestellt, wobei der Fokus auf einer einfachen, verständlichen aber auch korrekten Darstellung der Programmausführung liegt. Das Framework integriert das Debugger Adapter Protocol, um den Debugger unterschiedlicher Sprachen ansprechen und verwenden zu können.
In dieser Arbeit werden zunächst die Anforderungen für das generische Framework diskutiert. Anschließend werden bestehende Ansätze zur Visualisierung von Programmabläufen ausführlich untersucht und analysiert. Die Implementierung des Frameworks wird daraufhin detailliert beschrieben, wobei besonderer Wert auf die Erweiterbarkeit unterschiedlicher Sprachen gelegt wird.
Um die Eignung des Frameworks zu evaluieren, werden mehrere Aufgaben aus dem ersten Modul mit der jeweiligen Programmiersprache des Studiengangs Angewandte Informatik der Hochschule Offenburg betrachtet. Die Ergebnisse zeigen, dass das Framework mit den Aufgaben umgehen und diese korrekt und verständlich darstellen kann.
Die vorliegende Masterthesis analysiert die Anwendungsbereiche und Einsatzmöglichkeiten von ChatGPT im Social-Media-Marketing sowie die Vorteile, aber auch die möglichen Herausforderungen, die sich aus dem Einsatz von ChatGPT in diesem Bereich ergeben. In einer Ära, in der Künstliche Intelligenz, kurz KI, zunehmend die Marketinglandschaft prägt, wird die Integration von ChatGPT in Social-Media-Strategien immer bedeutsamer. Der Fokus liegt dabei auf der Identifizierung von vielfältigen Einsatzmöglichkeiten von ChatGPT in folgenden potenziellen Bereichen: Content Marketing, Kundenkommunikation, Influencer Marketing und Community Management.
Die Zielsetzung besteht darin, die Auswirkungen und Potenziale von ChatGPT auf die Effizienz, Relevanz und Qualität von Social-Media-Marketing zu bewerten. Die Forschungsmethodik basiert auf einer umfassenden Literaturrecherche und Experteninterviews, um Erkenntnisse über Best Practices und Herausforderungen beim Einsatz von ChatGPT zu gewinnen.
Die Ergebnisse dieser Arbeit bieten wertvolle Einblicke für Marketingexperten und Unternehmen, die die Integration von ChatGPT in ihre Social-Media-Strategien in Betracht ziehen. Diese Kurzfassung liefert einen Überblick über die wichtigsten Aspekte dieser Forschung und die erzielten Erkenntnisse, die die Zukunft des Social-Media-Marketing maßgeblich beeinflussen können. Die Erkenntnisse aus der Literaturrecherche, der Auswertung der Experteninterviews sowie die Gegenüberstellung der Ergebnisse dieser beiden Forschungsmethoden zeigen, dass der Einsatz von ChatGPT im Kontext von Social-Media-Marketing vor allem bei der Arbeit mit textlichen Inhalten sinnvoll, effizient und ressourcensparend sein kann, z.B. bei der Ideengenerierung, Korrektur, Übersetzung, Zusammenfassung oder der Erstellung erster Textvorlagen. In allen anderen Bereichen fungiert ChatGPT vor allem als Rat- und Ideengeber sowie als Informationsquelle, deren Wahrheitsgehalt jedoch stets überprüft werden sollte.
Aufgrund der Dynamik und der stetigen Weiterentwicklung des Feldes der KI sollte in Zukunft weitere Forschung in diesem Bereich betrieben werden.
Study of impact of change in market economics of Biosimilars due to SPC waiver on EU 469/2009
(2023)
This research was conducted to understand and investigate the impact of SPC waiver EU 933/2019 made as an amendment to EU 469/2019. The research was conducted for analysis and extraction of the data to compile the exact number of biological products impacted with the SPC waiver. The highest sale top-5 products were identified according to the expert’s opinion. The sales revenue opportunity valuable to the top-5 products in the top-5 non-EU markets for early exports is investigated. Additionally, a survey was conducted to assess the readiness of the industry for these changes. The information from this study will be very useful to students of the biopharmaceutical market research and to the stakeholders from the biopharmaceutical industry.
Linux and Linux-based operating systems have been gaining more popularity among the general users and among developers. Many big enterprises and large companies are using Linux for servers that host their websites, some even require their developers to have knowledge about Linux OS. Even in embedded systems one can find many Linux-based OS that run them. With its increasing popularity, one can deduce the need to secure such a system that many personnel rely on, be it to protect the data that it stores or to protect the integrity of the system itself, or even to protect the availability of the services it offers. Many researchers and Linux enthusiasts have been coming up with various ways to secure Linux OS, however new vulnerabilities and new bugs are always found, by malicious attackers, with every update or change, which calls for the need of more ways to secure these systems.
This Thesis explores the possibility and feasibility of another way to secure Linux OS, specifically securing the terminal of such OS, by altering the commands of the terminal, getting in the way of attackers that have gained terminal access and delaying, giving more time for the response teams and for forensics to stop the attack, minimize the damage, restore operations, and to identify collect and store evidence of the cyber-attack. This research will discuss the advantages and disadvantages of various security measures and compare and contrast with the method suggested in this research.
This research is significant because it paints a better picture of what the state of the art of Linux and Linux-based operating systems security looks like, and it addresses the concerns of security enthusiasts, while exploring new uncharted area of security that have been looked at as a not so significant part of protecting the OSes out of concern of the various limitations and problems it entails. This research will address these concerns while exploring few ways to solve them, as well as addressing the ideal areas and situations in which the proposed method can be used, and when would such method be more of a burden than help if used.
Der Bedarf an fortschrittlichen Bildungstechnologien wächst: Learning Experience Plattformen (LXP) erlangen angesichts der rasanten technologischen Entwicklungen und der daraus resultierenden Veränderungen des Lernverhaltens immer größere Relevanz. Diese Masterarbeit befasst sich mit der Konzeption und Konfiguration eines User-Interfaces für eine Learning Experience Plattform, speziell für die Moodle-Plattform der Hochschule Offenburg. Rahmengebend ist das KompiLe-Projekt, das durch das Bund-Länder- Programm Künstliche Intelligenz in der Hochschulbildung gefördert wird.
Als zentrales Ergebnis wurde ein spezifisches User-Interface für eine Learning Experience Plattform entworfen. Hierbei lag der Fokus insbesondere auf den Bereichen Dashboard, Meine Kurse und einen exemplarischen Kurs, die die wesentlichen Eigenschaften einer LXP repräsentieren sollten.
In einer Umfrage äußerten 55 Studierende und Lehrende ihre Vorstellungen und Präferenzen hinsichtlich der Elemente für das User-Interface. Unter Berücksichtigung dieser Erkenntnisse, kombiniert mit vorherigen Recherchen und einem Prototyp, wurde die finale Konfiguration entwickelt.
Im Vergleich zum vorherigen Design, das lediglich eine Zeitleiste zeigte, bietet das aktualisierte Dashboard erweiterte Möglichkeiten: Eine integrierte Kursübersicht ermöglicht es den Lernenden, direkt vom Dashboard aus auf Kurse zuzugreifen. Nutzer*innen haben die Möglichkeit, in der Kursübersicht zu filtern und durch die Auswahl von Favoriten ihre bevorzugten Kurse zu markieren. In einer Umfrage befürworteten fast 90% diese Funktion. Es wurde ein Interessen-Tag auf dem Dashboard hinzugefügt, der später zu dem Profil verlinken soll. Das Dashboard und die Kursseite wurden durch die Einführung einer linken Spalte aufgewertet, was zu einer erhöhten Symmetrie führte. Zudem wurden auf der Kursseite die ersten personalisierten Elemente wie Top bewertete Aktivitäten und Am häufigsten abgeschlossene Objekte hinzugefügt. Gamification- Elemente erfreuten sich großer Beliebtheit mit einer Zustimmung von 80%. Das Einbinden eines neuen modernen Gamification-Elements in Moodle erschien im Vergleich zu bereits verwendeten Elementen recht aufwändig und deshalb wurde sich zunächst dagegen entschieden. Im Kontext des sozialen Austauschs und der Interaktion war es auffällig, dass die Mehrheit der Studierenden es vorzog, ihre Online-Präsenz zu verbergen und das Moodle-Forum gegenüber anderen Interaktionsmöglichkeiten bevorzugte. Weniger signifikante Veränderungen fanden im Bereich Meine Kurse statt.
In recent years, the demand for reliable power, driven by sensitive electronic equipment, has surged. Even minor deviations from the nominal supply can lead to malfunctions or failure. Despite technological advancements, power quality issues persist due to various factors like short circuits, overloads, voltage fluctuations, unbalanced loads, and non-linear loads.
This thesis extensively explores power quality anomalies in industrial and commercial sectors, using power system data as the primary analytical resource. It addresses the critical need for power supply reliability in today's evolving power grid industry, affected by non-linear loads, renewable energy integration, and electric vehicles. This field of study is paramount for ensuring power supply reliability and stability in the evolving power grid industry.
The core of this thesis involves a comprehensive investigation of power quality, with a focus on frequency, power, and harmonics in voltage and current signals. The research employs Python programming for advanced data analysis, utilizing techniques such as advanced Fast Fourier Transformation (FFT) analysis. The primary objective is to provide valuable insights aimed at elevating power supply quality and enhancing reliability in both industrial and commercial environments.
The cellulase-producing Trichoderma reesei strain RL-P37 exhibits significant potential, yielding 7.3 g/L of cellulase in 241 hours. Microscopic investigations reveal a link between spore formation and enzyme production, suggesting the need for research into the intricate relationship between enzyme production, stress responses, and the nutritional prerequisites of fungi. Comparatively, the use of sodium hydroxide (NaOH) treatment, as opposed to water treatment, results in the reduction of micronutrient content and carbon source extraction as filtrate. Despite these challenges, research by He et al. (2021) highlights NaOH's efficiency in cellulose extraction from plant-based sources. Using NaOH pretreatment can be proven as effective by designing a proper cultivation method. The selection of inducers for enzyme induction gains importance, with soluble inducers, as emphasized by Zhang et al. (2022), exhibiting superior effectiveness. Hence, adopting soluble inducers in designing cultivation methods for improved enzyme production in shaking flasks is recommended. Enzymatic treatment of bio-waste, as outlined by Hu et al. (2021), shows promise in augmenting essential component content by breaking down plant cell walls and intercellular compartments. However, the feasibility of using an artificial bio-waste medium for cultivating Trichoderma reesei is questioned. Investigating the impact of micronutrient levels, particularly the inhibitory role of zinc, on fungal growth becomes essential. These findings underscore the necessity for ongoing research and optimization in cellulase production, emphasizing both strain productivity and cultivation methodologies.