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Strong security measures are required to protect sensitive data and provide ongoing service as a result of the rising reliance on online applications for a range of purposes, including e-commerce, social networking, and commercial activities. This has brought to light the necessity of strengthening security measures. There have been multiple incidents of attackers acquiring access to information, holding providers hostage with distributed denial of service attacks, or accessing the company’s network by compromising the application.
The Bundesamt für Sicherheit in der Informationstechnik (BSI) has published a comprehensive set of information security principles and standards that can be utilized as a solid basis for the development of a web application that is secure.
The purpose of this thesis is to build and construct a secure web application that adheres to the requirements established in the BSI guideline. This will be done in order to answer the growing concerns regarding the security of web applications. We will also evaluate the efficacy of the recommendations by conducting security tests on the prototype application and determining whether or not the vulnerabilities that are connected with a web application that is not secure have been mitigated.
The research employed HPTLC Pro System and other HPTLC instruments from CAMAG® to conduct various laboratory tests, aiming to compile a database for subsequent analyses. Utilizing MATLAB, distinct codes were developed to reveal patterns within analyzed biomasses and pyrolysis oils (sewage sludge, fermentation residue, paper sludge, and wood). Through meticulous visual and numerical analysis, shared characteristics among different biomasses and their respective pyrolysis oils were revealed, showcasing close similarities within each category. Notably, minimal disparity was observed in fermentation residue and wood biomasses with a similarity coefficient of 0.22. Similarly, for pyrolysis oils, the minimal disparity was found in fermentation residues 1 and 3, with a disparity coefficient of 1.41. Despite higher disparity coefficients in certain results, specific biomasses and pyrolysis oils, such as fermentation residue and sewage sludge, exhibited close similarities, with disparity coefficients of 0.18 and 0.55, respectively. The database, derived from triplicate experimentation, now serves as a valuable resource for rapid analysis of newly acquired raw materials. Additionally, the utility of HPTLC PRO as an investigation tool, enabling simultaneous analysis of up to five samples, was emphasized, although areas for improvement in derivatization methods were identified.
Though the basic concept of a ledger that anyone can view and verify has been around for quite some time, today’s blockchains bring much more to the table including a way to incentivize users. The coins given to the miner or validator were the first source of such incentive to make sure they fulfilled their duties. This thesis draws inspiration from other peer efforts and uses this same incentive to achieve certain goals. Primarily one where users are incentivised to discuss their opinions and find scientific or logical backing for their standpoint. While traditional chains form a consensus on a version of financial "truth", the same can be applied to ideological truths too. To achieve this, creating a modified or scaled proof of stake consensus mechanism is explored in this work. This new consensus mechanism is a Reputation Scaled - Proof of Stake. This reputation can be built over time by voting for the winning side consistently or by sticking to one’s beliefs strongly. The thesis hopes to bridge the gap in current consensus algorithms and incentivize critical reasoning.
Garbage in, Garbage out: How does ambiguity in data affect state-of-the-art pedestrian detection?
(2024)
This thesis investigates the critical role of data quality in computer vision, particularly in the realm of pedestrian detection. The proliferation of deep learning methods has emphasised the importance of large datasets for model training, while the quality of these datasets is equally crucial. Ambiguity in annotations, arising from factors like mislabelling, inaccurate bounding box geometry and annotator disagreements, poses significant challenges to the reliability and robustness of the pedestrian detection models and their evaluation. This work aims to explore the effects of ambiguous data on model performance with a focus on identifying and separating ambiguous instances, employing an ambiguity measure utilizing annotator estimations of object visibility and identity. Through accurate experimentation and analysis, trade-offs between data cleanliness and representativeness, noise removal and retention of valuable data emerged, elucidating their impact on performance metrics like the log average miss-rate, recall and precision. Furthermore, a strong correlation between ambiguity and occlusion was discovered with higher ambiguity corresponding to greater occlusion prevalence. The EuroCity Persons dataset served as the primary dataset, revealing a significant proportion of ambiguous instances with approximately 8.6% ambiguity in the training dataset and 7.3% in the validation set. Results demonstrated that removing ambiguous data improves the log average miss-rate, particularly by reducing the false positive detections. Augmentation of the training data with samples from neighbouring classes enhanced the recall but diminished precision. Error correction of wrong false positives and false negatives significantly impacts model evaluation results, as evidenced by shifts in the ECP leaderboard rankings. By systematically addressing ambiguity, this thesis lays the foundation for enhancing the reliability of computer vision systems in real-world applications, motivating the prioritisation of developing robust strategies to identify, quantify and address ambiguity.
The interest of scientists to study motion sequences exists in the fields of sports science, clinical analysis and computer animation for quite some time. While in the last decades mainly markerbased motion capture systems have been used to evaluate movements, the interest in markerless systems is growing more and more. Nevertheless, in the field of clinical analysis, markerless methods have not yet proven their value, partly due to a lack of studies evaluating the quality of the obtained data. Therefore, this study aims to validate two markerless motion capture softwares from Simi Reality Motion Systems. The software Simi Shape, which is a mixture of traditional image-based tracking supported by an artificial intelligence net (AI net), and the software Crush, that uses a completely AI-based method. For this purpose, all motion data was recorded with two in-house motion capture systems. One system for recording the movements for a markerbased evaluation as gold standard and one system for markerless tracking. Within a laboratory environment, eight cameras per system were mounted around the area of motion. By placing two cameras in the same position and using the same calibration, deviations in the image data between those for markerbased and markerless tracking were extremely minimal. Based on this data, markerbased tracking was performed using the Simi Motion program, markerless tracking was performed using the Simi Shape software system and the latest software from Simi Reality Motion Systems, Crush. When comparing the markerless data with the markerbased data, an average root mean square error of 0,038 m was calculated for Simi Shape and a deviation of 0,037 m for Crush. In a direct comparison of the two markerless systems, a root mean square error of 0,019 m was scored. Based on these data, conclusions could be drawn about the accuracies of the two markerless systems. The obtained kinematic data of the tracking are in the range of high accuracy, which is limited to a deviation of less than 0,05 m according to the literature.
As the Industry 4.0 is evolving, the previously separated Operational Technology (OT) and Information Technology (IT) is converging. Connecting devices in the industrial setting to the Internet exposes these systems to a broader spectrum of cyber-attacks. The reason is that since OT does not have much security measures as much as IT, it is more vulnerable from the attacker's perspective. Another factor contributing to the vulnerability of OT is that, when it comes to cybersecurity, industries have focused on protecting information technology and less prioritizing the control systems. The consequences of a security breach in an OT system can be more adverse as it can lead to physical damage, industrial accidents and physical harm to human beings. Hence, for the OT networks, certificate-based authentication is implemented. This involves stages of managing credentials in their communication endpoints. In the previous works of ivESK, a solution was developed for managing credentials. This involves a CANopen-based physical demonstrator where the certificate management processes were developed. The extended feature set involving certificate management will be based on the existing solution. The thesis aims to significantly improve such a solution by addressing two key areas that is enhancing functionality and optimizing real-time performance. Regarding the first goal, firstly, an analysis of the existing feature set shall be carried out, where the correct functionality shall be guaranteed. The limitations from the previously implemented system will be addressed and to make sure it can be applied to real world scenarios, it will be implemented and tested in the physical demonstrator. This will lay a concrete foundation that these certificate management processes can be used in the industries in large-scale networks. Implementation of features like revocation mechanism for certificates, automated renewal of the credentials and authorization attribute checks for the certificate management will be implemented. Regarding the second goal, the impact of credential management processes on the ongoing CANopen real-time traffic shall be a studied. Since in real life scenarios, mission-critical applications like Industrial control systems, medical devices, and transportation networks rely on real-time communication for reliable operation, delays or disruptions caused by credential management processes can have severe consequences. Optimizing these processes is crucial for maintaining system integrity and safety. The effect to minimize the disturbance of the credential management processes on the normal operation of the CANopen network shall be characterized. This shall comprise testing real-time parameters in the network such as CPU load, network load and average delay. Results obtained from each of these tests will be studied.
Increasing global energy demand and the need to transition to sustainable energy sources to mitigate climate change, highlights the need for innovative approaches to improve the resilience and sustainability of power grids. This study focuses on addressing these challenges in the context of Morocco's evolving energy landscape, where increasing energy demand and efforts to integrate renewable energy require grid reinforcement strategies. Using renewable energy sources such as photovoltaic systems and energy storage technologies, this study aims to develop a methodology for strengthening rural community grids in Morocco.
Traditional reinforcement measures such as line and transformer upgrades will be investigated as well as the integration of power generation from photovoltaic systems, which offer a promising way to utilise Morocco's abundant solar resources. In addition, energy storage systems will be analysed as potential solutions to the challenges of grid stability and resilience. Using comprehensive data analysis, scenario planning and simulation methods with the open-source simulation software Panda Power, this study aims to assess the impact of different grid reinforcement measures, including conventional methods, photovoltaic integration, and the use of energy storage, on grid performance and sustainability. The results of this study provide valuable insights into the challenges and opportunities of transitioning to a more resilient and sustainable energy future in Morocco.
Based on a rural medium-voltage grid in Souihla, Morocco, three scenarios were carried out to assess the impact of demand growth in 2030 and 2040. The first scenario focuses on conventional grid reinforcement measures, while the second scenario incorporates energy from residential photovoltaic systems. The third scenario analyses the integration of storage systems and their impact on grid reinforcement in 2030.
The simulations with energy from photovoltaic systems show a reduction in grid reinforcement measures compared to the scenario without solar energy. In addition, the introduction of a storage system in 2030 led to a significant reduction in the required installed transformer capacity and fewer congested lines. Furthermore, the results emphasized the role of storage in stabilizing grid voltage levels.
In summary, the results highlighted the potential benefits of integrating energy from photovoltaics and storage into the grid. This integration not only reduces the need for transformers and overall grid infrastructure but also promotes a more efficient and sustainable energy system.
"Ad fontes!"
Francesco Petrarca (1301–1374)
In the beginning, there was an idea: the reconstruction of the first "Iron Hand" of the Franconian imperial knight Götz von Berlichingen (1480–1562). We found that with this historical prosthesis, simple actions for daily use, such as holding a wine glass, a mobile phone, a bicycle handlebar grip, a horse’s reins, or some grapes, are possible without effort. Controlling this passive artificial hand, however, is based on the help of a healthy second hand.
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.