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The primary objective of this thesis is to examine the lean accounting transformation, which involves applying lean management principles to the accounting domain. In recent years, various sectors, including manufacturing, healthcare, and services, have experienced success with lean management practices. Nevertheless, the implementation of lean accounting within financial management has not been as extensively explored. This research aims to bridge that gap by scrutinizing the benefits and potential drawbacks of adopting lean accounting practices in business operations.
This research uses a combination of qualitative techniques and an extensive literature review to better understand the present subject matter. By describing the ideas of lean management and standard accounting and highlighting the fundamental distinctions between the two systems, the literature study lays a theoretical framework. The case studies illustrate the benefits of adopting lean accounting processes with real-world examples of firms that have made the transition effectively.
In the quantitative analysis of lean accounting's impact, both financial and operational factors are examined extensively. The results indicate that companies embracing lean accounting practices experience significant improvements in productivity, cost reduction, and decisionmaking quality. By highlighting the potential gains to be made by incorporating lean techniques into accounting procedures, this study adds to the current body of information on lean management. The findings offer practical implications for accounting professionals, business leaders, and policymakers interested in leveraging lean accounting to drive organizational performance improvement. The thesis finishes with suggestions for further study in this area, lean accounting.
Risk-based Cybermaturity Assessment Model - Protecting the company against ransomware attacks
(2023)
Ransomware has become one of the most catastrophic attacks in the previous decade, hurting businesses of all sorts worldwide. So, no organization is safe, and most companies are reviewing their ransomware defensive solutions to avoid business and operational hazards. IT departments are using cybersecurity maturity assessment frameworks like CMMC, C2M2, CMMI, NIST, CIS, CPP, and others to analyze organization security capabilities. In addition to maturity assessment models for the process layer and human pillar, there are much research on the analysis, identification, and defense of cyber threats in product/software layers that propose state-of-the-art approaches.
This motivates a comprehensive ransomware cyber security solution. Then, a crucial question arises: “How companies can measure the security maturity of controls in a specific danger for example for Ransomware attack?” Several studies and frameworks addressed this subject.
Complexity of understanding the ransomware attack, Lack of comprehensive ransomware defense solutions and Lack of cybermaturity assessment model for ransomware defense solutions are different aspects of problem statement in this study. By considering the most important limitations to developing a ransomware defense cybermaturity assessment method, this study developed a cybermaturity assessment methodology and implemented a Toolkit to conduct cyber security self-assessment specifically for ransomware attack to provide a clearer vision for enterprises to analyze the security maturity of controls regardless of industry or size.
Die Vision vom "Internet der Dinge" prägt seit Jahren Forschung und Entwicklung, wenn es um smarte Technologien und die Vernetzung von Geräten geht. In der Zukunft wird die reale Welt zunehmend mit dem Internet verknüpft, wodurch zahlreiche Gegenstände (Dinge) des normalen Alltags dazu befähigt werden, zu interagieren und sowohl online als auch autark zu kommunizieren. Viele Branchen wie Medizin, Automobilbau, Energieversorgung und Unterhaltungselektronik sind gleichermaßen betroffen, wodurch trotz Risiken auch neues wirtschaftliches Potential entsteht. Im Bereich "Connected Home" sind bereits Lösungen vorhanden, mittels intelligenter Vernetzung von Haushaltsgeräten und Sensoren, die Lebensqualität in den eigenen vier Wänden zu erhöhen. Diese Arbeit beschäftigt sich mit dem Thread Protokoll; einer neuen Technologie zur Integration mehrerer Kommunikationsschnittstellen innerhalb eines Netzwerks. Darüber hinaus wird die Implementierung auf Netzwerkebene (Network Layer) vorgestellt, sowie aufbereitete Informationen bezüglich verwendeter Technologien dargestellt.
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.
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.
This paper gives an overview of the impact that the corona pandemic has on the export industry in Canada and analyzes the different Canadian government measures for exporters. In addition, the measures are subsequently evaluated in order to identify if the support measures can help Canadian exporters to overcome the crisis. The basis of this paper are semi-structured expert interviews with experts from the financial sector, scientific literature and studies. The results have shown that the COVID-19 pandemic has a major impact on Canada’s export economy and it’s GDP. Trade is only possible to a limited extent, as many borders are closed. The Canadian government reacted with an economic response plan to support Canadian individuals and businesses. This paper depicts and assesses the most eligible measures for export companies.
This thesis evaluates and compares current Full-Stack JavaScript Technologies. Through extensive research on the state of the art of JavaScript and its related frameworks, different aspects of FullStack Development are analysed to judge the popularity of technologies.
The language JavaScript and the idea of Full-Stack Development are presented with the functionality of different frameworks. The JavaScript runtime Node.js was examined and marked as the most influential JavaScript technology, which opened up many opportunities.
As technology stacks MERN, MEAN and MEVN were investigated, featuring the base technologies Node.js, MongoDB and Express.js. It was discovered that front-end frameworks have the most influence on which variant of Full-Stack can be chosen. Comparison criteria between the technology stacks were the learning curve, the maintainability, modularity and media integration. These criteria were extracted from research and a questionnaire conducted with students of the University of Applied Sciences Offenburg.
For the purposes of testing and experiencing a Full-Stack JavaScript application, the game RemArrow, based on the 1979s game Simon, was designed and implemented. The comparison with predefined criteria shows the result that the MERN stack with React.js is the best to learn and promises the most potential. Arising JavaScript technologies and their popularity are very dependent on the industry and skill set of the developer.
In conclusion, it can be established that the concept of Full-Stack Development is currently very interesting and more than just a trend. It has potential of becoming a new kind of web development, and part of the curriculum taught at universities. Expert knowledge is needed but there is a high demand and much potential for Full-Stack JavaScript Developers.
This paper describes a project absolved to increase the material flow through the LTCC production of the Bosch Anderson Plant in South Carolina, USA. To archive this goal the regarded value stream is introduced first. The bottleneck, which is limiting the material flow is found and eliminated in order to increase the output of the machine and consequently improve the material flow through the whole value stream. The completed projects made for this purpose result in a 13% increase. To control the material flow the inventory sizes are determined. The inventories, from which the size is desired to be determined, include climatization processes to dry the pastes that are applied in the previous process steps. Therefore, a separation of the parts in the production process climatization and the buffer is necessary first. After that the buffer can be eliminated and the inventory areas minimized. The results are smaller and controlled buffer sizes that make part of the floor space unnecessary. A welcomed side effect is the solution to a production problem of warped parts because of too long climatization times. Observations over time show that the results of the buffer limitations are just right to improve the material flow through the LTCC production.
Communication protocols enable information exchange between different information systems. If protocol descriptions for these systems are not available, they can be reverse-engineered for interoperability or security reasons. This master thesis describes the analysis of such a proprietary binary protocol, named the DVRIP or Dahua private protocol from Dahua Technology. The analysis contains the identification of the DVRIP protocol header format, security mechanisms and vulnerabilities inside the protocol implementation. With the revealing insights of the protocol, an increase of the overall security is achieved. This thesis builds the foundation for further targeted security analyses.
Webassembly is a new technology to create application in a new way. Webassembly is being developed since 2017 by the worldwide web consortium (w3c). The primary task of webassembly is to improve web applications.
Today, more and more applications are being created as web applications. Web applications have some advantages - they are platform independent and even mobile platforms can run them, and no installation is needed apart from a modern web browser.
Currently, web applications are being developed in JavaScript (JS), hypertext mark-up language 5 (HTML 5), and cascading style sheets (CSS).
These technologies are not made for huge web applications, but they should not be replaced by webassembly; rather, webassembly is an extension to the currently existing technology.
The purpose of webassembly is to fix or improve the problems in web application development.
This master’s thesis reviews all of the aspects and checks whether the promises of webassembly are kept and where problems still exist.
This thesis deals with the implementation of character controls and combat system of the Action Adventure 'Scout 3D'. The game development was realized with the game engine Unity 3D. In the first part, the architecture of a typical game engine is explained. The single components are describes step by step. Then, five well-known game engines are compared and evaluated. In the next chapter, a short overview about design and architecture patterns is worked out. The features of Unity, that are used for the implementation, and Unity's animation system 'Mecanim, are described finally. The second part includes the requirement definitions for the game 'Scout COD' which define player input, different conditions that allow or disallow several activities and the behaviour of enemies. With the help of patterns the architecture of the game is designed. Then, the implementation is explained by means of code snippets.
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.
The effects of climate change, including severe storms, heat waves, and melting glaciers, are highlighted as an urgent concern, emphasising the need to decrease carbon emissions to restrict global warming to 1.5°C. To accomplish this goal, it is vital to substitute fossil fuel-based power plants with renewable energy sources like solar, wind, hydro, and biofuels. Despite some progress being made, the proportion of renewables used in generating electricity is still lower than the levels needed for 2030 and 2050. Decarbonising the power grid is also critical in lowering the energy consumption of buildings, which is responsible for a substantial percentage of worldwide electricity usage. Even though there has been substantial expansion in the worldwide renewable energy market in the past 15 years, the transition to renewable energy sources also requires taking into account the importance of energy trading.
Peer-to-peer (P2P) electricity trading is an emerging type of energy exchange that can revolutionise the energy sector by providing a more decentralised and efficient way of trading energy. This research deals about P2P electricity trading in a carbon-neutral scenario. 'Python for Power System Analysis' (PyPSA) was used to develop models through which the P2P effect was tested. Data for the entire state of Baden-Württemberg (BW) was collected. Three scenarios were taken into consideration while developing models: 2019 (base), 2030 (coal phase-out), and 2040(climate neutral). Alongside this, another model with no P2P trading was developed to make a comparison. In addition, the use case of community storage in a P2P trading network is also presented.
The research concludes that P2P has a significant positive effect on a pathway to achieve climate neutrality. The findings show that the share of renewables in electricity generation is increasing compared to conventional sources in BW, which can be traded to meet the demand. From the storage analysis, it can be concluded that community storage can be effectively utilised in P2P trading. While the emissions are reduced, the operating costs are also reduced when the grid has P2P trading available. By highlighting the benefits of P2P trading, this research contributed to the growing body of research on the effectiveness of P2P trading in an electricity network grid.
The Lattice Boltzmann Method is a useful tool to calculate fluid flow and acoustic effects at the same time. Although the acoustic perturbation is much smaller than normal pressure differences in fluid flow, this direct calculation is a great advantage of the Lattice Boltzmann Method (LBM). But each border used in calculation produces a multitude of reflections with the acoustic waves, which lead to an unusable result. Therefore, it is worked on different absorbing techniques.
In this thesis three absorbing layer techniques are described, explained and reviewed with different simulations. The absorbing layers are implemented in a basic LBM code in C++, and with this umpteen simulations within a box were performed to compare the different absorbing layers. The Doppler effect and a cylinder flow are also examined to compare the damping efficiencies.
The three studied absorbing techniques are the sponge layer, the perfectly matched layer and a force based Term II absorbing layer. The sponge layer is easy to implement but gives worse results than a calculation without any absorbing layer. The perfectly matched layer and a force based absorbing term provide very good results but the perfectly matched layer has problems with instability. The force based absorbing layer represents the best compromise between the additional computation time due the absorbing layer and the achieved damping efficiency.
As e-commerce platforms have grown in popularity, new difficulties have emerged, such as the growing use of bots—automated programs—to engage with e-commerce websites. Even though some algorithms are helpful, others are malicious and can seriously hurt e-commerce platforms by making fictitious purchases, posting fictitious evaluations, and gaining control of user accounts. Therefore, the development of more effective and precise bot identification systems is urgently needed to stop such actions. This thesis proposes a methodology for detecting bots in E-commerce using machine learning algorithms such as K-nearest neighbors, Decision Tree, Random Forest, Support Vector Machine, and Neural Network. The purpose of the research is to assess and contrast the output of these machine learning methods. The suggested approach will be based on data that is readily accessible to the public, and the study’s focus will be on the research of bots in e-commerce.
The purpose of the study is to provide an overview of bots in e-commerce, as well as information on the different kinds and traits of bots, as well as current research on bots in e-commerce and associated work on bot detection in e-commerce. The research also seeks to create a more precise and effective bot detection system as well as find critical factors in detecting bots in e-commerce.
This research is significant because it sheds light on the increasing issue of bots in e-commerce and the requirement for more effective bot detection systems. The suggested approach for using machine learning algorithms to identify bots in ecommerce can give e-commerce platforms a more precise and effective bot detection system to stop malicious bot activities. The study’s results can also be used to create a more effective bot detection system and pinpoint key elements in detecting bots in e-commerce.
Singapore’s success in transforming itself from a poor, vulnerable economy to one of the richest countries in the world (IMF, 2016) is nothing short of inspirational to many small economies around the globe. Given its lack of resources, Singapore relied upon foreign investors to fuel its growth not only through cash injection into the economy in the form of Foreign Direct Investments (FDI) but also to help upgrade its skills and technological stock. This study looks at how Singapore inspired many Multi-National Corporations (MNCs) into pouring a large sum of investments into this small ailing citystate and if this idea can be generalized to apply it in other economies, especially in Oman.
In a bid to explain the large flow of Capital into an economy, this study moves on further to review most prominent literature in the field since Macdougall (1958) first laid the groundwork for the subsequent theories on FDI. Based on the review of several previous studies, the most significant determinants of FDI were found to be government policy and political stability, inflation rate as a proxy for economic stability, quality of infrastructure and institutions, market size of the host country, openness to trade, tax policies and access to low cost factors of production.
Through a case study method with the inductive approach, this study finds that Singapore excels in all of the determinants of FDI except for the market size of the host country and access to low-cost factors of production. However, it more than compensates for these shortcomings with its strategic geographical location and numerous bilateral and regional trade agreements that give it access to markets around the region. Oman like Singapore ranks well in many of these determinants that make it a potential destination for investment. However, the sultanate could gain more interest from the MNC’s to help its growth by optimizing its policies to lower existing barriers, easing immigration laws to meet the short term skill shortage, allowing for 100 percent foreign ownership, allowing for more liberal property rights, working to improve corruption perception and opting for more trade agreements to give it easy access to larger markets. Moreover, the economy’s heavy reliance on hydrocarbon exports is seen as a major risk by investors as it creates an economic vulnerability which could potentially overshadow many other benefits of investing in the sultanate. Besides the aforementioned determinants, a lot also depends on the success of Oman’s diversification plans.
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.
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.
G.R.E.C is a adventure game, set in an dystopien industrial world, where you are a scavenger for hire. Explore the village of Vankhart Valley and grab everything valuable you can get your hands on.
Your trusty old jump boots will help you avoiding the nasty and deadly spores that changed the world of G.R.E.C forever.
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.