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The current thesis conducts the study on the integration of digitalization techniques aimed at improving energy supply efficiency in off-grid energy systems. The primary objective is to fortify the security of energy supply in remote areas, particularly in instances of adverse weather conditions, unanticipated changes in load and fluctuations in the performance of renewable energy systems. This objective is to be achieved through the implementation of a smart load management strategy in stand-alone photovoltaic systems (SAPVS). This strategy involves deployment of forecasting algorithms on an edge device that operates with limited processing resources in an environment characterized for the lack of internet connection. The edge device is designed to interact with a smart home gateway that prioritizes, and schedules smart appliances based on the forecasted state of charge (SOC) in the 36-hours ahead of the SAPVS operation (the implementation of the loads schedule deployed on the Home Assistant device is out of the scope of the tasks implemented for this project).
The edge device, developed using a Raspberry Pi 3B+, was specifically intended for being implemented along with a SAPVS, in remote areas such as health stations in Africa and tropical islands, providing communities with a reliable source of electrical energy. The deployment of the strategy was carried out in four phases. The first phase involved the implementation of an Extraction-Transformation-Load (ETL) pipeline, where data was gathered from various heterogeneous hardware sources of an implemented test system that served as the enabler and testbench of this research, this test stand is composed of power electronics components such as an inverter, a MPPT solar charge controller, a smart meter, and a BOS LiFePo4 battery prototype. In the transformation stage, a data model was developed to identify the most critical parameters of the energy system, and to eliminate outliers and null values. In the load stage, a local SQL database was established for saving and structuring the data gathered and to ensure high-quality data with defined units and casting.
The second phase involved data analysis to identify the relevant features and potential exogenous variables for the forecasting model to implement. In the third phase, an Auto Regressive Moving Average (ARMA) model with two selected exogenous variables was implemented to forecast the AC load consumption profile for the 36- hours ahead of the off-grid system operation. The final phase involved the information exchange with the Home Assistant device, by transferring to it from the edge device the battery SOC present value and the predicted 36-hour ahead AC load profile information for prioritization and scheduling of loads; this through an MQTT interface.
The outcome of the experiment was a successful deployment of a data engineering and data forecasting approach that enabled data quality strategy implementation, local database storage, and forecasting algorithms on a processing and internet-constrained edge device. The interface with a home assistant implementation resulted in the successful execution of smart load management endeavors in an off-grid system, thereby enhancing the energy security of supply and contributing to the advancement of data-driven strategies in the rural electrification sector.
This thesis emphasizes the significance of digitalization strategies in smart SAPVS and highlights the potential of edge computing solutions in achieving seamless energy management in smart homes.
The purpose of this master's thesis was to set up a test bed for the absorption of chemical compounds by carbon-based sorbents and polymers and to develop a method for the detection of these substances applied by liquid chromatography.
The study made it possible to demonstrate the effectiveness of both polymers and biochars sorbents for the adosorption of specific substances. The results obtained open new paths on the study of biochar for the treatment of contaminated water. Some biochars made from plant-based materials have been shown to be almost as effective as commercial products used in plants. The developed chromatography method allows efficient separation of substances and their detection.
Conceptualization and implementation of automated optimization methods for private 5G networks
(2023)
Today’s companies are adjusting to the new connectivity realities. New applications require more bandwidth, lower latency, and higher reliability as industries become more distributed and autonomous. Private 5th Generation (5G) networks known as 5G Non-Public Networks (5G-NPN), is a novel 3rd Generation Partnership Project (3GPP)- based 5G network that can deliver seamless and dedicated wireless access for a particular industrial use case by providing the mentioned application’s requirements. To meet these requirements, several radio-related aspects and network parameters should be considered. In many cases, the behavior of the link connection may vary based on wireless conditions, available network resources, and User Equipment (UE) requirements. Furthermore, Optimizing these networks can be a complex task due to the large number of network parameters and KPIs that need to be considered. For these reasons, traditional solutions and static network configuration are not affordable or simply impossible. Despite the existence of papers in the literature that address several optimization methods for cellular networks in industrial scenarios, more insight into these existing but complex or unknown methods is needed.
In this thesis, a series of optimization methods were implemented to deliver an optimal configuration solution for a 5G private network. To facilitate this implementation, a testing system was implemented. This system enables remote control over the UE and 5G network, establishment of a test environment, extraction of relevant KPI reports from both UE and network sides, assessment of test results and KPIs, and effective utilization of the optimization and sampling techniques.
The research highlights the advantageous aspects of automated testing by using OFAT, Simulated Annealing, and Random Forest Regressor methods. With OFAT, as a common sampling method, a sensitivity analysis and an impact of each single parameter variation on the performance of the network were revealed. With Simulated Annealing, an optimal solution with MSE of roughly 10 was revealed. And, in the Random Forest Regressor, it was seen that this method presented a significant advantage over the simulated annealing method by providing substantial benefits in time efficiency due to its machine- learning capability. Additionally, it was seen that by providing a larger dataset or using some other machine-learning techniques, the solution might be more accurate.
Self-sufficient enzymes belong to the cytochrome P450 (CYP) group and are known for their superior hydroxylation catalytic activity. In the pursuit of identifying new pesticides to combat antimicrobial-resistant pathogens, we employed BM3 wild type (BM3-WT), the fastest monohydroxylating CYP, along with its seven homologs, to investigate the production of potential hydroxylated derivatives from the established pesticide, 4-oxocrotonic acid using high-pressure liquid chromatography (HPLC) method. Following the recombinant production of BM3-WT and three other homologs in E. coli, and their subsequent purification using Immobilized Metal Affinity Chromatography (IMAC), a novel enzyme assay approach was developed as a substitute for the carbon monoxide (CO) assay. This new method relied on the measurement of NADPH consumption at 340 nm by BM3-WT for palmitic acid. Leveraging this established technique, we explored the substrate specificity of BM3-WT and its homologs not only on palmitic acid but also on other structurally similar compounds, including 4-oxocrotonic acid. The results obtained from the established NADPH assay indicate that all tested enzymes displayed greater catalytic activity on 4-oxocrotonic acid in comparison to other substrates with similar structures. However, the impact of BM3-WT and its homologs on 4-oxocrotonic acid varied in terms of product specificity. Enzymes such as Poh, Trr and Bas-CYP D exhibited specificity in producing solely monohydroxylated products, while others tended to yield dehydroxylated and ketol metabolites.
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.
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.
Organized by the Fraunhofer Additive Manufacturing Alliance, the bi-annual Direct Digital Manufacturing Conference brings together researchers, educators and practitioners from around the world. The conference covers the entire range of topics in additive manufacturing, starting with methodologies, design and simulation, right up to more application-specific topics, e.g. from the realm of medical engineering and electronics.
The Internet of Things is spreading significantly in every sector, including the household, a variety of industries, healthcare, and emergency services, with the goal of assisting all of those infrastructures by providing intelligent means of service delivery. An Internet of Vulnerabilities (IoV) has emerged as a result of the pervasiveness of the Internet of Things (IoT), which has led to a rise in the use of applications and devices connected to the IoT in our day-to-day lives. The manufacture of IoT devices are growing at a rapid pace, but security and privacy concerns are not being taken into consideration. These intelligent Internet of Things devices are especially vulnerable to a variety of attacks, both on the hardware and software levels, which leaves them exposed to the possibility of use cases. This master’s thesis provides a comprehensive overview of the Internet of Things (IoT) with regard to security and privacy in the area of applications, security architecture frameworks, a taxonomy of various cyberattacks based on various architecture models, such as three-layer, four-layer, and five-layer. The fundamental purpose of this thesis is to provide recommendations for alternate mitigation strategies and corrective actions by using a holistic rather than a layer-by-layer approach. We discussed the most effective solutions to the problems of privacy and safety that are associated with the Internet of Things (IoT) and presented them in the form of research questions. In addition to that, we investigated a number of further possible directions for the development of this research.
As cyber threats continue to evolve, it is becoming increasingly important for organizations to have a Security Operations Center (SOC) in place to effectively defend against them. However, building and maintaining a SOC can be a daunting task without clear guidelines, policies, and procedures in place. Additionally, most current SOC solutions used by organizations are outdated, lack key features and integrations, and are expensive to maintain and upgrade. Moreover, proprietary solutions can lead to vendor lock-in, making it difficult to switch to a different solution in the future.
To address these challenges, this thesis proposes a comprehensive SOC framework and an open-source SOC solution that provides organizations with a flexible and cost-effective way to defend against modern cyber threats. The research methodology involved conducting a thorough literature review of existing literature and research on building and maintaining a SOC, including using SOC as a service. The data collected from the literature review was analyzed to identify common themes, challenges, and best practices for building and maintaining a SOC.
Based on the data collected, a comprehensive framework for building and maintaining a SOC was developed. The framework addresses essential areas such as the scope and purpose of the SOC, governance and leadership, staffing and skills, technologies and tools, processes and procedures, service level agreements (SLAs), and evaluation and measurement. This framework provides organizations with the necessary guidance and resources to establish and effectively operate a SOC, as well as a reference for evaluating the service provided by SOC service providers.
In addition to the SOC framework, a modern open-source SOC solution was developed, which emphasizes several key measures to help organizations defend against modern cyber threats. These measures include real-time, actionable threat intelligence, rapid and effective incident response, continuous security monitoring and alerting, automation, integration, and customization. The use of open-source technologies and a modular architecture makes the solution cost-effective, allowing organizations to scale it up or down as needed.
Overall, the proposed SOC framework and open-source SOC solution provide organizations with a comprehensive and systematic approach for building and maintaining a SOC that is aligned with the needs and objectives of the organization. The open-source SOC solution provides a flexible and cost-effective way to defend against modern cyber threats, helping organizations to effectively operate their SOC and reduce their risk of security incidents and breaches.