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The Internet of Things (IoT) is the enabler for new innovations in several domains. It allows the connection of digital services with physical entities in the real world. These entities are devices of different categories and sizes range from large machinery to tiny sensors. In the latter case, devices are typically characterized by limited resources in terms of computational power, available memory and sometimes limited power supply. As a consequence, the use of security algorithms requires of them to work within the limited resources. This means to find a suitable implementation and configuration for a security algorithm, that performs properly on the device, which may become a challenging task. On the other side, there is the desire to protect valuable assets as strong as possible. Usually, security goals are recorded in security policies, but they do not consider resource availability on the involved device and its power consumption while executing security algorithms. This paper presents an IoT security configuration tool that helps the designer of an IoT environment to experiment with the trade-off between maximizing security and extending the lifetime of a resource constrained IoT device. The tool is controlled with high-level description of security goals in the form of policies. It allows the designer to validate various (security) configurations for a single IoT device up to a large sensor network.
Das Interesse am Lehrkonzept des Inverted Classroom (ICM) erfreut sich in den letzten Jahren zunehmender Beliebtheit und mit Beginn der Corona-Pandemie und dem damit verbundenen Umstieg auf Online-gestützt Lehrformate ist es noch einmal deutlich gestiegen. Beim ICM wird die Phase der Wissensvermittlung aus der Präsenzphase der traditionellen Lehrveranstaltung umgedreht: Was bisher während der gemeinsamen Veranstaltungszeit präsentiert wurde, wird nun über Texte, Videos u.a. in eine vorgelagerte Selbstlernphase aus der Veranstaltungszeit ausgelagert. Die gemeinsame Präsenzzeit wird für aktives Lernen, Vertiefung, Diskussion oder andere aktive Formate genutzt. Das Inverted Classroom Modell wird Disziplin- und veranstaltungsübergreifend in der Lehre sowohl in Schulen wie auch Hochschulen genutzt.
Die von Sutherland und Schwaber entwickelte Scrum-Methodik ist ein etabliertes Vorgehensmodell in der Software-Entwicklung und dem Projektmanagement. Scrum bietet durch definierte Rollen, Artefakte und Ereignisse einen Rahmen in dem inkrementell an der Entwicklung eines Produktes gearbeitet werden kann. Diese Inkremente werden in Arbeitszyklen (Sprints) erarbeitet, bei denen die stetige Verbesserung des Produktes und der Arbeitsweise im Fokus stehen. Mit eduScrum oder Scrum4Schools wird Scrum in die Lehre übertragen.
Es liegt auf der Hand, dass sich die Konzepte des ICM und Scrum sehr gut ergänzen und die Scrum Methodik einen formalen Rahmen für ICM bieten kann.
Der Beitrag beschreibt die Umsetzung dieser Kombination agiler Methodiken aus Scrum im Kontext des Inverted Classroom in einer Informatik-Grundlagenveranstaltung an der Hochschule Osnabrück.
Im Modul Algorithmen und Datenstrukturen ist das Inverted Classroom Modell mit der Scrum-Methodik kombiniert. Die Studierenden erarbeiten die Inhalte des Moduls im Lernmanagementsystem mithilfe von Videoaufzeichnungen, digitalem Skript und interaktiven Übungseinheiten. Der Wegfall der klassischen Vorlesung ermöglicht mehr Zeit zur Beantwortung von Fragen, Diskussionen sowie der Reflexion des Erlernten durch Hörsaal-Quizze. Die Themen der Veranstaltung werden vorgegeben, aber die Bearbeitung erfolgt individuell und die Studierenden gestalten ihre eigenen Lernprozesse. Theorie und Praxis der Veranstaltung werden analog zur Scrum-Methodik in mehrwöchigen Sprints im Team bearbeitet. Die Aufgaben sind in den Kontext einer virtuellen Betriebssystemumgebung eingebettet und bauen aufeinander auf. Das Softwareprojekt wird hierzu als GitLab-Repository zur Verfügung gestellt. Die Verwendung von Git und integrierten Test-Routinen entsprechen einer realitätsnahen Vorgehensweise, wie sie in der Softwareentwicklung allgemein gängige Praxis ist.
Oleamide is used as a lubricant in the manufacturing and application of polypropylene (PP) medical devices. Samples of PP were prepared with 0, 1500, and 15 000 ppm oleamide content as lubricant. The samples were either left non-sterile, sterilized with ethylene oxide (ETO), γ-radiation (γ) or autoclaved (A) and stored for up to 4 weeks. To determine the oleamide bulk-to-surface distribution depending on sterilization method and storage time an extraction method and a washing technique were applied. The oleamide content was determined by gas chromatography (GC-FID) and compared with the coefficient of friction (COF). The COF dependent on the measured lubricant content at the surface. The content of lubricant on the surface depends on the type of sterilization: ETO increased the lubricant content to some extent, γ-sterilization and autoclaving reduced it. After storage, no migration of the lubricant to the surface could be detected.
In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS.
Bamboo is an environmentally friendly alternative to conventional materials in mechanical engineering such as steel or aluminium. Bamboo is the fastest growing plant in the world. Instead of releasing CO2 during the manufacturing process, bamboo absorbs CO2 as it grows.
In addition to the sustainability aspect, bamboo tubes also offer excellent properties as a lightweight construction material, which have been optimised through evolution. Bamboo tubes have high strength and stiffness at low weight when used as tension-compression bars or bending beams. Bamboo has strong, high-density fibres at the boundary area, where bending stresses are greatest. Towards the inside, where the stresses are lower, the bamboo becomes porous to optimise weight. This, together with knots arranged in regular intervals, counteracts buckling.
In mobile applications such as cars and bicycles, lightweight construction is sought for energy efficiency reasons. Because of its excellent lightweight properties, the project investigated whether bamboo could be used in mobile, automotive or agricultural engineering. For example, a bamboo bicycle frame has been developed with the aim to be as light as possible. There are bamboo bicycles on the market, but they can only be made one at a time by hand. The bamboo tubes are joined together and functional elements such as the bottom bracket and headset are integrated by wrapping them in resin-impregnated natural or carbon fibres. This makes the joints very heavy. A different approach is taken here: the bamboo tubes are drilled out slightly to achieve a defined internal diameter, and then short aluminium tubes are glued into the bamboo canes from the inside. To prevent the cane from breaking in the circumferential direction, i.e. perpendicular to the fibre direction, the bamboo tubes are wrapped in a thin layer of natural or carbon fibre impregnated with synthetic resin. The aluminium tubes and functional elements are welded or soldered together beforehand.
The design of the bicycle frame, i.e. the dimensioning of the bamboo tubes and joints, was based on extensive bending and tensile tests to determine the strength properties of the natural material bamboo. The bonding between the bamboo cane and the aluminium tube was also investigated experimentally. Finally, several prototype bicycle frames were made and tested for durability according to DIN-EN-14764. The frames passed the tests.
The result is a bamboo bicycle that is manufactured with standardised connectors and joints. The assembly concept developed allows both fully automated and semi-automated series production of bamboo bicycles.
The energy transition involves various challenges. One key aspect is the decentralization of power generation, which requires new actors. In order to integrate these into the system in the best possible way, there are various approaches e.g. in cooperation in citizens' initiatives or cooperatives (Dorniok, 2016).
Cooperation in general can enable the implementation of certain business models or can increase profitability by the exploitation of economies of scale (Skovsgaard & Jacobsen, 2017; Theurl, 2010). Synergy effects result from the utilization of know-how, different technologies or resources of the partners involved to complement the own competencies and services (Eggers & Engelbrecht, 2005; Sander, 2009). Cooperation exists in various industries and enable the participating companies to compensate their size-related resource deficits (Glaister & Buckley, 1996; Todeva & Knoke, 2005). This creates the opportunity to develop innovations, open up new markets, exploit newly created economies of scale and share costs and risks (Franco & Haase, 2015). In agriculture, cooperation in the form of cooperatives have been of essential importance for a long time, especially with the aim of exploiting synergy effects (Bareille et al., 2017). In the field of renewable energy development, cooperation in form of citizen cooperatives make a significant contribution to the participation of citizens in political, social and financial aspects of the energy transition (Huybrechts & Mertens, 2014). Energy cooperatives are frequently discussed as a potential actor in the energy transition and are increasingly being established to advance the common interests of stakeholders. For example, the joint operation of decentralized power generation plants can involve new actors in the energy transition through regional cooperation (Walk, 2014).
Existing biogas plants in Germany need new business models after the 20-year Renewable Energy Sources Act feed-in tariff expires. For continued operation, a business model innovation is needed, which can be realized based on the different technical utilization pathways. Cooperation can have a significant impact on the profitability of the different business models, especially by exploiting synergy effects (Karlsson et al., 2019). In addition, cooperation can help to ensure that existing plants continue to operate at all.
Currently, the most widespread use of biogas in Germany is in the coupled generation of electricity and heat. Additionally, there is the possibility of upgrading biogas to biomethane or biogenic hydrogen path (Mertins & Wawer, 2022).
Different options for cooperative business models that exist in the biogas utilization pathways are presented. The focus is on explaining the advantages of a joint approach compared to single-farm business models and identifying the relevant actors. Subsequently, drivers and barriers for the different cooperative business models are identified and classified based on 20 semi-structured interviews with plant operators in the administrative district of Osnabrück. The aim is to identify drivers and barriers for cooperative post-EEG operation. As a result, political instruments are to be found that make it possible to involve relevant actors and thus stimulate the best possible continued operation from the point of view of the energy system. The results are structured according to the PESTEL analysis. This assigns drivers and barriers to the categories political, economic, sociocultural, technological, ecological and legal (Kaufmann, 2021). The analysis of the interviews is supplemented and validated by a literature review.
Drivers and barriers for cooperative business models are manifold and can vary mainly depending on the plant and the operator.
Drivers
• Political
o Promotion of renewable energies: reduce dependence on fossil (Russian) fuels
• Economic
o Expectation of synergies (information sharing, shared risk, economies of scale)
o Planning security (fixed supply or purchase contracts)
o Access to new markets (not accessible by single-farm business models)
o Cost savings by sharing infrastructure, technology
o Positive return expectation
• Sociocultural
o Motivating, innovative environment
o Lowers barriers to participation in new markets
o Target-oriented partnerships
o Better use of capacities and strengths
o Strengthening regional value creation
• Technological
o Economies of scale (efficiency)
o Available, mature technology
o Storable, transportable gas
o Well-developed infrastructure
• Ecological
o Increase in plant efficiency
o Reduction of greenhouse gas emissions
o Promotion of the circular economy by utilization of organic waste and agricultural residues
o Improving soil quality (fermentation residues as fertilizer)
Barriers
• Political
o Competition to other renewable energies
• Economic
o Uncertainty about future development of energy markets
o Disagreements between the cooperation partners
o Lack of flexibility due to longer-term contractual obligations
o Allocation of profits
• Sociocultural
o Cooperation with current competitor
o Cultural differences and lack of trust
o Acceptance by the general public (e.g. overproduction of maize)
• Technological
o Different technology that is difficult to combine
o Data protection
• Ecological
o Competition for agricultural land
o Use of monocultures
o Emissions from plant
o Pollution from transport
• Legal
o Legal requirements and regulations
o Unfavorable regulatory environment, e.g. long permitting process
One finding is that uncertainty is a major barrier for plant operators. This includes uncertainty about regulatory frameworks and political requirements, as well as about the general development of the energy markets. In addition, social factors such as lack of reliability and disagreement about revenue sharing are a potential barrier. A key driver for the implementation of cooperative business models is the expectation of synergy effects. In addition, operators are driven by a positive expectation of returns and the responsibility for securing the energy supply in times of crisis.
The drivers identified can now be used to develop strategies to advance cooperative business models. In particular, synergy effects should be exploited so that operators can benefit from cooperation. The advantages can also be highlighted and communicated to increase acceptance among the general public. Another important step is to reduce the barriers discussed above. In order to reduce social barriers in particular, it may be advisable to include an external partner in the cooperation, such as a municipal utility that operates an upgrading plant and concludes purchase agreements with the individual partners. In addition, it would be politically expedient to provide the operators with a clear framework for the future in order to reduce uncertainties. As a further aspect, knowledge transfer on new technologies and markets should take place.
Artificial intelligence (AI) and human-machine interaction (HMI) are two keywords that usually do not fit embedded applications. Within the steps needed before applying AI to solve a specific task, HMI is usually missing during the AI architecture design and the training of an AI model. The human-in-the-loop concept is prevalent in all other steps of developing AI, from data analysis via data selection and cleaning to performance evaluation. During AI architecture design, HMI can immediately highlight unproductive layers of the architecture so that lightweight network architecture for embedded applications can be created easily. We show that by using this HMI, users can instantly distinguish which AI architecture should be trained and evaluated first since a high accuracy on the task could be expected. This approach reduces the resources needed for AI development by avoiding training and evaluating AI architectures with unproductive layers and leads to lightweight AI architectures. These resulting lightweight AI architectures will enable HMI while running the AI on an edge device. By enabling HMI during an AI uses inference, we will introduce the AI-in-the-loop concept that combines AI's and humans' strengths. In our AI-in-the-loop approach, the AI remains the working horse and primarily solves the task. If the AI is unsure whether its inference solves the task correctly, it asks the user to use an appropriate HMI. Consequently, AI will become available in many applications soon since HMI will make AI more reliable and explainable.
Currently, soil nutrient analysis involves two separate processes for soil sampling and nutrient analysis: 1. field soil sampling and 2. laboratory analysis. These two - separate - main work processes are combined and conceptualised for a mobile field laboratory so that soil sampling and analysis can be carried out simultaneously in the field. The module-based field laboratory "soil2data" can carry out these two main work processes in parallel and consists of 5 different task-specific modules that build on each other: app2field, field2soil, app2liquid, liquid2data and data2app. The individual modules were designed and built for the sub-process steps and adapted to the special features of the mobile field laboratory "soil2data". The biggest advantage is that the analysis results are available immediately, and a fertiliser recommendation can be generated instantly. For further analyses, the results are stored in the data cloud. The soil material remains in the field. In the ongoing project "Prototypes4soil2data", the mobile field laboratory soil2data is being further developed into a prototype with a modular structure.
While developing traffic-based cognitive enhancement technology (CET), such as bike accident prevention systems, it can be challenging to test and evaluate them properly. After all, the real-world scenario could endanger the subjects’ health and safety. Therefore, a simulator is needed, preferably one that is realistic yet low cost. This paper introduces a way to use the video game Grand Theft Auto V (GTA V) and its sophisticated traffic system as a base to create such a simulator, allowing for the safe and realistic testing of dangerous traffic situations involving cyclists, cars, and trucks. The open world of GTA V, which can be explored on foot and via various vehicles, serves as an immersive stand-in for the real world. Custom modification scripts of the game give the researchers control over the experiment scenario and the output data to be evaluated. An off-the-shelf bicycle equipped with three sensors serves as a realistic input device for the subject’s movement direction and speed. The simulator was used to test two early-stage CET concepts enabling cyclists to sense dangerous traffic situations, such as trucks approaching from behind the cyclist. Thus, this paper also presents the user evaluation of the cycling simulator and the CET used by the subjects to sense dangerous traffic situations. With the knowledge of the first iteration of the user-centered design (UCD) process, this paper concludes by naming improvements for the cycling simulator and discussing further research directions for CET that enable users to sense dangerous situations better.