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In der Schriftenreihe „Voneinander Lehren lernen“ publiziert das LearningCenter der Hochschule Osnabrück anwendungsbezogene Beiträge zur Qualitätsentwicklung in Studium und Lehre. Die Schriftenreihe ist an das Konzept des „Scholarship of Teaching and Learning“ (SoTL) angelehnt. Demnach soll sie insbesondere den Fachlehrenden verschiedener Studiengänge als Plattform dienen, um ihre eigenen Erfahrungen, Ideen und Konzepte zur Lehr- und Studiengangentwicklung systematisch zu reflektieren und entsprechende Erkenntnisse für andere nutzbar zu machen. Ziel ist es, den Diskurs über hochschuldidaktische Themen in die Fächer zu tragen und so die Qualität der Lehr-Lernprozesse in den Studiengängen zu fördern. In diesem fünften Band der Schriftenreihe werden Projekte der Hochschule Osnabrück beschrieben, deren Umsetzung durch verschiedene Förderlinien oder durch Studienqualitätsmittel unterstützt wurde. Die Textbeiträge sind sowohl inhaltlich als auch didaktisch-methodisch sehr vielschichtig. Eine Gemeinsamkeit liegt jedoch darin, dass sie jeweils eine konstruktive hochschuldidaktische Reaktion auf zukunftsbezogene Trends und daraus resultierende Kompetenz-Anforderungen an Hochschulabsolvent*innen widerspiegeln. Der Terminologie des Zukunftsinstituts folgend sind es primär die Megatrends „Konnektivität“, „New Work“, „Gesundheit“, „Wissenskultur“ und „Globalisierung“, die in den Beiträgen implizit oder explizit thematisiert werden.
Water retention properties of wood fiber based growing media and their impact on irrigation strategy
(2024)
Distribution of water and air in growing media during ebb-and-flow irrigation depends on water storage properties (water retention curve) and water transport properties (hydraulic conductivity) of the materials. Growing media with their high number of coarse pores are known to exhibit strong hysteresis, i.e., differences in the water retention properties during drying and wetting cycles. To account for potential ecological disadvantages of peat, wood fibers are commonly used as substitutes for peat in growing media. However, the wood fibers generally have higher air capacities and hydraulic conductivities and lower water capacities compared to peat which may results in necessary adaptions of the irrigation strategy. Tools to optimize irrigation systems are physically based water transport models, such as HYDRUS-1D, which is commonly used to describe water transport in soils, but not often for growing media. In this study, white peat and pure wood fibers were used to describe differences in their water retention behavior. Water retention curves (drying cycles) and hydraulic conductivities were measured with standard analytical procedures. Hysteresis of the water retention curves was analytically determined based on their capillary rise properties. The results were used with a modified HYDRUS-1D model to test model quality against measured water contents during ebb-and-flow irrigation cycles and to optimize the irrigation strategy for the different materials. The results showed that the model quality was sufficiently good only if the strong hysteresis of the water retention curves was considered during the simulation process. Different strategies were tested to modify ebb-and-flow irrigation (irrigation frequency, irrigation duration and irrigation height) in that way that the water suction in the root zone was similar to that of the peat material. Simulation results showed that significant improvements could only be reached by increasing the flooding depth in ebb-and-flow systems to ensure an optimum water supply of plants in the wood fiber based growing media.
Test von Schnellverfahren zur Bestimmung der Benetzungseigenschaften von Kultursubstraten (Abstract)
(2024)
Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive "data
problem" has initialized a shift to synthetic data. One downside of synthetic data is known as the "reality gap", a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.
The 3GPP release 16 integrates TSN functionality into 5G and standardizes various options for TSN time synchronization over 5G such as transparent mode and bridge mode. The time domains for the TSN network and the 5G network are kept separate with an option to synchronize either of the networks to the other. The TSN time synchronization over 5G is possible either by using the IEEE 1588 generalized Precision Time Protocol (gPTP) based on UDP/IP multicast or via IEEE 802.1AS based on Ethernet PDUs. The INET and Simu5G simulation frameworks, which are both based on the OMNeT++ discrete event simulator, are widely used for simulating TSN and 5G networks. The INET framework comprises the 802.1AS based time synchronization mechanism, and Simu5G provides the 5G user plane carrying IP PDUs. We modified the 802.1AS-based synchronization model of INET so that it works over UDP/IP. With that, it is possible to synchronize TSN slaves (connected to 5G UEs), across a 5G network, with a TSN master clock, present within a TSN network, that is connected to the 5G core network. Our simulation results show that 500 microseconds of synchronization accuracy can be achieved with the corrected asymmetric propagation delay of uplink and downlink between the gNodeB (gNB) and the User Equipment (UE). Furthermore, the synchronization accuracy can be improved if the delay difference between uplink and downlink is known.
Durch die Auswirkungen des Klimawandels – besonders durch Hitze – geraten viele indigene Baumarten innerhalb der nächsten 75 Jahre voraussichtlich an den Rand ihrer Existenz. Der Stadtstandort stellt eine zusätzliche Herausforderung dar, der durch menschliche Aktivitäten negativ, aber auch positiv beeinflusst werden kann. Besonders die Wasserverfügbarkeit kann durch geeignete vegetationstechnische Maßnahmen und intelligente Profilierung von Oberflächen befördert werden. Die Vegetation wird sich verändern. Mit gebietseinheimischen Genotypen und natürlicher Migration hitzeverträglicher Arten alleine lassen sich unsere Probleme nicht lösen. Wir brauchen Bäume in der Stadt, die beschatten und verdunsten.
Lösungsansätze sind die vielfältige Anpflanzung hitzeresistenter Genotypen indigener Arten, neuer, submediterraner Arten aus Süd- und Südosteuropa (assisted migration) sowie klimatoleranter Arten anderer Kontinente. Es ist allerdings davon auszugehen, dass sich diese Arten dann bei uns auch etablieren werden. Und das ist bei der durch die Eiszeiten verarmten Gehölzflora Mitteleuropas und für lebenswerte Städte auch gut so!
Recent real-time networking developments have enabled ultra reliability, very low latency and high data rates in wired networks. Wireless networking developments have also shown that they can achieve very high data rates with consistency, but they still lack in providing ultra reliability and extremely low latency. Time Sensitive Networking (TSN) developments have brought these capabilities in Industry automation and Automotive industry too. Although TSN is standardized for wired networks for a long time, for wireless networks it will be standardized within the IEEE 802.11be standard for Wi-Fi and 3GPP Release 17 for 5G in the near future. This paper provides an overview of TSN in wired and wireless networks with the aim of comparing different simulators and presenting their offered functionality and shortcomings. These tools can be used to make oneself familiar with TSN algorithms, standards, and for the development and testing of time sensitive networks. Afterwards, the paper discusses open research questions for using TSN over wireless networks.