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Globalization has not only changed our society, it has also had a profound effect on education. Many schools deal with student populations which, due to migration, are increasingly multilingual. Politically, few argue against the importance of multilingualism; rather, it is promoted. However, in practical terms the challenges associated with teaching and educational policies have increased as a result of linguistic diversity among student bodies. Moreover, reading is certainly regarded as a key learning skill, but how is the students’ life-world multilingualism (LWMUL) taken into consideration? Previous research suggests that there are significant links between teachers’ beliefs and practices, making this a compelling issue. The overall aim of this study was thus to gain a deeper understanding on teachers’ beliefs and strategies when teaching reading in multilingual settings. Using a cross-disciplinary, qualitative research methodology approach, the empirical inquiry consists of case studies with different, linguistically diverse settings. The case studies include classroom observations as well as teacher interviews in German, Swedish and Chilean grade 4 classrooms. After a qualitative content analysis in three analysis procedures, the results suggest dualistic beliefs being exhibited by the teachers. The separation of languages is believed to be of major importance, thus providing space almost exclusively for the academic language of instruction. This is reflected in the teachers’ strategies, leading to a static implementation, in which the students’ life-world multilingual resources (MULR) are generally not included. A lack of professional competence could be observed in issues regarding multilingualism, allowing beliefs rather than evidence-based knowledge to be the deciding factor in the practice. Four types of strategies for teaching reading in multilingual settings were identified, and an inattentive type of strategy, including a blindness to difference, seems to dominate.
In this thesis a swarm intelligent approach for controlling and coordinating a multi- agent system is studied. Considering an example where bodyguard agents have to protect one or more VIP agents, it is shown that the swarm intelligent approach can be a flexible and robust way to control a multi-agent system.
The power density of electric machines is a critical factor in various applications, i.e. like the power train. A major factor to improve the power density is boosting the electric current density, which increases the losses in the limited volume of the electric machine. This results in a need for an optimized thermal design and efficient cooling. The dissipation of heat can be achieved in a multitude of ways, ranging from air cooling to highly integrated cooling solutions. In this paper, this variety is shown and analyzed with a focus on water cooling. Further various structures in electric machines are presented.
A planar testbench is built to systematically analyze water cooling geometries. The focus lies in providing different power loss distributions along cooling channels, accurate temperature readings in a multitude of locations, as well as the pressure drop across the channel. The test bench results are aligned with simulations and simplified analytical evaluation to support the development process.
The main goal in this paper is to determine temperature gradients in the material close to the stator to quantize the potential for future cooling jacket designs. One question ,to answer is: How large the gradient is considering a realistic power loss distribution. Another sensible point are the different thermal expansions of aluminum used in cooling jackets and the steel core of the stator. This can be bypassed by using a steel cooling jacket. In this case, the performance of a steel cooling jacket compared to an aluminum version is investigated and also if light weight construction can compensate the lower thermal conductivity of steel.
After the analysis, an outlook about future changes of the measurement methods are given and first potentials for future cooling jackets are proposed.
In today's AI-driven era, computer vision, including autonomous driving, robotics, and healthcare, is prevalent. How-ever, acquiring ample data while managing resources and privacy constraints is challenging. This article proposes a solution: synthetic data generation. We use CAD software to craft intricate 3D models, process them in Blender, and evaluate quality using metrics like Structural Similarity and PSNR (Peak Signal to Noise Ratio). Synthetic data achieves up to 90% similarity with real data and an average PSNR of 21dB. Our method offers a streamlined, dependable ap-proach for enhancing computer vision, especially in object detection, addressing data acquisition challenges.
The vortex tube can separate a mass flow into a hot and cold mass flow. In this paper, the energy balance in the boundary layer of the vortex tube is analyzed with respect to a possible effect of temperature separation in the boundary layer by the viscous term of the enthalpy balance equation. A Large Eddy Simulation is used to generate the velocity profiles used for the computation of the viscous source terms. The dominant contributions of the source terms in the boundary layer of the vortex tube are identified and computed from the velocity fields. It is demonstrated how the strong velocity gradients in the boundary layer create a viscous flux of energy. An implementation of balance equations both with and without source term show the effect of energy separation in the boundary layer of the vortex tube.
The requirement of air conditioning in modern passenger cars need to fullfill enviromental standards with respect to refrigerant concerning global warming potential and ozone depletion with flour chlor carbon hydrogen mixtures.
This leads to compromises with respect to energy onsumption and cost of components.
As an alternative to standard refrigeration cycles, an inexpensive direct air cooling process is presented and discussed with respect to performance.
The process is specially suited for electric vehicles or vehicles equipped with an electric boosting device (E-charger).
One of the great challenges facing Europe today is the fast integration of migrants into society and the labour
market. This is made even more challenging by the fact that a great number of migrants only have a low level of education. The EU-funded project Fast Track Integration in European Regions (FIER) allows European
project-partners from regions with a high influx of refugees to interconnect in order to develop, test and evaluate joint innovative measures and strategies for a sustainable and fast labour-market-integration.
Based on this background and as part of the FIER-project “Language training on the job (LaTJo)”, the Akademie
für wissenschaftliche Weiterbildung der Pädagogischen Hochschule Weingarten (AWW) has developed a training program for the position of “Mentor for Language Learning at the Workplace”. The training enables Germanspeaking
employees of the participating companies to assist
their new colleagues with their integration at the workplace, especially by mentoring them in the field of work-related language acquisition. Here the workplace serves as a valuable language-learning-space, and the job-related actions and activities as natural prompters for language mentoring. The training is presented in detail in this manual.
Battery electric vehicle (BEV) adoption and complex powertrains
pose new challenges to automotive industries, requiring
comprehensive testing and validation strategies for reliability and
safety. Hardware-in-the-loop (HIL) based real-time simulation is
important, with cooperative simulation (co-simulation) being an
effective way to verify system functionality across domains. Fault
injection testing (FIT) is crucial for standards like ISO 26262.
This study proposes a HIL-based real-time co-simulation
environment that enables fault injection tests in BEVs to allow
evaluation of their effects on the safety of the vehicle. A Typhoon
HIL system is used in combination with the IPG CarMaker
environment. A four-wheel drive BEV model is built, considering
high-fidelity electrical models of the powertrain components
(inverter, electric machine, traction battery) and the battery
management system (BMS). Additionally, it enables validation of
driving dynamics, routes and environmental influences and provides
a precise analysis of the effect of powertrain system faults on driving
behavior. A possible case for a fault injection is to introduce a shootthrough fault in the inverter. Through the co-simulation, it is possible
to analyze the effects on the powertrain and the vehicle dynamics in
different driving situations (e.g. snow). This work demonstrates that
co-simulation is a valuable tool for the development and validation of
BEVs, and presents specific fault cases introduced into the
powertrain and the resulting effects tested under different driving
conditions. In addition, the study discusses the system's limitations
and future possibilities such as controller hardware integration
(Controller-HIL) and autonomous driving system validation.
Robotic grasping has been a prevailing problem ever since humans began creating robots to execute human-like tasks. The problems are usually due to the involvement of moving parts and sensors. Inaccuracy in sensor data usually leads
to unexpected results. Researchers have used a variety of sensors for improving manipulation tasks in robots.
This thesis focuses specifically on grasping unknown objects using mobile service robots. An approach using convolutional neural networks to generate grasp points
in a scene using RGBD sensor data is proposed. Two neural networks that perform grasp detection in a top down scenario are evaluated, enhanced and compared in
a more general scenario. Experiments are performed in a simulated environmentas well as the real world.
The results are used to understand how difference in
sensor data can affect grasping and enhancements are made to overcome these effects and to optimize the solution.
This thesis is an improvement on the works of Douglas Morrison, Peter Corke and Jürgen Leitner in their work Closing the Loop for Robotic Grasping: A Real-time,
Generative Grasp Synthesis Approach and Fu-Jen Chu, Ruinian Xu and Patricio A. Vela in their work Real-world Multi-object, Multi-grasp Detection.
Robotic grasping has been a prevailing problem ever since
humans began creating robots to execute human-like tasks. The problems
are usually due to the involvement of moving parts and sensors. Inaccuracy in sensor data usually leads to unexpected results. Researchers have
used a variety of sensors for improving manipulation tasks in robots.
We focus specifically on grasping unknown objects using mobile service
robots. An approach using convolutional neural networks to generate
grasp points in a scene using RGBD sensor data is proposed. Two convolutional neural networks that perform grasp detection in a top down
scenario are evaluated, enhanced and compared in a more general scenario. Experiments are performed in a simulated environment as well as
the real world. The results are used to understand how the difference in
sensor data can affect grasping and enhancements are made to overcome
these effects and to optimize the solution.