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Since February 2019, the „Katholische Erwachsenenbildung“ has been collaborating with the Institute of Music of the University of Applied Sciences Osnabrück. An institute for adult education provides the framework for a concept in which German as a second language lessons are accompanied by musicalisation. In line with elemental musical practice, whole-body experiences with voice, movement, and body-percussion play an important role. Students with the career goal of „Educating Artist“ work alongside language teachers in this project, and are mentored by university teachers. The young musicians gain monitored teaching experience and have a great opportunity to witness and help shape the linguistic and social integration of refugees.
A consequence of increasing migration is that a large number of people need to learn the language of the country of immigration. Music and language are phenomenons that share many common characteristics, such as melody, rhythm, and timbre. Music draws attention and can cause positive emotions. Music and movement are deeply rooted in the communication of emotional states und are considered to be the evolutionary biological basis for language. Thus the close relationship between language, music, and dance is evident: They all rely on differentiating perception, are able, as systems designed for social interactions, to connect people, and allow for both collective and individual expression.
The contents and procedures of the lessons are documented in a digital diary. The entire team meets at regular intervals, in order to reflect on the experiences and conduct further planning. For these purposes, video documentation of the lessons is also used. The project will end in November 2019 with a language exam; a musical final presentation is also planned. By then at the latest, findings will be available as to if and how the musical course content was able to support language acquisition. The collaboration enables the partners to realize the combining of different objectives (learning a second language, cultural participation and music making) by bringing experts together.
Taking the transdisciplinary research study “Green fingers for a climate resilient city”, funded by the German Ministry of education and research (BMBF), as an example, this paper follows the hypothesis that processes of landscape planning and designing multifunctional green spaces and processes of co-creation need to be combined to stimulate climate resilient city transformation. The findings are that efforts to combine these processes benefit from making complex climate-resilient city planning accessible for people of different professional backgrounds. The paper showcases how storytelling (Schmidt 2019), mapping (Langner 2009) and guided walks (Schultz 2019) are means to mutually engage with, perceive and understand multifunctional green spaces, inspire ownership, and build capacity for the city’s climate-resilient transformation.
This article proposes the concept of a simulation framework for environmental sensors with multilevel abstraction in agricultural scenarios. The implementation case study is a simulation of a grain-harvesting scenario enabled by LiDAR sensors. Environmental sensor models as well as kinematics and dynamic behavior of machines are based on the robotics simulator Gazebo. Models for powertrain, machine process aggregates and peripheral simulation components are implemented with the help of MATLAB/ Simulink and with the robotics middleware Robot Operating System (ROS). This article deals with the general concept of a multilevel simulation framework and in particular with sensor and environmental modeling.
Background: Singers belong to the group of professional voice users with the highest demands regarding voice quality and vocal load. Thus, they also have a high risk of developing a voice disorder, which in return has major impact on their ability to work. Besides voice disorders caused by organic changes, there are functional voice disorders caused by, e.g., a hypertonia of the larynx, shoulder and neck muscles or insufficient breathing patterns. In these cases, physiotherapy can be one component of a multidisciplinary approach to treatment.
The purpose of this presentation is, based on anatomical considerations and current evidence, to inform about and demonstrate physiotherapy techniques for treating singers with functional voice disorders.
Approach of Presentation: A case from a special physiotherapy outpatient clinic for vocalists will be described. Based on this example, information on the evidence of physiotherapy approaches for functional voice disorders will be provided. Afterwards, some practical hands-on techniques will be demonstrated for participants to try.
Content of Presentation: This workshop will focus on the physiotherapy treatment for a vocalist with functional voice disorders. The vocalist experienced changed pitch and hypertonia in both the muscles of the shoulder-neck region and the extrinsic laryngeal muscles. Paralaryngeal manual techniques, in addition to posture and breathing exercises, will be demonstrated with the purpose of mobilizing the larynx and relaxing the hypertonic muscles.
Conclusions and Practical Relevance: This workshop highlights the special potential of physical therapy in the treatment of functional voice disorders in singers.
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.
The market for external ratings is dominated worldwide as well as in the European Union (EU) by three major credit rating agencies (CRAs). These “Big Three” are Standard & Poor's (S&P), Moody's and Fitch Ratings. Due to the oligopolistic market structure and possible involvement in the 2008 financial crisis, the rating agencies have constantly come under criticism. This was associated with stricter regulatory requirements to ease the situation. The EU-Regulation on credit rating agencies („CRA-Regulation“) coming into force 2009 and its amendments in 2011 and in 2013 have mainly governed such regulation. The aim of the article is to analyse potential regulatory impact on the still inherent oligopolistic situation on the EU rating market in the context of the CRA-Regulation. Selected key figures are used to observe over a defined period of time if and how the dominance has changed. The motivation for this article is the observation, that political and private efforts to establish a European rating agency as a counterweight to the three major agencies and other approaches to increase competition in the rating market, followed, which has not been resounding to date. In summary, it is shown that new agencies have a potential impact on the EU rating market and that the three major rating agencies still dominate the market but within a changed environment.
This paper presents an optimized algorithm for estimating static and dynamic gait parameters. We use a marker- and contact-less motion capture system that identifies 20 joints of a person walking along a corridor.
Based on the proposed gait cycle detection basic metrics as walking frequency, step/stride length, and support phases are estimated automatically. Applying a rigid body model, we are capable to calculate static and dynamic gait stability metrics. We conclude with initial results of a clinical study evaluating orthopaedic technical support.
Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images. To this end, algorithms of the YoloV5 family of pre-trained models were applied to 885 images containing at least one of the two wound types. The YoloV5m6 model provided the highest precision (0.942) and a high recall value (0.837). Its mAP_0.5:0.95 was 0.642. While the latter value is comparable to the ones reported in the literature, precision and recall were considerably higher. In conclusion, our results on good wound detection and classification may reveal a path towards (semi-) automated entry of wound information in patient records. To strengthen the trust of clinicians, we are currently incorporating a dashboard where clinicians can check the validity of the predictions against their expertise.