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Vertical or shared?

University teacher teams can work toward educational change through the process of team learning behavior, which involves sharing and discussing practices to create new knowledge. However, teachers do not routinely engage in learning behavior when working in such teams and it is unclear how leadership support can overcome this problem. Therefore, this study examines when team leadership behavior supports teacher teams in engaging in learning behavior. We studied 52 university teacher teams (281 respondents) involved in educational change, resulting in two key findings. First, analyses of multiple leadership types showed that team learning behavior was best supported by a shared transformational leadership style that challenges the status quo and stimulates team members’ intellect. Mutual transformational encouragement supported team learning more than the vertical leadership source or empowering and initiating structure styles of leadership. Second, moderator analyses revealed that task complexity influenced the relationship between vertical empowering team leadership behavior and team learning behavior. Specifically, this finding suggests that formal team leaders who empower teamwork only affected team learning behavior when their teams perceived that their task was not complex. These findings indicate how team learning behavior can be supported in university teacher teams responsible for working toward educational change. Moreover, these findings are unique because they originate from relating multiple team leadership types to team learning behavior, examining the influence of task complexity, and studying this in an educational setting.

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In-depth assessment of the situation of the textile and clothing sector in the EU and prospects

The in-depth assessment of the situation of the European textile and clothing sector is composed by six independent reports with a close focus on key aspects useful to understand the dynamics and the development of the textile and clothing industry, drivers of change – most notably the impact of the financial crisis – and identification of policy responses and best practices. This has been done in six specific tasks leading to the six reports: Task 1 Survey on the situation of the EU textile and clothing sector Task 2 Report on research and development Task 3 Report on SME situation Task 4 Report on restructuring Task 5 Report on training and Education Task 6 Report on innovation practices Task 6 focused on understanding how European textile & clothing companies are engaged into innovation practices. Hence key questions regard what is critical to transform knowledge and Research and Development (R&D) into good selling marketable products and which are the driving forces and relationships towards a better competitive performance through innovation. The analysis was carried out and the trends were then verified in selected regional cases: Lombardia Piemonte, Baden Württemberg, North Portugal and Galicia, Slovenia and Romania.

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In-depth assessment of the situation of the textile and clothing sector in the EU and prospects

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The Frisian dykes and their touristic and recreational offers

This report deals with the possibilities for touristic and recreational development on the Wadden Sea coast of the Dutch province of Friesland. The topic is approached against the background of possible dyke reinforcements which might become necessary in future years. The data on this topic were collected by conducting three interviews with stakeholders responsible for or influenced by these changes. In addition 70 questionnaires were handed out to respondents who are using the dykes for touristic or recreational purposes. Results of the research show that a complete reinforcement is not planned so far. Still, the people using the dykes have a certain demand for new developments in this field and are not completely satisfied with the current state. It also becomes clear that the possibilities for touristic improvements in the dyke area are limited in many ways. The protection is the main purpose of the dykes and interventions risking the safety are stopped by existing laws. Concluding it can be said that there is a potential for further touristic and recreational improvements in this area. Stakeholders and people using the dykes, all have a certain interest in new developments. Nevertheless, these changes can only be limited to small developments, building up on already existing tourist activities. Furthermore, stakeholders have to improve their cooperation in order to work towards a common goal.

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The Frisian dykes and their touristic and recreational offers

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Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity

Background: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. Objective: First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. Methods: A scoping review was conducted based on the framework by Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. Results: In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. Conclusions: The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.

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Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity

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ons of life's most precious gifts

Professional development of teacher educators is an important topic, because teacher educators need to maintain and enhance their expertise in order to educate our future teachers (Kools & Koster, n.d. ; Dengerink, Lunenberg & Kools, 2015). How do teacher educators fulfil this task, especially within the hectic timeframe of everyday work? I asked four colleges to participate in a group to share their experiences, actions or behaviour in the organisation about their development in their profession of being a teacher educator. My purpose is to bring awareness and movement into that group. My research focusses on teacher educators in a large teacher education department in the Netherlands and the opportunities for action available to them. During this study we are currently creating a learning environment in which mutual cooperation increases the learning potential of all participants. In this group participants take or make time to learn, giving words to their scopes . Researcher and participants discuss and explore on the basis of equality, reciprocity and mutual understanding. By deploying methods borrowed from ‘Appreciative Inquiry’(Massenlink et al., 2008) the enthusiasm of a study group is raised and the intrinsic motivation of the participants stimulated. Our study group will convene three times. Its goal is to stimulate cooperation among teacher educators through optimisation of existing qualities, a method that could be described as empowerment, or a process of collective reinforcement ‘To learn’ involves experiencing that what one does really matters, as well as developing one’s own persona in the local community. Intervention, action, reflection and study group meetings alternate in the course of our research. In addition to audio and video recordings, data consists of reports drawn up on the basis of member checks. Data is analysed qualitatively by coding the interview texts and reports. After applying the codes, the researcher discusses the coding in a research group and with the participants of the study group (membercheck). Working collaboratively can offer learning challenges that catalyse growth as a professional, teacher educators become acquainted and approach each other from the perspective of their respective professional and functional responsibilities. This study offers perspectives for other teacher educators to recognize these possibilities in their own situation. Moreover the study offers a description of a way to organise collegial exchange. The research is related to the RDC professional development of teacher educators.

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CURIOSITY-DRIVEN REINFORCEMENT LEARNING AGENT FOR MAPPING UNKNOWN INDOOR ENVIRONMENTS

Autonomously exploring and mapping is one of the open challenges of robotics and artificial intelligence. Especially when the environments are unknown, choosing the optimal navigation directive is not straightforward. In this paper, we propose a reinforcement learning framework for navigating, exploring, and mapping unknown environments. The reinforcement learning agent is in charge of selecting the commands for steering the mobile robot, while a SLAM algorithm estimates the robot pose and maps the environments. The agent, to select optimal actions, is trained to be curious about the world. This concept translates into the introduction of a curiosity-driven reward function that encourages the agent to steer the mobile robot towards unknown and unseen areas of the world and the map. We test our approach in explorations challenges in different indoor environments. The agent trained with the proposed reward function outperforms the agents trained with reward functions commonly used in the literature for solving such tasks.

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Reinforcement Learning for Collaborative Robots Pick-and-Place Applications:

The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an (Formula presented.) -greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet.

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Reinforcement Learning to send reminders at right moments in smartphone Exercise Application

Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the ‘right’ time to deliver a restricted number of notifications adaptively, with respect to users’ temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app’s other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.

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Reinforcement Learning to send reminders at right moments in smartphone Exercise Application

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Deep Reinforcement Learning Applied to a Robotic Pick-and-Place Application

Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. This can be achieved by off-the-shelf vision-based solutions, but most require prior knowledge about each object tobe manipulated. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative Robot (Cobot) using a depth camera. The solution uses deep Q-learning to process the color and depth images and generate a greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNNmodels (RexNext, MobileNet, MNASNet and DenseNet). Results showthat the best performance in our application was reached by MobileNet,with an average of 84 % accuracy after training in simulated environment.

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Vertical or shared?

In-depth assessment of the situation of the textile and clothing sector in the EU and prospects

The Frisian dykes and their touristic and recreational offers

Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity

ons of life's most precious gifts

CURIOSITY-DRIVEN REINFORCEMENT LEARNING AGENT FOR MAPPING UNKNOWN INDOOR ENVIRONMENTS

Reinforcement Learning for Collaborative Robots Pick-and-Place Applications:

Reinforcement Learning to send reminders at right moments in smartphone Exercise Application

Deep Reinforcement Learning Applied to a Robotic Pick-and-Place Application

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ONTZOUTEN RIJPEND SLIB VOOR DELTABESCHERMING

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Producten 623

Vertical or shared?

In-depth assessment of the situation of the textile and clothing sector in the EU and prospects

The Frisian dykes and their touristic and recreational offers

Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity

ons of life's most precious gifts

CURIOSITY-DRIVEN REINFORCEMENT LEARNING AGENT FOR MAPPING UNKNOWN INDOOR ENVIRONMENTS

Reinforcement Learning for Collaborative Robots Pick-and-Place Applications:

Reinforcement Learning to send reminders at right moments in smartphone Exercise Application

Deep Reinforcement Learning Applied to a Robotic Pick-and-Place Application

Projecten 1

ONTZOUTEN RIJPEND SLIB VOOR DELTABESCHERMING