Sustainable entrepreneurs need to balance social, environmental and economic goals. However, some put sustainable goals first whereas others prioritize economic outcomes. This suggests differences in types of sustainable entrepreneurs. We study sustainable entrepreneur archetypes based on their motivations to start a venture and on their entrepreneurial orientation (EO). This is important to understand since the degrees of EO and motivations determines not only the sustainability of a venture but also its success and activities of sustainable entrepreneurs. Based on the extant literature and an exploratory, mixed-method study of 24 sustainable fashion firms we put forward four distinct sustainable entrepreneurship archetypes which differ in motivation and EO, namely: Idealists, Evangelists, Realists and Opportunists. Implications for practice, theory and future research are suggested.
Objectives: This study assesses social workers’ orientation toward the evidence-based practice (EBP) process and explores which specific variables (e.g. age) are associated. Methods: Data were collected from 341 Dutch social workers through an online survey which included a Dutch translation of the EBP Process Assessment Scale (EBPPAS), along with 13 background/demographic questions. Results: The overall level of orientation toward the EBP process is relatively low. Although respondents are slightly familiar with it and have slightly positive attitudes about it, their intentions to engage in it and their actual engagement are relatively low. Respondents who followed a course on the EBP process as a student are more oriented toward it than those who did not. Social workers under 29 are more familiar with the EBP process than those over 29. Conclusions: We recommend educators to take a more active role in teaching the EBP process to students and social workers.
In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7◦ root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
The specific objective of HyScaling is to achieve a 25-30% cost reduction for levelized cost of hydrogen. This cost reduction will be achieved in 2030 when the HyScaling innovations have been fully implemented. HyScaling develops novel hardware (such as stacks & cell components), low-cost manufacturing processes, optimized integrated system designs and advanced operating and control strategies. In addition to the goal of accelerating implementation of hydrogen to decarbonize energy-intensive industry, HyScaling is built around industrial partners who are aiming to build a business on the HyScaling innovations. These include novel components for electrolysers (from catalysts to membranes, from electrode architectures to novel coatings) as well as electrolyser stacks and systems for different applications. For some innovations (e.g. a coating from IonBond, an electrode design from Veco) the consortium aims at starting commercialisation before the end of the program. A unique characteristic of the HyScaling program is the orientation on Use Cases. In addition to partners representing the Dutch manufacturing industry, end-users and technology providers are partner in the consortium. This enables the consortium to develop the electrolyser technology specifically for different applications. In order to be able to come to an assessment of the market for electrolysers and components, the use cases also include decentralized energy systems.Projectpartners:Nouryon, Tejin, Danieli Corus, VDL, Hauzer, VECO, lonbond, Fluor, Frames, Magneto, VONK, Borit, Delft IMP, ZEF, MTSA, SALD, Dotx control, Hydron Energy, MX, Polymers, VSL, Fraunhofer IPT, TNO, TU Delft, TU Eindhoven, ISPT, FMC.
