A loss of physical functioning (i.e., a low physical capacity and/or a low physical activity) is a common feature in patients with chronic obstructive pulmonary disease (COPD). To date, the primary care physiotherapy and specialized pulmonary rehabilitation are clearly underused, and limited to patients with a moderate to very severe degree of airflow limitation (GOLD stage 2 or higher). However, improved referral rates are a necessity to lower the burden for patients with COPD and for society. Therefore, a multidisciplinary group of healthcare professionals and scientists proposes a new model for referral of patients with COPD to the right type of exercise-based care, irrespective of the degree of airflow limitation. Indeed, disease instability (recent hospitalization, yes/no), the burden of disease (no/low, mild/moderate or high), physical capacity (low or preserved) and physical activity (low or preserved) need to be used to allocate patients to one of the six distinct patient profiles. Patients with profile 1 or 2 will not be referred for physiotherapy; patients with profiles 3–5 will be referred for primary care physiotherapy; and patients with profile 6 will be referred for screening for specialized pulmonary rehabilitation. The proposed Dutch model has the intention to get the right patient with COPD allocated to the right type of exercise-based care and at the right moment.
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In opdracht van het ministerie van Binnenlandse Zaken en Koninkrijksrelaties heeft het lectoraat Changing Role of Europe van De Haagse Hogeschool de rol van de Dutch Urban Envoy geëvalueerd. De betekenis, de inzet, het vervolg en de toekomstige invulling van de rol van de Dutch Urban Envoy komen aan bod. Op basis van de inzichten van 37 interviews met 39 betrokken partijen (van het Ministerie van BZK, Nederlandse steden, Europese steden, koepelorganisaties, Europese instellingen en andere ministeries binnen de Rijksoverheid) en deskresearch zijn de volgende conclusies en aanbevelingen geformuleerd.
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Blog post op de blog van het Amsterdam Creative Industries Network. Tijdens de Berlin Music Week hebben Inholland-onderzoekers Koos Zwaan en Sabine de Lat van het lectoraat Media, Cultuur en Burgerschap de eerste bevindingen gepresenteerd van een lopend onderzoeksproject getiteld ‘No Limits: The Value of Dutch Music in the Online Domain’.
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The focus of this project is on improving the resilience of hospitality Small and Medium Enterprises (SMEs) by enabling them to take advantage of digitalization tools and data analytics in particular. Hospitality SMEs play an important role in their local community but are vulnerable to shifts in demand. Due to a lack of resources (time, finance, and sometimes knowledge), they do not have sufficient access to data analytics tools that are typically available to larger organizations. The purpose of this project is therefore to develop a prototype infrastructure or ecosystem showcasing how Dutch hospitality SMEs can develop their data analytic capability in such a way that they increase their resilience to shifts in demand. The one year exploration period will be used to assess the feasibility of such an infrastructure and will address technological aspects (e.g. kind of technological platform), process aspects (e.g. prerequisites for collaboration such as confidentiality and safety of data), knowledge aspects (e.g. what knowledge of data analytics do SMEs need and through what medium), and organizational aspects (what kind of cooperation form is necessary and how should it be financed).Societal issueIn the Netherlands, hospitality SMEs such as hotels play an important role in local communities, providing employment opportunities, supporting financially or otherwise local social activities and sports teams (Panteia, 2023). Nevertheless, due to their high fixed cost / low variable business model, hospitality SMEs are vulnerable to shifts in consumer demand (Kokkinou, Mitas, et al., 2023; Koninklijke Horeca Nederland, 2023). This risk could be partially mitigated by using data analytics, to gain visibility over demand, and make data-driven decisions regarding allocation of marketing resources, pricing, procurement, etc…. However, this requires investments in technology, processes, and training that are oftentimes (financially) inaccessible to these small SMEs.Benefit for societyThe proposed study touches upon several key enabling technologies First, key enabling technology participation and co-creation lies at the center of this proposal. The premise is that regional hospitality SMEs can achieve more by combining their knowledge and resources. The proposed project therefore aims to give diverse stakeholders the means and opportunity to collaborate, learn from each other, and work together on a prototype collaboration. The proposed study thereby also contributes to developing knowledge with and for entrepreneurs and to digitalization of the tourism and hospitality sector.Collaborative partnersHZ University of Applied Sciences, Hotel Hulst, Hotel/Restaurant de Belgische Loodsensociëteit, Hotel Zilt, DM Hotels, Hotel Charley's, Juyo Analytics, Impuls Zeeland.
Renewable energy, particularly offshore wind turbines, plays a crucial role in the Netherlands' and EU energy-transition-strategies under the EU Green Deal. The Dutch government aims to establish 75GW offshore wind capacity by 2050. However, the sector faces human and technological challenges, including a shortage of maintenance personnel, limited operational windows due to weather, and complex, costly logistics with minimal error tolerance. Cutting-edge robotic technologies, especially intelligent drones, offer solutions to these challenges. Smaller drones have gained prominence through applications identifying, detecting, or applying tools to various issues. Interest is growing in collaborative drones with high adaptability, safety, and cost-effectiveness. The central practical question from network partners and other stakeholders is: “How can we deploy multiple cooperative drones for maintenance of wind turbines, enhancing productivity and supporting a viable business model for related services?” This is reflected in the main research question: "Which drone technologies need to be developed to enable collaborative maintenance of offshore wind turbines using multiple smaller drones, and how can an innovative business model be established for these services? In collaboration with public and private partners, Saxion, Hanze, and RUG will research the development of these collaborative drones and investigate the technology’s potential. The research follows a Design Science Research methodology, emphasizing solution-oriented applied research, iterative development, and rigorous evaluation. Key technological building blocks to be developed: • Morphing drones, • Intelligent mechatronic tools, • Learning-based adaptive interaction controllers and collaborations. To facilitate the sustainable industrial uptake of the developed technologies, appropriate sustainable business models for these technologies and services will be explored. The project will benefit partners by enhancing their operations and business. It will contribute to renewing higher professional education and may lead to the creation of spin-offs/spinouts which bring this innovative technology to the society, reinforcing the Netherlands' position as a leading knowledge economy.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
