Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.
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Since the first uptake of electric vehicles, policy makers are questioning how to rollout public charging infrastructure in an efficient manner, such that user convenience balances with costs of investment. In some metropolitan areas, the first phase of rollout has been passed, meaning an optimized deployment of future charging stations for electric vehicles (EVs) becomes important to improve the charging infrastructure and ensure customer satisfaction and sufficient service provision. Complex system literature shows that network vulnerability is an important metric, yet, charging infrastructure has not yet been a subject of these simulation models so far. This research, based on real-world data, provides a novel approach for improving the roll-out strategy of municipalities, by treating the charge infrastructure as a complex network of charging stations and defining vulnerability in respect to the availability of its surrounding charging stations within relevant walking distance.
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Technology designed to sense behavior, often neglects to directly incorporate subjective input from (elderly) users. This paper presents experiences in deploying technology that considers the elderly user and their subjective input as a way to enrich sensor data systems and empower the user. For this purpose, the paper draws on: (1) Observations of shortcomings in terms of capturing objective data from sensors as experienced in long-term deploymentt in the homes of older adults; (2) The design and evaluation of a wide range of applications especially designed to enable older adults to give subjective input on how they are doing, including an interactive television quiz, a talking picture frame and a tangible mood board, and (3) The development and field study of one application, the ‘Mood button’ in particular, that was tested in real-world sensing settings to work with a commercial sensing system. In doing this, this work aims to contribute towards successful sensing deployments and tools that give more control to the (elderly) end-user.
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People tend to be hesitant toward algorithmic tools, and this aversion potentially affects how innovations in artificial intelligence (AI) are effectively implemented. Explanatory mechanisms for aversion are based on individual or structural issues but often lack reflection on real-world contexts. Our study addresses this gap through a mixed-method approach, analyzing seven cases of AI deployment and their public reception on social media and in news articles. Using the Contextual Integrity framework, we argue that most often it is not the AI technology that is perceived as problematic, but that processes related to transparency, consent, and lack of influence by individuals raise aversion. Future research into aversion should acknowledge that technologies cannot be extricated from their contexts if they aim to understand public perceptions of AI innovation.
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At the department of electrical and electronic engineering of Fontys University of Applied Sciences we are defining a real-life learning context for our students, where the crossover with regional healthcare companies and institutes is maximized. Our innovative educational step is based on openly sharing electronic designs for health related measurement modalities as developed by our students. Because we develop relevant reference designs, the cross fertilization with society is large and so the learning cycle is short.
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This article delves into the acceptance of autonomous driving within society and its implications for the automotive insurance sector. The research encompasses two different studies conducted with meticulous analysis. The first study involves over 600 participants involved with the automotive industry who have not yet had the opportunity to experience autonomous driving technology. It primarily centers on the adaptation of insurance products to align with the imminent implementation of this technology. The second study is directed at individuals who have had the opportunity to test an autonomous driving platform first-hand. Specifically, it examines users’ experiences after conducting test drives on public roads using an autonomous research platform jointly developed by MAPFRE, Universidad Carlos III de Madrid, and Universidad Politécnica de Madrid. The study conducted demonstrates that the user acceptance of autonomous driving technology significantly increases after firsthand experience with a real autonomous car. This finding underscores the importance of bringing autonomous driving technology closer to end-users in order to improve societal perception. Furthermore, the results provide valuable insights for industry stakeholders seeking to navigate the market as autonomous driving technology slowly becomes an integral part of commercial vehicles. The findings reveal that a substantial majority (96% of the surveyed individuals) believe that autonomous vehicles will still require insurance. Additionally, 90% of respondents express the opinion that policies for autonomous vehicles should be as affordable or even cheaper than those for traditional vehicles. This suggests that people may not be fully aware of the significant costs associated with the systems enabling autonomous driving when considering their insurance needs, which puts the spotlight back on the importance of bringing this technology closer to the general public.
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Background The global nursing shortages exacerbated by the COVID-19 pandemic necessitated a drastic reorganization in nursing practices. Work routines, the composition of teams and subsequently mundane nursing practices were all altered to sustain the accessibility and quality of care. These dramatic changes demanded a reshaping of the nurses’ work environment. The aim of this study was to explore how nurses reshaped their work environment in the early stages of the COVID-19 pandemic. Methods A descriptive study comprising 26 semi-structured interviews conducted in a large Dutch teaching hospital between June and September 2020. Participants were nurses (including intensive care unit nurses), outpatient clinic assistants, nurse managers, and management (including one member of the Nurse Practice Council). The interviews were analysed with open, axial, and selective coding. Results We identified five themes: 1) the Nursing Staff Deployment Plan created new micro-teams with complementary roles to meet the care needs of COVID-19 infected patients; 2) nurse-led adaptations effectively managed the increased workload, thereby ensuring the quality of care; 3) continuous professional development ensured adequate competence levels for all roles; 4) interprofessional collaboration resulted in experienced solidarity, a positive atmosphere, and increased autonomy for nurses; and, 5) supportive managers reduced nurses’ stress and improved work conditions. Conclusions This study showed that nurses positively reshaped their work environment during the COVID-19 pandemic. They contributed to innovative solutions in an environment of equal interprofessional collaboration, which led to greater respect for their knowledge and competencies, enhanced their autonomy and improved management support.
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Background The global nursing shortages exacerbated by the COVID-19 pandemic necessitated a drastic reorganization in nursing practices. Work routines, the composition of teams and subsequently mundane nursing practices were all altered to sustain the accessibility and quality of care. These dramatic changes demanded a reshaping of the nurses’ work environment. The aim of this study was to explore how nurses reshaped their work environment in the early stages of the COVID-19 pandemic. Methods A descriptive study comprising 26 semi-structured interviews conducted in a large Dutch teaching hospital between June and September 2020. Participants were nurses (including intensive care unit nurses), outpatient clinic assistants, nurse managers, and management (including one member of the Nurse Practice Council). The interviews were analysed with open, axial, and selective coding. Results We identified five themes: 1) the Nursing Staff Deployment Plan created new micro-teams with complementary roles to meet the care needs of COVID-19 infected patients; 2) nurse-led adaptations effectively managed the increased workload, thereby ensuring the quality of care; 3) continuous professional development ensured adequate competence levels for all roles; 4) interprofessional collaboration resulted in experienced solidarity, a positive atmosphere, and increased autonomy for nurses; and, 5) supportive managers reduced nurses’ stress and improved work conditions. Conclusions This study showed that nurses positively reshaped their work environment during the COVID-19 pandemic. They contributed to innovative solutions in an environment of equal interprofessional collaboration, which led to greater respect for their knowledge and competencies, enhanced their autonomy and improved management support.
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Dit artikel is met toestemming overgenomen uit Microniek, 2020, nr 5 Robotics research groups around the world are using Robot Operating System (ROS)to develop their prototypes quickly. While the first version of ROS was aimed primarilyat the R&D community, its successor, ROS 2, has been redesigned completely to beindustrial grade and applicable in research, prototyping, deployment and production.This allows ROS 2 prototypes to evolve into products suitable for real-worldapplications. To explore the state of the art, Saxion University of Applied Sciencesand nine companies are developing an industrial mobile robot. This article describesexperiences from the development process and presents an outlook on the potentialof ROS 2 for industry.
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This paper introduces a novel distributed algorithm designed to optimize the deployment of access points within Mobile Ad Hoc Networks (MANETs) for better service quality in infrastructure less environments. The algorithm operates based on local, independent execution by each network node, thus ensuring a high degree of scalability and adaptability to changing network conditions. The primary focus is to match the spatial distribution of access points with the distribution of client devices while maintaining strong connectivity to the network root. Using autonomous decision-making and choreographed path-planning, this algorithm bridges the gap between demand-responsive network service provision and the maintenance of crucial network connectivity links. The assessment of the performance of this approach is motivated by using numerical results generated by simulations.
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