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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The flexible deployment of drones in the public domain, is in this article assessed from a legal philosophical perspective. On the basis of theories of Dworkin and Moore the distinction between individual rights and collective security policy goals is discussed. Mobile cameras in the public domain reflect how innovative technological tools challenge public authorities in new ways to balance between privacy and security. Furthermore, the different dimensions of privacy and the distinction between the three types of the value of privacy are reviewed. On the basis of the case study of the Dutch Drones Act, the article concludes that the flexible deployment of mobile cameras in the public domain is not legitimate from a normative perspective. The legal safeguards in the Netherlands are insufficient to protect the value of privacy. Therefore, further restrictions such as prior judicial review should be considered.
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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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Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to collect, manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. Participants in the interviews that we organized preparing this proposal indicated a need for guidance on how to develop DAC within their organization given their unique context (e.g. age and experience of the workforce, presence of legacy systems, high daily workload, lack of knowledge of digitalization). While a lot of attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC. From a structure perspective, the objective of the KIEM proposal will be to explore and solidify the partnership between Breda University of Applied Sciences (BUas), Avans University of Applied Sciences (Avans), Logistics Community Brabant (LCB), van Berkel Logistics BV, Smink Group BV, and iValueImprovement BV. This partnership will be used to develop the preliminary roadmap and pre-test it using action methodology. The action research protocol and preliminary roadmap thereby developed in this KIEM project will form the basis for a subsequent RAAK proposal.
Drones have been verified as the camera of 2024 due to the enormous exponential growth in terms of the relevant technologies and applications such as smart agriculture, transportation, inspection, logistics, surveillance and interaction. Therefore, the commercial solutions to deploy drones in different working places have become a crucial demand for companies. Warehouses are one of the most promising industrial domains to utilize drones to automate different operations such as inventory scanning, goods transportation to the delivery lines, area monitoring on demand and so on. On the other hands, deploying drones (or even mobile robots) in such challenging environment needs to enable accurate state estimation in terms of position and orientation to allow autonomous navigation. This is because GPS signals are not available in warehouses due to the obstruction by the closed-sky areas and the signal deflection by structures. Vision-based positioning systems are the most promising techniques to achieve reliable position estimation in indoor environments. This is because of using low-cost sensors (cameras), the utilization of dense environmental features and the possibilities to operate in indoor/outdoor areas. Therefore, this proposal aims to address a crucial question for industrial applications with our industrial partners to explore limitations and develop solutions towards robust state estimation of drones in challenging environments such as warehouses and greenhouses. The results of this project will be used as the baseline to develop other navigation technologies towards full autonomous deployment of drones such as mapping, localization, docking and maneuvering to safely deploy drones in GPS-denied areas.
Physical rehabilitation programs revolve around the repetitive execution of exercises since it has been proven to lead to better rehabilitation results. Although beginning the motor (re)learning process early is paramount to obtain good recovery outcomes, patients do not normally see/experience any short-term improvement, which has a toll on their motivation. Therefore, patients find it difficult to stay engaged in seemingly mundane exercises, not only in terms of adhering to the rehabilitation program, but also in terms of proper execution of the movements. One way in which this motivation problem has been tackled is to employ games in the rehabilitation process. These games are designed to reward patients for performing the exercises correctly or regularly. The rewards can take many forms, for instance providing an experience that is engaging (fun), one that is aesthetically pleasing (appealing visual and aural feedback), or one that employs gamification elements such as points, badges, or achievements. However, even though some of these serious game systems are designed together with physiotherapists and with the patients’ needs in mind, many of them end up not being used consistently during physical rehabilitation past the first few sessions (i.e. novelty effect). Thus, in this project, we aim to 1) Identify, by means of literature reviews, focus groups, and interviews with the involved stakeholders, why this is happening, 2) Develop a set of guidelines for the successful deployment of serious games for rehabilitation, and 3) Develop an initial implementation process and ideas for potential serious games. In a follow-up application, we intend to build on this knowledge and apply it in the design of a (set of) serious game for rehabilitation to be deployed at one of the partners centers and conduct a longitudinal evaluation to measure the success of the application of the deployment guidelines.