In textual modeling, models are created through an intermediate parsing step which maps textual representations to abstract model structures. Therefore, the identify of elements is not stable across different versions of the same model. Existing model differencing algorithms, therefore, cannot be applied directly because they need to identify model elements across versions. In this paper we present Textual Model Diff (tmdiff), a technique to support model differencing for textual languages. tmdiff requires origin tracking during text-to-model mapping to trace model elements back to the symbolic names that define them in the textual representation. Based on textual alignment of those names, tmdiff can then determine which elements are the same across revisions, and which are added or removed. As a result, tmdiff brings the benefits of model differencing to textual languages.
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Although basic features of journalism have remained the same over the last decades, the tasks journalists perform, the skills they need and the position they have within news organizations have changed dramatically. Usually the focus in the discourse on changes in journalism is on skills, especially on technical multi-media skills or research skills. In this paper we focus on changes in professional roles of journalists, arguing that these roles have changed fundamentally, leading to a new generation of journalists. We distinguish between different trends in journalism. Journalism has become more technical, ranging from editing video to programming. At the same time, many journalists are now more ‘harvesters’ and ‘managers’ of information and news instead of producers of news. Thirdly, journalists are expected to gather information from citizens and social media, and edit and moderate user-contributions as well. Lastly, many journalists are no longer employed by media but work as freelancers or independent entrepreneurs. We track these trends and provide a detailed description of developments with examples from job descriptions in the Netherlands.
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In deze podcast, in de serie "NHL Stenden onderzoekt", gaat host Marjan Teunissen het gesprek aan met Jolanda Tuinstra (lector Sociale Kwaliteit NHL Stenden), Nina Velden (student NHL Stenden) en oud-huisarts van Appelscha Herman Hoekstra over de bloeizone Appelscha. De Bloeizone Appelscha is geïnspireerd op de internationale Blue Zones, regio's waar mensen gezonder en langer leven dankzij factoren zoals voeding, beweging en sociale verbondenheid. De werkgroep Bloeizone Appelscha, die bestaat uit gemotiveerde mensen met uiteenlopende achtergronden, heeft de ambitie om de gezondheid en het gemeenschapsgevoel in Appelscha te versterken. Samen met het Atelier Sociaal Domein van NHL Stenden Hogeschool en de gemeente Ooststellingwerf hebben studenten en lokale partners actief bijgedragen aan dit doel.
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Buildings with innovative technologies and architectural solutions are needed as a means of support for future nursing homes alongside adequate care services. This study investigated how various groups of stakeholders from healthcare and technology envision the nursing home of the future in the presumed perspective of residents, care professionals and technical staff. This qualitative study gathered data via ten simultaneous monodisciplinary focus group sessions with 95 professional stakeholders. The sessions yielded eight main themes: person and well-being; relatives and interaction; care technology; safety and security; interior design, architecture and the built environment; vision and knowledge; communication; and maintenance and operation. These themes can be used for programming future nursing homes, and for prioritising design and technological solutions. The views between the groups of stakeholders are to a large extent similar, and the personal needs of the residents are the most prominent factor for practice.
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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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Tallinn University in co-operation with Tartu University has conducted a four month long research on deinstitutionalisation policies, financed by the Estonian Ministry of Social Affairs, between August and November 2015. The research programme consisted of an international review on the experiences of seven selected European countries and on focus groups and individual interviews among Estonian stakeholders related to deinstitutionalisation and community based services in the field of disability care and mental health. Based on the research the international research group suggested a number of considerations for the Estonian Case. Some of the most relevant are reported in this Research note.
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This article deals with automatic object recognition. The goal is that in a certain grey-level image, possibly containing many objects, a certain object can be recognized and localized, based upon its shape. The assumption is that this shape has no special characteristics on which a dedicated recognition algorithm can be based (e.g. if we know that the object is circular, we could use a Hough transform or if we know that it is the only object with grey level 90, we can simply use thresholding). Our starting point is an object with a random shape. The image in which the object is searched is called the Search Image. A well known technique for this is Template Matching, which is described first.
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This textbook is intended for a basic course in problem solving and program design needed by scientists and engineers using the TI-92. The TI-92 is an extremely powerful problem solving tool that can help you manage complicated problems quickly. We assume no prior knowledge of computers or programming, and for most of its material, high school algebra is sufficient mathematica background. It is advised that you have basic skills in using the TI-92. After the course you will become familiar with many of the programming commands and functions of the TI-92. The connection between good problem solving skills and an effective program design method, is used and applied consistently to most examples and problems in the text. We also introduce many of the programming commands and functions of the TI-92 needed to solve these problems. Each chapter ends with a number of practica problems that require analysis of programs as well as short programming exercises.
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Bij Vroeg Eropaf zoekt de gemeente contact met inwoners over wie een betaalachterstand is gemeld om hulp aan te bieden. Onderzoekers van Bureau Bartels, Verwey-Jonker Instituut, Centraal Bureau voor de Statistiek en Hogeschool Utrecht hebben samen met de Gemeente Amsterdam de Vroeg Eropaf-aanpak in Amsterdam tegen het licht gehouden: wat werkt? Wat zijn volgens dit onderzoek succesfactoren van de Vroeg Eropaf-aanpak? - Het outreachend werken/proactief benaderen van inwoners met betaalachterstanden. Dus: niet wachten tot inwoners zelf op zoek gaan naar hulp. - Combineren van meerdere vormen van communicatie (‘gecombineerde aanpak’). Dus: benaderen van inwoners via huisbezoek, telefoon, e-mail, WhatsApp, en alle andere manieren die je kunt bedenken. - Duidelijk en eerlijk zijn over mogelijke dienstverlening. Dus: verwachtingsmanagement. - Maken van écht contact. Dus: luisteren naar de inwoner en horen welke vragen, twijfels, angsten of oplossingen de inwoner heeft.
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In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.
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