Kees Hoogland beschrijft opvallend wetenchappelijk onderzoek naar rekenen-wiskunde. Dit keer zijn het artikelen over statistiek en kansberekening voor het basisonderwijs.
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Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.
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This paper outlines an investigation into the updating of fatigue reliability through inspection data by means of structural correlation. The proposed methodology is based on the random nature of fatigue fracture growth and the probability of damage detection and introduces a direct link between predicted crack size and inspection results. A distinct focus is applied on opportunities for utilizing inspection information for the updating of both inspected and uninspected (or uninspectable) locations.
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BACKGROUND: Blended physiotherapy, in which physiotherapy sessions and an online application are integrated, might support patients in taking an active role in the management of their chronic condition and may reduce disease related costs. The aim of this study was to evaluate the cost-effectiveness of a blended physiotherapy intervention (e-Exercise) compared to usual physiotherapy in patients with osteoarthritis of hip and/or knee, from the societal as well as the healthcare perspective.METHODS: This economic evaluation was conducted alongside a 12-month cluster randomized controlled trial, in which 108 patients received e-Exercise, consisting of physiotherapy sessions and a web-application, and 99 patients received usual physiotherapy. Clinical outcome measures were quality-adjusted life years (QALYs) according to the EuroQol (EQ-5D-3 L), physical functioning (HOOS/KOOS) and physical activity (Actigraph Accelerometer). Costs were measured using self-reported questionnaires. Missing data were multiply imputed and bootstrapping was used to estimate statistical uncertainty.RESULTS: Intervention costs and medication costs were significantly lower in e-Exercise compared to usual physiotherapy. Total societal costs and total healthcare costs did not significantly differ between groups. No significant differences in effectiveness were found between groups. For physical functioning and physical activity, the maximum probability of e-Exercise being cost-effective compared to usual physiotherapy was moderate (< 0.82) from both perspectives. For QALYs, the probability of e-Exercise being cost-effective compared to usual physiotherapy was 0.68/0.84 at a willingness to pay of 10,000 Euro and 0.70/0.80 at a willingness to pay of 80,000 Euro per gained QALY, from respectively the societal and the healthcare perspective.CONCLUSIONS: E-Exercise itself was significantly cheaper compared to usual physiotherapy in patients with hip and/or knee osteoarthritis, but not cost-effective from the societal- as well as healthcare perspective. The decision between both interventions can be based on the preferences of the patient and the physiotherapist.TRIAL REGISTRATION: NTR4224 (25 October 2013).
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This report provides the global community of hospitality professionals with critical insights into emerging trends and developments, with a particular focus on the future of business travel. Business travellers play a pivotal role within the tourism industry, contributing significantly to international travel, GDP, and business revenues.In light of recent disruptions and evolving challenges, this forward-looking study aims not only to reflect on the past but, more importantly, to anticipate future developments and uncertainties in the realm of business travel. By doing so, it offers strategic insights to help hospitality leaders navigate the ever-evolving landscape of the industry.Key findings from the Yearly Outlook include:• Recovery of International Travel: By 2024, international travel arrivals have surpassed 2019 levels by 2%, signalling a full recovery in the sector. In Amsterdam, there was a 13% decrease in business traveller numbers, offset by an increase in the average length of stay from 2.34 to 2.71 days. Notably, more business travellers opted for 3-star accommodations, marking a shift in preferences.• Future of Business Travel: The report outlines a baseline scenario that predicts a sustainable, personalised, and seamless business travel experience by 2035. This future will likely be driven by AI integration, shifts in travel patterns—such as an increase in short-haul trips, longer stays combining business and leisure—and a growing focus on sustainability.• Potential Disruptors: The study also analyses several potential disruptors to these trends. These include socio-political shifts that could reverse sustainability efforts, risks associated with AI-assisted travel, the decline of less attractive business destinations, and the impact of global geopolitical tensions.The Yearly Outlook provides practical recommendations for hospitality professionals and tourism policymakers. These recommendations focus on building resilience, anticipating changes in business travel preferences, leveraging AI and technological advancements, and promoting sustainable practices within the industry.
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New learning theory, underpinning the idea of teaching for self-directed learning, provides new conceptions of learning: the self-regulation of learning, the construct-character of knowledge, the social nature of learning, and a dynamic model of intelligence. What conceptions teachers hold may be related to their tolerance of uncertainty. We constructed a Learning Inventory, and administered this to teachers in Dutch senior secondary education, where an innovation is heading for more independent learning. We found empirical confirmation of the five dimensions underlying teachers' conceptions of learning, both for student learning and for their own learning. Tolerance of uncertainty explained the other four dimensions in conceptions of student learning, but not in teachers' conceptions of their own learning. Teachers generally endorse the process-oriented conceptions, although some differences are noted between teachers' conceptions of student learning and their own learning.
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Liveblogs are very popular with the public and journalists alike. The problem, though, is their credibility, given the uncertainty of the covered events and the immediacy of their production. Little is known about how journalists routinize the unexpected—to paraphrase Tuchman—when journalists report about an event that is still unfolding. This paper is about makers of liveblogs, livebloggers, so to speak, and the routines and conventions they follow. To better understand the relationship between those who do the “liveblogging” and how the “liveblogging” is done, we interviewed a selection of nine experienced livebloggers who cover breaking news, sports, and politics for the three most-visited news platforms in the Netherlands. Based on our results, we concluded that journalists working at different platforms follow similar routines and conventions for claiming, acquiring, and justifying knowledge. Journalists covering news in liveblogs must have expert knowledge, as well as technical and organizational skills. Liveblogging—in contrast to regular, online reporting—is best summarized as a social process instead of an autonomous production. These findings are important for three reasons: first, to understand how journalists cope with uncertainty covering events under immediate circumstances using liveblogs; second, to understand the workings of this popular format; and third, to contribute to literature about journalistic genres, discourse communities and, more specifically, generic requirements of liveblogs for effects of credibility to take place.
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Spontaneous speech is an important source of information for aphasia research. It is essential to collect the right amount of data: enough for distinctions in the data to become meaningful, but not so much that the data collection becomes too expensive or places an undue burden on participants. The latter issue is an ethical consideration when working with participants that find speaking difficult, such as speakers with aphasia. So, how much speech data is enough to draw meaningful conclusions? How does the uncertainty around the estimation of model parameters in a predictive model vary as a function of the length of texts used for training?
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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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