Evaluation of the hydrological performance of grassed swales usually needs long-term monitoring data. At present, suitable techniques for simulating the hydrological performance using limited monitoring data are not available. Therefore, current study aims to investigate the relationship between saturated hydraulic conductivity (Ks) fitting results and rainfall characteristics of various events series length. Data from a full-scale grassed swale (Enschede, the Netherlands) were utilized as long-term rainfall event series length (95 rainfall events) on the fitting outcomes. Short-term rainfall event series were extracted from these long-term series and used as input in fitting into a multivariate nonlinear model between Ks and its influencing rainfall indicators (antecedent dry days, temperature, rainfall, rainfall duration, total rainfall, and seasonal factor (spring, summer, autumn, and winter, herein refer as 1, 2, 3, and 4). Comparison of short-term and long-term rainfall event series fitting results allowed to obtain a representative short-term series that leads to similar results with those using long-term series. A cluster analysis was conducted based on the fitting results of the representative rainfall event series with their rainfall event characteristics using average values of influencing rainfall indicators. The seasonal index (average value of seasonal factors) was found to be the most representative short rainfall event series indicator. Furthermore, a Bayesian network was proposed in the current study to predict if a given short-term rainfall event series is representative. It was validated by a data series (58 rainfall events) from another full-scale grassed swale located in Utrecht, the Netherlands. Results revealed that it is quite promising and useful to evaluate the representativeness of short-term rainfall event series used for long-term hydrological performance evaluation of grassed swales.
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Evaluation and monitoring have become major issues for cultural events. On the one hand, more detailed information is needed to satisfy event sponsors, on the other hand, the nature of events is increasingly developing towards inclusive concepts (from consumer-oriented delivery to more experiential and social values). The focus on visitor experience presents event organizers with a need to go beyond traditional measurement instruments to evaluate their events. Qualitative approaches are therefore increasingly valued for the insight they provide into visitor experience. The use of visitor journeys and thematic analysis for evaluating, monitoring and improving cultural events will be discussed on the basis of research in cooperation with ‘Rotterdam Festivals’, an umbrella organization for festivals in the city of Rotterdam. On the basis of these results, the visitor journey method allows for better insight into consumer experiences. Apart from contributing to knowledge about the event experience, the method has also proven to be a powerful tool for event design (i.e. developing new strategies and concepts), and thus it can bridge the gap identified in the literature between event evaluation and design. This paper will serve three goals: to present the outcomes of the visitor journey approach, to discuss strengths and weaknesses when compared to other evaluation approaches, and discuss the value of visitor journeys for evaluation and event design.
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Introduction: In March 2014, the New South Wales (NSW) Government (Australia) announced the NSW Integrated Care Strategy. In response, a family-centred, population-based, integrated care initiative for vulnerable families and their children in Sydney, Australia was developed. The initiative was called Healthy Homes and Neighbourhoods. A realist translational social epidemiology programme of research and collaborative design is at the foundation of its evaluation. Theory and Method: The UK Medical Research Council (MRC) Framework for evaluating complex health interventions was adapted. This has four components, namely 1) development, 2) feasibility/piloting, 3) evaluation and 4) implementation. We adapted the Framework to include: critical realist, theory driven, and continuous improvement approaches. The modified Framework underpins this research and evaluation protocol for Healthy Homes and Neighbourhoods. Discussion: The NSW Health Monitoring and Evaluation Framework did not make provisions for assessment of the programme layers of context, or the effect of programme mechanism at each level. We therefore developed a multilevel approach that uses mixed-method research to examine not only outcomes, but also what is working for whom and why.
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In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
266 woorden Op school kan de situatie zich voordoen dat de leerkracht onvoldoende tegemoet kan komen aan de extra ondersteuning die leerlingen met autisme nodig hebben. De klas kan te groot zijn, de leerkracht kan handelingsverlegen zijn, etc.. In dit projectplan wordt onderbouwd wat de relevantie is voor de dagelijkse praktijk van de leerkracht en de leerling met autisme en daaraan gerelateerde problemen. Tevens wordt onderbouwd waarom beeldende therapie theoretisch en empirisch kan bijdragen als creatieve oplossing voor kinderen met aan autisme gerelateerde problemen die in de klas extra aandacht vragen. Deze kinderen hebben een andere manier van informatie verwerken, kunnen zich vaak verbaal moeilijk uiten en hebben vaak sociale problemen. Deze kinderen lopen risico op verslavingsproblematiek (33%) en eenzaamheid, angst en depressie op volwassen leeftijd (80%). Kunstvormen in een leeromgeving bieden andere mogelijkheden voor kinderen om zich te uiten en om samen te werken. In dit projectplan wordt beschreven waarom het zinvol is te onderzoeken wat de effectiviteit is van beeldende therapie voor kinderen met autisme in primair (speciaal) onderwijs, ter preventie van risicogedrag. Het behandelprogramma ‘Zelf in beeld, beeldende therapie voor kinderen met autisme (bijlage 1) lijkt veelbelovende resultaten op te leveren (Schweizer, 2020). Om een indruk van de resultaten van praktijkgericht onderzoek naar ‘Zelf in beeld’ te krijgen kunt u de korte animatie bekijken (3 min): https://youtu.be/cVAAzRHZnb0 In dit vervolgproject wordt verkend in hoeverre ‘Zelf in beeld’ van toegevoegde waarde van kan zijn voor kind, leerkracht en ouders, binnen de setting van Speciaal Onderwijs. Dit project heeft een innovatief karakter omdat er een nieuwe vorm van (preventief) werken binnen passend onderwijs wordt toegepast en onderzocht.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
