Current research on data in policy has primarily focused on street-level bureaucrats, neglecting the changes in the work of policy advisors. This research fills this gap by presenting an explorative theoretical understanding of the integration of data, local knowledge and professional expertise in the work of policy advisors. The theoretical perspective we develop builds upon Vickers’s (1995, The Art of Judgment: A Study of Policy Making, Centenary Edition, SAGE) judgments in policymaking. Empirically, we present a case study of a Dutch law enforcement network for preventing and reducing organized crime. Based on interviews, observations, and documents collected in a 13-month ethnographic fieldwork period, we study how policy advisors within this network make their judgments. In contrast with the idea of data as a rationalizing force, our study reveals that how data sources are selected and analyzed for judgments is very much shaped by the existing local and expert knowledge of policy advisors. The weight given to data is highly situational: we found that policy advisors welcome data in scoping the policy issue, but for judgments more closely connected to actual policy interventions, data are given limited value.
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The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
IntroductionMechanical power of ventilation, a summary parameter reflecting the energy transferred from the ventilator to the respiratory system, has associations with outcomes. INTELLiVENT–Adaptive Support Ventilation is an automated ventilation mode that changes ventilator settings according to algorithms that target a low work–and force of breathing. The study aims to compare mechanical power between automated ventilation by means of INTELLiVENT–Adaptive Support Ventilation and conventional ventilation in critically ill patients.Materials and methodsInternational, multicenter, randomized crossover clinical trial in patients that were expected to need invasive ventilation > 24 hours. Patients were randomly assigned to start with a 3–hour period of automated ventilation or conventional ventilation after which the alternate ventilation mode was selected. The primary outcome was mechanical power in passive and active patients; secondary outcomes included key ventilator settings and ventilatory parameters that affect mechanical power.ResultsA total of 96 patients were randomized. Median mechanical power was not different between automated and conventional ventilation (15.8 [11.5–21.0] versus 16.1 [10.9–22.6] J/min; mean difference –0.44 (95%–CI –1.17 to 0.29) J/min; P = 0.24). Subgroup analyses showed that mechanical power was lower with automated ventilation in passive patients, 16.9 [12.5–22.1] versus 19.0 [14.1–25.0] J/min; mean difference –1.76 (95%–CI –2.47 to –10.34J/min; P < 0.01), and not in active patients (14.6 [11.0–20.3] vs 14.1 [10.1–21.3] J/min; mean difference 0.81 (95%–CI –2.13 to 0.49) J/min; P = 0.23).ConclusionsIn this cohort of unselected critically ill invasively ventilated patients, automated ventilation by means of INTELLiVENT–Adaptive Support Ventilation did not reduce mechanical power. A reduction in mechanical power was only seen in passive patients.Study registrationClinicaltrials.gov (study identifier NCT04827927), April 1, 2021URL of trial registry recordhttps://clinicaltrials.gov/study/NCT04827927?term=intellipower&rank=1
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The PhD research by Joris Weijdom studies the impact of collective embodied design techniques in collaborative mixed-reality environments (CMRE) in art- and engineering design practice and education. He aims to stimulate invention and innovation from an early stage of the collective design process.Joris combines theory and practice from the performing arts, human-computer interaction, and engineering to develop CMRE configurations, strategies for its creative implementation, and an embodied immersive learning pedagogy for students and professionals.This lecture was given at the Transmedia Arts seminar of the Mahindra Humanities Center of Harvard University. In this lecture, Joris Weijdom discusses critical concepts, such as embodiment, presence, and immersion, that concern mixed-reality design in the performing arts. He introduces examples from his practice and interdisciplinary projects of other artists.About the researchMultiple research areas now support the idea that embodiment is an underpinning of cognition, suggesting new discovery and learning approaches through full-body engagement with the virtual environment. Furthermore, improvisation and immediate reflection on the experience itself, common creative strategies in artist training and practice, are central when inventing something new. In this research, a new embodied design method, entitled Performative prototyping, has been developed to enable interdisciplinary collective design processes in CMRE’s and offers a vocabulary of multiple perspectives to reflect on its outcomes.Studies also find that engineering education values creativity in design processes, but often disregards the potential of full-body improvisation in generating and refining ideas. Conversely, artists lack the technical know-how to utilize mixed-reality technologies in their design process. This know-how from multiple disciplines is thus combined and explored in this research, connecting concepts and discourse from human-computer interaction and media- and performance studies.This research is a collaboration of the University of Twente, Utrecht University, and HKU University of the Arts Utrecht. This research is partly financed by the Dutch Research Council (NWO).Mixed-reality experiences merge real and virtual environments in which physical and digital spaces, objects, and actors co-exist and interact in real-time. Collaborative Mix-Reality Environments, or CMRE's, enable creative design- and learning processes through full-body interaction with spatial manifestations of mediated ideas and concepts, as live-puppeteered or automated real-time computer-generated content. It employs large-scale projection mapping techniques, motion-capture, augmented- and virtual reality technologies, and networked real-time 3D environments in various inter-connected configurations.This keynote was given at the IETM Plenary meeting in Amsterdam for more than 500 theatre and performing arts professionals. It addresses the following questions in a roller coaster ride of thought-provoking ideas and examples from the world of technology, media, and theatre:What do current developments like Mixed Reality, Transmedia, and The Internet of Things mean for telling stories and creating theatrical experiences? How do we design performances on multiple "stages" and relate to our audiences when they become co-creators?Contactjoris.weijdom@hku.nl / LinkedIn profileThis research is part of the professorship Performative Processes
