Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
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Background. A number of parenting programs, aimed at improving parenting competencies, have recently been adapted or designed with the use of online technologies. Although web-based services have been claimed to hold promise for parent support, a meta-analytic review of online parenting interventions is lacking. Method. A systematic review was undertaken of studies (n = 19), published between 2000 and 2010, that describe parenting programs of which the primary components were delivered online. Seven programs were adaptations of traditional, mostly evidencebased, parenting interventions, using the unique opportunities of internet technology. Twelve studies (with in total 54 outcomes, Ntot parents = 1,615 and Ntot children = 740) were included in a meta-analysis. Results. The meta-analysis showed a statistically signifi cant medium effect across parents outcomes (ES = 0.67; se = 0.25) and child outcomes (ES = 0.42; se = 0.15). Conclusions. The results of this review show that web-based parenting programs with new technologies offer opportunities for sharing social support, consulting professionals and training parental competencies. The metaanalytic results show that guided and self-guided online interventions can make a signifi cant positive contribution for parents and children. The relation with other metaanalyses in the domains of parent education and web-based interventions is discussed.
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Long-term care facilities are currently installing dynamic lighting systems with the aim to improve the well-being and behaviour of residents with dementia. The aim of this study was to investigate the implementation of dynamic lighting systems from the perspective of stakeholders and the performance of the technology. Therefore, a questionnaire survey was conducted with the management and care professionals of six care facilities. Moreover, light measurements were conducted in order to describe the exposure of residents to lighting. The results showed that the main reason for purchasing dynamic lighting systems lied in the assumption that the well-being and day/night rhythmicity of residents could be improved. The majority of care professionals were not aware of the reasons why dynamic lighting systems were installed. Despite positive subjective ratings of the dynamic lighting systems, no data were collected by the organizations to evaluate the effectiveness of the lighting. Although the care professionals stated that they did not see any large positive effects of the dynamic lighting systems on the residents and their own work situation, the majority appreciated the dynamic lighting systems more than the old situation. The light values measured in the care facilities did not exceed the minimum threshold values reported in the literature. Therefore, it seems illogical that the dynamic lighting systems installed in the researched care facilities will have any positive health effects.
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In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
To enable circularity new tools are needed. Regulatory compliance with the European Commission has introduced the Digital Product Passport (DPP) as part of the Ecodesign for Sustainable Products Regulation (ESPR). This framework requires traceability across all production tiers, including Tier 4, which covers raw material origins. The textile clothing leather and footwear (TCLF) sector has been identified as priority categories for DPP adoption, with mandatory compliance set between 2027 and 2030. DPP system standardizes lifecycle value chain data and includes information on material origin, manufacturing, assembly, and end-of-life handling. For the Dutch textile sector, comprising of almost 11,000 companies, DPP implementation presents significant challenges due to fragmented data infrastructure and long product lifecycles. Traditional identifiers (e.g., QR-codes, RFID) are often damaged or removed, limiting their effectiveness. Molecular characterization—using established techniques like spectral and chemical analysis—is emerging as the only reliable long-term solution for persistent, product-embedded identification. These molecular methods allow precise validation of fiber content, wear analysis, and recyclability, addressing compliance and end-of-life traceability issues. The Molecular Digital Physical Digital Product Passport (M-DPP) initiative demonstrates a practical application of these techniques for wool and cotton. It employs co-design to ensure regulatory alignment and develops an open-source API to support automated validation, extended producer responsibility (EPR), return and reuse (RE), textile lifecycle recovery (TLR), and material sorting and recycling (MSR). Smart contract functionality enables automated execution within deposit-refund systems, improving traceability and circularity. An iterative, design-thinking methodology underpins system development, ensuring adaptability to evolving standards. Pilot testing in collaboration with fashion and interior partners will validate the molecular sensing and data integration approach. Dissemination and scaling will occur through partnerships with NewTexEco, Circolab, DCTV, and TNO’s Center of Excellence for DPPs, aligning with European standardization efforts and enabling sector-wide adoption.
Structural and functional knowledge of proteins, which are essential in biological processes, is fundamental for our understanding of the Chemistry of Life. Structural biology - the field that studies the structure and function of proteins – has seen several revolutions over the last few years. Single particle analysis (SPA), where individual macromolecular assemblies are imaged under cryogenic conditions within highly automated electron microscopes, has been used to elucidate the structures of many novel and important proteins and complexes. Deep-learning–based computational techniques provided systematic predictions of an million three-dimensional protein structures. Cryo-electron tomography (ET) combined with sub-tomogram averaging (STA) enabled the investigation of conformational states of large macromolecular complexes. We expect in situ structural biology, where macromolecular assemblies are studied within the interior of focused-ion-beam milled frozen cells, to become the next revolution in our field. Such revolution would require well prepared vitreous samples (cells, tissue slices, organoids): the sample should be cooled fast enough to prevent the formation of crystalline ice. Previously, we developed the technology to prepare SPA samples using jets of cryogenic fluid directed onto the sample. This device, the VitroJet, has been further developed into a commercial product by CryoSol-World and has been sold worldwide. Here, we wish to advance the jetting technology such that it can vitrify cells. Crucial aspects are the speed of the jets and the timing and reproducibility of the fronts of the cryogens arriving onto the sample. We will design, build, characterise and refine a next generation of the ethane cup, a core component within the VitroJet. If successful, we should be able to increase its vitrification potential as well as its reproducibility by more than one order of magnitude. This technology will enable in situ structural biology studies necessary to understand the Chemistry of Life.
