Inkjet printing is a rapidly growing technology for depositing functional materials in the production of organic electronics. Challenges lie among others in the printing of high resolution patterns with high aspect ratio of functional materials to obtain the needed functionality like e.g. conductivity. μPlasma printing is a technology which combines atmospheric plasma treatment with the versatility of digital on demand printing technology to selectively change the wetting behaviour of materials. In earlier research it was shown that with μPlasma printing it is possible to selectively improve the wetting behaviour of functional inks on polymer substrates using atmospheric air plasma. In this investigation we show it is possible to selectively change the substrate wetting behaviour using combinations of different plasmas and patterned printing. For air and nitrogen plasmas, increased wetting of printed materials could be achieved on both polycarbonate and glass substrates. A minimal track width of 320 μm for a 200 μm wide plasma needle was achieved. A combination of N2 with HMDSO plasma increases the contact angle for water up from <100 to 1050 and from 320 to 460 for DEGDMA making the substrate more hydrophobic. Furthermore using N2-plasma in combination with a N2/HMDSO plasma, hydrophobic tracks could be printed with similar minimal track width. Combining both N2 -plasma and N2/HMDSO plasma treatments show promising results to further decrease the track width to even smaller values.
Literature highlights the need for research on changes in lumbar movement patterns, as potential mechanisms underlying the persistence of low-back pain. Variability and local dynamic stability are frequently used to characterize movement patterns. In view of a lack of information on reliability of these measures, we determined their within- and between-session reliability in repeated seated reaching. Thirty-six participants (21 healthy, 15 LBP) executed three trials of repeated seated reaching on two days. An optical motion capture system recorded positions of cluster markers, located on the spinous processes of S1 and T8. Movement patterns were characterized by the spatial variability (meanSD) of the lumbar Euler angles: flexion–extension, lateral bending, axial rotation, temporal variability (CyclSD) and local dynamic stability (LDE). Reliability was evaluated using intraclass correlation coefficients (ICC), coefficients of variation (CV) and Bland-Altman plots. Sufficient reliability was defined as an ICC ≥ 0.5 and a CV < 20%. To determine the effect of number of repetitions on reliability, analyses were performed for the first 10, 20, 30, and 40 repetitions of each time series. MeanSD, CyclSD, and the LDE had moderate within-session reliability; meanSD: ICC = 0.60–0.73 (CV = 14–17%); CyclSD: ICC = 0.68 (CV = 17%); LDE: ICC = 0.62 (CV = 5%). Between-session reliability was somewhat lower; meanSD: ICC = 0.44–0.73 (CV = 17–19%); CyclSD: ICC = 0.45–0.56 (CV = 19–22%); LDE: ICC = 0.25–0.54 (CV = 5–6%). MeanSD, CyclSD and the LDE are sufficiently reliable to assess lumbar movement patterns in single-session experiments, and at best sufficiently reliable in multi-session experiments. Within-session, a plateau in reliability appears to be reached at 40 repetitions for meanSD (flexion–extension), meanSD (axial-rotation) and CyclSD.
MULTIFILE
Movement behaviors, that is, both physical activity and sedentary behavior, are independently associated with health risks. Although both behaviors have been investigated separately in people after stroke, little is known about the combined movement behavior patterns, differences in these patterns between individuals, or the factors associated with these patterns. Therefore, the objectives of this study are (1) to identify movement behavior patterns in people with first-ever stroke discharged to the home setting and (2) to explore factors associated with the identified patterns.
National forestry Commission (SBB) and National Park De Biesbosch. Subcontractor through NRITNational parks with large flows of visitors have to manage these flows carefully. Methods of data collection and analysis can be of help to support decision making. The case of the Biesbosch National Park is used to find innovative ways to figure flows of yachts, being the most important component of water traffic, and to create a model that allows the estimation of changes in yachting patterns resulting from policy measures. Recent policies oriented at building additional waterways, nature development areas and recreational concentrations in the park to manage the demands of recreation and nature conservation offer a good opportunity to apply this model. With a geographical information system (GIS), data obtained from aerial photographs and satellite images can be analyzed. The method of space syntax is used to determine and visualize characteristics of the network of leisure routes in the park and to evaluate impacts resulting from expected changes in the network that accompany the restructuring of waterways.
Climate change is one of the most critical global challenges nowadays. Increasing atmospheric CO2 concentration brought by anthropogenic emissions has been recognized as the primary driver of global warming. Therefore, currently, there is a strong demand within the chemical and chemical technology industry for systems that can covert, capture and reuse/recover CO2. Few examples can be seen in the literature: Hamelers et al (2013) presented systems that can use CO2 aqueous solutions to produce energy using electrochemical cells with porous electrodes; Legrand et al (2018) has proven that CDI can be used to capture CO2 without solvents; Shu et al (2020) have used electrochemical systems to desorb (recover) CO2 from an alkaline absorbent with low energy demand. Even though many efforts have been done, there is still demand for efficient and market-ready systems, especially related to solvent-free CO2 capturing systems. This project intends to assess a relatively efficient technology, with low-energy costs which can change the CO2 capturing market. This technology is called whorlpipe. The whorlpipe, developed by Viktor Schauberger, has shown already promising results in reducing the energy and CO2 emissions for water pumping. Recently, studies conducted by Wetsus and NHL Stenden (under submission), in combination with different companies (also members in this proposal) have shown that vortices like systems, like the Schauberger funnel, and thus “whorlpipe”, can be fluid dynamically represented using Taylor-Couette flows. This means that such systems have a strong tendency to form vortices like fluid-patterns close to their air-water interface. Such flow system drastically increase advection. Combined with their higher area to volume ratio, which increases diffusion, these systems can greatly enhance gas capturing (in liquids), and are, thus, a unique opportunity for CO2 uptake from the air, i.e. competing with systems like conventional scrubbers or bubble-based aeration.
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.
