In this study, we investigated the effects of wearing a police uniform and gear on officers’ performance during the Physical Competence Test (PCT) of the Dutch National Police. In a counterbalanced within-subjects design, twenty-seven police officers performed the PCT twice, once wearing sportswear and once wearing a police uniform. The results showed clear indications that wearing a police uniform influenced the performance on the PCT. Participants were on average 14 seconds slower in a police uniform than in sportswear. Furthermore, performing the test in uniform was accompanied by higher RPE-scores and total physiological load. It seems that wearing a police uniform during the test diminishes the discrepancy between physical fitness needed to pass the simulated police tasks in the PCT and the job-specific physical fitness that is required during daily police work. This suggests that wearing a police uniform during the test will increase the representativeness of the testing environment for the work field.
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The standardized Mensendieck test (SMT) was developed to quantify posture, movement, gait, and respiration. In the hands of an experienced therapist, the SMT is proven to be a reliable tool. It is unclear whether posture, movement, gait, and respiration are related to the degree of functional disability in patients with chronic pain. The objective of this study was to assess the reliability and convergent validity of the SMT in a heterogeneous sample of 50 patients with chronic pain. Methods: Internal consistency was determined by Cronbach’s α and interrater reliability by the intraclass correlation coefficient (ICC). Convergent validity was assessed by determining the Spearman rank correlation coefficient between the movement quality measured in the SMT and functional limitation measured on the disability rating index (DRI). Results: The internal consistency was Cronbach’s α 0.91. Substantial reliability was found for the items: movement (ICC = 0.68), gait (ICC = 0.69), sitting posture (ICC = 0.63), and respiration (ICC = 0.64). Insufficient reliability was found for standing posture (ICC = 0.23). A moderate correlation was found between average test score SMT and the DRI (r = −0.37) and respiration and DRI (r = −0.45). Discussion: The SMT is a reasonably reliable tool to assess movement, gait, sitting posture, and respiration. None of the items in the domain standing posture has sufficient reliability. A thorough study of this domain should be considered. The results show little evidence for convergent validity. Several items of the SMT correlated moderately with functional limitation with the DRI. These items were global movement, hip flexion, pelvis rotation, and all respiration items.
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Background There currently is no field test available for measuring maximal exercise capacity in people with stroke. Objective To determine the feasibility, reproducibility and validity of the Shuttle Test (ST) to measure exercise capacity in people with stroke. Design Longitudinal study design. Setting Rehabilitation department, day care centres from a nursing home and private practices specialized in neuro rehabilitation. Subjects People with subacute or chronic stroke. Interventions A standardized protocol was used to determine feasibility, reproducibility and validity of the 10-meter Shuttle Test (10mST). Main measures Number of shuttles completed, 1stVentilatory Threshold (1stVT). Results The associations of the number of shuttles completed and cardiopulmonary capacity as measured with a portable gas analyser were r > 0.7, confirming good convergent validity in subacute and chronic people with stroke. Criterion validity, however, indicates it is not a valid test for measuring maximal cardiopulmonary capacity (VO2max). Only 60% of participants were able to reach the 1stVT. Higher cardiopulmonary capacity and a higher total score of the lower extremity Motricity Index contributed significantly to a higher number of shuttles walked (p = 0.001). Conclusions The Shuttle Test may be a safe and useful exercise test for people after stroke, but may not be appropriate for use with people who walk slower than 2 km/h or 0.56 m/s.
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The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
Fungal colorants offer a sustainable alternative to synthetic colors, which are derived from fossil fuels and contribute to environmental pollution. While fungal colorants could be effectively produced through precision fermentation by microorganisms, their adoption in industry remains limited due to challenges in processing, formulation, and application. ColorFun aims to bridge the gap between laboratory research, artisanal practices, and industrial needs by developing a scalable and adaptable colorant processing system. Building on the TUFUCOL project, which focused on optimizing fungal fermentation, ColorFun consortium gears the focus to downstream processing and industrial applications by using green chemistry. Many SMEs have explored fungal colorants using traditional methods, but due to lack of consistency and reproducibility, they are unsuitable for large-scale production. Meanwhile, lab research usually does not translate directly to industrial applications. Researchers can fine-tune processes under controlled conditions while large-scale production requires consistent formulations that work across different material substrates and processing environments. Without bridging these gaps, fungal colorants remain confined to research and small-scale applications rather than becoming viable industrial alternatives. Instead of developing separate solutions for each sector, ColorFun is working towards a set of standardized extraction and stabilization methods for a stable base colorant product. This pre-processed colorant can then be adjusted by different industries to meet their specific needs. This approach ensures both efficiency in production and flexibility in application. Professionals will collaborate in a test-improve-test circle, ColorFun will refine these formulations to ensure they work in real-world conditions. Students will be involved in the project, contributing to curriculum developments in biotechnology, chemistry, and materials science. Combining efforts, ColorFun lowers the barriers aiding fungal colorants to become a mainstream alternative to synthetic feedstocks. By making these colorants scientifically validated, industrially viable, and commercially adaptable, the project helps accelerate the transition to sustainable color solutions and circular economy.
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.
