This paper proposes an amendment of the classification of safety events based on their controllability and contemplates the potential of an event to escalate into higher severity classes. It considers (1) whether the end-user had the opportunity to intervene into the course of an event, (2) the level of end-user familiarity with the situation, and (3) the positive or negative effects of end-user intervention against expected outcomes. To examine its potential, we applied the refined classification to 296 aviation safety investigation reports. The results suggested that pilots controlled only three-quarters of the occurrences, more than three-thirds of the controlled cases regarded fairly unfamiliar situations, and the flight crews succeeded to mitigate the possible negative consequences of events in about 71% of the cases. Further statistical tests showed that the controllability-related characteristics of events had not significantly changed over time, and they varied across regions, aircraft, operational and event characteristics, as well as when fatigue had contributed to the occurrences. Overall, the findings demonstrated the value of using the controllability classification before considering the actual outcomes of events as means to support the identification of system resilience and successes. The classification can also be embedded in voluntary reporting systems to allow end-users to express the degree of each of the controllability characteristics so that management can monitor them over time and perform internal and external benchmarking. The mandatory reports concerned, the classification could function as a decision-making parameter for prioritising incident investigations.
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Cervical spinal manipulations (CSM) are frequently employed techniques to alleviate neck pain and headache. Minor and major complications following CSM have been described, though clear consensus on definition and the classification of the complications had not yet been achieved. As a result, incidence rates may be underestimated. The aim of this study was to develop a consensus-based classification of adverse events following cervical spinal manipulations which has good feasibility in clinical practice and research. Design: A three round Delphi-study. Medical specialists, manual therapists, and patients (n=30) participated in an online survey. In Round 1, participants were invited to select a classification system of adverse events. Potential complications were inventoried and detailed in accordance with the ICF and the ICD-10. In Round 2, panel members categorized the potential complications in their selected classification. During the third round, it was inquired of the participants whether they concurred with the answer of the majority of participants. Results: Thirty four complications were defined. Consensus was achieved for 29 complications for all durations [hours, days, weeks]. For the remaining five complications, consensus was reached for two of the three durations [hours, days, weeks]. Conclusions: A consensus-based classification system of adverse events after cervical spinal manipulation was developed which comprises patients’ and clinicians’ perspectives and has only a small number of categories. The classification system includes a precise description of potential adverse events and is based on international accepted classifications (ICD-10 and ICF). This classification system may be useful for utilization in both clinical practice and research.
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Objective: We aimed to identify published classification systems with a targeted treatment approach (treatment-based classification systems (TBCSs)) for patients with non-specific neck pain, and assess their quality and effectiveness. Design: Systematic review. Data sources: MEDLINE, CINAHL, EMBASE, PEDro and the grey literature were systematically searched from inception to December 2019. Study appraisal and synthesis: The main selection criterium was a TBCS for patients with non-specific neck pain with physiotherapeutic interventions. For data extraction of descriptive data and quality assessment we used the framework developed by Buchbinder et al. We considered as score of ≤3 as low quality, a score between 3 and 5 as moderate quality and a score ≥5 as good quality. To assess the risk of bias of studies concerning the effectiveness of TBCSs (only randomized clinical trials (RCTs) were included) we used the PEDro scale. We considered a score of ≥ six points on this scale as low risk of bias. Results: Out of 7664 initial references we included 13 studies. The overall quality of the TBCSs ranged from low to moderate. We found two RCTs, both with low risk of bias, evaluating the effectiveness of two TBCSs compared to alternative treatments. The results showed that both TBCSs were not superior to alternative treatments. Conclusion: Existing TBCSs are, at best, of moderate quality. In addition, TBCSs were not shown to be more effective than alternatives. Therefore using these TBCSs in daily practice is not recommended.
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This paper focuses on the topical and problematic area of social innovations. The aim of this paper is to develop an original approach to the allocation of social innovations, taking into account characteristics such as the degree of state participation, the scope of application, the type of initiations as well as the degree of novelty, which will be elaborated on further in this article. In order to achieve this goal, the forty-two most successful social innovations were identified and systematized. The results of this study demonstrated that 73.5% of social innovations are privately funded, most of them operating on an international level with a high degree of novelty. Moreover, 81% of all social innovations are civic initiatives. Social innovations play an important role in the growth of both developed and less developed countries alike as highlighted in our extensive analysis
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Office well-being aims to explore and support a healthy, balanced and active work style in office environments. Recent work on tangible user interfaces has started to explore the role of physical, tangible interfaces as active interventions to explore how to tackle problems such as inactive work and lifestyles, and increasingly sedentary behaviours. We identify a fragmented research landscape on tangible Office well-being interventions, missing the relationship between interventions, data, design strategies, and outcomes, and behaviour change techniques. Based on the analysis of 40 papers, we identify 7 classifications in tangible Office well-being interventions and analyse the intervention based on their role and foundation in behaviour change. Based on the analysis, we present design considerations for the development of future tangible Office well-being design interventions and present an overview of the current field and future research into tangible Office well-being interventions to design for a healthier and active office environment.
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Purpose: Classification is a defining factor for competition in wheelchair sports, but it is a delicate and time-consuming process with often questionable validity. New inertial sensor-based measurement methods applied in match play and field tests allow for more precise and objective estimates of the impairment effect on wheelchair-mobility performance. The aim of the present research was to evaluate whether these measures could offer an alternative point of view for classification. Methods: Six standard wheelchair-mobility performance outcomes of different classification groups were measured in match play (n = 29), as well as best possible performance in a field test (n = 47). Results: In match results, a clear relationship between classification and performance level is shown, with increased performance outcomes in each adjacent higher-classification group. Three outcomes differed significantly between the low- and mid-classified groups, and 1, between the mid- and high-classified groups. In best performance (field test), there was a split between the low- and mid-classified groups (5 out of 6 outcomes differed significantly) but hardly any difference between the mid- and high-classified groups. This observed split was confirmed by cluster analysis, revealing the existence of only 2 performance-based clusters. Conclusions: The use of inertial sensor technology to obtain objective measures of wheelchair-mobility performance, combined with a standardized field test, produced alternative views for evidence-based classification. The results of this approach provide arguments for a reduced number of classes in wheelchair basketball. Future use of inertial sensors in match play and field testing could enhance evaluation of classification guidelines, as well as individual athlete performance. DOI: https://doi.org/10.1123/ijspp.2017-0326 LinkedIn: https://www.linkedin.com/in/rienkvdslikke/ https://www.linkedin.com/in/moniqueberger/ https://www.linkedin.com/in/annemarie-de-witte-9582b154/
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Athlete impairment level is an important factor in wheelchair mobility performance (WMP) in sports. Classification systems, aimed to compensate impairment level effects on performance, vary between sports. Improved understanding of resemblances and differences in WMP between sports could aid in optimizing the classification methodology. Furthermore, increased performance insight could be applied in training and wheelchair optimization. The wearable sensor-based wheelchair mobility performance monitor (WMPM) was used to measure WMP of wheelchair basketball, rugby and tennis athletes of (inter-)national level during match-play. As hypothesized, wheelchair basketball athletes show the highest average WMP levels and wheelchair rugby the lowest, whereas wheelchair tennis athletes range in between for most outcomes. Based on WMP profiles, wheelchair basketball requires the highest performance intensity, whereas in wheelchair tennis, maneuverability is the key performance factor. In wheelchair rugby, WMP levels show the highest variation comparable to the high variation in athletes’ impairment levels. These insights could be used to direct classification and training guidelines, with more emphasis on intensity for wheelchair basketball, focus on maneuverability for wheelchair tennis and impairment-level based training programs for wheelchair rugby. Wearable technology use seems a prerequisite for further development of wheelchair sports, on the sports level (classification) and on individual level (training and wheelchair configuration).
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Author supplied Business rules play a critical role in an organization’s daily activities. With the increased use of business rules (solutions) the interest in modelling guidelines that address the manageability of business rules has increased as well. However, current research on modelling guidelines is mainly based on a theoretical view of modifications that can occur to a business rule set. Research on actual modifications that occur in practice is limited. The goal of this study is to identify modifications that can occur to a business rule set and underlying business rules. To accomplish this goal we conducted a grounded theory study on 229 rules set, as applied from March 2006 till June 2014, by the National Health Service. In total 3495 modifications have been analysed from which we defined eleven modification categories that can occur to a business rule set. The classification provides a framework for the analysis and design of business rules management architectures.
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This paper investigate to use of information technology, i.e. machine learning algorithms for water assessment in Timor-Leste. It is essential to access clean water to ensure the safety for humans and others livings in this world. The Water Quality Index (WQI) is the standard tool for assessing water quality, which can be calculated from physicochemical and microbiological parameters. However, in developing countries, it is continuing need to bring water and energy for the most disadvantaged, make it necessary to find new solutions. In such case, missing-value imputation and machine learning models are useful for classifying water samples into suitable or unsuitable with significant accuracy. Some imputation methods were tested, and four machine learning algorithms were explored: logistic regression, support vector machine, random forest, and Gaussian naïve Bayes. We obtained a dataset with 368 observations from 26 groundwater sampling points in Dili city of Timor-Leste. According to experimental results, it is found that 64% of the water samples are suitable for human consumption. We also found k-NN imputation and random forest method were the clear winners, achieving 96% accuracy with three-fold cross validation. The analysis revealed that some parameters significantly affected the classification results.
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
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