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Peer-reviewed artikel over semantische segmentatie van point clouds.
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Meten is weten. Daar zijn we het allemaal over eens. Maar hoe zorg je er voor dat je op het goede moment en met het juiste meetinstrument een zinvolle meting doet? Meten is immers geen doel op zich. Onlangs kwam een nieuwe uitgave van het boek Meten in de praktijk: Stappenplan voor het gebruik van meetinstrumenten in de praktijk uit. Hoog tijd om stil te staan bij meten in de dagelijkse ergotherapiepraktijk en daar een stappenplan voor te presenteren.
Artrose is een degeneratieve aandoening van het kraakbeen, waarbij ook de andere structuren in de gewrichten betrokken zijn. De aandoening kan leiden tot beperkingen in het dagelijks functioneren. De huidige kennis betreffende de effecten van artrose op arbeidsparticipatie is onvolledig. In de literatuur zijn slechts enkele studies gevonden met een adequate opzet, die geldige conclusies over dit effect opleverden. In dit onderzoek wordt de arbeidsparticipatie van mensen met beginnende artrose beschreven bij de baseline meting van de CHECK-studie (Cohort Heup En Cohort Knie).
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Parental involvement is a crucial force in children’s development, learning and success at school and in life [1]. Participation, defined by the World Health Organization as ‘a person’s involvement in life situations’ [2] for children means involvement in everyday activities, such as recreational, leisure, school and household activities [3]. Several authors use the term social participation emphasising the importance of engagement in social situations [4, 5]. Children’s participation in daily life is vital for healthy development, social and physical competencies, social-emotional well-being, sense of meaning and purpose in life [6]. Through participation in different social contexts, children gather the knowledge and skills needed to interact, play, work, and live with other people [4, 7, 8]. Unfortunately, research shows that children with a physical disability are at risk of lower participation in everyday activities [9]; they participate less frequently in almost all activities compared with children without physical disabilities [10, 11], have fewer friends and often feel socially isolated [12-14]. Parents, in particular, positively influence the participation of their children with a physical disability at school, at home and in the community [15]. They undertake many actions to improve their child’s participation in daily life [15, 16]. However, little information is available about what parents of children with a physical disability do to enable their child’s participation, what they come across and what kind of needs they have. The overall aim of this thesis was to investigate parents’ actions, challenges, and needs while enhancing the participation of their school-aged child with a physical disability. In order to achieve this aim, two steps have been made. In the first step, the literature has been examined to explore the topic of this thesis (actions, challenges and needs) and to clarify definitions for the concepts of participation and social participation. Second, for the purposes of giving breadth and depth of understanding of the topic of this thesis a mixed methods approach using three different empirical research methods [17-19], was applied to gather information from parents regarding their actions, challenges and needs.
The main aim of this study was to determine the agreement in classification between the modified KörperKoordinations Test für Kinder (KTK3+) and the Athletic Skills Track (AST) for measuring fundamental movement skill levels (FMS) in 6- to 12-year old children. 3,107 Dutch children (of which 1,625 are girls) between 6 and 12 years of age (9.1 ± 1.8 years) were tested with the KTK3+ and the AST. The KTK3+ consists of three items from the KTK and the Faber hand-eye coordination test. Raw scores from each subtest were transformed into percentile scores based on all the data of each grade. The AST is an obstacle course consisting of 5 (grades 3 till 5, 6–9 years) or 7 (grades 6 till 8, 9–12 years) concatenated FMS that should be performed as quickly as possible. The outcome measure is the time needed to complete the track. A significant bivariate Pearson correlation coefficient of 0.51 was found between the percentile sum score of the KTK3+ and the time to complete the AST, indicating that both tests measure a similar construct to some extent. Based on their scores, children were classified into one of five categories: <5, 5–15, 16–85, 86–95 or >95%. Cross tabs revealed an agreement of 58.8% with a Kappa value of 0.15 between both tests. Less than 1% of the children were classified more than two categories higher or lower. The moderate correlation between the KTK3+ and the AST and the low classification agreement into five categories of FMS stress the importance to further investigate the test choice and the measurement properties (i.e., validity and reliability) of both tools. PE teachers needs to be aware of the context in which the test will be conducted, know which construct of motor competence they want to measure and know what the purpose of testing is (e.g., screening or monitoring). Based on these considerations, the most appropriate assessment tool can be chosen.
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This paper presents a Decision Support System (DSS) that helps companies with corporate reputation (CR) estimates of their respective brands by collecting provided feedbacks on their products and services and deriving state-of-the-art key performance indicators. A Sentiment Analysis Engine (SAE) is at the core of the proposed DSS that enables to monitor, estimate, and classify clients’ sentiments in terms of polarity, as expressed in public comments on social media (SM) company channels. The SAE is built on machine learning (ML) text classification models that are cross-source trained and validated with real data streams from a platform like Trustpilot that specializes in user reviews and tested on unseen comments gathered from a collection of public company pages and channels on a social networking platform like Facebook. Such crosssource opinion analysis remains a challenge and is highly relevant in the disciplines of research and engineering in which a sentiment classifier for an unlabeled destination domain is assisted by a tagged source task (Singh and Jaiswal, 2022). The best performance in terms of F1 score was obtained with a multinomial naive Bayes model: 0,87 for validation and 0,74 for testing.
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.
Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% 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 metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
ObjectiveTo compare estimates of effect and variability resulting from standard linear regression analysis and hierarchical multilevel analysis with cross-classified multilevel analysis under various scenarios.Study design and settingWe performed a simulation study based on a data structure from an observational study in clinical mental health care. We used a Markov chain Monte Carlo approach to simulate 18 scenarios, varying sample sizes, cluster sizes, effect sizes and between group variances. For each scenario, we performed standard linear regression, multilevel regression with random intercept on patient level, multilevel regression with random intercept on nursing team level and cross-classified multilevel analysis.ResultsApplying cross-classified multilevel analyses had negligible influence on the effect estimates. However, ignoring cross-classification led to underestimation of the standard errors of the covariates at the two cross-classified levels and to invalidly narrow confidence intervals. This may lead to incorrect statistical inference. Varying sample size, cluster size, effect size and variance had no meaningful influence on these findings.ConclusionIn case of cross-classified data structures, the use of a cross-classified multilevel model helps estimating valid precision of effects, and thereby, support correct inferences.
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