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Eating habits study extracorporeal living support with regard to the respiratory system failure in kids with principal immunodeficiencies.
The purpose of this study was to compare model estimates of linear sprint mechanical characteristics using timing gates with and without time correction. High-level female soccer players (n = 116) were evaluated on a 35-m linear sprint with splits at 5, 10, 20, 30, and 35 m. A mono-exponential function was used to model sprint mechanical metrics in three ways without a time correction, with a fixed (+0.3 s) time correction, and with an estimated time correction. Separate repeated-measures ANOVAs compared the sprint parameter estimates between models and also the residuals between models. Differences were identified between all modeled sprint mechanical metrics; however, comparable estimates to the literature occurred when either time correction was used. Bias for both time-corrected models was reduced across all sprint distances compared to the uncorrected model. This study confirms that a time correction is warranted when using timing gates at the start line to model sprint mechanical metrics. However, determining whether fixed or estimated time corrections provide greater accuracy requires further investigation.Public healthcare has a history of cautious adoption for artificial intelligence (AI) systems. The rapid growth of data collection and linking capabilities combined with the increasing diversity of the data-driven AI techniques, including machine learning (ML), has brought both ubiquitous opportunities for data analytics projects and increased demands for the regulation and accountability of the outcomes of these projects. As a result, the area of interpretability and explainability of ML is gaining significant research momentum. While there has been some progress in the development of ML methods, the methodological side has shown limited progress. This limits the practicality of using ML in the health domain the issues with explaining the outcomes of ML algorithms to medical practitioners and policy makers in public health has been a recognized obstacle to the broader adoption of data science approaches in this domain. This study builds on the earlier work which introduced CRISP-ML, a methodology that determions discussed in the project. The approach used ensured that interpretability requirements were met, taking into account public healthcare specifics, regulatory requirements, project stakeholders, project objectives, and data characteristics. The study concludes with the three main directions for the development of the presented cross-industry standard process.This article discusses possible search engine page rank biases as a consequence of search engine profile information. After describing search engine biases, their causes, and their ethical implications, we present data about the Google search engine (GSE) and DuckDuckGo (DDG) for which only the first uses profile data for the production of page ranks. We analyze 408 search engine screen prints of 102 volunteers (53 male and 49 female) on queries for job search and political participation. For job searches via GSE, we find a bias toward stereotypically "female" jobs for women but also for men, although the bias is significantly stronger for women. For political participation, the bias of GSE is toward more powerful positions. Contrary to our hypothesis, this bias is even stronger for women than for men. Our analysis of DDG does not give statistically significant page rank differences for male and female users. We, therefore, conclude that GSE's personal profiling is not reinforcing a gender stereotype. Although no gender differences in page ranks was found for DDG, DDG usage in general gave a bias toward "male-dominant" vacancies for both men and women. We, therefore, believe that search engine page ranks are not biased by profile ranking algorithms, but that page rank biases may be caused by many other factors in the search engine's value chain. We propose ten search engine bias factors with virtue ethical implications for further research.Alzheimer's disease (AD) has its onset many decades before dementia develops, and work is ongoing to characterise individuals at risk of decline on the basis of early detection through biomarker and cognitive testing as well as the presence/absence of identified risk factors. Epacadostat Risk prediction models for AD based on various computational approaches, including machine learning, are being developed with promising results. However, these approaches have been criticised as they are unable to generalise due to over-reliance on one data source, poor internal and external validations, and lack of understanding of prediction models, thereby limiting the clinical utility of these prediction models. We propose a framework that employs a transfer-learning paradigm with ensemble learning algorithms to develop explainable personalised risk prediction models for dementia. Our prediction models, known as source models, are initially trained and tested using a publicly available dataset (n = 84,856, mean age = 69 years) with 1 and applying the "knowledge" to another dataset from a different and undiagnosed population for the early detection and prediction of dementia risk, and the ability to visualise the interaction of the risk factors that drive the prediction. This approach has direct clinical utility.In the past few years, the importance of electric mobility has increased in response to growing concerns about climate change. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of electric vehicles (EVs). To mitigate the problem, the need for optimal route planning algorithms emerged. In this paper, we propose a mathematical formulation of the EV-specific routing problem in a graph-theoretical context, which incorporates the ability of EVs to recuperate energy. Furthermore, we consider a possibility to recharge on the way using intermediary charging stations. As a possible solution method, we present an off-policy model-free reinforcement learning approach that aims to generate energy feasible paths for EV from source to target. The algorithm was implemented and tested on a case study of a road network in Switzerland. The training procedure requires low computing and memory demands and is suitable for online applications. The results achieved demonstrate the algorithm's capability to take recharging decisions and produce desired energy feasible paths.
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