A testing protocol was developed to measure accumulation of vertical plastic deformation in fouled railway ballast under cyclic traffic loading. Large-scale cyclic triaxial (LSCT) test equipment was constructed to test fouled ballast under various stress conditions. A method of introducing fouling material and moisture to ballast specimens was critical to the resulting deformational behavior. Specimens for LSCT testing should be prepared by mixing ballast with relatively dry fouling material (moisture <5 % for granular fouling and <15 % for clay-based fouling) prior to compaction to prevent a heterogeneous distribution of fouling within the specimen. A full-scale track model experiment (FSTME) was built to determine a representative state of stress (RSS) for railway ballast for use in the LSCT testing. The RSS allows for testing of ballast under consistent stresses to compare the effect of material characteristics of fouling such as fouling content and moisture content. A RSS of 90 kPa (confining) and 300 kPa (deviator) for a train axle load of 264 kN was suggested based on the FSTME results. Measured deformation of fouled ballast using the proposed testing protocol was compared with a published vertical deformation of railway track in a track test section. Results of this study indicate that the proposed testing protocol can simulate the vertical plastic deformation of railway ballast at a specified stress level.
BACKGROUND: Lifestyle is one factor that forms the nurses’ health, particularly those who work in shiftwork schedules. AIMS: The aim of this study was to design and test a model for health promotion of Iranian nurses. In this model, nurses’ lifestyle was considered as the precedent, physical and mental health as the outcomes, and sleep disturbance and chronic fatigue as the mediators. METHODS: A cross-sectional study using structural equation modeling was conducted among 240 shiftworker nurses in Iran. The data collection was performed using the Persian versions of the Survey of Shiftworkers Questionnaire and Life Style Questionnaire. Bootstrap in Preacher and Hayes’ Macro program was used for testing mediation. RESULTS: Lifestyle had a weak significant direct effect on physical (β = 0.13, p < .04) and mental health (β = 0.12, p < .02), and it had a significant indirect effect on physical health via chronic fatigue (β = −0.11, p < .001) and sleep disturbance (β = −0.05, p < .01). This variable only had a significant indirect effect on mental health via chronic fatigue (β = −0.19, p < .001). The final model proposed a new significant path between sleep disturbances and chronic fatigue (β = 0.22, p < .001). CONCLUSIONS: Therefore, the hospital officials can enhance the nurses’ physical and mental well-being by providing interventions and training courses on aspects of healthy lifestyles, such as physical activity, avoidance of smoking, and maintenance of body weight.
ObjectivesThis systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).DesignA systematic review was performed.SettingThe databases including Medline (PubMed), Scopus and Embase (Ovid) were searched between 2010 and 2021, to extract published articles in English, describing ML-based models utilising vital sign variables to predict in-hospital mortality for patients admitted at EDs. Critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist was used for study planning and data extraction. The risk of bias for included papers was assessed using the prediction risk of bias assessment tool.ParticipantsAdmitted patients to the ED.Main outcome measureIn-hospital mortality.ResultsFifteen articles were included in the final review. We found that eight models including logistic regression, decision tree, K-nearest neighbours, support vector machine, gradient boosting, random forest, artificial neural networks and deep neural networks have been applied in this domain. Most studies failed to report essential main analysis steps such as data preprocessing and handling missing values. Fourteen included studies had a high risk of bias in the statistical analysis part, which could lead to poor performance in practice. Although the main aim of all studies was developing a predictive model for mortality, nine articles did not provide a time horizon for the prediction.ConclusionThis review provided an updated overview of the state-of-the-art and revealed research gaps; based on these, we provide eight recommendations for future studies to make the use of ML more feasible in practice. By following these recommendations, we expect to see more robust ML models applied in the future to help clinicians identify patient deterioration earlier.
Prediction of alcohol use disorder (AUD) may reduce the number of deaths caused by alcohol-related diseases. However, prediction of AUD based on patients' historical clinical data is still an open research objective. This study proposes a method to predict AUD from electronic health record (EHR) data through supervised machine learning. The study creates a dataset based on the combination of EHR data with patient reported data from 2,571 patients in the Region of Southern Denmark. After that, the dataset is labeled into two categories, AUD positive (457) and AUD negative (2,114). This unique dataset is used to validate the proposed method for prediction of AUD using machine learning methods based on historical clinical data from EHRs.
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