The Effect of Caffeine on Motor Task Performance

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Delirium prediction in the ICU: designing a screening tool for preventive interventions

Introduction Delirium occurrence is common and preventive strategies are resource intensive. Screening tools can prioritize patients at risk. Using machine learning, we can capture time and treatment effects that pose a challenge to delirium prediction. We aim to develop a delirium prediction model that can be used as a screening tool. Methods From the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care version III (MIMIC-III) database, patients with one or more Confusion Assessment Method-Intensive Care Unit (CAM-ICU) values and intensive care unit (ICU) length of stay greater than 24 h were included in our study. We validated our model using 21 quantitative clinical parameters and assessed performance across a range of observation and prediction windows, using different thresholds and applied interpretation techniques. We evaluate our models based on stratified repeated cross-validation using 3 algorithms, namely Logistic Regression, Random Forest, and Bidirectional Long Short-Term Memory (BiLSTM). BiLSTM represents an evolution from recurrent neural network-based Long Short-Term Memory, and with a backward input, preserves information from both past and future. Model performance is measured using Area Under Receiver Operating Characteristic, Area Under Precision Recall Curve, Recall, Precision (Positive Predictive Value), and Negative Predictive Value metrics. Results We evaluated our results on 16 546 patients (47% female) and 6294 patients (44% female) from eICU-CRD and MIMIC-III databases, respectively. Performance was best in BiLSTM models where, precision and recall changed from 37.52% (95% confidence interval [CI], 36.00%–39.05%) to 17.45 (95% CI, 15.83%–19.08%) and 86.1% (95% CI, 82.49%–89.71%) to 75.58% (95% CI, 68.33%–82.83%), respectively as prediction window increased from 12 to 96 h. After optimizing for higher recall, precision and recall changed from 26.96% (95% CI, 24.99%–28.94%) to 11.34% (95% CI, 10.71%–11.98%) and 93.73% (95% CI, 93.1%–94.37%) to 92.57% (95% CI, 88.19%–96.95%), respectively. Comparable results were obtained in the MIMIC-III cohort. Conclusions Our model performed comparably to contemporary models using fewer variables. Using techniques like sliding windows, modification of threshold to augment recall and feature ranking for interpretability, we addressed shortcomings of current models

Cycling comfort on asphalt pavement: Influence of the pavement-tyre interface on vibration

Attainment of cycling comfort on urban roads encourages travelers to use bicycles more often, which has social and environment benefits such as to reduce congestion, air pollution and carbon emissions. Cycling vibration is responsible for the cyclists’ perception of (dis)comfort. How asphalt pavement's surface characteristics relate to cycling comfort, however, remains undiscovered. In this study, the cycling vibration intensity on 46 sections of 24 urban roads was tested using a dynamic cycling comfort measure system while the cyclists’ perception of vibration was identified via questionnaires; the cycling comfort was then defined based on the cycling vibration. To record the accurate pavement-tyre interface under a stable environment, a total of 19 pavement sections were scanned using a 3D digital camera. These 3D models were then 3D printed, which are used to conduct the pressure film test using a self-developed pavement-tyre interface test system. Three ranges of pressure films were adopted to characterize the pavement-tyre interface via 9 parameters, namely contact area (A c ), unit bearing area (B u ), stress intensity (S i ), stress uniformity (S u ), kurtosis (S ku ), spacing (Sp a ), maximum peak spacing (Sp max ), radius ratio (R r ) and fractal dimension (F d ), in consideration of the area characteristics, pressure amplitude, peak spacing and shape of the interface. Finally, the significant interface parameters were identified, and the regression model between interface parameters and cycling comfort was established. Results show that the cycling vibration was described to be ‘very comfortable’ when the human exposure to vibration level (a wv ) was less than 1.78 m/s 2 ; ‘comfortable’ when the a wv was between 1.78 m/s 2 and 2.20 m/s 2 ; and ‘uncomfortable’ when the a wv was greater than 2.20 m/s 2 . The average stress on rear wheel-pavement interface is higher than that of the front wheel. B u-0.6 , Sp a-0.6 , and F d-0.6 are significant to cycling vibration. The 2LW pressure film is recommended for use to measure the bicycle pavement-tyre interface. The recommended interface characteristics are less than 7 mm 2 of the unit bearing area, 6 mm of average spacing and 2.38 of fractal dimension. Finally, dense asphalt mixture performs better in providing cycling comfort than the gap-graded asphalt mixture. Results of this study contribute to current knowledge on bike lane comfort and pavement design, the findings should be interested in cyclists, transport planners, and road authorities

Russian life and religion : a comparative study pre and post 1991.

College of Liberal Arts; Foreign Languages Department; Advisor: Dr. Sandra Carpenter; Date: November 201

United States Road Assessment Program (usRAP) Feasibility Assessment and Pilot Program

The level of safety for motorists on U.S. roads varies widely. Controlled-access freeways, with no at-grade intersections or driveways, provide the highest level of safety among road types. Other safety enhancing features of roadways include medians, roadside clear zones, guardrails, median barriers, and intersection turn lanes. Highway agencies have limited funds for improving the safety features of roadways, so it is important that their investment decisions are made in a way that provides maximum benefits to motorists and to the public at large