Permission form synopses to improve parents' understanding of research: a randomized trial.

ObjectiveWe hypothesized that, among parents of potential neonatal research subjects, an accompanying cover sheet added to the permission form (intervention) would increase understanding of the research, when compared to a standard form (control).Study designThis pilot study enrolled parents approached for one of two index studies: one randomized trial and one observational study. A one-page cover sheet described critical study information. Families were randomized 1:1 to receive the cover sheet or not. Objective and subjective understanding and satisfaction were measured.ResultsThirty-two parents completed all measures (17 control, 15 intervention). There were no differences in comprehension score (16.8±5.7 vs 16.3±3.5), subjective understanding (median 6 vs 6.5), or overall satisfaction with consent (median 7 vs 6.5) between control and intervention groups (all P>0.50).ConclusionA simplified permission form cover sheet had no effect on parents' understanding of studies for which their newborns were being recruited

Study of thermal bubble motion in microchannel

This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.The micro capillary pumped loop system (MCPL) is a highly efficient device for heat transfer because the main driving force is a result of thermo-bubbles in micro-channel. In this study, the scaling effect with respect to the dimensional geometry of MCPL was studied for improving the heat transfer performance. The results showed that when a larger heating power was provided by microheater, the growing rate of thermal bubble was faster. Generally speaking, injecting a larger amount of working fluids resulted in faster thermo bubble motion. When the size of channel was scaled down, the nucleation of thermal bubble occurred easily and a positive performance of heat transfer was expected. These findings will be useful to the further optimal design of MCPL.This study is funded by the National Science Council of the Republic of China, Taiwan, under Contract No. NSC- 98-2221-E-197 –018

Effects of correlation-based VM allocation criteria to cloud data centers

2016-2017 > Academic research: refereed > Refereed conference paper201804_a bcmapreprint_postprin

An EEG-based brain-computer interface for dual task driving detection

The development of brain-computer interfaces (BCI) for multiple applications has undergone extensive growth in recent years. Since distracted driving is a significant cause of traffic accidents, this study proposes one BCI system based on EEG for distracted driving. The removal of artifacts and the selection of useful brain sources are the essential and critical steps in the application of electroencephalography (EEG)-based BCI. In the first model, artifacts are removed, and useful brain sources are selected based on the independent component analysis (ICA). In the second model, all distracted and concentrated EEG epochs are recognized with a self-organizing map (SOM). This BCI system automatically identified independent components with artifacts for removal and detected distracted driving through the specific brain sources which are also selected automatically. The accuracy of the proposed system approached approximately 90% for the recognition of EEG epochs of distracted and concentrated driving according to the selected frontal and left motor components. © 2013

Theta and alpha oscillations in attentional interaction during distracted driving

© 2018 Wang, Jung and Lin. Performing multiple tasks simultaneously usually affects the behavioral performance as compared with executing the single task. Moreover, processing multiple tasks simultaneously often involve more cognitive demands. Two visual tasks, lane-keeping task and mental calculation, were utilized to assess the brain dynamics through 32-channel electroencephalogram (EEG) recorded from 14 participants. A 400-ms stimulus onset asynchrony (SOA) factor was used to induce distinct levels of attentional requirements. In the dual-task conditions, the deteriorated behavior reflected the divided attention and the overlapping brain resources used. The frontal, parietal and occipital components were decomposed by independent component analysis (ICA) algorithm. The event- and response-related theta and alpha oscillations in selected brain regions were investigated first. The increased theta oscillation in frontal component and decreased alpha oscillations in parietal and occipital components reflect the cognitive demands and attentional requirements as executing the designed tasks. Furthermore, time-varying interactive over-additive (O-Add), additive (Add) and under-additive (U-Add) activations were explored and summarized through the comparison between the summation of the elicited spectral perturbations in two single-task conditions and the spectral perturbations in the dual task. Add and U-Add activations were observed while executing the dual tasks. U-Add theta and alpha activations dominated the posterior region in dual-task situations. Our results show that both deteriorated behaviors and interactive brain activations should be comprehensively considered for evaluating workload or attentional interaction precisely

Cluster-based informed agents selection for flocking with a virtual leader

2014-2015 > Academic research: refereed > Refereed conference paperAccepted ManuscriptPublishe

A stable matching-based virtual machine allocation mechanism for cloud data centers

2016-2017 > Academic research: refereed > Refereed conference paper201803_a bcwhpreprint_postprin

Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering

© 2017 IEEE. Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-statedrift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-statedrift detector to improve the detection accuracy

Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity