An EEG-based perceptual function integration network for application to drowsy driving

© 2015 Elsevier B.V. All rights reserved. Drowsy driving is among the most critical causes of fatal crashes. Thus, the development of an effective algorithm for detecting a driver's cognitive state demands immediate attention. For decades, studies have observed clear evidence using electroencephalography that the brain's rhythmic activities fluctuate from alertness to drowsiness. Recognition of this physiological signal is the major consideration of neural engineering for designing a feasible countermeasure. This study proposed a perceptual function integration system which used spectral features from multiple independent brain sources for application to recognize the driver's vigilance state. The analysis of brain spectral dynamics demonstrated physiological evidenced that the activities of the multiple cortical sources were highly related to the changes of the vigilance state. The system performances showed a robust and improved accuracy as much as 88% higher than any of results performed by a single-source approach

Provision of reinforcement in concrete solids using the generalized genetic algorithm

A generalized genetic algorithm has been developed to find the global optimal reinforcement contents for a concrete solid structure subjected to a general three-dimensional (3D) stress field. Feasible solutions were examined based on the genetic algorithm, and the heterogeneous strategy used ensures that all of the local optimal regions are searched and the most optimal reinforcement content found. The effectiveness of the proposed approach has been validated by comparing the steel contents evaluated using the present method with those obtained from other available methods. A more economic design is achieved by the proposed algorithm. The method developed provides the designer with a valuable tool for the determination of reinforcements in complicated solid concrete structures. © 2011 American Society of Civil Engineers.postprin

Developing an EEG-based on-line closed-loop lapse detection and mitigation system

© 2014 Wang, Huang, Wei, Huang, Ko, Lin, Cheng and Jung. In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments

A systematic simulation methodology for LNG ship operations in port waters: a case study in Meizhou Bay

With the increment for liquefied natural gas (LNG) demand, LNG carriers are becoming larger in size. The operational safety of the carriers and the associated terminals is increasingly attracting attention. This is particularly true when a large LNG vessel approaches a terminal, requiring a detailed investigation of ship handling in port waters, especially in certain unusual cases. A full mission simulator provides an effective tool for research and training in operations of both port terminals and ships. This paper presents an experimental design methodology of the full mission simulation. The details as to how the simulation is achieved are described, and the simulation strategies applicable to LNG ships are specified. A typical case study is used to demonstrate and verify the proposed design methodology. The proposed methodology of the full mission simulation provides guidance for port safety research, risk evaluation and seafarer training. © 2017 Institute of Marine Engineering, Science & Technolog

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

© 2014 Huang, Lin, Ko, Liu, Su and Lin. Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician's expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 ± 4.0 (SD) % [average kappa 0.68 ± 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare

Classification of migraine stages based on resting-state EEG power

© 2015 IEEE. Migraine is a chronic neurological disease characterized by recurrent moderate to severe headaches during a period like one month often in association with symptoms in human brain and autonomic nervous system. Normally, migraine symptoms can be categorized into four different stages: inter-ictal, pre-ictal, ictal, and post-ictal stages. Since migraine patients are difficulty knowing when they will suffer migraine attacks, therefore, early detection becomes an important issue, especially for low-frequency migraine patients who have less than 5 times attacks per month. The main goal of this study is to develop a migraine-stage classification system based on migraineurs' resting-state EEG power. We collect migraineurs' O1 and O2 EEG activities during closing eyes from occipital lobe to identify pre-ictal and non-pre-ictal stages. Self-Constructing Neural Fuzzy Inference Network (SONFIN) is adopted as the classifier in the migraine stages classification which can reach the better classification accuracy (66%) in comparison with other classifiers. The proposed system is helpful for migraineurs to obtain better treatment at the right time

Transformation Pathways of Silica under High Pressure

Concurrent molecular dynamics simulations and ab initio calculations show that densification of silica under pressure follows a ubiquitous two-stage mechanism. First, anions form a close-packed sub-lattice, governed by the strong repulsion between them. Next, cations redistribute onto the interstices. In cristobalite silica, the first stage is manifest by the formation of a metastable phase, which was observed experimentally a decade ago, but never indexed due to ambiguous diffraction patterns. Our simulations conclusively reveal its structure and its role in the densification of silica.Comment: 14 pages, 4 figure

Factors associated with reporting multiple causes of death

BACKGROUND: There is analytical potential for multiple cause of death data collected from death certificates. This study examines relationships of multiple causes of death as a function of factors available on the death certificate (demographics of decedent, place of death, type of certifier, disposal method, whether an autopsy was performed, and year of death). METHODS: Data from 326,332 Minnesota death certificates from 1990–1998 are examined. Underlying and non-underlying causes of death are examined (based on record axis codes) as well as demographic and death-related covariates. Associations between covariates and prevalence of multiple causes of death and conditional probability of underlying compared to non-underlying causes of death are examined. The occurrence of ischemic heart disease or diabetes as underlying causes are specifically examined. RESULTS: Both the probability of multiple causes of death and the proportion of underlying cause compared to non-underlying cause of death are associated with demographic characteristics of the deceased and other non-medical conditions related to filing death certificate such as place of death. CONCLUSIONS: Multiple cause of death data provide a potentially useful way of looking for inaccuracies in reporting of causes of death. Differences across demographics in the proportion of time a cause is selected as underlying compared to non-underlying exist and can potentially provide useful information about the overall impact of causes of death in different populations

Nanoquartz in Late Permian C1 coal and the high incidence of female lung cancer in the Pearl River Origin area: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>The Pearl River Origin area, Qujing District of Yunnan Province, has one of the highest female lung cancer mortality rates in China. Smoking was excluded as a cause of the lung cancer excess because almost all women were non-smokers. Crystalline silica embedded in the soot emissions from coal combustion was found to be associated with the lung cancer risk in a geographical correlation study. Lung cancer rates tend to be higher in places where the Late Permian C1 coal is produced. Therefore, we have hypothesized the two processes: C1 coal combustion --> nanoquartz in ambient air --> lung cancer excess in non-smoking women.</p> <p>Methods/Design</p> <p>We propose to conduct a retrospective cohort study to test the hypothesis above. We will search historical records and compile an inventory of the coal mines in operation during 1930–2009. To estimate the study subjects' retrospective exposure, we will reconstruct the historical exposure scenario by burning the coal samples, collected from operating or deserted coal mines by coal geologists, in a traditional firepit of an old house. Indoor air particulate samples will be collected for nanoquartz and polycyclic aromatic hydrocarbons (PAHs) analyses. Bulk quartz content will be quantified by X-ray diffraction analysis. Size distribution of quartz will be examined by electron microscopes and by centrifugation techniques. Lifetime cumulative exposure to nanoquartz will be estimated for each subject. Using the epidemiology data, we will examine whether the use of C1 coal and the cumulative exposure to nanoquartz are associated with an elevated risk of lung cancer.</p> <p>Discussion</p> <p>The high incidence rate of lung cancer in Xuan Wei, one of the counties in the current study area, was once attributed to high indoor air concentrations of PAHs. The research results have been cited for qualitative and quantitative cancer risk assessment of PAHs by the World Health Organization and other agencies. If nanoquartz is found to be the main underlying cause of the lung cancer epidemic in the study area, cancer potency estimates for PAHs by the international agencies based on the lung cancer data in this study setting should then be updated.</p