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关键词： Electroencephalogram (EEG)   Texture transformation   Textural feature extraction   Driver fatigue detection   MENTAL FATIGUE   CLASSIFICATION   SYSTEM   SAFETY   TREE  

摘要： Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.

关键词： socioeconomics   child growth   dietary change   statistical decomposition   household consumption and expenditure survey (HCES)   DIETARY PATTERNS   RISK   UNDERNUTRITION   CONSUMPTION   IMPROVEMENT   PREDICTORS   FOOD  

摘要： Background: Bangladesh has experienced rapid reductions in child undernutrition and poverty, increases in maternal education, and dietary change over the past 3 decades. Objective: We aimed to quantify the determinants of the improvement in child nutritional status among preschool-aged children in Bangladesh from 1992 to 2005. Methods: We utilized data from 4 rounds of 2 linked and seasonally balanced survey systems: the Bangladesh Household [Income and] Expenditure Surveys (H[I]ES) and the Child [and Mother] Nutrition Survey (C[M]NS). We analyzed 10,780 children aged 6?59 mo, divided into 2 age groups (6?23 mo and 24?59 mo). We used Blinder?Oaxaca decomposition to assess the impact of changing determinants on nutritional status over time, guided by the UNICEF conceptual framework for the causes of child malnutrition. Results: There were significant improvements in child growth over time for all z-score measures?length/height-forage (LAZ/HAZ), weight-for-length/height (WLZ/WHZ), and weight-for-age (WAZ)?and in many potential determinants of child growth across domains of the UNICEF framework. Among younger children, decomposition explained 67% of the observed change in LAZ, 130% of WLZ, and 73% of WAZ. Among older children, decomposition explained 41% of the observed change in HAZ and 36% of WAZ. Drivers varied, with improvements in care of children as the only driver in both age groups and for all growth measures. Declines in disease prevalence drove improvements in weight-based measures. For younger children, household diets and household environments were significant drivers of improvement in LAZ and WAZ. For older children, increasing income was the largest driver of HAZ and WAZ. Conclusions: Increasing income did not independently drive improvements for younger children but drove improved growth among children aged 2?4 y. This points to the need to focus on nutrition-specific and nutrition-sensitive interventions to decrease child undernutrition in the vulnerab

关键词： neural network   MET   exon skipping   genetic variants   deep learning  

摘要： Background: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. Methods: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. Conclusions: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool.

关键词： Customer touch points   Customer comeback   Social media   Second-lifetime spending   Predictive modeling   CHURN PREDICTION   WIN-BACK   BRAND   IMPACT   MODEL   FIELD  

摘要： Customer comeback, or the return of previous customers to the company without receiving a win-back offer, has received little academic attention. By tapping into a rich transactional database enhanced with social media data, we argue that a multitude of touch points after defection (such as social media) can accurately inform managers about customers for whom win-back offers may not be relevant. Econometric analysis reveals positive links of Facebook likes and event attendances after defection with customer comeback, next to a significant concave relationship of first-lifetime behavior. From a predictive point of view, touch points after defection are more informative than first-lifetime behavior. Finally, comeback customers spend, on average, more than newly acquired customers, and lower-profile comeback customers reduce their spending with the firm upon return. Based on our multimethod analysis, we demonstrate the value of comeback analyses and derive several actionable insights and recommendations for both theory and practice.

关键词： Fatigue   Sleepiness   Partially automated driving   Level 2   FATIGUE   PERFORMANCE   NIGHT   DROWSINESS   TASKS   EEG   SIMULATOR   ROAD   PHYSIOLOGY   INDICATOR  

摘要： The objective of this study was to compare the development of sleepiness during manual driving versus level 2 partially automated driving, when driving on a motorway in Sweden. The hypothesis was that partially auto-mated driving will lead to higher levels of fatigue due to underload. Eighty-nine drivers were included in the study using a 2 ? 2 design with the conditions manual versus partially automated driving and daytime (full sleep) versus night-time (sleep deprived). The results showed that night-time driving led to markedly increased levels of sleepiness in terms of subjective sleepiness ratings, blink durations, PERCLOS, pupil diameter and heart rate. Partially automated driving led to slightly higher subjective sleepiness ratings, longer blink durations, decreased pupil diameter, slower heart rate, and higher EEG alpha and theta activity. However, elevated levels of sleepiness mainly arose from the night-time drives when the sleep pressure was high. During daytime, when the drivers were alert, partially automated driving had little or no detrimental effects on driver fatigue. Whether the negative effects of increased sleepiness during partially automated driving can be compensated by the positive effects of lateral and longitudinal driving support needs to be investigated in further studies.

关键词： Simulated driving   Real car driving   Relative validity   Adaptation   Driving behavior   Subjective ratings   MENTAL WORKLOAD   STEERING ADAPTATION   CRUISE CONTROL   BEHAVIOR   VALIDITY   SPEED   REAL   ROAD   DISTRACTION   VALIDATION  

摘要： Car manufacturers expect driving simulators to be reliable research and development tools. Questions arise, however, as to whether drivers? behavior on simulators exactly matches that observed when they are driving real cars. Drivers? performances and their subjective feelings about their driving were compared between two groups during a 40-min driving test on the same circuit in a real car (n = 20) and a high-fidelity dynamic simulator (n = 27). Their speed and its variability, the braking force and the engine revolutions per minute (rpm) were recorded five times on a straight line and three times on a curve. The differences observed in these measurements between circuit driving (CD) and simula-tor driving (SD) from the 6th to 40th minute showed no significant changes during the drive. The drivers also completed the NASA Raw Task Load Index (NASA RTLX) question-naire and the Simulator Sickness Questionnaire (SSQ) and estimated the ease and standard of their own driving performances. These subjective feelings differed significantly between the two groups throughout the experiment. The SD group?s scores on the NASA RTLX and SSQ questionnaires increased with time and the CD group?s perceived driving quality and ease increased with time, reaching non-significantly different levels from their usual car driving standards by the end of the drive. These findings show the existence of a fairly good match between real-life and simulated driving, which stabilized six minutes after the start of the test, regardless of whether the road was straight or curved. These objective findings and subjective assessments suggest possible ways of improving the match between dri-vers? performances on simulators and their real-life driving behavior. ? 2021 Elsevier Ltd. All rights reserved.

关键词： Contact mixing patterns   Human mobility   COVID-19   Transmission model   SIR model   CORONAVIRUS INFECTIONS   EPIDEMIOLOGY  

摘要： BackgroundApplying heavy nationwide restrictions is a powerful method to curtail COVID-19 transmission but poses a significant humanitarian and economic crisis. Thus, it is essential to improve our understanding of COVID-19 transmission, and develop more focused and effective strategies. As human mobility drives transmission, data from cellphone devices can be utilized to achieve these *** analyzed aggregated and anonymized mobility data from the cell phone devices of>3 million users between February 1, 2020, to May 16, 2020 - in which several movement restrictions were applied and lifted in Israel. We integrated these mobility patterns into age-, risk- and region-structured transmission model. Calibrated to coronavirus incidence in 250 regions covering Israel, we evaluated the efficacy and effectiveness in decreasing morbidity and mortality of applying localized and temporal lockdowns (stay-at-home order).ResultsPoorer regions exhibited lower and slower compliance with the restrictions. Our transmission model further indicated that individuals from impoverished areas were associated with high transmission rates. Considering a horizon of 1-3years, we found that to reduce COVID-19 mortality, school closure has an adverse effect, while interventions focusing on the elderly are the most efficient. We also found that applying localized and temporal lockdowns during regional outbreaks reduces the overall mortality and morbidity compared to nationwide lockdowns. These trends were consistent across vast ranges of epidemiological parameters, and potential seasonal *** resources should be devoted to helping impoverished regions. Utilizing cellphone data despite being anonymized and aggregated can help policymakers worldwide identify hotspots and apply designated strategies against future COVID-19 outbreaks.