Efficient Sampling Policy for Selecting a Good Enough Subset

The note studies the problem of selecting a good enough subset out of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior probability of correctly selecting a good subset. We formulate the dynamic sampling decision as a stochastic control problem in a Bayesian setting. In an approximate dynamic programming paradigm, we propose a sequential sampling policy based on value function approximation. We analyze the asymptotic property of the proposed sampling policy. Numerical experiments demonstrate the efficiency of the proposed procedure

Impact of Indoor Mobility Behavior on the Respiratory Infectious Diseases Transmission Trends

The importance of indoor human mobility in the transmission dynamics of respiratory infectious diseases has been acknowledged. Previous studies have predominantly addressed a single type of mobility behavior such as queueing and a series of behaviors under specific scenarios. However, these studies ignore the abstraction of mobility behavior in various scenes and the critical examination of how these abstracted behaviors impact disease propagation. To address these problems, this study considers people's mobility behaviors in a general scenario, abstracting them into two main categories: crowding behavior, related to the spatial aspect, and stopping behavior, related to the temporal aspect. Accordingly, this study investigates their impacts on disease spreading and the impact of individual spatio-temporal distribution resulting from these mobility behaviors on epidemic transmission. First, a point of interest (POI) method is introduced to quantify the crowding-related spatial POI factors (i.e., the number of crowdings and the distance between crowdings) and stopping-related temporal POI factors (i.e., the number of stoppings and the duration of each stopping). Besides, a personal space determined with Voronoi diagrams is used to construct the individual spatio-temporal distribution factor. Second, two indicators (i.e., the daily number of new cases and the average exposure risk of people) are applied to quantify epidemic transmission. These indicators are derived from a fundamental model which accurately predicts disease transmission between moving individuals. Third, a set of 200 indoor scenarios is constructed and simulated to help determine variable values. Concurrently, the influences and underlying mechanisms of these behavioral factors on disease transmission are examined using structural equation modeling and causal inference modeling.....

ESSM: An Extractive Summarization Model with Enhanced Spatial-Temporal Information and Span Mask Encoding

Extractive reading comprehension is to extract consecutive subsequences from a given article to answer the given question. Previous work often adopted Byte Pair Encoding (BPE) that could cause semantically correlated words to be separated. Also, previous features extraction strategy cannot effectively capture the global semantic information. In this paper, an extractive summarization model is proposed with enhanced spatial-temporal information and span mask encoding (ESSM) to promote global semantic information. ESSM utilizes Embedding Layer to reduce semantic segmentation of correlated words, and adopts TemporalConvNet Layer to relief the loss of feature information. The model can also deal with unanswerable questions. To verify the effectiveness of the model, experiments on datasets SQuAD1.1 and SQuAD2.0 are conducted. Our model achieved an EM of 86.31% and a F1 score of 92.49% on SQuAD1.1 and the numbers are 80.54% and 83.27% for SQuAD2.0. It was proved that the model is effective for extractive QA task

Identification and diagnosis of concurrent faults in rotor-bearing system with WPT and zero space classifiers

An effective method for identifying and diagnosing the concurrent fault combined by two or more single faults is yet to be further developed because most existing approaches focus on single faults. On the other hand, rotor-bearing system is an important part of rotating machinery. Therefore a new fault identification and diagnosis method based on wavelet packet transform (WPT) and zero space classifiers is presented in this paper. Firstly, the vibration signals collected from the rotor-bearing system are decomposed into several time-frequency compositions by WPT. Then the appropriate composition characterizing fault signatures is selected to extract features for constructing zero space classifiers. Finally, the effectiveness of the proposed method is demonstrated by an experiment carried out on a machinery fault simulator. The experimental results show that the proposed approach is feasible and effective to identify and diagnose the concurrent faults in a rotor-bearing system

Fault diagnosis of rotating machinery based on noise reduction using empirical mode decomposition and singular value decomposition

Vibration signals collected from a faulty rotating machine include in general impulse information reflecting fault types, irrelevant vibration components caused by other normal mechanical parts, and other environmental noise. Cleaning the obtained vibration signals can prove practical significance for the fault diagnosis of rotating machinery. To address this issue, this paper proposes a new fault diagnosis method based on noise reduction technology using empirical mode decomposition (EMD) and singular value decomposition (SVD). In this approach, EMD is first applied to decompose the collected vibration signal into a set of intrinsic mode functions (IMFs) and residual signal. Then the first several IMFs including bearing characteristic damage frequencies (CDFs) and higher frequency components are selected to do further noise reduction by SVD for features, and the other remaining decomposition components of EMD are abandoned as noise. Finally, the fault diagnosis of rotating machinery is realized by these obtained features using a support vector machine (SVM) model. Experimental results testify that the proposed method is effective for mechanical fault diagnosis

Ambient air pollution in relation to diabetes and glucose-homoeostasis markers in China: a cross-sectional study with findings from the 33 Communities Chinese Health Study

Background: Health effects of air pollution on diabetes have been scarcely studied in developing countries. We aimed to explore the associations of long-term exposure to ambient particulate matter (PM) and gaseous pollutants with diabetes prevalence and glucose-homoeostasis markers in China. Methods: Between April 1 and Dec 31, 2009, we recruited a total of 15 477 participants aged 18–74 years using a random number generator and a four-staged, stratified and cluster sampling strategy from a large cross-sectional study (the 33 Communities Chinese Health Study) from three cities in Liaoning province, northeastern China. Fasting and 2 h insulin and glucose concentrations and the homoeostasis model assessment of insulin resistance index and β-cell function were used as glucose-homoeostasis markers. Diabetes was defined according to the American Diabetes Association's recommendations. We calculated exposure to air pollutants using data from monitoring stations (PM with an aerodynamic diameter of 10 μm or less [PM10], sulphur dioxide, nitrogen dioxide, and ozone) and a spatial statistical model (PM with an aerodynamic diameter of 1 μm or less [PM1] and 2·5 μm or less [PM2·5]). We used two-level logistic regression and linear regression analyses to assess associations between exposure and outcomes, controlling for confounders. Findings: All the studied pollutants were significantly associated with increased diabetes prevalence (eg, the adjusted odds ratios associated with an increase in IQR for PM1, PM2·5, and PM10 were 1·13, 95% CI 1·04–1·22; 1·14, 1·03–1·25; and 1·20, 1·12–1·28, respectively). These air pollutants were also associated with higher concentrations of fasting glucose (0·04–0·09 mmol/L), 2 h glucose (0·10–0·19 mmol/L), and 2 h insulin (0·70–2·74 μU/L). No association was observed for the remaining biomarkers. Stratified analyses indicated greater effects on the individuals who were younger (<50 years) or overweight or obese. Interpretation: Long-term exposure to air pollution was associated with increased risk of diabetes in a Chinese population, particularly in individuals who were younger or overweight or obese. Funding: The National Key Research and Development Program of China, the National Natural Science Foundation of China, the Fundamental Research Funds for the Central Universities, the Guangdong Province Natural Science Foundation, the Career Development Fellowship of Australian National Health and Medical Research Council, and the Early Career Fellowship of Australian National Health and Medical Research Council

The associations of residential greenness with fetal growth in utero and birth weight: A birth cohort study in Beijing, China

Background: Although studies have examined the association between residential greenness and birth weight, there is no evidence regarding the association between residential greenness and fetal growth in utero. We aimed to investigate the associations of residential greenness with both fetal growth in utero and birth weight. Methods: A birth cohort (2014–2017) with 18,665 singleton pregnancies was established in Tongzhou Maternal and Child hospital of Beijing, China. Residential greenness was matched with maternal residential address and estimated from remote satellite data using normalized difference vegetation index with 200 m and 500 m buffers (NDVI-200 and NDVI-500). Fetal parameters including estimated fetal weight (EFW), abdominal circumference (AC), head circumference (HC) an

Mortality risk attributable to wildfire-related PM2·5 pollution: a global time series study in 749 locations

Summary Background Many regions of the world are now facing more frequent and unprecedentedly large wildfires. However, the association between wildfire-related PM2·5 and mortality has not been well characterised. We aimed to comprehensively assess the association between short-term exposure to wildfire-related PM2·5 and mortality across various regions of the world. Methods For this time series study, data on daily counts of deaths for all causes, cardiovascular causes, and respiratory causes were collected from 749 cities in 43 countries and regions during 2000–16. Daily concentrations of wildfire-related PM2·5 were estimated using the three-dimensional chemical transport model GEOS-Chem at a 0·25° × 0·25° resolution. The association between wildfire-related PM2·5 exposure and mortality was examined using a quasi-Poisson time series model in each city considering both the current-day and lag effects, and the effect estimates were then pooled using a random-effects meta-analysis. Based on these pooled effect estimates, the population attributable fraction and relative risk (RR) of annual mortality due to acute wildfire-related PM2·5 exposure was calculated. Findings 65·6 million all-cause deaths, 15·1 million cardiovascular deaths, and 6·8 million respiratory deaths were included in our analyses. The pooled RRs of mortality associated with each 10 μg/m3 increase in the 3-day moving average (lag 0–2 days) of wildfire-related PM2·5 exposure were 1·019 (95% CI 1·016–1·022) for all-cause mortality, 1·017 (1·012–1·021) for cardiovascular mortality, and 1·019 (1·013–1·025) for respiratory mortality. Overall, 0·62% (95% CI 0·48–0·75) of all-cause deaths, 0·55% (0·43–0·67) of cardiovascular deaths, and 0·64% (0·50–0·78) of respiratory deaths were annually attributable to the acute impacts of wildfire-related PM2·5 exposure during the study period.Australian Research Council, Australian National Health & Medical Research Council.Peer reviewe

Maternal exposure to ambient air pollution and congenital heart defects in China

Background: Evidence of maternal exposure to ambient air pollution on congenital heart defects (CHD) has been mixed and are still relatively limited in developing countries. We aimed to investigate the association between maternal exposure to air pollution and CHD in China.Method: This longitudinal, population-based, case-control study consecutively recruited fetuses with CHD and healthy volunteers from 21 cities, Southern China, between January 2006 and December 2016. Residential address at delivery was linked to random forests models to estimate maternal exposure to particulate matter with an aerodynamic diameter of ≤1 µm (PM1), ≤2.5 µm, and ≤10 µm as well as nitrogen dioxides, in three trimesters. The CHD cases were evaluated by obstetrician, pediatrician, or cardiologist, and confirmed by cardia ultrasound. The CHD subtypes were coded using the International Classification Diseases. Adjusted logistic regression models were used to assess the associations between air pollutants and CHD and its subtypes.Results: A total of 7055 isolated CHD and 6423 controls were included in the current analysis. Maternal air pollution exposures were consistently higher among cases than those among controls. Logistic regression analyses showed that maternal exposure to all air pollutants during the first trimester was associated with an increased odds of CHD (e.g., an interquartile range [13.3 µg/m3] increase in PM1 was associated with 1.09-fold ([95% confidence interval, 1.01-1.18]) greater odds of CHD). No significant associations were observed for maternal air pollution exposures during the second trimester and the third trimester. The pattern of the associations between air pollutants and different CHD subtypes was mixed.Conclusions: Maternal exposure to greater levels of air pollutants during the pregnancy, especially the first trimester, is associated with higher odds of CHD in offspring. Further longitudinal well-designed studies are warranted to confirm our findings

Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities

BACKGROUND: Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures. METHODS: We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach. RESULTS: We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-μg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively. CONCLUSIONS: Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies