2,803 research outputs found
Offshore Records of Earthquake Triggered Events: Observations from Chirp Sonar Profiles and Cores
No full textTransurethral RollerLoop Vapor Resection of Prostate for Treatment of Symptomatic Benign Prostatic Hyperplasia: A 2-Year Follow-up
No full textOffshore Records of Submarine hazards off Southwestern Taiwan: Earthquake versus Flooding Events
No full textTissue factor activity of SW-480 human colon adenocarcinoma cells is modulated by thrombin and protein kinase C activator.
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Short-term effects of air pollution and temperature on daily morbidity in Chiang Mai Thailand
Full text linkAir pollution is associated with mortality and morbidity worldwide. Hot and cold temperature is also related to increased deaths and possibly hospital visits and admissions in many settings. Climate change is anticipated to pose increasing risks of deaths and illnesses associated with air pollution and temperature variations, particularly in developing world. To date, research studies about health effects of air pollution and temperature have been conducted in developed countries with cool climate more than in developing countries with subtropical or tropical climate. Furthermore, studies to identify susceptible populations are still limited. This study aims to investigate heath effects of air pollution and temperature and to identify people who are more susceptible to air pollution and temperature in a developing, tropical country, Thailand.
A regression analysis of retrospective time series data was employed to assess the shortterm effects of air pollution and temperature on daily out-patient visits and hospital admissions in Chiang Mai, Thailand, from October 2002 to September 2006. Generalised negative binomial regression was used to model the relationships between the exposure and health outcomes, controlling for seasonal patterns and other possible potential confounders. Lag effects up to 4 days for air pollution, and up to 13 days for temperature were considered. Effect modification by age, sex, occupation, season, and previous out-patient visits before admissions were also examined.
There were positive, but not significant, effects of air pollution for some pollutants (particularly for S02), with notably larger effect sizes compared to previous studies in Western countries. There was evidence of hot temperature effects (though wide confidence intervals), with an increase in diabetic visits of 26.3% (95% Cl, 7.1% to 49.0%), and in circulatory visits of 19.2% (95% Cl, 7.0% to 32.8%) for each 1°C increase in temperature above 29°C. There was a rise of both the visits (3.7% increase, 95% Cl, 1.5% to 5.9%) and admissions (5.8% increase, 95% Cl, 2.3% to 9.3%) due to intestinal infectious disease for each 1°C increase across the whole temperature range. Despite no statistically significant differences between subgroups, air pollution effects were stronger in the elderly, females and manual workers, whereas temperature effects were stronger in the elderly, male and unemployed people.
This study suggests that while there was little evidence of air pollution effects, there was significant evidence of high temperature effects on daily morbidity in Chiang Mai. The elderly seemed to be more vulnerable to the daily changes of both air pollution and temperature
The use of machine learning to identify the correctness of HS Code for the customs import declarations
No full textAs an increasing volume of international trade activities around the world, the amount of cross-boarder import declarations grows rapidly, resulting in an unprecedented scale of potentially fraudulent transactions, in particular false commodity code (e.g., HS Code). The incorrect HS Code will cause duty risk and adversely impact the revenue collection. Physical investigation by the customs administrations is impractical due to the substantial quantity of declarations. This paper provides an automatic approach by harnessing the power of machine learning techniques to relief the burden of customs targeting officers. We introduced a novel model based on the off-the-shelf embedding encoder to identify the correctness of HS Code without any human effort. Determining whether the HS Code is correctly matched with commodity description is a classification task, so the labelled data is typically required. However, the lack of gold standard labelled data sets in customs domain limits the development of supervised-based approach. Our model is developed by the unsupervised mechanism and trained on the unlabelled historical declaration records, which is robust and able to be smoothly adapted by the different customs administrations. Rather than typically classifying whether the HS Code is correct or not, our model predicts the score to indicate the degree of the HS Code being correct. We have evaluated our proposed model on the ground-truth data set provided by Dutch customs officers. Results show promising performance of 71% overall accuracy.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Information and Communication Technolog
Multipotential Mesenchymal Stem Cells from Femoral Bone Marrow near the site of Osteonecrosis
No full textImpaired T-lymphocyte proliferation function in biliary atresia patients with chronic cholestatic jaundice after Kasai operation.
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