Search

The early diagnosis and treatment of foreign body aspiration (FBA) can significantly improve the overall prognosis of children. There are significant differences in the epidemiology and clinical characteristics of FBA in different regions. Therefore, we conducted a real-world study in the western region of China with over 4000 patients. The aim of this study was to improve the understanding of FBA in terms of its types, the specific months of its occurrence, and the distribution of primary caregiver characteristics in western China. We collected the clinical and epidemiological data of children who were diagnosed with FBA in our hospital over the past 20 years through a big data centre. We matched the data of healthy children who underwent routine physical examinations at the paediatric health clinic during the same period to analyse the differences in the data of actual guardians. A total of 4227 patients from five provinces were included in this study. Foreign bodies were removed by rigid bronchoscopy in 99.4% (4202/4227) of patients, with a median age of 19 months and a median surgical duration 16 min. January was the most common month of onset for 1725 patients, followed by February, with 1027 patients. The most common types of foreign objects were melon peanuts, seeds and walnuts, accounting for 47.2%, 15.3%, and 10.2%, respectively. In the FBA group, the proportion of grandparents who were primary caregivers was 70.33% (2973/4227), which was significantly greater than the 63.05% in the healthy group (2665/4227) (P < 0.01). FBA most commonly occurs in January and February. More than 60% of FBAs occur between the ages of 1 and 2 years, and the incidence of FBA may be greater in children who are cared for by grandparents. A rigid bronchoscope can be used to remove most aspirated foreign bodies in a median of 16 min. [ABSTRACT FROM AUTHOR]

In today's economy, information technology (IT) is vitally important, and the increasing use of the Internet, telecommunications services, and internal IT networks in organizations have led to rapid growth in the demands on big data processing. In general, site selection is a fundamental part of the design of a big data center (BDC), and a poor site decision can affect the sustainability of the facility. To construct a comprehensive assessment framework for a BDC, the following three categories of indicators are determined based on the "Specification for Design of Data Center" in GB50174-2017 of China: economic factors, natural climate environment factors, and energy resources factors. After explaining the rationality of choosing these indicators in detail, an integrated method that combines the multi-criteria decision-making (MCDM) method and the multi-choice goal programming (MCGP) model is proposed. The proposed approach uses two phases to conduct the decision procedure. First, the preference ranking organization method for enrichment evaluation (PROMETHEE) method is applied to evaluate the economic factors. Then, the evaluation results are added to the MCGP model as one of the goals of multi-objective programming. Second, the remaining five sub-indicators and the evaluation results generated from the first phase are formulated as a complete MCGP model. Finally, an empirical study on the site selection for the BDC is implemented based on the proposed method. The result shows that Guiyang is the most suitable place for locating a BDC in China. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the era of big data astronomy, next generation telescopes and large sky surveys produce data sets at the TB or even PB level. Due to their large data volumes, these astronomical data sets are extremely difficult to transfer and analyze using personal computers or small clusters. In order to offer better access to data, data centers now generally provide online science platforms that enable analysis close to the data. The Chinese Virtual Observatory (China-VO) is one of the member projects in the International Virtual Observatory Alliance and it is dedicated to providing a research and education environment where globally distributed astronomy archives are simple to find, access, and interoperate. In this study, we summarize highlights of the work conducted at the China-VO, as well the experiences and lessons learned during the full life-cycle management of astronomical data. Finally, We discuss the challenges and future trends for astronomical science platforms. [ABSTRACT FROM AUTHOR]

We aim to establish National Big Data Centre of Respiratory Disease in China. The big data platform has supported research management, advanced data filters, data analysis, data demonstration, image identification, machine learning, medical record quality control, multi-centre collaboration, and et al. National Big Data Centre of Respiratory Disease in China was established initially, which could support the implementation and landing of major national projects in a next few years. [Extracted from the article]