University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Population & Control theory. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

The prevalence of autism spectrum disorders in China: a comprehensive meta-analysis

Abstract: There are conflicting prevalence estimates of autism spectrum disorders (ASDs) in mainland China (China thereafter). This study is a comprehensive meta-analysis of the pooled prevalence of ASDs in the general population in China. Study investigators independently conducted a systematic literature search of the following databases: PubMed, EMBASE, PsycINFO, China National Knowledge Infrastructure, Chinese biomedical literature service system, and Wan Fang. Studies reporting prevalence of ASDs and autism in Chinese population were identified and analysed using the Comprehensive Meta-Analysis program with the random effects model. Forty-four studies were included in the meta-analysis comprising 2,337,321 subjects of whom 46.66 % were females. The mean age of subjects ranged from 1.6 to 8 years. Based on diagnostic criteria the pooled prevalence of ASDs was 39.23 per 10,000 (95% CI: 28.44-50.03 per 10,000, I2=89.2%); specifically, the prevalence of autism was 10.18 per 10,000 (95% CI: 8.46-11.89 per 10,000, I2=92.5%). Subgroup analyses revealed significant difference in the prevalence of ASDs between genders (72.77 per 10,000 in males vs. 16.45 per 10,000 in females). In conclusion, the prevalence of ASDs and autism in China was found generally lower than those reported in other countries. Further studies are needed to clarify the variation in prevalence.

A Novel ECG Data Compression Method Using Adaptive Fourier Decomposition With Security Guarantee in e-Health Applications

Abstract: This paper presents a novel electrocardiogram (ECG) compression method for e-health applications by adapting an adaptive Fourier decomposition (AFD) algorithm hybridized with a symbol substitution (SS) technique. The compression consists of two stages: first stage AFD executes efficient lossy compression with high fidelity; second stage SS performs lossless compression enhancement and built-in data encryption, which is pivotal for e-health. Validated with 48 ECG records from MIT-BIH arrhythmia benchmark database, the proposed method achieves averaged compression ratio (CR) of 17.6–44.5 and percentage root mean square difference (PRD) of 0.8–2.0% with a highly linear and robust PRD-CR relationship, pushing forward the compression performance to an unexploited region. As such, this paper provides an attractive candidate of ECG compression method for pervasive e-health applications.

Improving Multilingual Semantic Interoperation in Cross-Organizational Enterprise Systems Through Concept Disambiguation

Abstract: For the multilingual semantic interoperations in cross-organizational enterprise systems and e-commerce systems, semantic consistency is a research issue that has not been well resolved. This paper contributes to improving multilingual semantic interoperation by proposing a concept-connected near synonym (NSG) framework for concept disambiguation. NSG framework provides a vocabulary preprocessing process of collaborative vocabulary editing, which further ensures semantically consistent vocabulary for building semantically consistent business processes and documents between context-different information systems. The vocabulary preprocessing offered by NSG automates the process of finding potential near synonym sets and identifying collaboratively editable near synonym sets. The realization of NSG framework includes a probability model that computes concept values between concepts based on a newly introduced semantic relatedness method-SRCT. In this paper, SRCT-based methods are implemented and compared with some existing semantic relatedness methods. Experiments have shown that SRCT-based methods outperform the existing methods. This paper has made an improvement on the existing methods of semantic relatedness and reduces the collaboration cost of collaborative vocabulary editing.