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Chung C.-J.,Central Taiwan University of Science and Technology | Long H.-Y.,Feng Chia University
Acta Biomaterialia

This study attempts to enhance the osseointegration of titanium implants by adopting a micro-arc treatment (MAT) capable of replacing calcium (Ca) with different percentages of strontium (Sr) in order to fabricate strontium-containing hydroxyapatite (Sr-HAp) coatings. Sr, regarded as a significant therapy promoting bone mass and bone strength, has a dual mechanism, enhancing osteoblast differentiation and inhibiting osteoclast differentiation. This study also investigates how Sr content affects the microstructure of and osteoblast/osteoclast growth on the coatings. Experimental results indicate that an increase in the Sr content in the electrolyte bath results in a greater degree of Sr substitution at Ca sites within the HAp phase, facilitating the formation of Sr-HAp coatings with Sr fully solid soluble in the HAp phase. Irrespective of the Sr content, most coatings are similar in porous morphology and pore size. Additionally, the Sr-HAp coating shows higher osteoblast compatibility than raw titanium metal and the HAp coating. Moreover, cell adhesion and proliferation after 48 h was greater than that after 4 h, indicating that Sr can stimulate osteoblast adhesion and proliferation. Further, Sr significantly inhibits osteoclast differentiation when the Sr-HAp coatings exceed 38.9 at.% Sr. © 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved. Source

Lin Y.-C.,Central Taiwan University of Science and Technology
Safety Science

Objectives: This study investigated the critical factors influencing the job involvement of emergency medical technicians (EMTs) of the Fire and Disaster Prevention and Rescue Bureau after it was restructured from the original National Fire Agency, and examined how they confronted continual massive disasters resulting from global climate changes and the demand for better quality rescue services. Methods: 645 Questionnaires were sent out and the responses analyzed. Results: Job negativism increased with the age, life experience and work experience of the EMT. Variables directly affecting job involvement included professional competency and training. In addition, organizations with 16-20 personnel showed greater dedication. However, the professional competency of EMT was not influenced by the constitutional differences of EMT service location. Conclusions: When drafting emergency policies, fire departments should outline appropriate job regulations and manpower distribution in order to increase job inducement and satisfaction, and provide regular training to boost professional competency. In addition, medical rescue budget should be increased to provide substantial logistical support such as ambulances and equipment. © 2012 Elsevier Ltd. Source

Weng C.-H.,Central Taiwan University of Science and Technology | Chen Y.-L.,National Central University
Knowledge and Information Systems

Association rule mining is an important data analysis method that can discover associations within data. There are numerous previous studies that focus on finding fuzzy association rules from precise and certain data. Unfortunately, real-world data tends to be uncertain due to human errors, instrument errors, recording errors, and so on. Therefore, a question arising immediately is how we can mine fuzzy association rules from uncertain data. To this end, this paper proposes a representation scheme to represent uncertain data. This representation is based on possibility distributions because the possibility theory establishes a close connection between the concepts of similarity and uncertainty, providing an excellent framework for handling uncertain data. Then, we develop an algorithm to mine fuzzy association rules from uncertain data represented by possibility distributions. Experimental results from the survey data show that the proposed approach can discover interesting and valuable patterns with high certainty. © 2009 Springer-Verlag London Limited. Source

Chang Y.-C.,Fong Yuan Hospital | Lo H.-H.,Central Taiwan University of Science and Technology
Diagnostic Microbiology and Infectious Disease

No literature is available on the prevalence and clinical aspects of beta-haemolytic group G Streptococcus anginosus group in central Taiwan. In this study, we used 16S rRNA gene sequencing and 16S-23S rDNA intergenic spacer sequencing (where necessary) as the gold standard for molecular identification. Twenty-seven S. anginosus group isolates were identified from 273 beta-haemolytic GGS isolates collected from patients in central Taiwan between February 2007 and August 2011. Of the 27 isolates, 22 were S. anginosus and 5 were Streptococcus constellatus. The 3 commercial methods, Rapid ID 32 Strep, API 20 Strep, and Vitek 2 GP card, identified 77.8%, 40.7%, and 37.0% of S. anginosus group isolates, respectively, with acceptable %ID or probability level. All the S. constellatus isolates possessed the lmb gene (encoding laminin-binding protein); however, none of the S. anginosus isolates possessed this gene. All the 27 isolates were susceptible to penicillin. Five S. anginosus group isolates (18.5%) were resistant to erythromycin. The resistance genes, ermB and mefA, were detected in 3 (2 S. anginosus and 1 S. constellatus) and 2 (2 S. anginosus) isolates, respectively. Pulsed field gel electrophoresis showed that most S. anginosus group isolates were genetically diverse. This is the first study to evaluate 3 commercial methods for the identification of beta-haemolytic group G S. anginosus group species, and only the Rapid ID 32 Strep system showed considerable ability. The clinical aspects, susceptibility pattern, and molecular epidemiology of beta-haemolytic group G S. anginosus group isolates from central Taiwan were also first presented. © 2013 Elsevier Inc. Source

Weng C.-H.,Central Taiwan University of Science and Technology
Knowledge-Based Systems

Association rule mining is an important data analysis method for the discovery of associations within data. There have been many studies focused on finding fuzzy association rules from transaction databases. Unfortunately, in the real world, one may have available relatively infrequent data, as well as frequent data. From infrequent data, we can find a set of rare itemsets that will be useful for teachers to find out which students need extra help in learning. While the previous association rules discovery techniques are able to discover some rules based on frequency, this is insufficient to determine the importance of a rule composed of frequency-based data items. To remedy this problem, we develop a new algorithm based on the Apriori approach to mine fuzzy specific rare itemsets from quantitative data. Finally, fuzzy association rules can be generated from these fuzzy specific rare itemsets. The patterns are useful to discover learning problems. Experimental results show that the proposed approach is able to discover interesting and valuable patterns from the survey data. © 2010 Elsevier B.V. All rights reserved. Source

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