This paper proposes a new safe measurement-based estimation method for Worst-Case Execution Time (WCET) of programs in real-time systems. The latest progress in Pattern Recognition of learning to detect unseen object classes by between-class attribute transfer has been used for automatic test-data generation in our method. Based on control flow graph partition, execution profiles of each basic block and probabilities of their executions can be extracted during program executions driven by test data. Afterwards, a critical path can be identified by calculating its execution probability among all feasible paths. With measurement for critical paths, WCET can be obtained by adding static analysis of hardware features to measurement results. The objective of this paper is not to present finished or almost finished work. Instead we hope to trigger discussion and solicit feedback from the community in order to avoid pitfalls experienced by others and to help focus our research.

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BackgroundPubMed is a widely used database for scientists to find biomedical-related literature. Due to the complexity of the selected research subject and its interdisciplinary nature, as well as the exponential growth in the number of disparate pieces of biomedical literature, it is an overwhelming challenge for scientists to define the right search strategies and quickly locate all related information. Specialized subsets and groupings of controlled vocabularies, such as Medical Subject Headings (MeSH), can enhance information retrieval in specialized domains, such as stem cell research. There is a need to develop effective search strategies and convenient solutions for knowledge organization in stem cell research. The understanding of the interrelationships between these MeSH terms also facilitates the building of knowledge organization systems in related subject fields.MethodsThis study collected empirical data for MeSH-related terms from stem cell literature and developed a novel approach that uses both automation and expert-selection to create a set of terms that supports enhanced retrieval. The selected MeSH terms were reconstructed into a classified thesaurus that can guide researchers towards a successful search and knowledge organization of stem cell literature.ResultsFirst, 4253 MeSH terms were harvested from a sample of 5527 stem cell related research papers from the PubMed database. Next, unrelated terms were filtered out based on term frequency and specificity. Precision and recall measures were used to help identify additional valuable terms, which were mostly non-MeSH terms. The study identified 15 terms that specifically referred to stem cell research for information retrieval, which would yield a higher precision (97.7 %) and recall (94.4 %) rates in comparison to other approaches. In addition, 128 root MeSH terms were selected to conduct knowledge organization of stem cell research in categories of anatomy, disease, and others.ConclusionsThis study presented a novel strategy and procedure to reengineer term selections of the MeSH thesaurus for literature retrieval and knowledge organization using stem cell research as a case. It could help scientists to select their own search terms and build up a thesaurus-based knowledge organization system in interested and interdisciplinary research subject areas.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0298-z) contains supplementary material, which is available to authorized users.

As the visual storage of objective things, images are usually used everywhere in the field of electronic information. Images of objects are collected or drawn to make electronic image files, which are stored into image files, and may be classified into different folders. Image users, such as accounting, HR and so on, often need to reprocess the images and save them into a word file when they use the images. Such manual processing of images is time costing and labor costing. In this paper, a method of automatically inserting and processing images is proposed and implemented. In the method, the sequence of inserting files is recorded in an excel file. Only the name of image file and the excel file need to be maintained. It is convenient to use the method to process images, and the method can be implemented automatically. So it is an effective way to help staffs to edit and insert images.

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