RESUMEN
Spray combustion is important for different engines. The understanding of spray combustion should be further promoted especially in the non-dilute region, and there is lack of well-defined spray experiments. In this study, an experimental platform was developed. Using this platform, a cylindrical quasi-laminar spray can be formed and ignited by a thin and straight hot wire, making it a simple configuration. Two image diagnostic methods were also developed to capture in situ microscopic droplet images and macroscopic droplet-flame images synchronously. Different image analysis algorithms were developed to obtain droplet statistics (diameter, velocity, and number density) and flame information (size, location, and flame propagation speed) from the raw images. The design, diagnostic methods, and image analysis methods are detailedly presented. This experimental platform can cover a wide range of operating conditions, with Gig in a range of 0.01-0.06 and temperature in a range from room temperature to 1400 K. In addition, this platform is small in size and is capable of further implanting into a ground-based microgravity facilitaty. The whole experimental system can be applied in spray ignition and combustion studies and can provide legitimate data for further model development.
RESUMEN
BACKGROUND: The exploration of cancer vaccines has yielded a multitude of studies, resulting in a diverse collection of information. The heterogeneity of cancer vaccine data significantly impedes effective integration and analysis. While CanVaxKB serves as a pioneering database for over 670 manually annotated cancer vaccines, it is important to distinguish that a database, on its own, does not offer the structured relationships and standardized definitions found in an ontology. Recognizing this, we expanded the Vaccine Ontology (VO) to include those cancer vaccines present in CanVaxKB that were not initially covered, enhancing VO's capacity to systematically define and interrelate cancer vaccines. RESULTS: An ontology design pattern (ODP) was first developed and applied to semantically represent various cancer vaccines, capturing their associated entities and relations. By applying the ODP, we generated a cancer vaccine template in a tabular format and converted it into the RDF/OWL format for generation of cancer vaccine terms in the VO. '12MP vaccine' was used as an example of cancer vaccines to demonstrate the application of the ODP. VO also reuses reference ontology terms to represent entities such as cancer diseases and vaccine hosts. Description Logic (DL) and SPARQL query scripts were developed and used to query for cancer vaccines based on different vaccine's features and to demonstrate the versatility of the VO representation. Additionally, ontological modeling was applied to illustrate cancer vaccine related concepts and studies for in-depth cancer vaccine analysis. A cancer vaccine-specific VO view, referred to as "CVO," was generated, and it contains 928 classes including 704 cancer vaccines. The CVO OWL file is publicly available on: http://purl.obolibrary.org/obo/vo/cvo.owl , for sharing and applications. CONCLUSION: To facilitate the standardization, integration, and analysis of cancer vaccine data, we expanded the Vaccine Ontology (VO) to systematically model and represent cancer vaccines. We also developed a pipeline to automate the inclusion of cancer vaccines and associated terms in the VO. This not only enriches the data's standardization and integration, but also leverages ontological modeling to deepen the analysis of cancer vaccine information, maximizing benefits for researchers and clinicians. AVAILABILITY: The VO-cancer GitHub website is: https://github.com/vaccineontology/VO/tree/master/CVO .