An ontology for integrated machining and inspection process planning focusing on resource capabilities

The search for and assignment of resources is extremely important for the efficient planning of any process in a distributed environment, such as the collaborative product integrated development process. These environments require a degree of semantic interoperability, which currently can only be provided by ontological models. However, the ontological proposals centred on Resources for Machining and nspection Process Planning have a limited reach, do not adopt a unified view of machining and inspection, and fail to express knowledge in the manner required by some of the planning tasks, as is the case with those concerned with resource assignment and plan validation. With the aim of providing a solution to these shortcomings the manufacturing and inspection resource capability (MIRC) ontology has been developed, as a specialist offshoot of the product and processes development resources capability ontology. This ontology considers resource capabilities to be a characteristic of the resource executing any activity present in an integrated process plan. Special attention is given to resource preparation activities, due to their influence on the quality of the final product. After describing the MIRC ontology, a case study demonstrates how the ontology supports the process planning for any level, approach or plan strategy.This work has been possible thanks to the funding received from the Spanish Ministry of Science and Education through the COAPP Research Project [reference DPI2007-66871-C02-01/02].Solano García, L.; Romero Subirón, F.; Rosado Castellano, P. (2016). An ontology for integrated machining and inspection process planning focusing on resource capabilities. International Journal of Computer Integrated Manufacturing. 29(1):1-15. doi:10.1080/0951192X.2014.1003149S11529

Embedding nursing interventions into the World Health Organization’s International Classification of Health Interventions (ICHI)

Objective: The International Classification of Health Interventions (ICHI) is currently being developed. ICHI seeks to span all sectors of the health system. Our objective was to test the draft classification’s coverage of interventions commonly delivered by nurses, and propose changes to improve the utility and reliability of the classification for aggregating and analyzing data on nursing interventions. Materials and methods: A two-phase content mapping method was used: (1) three coders independently applied the classification to a data set comprising 100 high-frequency nursing interventions; (2) the coders reached consensus for each intervention and identified reasons for initial discrepancies. Results: A consensus code was found for 80 of the 100 source terms: for 34% of these the code was semantically equivalent to the source term, and for 64% it was broader. Issues that contributed to discrepancies in Phase 1 coding results included concepts in source terms not captured by the classification, ambiguities in source terms, and uncertainty of semantic matching between ‘action’ concepts in source terms and classification codes. Discussion: While the classification generally provides good coverage of nursing interventions, there remain a number of content gaps and granularity issues. Further development of definitions and coding guidance is needed to ensure consistency of application. Conclusion: This study has produced a set of proposals concerning changes needed to improve the classification. The novel method described here will inform future health terminology and classification content coverage studies

Understanding Uncertainties in Thermographic Imaging

7 p.The present article proposes a workflow based on free/open-source software solutions for the acquisition of competences in engineering courses related to the use of thermographic images. The approach is aimed to three-dimensional visualization techniques over thermographic images to improve the comprehension and interpretation of the different error sources that affects the measurements, and therefore the conclusions and analysis derived from them. The present work is framed inside the virtual laboratories discipline, as the new learning material can be employed for the acquisition of competences and skills. Additionally, it can be used for the evaluation of competences in asynchronous and e-learning programs. The learning materials could be easily deployed in a learning management system, allowing the students to work with the models by means of open-source solutions easily, both in asynchronous and face-to-face courses. Consequently, the present approach will improve the application of professional techniques, so the future professionals will reach the working market better prepared.S

Mapping the Influence of Food Waste in Food Packaging Environmental Performance Assessments

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149217/1/jiec12743-sup-0001-SuppInfoS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149217/2/jiec12743.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149217/3/jiec12743_am.pd

Aqua regia extractable selenium concentrations of some Scottish topsoils measured by ICP-MS and the relationship with mineral and organic soil components

The definitive version is available at www3.interscience.wiley.com.Peer reviewedPreprin

Defining complementary tools to the IVI. The Infrastructure Degradation Index (IDI) and the Infrastructure Histogram (HI)

[EN] The Infrastructure Value Index (IVI) is quickly becoming a standard as a valuable tool to quickly assess the state of urban water infrastructure. However, its simple nature (as a single metric) can mask some valuable information and lead to erroneous conclusions. This paper introduces two complementary tools to IVI: The Infrastructure Degradation Index (IDI) and the Infrastructure Histogram (HI). The IDI is focused on time (compared to the IVI, focused on value), represents an intuitive concept and behaves in a linear way. The joint analysis of IVI and IDI provides results in a more complete understanding of the state of the assets, while maintaining the simplicity of the tools. The Infrastructure Histogram allows for a full evaluation of the infrastructure state and provides a detailed picture of network age compared to its expected life, as well as an order of magnitude of the required investments in the following years.Cabrera Rochera, E.; Estruch-Juan, ME.; Gomez Selles, E.; Del Teso-March, R. (2019). Defining complementary tools to the IVI. The Infrastructure Degradation Index (IDI) and the Infrastructure Histogram (HI). Urban Water Journal. 16(5):343-352. https://doi.org/10.1080/1573062X.2019.1669195S343352165Alegre, H., Vitorino, D., & Coelho, S. (2014). Infrastructure Value Index: A Powerful Modelling Tool for Combined Long-term Planning of Linear and Vertical Assets. Procedia Engineering, 89, 1428-1436. doi:10.1016/j.proeng.2014.11.469Amaral, R., Alegre, H., & Matos, J. S. (2016). A service-oriented approach to assessing the infrastructure value index. Water Science and Technology, 74(2), 542-548. doi:10.2166/wst.2016.250Aware-p.org. 2014. “AWARE-P/Software.” Accessed 25 November 2018. http://www.aware-p.org/np4/software/Baseform. 2018. “Baseform.” Accessed 24 November 2018. https://baseform.com/np4/productCanal de Isabel II Gestión. 2012. Normas Para Redes de Abastecimiento. [Standards for Water Supply Networks.]. https://www.canalgestion.es/es/galeria_ficheros/pie/normativa/normativa/Normas_redes_abastecimiento2012_CYIIG.pdfFrost, and Sullivan. 2011. “Western European Water and Wastewater Utilities Market.” https://store.frost.com/western-european-water-and-wastewater-utilities-market.html#section1Leitão, J. P., Coelho, S. T., Alegre, H., Cardoso, M. A., Silva, M. S., Ramalho, P., … Carriço, N. (2014). Moving urban water infrastructure asset management from science into practice. Urban Water Journal, 13(2), 133-141. doi:10.1080/1573062x.2014.939092Marchionni, V., Cabral, M., Amado, C., & Covas, D. (2016). Estimating Water Supply Infrastructure Cost Using Regression Techniques. Journal of Water Resources Planning and Management, 142(4), 04016003. doi:10.1061/(asce)wr.1943-5452.0000627Marchionni, V., Lopes, N., Mamouros, L., & Covas, D. (2014). Modelling Sewer Systems Costs with Multiple Linear Regression. Water Resources Management, 28(13), 4415-4431. doi:10.1007/s11269-014-0759-zPulido-Velazquez, M., Cabrera Marcet, E., & Garrido Colmenero, A. (2014). Economía del agua y gestión de recursos hídricos. Ingeniería del agua, 18(1), 95. doi:10.4995/ia.2014.3160Rokstad, M. M., Ugarelli, R. M., & Sægrov, S. (2015). Improving data collection strategies and infrastructure asset management tool utilisation through cost benefit considerations. Urban Water Journal, 13(7), 710-726. doi:10.1080/1573062x.2015.102469