Effectiveness of infrared thermography in monitoring ventilation performance during cardiopulmonary resuscitation training

[EN] Providing reliable systems to assess ventilation outcomes in simulation-based scenarios is paramount to improve the performance during cardiopulmonary resuscitation (CPR) in real situations. The aim of this study is to investigate the reliability of infrared thermography (IRT) in monitoring the quality of resuscitative breaths in undergraduate nursing students during a simulated CPR-based clinical practice. We recruited a convenience sample of 21 volunteer students in the second year of the Bachelor of Nursing. Participants were instructed to perform CPR following the European Resuscitation Council guidelines in training manikins from Laerdal Medical® during two consecutive minutes. Demographic and knowledge data about CPR performance were collected with a questionnaire whilst ventilation quality parameters (volume, rate, time spent) were provided by the manikin software. Thermographic images from the manikin´s peripheral mouth region were recorded at the end of each CPR ventilation cycle. The temperature profile was examined at baseline and after 1 and 2 minutes of ventilation performance. A temperture increment of 1.357 ºC was observed when comparing the maximum temperature at minute 1 with regard to baseline, whilst a significative decrease was obtained between minute 1 and minute 2 (0.457ºC) of the study. The comparison between the number of ventilations and the temperature variation after 1 minute of CPR training produced good correlation values (rho = 0.658, p=0.0019). A positive association was also observed between IRT and t ventilation volume values (r = 0.503, p=0.02). Our results indicate that infrared thermography is a promising tool for assessing ventilation performance in CPR practice, thus enabling its potential use as predictor of the quality of resuscitative breaths in simulation-based scenarios

An ontology framework for developing platform-independent knowledge-based engineering systems in the aerospace industry

This paper presents the development of a novel knowledge-based engineering (KBE) framework for implementing platform-independent knowledge-enabled product design systems within the aerospace industry. The aim of the KBE framework is to strengthen the structure, reuse and portability of knowledge consumed within KBE systems in view of supporting the cost-effective and long-term preservation of knowledge within such systems. The proposed KBE framework uses an ontology-based approach for semantic knowledge management and adopts a model-driven architecture style from the software engineering discipline. Its phases are mainly (1) Capture knowledge required for KBE system; (2) Ontology model construct of KBE system; (3) Platform-independent model (PIM) technology selection and implementation and (4) Integration of PIM KBE knowledge with computer-aided design system. A rigorous methodology is employed which is comprised of five qualitative phases namely, requirement analysis for the KBE framework, identifying software and ontological engineering elements, integration of both elements, proof of concept prototype demonstrator and finally experts validation. A case study investigating four primitive three-dimensional geometry shapes is used to quantify the applicability of the KBE framework in the aerospace industry. Additionally, experts within the aerospace and software engineering sector validated the strengths/benefits and limitations of the KBE framework. The major benefits of the developed approach are in the reduction of man-hours required for developing KBE systems within the aerospace industry and the maintainability and abstraction of the knowledge required for developing KBE systems. This approach strengthens knowledge reuse and eliminates platform-specific approaches to developing KBE systems ensuring the preservation of KBE knowledge for the long term

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable