Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

Facilitation of learning in specialist nursing training in the PICU : the supervisors´ concerns in the learning situation

With the aim to unfold nurses’ concerns of the supervision of the student in the clinical caring situation of the vulnerable child, clinical nurses situated supervision of postgraduate nursing students in the Pediatric Intensive Care Unit (PICU) are explored. A qualitative approach, interpretive phenomenology, with participant observations and narrative interviews, was used. Two qualitative variations of patterns of meaning for the nurses’ clinical facilitation were disclosed in this study. Learning by doing theme supports the students learning by doing through performing skills and embracing routines. The reflecting theme supports thinking and awareness of the situation. As the supervisor often serves as a role model for the student this might have an immediate impact on how the student applies nursing care in the beginning of his or her career. If the clinical supervisor narrows the perspective and hinders room for learning the student will bring less knowledge from the clinical education than expected, which might result in reduced nursing quality.