Impact of Immersive Training on Senior Chemical Engineering Students\u27 Prioritization of Process Safety Decision Criteria

Every year new safety features and regulations are employed within the process industry to reduce risks associated with operations. Despite these advancements chemical plants remain hazardous places, and the role of the engineer will always involve risk mitigation through real time decision making. Results from a previous study by Kongsvik et al., 2015 indicated that there were three types of decisions in major chemical plants: strategic decisions, operational decisions, and instantaneous decisions. The study showed the importance for improving upon engineers’ operational and instantaneous choices when tasked with quick solutions in the workforce. In this research study, we dive deeper to understand how senior chemical engineering students’ prioritize components of decision making such as budget, productivity, relationships, safety, and time, and how this prioritization may change as a result of participation in a digital immersive training environment called Contents Under Pressure. More specifically, we seek to address the following two research questions: (1) How do senior chemical engineering students prioritize safety in comparison to criteria such as budget, personal relationships, plant productivity, and time in a process safety context, and (2) How does senior chemical engineering students’ prioritization of decision making criteria (budget, personal relationships, plant productivity, safety, and time) change after exposure to a virtual process safety decision making environment? As part of this study, 187 senior chemical engineering students from three separate institutions completed a pre- and post-reflection survey around their engagement with Contents Under Pressure and asked them to rank their prioritizations of budget, productivity, relationships, safety, and time. Data was analyzed using descriptive statistics, and Friedman and Wilcoxon-sign-rank post hoc analyses were completed to determine any statistical differences between the rankings of decision making factors before and after engagement with Contents Under Pressure. Simulating process safety decision making with interactive educational supports may increase students’ understanding of genuine workplace environments and factors that contribute to process safety, without the real world hazards that result from poor decision making. By understanding how students prioritize these factors, chemical engineering curricula can be adapted to focus on the areas of process safety decision making where students need the largest improvement, thereby better preparing them to enter the engineering workforce

Classification of time series by shapelet transformation

Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best $k$ shapelets, which are used to produce a transformed dataset, where each of the $k$ features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselve

Nationales Fischereidatenerhebungsprogramm: Aktivitäten und Ausblick

Within the frame of the EU Data Collection Regulation (DCR), Germany is obliged since 2002 to collect basic fisheries data to support the Common Fisheries Policy. Various governmental institutions are involved in the collection of landings and effort data, biological and economic data of the German fisheries. About 200 trips on commercial fishery vessels were sampled from 2002 to 2006. Additional stock data are collected on research surveys. The landings of cod in the recreational fisheries in the North and Baltic Seas were recorded within a pilot study. In order to assess the economic situation of the fishing fleet and processing industry, economic data were collected. The collected data are being stored in a national database and being made available for scientific working groups. At present, the legal regulations within the DCR framework are being reviewed and adapted to the changing requirements of fisheries management