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This paper proposes a functional modelling method, called Multilevel Flow Modelling (MFM) for identification of safety objectives and functions of emergency shutdown (ESD) system in the design phase for emergency shutdown safely. Firstly, the required information for designing safety objectives and functions for an emergency shutdown is analysed. The information includes process topology, the initial state of the process, the shutdown objectives, and other constraints, such as design and operational limits of unit operations and hazardous and environmental constraints. Secondly, a procedure is proposed based on the required information by using MFM. The procedure incorporates steps: 1) The MFM model of the process in normal operations are built by following modelling procedures, 2) Initiate states are defined and changing conditions are identified of the process when it shuts down unplanned by using the objective-function tree in the MFM modelling, 3) The goals of the shutdown operation are defined, 4) With the consideration of the shutdown operating goals and changing conditions, the first safety function during shutdown process are identified by using MFM causal reasoning, 5) By using MFM consequence reasoning based on the first identified safety function represented in the MFM model, the rest of the safety functions are identified, and the emergency shutdown procedures are generated. This procedure is demonstrated by designing safety functions in an emergency shut down of a seawater deaeration process in a seawater injection system. The results show that the produced emergency shut down procedure based on the proposed method is feasible and that it can be validated against the real operating procedure.

This paper presents a real-time joint trajectory interpolation system for the purpose of frequency scaling the low cycle time of a robot controller, allowing a Python application to real-time control the robot at a moderate cycle time. Interpolation is based on quintic Hermite piece-wise splines. The splines are calculated in real-time, in a piecewise manner between the high-level, long cycle time trajectory points, while sampling of these splines at an appropriate, shorter cycle time for the real-time requirement of the lower-level system. The principle is usable in general, and the specific implementation presented is for control of the Panda robot from Franka Emika. Tracking delay analysis is presented based on a cosine trajectory. A simple test application has been implemented, demonstrating real-time feeding of a pre-calculated trajectory for cutting with a knife. Estimated forces on the robot wrist are recorded during cutting and presented in the paper.

Manufacturing of large steel tube structures is faced with excessive welding, fit-up and rework times in tube joints, due to various types of deviation from nominal shape of the tubes. This article presents a procedure and geometry calculus for generating cutting and welding paths based on measured geometries. The procedure poses the two measured meshes as per construction specification and invokes a mesh intersection procedure to get the mesh intersection path; performs an optional smoothing; interpolates the smoothed path to a specified angular resolution; estimates the two surface normal vectors and the two surface tangents in the plane spanned by the normals at each interpolation point; calculates the cutting tool and welding tool approach directions for obtaining the specified welding groove geometry at each interpolation point; and finally stores all the data parameterized by the interpolation angle. Illustrations of results with both synthetic, representative meshes and meshes obtained from scanning of actual tubes at the shop-floor at a manufacturer are presented. The reference implementation for the developed software tool is based on Python and uses the mesh modeller from the 3D creation suite Blender as platform.

Abstract Background Risk stratification of patients in the emergency department can be strengthened using prognostic biomarkers, but the impact on patient prognosis is unknown. The aim of the TRIAGE III trial was to investigate whether the introduction of the prognostic and nonspecific biomarker: soluble urokinase plasminogen activator receptor (suPAR) for risk stratification in the emergency department reduces mortality in acutely admitted patients. Methods The TRIAGE III trial was a cluster-randomized interventional trial conducted at emergency departments in the Capitol Region of Denmark. Eligible hospitals were required to have an emergency department with an intake of acute medical and surgical patients and no previous access to suPAR measurement. Three emergency departments were randomized; one withdrew shortly after the trial began. The inclusion period was from January through June of 2016 consisting of twelve cluster-periods of 3-weeks alternating between intervention and control and a subsequent follow-up of ten months. Patients were allocated to the intervention if they arrived in interventional periods, where suPAR measurement was routinely analysed at arrival. In the control periods suPAR measurement was not performed. The main outcome was all-cause mortality 10 months after arrival of the last patient in the inclusion period. Secondary outcomes included 30-day mortality. Results The trial enrolled a consecutive cohort of 16,801 acutely admitted patients; all were included in the analyses. The intervention group consisted of 6 cluster periods with 8900 patients and the control group consisted of 6 cluster periods with 7901 patients. After a median follow-up of 362 days, death occurred in 1241 patients (13.9%) in the intervention group and in 1126 patients (14.3%) in the control group. The weighted Cox model found a hazard ratio of 0.97 (95% confidence interval, 0.89 to 1.07; p = 0.57). Analysis of all subgroups and of 30-day all-cause mortality showed similar results. Conclusions The TRIAGE III trial found no effect of introducing the nonspecific and prognostic biomarker suPAR in emergency departments on short- or long-term all-cause mortality among acutely admitted patients. Further research is required to evaluate how prognostic biomarkers can be implemented in routine clinical practice. Trial registration clinicaltrials.gov, NCT02643459. Registered 31 December 2015.

Decision support systems are a key focus of research on developing control rooms to aid operators in making reliable decisions and reducing incidents caused by human errors. For this purpose, models of complex systems can be developed to diagnose causes or consequences for specific alarms. Models applied in safety systems of complex and safety-critical systems require rigorous and reliable model building and testing. Multilevel flow modeling is a qualitative and discrete method for diagnosing faults and has previously only been validated by subjective and qualitative means. To ensure reliability during operation, this work aims to synthesize a procedure to measure model performance according to diagnostic requirements. A simple procedure is proposed for validating and evaluating the concept of multilevel flow modeling. For this purpose, expert statements, dynamic process simulations, and pilot plant experiments are used for validation of simple multilevel flow modeling models of a hydrocyclone unit for oil removal from produced water. Keywords: Fault Diagnosis, Model Validation, Multilevel Flow Modeling, Produced Water Treatment

With the expansion of robotic applications in the industrial domain, it is important that the robots can execute their tasks in a safe and reliable way. A monitoring system can be implemented to ensure the detection of abnormal situations of the robots and report the abnormality to their human supervisors or cooperators. In this work, we focus on developing a modeling framework for monitoring robotic system based on means-end analysis and the concept of action phases from action theory. A circular cascaded action phase structure is proposed for building the model of cyclical robotic events. This functional model provide a formal way of decompose robotic tasks and analyze each level of conditions for an action to be executed successfully. It can be used for monitoring robotic systems by checking the preconditions in the action phases and identifying the failure modes. The proposed method is demonstrated by using a simulated robotic manipulation system. The simulation results demonstrate the feasibility of the developed functional model in finding errors during the execution monitoring.

INTRODUCTION:Triage systems with limited room for clinical judgment are used by emergency departments (EDs) worldwide. The Copenhagen Triage Algorithm (CTA) is a simplified triage system with a clinical assessment. METHODS:The trial was a non-inferiority, two-center cluster-randomized crossover study where CTA was compared to a local adaptation of Adaptive Process Triage (ADAPT). CTA involves initial categorization based on vital signs with a final modification based on clinical assessment by an ED nurse. We used 30-day mortality with a non-inferiority margin at 0.5%. Predictive performance was compared using Receiver Operator Characteristics. RESULTS:We included 45,347 patient visits, 23,158 (51%) and 22,189 (49%) were triaged with CTA and ADAPT respectively with a 30-day mortality of 3.42% and 3.43% (P = 0.996) a difference of 0.01% (95% CI: -0.34 to 0.33), which met the non-inferiority criteria. Mortality at 48 hours was 0.62% vs. 0.71%, (P = 0.26) and 6.38% vs. 6.61%, (P = 0.32) at 90 days for CTA and ADAPT. CTA triaged at significantly lower urgency level (P

The paper presents functional modelling and its application for diagnosis in nuclear power plants. Functional modelling is defined and its relevance for coping with the complexity of diagnosis in large scale systems like nuclear plants is explained. The diagnosis task is analyzed and it is demonstrated that the levels of abstraction in models for diagnosis must reflect plant knowledge about goals and functions which is represented in functional modelling. Multilevel flow modelling (MFM), which is a method for functional modelling, is introduced briefly and illustrated with a cooling system example. The use of MFM for reasoning about causes and consequences is explained in detail and demonstrated using the reasoning tool, the MFMSuite. MFM applications in nuclear power systems are described by two examples: a PWR; and an FBR reactor. The PWR example show how MFM can be used to model and reason about operating modes. The FBR example illustrates how the modelling development effort can be managed by proper strategies including decomposition and reuse.

Regeringen, Kommunernes Landsforening og Danske Regioner indgik i efteråret 2012 aftalen om "Gode grunddata til alle - en kilde til vækst og effektivisering". Grunddataprogrammet blev dermed født, og Danmark fik en officiel og autoritativ infrastrukturmodel for geografisk information. Arbejdet har kørt parallelt med implementeringen af INSPIRE-direktivet, og selvom de to udviklingsarbejder har haft hvert deres bane, har der været og er stadig god synergi mellem de to infrastruktur-arbejder. Artiklen præsenterer grunddataprogrammet og gør status på implementeringen heraf.

In the autumn of 2012, the Danish government, Local Government Denmark and Danish Regions signed the agreement about 'Good basic data for everyone – a source of growth and efficiency'. The Basic Data Programme was born, and Denmark had an official and authoritative infrastructure model for spatial information. Work has run in parallel to the implementation of the INSPIRE Directive, and although the two development works have had each their trajectory, there has been, and still is, good synergy between the two infrastructure projects. This article presents the Basic Data Programme and takes stock of the implementation of the same.

Abstract Background We previously performed a pragmatic cluster randomized controlled trial (RCT) in general practices and older adult care organizations in Poland, the Netherlands, Norway, and Sweden. We found that a multifaceted antibiotic stewardship intervention (ASI) substantially reduced antibiotic use for suspected urinary tract infections (UTIs) in frail older adults compared with usual care. We aimed to evaluate the implementation process of the ASI to provide recommendations for clinical practice. Methods We conducted a process evaluation alongside the cluster RCT. The ASI consisted of a decision-tool and a toolbox, which were implemented using a participatory-action-research (PAR) approach with sessions for education and evaluation. We documented the implementation process of the intervention and administered a questionnaire to health care professionals (HCPs) from participating organizations in the intervention and usual care clusters. We evaluated the multiple components of the intervention and its implementation following a structured framework. Results The questionnaire was completed by 254 HCPs from the 38 participating clusters. All components were largely delivered according to plan and evaluated as useful. The decision-tool and toolbox materials were reported to facilitate decision-making on UTIs. Regarding the PAR approach, educational sessions focusing on the distinction between UTIs and asymptomatic bacteriuria were held in all 19 intervention clusters. In 17 out of these 19 clusters, evaluation sessions took place, which were reported to help remind HCPs to implement the ASI. During both sessions, HCPs valued the reflection that took place and the resulting awareness of their behavior. It allowed them to explore implementation barriers and to tailor their local implementation process to overcome these. For example, HCPs organized extra educational sessions or revised local policies to incorporate the use of the decision-tool. Various HCPs took key roles in implementation. Staff changes and the COVID-19 pandemic were important contextual barriers. Conclusions We found each component of the multifaceted ASI and its implementation to have added value in the process to improve antibiotic prescribing for suspected UTIs in a heterogeneous older adult care setting. We recommend using a multifaceted, multidisciplinary approach that enables HCPs to reflect on their current practice and accordingly tailor local implementation. Trial registration ClinicalTrials.gov NCT03970356. Registered on May 31, 2019.