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Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems' instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support. (c) 2021 The Author(s). Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.

Emergency response planning associated with written procedures play a key role in the process safety to ensure that accident can be properly managed. Although the extensive use of digital techniques have significantly reduced the accident risk by early warnings and operation guidance, the Fukushima nuclear disaster reminds us that the unexpected events may still exceed existing system capability to indicate appropriate responding measures. A novel digitalized approach is hence required for planning decision support. This article presents a joint utilizing of two modeling and analysis methodologies, which are complementary to each other to provide comprehensive insights for decision-making in the accident emergency response. Multilevel Flow Modeling (MFM) is applied to develop an intelligent reasoning system, which can harness plant’s qualitative functional knowledge to search available equipments and corresponding action sequences potentially being able to manage the accident. Go-Flow is a success-oriented reliability analysis method specially suitable for quantitative risk assessment in the situations involving configuration changes. The underlying configuration of each MFM-driven emergency response measure will update the understanding of goal achievement and modify the Go-Flow chart, by which plan’s effectiveness of risk reduction can be evaluated. Moreover, Go-Flow is also used to conduct the risk matrix assessment for identifying critical components, which can provide a reference for effective assignment of resources in the long-term accident management. This work is expected to strengthen operator’s ability of emergency response planning from perspective of both measure development and optimization. An accident scenario similar to what occurred in the Fukushima Daiichi nuclear power plant has been used to demonstrate the application of the propose approach.

Isolation of abnormal changes in process variables is an integral component of fault diagnosis, as it provides evidential information for determining the root cause of a detected abnormal event. This task is challenging when the approach to diagnosis does not incorporate knowledge of the process’ nominal behavior, but is instead established solely on historical process data. Though isolation of abnormal changes in variables may be facilitated by including historical process data for faults that have been previously diagnosed, inconclusive results will remain for unfamiliar faults. This paper presents a method for isolating abnormal changes in process variables with an autoencoder (AE) - a type of neural network configured for latent projection — and without prior knowledge of nominal process behavior or faults. The AE is optimized with nominal process data as well as a sparsity constraint to produce a sparse network. Probing into the sparse AE allows one to gain insight into the correlations that exist among the process variables during normal process operation. Movements in the AE’s reconstruction space are interrogated alongside the acquired knowledge to isolate the abnormal changes in process variables. The method is demonstrated with a simulation of a nonlinear triple tank process, and is shown to isolate abnormal changes in variables for both simple and complex faults.

The aim of the present study was to investigate the relationship between bicycle helmet use and safety behavior at signalized intersections. Two hypotheses were investigated: The first states that bicycle helmet use leads to risker behavior because of the increased sense of protection (risk compensation), the other states that helmeted cyclists have a general inclination toward safer behavior (safety package) and that helmet use is one of several behaviors for improving safety.Based on video recordings of 1031 cyclists at 12 signalized intersections in Denmark, two indicators of risky behavior were compared between helmeted and unhelmeted cyclists: Speed and time after the onset of yellow at which the cyclists crossed the stop line. Linear regression models were developed with gender, type of bicycle, and intersection characteristics as predictor variables, in addition to helmet use.Helmeted and unhelmeted cyclists do not differ in how many seconds after the onset of yellow they cross the stopping line. This is consistent with the absence of both risk compensation and safety package, alternatively with a general inclination of helmeted cyclists toward safer behavior which is about offset by risk compensation. Helmeted cyclists had higher speed on average, which indicates that risk compensation may occur. However, the higher speed may also be due to the generally better fitness of helmeted cyclists which is likely to result from larger cycling volumes. Moreover, the effect of helmet use on speed may be overestimated because of a lack of control for potential confounding variables. The results show further that, regardless of helmet use, before-red (lights on a separate bicycle signal shift to red before the main signal) is related to later crossings of the stop lane after the onset of yellow and that cyclists stop earlier on average at intersections with right-turn signals.The results do not provide support for the position that promoting or mandating bicycle helmet use will have adverse safety effects because of more risky behavior among helmeted cyclists.

Continuous glucose monitoring (CGM) has revolutionized the world of diabetes and transformed the approach to diabetes care. In this context, an expert panel has reached consensus on clinical targets for CGM data interpretation based on eight CGM metrics. At least 70% of 14 consecutive CGM days (referred to as a period) are recommended to assess glycemic control based on the metrics. In clinical practice less CGM data may be available. Therefore, the primary aim of this study is to explore the ability to recover the consensus metrics utilizing less than 14 days of CGM data (intra-period). As a secondary aim, we investigate the recovery considering two consecutive periods (inter-period). The analyses are based on real-world CGM data from 484 diabetes users (4726 periods) acquired from the Cornerstones4Care® Powered by Glooko app. Using up to 14 accumulated days, the consensus metrics are calculated for each user and period, and compared to the fully 14 accumulated intra- and inter-period days. Relatively low deviations were observed for time in range (TIR) and average based metrics when using less than 14 days, however, we observed large deviations in metrics characterizing infrequent events such as time below range (TBR). Furthermore, the consensus metrics obtained in two consecutive 14 day periods have clear discrepancies (inter-period). Recovering consensus metrics using less than 14 days might still be valuable in terms of interpreting CGM data in certain clinical contexts. However, caution should be taken if treatment decisions would be made with less than 14 days of data on critical metrics such as TBR, since the metrics characterizing infrequent events deviate substantially when less data are available. Substantial deviation is also seen when comparing across two consecutive periods, which means that care should be taken not to over-generalize consensus metric based glycemic control conclusions from one period to subsequent periods.

Background: This comparative handling study investigated user satisfaction and insulin pump handling with a prefilled insulin cartridge versus a self-filled insulin reservoir in insulin pump users with type 1 diabetes (T1D). Methods: Adult (n = 105) and adolescent (n = 25) participants performed insulin pump preparations using a prefilled insulin cartridge and self-filled insulin reservoir. User satisfaction, insulin pump preparation time, and residual air in infusion set tubing were assessed for each insulin filling method. Post hoc analysis evaluated training time. Results: User satisfaction scores were statistically significantly different in favor of the prefilled insulin cartridge versus the self-filled insulin reservoir (mean [SD]: overall, 4.0 [0.5] vs 3.3 [0.9]; burden on the user, 1.8 [0.6] vs 2.9 [1.0]; user inconvenience, 2.0 [0.7] vs 2.8 [1.1]; device effectiveness, 3.9 [0.7] vs 3.6 [0.9]; all P < .001). Insulin pump preparation time and residual air measurements were significantly different and numerically lower for the prefilled insulin cartridge versus the self-filled insulin reservoir (mean [SD]: preparation time, 124.4 [30.3] vs 237.8 [64.2] seconds, P < .001; residual air, 2.3 [26.3] vs 10.0 [63.3] mm, P = .007). Training time was shorter with the prefilled insulin cartridge versus the self-filled insulin reservoir (mean [min; max]: 193.1 [36; 453] vs 535.8 [124; 992] seconds). Conclusions: Adult and adolescent insulin pump users were more satisfied with the prefilled insulin cartridge versus the self-filled insulin reservoir when preparing an insulin pump. The prefilled insulin cartridge was associated with reduced insulin pump preparation time and reduced training time versus the self-filled insulin reservoir.

Although function model has been widely applied to develop various operator decision support systems, the modeling process is essentially a manual work, which takes significant efforts on knowledge acquisition. It would greatly improve the efficiency of modeling if relevant information can be automatically retrieved from engineering documents. This paper investigates the possibility of automated transformation from P&IDs to a function model called MFM via AutomationML. Semantics and modeling patterns of MFM are established in AutomationML, which can be utilized to convert plant topology models into MFM models. The proposed approach is demonstrated with a small use case. Further topics for extending the study are also discussed.

A large part of keeping a process running safely is ensuring that all equipment is functioning well. To do so, routine preventive maintenance needs to be conducted. This paper investigates the feasibility of a functional modeling method-Multilevel Flow Modeling (MFM) extension with roles for modeling of mechanical components functionalities. The method is applied on a case study of a test bench. Consequently, it studies the effects of physical structure failures on functions propagated upwards to objectives. This work can benefit in making the maintenance plan with further experimental study for linking condition monitoring data with the extended MFM models.

Introduction Systematic triage is performed in the Emergency Department (ED) to assess the urgency of care for each patient. The Copenhagen Triage Algorithm (CTA) is a newly developed, evidence-based triage system, however the interrater agreement remains unknown. Method This was a prospective cohort study. The collection of data was conducted in the three sections (Acute/Cardiology, Medicine and Surgery) of the ED of Herlev Hospital. Patients were assessed independently by two different nurses using CTA. The interrater variability of CTA was calculated using Fleiss kappa. The analysis was stratified according to less or more than 2 years of ED experience. Results A total of 110 patients were included of which 10 were excluded due to incomplete data. The raters agreed on triage category 80 % of the time corresponding to a kappa value of 0.70 (95% confidence interval 0.57-0.83). Stratified on ED sections, the agreement was 83 % in the Acute/Cardiology section corresponding to a kappa value of 0.73 (0.55-0.91), 79 % in the Medicine section corresponding to a kappa value of 0.64 (0.39-0.89) and 0.56 % in the Surgery section corresponding to a kappa value of 0.56 (0.21-0.90). The experienced raters had an interrater agreement of 0.73 (0.56-0.90), while the less experienced raters had an agreement of 0.76, (0.28-1.24). Conclusion A substantial interrater agreement was found for the Copenhagen triage algorithm.

BACKGROUND Patient support apps have risen in popularity and provide novel opportunities for self-management of diabetes. Such apps offer patients to play an active role in monitoring their condition, thereby increasing their own treatment responsibility. Although many health apps require active user engagement to be effective, there is little evidence exploring engagement with mobile health (mHealth). OBJECTIVE This study aims to analyze the extent to which users engage with mHealth for diabetes and identify patient characteristics that are associated with engagement. METHODS The analysis is based on real-world data obtained by Novo Nordisk’s Cornerstones4Care Powered by Glooko diabetes support app. User engagement was assessed as the number of active days and using measures expressing the persistence, longevity, and regularity of interaction within the first 180 days of use. Beta regressions were estimated to assess the associations between user characteristics and engagement outcomes for each module of the app. RESULTS A total of 9051 individuals initiated use after registration and could be observed for 180 days. Among these, 55.39% (5013/9051) used the app for one specific purpose. The average user activity ratio varied from 0.05 (medication and food) to 0.55 (continuous glucose monitoring), depending on the module of the app. Average user engagement was lower if modules required manual data entries, although the initial uptake was higher for these modules. Regression analyses further revealed that although more women used the app (2075/3649, 56.86%), they engaged significantly less with it. Older people and users who were recently diagnosed tended to use the app more actively. CONCLUSIONS Strategies to increase or sustain the use of apps and availability of health data may target the mode of data collection and content design and should take into account privacy concerns of the users at the same time. Users’ engagement was determined by various user characteristics, indicating that particular patient groups should be targeted or assisted when integrating apps into the self-management of their disease.

Background Patient support apps have risen in popularity and provide novel opportunities for self-management of diabetes. Such apps offer patients to play an active role in monitoring their condition, thereby increasing their own treatment responsibility. Although many health apps require active user engagement to be effective, there is little evidence exploring engagement with mobile health (mHealth). Objective This study aims to analyze the extent to which users engage with mHealth for diabetes and identify patient characteristics that are associated with engagement. Methods The analysis is based on real-world data obtained by Novo Nordisk’s Cornerstones4Care Powered by Glooko diabetes support app. User engagement was assessed as the number of active days and using measures expressing the persistence, longevity, and regularity of interaction within the first 180 days of use. Beta regressions were estimated to assess the associations between user characteristics and engagement outcomes for each module of the app. Results A total of 9051 individuals initiated use after registration and could be observed for 180 days. Among these, 55.39% (5013/9051) used the app for one specific purpose. The average user activity ratio varied from 0.05 (medication and food) to 0.55 (continuous glucose monitoring), depending on the module of the app. Average user engagement was lower if modules required manual data entries, although the initial uptake was higher for these modules. Regression analyses further revealed that although more women used the app (2075/3649, 56.86%), they engaged significantly less with it. Older people and users who were recently diagnosed tended to use the app more actively. Conclusions Strategies to increase or sustain the use of apps and availability of health data may target the mode of data collection and content design and should take into account privacy concerns of the users at the same time. Users’ engagement was determined by various user characteristics, indicating that particular patient groups should be targeted or assisted when integrating apps into the self-management of their disease.

Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to conventional time-series forecasting using Autoregressive Integrated Moving Average (ARIMA). A prediction horizon up to 90 min into the future is considered. In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed. We find that the population-based RNN model is the best performing model across the considered prediction horizons without the need of patient-specific data. This demonstrates the potential of RNNs for STBG prediction in diabetes patients towards detecting/mitigating severe events in the STBG, in particular hypoglycemic events. However, further studies are needed in regards to the robustness and practical use of the investigated STBG prediction models., Comment: Accepted to EMBC 2020

It is crucial to provide operators supports for operation planning in complex process plants. This paper presents an operation searching approach used for synthesis of valve and pump operations to establish specific pipe routes. Instead of modeling pipeline fragments, a functional modeling methodology called Multilevel Flow Modeling (MFM) is adopted to represent plant knowledge. Relying on causality feature of MFM, one can identify required state changes of specific functions in the model, and accordingly operations. Since relationships between functions, components, and operations are independent to the modeling object, these generic principles can be implemented into a rule-based reasoning system for inferring operational conditions of pipe routes, namely, operations. An example in the literature has been used to demonstrate the presented approach and application of the software.

Intelligent fault diagnosis systems can be a major aid to human operators charged with the high-level control of industrial plants. Such systems aim for high diagnostic accuracy while retaining the ability to produce results that can be interpreted by human experts on site. Signed directed graphs have been shown to be a viable method for plant-wide diagnosis that can incorporate both quantitative information about the process condition as well as qualitative information about the system topology and the functions of its components. Their range of application in industrial settings has been limited due to difficulties regarding the interpretation of results and consistent graph generation. This contribution addresses these issues by proposing an automated generation of signed directed graphs of industrial processes in the chemical, petroleum and nuclear industries using Multilevel Flow Modeling; a functional modeling method designed for operator support. The approach is demonstrated through a case study conducted on the Tennessee Eastman Process, showing that Multilevel Flow Modeling can facilitate a consistent modeling process for signed directed graphs. Finally, the resulting benefits regarding qualitative reasoning for plant-wide diagnosis are discussed.

The complexity of modern industrial plants poses significant challenges for the design of effective operator interfaces. Although established practices can significantly reduce the frequency of alarms, operators often cannot resolve the failure cascades commonly occurring during emergency situations. Automating control rooms by incorporating design and operation knowledge about the systems can significantly improve operator efficacy. Intelligent support systems should reduce the amount of information and provide more context to the operators. The operators focus should be shifted from information acquisition to taking informed decisions about mitigation steps. This contribution gives a brief review of the development of Multilevel Flow Modeling (MFM) and its application to provide operators with decision support and situation awareness, focusing on implementations directly utilising the knowledge represented in MFM. Finally, current efforts toward a comprehensive intelligent human machine interface for operators are outlined.

Multilevel Flow Modelling (MFM) was proposed as a knowledge representation method for Hazard and Operability Studies (HAZOPs). MFM reasoning software can facilitate the cause-consequence analysis during the HAZOP analysis of whole life cycle of the plant. Recent studies have further confirmed that MFM offers the opportunity to redeploy the insights achieved by the HAZOP team to assist an operator dealing with an abnormal event. However, past studies into MFM-based HAZOP have been lacking in the specification of the principle. This principle makes MFM possible to determine safeguards for studied hypothetical events. This paper proposes such principle, which further increases the application of computer aids in HAZOP studies. The paper provides an analysis and classification of different types of safeguards on the functional level and introduces the safeguards into MFM methodology. It further presents an MFM-specific barrier function and its reasoning rules. The safeguard representation and reasoning transparency explicitly the relationship between suitable safeguards and hypothetical events given knowledge about the complex interdependencies between process design, equipment design, safety barriers and instrumentation. Based on the principles developed, an existing MFM model of a typical oil and gas process module is extended with explicit safety functions and used as an example for utilizing the specified principle for identification of safeguards. Potential safeguards for the process module are produced as the results.

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 en...

Several large trials have evaluated the effect of CT screening based on specific symptoms, with varying outcomes. Screening of patients with CT based on their prognosis alone has not been examined before. For moderate-to-high risk patients presenting in the emergency department (ED), the potential gain from a CT scan might outweigh the risk of radiation exposure. We hypothesized that an accelerated “multiple rule out” CT screening of moderate-to-high risk patients will detect many clinically unrecognized diagnoses that affect change in treatment. Patients ≥ 40 years, triaged as high-risk or moderate-to-high risk according to vital signs, were eligible for inclusion. Patients were scanned with a combined ECG-gated and dual energy CT scan of cerebrum, thorax, and abdomen. The impact of the CT scan on patient diagnosis and treatment was examined prospectively by an expert panel. A total of 100 patients were included in the study, (53% female, mean age 73 years [age range, 43–93]). The scan lead to change in treatment or additional examinations in 37 (37%) patients, of which 24 (24%) were diagnostically significant, change in acute treatment in 11 (11%) cases and previously unrecognized malignant tumors in 10 (10%) cases. The mean size specific radiation dose was 15.9 mSv (± 3.1 mSv). Screening with a multi-rule out CT scan of high-risk patients in an ED is feasible and result in discovery of clinically unrecognized diagnoses and malignant tumors, but at the cost of radiation exposure and downstream examinations. The clinical impact of these findings should be evaluated in a larger randomized cohort.

Making sense of alarms can be difficult on oil and gas platforms. Multilevel Flow Modeling provides a structure for modelling plant functionality and inferring causes for alarms and predicting consequences. Currently, Multilevel Flow Modeling has limited application for on-line fault diagnosis. Based on a fault emulated on a pilot plant for offshore produced water treatment, Multilevel Flow Modeling is used for reasoning about causes for triggered alarms. The inferred causes are analysed to investigate the current maturity of Multilevel Flow Modeling for on-line diagnosis.

Diabetes has become a major public health problem in the world. In this context, early assessment of glycemic control is essential in order to avoid life-threatening health complications. A panel of diabetes experts have recently proposed a list of recommendations when using Continuous Glucose Monitoring (CGM) for glycemic control assessment including a minimum of two weeks of CGM data. A recent study has further introduced a metric called Glucose Profile Indicator (GPI) for CGM based diabetes management including a subset of the recommended CGM metrics. In this pilot study, it was investigated if less than two weeks of CGM data would impact the performance of GPI compared to the proposed two weeks of CGM data. Furthermore, logistic regression (LR) was used to examine if an improvement could be achieved taking as input the CGM metrics used to quantify GPI. The population mean accuracy for accumulated day 1 to 13 varied between 72.8 ± 2.0% − 98.3 ± 0.4% with no clear sign of improvement using LR. Hence, this indicates a trade-off between the amount of available CGM data and the precision in which the GPI outcome using all 14 days can be achieved when considering features of the GPI alone. Future work is needed to investigate if this trade-off can be improved by the use of additional features of the CGM.

The aim of the present study was to investigate the relationship between bicycle helmet use and safety behavior at signalized intersections. Two hypotheses were investigated: The first states that bicycle helmet use leads to risker behavior because of the increased sense of protection (risk compensation), the other states that helmeted cyclists have a general inclination toward safer behavior (safety package) and that helmet use is one of several behaviors for improving safety. Based on video recordings of 1031 cyclists at 12 signalized intersections in Denmark, two indicators of risky behavior were compared between helmeted and unhelmeted cyclists: Speed and time after the onset of yellow at which the cyclists crossed the stop line. Linear regression models were developed with gender, type of bicycle, and intersection characteristics as predictor variables, in addition to helmet use. Helmeted and unhelmeted cyclists do not differ in how many seconds after the onset of yellow they cross the stopping line. This is consistent with the absence of both risk compensation and safety package, alternatively with a general inclination of helmeted cyclists toward safer behavior which is about offset by risk compensation. Helmeted cyclists had higher speed on average, which indicates that risk compensation may occur. However, the higher speed may also be due to the generally better fitness of helmeted cyclists which is likely to result from larger cycling volumes. Moreover, the effect of helmet use on speed may be overestimated because of a lack of control for potential confounding variables. The results show further that, regardless of helmet use, before-red (lights on a separate bicycle signal shift to red before the main signal) is related to later crossings of the stop lane after the onset of yellow and that cyclists stop earlier on average at intersections with right-turn signals. The results do not provide support for the position that promoting or mandating bicycle helmet use will have adverse safety effects because of more risky behavior among helmeted cyclists.

For decades, efforts have been made to automate the HAZOP process. The motivation has mainly been to displace expensive manual HAZOP approaches, that are furthermore known to suffer from systemic quality issues related to system complexity, uncertainty, vagueness and level of knowledge completeness. With offset in a review of the main historic arguments for automating the HAZOP analysis, and an outline of the particular benefits of employing Multilevel Flow Modelling (MFM) theory in this context, this paper emphasises the opportunity to redeploy the insights achieved by the HAZOP team to assist an operator facing an abnormal event years later. By means of a detailed analysis of an actual catastrophic failure of a FPSO compression module, the paper demonstrates how MFM enabled HAZOP captures explicitly tacit expert knowledge about the complex interdependencies between process design, equipment design, safety barriers and instrumentation. The paper further describes a methodology to interpret measurements online by means of the MFM analysis, thereby establishing real-time cause and consequence analysis in sufficient time to interrupt the escalation from a benign sensor malfunction to a topside explosion. The paper concludes by outlining a knowledge management framework centred in MFM of the technical, operational and organizational safety barriers, which would make hitherto tacit knowledge explicitly available at all critical decision points during the lifecycle of a process plant, from design and HAZOP to commissioning, operation and decommissioning, as well as in any plant modification required along the way. The MFM theory was adapted and extended to capture the experienced failure mode and thereby facilitate HAZOP automation and subsequent intelligent real-time operator decision support.

A real-time decision support solution for control room operators that targets increasing production efficiency by reducing plant upsets and disturbances has been developed. The solution relies on process engineering and plant knowledge combined with AI tools like ontology and rule-based reasoning. The aim is to enhance the basis for rapid and intelligent decision-making and increase human performance when abnormal situations occur. The solution is based on "Multilevel Flow Modeling" (MFM), which is a modeling language that builds on a systematic representation of relations between objectives and functions of plant equipment in a means-ends structure. The aim of MFM in this context is to develop models that allows for reasoning about causes and consequences of events, or upsets in process plants, such as oil and gas production facilities. A key feature of MFM is its compatibility with human cognition and results of MFM reasoning can therefore be communicated to operators in an intuitive way. In their daily life, control room operators are presented with process variables through a SCADA system and alarms to notify them that something is not right. Their task is to maintain the production in in the plant and avoid unnecessary disturbances and shut-downs, for which they often rely on the information they receive through the systems as well as their experience. When abnormal events occur, operators often need to respond fast, in order to avoid plant upsets, however, there are no solutions to support them with situation analysis and root cause identification. The MFM based solution samples plant data and develops "failure trees" in real-time that are presented to panel operators in a simplistic and logical way. The solution has been developed in an industry-academia collaboration, in close collaboration with two operators. Initial online testing has been done on pilot plant basis, whereas the piloting on offshore fields is currently ongoing. The modeling work is done in close collaboration with engineers and operators familiar with the targeted plant facilities, in order to secure that all relevant operational aspects are adequately covered. In parallel, a UX development path is ongoing and is part of the integrated test program. The novelty of the decision support solution as well as knowledge about how it impacts control room operators in their daily work is discussed. The learnings and experience from offshore pilot testing are also presented.

BACKGROUND: This study aimed to improve early risk stratification in the emergency department by creating a simple blood test score based on routine biomarkers and assess its predictive ability for 30-day mortality of acutely admitted patients.METHODS: This was a secondary analysis of data from the TRIAGE II study. It included unselected acutely admitted medical and surgical patients, who had albumin, C-reactive protein, creatinine, haemoglobin, leukocytes, potassium, sodium and thrombocytes levels analysed upon admission. Patients were classified according to the number of biomarker results outside the reference range into four risk groups termed "very low", "low", "intermediate", and "high" with 0-1, 2-3, 4-5 and 6-8 abnormal biomarker results, respectively. Logistic regression was used to calculate odds ratios for 30-day mortality and receiver operating characteristic was used to test the discriminative value. The primary analysis was done in patients triaged with ADAPT (Adaptive Process Triage). Subsequently, we analysed two other cohorts of acutely admitted patients.RESULTS: The TRIAGE II cohort included 17,058 eligible patients, 30-day mortality was 5.2%. The primary analysis included 7782 patients. Logistic regression adjusted for age and sex showed an OR of 24.1 (95% CI 14.9-41.0) between the very low- and the high-risk group. The area under the curve (AUC) was 0.79 (95% CI 0.76-0.81) for the blood test score in predicting 30-day mortality. The subsequent analyses confirmed the results.CONCLUSIONS: A blood test score based on number of routine biomarkers with an abnormal result was a predictor of 30-day mortality in acutely admitted patients.

Increasing complexity of industrial processes has made statistical methods for process monitoring and diagnosis a more attractive alternative to model-based methods. A primary reason is that statistical approaches can be formulated to rely less on process knowledge. Since multivariable processes can exhibit complex, nonlinear dynamics, there is a need for methods capable of diagnosing nonlinear process data. A Monte Carlo simulation was conducted on a numerical model of the quadruple tank process (QTP) - a novel multivariate nonlinear process. The simulation was designed so that the QTP exhibited bipartite nonlinear behavior. Reference data obtained from the simulation was used to obtain principal component analysis (PCA) and autoencoder (AE) models. The models generated residuals that were used to monitor the condition of the process. The results showed that AEs, which have nonlinear functionalities, performed better than PCA models at generating residuals.

Introduction: Insulin initiation in type 2 diabetes (T2D) can be a complex process. In U.S., more than 60% of T2Ds treated with insulin do not reach recommended glycemic targets. Aim: To investigate the feasibility of using a simple dose response model for predicting the basal insulin dose needed to reach glycemic target for a person with T2D. Methods: To support initiation of a long acting insulin (insulin Degludec), we used CGM and injection data during insulin initiation to create an individual dose response model. Insulin naïve persons with T2D were included. Patients wore a CGM until the glycemic target was reached. CGM data from the first two weeks were used to identify the lowest glucose value of a day. The estimated safe and effective insulin dose (the 2-week dose estimates) were obtained by combining these lowest daily glucose periods with corresponding insulin doses, and extrapolating to a target range of 4-6 mmol/L. At end of study, the 2-week dose estimate was compared to the actual dose needed for glycemic control to assess how safe and effective the initial estimate would have been. Results: Of the first 7 of 10 patients who completed the study, 5 were males of average age 60.3 (9.1) years, BMI 30.5 (4.1) kg/m2 and diabetes duration 15.8 (8.8) years. The observed actual dose for reaching the glycemic target was 49.9 (31.4) IU, and the 2-week dose prediction for reaching 4 and 6 mmol/L was 31.4 (12.6) IU and 45.5 (17.7) IU, respectively. At the end of study, a dose range for reaching the 4-6 mmol/L range was estimated using all the collected data, and all estimates were within or below this range. Conclusion: Preliminary results from this feasibility study show that the insulin Degludec dose predicted two weeks after initiation was within or below the dose range needed for optimal control, thus the prediction errors were on the safe side in all cases. The results indicate that linear glucose and insulin extrapolation can be a safe and effective way to support initiation of insulin Degludec for people with T2D. Disclosure T. Aradóttir: None. H. Bengtsson: None. M.L. Jensen: Employee; Self; Novo Nordisk A/S. N.K. Poulsen: None. D. Boiroux: None. S. Schmidt: Speaker's Bureau; Self; Novo Nordisk A/S. K. Nørgaard: Advisory Panel; Self; Abbott, Medtronic, Novo Nordisk A/S. Speaker's Bureau; Self; Bayer US, Medtronic, Roche Diabetes Care, Rubin Medical, Sanofi, Zealand Pharma A/S. Stock/Shareholder; Self; Novo Nordisk A/S. Funding Novo Nordisk A/S; Innovation Fund Denmark (5189-00033B); Novo Nordisk Foundation

In this paper, a hierarchical management system is proposed to integrate electric vehicles (EVs) into a distribution grid. Three types of actors are included in the system: Distribution system operators (DSOs), Fleet operators (FOs) and EV owners. In contrast to a typical hierarchical control system where the upper level controller directly controls the lower level subordinated nodes, this study aims to integrate two common indirect control methods:market-based control and price-based control into the hierarchical electric vehicles management system. Specifically, on the lower level of the hierarchy, the FOs coordinate the charging behaviors of their EV users using a price-based control method. A parametric utility model is used on the lower level to characterize price elasticity of electric vehicles and thus used by the FO to coordinate the individual EV charging. On the upper level of the hierarchy, the distribution system operator uses the market-based control strategy to coordinate the limited power capacity of power transformer with fleet operators. To facilitate the application of the two indirect control methods into the system, a model describing decision tasks in control is used to specify the essential functions that are needed in the control system. The simulations illustrate the effectiveness of the proposed solutions.

The purpose of this paper is to present a multi-agent based modeling technology for simulating and operating a hierarchical energy management of a power distribution system with focus on EVs integration. The proposed multi-agent system consists of four types of agents: i) Distribution system operator (DSO) technical agent and ii) DSO market agents that both belong to the top layer of the hierarchy and their roles are to manage the distribution network by avoiding grid congestions and using congestion prices to coordinate the energy scheduled; iii) Electric vehicle virtual power plant agents are in the middle level of the hierarchy and their roles are to manage the charge process of the electric vehicles; iv) Electric vehicle agents are placed at the bottom layer of the hierarchy and they represent electric vehicle owners with different users’ profiles. To demonstrate the coordination behavior of the proposed system, a multi-agent simulation platform is developed based on the co-simulation environment of JACK, Matlab and GAMS. The aim of the multi-agent system is to simulate the collaborative (all agents contribute to achieve an optimized global performance) but also competitive environment (each agent will try to increase its utilities or reduce its costs).

Electric vehicles can become integral parts of a smart grid, since they are capable of providing valuable services to power systems other than just consuming power. On the transmission system level, electric vehicles are regarded as an important means of balancing the intermittent renewable energy resources such as wind power. This is because electric vehicles can be used to absorb the energy during the period of high electricity penetration and feed the electricity back into the grid when the demand is high or in situations of insufficient electricity generation. However, on the distribution system level, the extra loads created by the increasing number of electric vehicles may have adverse impacts on grid. These factors bring new challenges to the power system operators. To coordinate the interests and solve the conflicts, electric vehicle fleet operators are proposed both by academics and industries. This paper presents a review and classification of methods for smart charging (including power to vehicle and vehicle-to-grid) of electric vehicles for fleet operators. The study firstly presents service relationships between fleet operators and other four actors in smart grids; then, modeling of battery dynamics and driving patterns of electric vehicles, charging and communications standards are introduced; after that, three control strategies and their commonly used algorithms are described; finally, conclusion and recommendations are made.

Established control room systems in processing industry are prone to overload operators during severe plant upsets. Automatic diagnostic assistants can assist operators in high load situations, but the system needs to be aware of configuration changes. Standard Operating Procedures detail when and how the plant configuration is to be changed. Tracking the correct execution of a procedure is relevant to both, detecting errors during procedure execution and adapting diagnostic models. However, plant operators tend to deviate from the prescribed procedures. Therefore, the reference for correct procedure execution needs to be established from the procedure documentation but also capturing operator habits. We describe methods for representing documented procedures and validating the procedures against log files of the control system. We propose a fast approach of detecting procedure executions and action sets associated with procedure steps. The presented methods are demonstrated in an industrial case study of a filtration system.

When severe accidents happen in nuclear power plants (NPPs), although there are candidate high-level actions (CHLAs) that can be selected from the severe accident management guidance (SAMG) to mitigate consequences, operators may still have cognitive challenges to identify available plant sources and then formulate executable action plans to implement the chosen actions. The difficulties faced by humans may be resolved by a decision support system taking advantages of computers’ knowledge representation and inferential capability. This article presents a systematic approach for synthesizing operating procedures that can achieve operational goals with currently available plant resources. Multilevel flow modeling (MFM), whose representation has been proved to be consistent with the general emergency procedure development, is used as knowledge basis for the action planning. A rule reasoning method is proposed to infer about actions from perspective of functions’ state transition. A model-based and rule-based operating procedure synthesis (OPS) system is developed with a rule engine. The OPS system is applied to elaborate existing high level strategies for dealing with station blackout (SBO) in a BWR plant as multiple executable action plans. The results shows some untypical ways of implementing available systems for achieving current safety goals, which may be considered in the further SAM. Moreover, what critical plant resources should be well prepared in advance is also indicated from the case study. Finally, limitation of MFM and the develop OPS system on planning SAM strategies are discussed.

Prophylactic coagulation factor replacement is increasingly the treatment modality of choice for people with haemophilia (PWH). Currently available recombinant factor products require reconstitution from a lyophilised powder and diluent, and a range of infusion systems is available to assist in this process. This study aimed to understand the properties of a reconstitution/infusion system that are most important to PWH and carers of children with haemophilia (CWH), and to assess two available systems produced by Novo Nordisk for the reconstitution and infusion of activated recombinant factor VII and recombinant factor VIII: the original infusion system and the newer MixPro® system. Both were tested by a group of 67 PWH or carers of CWH who were naïve to them. Participants rated the performance of each system against 18 predefined parameters using the 7-point Likert scale, and ranked the importance of these parameters to the design of an infusion system. They also directly compared the performance of the two systems and provided qualitative feedback. Overall, MixPro® was preferred to the original system by 94% of study participants. This was reflected in the performance scores for individual parameters, with scores in 16/18 parameters being significantly higher for MixPro® (p

A numerically controlled processing machine, integrated over EtherCAT with full local redundancy in the axes- to task-space mapping has been designed and built in a laboratory. The redundancy arises from a set of slow, long-ranging base axes manipulating a set of fast, short-ranging tool axes, which again holds and manipulates the tool. The principle of the machine is time-efficient in manufacturing applications with high task detail and where the tool process is faster than the long-ranging axes. This paper will give an overview of construction of the machine and experimental trajectory planning.

There is a lack of disease-modifying treatments in hypertrophic cardiomyopathy (HCM). The aim of this randomised, placebo-controlled study was to assess if losartan could improve or ameliorate deterioration of cardiac function and exercise capacity.Echocardiography, exercise test and MRI or CT were performed at baseline and after 12 months in 133 patients (52±13 years, 35% female) randomly allocated to losartan (100 mg/day) or placebo.Losartan had no effect on systolic function compared with placebo (mean difference for left ventricular ejection fraction (LVEF) 0% (95% CI -3% to 4%), p=0.84 or global longitudinal strain 0.7% (95% CI -0.2% to 1.6%), p=0.13). Neither Doppler measures of diastolic function, left atrial volume (mean difference 2 mL/m(2) (95% CI -4 to 8 mL/m(2)) p=0.53) nor exercise capacity (mean difference -0.3 metabolic equivalents (METS) (95% CI -1.0 to 0.3 METS), p=0.28) differed between the treatment groups. At follow-up, there was further progression of disease, with the most prominent impairment being an increase in left atrial volume of 6 mL/m(2) (95% CI 3 to 9 mL/m(2), p0.0001) in both groups combined. LVEF decreased (mean change -2%, (95% CI -3% to -1%), p=0.037) and 4% of patients had end-stage HCM with a LVEF of less than 50% at the end of the study.Treatment with losartan had no effect on cardiac function or exercise capacity compared with placebo. Losartan fail to improve myocardial performance and failed to alter the progression of the disease. These findings do not support the use of angiotensin II receptor blockers as disease modifiers in adult patients with overt HCM.NCT01447654-results.

Electric vehicles (EVs) are widely regarded as valuable assets in the smart grid as distributed energy resources in addition to their primary transportation function. However, connecting EVs to the distribution network and recharging the EV batteries without any control may overload the transformers and cables during peak hours when the penetration of EVs is relatively high. In this study, a two level hierarchical control method for integrating EVs into the distribution network is proposed to coordinate the self-interests and operational constraints of two actors, the EV owner and Distribution system operator (DSO), facilitated by the introduction of the fleet operator (FO) and the grid capacity market operator (CMO). Unlike the typical hierarchical control system where the upper level controller commands the low level unit to execute the actions, in this study, market based control are applied both in the upper and low level of the hierarchical system. Specifically, in the upper level of the hierarchy, distribution system operator uses market based control to coordinate the fleet operator׳s power schedule. In the low level of the hierarchy, the fleet operator use market based control to allocate the charging power to the individual EVs, by using market based control, the proposed method considers the flexibility of EVs through the presence of the response-weighting factor to the shadow price sent out by the FO. Furthermore, to fully demonstrate the coordination behavior of the proposed control strategy, we built a multi-agent system (MAS) that is based on the co-simulation environment of JACK, Matlab and Simulink. A use case of the MAS and the results of running the system are presented to intuitively illustrate the effectiveness of the proposed solutions.

This paper presents an approach for a complete sewing process, including the material handling and preparation before the sewing operation as well as material handling after the finished sewing. The complete process is divided into different tasks. The tasks include localization and stacking of the parts to be sewn, initial alignment and feeding of the aligned stack to the sewing machine, the previously-published sewing operation as well as the post sewing handling. Additionally, the sensor system for the handling process is described. This RGB-D-camera-based system is used to detect the corner poses for the two parts. While the presented tasks have been experimented with, solutions for the integration of sequent sewing processes are discussed.

Industrial processes contain inherent risks and to assess such risks the use of Process Hazard Analysis (PHA) are commonly applied. The benefits of first principles qualitative and quantitative models and empirical knowledge support for PHA are explored. The use of qualitative hazard analysis based on functional modelling to guide hazard analysis is emphasized. The study proposes a framework for investigating potential hazard scenarios based on functional modeling, e.g. Multilevel Flow Modeling of process system. Subsequently quantitative hazard analysis is used to prioritize the hazard scenarios with highest potential. MFM represents the process on several levels of abstraction and supports logical inference. The potential hazard scenarios are ranked by likelihood and severity. Judgment about likelihood, severity and the tolerability of the resulting risk is made on a subjective basis using the empirical knowledge of the PHA team members. The potential hazard scenario generation procedure can be computer-aided. The proposed framework is applied for a water injection system to increase oil recovery from existing reservoir with better safety performance. Qualitative functional models are efficient for detailed description of possible accident scenarios. Domino effects can be visualized in an MFM model. The possible major accident scenarios are selected by likelihood and severity of their effects. The major accident scenarios can be examined in detail through further quantitative analysis. The hazard analysis of a water injection system demonstrates the feasibility and applicability of the proposed framework. In industrial practice, formulation of the major accident scenario is usually based on historical incidents and the outcome of HAZOP/HAZID type hazard identification studies. Such studies are dependent on the team and their collective knowledge rather than being systematic and objective in nature. The proposed framework performs systematic and comprehensive plant failure and consequence path generation by a computer-aided tool.

Newchallengesarearisinginmanagingpowersystems asthesesystemsbecomemorecomplexduetotheuseofhighlevelsof distributed generation, mainly based on renewable energy sources, and due to the competitive environment within the power sector. At the same time, the use of phasor measurement units (PMUs) provides more information and enables wide-area monitoring with accurate timing. One of the challenges in the near future is con- verting the high quantity and quality of information provided by PMUs into useful knowledge about operational state of a global system. The use of real-time simulation in closed loop is essential to develop and validate new real-time applications of wide-area PMU data. This paper presents a simulation platform developed within the research project Secure Operation of Sustainable Power Sys- tems (SOSPO). The SOSPO simulation platform (SOSPO-SP) functions in a closed loop integrating new real-time assessment methodstoprovideusefulinformationtooperatorsinpowersystem control centers, and to develop new control methodologies that handle emergency situations and avoid power system blackouts. Index Terms—Phasor measurement units (PMUs), real-time simulation, simulation platforms, stability assessment methods, visualization.

Approximately 90% of the people with diabetes have type 2 diabetes (T2D), and more than half of the diabetes patients on insulin fail to reach the treatment targets. The reasons include fear of hypoglycemia, complexity of treatment, and work load related to treatment intensification. This paper proposes a model predictive control (MPC) based dose guidance algorithm to identify an individual’s optimal dosing of long acting insulin. We present a model for simulating the effect of long acting insulin on fasting glucose in T2D. We do this by adapting previous models such that slow and non-linear dynamics are identifiable from clinical data. For dose guidance, we use MPC with a novel approach to sub-frequency actuation, to increase safety between input samples. To test the controller, we simulate scenarios with biological variations and different levels of adherence to treatment. The results are compared to a standard of care (SOC) method in insulin dose adjustments.

Generating and defining Major Accident Scenarios (MAS) are commonly agreed as the key step for quantitative risk assessment (QRA). The aim of the study is to explore the feasibility of using Multilevel Flow Modeling (MFM) methodology to formulating MAS. Traditionally this is usually done based on historical incidents or the outcome of HAZOP/HAZID. This paper suggests using MFM to model the plant, and then performs systematic reasoning based on the model to produce casual paths of plant failure scenarios. The cause trees generated by MFM are transformed into fault trees, which are then used to calculate likelihood of each MAS. Combining the likelihood of each scenario with a qualitative risk matrix, each major accident scenario is thereby ranked for consideration for detailed consequence analysis. The methodology is successfully highlighted using part of BMA-process for production of hydrogen cyanide as case study.