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| 2. |
- Ekelund, Maria, et al.
(författare)
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Lifetime prediction : different strategies by example
- 2009
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Ingår i: POLYMER DEGRADATION AND PERFORMANCE. - Washington, DC : American Chemical Society. - 9780841269781 ; , s. 159-169
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Konferensbidrag (refereegranskat)abstract
- Two different approaches for lifetime prediction are presented. The underlying lifetime limiting processes have been identified in two cases. Mathematical expressions of chemical/physical relevance were used for the lifetime predictions for PE hot-water pipes and cables insulated with plasticized PVC. Accelerated testing, extrapolation and validation of the extrapolation by assessment of the remaining lifetime of objects aged during service conditions for 25 years were successfully applied to cables insulated with chlorosulfonated polyethylene. Polyolefin pipes exposed to chlorinated water showed a very complex deterioration scenario and it was only possible to find a method suitable for predicting the time for the depletion of the stabilizer system.
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| 3. |
- Ekelund, Maria, et al.
(författare)
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Long-term performance of poly(vinyl chloride) cables. Part 1 : Mechanical and electrical performances
- 2007
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Ingår i: Polymer degradation and stability. - : Elsevier BV. - 0141-3910 .- 1873-2321. ; 92:4, s. 617-629
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Tidskriftsartikel (refereegranskat)abstract
- Cables insulated with plasticized poly(vinyl chloride) were aged in air at temperatures between 80 degrees C and 180 degrees C and their conditions were assessed by indenter modulus measurements, tensile testing, infrared (IR) spectroscopy and differential scanning calorimetry (DSC). Electrical testing of oven-aged cable samples was performed in order to relate the electrical functionality during a high-energy line break (HELB) to the mechanical properties and to establish a lifetime criterion. The mechanical data taken at room temperature after ageing could be superimposed with regard to ageing time and temperature. The ageing-temperature shift factor showed an Arrhenius temperature dependence. The jacketing material showed an immediate increase in stiffness (indenter modulus and Young's modulus) and a decrease in the strain at break on ageing; these changes were dominated by loss of plasticizer by migration which was confirmed by IR spectroscopy and DSC. The core insulation showed smaller changes in these mechanical parameters; the loss of plasticizer by migration was greatly retarded by the closed environment, according to data obtained by IR spectroscopy and DSC, and the changes in the mechanical parameters were due to chemical degradation (dehydrochlorination). A comparison of data obtained from this study and data from other studies indicates that extrapolation of data for the jacketing insulation can be performed according to the Arrhenius equation even down to service temperatures (20-50 degrees C). The low-temperature deterioration of the jacketing is, according to this scheme, dominated by loss of plasticizer by migration.
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| 4. |
- Ekelund, Maria, et al.
(författare)
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Long-term performance of poly(vinyl chloride) cables, Part 2 : Migration of plasticizer
- 2008
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Ingår i: Polymer degradation and stability. - : Elsevier BV. - 0141-3910 .- 1873-2321. ; 93:9, s. 1704-1710
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Tidskriftsartikel (refereegranskat)abstract
- Cable samples with plasticized poly(vinyl chloride) insulations were aged in air at temperatures between 80 and 155 degrees C. The concentrations of the plasticizer (di-(2-ethylhexyl) phthalate, DEHP) in the insulations of the aged cables were determined by extraction of samples in tetrahydrofuran followed by analysis of the extract by liquid chromatography. The plasticizer concentration data for different ageing times were analysed by numerical methods, fitting Fick's second law with a concentration-dependent diffusivity. The analysis showed that the transport of the plasticizer to the surrounding air phase was controlled by diffusion at 120 and 155 degrees C with an activation energy of 89 kJ mol(-1). The evaporation of the plasticizer from the outer boundary was rate controlling at lower temperatures (<= 100 degrees C), The rate of evaporation was initially constant and independent of the plasticizer concentration at both 80 and 100 degrees C. The activation energy for the initial DEHP loss rate from PVC at these temperatures was the same as that obtained for evaporation of pure DEHP on a glass plate at 60-100 degrees C measured by thermogravimetry, 98 2 kJ mol-1. Furthermore, the evaporation rate of pure DEHP on a glass plate was also of the same order of magnitude as the rate of plasticizer loss from the cable insulation. Extrapolation of the plasticizer loss rate data (from the cable at 80 degrees C and from pure liquid DEHP at temperatures between 60 and 100 IQ to 25 degrees C predicted a maximum loss of plasticizer of 1% over 25 years. This is in accordance with earlier presented data and with the data presented in this report.
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| 6. |
- Ekelund, Maria, et al.
(författare)
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Thermal ageing assessment of EPDM-chlorosulfonated polyethylene insulated cables using line resonance analysis (LIRA)
- 2011
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Ingår i: Polymer testing. - : Elsevier BV. - 0142-9418 .- 1873-2348. ; 30:1, s. 86-93
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Tidskriftsartikel (refereegranskat)abstract
- Two cables with chlorosulfonated polyethylene jackets and EPDM core insulations, but having different designs and geometries, were aged at 140 degrees C for different periods of time mimicking ageing at 50 degrees C for an exposure time of more than one hundred years. The cable samples were aged in dry air and dry nitrogen. The cable samples were studied with indenter modulus measurements, tensile tests, infrared spectroscopy and line resonance analysis (LIRA). The main question was whether universal correlations could be established between the two classical methods (indenter and tensile testing) and LIRA. The global ageing indicator (CBAC2) obtained by LIRA showed good correlation with the indenter modulus of the jacketing and with the mechanical properties of the core insulation. Almost universal CBAC2 values were obtained for samples reaching a critical state; the latter being defined according to a criterion based on LOCA test data. Infrared spectroscopy showed that the core insulation degraded by an essentially oxygen-free mechanism, with a gradual increase in the concentration of vinyl and vinylene groups. (C) 2010 Elsevier Ltd. All rights reserved.
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| 7. |
- Heyman, Ellen Tolestam, et al.
(författare)
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Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths
- 2021
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Ingår i: Journal of Emergency Medicine. - Philadelphia, PA : Elsevier BV. - 0736-4679 .- 1090-1280. ; 61:6, s. 763-773
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit.OBJECTIVES: To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge.METHODS: In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM).RESULTS: Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models.CONCLUSION: In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.
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| 8. |
- Björkelund, Anders, et al.
(författare)
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Machine learning compared with rule‐in/rule‐out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations
- 2021
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Ingår i: Journal of the American College of Emergency Physicians Open. - Hoboken, NJ : John Wiley & Sons. - 2688-1152. ; 2:2
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Tidskriftsartikel (refereegranskat)abstract
- AbstractObjectiveComputerized decision-support tools may improve diagnosis of acute myocardial infarction (AMI) among patients presenting with chest pain at the emergency department (ED). The primary aim was to assess the predictive accuracy of machine learning algorithms based on paired high-sensitivity cardiac troponin T (hs-cTnT) concentrations with varying sampling times, age, and sex in order to rule in or out AMI.MethodsIn this register-based, cross-sectional diagnostic study conducted retrospectively based on 5695 chest pain patients at 2 hospitals in Sweden 2013–2014 we used 5-fold cross-validation 200 times in order to compare the performance of an artificial neural network (ANN) with European guideline-recommended 0/1- and 0/3-hour algorithms for hs-cTnT and with logistic regression without interaction terms. Primary outcome was the size of the intermediate risk group where AMI could not be ruled in or out, while holding the sensitivity (rule-out) and specificity (rule-in) constant across models.ResultsANN and logistic regression had similar (95%) areas under the receiver operating characteristics curve. In patients (n = 4171) where the timing requirements (0/1 or 0/3 hour) for the sampling were met, using ANN led to a relative decrease of 9.2% (95% confidence interval 4.4% to 13.8%; from 24.5% to 22.2% of all tested patients) in the size of the intermediate group compared to the recommended algorithms. By contrast, using logistic regression did not substantially decrease the size of the intermediate group.ConclusionMachine learning algorithms allow for flexibility in sampling and have the potential to improve risk assessment among chest pain patients at the ED.
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| 10. |
- Forberg, Jakob L, et al.
(författare)
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An artificial neural network to safely reduce the number of ambulance ECGs transmitted for physician assessment in a system with prehospital detection of ST elevation myocardial infarction
- 2012
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Ingår i: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. - : Springer Science and Business Media LLC. - 1757-7241. ; 20:1, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Pre-hospital electrocardiogram (ECG) transmission to an expert for interpretation and triage reduces time to acute percutaneous coronary intervention (PCI) in patients with ST elevation Myocardial Infarction (STEMI). In order to detect all STEMI patients, the ECG should be transmitted in all cases of suspected acute cardiac ischemia. The aim of this study was to examine the ability of an artificial neural network (ANN) to safely reduce the number of ECGs transmitted by identifying patients without STEMI and patients not needing acute PCI. Methods: Five hundred and sixty ambulance ECGs transmitted to the coronary care unit (CCU) in routine care were prospectively collected. The ECG interpretation by the ANN was compared with the diagnosis (STEMI or not) and the need for an acute PCI (or not) as determined from the Swedish coronary angiography and angioplasty register. The CCU physician's real time ECG interpretation (STEMI or not) and triage decision (acute PCI or not) were registered for comparison. Results: The ANN sensitivity, specificity, positive and negative predictive values for STEMI was 95%, 68%, 18% and 99%, respectively, and for a need of acute PCI it was 97%, 68%, 17% and 100%. The area under the ANN's receiver operating characteristics curve for STEMI detection was 0.93 (95% CI 0.89-0.96) and for predicting the need of acute PCI 0.94 (95% CI 0.90-0.97). If ECGs where the ANN did not identify a STEMI or a need of acute PCI were theoretically to be withheld from transmission, the number of ECGs sent to the CCU could have been reduced by 64% without missing any case with STEMI or a need of immediate PCI. Conclusions: Our ANN had an excellent ability to predict STEMI and the need of acute PCI in ambulance ECGs, and has a potential to safely reduce the number of ECG transmitted to the CCU by almost two thirds.
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