| 1. |
- Bech-Hanssen, Odd, 1956, et al.
(författare)
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Grading right ventricular dysfunction in left ventricular disease using echocardiography: a proof of concept using a novel multiparameter strategy.
- 2021
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Ingår i: ESC heart failure. - : Wiley. - 2055-5822. ; 8:4, s. 3223-3236
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Tidskriftsartikel (refereegranskat)abstract
- Grading right ventricular dysfunction (RVD) in patients with left ventricular (LV) disease has earned little attention. In the present study, we established an echocardiographic RVD score and investigated how increments of the score correspond to RVD at right heart catheterization.We included 95 patients with LV disease consecutively referred for heart transplant or heart failure work-up with catheterization and echocardiography within 48h. The RVD score (5 points) included well-known characteristics of the development from compensated to decompensated right ventricular (RV) function: pulmonary hypertension, reduced RV strain, RV area dilatation, moderate/severe tricuspid regurgitation, and increased right atrial pressure (RAP) by echocardiography. Comparing three groups with increments of RVD score [1 (mild), 2-3 (moderate), and 4-5 (severe)] showed more advanced RVD with increasing RV end-diastolic pressure (P<0.001) and signs of uncoupling to load (reduced ratio between RV and pulmonary artery elastance, P<0.001) and more spherical RV shape (RV area/length, P<0.001). Receiver operating characteristic curve analysis for detection of severe RV (RAP≥10mmHg) showed for the RVD score an area under the curve of 0.88 compared with 0.69, 0.68, and 0.64 for RV strain, tricuspid annular plane systolic excursion, and fractional area change, respectively. A patient with RVD score≥4 had a 6.7-fold increase in likelihood of severe RVD, and no patient with RVD score≤1 had severe RVD.In this proof of concept study, a novel RVD score outperformed the widely used longitudinal parameters regarding grading of RVD severity, with a potential role for refined diagnosis, follow-up, and prognosis assessment in heart failure patients.
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| 2. |
- Bobbio, Emanuele, et al.
(författare)
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Association between central haemodynamics and renal function in advanced heart failure: a nationwide study from Sweden.
- 2022
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Ingår i: ESC heart failure. - : Wiley. - 2055-5822. ; 9:4, s. 2654-2663
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Tidskriftsartikel (refereegranskat)abstract
- Renal dysfunction in patients with heart failure (HF) has traditionally been attributed to declining cardiac output and renal hypoperfusion. However, other central haemodynamic aberrations may contribute to impaired kidney function. This study assessed the relationship between invasive central haemodynamic measurements from right-heart catheterizations and measured glomerular filtration rate (mGFR) in advanced HF.All patients referred for heart transplantation work-up in Sweden between 1988 and 2019 were identified through the Scandiatransplant organ-exchange organization database. Invasive haemodynamic variables and mGFR were retrieved retrospectively. A total of 1001 subjects (49±13years; 24% female) were eligible for the study. Analysis of covariance adjusted for age, sex, and centre revealed that higher right atrial pressure (RAP) displayed the strongest relationship with impaired GFR [β coefficient -0.59; 95% confidence interval (CI) -0.69 to -0.48; P<0.001], followed by lower mean arterial pressure (MAP) (β coefficient 0.29; 95% CI 0.14-0.37; P<0.001), and finally reduced cardiac index (β coefficient 3.51; 95% CI 2.14-4.84; P<0.003). A combination of high RAP and low MAP was associated with markedly worse mGFR than any other RAP/MAP profile, and high renal perfusion pressure (RPP, MAP minus RAP) was associated with superior renal function irrespective of the degree of cardiac output.In patients with advanced HF, high RAP contributed more to impaired GFR than low MAP. A higher RPP was more closely related to GFR than was high cardiac index.
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| 3. |
- Bobbio, Emanuele, et al.
(författare)
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Clinical Outcomes and Predictors of Long-Term Survival in Patients With and Without Previously Known Extracardiac Sarcoidosis Using Machine Learning: A Swedish Multicenter Study.
- 2023
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Ingår i: Journal of the American Heart Association. - 2047-9980. ; 12:15
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Tidskriftsartikel (refereegranskat)abstract
- Background Cardiac involvement can be an initial manifestation in sarcoidosis. However, little is known about the association between various clinical phenotypes of cardiac sarcoidosis (CS) and outcomes. We aimed to analyze the relation of different clinical manifestations with outcomes of CS and to investigate the relative importance of clinical features influencing overall survival. Methods and Results A retrospective cohort of 141 patients with CS enrolled at 2 Swedish university hospitals was studied. Presentation, imaging studies, and outcomes of de novo CS and previously known extracardiac sarcoidosis were compared. Survival free of primary composite outcome (ventricular arrhythmias, heart transplantation, or death) was assessed. Machine learning algorithm was used to study the relative importance of clinical features in predicting outcome. Sixty-two patients with de novo CS and 79 with previously known extracardiac sarcoidosis were included. De novo CS showed more advanced New York Heart Association class (P=0.02), higher circulating levels of NT-proBNP (N-terminal pro-B-type natriuretic peptide) (P<0.001), and troponins (P<0.001), as well as a higher prevalence of right ventricular dysfunction (P<0.001). During a median (interquartile range) follow-up of 61 (44-77) months, event-free survival was shorter in patients with de novo CS (P<0.001). The top 5 features predicting worse event-free survival in order of importance were as follows: impaired tricuspid annular plane systolic excursion, de novo CS, reduced right ventricular ejection fraction, absence of β-blockers, and lower left ventricular ejection fraction. Conclusions Patients with de novo CS displayed more severe disease and worse outcomes compared with patients with previously known extracardiac sarcoidosis. Using machine learning, right ventricular dysfunction and de novo CS stand out as strong overall predictors of impaired survival.
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| 4. |
- Bobbio, Emanuele, et al.
(författare)
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Diagnosis, management, and outcome of cardiac sarcoidosis and giant cell myocarditis: a Swedish single center experience.
- 2022
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Ingår i: BMC cardiovascular disorders. - : Springer Science and Business Media LLC. - 1471-2261. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- Cardiac sarcoidosis (CS) and giant cell myocarditis (GCM) are rare diseases that share some similarities, but also display different clinical and histopathological features. We aimed to compare the demographics, clinical presentation, and outcome of patients diagnosed with CS or GCM.We compared the clinical data and outcome of all adult patients with CS (n=71) or GCM (n=21) diagnosed at our center between 1991 and 2020.The median (interquartile range) follow-up time for patients with CS and GCM was 33.5 [6.5-60.9] and 2.98 [0.6-40.9] months, respectively. In the entire cohort, heart failure (HF) was the most common presenting manifestation (31%), followed by ventricular arrhythmias (25%). At presentation, a left ventricular ejection fraction of<50% was found in 54% of the CS compared to 86% of the GCM patients (P=0.014), while corresponding proportions for right ventricular dysfunction were 24% and 52% (P=0.026), respectively. Advanced HF (NYHA≥IIIB) was less common in CS (31%) than in GCM (76%). CS patients displayed significantly lower circulating levels of natriuretic peptides (P<0.001) and troponins (P=0.014). Eighteen percent of patients with CS included in the survival analysis reached the composite endpoint of death or heart transplantation (HTx) compared to 68% of patients with GCM (P<0.001).GCM has a more fulminant clinical course than CS with severe biventricular failure, higher levels of circulating biomarkers and an increased need for HTx. The histopathologic diagnosis remained key determinant even after adjustment for markers of cardiac dysfunction.
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| 5. |
- Everitt, Niklas, 1987-, et al.
(författare)
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A Geometric Approach to Variance Analysis of Cascaded Systems
- 2013
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Ingår i: Proceedings of the 52nd Conference On Decision And Control. - : IEEE conference proceedings. - 9781467357173 ; , s. 6496-6501
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Konferensbidrag (refereegranskat)abstract
- Modeling complex and interconnected systems is a key issue in system identification. When estimating individual subsystems of a network of interconnected system, it is of interest to know the improvement of model-accuracy in using different sensors and actuators. In this paper, using a geometric approach, we quantify the accuracy improvement from additional sensors when estimating the first of a set of subsystems connected in a cascade structure. We present results on how the zeros of the first subsystem affect the accuracy of the corresponding model. Additionally we shed some light on how structural properties and experimental conditions determine the accuracy. The results are particularized to FIR systems, for which the results are illustrated by numerical simulations. A surprising special case occurs when the first subsystem contains a zero on the unit circle; as the model orders grows large, thevariance of the frequency function estimate, evaluated at thecorresponding frequency of the unit-circle zero, is shown to be the same as if the other subsystems were completely known.
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| 6. |
- Everitt, Niklas, et al.
(författare)
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An empirical Bayes approach to identification of modules in dynamic networks
- 2018
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Ingår i: Automatica. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0005-1098 .- 1873-2836. ; 91, s. 144-151
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Tidskriftsartikel (refereegranskat)abstract
- We present a new method of identifying a specific module in a dynamic network, possibly with feedback loops. Assuming known topology, we express the dynamics by an acyclic network composed of two blocks where the first block accounts for the relation between the known reference signals and the input to the target module, while the second block contains the target module. Using an empirical Bayes approach, we model the first block as a Gaussian vector with covariance matrix (kernel) given by the recently introduced stable spline kernel. The parameters of the target module are estimated by solving a marginal likelihood problem with a novel iterative scheme based on the Expectation-Maximization algorithm. Additionally, we extend the method to include additional measurements downstream of the target module. Using Markov Chain Monte Carlo techniques, it is shown that the same iterative scheme can solve also this formulation. Numerical experiments illustrate the effectiveness of the proposed methods.
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| 7. |
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| 8. |
- Everitt, Niklas, et al.
(författare)
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Identification of modules in dynamic networks : An empirical Bayes approach
- 2016
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Ingår i: 2016 IEEE 55th Conference on Decision and Control, CDC 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509018376 ; , s. 4612-4617
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Konferensbidrag (refereegranskat)abstract
- We address the problem of identifying a specific module in a dynamic network, assuming known topology. We express the dynamics by an acyclic network composed of two blocks where the first block accounts for the relation between the known reference signals and the input to the target module, while the second block contains the target module. Using an empirical Bayes approach, we model the first block as a Gaussian vector with covariance matrix (kernel) given by the recently introduced stable spline kernel. The parameters of the target module are estimated by solving a marginal likelihood problem with a novel iterative scheme based on the Expectation Maximization algorithm. Numerical experiments illustrate the effectiveness of the proposed method.
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| 9. |
- Everitt, Niklas, 1987-
(författare)
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Module identification in dynamic networks: parametric and empirical Bayes methods
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The purpose of system identification is to construct mathematical models of dynamical system from experimental data. With the current trend of dynamical systems encountered in engineering growing ever more complex, an important task is to efficiently build models of these systems. Modelling the complete dynamics of these systems is in general not possible or even desired. However, often, these systems can be modelled as simpler linear systems interconnected in a dynamic network. Then, the task of estimating the whole network or a subset of the network can be broken down into subproblems of estimating one simple system, called module, embedded within the dynamic network.The prediction error method (PEM) is a benchmark in parametric system identification. The main advantage with PEM is that for Gaussian noise, it corresponds to the so called maximum likelihood (ML) estimator and is asymptotically efficient. One drawback is that the cost function is in general nonconvex and a gradient based search over the parameters has to be carried out, rendering a good starting point crucial. Therefore, other methods such as subspace or instrumental variable methods are required to initialize the search. In this thesis, an alternative method, called model order reduction Steiglitz-McBride (MORSM) is proposed. As MORSM is also motivated by ML arguments, it may also be used on its own and will in some cases provide asymptotically efficient estimates. The method is computationally attractive since it is composed of a sequence of least squares steps. It also treats the part of the network of no direct interest nonparametrically, simplifying model order selection for the user.A different approach is taken in the second proposed method to identify a module embedded in a dynamic network. Here, the impulse response of the part of the network of no direct interest is modelled as a realization of a Gaussian process. The mean and covariance of the Gaussian process is parameterized by a set of parameters called hyperparameters that needs to be estimated together with the parameters of the module of interest. Using an empirical Bayes approach, all parameters are estimated by maximizing the marginal likelihood of the data. The maximization is carried out by using an iterative expectation/conditional-maximization scheme, which alternates so called expectation steps with a series of conditional-maximization steps. When only the module input and output sensors are used, the expectation step admits an analytical expression. The conditional-maximization steps reduces to solving smaller optimization problems, which either admit a closed form solution, or can be efficiently solved by using gradient descent strategies. Therefore, the overall optimization turns out computationally efficient. Using markov chain monte carlo techniques, the method is extended to incorporate additional sensors.Apart from the choice of identification method, the set of chosen signals to use in the identification will determine the covariance of the estimated modules. To chose these signals, well known expressions for the covariance matrix could, together with signal constraints, be formulated as an optimization problem and solved. However, this approach does neither tell us why a certain choice of signals is optimal nor what will happen if some properties change. The expressions developed in this part of the thesis have a different flavor in that they aim to reformulate the covariance expressions into a form amenable for interpretation. These expressions illustrate how different properties of the identification problem affects the achievable accuracy. In particular, how the power of the input and noise signals, as well as model structure, affect the covariance.
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| 10. |
- Everitt, Niklas, et al.
(författare)
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On the Effect of Noise Correlation in Parameter Identification of SIMO Systems
- 2015
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 48:28, s. 326-331
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Tidskriftsartikel (refereegranskat)abstract
- The accuracy of identified linear time-invariant single-input multi-output (SIMO) models can be improved when the disturbances affecting the output measurements are spatially correlated. Given a linear parametrization of the modules composing the SIMO structure, we show that the correlation structure of the noise sources and the model structure of the othe modules determine the variance of a parameter estimate. In particular we show that increasing the model order only increases the variance of other modules up to a point. We precisely characterize the variance error of the parameter estimates for finite model orders. We quantify the effect of noise correlation structure, model structure and signal spectra.
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| 11. |
- Everitt, Niklas, et al.
(författare)
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On the Variance Analysis of identified Linear MIMO Models
- 2015
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Ingår i: IEEE Explore. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- We study the accuracy of identified linear time-invariant multi-input multi-output (MIMO) systems. Under a stochastic framework, we quantify the effect of the spatial correlation and choice of model structure on the covariance matrix of the transfer function estimates. In particular, it is shown how the variance of a transfer function estimate depends on signal properties and model orders of other modules composing the MIMO system.
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| 12. |
- Everitt, Niklas, et al.
(författare)
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Open-loop asymptotically efficient model reduction with the Steiglitz–McBride method
- 2018
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 89, s. 221-234
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Tidskriftsartikel (refereegranskat)abstract
- In system identification, it is often difficult to use a physical intuition when choosing a noise model structure. The importance of this choice is that, for the prediction error method (PEM) to provide asymptotically efficient estimates, the model orders must be chosen according to the true system. However, if only the plant estimates are of interest and the experiment is performed in open loop, the noise model can be over-parameterized without affecting the asymptotic properties of the plant. The limitation is that, as PEM suffers in general from non-convexity, estimating an unnecessarily large number of parameters will increase the risk of getting trapped in local minima. Here, we consider the following alternative approach. First, estimate a high-order ARX model with least squares, providing non-parametric estimates of the plant and noise model. Second, reduce the high-order model to obtain a parametric model of the plant only. We review existing methods to do this, pointing out limitations and connections between them. Then, we propose a method that connects favorable properties from the previously reviewed approaches. We show that the proposed method provides asymptotically efficient estimates of the plant with open-loop data. Finally, we perform a simulation study suggesting that the proposed method is competitive with state-of-the-art methods.
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| 13. |
- Everitt, Niklas, 1987-, et al.
(författare)
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Variance Analysis of Linear SIMO Models with Spatially Correlated Noise
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Substantial improvement in accuracy of identied linear time-invariant single-input multi-output (SIMO) dynamical models ispossible when the disturbances aecting the output measurements are spatially correlated. Using an orthogonal representation for the modules composing the SIMO structure, in this paper we show that the variance of a parameter estimate of a module is dependent on the model structure of the other modules, and the correlation structure of the disturbances. In addition, we quantify the variance-error for the parameter estimates for finite model orders, where the effect of noise correlation structure, model structure and signal spectra are visible. From these results, we derive the noise correlation structure under which the mentioned model parameterization gives the lowest variance, when one module is identied using less parameters than the other modules.
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| 14. |
- Everitt, Niklas, et al.
(författare)
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Variance analysis of linear SIMO models with spatially correlated noise
- 2017
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Ingår i: Automatica. - : Elsevier. - 0005-1098. ; 77, s. 68-81
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we address the identification of linear time-invariant single-input multi-output (SIMO) systems. In particular, we assess the performance of the prediction error method by quantifying the variance of the parameter estimates. Using an orthonormal representation for the modules composing the SIMO structure, we show that the parameter estimate of a module depends on the model structure of the other modules, and on the correlation structure of the output disturbances. We provide novel results which quantify the variance-error of the parameter estimates for finite model orders, where the effects of noise correlation structure, model structure and input spectrum are visible. In particular, we show that a sensor does not increase the accuracy of a module if common dynamics have to be estimated. When a module is identified using less parameters than the other modules, we derive the noise correlation structure that gives the minimum total variance. The implications of our results are illustrated through numerical examples and simulations.
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| 15. |
- Everitt, Niklas, 1987-, et al.
(författare)
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Variance Results for Parallel Cascade Serial Systems
- 2014
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Ingår i: Proceedings of 19th IFAC World Congress. - : Elsevier BV.
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Konferensbidrag (refereegranskat)abstract
- Modelling dynamic networks is important in different fields of science. At present, little is known about how different inputs and sensors contribute to the statistical properties concerning an estimate of a specific dynamic system in a network. We consider two forms of parallel serial structures, one multiple-input-multiple-output structure and one single-input multiple-output structure. The quality of the estimated models is analysed by means of the asymptotic covariance matrix, with respect to input signal characteristics, noise characteristics, sensor locations and previous knowledge about the remaining systems in the network. It is shown that an additive property applies to the information matrix for the considered structures. The impact of input signal selection, sensor locations and incorporation of previous knowledge isillustrated by simple examples.
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| 16. |
- Galrinho, Miguel, et al.
(författare)
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ARX modeling of unstable linear systems
- 2017
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Ingår i: Automatica. - : Elsevier. - 0005-1098. ; 75, s. 167-171
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Tidskriftsartikel (refereegranskat)abstract
- High-order ARX models can be used to approximate a quite general class of linear systems in a parametric model structure, and well-established methods can then be used to retrieve the true plant and noise models from the ARX polynomials. However, this commonly used approach is only valid when the plant is stable or if the unstable poles are shared with the true noise model. In this contribution, we generalize this approach to allow the unstable poles not to be shared, by introducing modifications to correctly retrieve the noise model and noise variance.
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| 17. |
- Galrinho, Miguel, et al.
(författare)
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Incorporating noise modeling in dynamic networks using non-parametric models
- 2017
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 50:1, s. 10568-10573
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Tidskriftsartikel (refereegranskat)abstract
- For identification of systems in dynamic networks, two-stage and instrumental variable methods are common time-domain methods. These methods provide consistent estimates of a chosen module of the network without estimating other parts of the network or noise models. However, disregarding noise modeling may come at a cost in estimation error. To capture the noise contribution, we propose the following procedure: first, we estimate a non-parametric model of an appropriate part of the network; second, we estimate the module of interest using signals simulated with the non-parametric model. The simulated signals are derived from an asymptotic maximum likelihood criterion. Preliminary simulations suggest that the propose method is competitive with existing approaches and is particularly beneficial with colored noise.
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| 18. |
- Hjalmarsson, Anders, et al.
(författare)
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Mind the gap : Exploring stakeholders' value with open data assessment
- 2015
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Ingår i: 2015 48<sup>th</sup> Hawaii International Conference on System Sciences. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781479973675 ; , s. 1314-1323
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Konferensbidrag (refereegranskat)abstract
- There is an ongoing movement in society to provide open data as an enabler for innovation and growth. As more data is passed on beyond organizational borders to trigger third party development of services, the expectations on what could be developed using open data increase. Developers have been observed to express frustration over different challenges in the wake of open data provision, such as localization issues, lack of quality in data etc. Knowledge and tools are missing to assess available open data. This absence risks ensuring continual improvement of open data markets and the management of hampering gaps between developers' requests of data and data providers' provision of data. This paper explores the benefits with open data assessment based on an exploratory single case research design.
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| 19. |
- Hjalmarsson, Clara, 1969, et al.
(författare)
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Severe heart failure in a unique case of cobalamin-C-deficiency resolved with LVAD implantation and subsequent heart transplantation.
- 2024
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Ingår i: Molecular genetics and metabolism reports. - 2214-4269. ; 39
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Cobalamin c deficiency (cblC), an inborn error of vitamin B12 metabolism, is caused by mutations of the MMACHC gene. It usually leads to a multisystemic disease; 50% of all patients with cblC have various structural heart defects. Severe congestive heart failure (HF) may also occur and its prognosis is poorly documented. Case report We present the case of a young man who had been diagnosed with cblC due to C331T mutation in the MMACHC gene at the age of 3days and had been treated with substitution therapy (OH-Cbl, mecobalamine, carnitine, betaine, and calcium folinate) since then. He had mildly impaired cognitive function; an ectopic hypophysis/pituitary insufficiency, with adequate hormone replacement therapy; obstructive sleep apnea syndrome, treated with CPAP, bronchial asthma, and obesity (BMI of 30). The liver and kidney functions were normal. He developed severe dilated cardiomyopathy and HF at the age of 12y. With medical treatment, his condition improved and he was stable (NYHA class II) for several years. Six years later, his status deteriorated rapidly, as he developed advanced HF, INTERMACS 3. The cardiac ultrasound revealed dilated ventricles with severely depressed ejection fraction (EF), increased filling pressures, and pulmonary hypertension (sPAP 60mmHg). Cardiac MRI showed extremely dilated chambers (LVedv 609mL, RVedv 398mL) with pronounced non-compaction, and a left ventricle EF of 13%. A primary prophylactic ICD and a left ventricular assist device (LVAD/HM3) were implanted, and the patient was subsequently listed for heart transplantation (HTx). After 25months on the waiting list, he underwent an uncomplicated HTx. However postoperatively, he got two episodes of cardiac tamponade, as well as mediastinitis, treated with antibiotics and vaccum assisted closure. He developed severe kidney failure, which fully recovered after two months, and was treated successfully for an early moderate allograft rejection (ISHT 2). At the latest outward visit, twelve months after HTx, the patient was doing excellent. Summary To the best of our knowledge, this is the first ever reported case of a patient with CblC undergoing an LVAD implantation and subsequently a HTx. Although both interventions were complicated with bleeding events, this seems to be a treatment option for advanced HF in patients with CblC.
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| 20. |
- Hjalmarsson-Jordanius, Anders, et al.
(författare)
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Autonomous Transport : Transforming Logistics through Driverless Intelligent Transportation
- 2018
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Ingår i: Transportation Research Record. - : SAGE Publications. - 0361-1981 .- 2169-4052. ; 2672:7, s. 24-33
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Tidskriftsartikel (refereegranskat)abstract
- How can autonomous technology be used beyond end-customer autonomous driving features? This position paper addresses this problem by exploring a novel autonomous transport solution applied in the automotive logistics domain. We propose that factory-complete cars can be transformed to become their own autonomous guided vehicles and thus transport themselves when being moved from the factory for shipment. Cars equipped with such a system are driverless and use an onboard autonomous transport solution combined with the advanced driver assistance systems pre-installed in the car for end-customer use. The solution uses factory-equipped sensors as well as the connectivity infrastructure installed in the car. This means that the solution does not require any extra components to enable the car to transport itself autonomously to complete a transport mission in the logistics chain. The solution also includes an intelligent off-board traffic control system that defines the transport mission and manages the interaction between vehicles during systems operation. A prototype of the system has been developed which was tested successfully in live trials at the Volvo Car Group plant in Gothenburg Sweden in 2017. In the paper, autonomous transport is positioned in between autonomous guided vehicles and autonomous driving technology. A review of the literature on autonomous vehicle technology offers contextual background to this positioning. The paper also presents the solution and displays lessons learned from the live trials. Finally, other use areas are introduced for driverless autonomous transport beyond the automotive logistics domain that is the focus of this paper.
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| 21. |
- Hjalmarsson-Jordanius, Anders, et al.
(författare)
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Disrupting Automotive Logistics : Through a Combined Intelligent and Autonomous Transport Solution
- 2017
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Konferensbidrag (refereegranskat)abstract
- This technical paper addresses a novel and scalable autonomous transport system, applied in theautomotive logistics context. Production cars will be utilized as their own means of transportation in thelogistic chain, requiring no human driver. The cars will be guided by a novel intelligent transport solutioncombined with existing on-board Advanced Driver Assistance Systems (ADAS) functionality. Thisincludes using implemented sensors in the production car, fusion of sensor data collected by the car,and connectivity with existing and future mobile networks communication technology. An intelligent offboardtraffic control system will manage each production car in the logistic flow and direct the car frompoint A to point B, as well as manage the interaction between cars in the flow. A prototype of the systemhas been developed, implemented in a production car and during 2017, being tested in live car-trials inSweden. In this technical paper, we describe this evolution in vehicle logistics with a focus on its onboardcore sub-system and the off-board traffic control system. We pinpoint design features in thesystem, as well as discuss the capacity for the system to disrupt contemporary models of automotivelogistics.
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| 22. |
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| 23. |
- Lannemyr, Lukas, 1974, et al.
(författare)
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Differential Effects of Levosimendan and Dobutamine on Glomerular Filtration Rate in Patients With Heart Failure and Renal Impairment:ARandomized Double-Blind Controlled Trial.
- 2018
-
Ingår i: Journal of the American Heart Association. - 2047-9980. ; 7:16
-
Tidskriftsartikel (refereegranskat)abstract
- Background The management of the cardiorenal syndrome in advanced heart failure is challenging, and the role of inotropic drugs has not been fully defined. Our aim was to compare the renal effects of levosimendan versus dobutamine in patients with heart failure and renal impairment. Methods and Results In a randomized double-blind study, we assigned patients with chronic heart failure (left ventricular ejection fraction <40%) and impaired renal function (glomerular filtration rate <80mL/min per 1.73m2) to receive either levosimendan (loading dose 12μg/kg+0.1μg/kg per minute) or dobutamine (7.5 μg/kg per minute) for 75minutes. A pulmonary artery catheter was used for measurements of systemic hemodynamics, and a renal vein catheter was used to measure renal plasma flow by the infusion clearance technique for PAH (para-aminohippurate) corrected by renal extraction of PAH . Filtration fraction was measured by renal extraction of chromium ethylenediamine tetraacetic acid. A total of 32 patients completed the study. Following treatment, the levosimendan and dobutamine groups displayed similar increases in renal blood flow (22% and 26%, respectively) with no significant differences between groups. Glomerular filtration rate increased by 22% in the levosimendan group but remained unchanged in the dobutamine group ( P=0.012). Filtration fraction was not affected by levosimendan but decreased by 17% with dobutamine ( P=0.045). Conclusions In patients with chronic heart failure and renal impairment, levosimendan increases glomerular filtration rate to a greater extent than dobutamine and thus may be the preferred inotropic agent for treating patients with the cardiorenal syndrome. Clinical Trial Registration URL: https://www.clinicaltrials.gov . Unique identifier: NCT 02133105.
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| 24. |
- Ljungman, Charlotta, 1977, et al.
(författare)
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Differences in phenotypes, symptoms, and survival in patients with cardiomyopathy-a prospective observational study from the Sahlgrenska CardioMyoPathy Centre.
- 2023
-
Ingår i: Frontiers in cardiovascular medicine. - 2297-055X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Cardiomyopathy is the fourth most common cause of heart failure. The spectrum of cardiomyopathies may be impacted by changes in environmental factors and the prognosis may be influenced by modern treatment. The aim of this study is to create a prospective clinical cohort, the Sahlgrenska CardioMyoPathy Centre (SCMPC) study, and compare patients with cardiomyopathies in terms of phenotype, symptoms, and survival.The SCMPC study was founded in 2018 by including patients with all types of suspected cardiomyopathies. This study included data on patient characteristics, background, family history, symptoms, diagnostic examinations, and treatment including heart transplantation and mechanical circulatory support (MCS). Patients were categorized by the type of cardiomyopathy on the basis of the diagnostic criteria laid down by the European Society of Cardiology (ESC) working group on myocardial and pericardial diseases. The primary outcomes were death, heart transplantation, or MCS, analyzed by Kaplan-Meier and Cox proportional regression, adjusted for age, gender, LVEF and QRS width on ECG in milliseconds.In all, 461 patients and 73.1% men with a mean age of 53.6±16 years were included in the study. The most common diagnosis was dilated cardiomyopathy (DCM), followed by cardiac sarcoidosis and myocarditis. Dyspnea was the most common initial symptom in patients with DCM and amyloidosis, while patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) presented with ventricular arrythmias. Patients with ARVC, left-ventricular non-compaction cardiomyopathy (LVNC), hypertrophic cardiomyopathy (HCM), and DCM had the longest time from the debut of symptoms until inclusion in the study. Overall, 86% of the patients survived without heart transplantation or MCS after 2.5 years. The primary outcome differed among the cardiomyopathies, where the worst prognosis was reported for ARVC, LVNC, and cardiac amyloidosis. In a Cox regression analysis, it was found that ARVC and LVNC were independently associated with an increased risk of death, heart transplantation, or MCS compared with DCM. Further, female gender, a lower LVEF, and a wider QRS width were associated with an increased risk of the primary outcome.The SCMPC database offers a unique opportunity to explore the spectrum of cardiomyopathies over time. There is a large difference in characteristics and symptoms at debut and a remarkable difference in outcome, where the worst prognosis was reported for ARVC, LVNC, and cardiac amyloidosis.
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| 25. |
- Mårtensson, Jonas, et al.
(författare)
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Covariance analysis in SISO linear systems identification
- 2017
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 77, s. 82-92
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we analyze the asymptotic covariance of models of causal single-input single-output linear time invariant systems. Expressions for the asymptotic (co)variance of system properties estimated using the prediction error method are derived. These expressions delineate the impacts of model structure, model order, true system dynamics, and experimental conditions. A connection to results on frequency function estimation is established. Also, simple model structure independent upper bounds are derived. Explicit variance expressions and bounds are provided for common system properties such as impulse response coefficients and non-minimum phase zeros. As an illustration of the insights the expressions provide, they are used to derive conditions on the input spectrum which make the asymptotic variance of non-minimum phase zero estimates independent of the model order and model structure.
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| 26. |
- Mårtensson, Jonas, et al.
(författare)
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Variance Analysis in SISO Linear Systems Identification
- 2015
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Ingår i: Automatica. - 0005-1098 .- 1873-2836.
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Tidskriftsartikel (refereegranskat)abstract
- Causal single input single output linear time invariant systems are considered. Expressions for the asymptotic (co)variance of system properties estimated using the prediction error method are derived. These expressions delineate the impacts of model structure, model order, true system dynamics, and experimental conditions. A connection to results on frequency function estimation is established. Also, simple model structure independent upper bounds are derived. Explicit variance expressions and bounds are provided for common system properties such as impulse response coefficients and non-minimum phase zeros. As an illustration of the insights the expressions provide, they are used to derive conditions on the input spectrum which makethe asymptotic variance of non-minimum phase zero estimates independent of the model order and model structure.
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