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Over the last years, case-based reasoning has shown to be a promising AI-method in the legal domain, in which a fortiori reasoning is utilized to find similar cases from the past. However, this approach limits the decision making process by not being able to make a prediction for every new case, resulting in a significant number of cases remaining undecided. This thesis discusses the development and evaluation of a newly created case-based reasoning (C-BR) model designed to address this issue, by combining two previously designed models: the aforementioned legal C-BR model and a traditional similarity measure-based C-BR model. Similarity measures are commonly employed in C-BR models to retrieve previous cases in order to solve new problems. The combined approach proposed in this case study includes a fortiori reasoning as well, which involves a formal model of legal reasoning to retrieve similar cases from the past. This combined approach aims to improve the accuracy and reliability of C-BR models. The combined model was tested on a decision making problem of the CBR (‘Centraal Bureau Rijvaardigheidsbewijzen’: Dutch Central Office of Driving Certification), in which a decision of the fitness to drive of individuals was made based on their health deviations. The performance of the combined C-BR model was compared to models solely utilizing one of the two approaches. While results indicate that the combination of both techniques is a promising approach in C-BR, this model does not outperform the more traditional approach yet, making less humanlike decisions than traditional C-BR with a similarity measure. However, this study emphasizes the necessity of further research into the applicability of the integrated model in other domains. Future studies could investigate the potential of the combined C-BR model in datasets that are less complex. This could increase the performance of case-based reasoning models in AI, making these models applicable in even more domains to a

Hyperthermia treatment consists of elevating the temperature of the tumor to increase the effectiveness of radiotherapy and chemotherapy. Hyperthermia treatment planning (HTP) is an important tool to optimize treatment quality using pre-treatment temperature predictions. The accuracy of these predictions depends on modeling uncertainties such as tissue properties and positioning. In this study, we evaluated if HTP accuracy improves when the patient is imaged inside the applicator at the start of treatment. Because perfusion is a major uncertainty source, the importance of accurate treatment position and anatomy was evaluated using different perfusion values. Volunteers were scanned using MR imaging without ('planning setup') and with the MR-compatible hyperthermia device ('treatment setup'). Temperature-based quality indicators were used to assess the differences between the standard, apparent and the optimized hyperthermia dose. We conclude that pre-treatment imaging can improve HTP predictions accuracy but also, that tissue perfusion modelling is crucial if temperature-based optimization is applied., RST/Applied Radiation & Isotopes

Hyperthermia treatment consists of elevating the temperature of the tumor to increase the effectiveness of radiotherapy and chemotherapy. Hyperthermia treatment planning (HTP) is an important tool to optimize treatment quality using pre-treatment temperature predictions. The accuracy of these predictions depends on modeling uncertainties such as tissue properties and positioning. In this study, we evaluated if HTP accuracy improves when the patient is imaged inside the applicator at the start of treatment. Because perfusion is a major uncertainty source, the importance of accurate treatment position and anatomy was evaluated using different perfusion values. Volunteers were scanned using MR imaging without (“planning setup”) and with the MR-compatible hyperthermia device (“treatment setup”). Temperature-based quality indicators were used to assess the differences between the standard, apparent and the optimized hyperthermia dose. We conclude that pre-treatment imaging can improve HTP predictions accuracy but also, that tissue perfusion modelling is crucial if temperature-based optimization is applied.

Hyperthermia treatment consists of elevating the temperature of the tumor to increase the effectiveness of radiotherapy and chemotherapy. Hyperthermia treatment planning (HTP) is an important tool to optimize treatment quality using pre-treatment temperature predictions. The accuracy of these predictions depends on modeling uncertainties such as tissue properties and positioning. In this study, we evaluated if HTP accuracy improves when the patient is imaged inside the applicator at the start of treatment. Because perfusion is a major uncertainty source, the importance of accurate treatment position and anatomy was evaluated using different perfusion values. Volunteers were scanned using MR imaging without ('planning setup') and with the MR-compatible hyperthermia device ('treatment setup'). Temperature-based quality indicators were used to assess the differences between the standard, apparent and the optimized hyperthermia dose. We conclude that pre-treatment imaging can improve HTP predictions accuracy but also, that tissue perfusion modelling is crucial if temperature-based optimization is applied.

Optimization-based controllers, such as Model Predictive Control (MPC), have attracted significant research interest due to their intuitive concept, constraint handling capabilities, and natural application to multi-input multi-output systems. However, the computational complexity of solving a receding horizon problem at each time step remains a challenge for the deployment of MPC. This is particularly the case for systems constrained by many inequalities. Recently, we introduced the concept of constraint-adaptive MPC (ca-MPC) to address this challenge for linear systems with hard constraints. In ca-MPC, at each time step, a subset of the constraints is removed from the optimization problem, thereby accelerating the optimization procedure, while resulting in identical closed-loop behavior. The present paper extends this framework to soft-constrained MPC by detecting and removing constraints based on sub-optimal predicted input sequences, which is rather easy for soft-constrained MPC due to the receding horizon principle and the inclusion of slack variables. We will translate these new ideas explicitly to an offset-free output tracking problem. The effectiveness of these ideas is demonstrated on a two-dimensional thermal transport model, showing a three order of magnitude improvement in online computational time of the MPC scheme.

OBJECTIVES: Current choice models in healthcare (and beyond) can provide suboptimal predictions of healthcare users' decisions. One reason for such inaccuracy is that standard microeconomic theory assumes that decisions of healthcare users are made in a social vacuum. Healthcare choices, however, can in fact be (entirely) socially determined. To achieve more accurate choice predictions within healthcare and therefore better policy decisions, the social influences that affect healthcare user decision-making need to be identified and explicitly integrated into choice models. The purpose of this study is to develop a socially interdependent choice framework of healthcare user decision-making.DESIGN: A mixed-methods approach will be used. A systematic literature review will be conducted that identifies the social influences on healthcare user decision-making. Based on the outcomes of a systematic literature review, an interview guide will be developed that assesses which, and how, social influences affect healthcare user decision-making in four different medical fields. This guide will be used during two exploratory focus groups to assess the engagement of participants and clarity of questions and probes. The refined interview guide will be used to conduct the semistructured interviews with healthcare professionals and users. These interviews will explore in detail which, and how, social influences affect healthcare user decision-making. Focus group and interview transcripts will be analysed iteratively using a constant comparative approach based on a mix of inductive and deductive coding. Based on the outcomes, a social influence independent choice framework for healthcare user decision-making will be drafted. Finally, the Delphi technique will be employed to achieve consensus about the final version of this choice framework.ETHICS AND DISSEMINATION: This study was approved by the Erasmus School of Health Policy and Management Research Ethics Review Co

The full text of this thesis is not available due to privacy or embargo reasons.

In model predictive control (MPC) for large-scale applications, the computational limitations for on-line optimization often lead to the use of (relatively) short prediction horizons. In this paper, we show that as a result, the controller optimizes over only a fraction of the dynamics of the large-scale system. Based on this observation, which we will formalize, we propose a method to construct reduced-order models of minimal order, by exploiting the system-theoretic concept of finite-horizon observability, that exactly match the response of the large-scale system within a finite horizon. These so-called finite-horizon minimal realizations are used to implement equivalent MPC schemes with reduced computational effort (or the same computational effort but with a larger prediction horizon) without sacrificing accuracy/performance (as the equivalent optimization problem has the same optimizers as the original MPC problem). By computing finite-horizon minimal realizations, we can determine the dynamics as "seen"by the MPC, which can provide useful design insights, in particular, when tuning the prediction horizon. We demonstrate the strengths of our results in a numerical case study.

Accurate state estimates are required for increasingly complex systems, to enable, for example, feedback control. However, available state estimation schemes are not necessarily real-time feasible for certain large-scale systems. Therefore, we develop in this paper, a real-time feasible state-estimation scheme for a class of large-scale systems that approximates the steady state Kalman filter. In particular, we focus on systems where the state-vector is the result of discretizing the spatial domain, as typically seen in Partial Differential Equations. In such cases, the correlation between states in the state-vector often have an intuitive interpretation on the spatial domain, which can be exploited to obtain a significant reduction in computational complexity, while still providing accurate state estimates. We illustrate these strengths of our method through a hyperthermia cancer treatment case study. The results of the case study show significant improvements in the computation time, while simultaneously obtaining good state estimates, when compared to Ensemble Kalman filters and Kalman filters using reduced-order models.

Reducing the computation time of model predictive control (MPC) is important, especially for systems constrained by many state constraints. In this paper, we propose a new online constraint removal framework for linear systems, for which we coin the term constraint-adaptive MPC (ca-MPC). In so-called exact ca-MPC, we adapt the imposed constraints by removing, at each time-step, a subset of the state constraints in order to reduce the computational complexity of the receding-horizon optimal control problem, while ensuring that the closed-loop behavior is identical to that of the original MPC law. We also propose an approximate ca-MPC scheme in which a further reduction of computation time can be accomplished by a tradeoff with closed-loop performance, while still preserving recursive feasibility, stability, and constraint satisfaction properties. The online constraint removal exploits fast backward and forward reachability computations combined with optimality properties.

Purpose: A crucial aspect of quality assurance in thermal therapy is periodic demonstration of the heating performance of the device. Existing methods estimate the specific absorption rate (SAR) from the temperature rise after a short power pulse, which yields a biased estimate as thermal diffusion broadens the apparent SAR pattern. To obtain an unbiased estimate, we propose a robust frequency-domain method that simultaneously identifies the SAR as well as the thermal dynamics. Methods: We propose a method consisting of periodic modulation of the FUS power while recording the response with MR thermometry (MRT). This approach enables unbiased measurements of spatial Fourier coefficients that encode the thermal response. These coefficients are substituted in a generic thermal model to simultaneously estimate the SAR, diffusivity, and damping. The method was tested using a cylindrical phantom and a 3 T clinical MR-HIFU system. Three scenarios with varying modulation strategies are chosen to challenge the method. The results are compared to the well-known power pulse technique. Results: The thermal diffusivity is estimated at 0.151 mm 2s -1 with a standard deviation of 0.01 mm 2s -1 between six experiments. The SAR estimates are consistent between all experiments and show an excellent signal-to-noise ratio (SNR) compared to the well established power pulse method. The frequency-domain method proved to be insensitive to B 0-drift and non steady-state initial temperature distributions. Conclusion: The proposed frequency-domain estimation method shows a high SNR and provided reproducible estimates of the SAR and the corresponding thermal diffusivity. The findings suggest that frequency-domain tools can be highly effective at estimating the SAR from (biased) MRT data acquired during periodic power modulation.

Purpose: A crucial aspect of quality assurance in thermal therapy is periodic demonstration of the heating performance of the device. Existing methods estimate the specific absorption rate (SAR) from the temperature rise after a short power pulse, which yields a biased estimate as thermal diffusion broadens the apparent SAR pattern. To obtain an unbiased estimate, we propose a robust frequency-domain method that simultaneously identifies the SAR as well as the thermal dynamics. Methods: We propose a method consisting of periodic modulation of the FUS power while recording the response with MR thermometry (MRT). This approach enables unbiased measurements of spatial Fourier coefficients that encode the thermal response. These coefficients are substituted in a generic thermal model to simultaneously estimate the SAR, diffusivity, and damping. The method was tested using a cylindrical phantom and a 3 T clinical MR-HIFU system. Three scenarios with varying modulation strategies are chosen to challenge the method. The results are compared to the well-known power pulse technique. Results: The thermal diffusivity is estimated at 0.151 mm2s–1 with a standard deviation of 0.01 mm2s–1 between six experiments. The SAR estimates are consistent between all experiments and show an excellent signal-to-noise ratio (SNR) compared to the well established power pulse method. The frequency-domain method proved to be insensitive to B 0-drift and non steady-state initial temperature distributions. Conclusion: The proposed frequency-domain estimation method shows a high SNR and provided reproducible estimates of the SAR and the corresponding thermal diffusivity. The findings suggest that frequency-domain tools can be highly effective at estimating the SAR from (biased) MRT data acquired during periodic power modulation.

Reducing the computation time of model predictive control (MPC) is important, especially for systems constrained by many state constraints. In this paper, we propose a new online constraint removal framework for linear systems, for which we coin the term constraint-adaptive MPC (ca-MPC). In so-called exact ca-MPC, we adapt the imposed constraints by removing, at each time-step, a subset of the state constraints in order to reduce the computational complexity of the receding-horizon optimal control problem, while ensuring that the closed-loop behavior is identical to that of the original MPC law. We also propose an approximate ca-MPC scheme in which a further reduction of computation time can be accomplished by a tradeoff with closed-loop performance, while still preserving recursive feasibility, stability, and constraint satisfaction properties. The online constraint removal exploits fast backward and forward reachability computations combined with optimality properties.

Although the industrial release of pollutant substances into North West European river delta has been decreasing over the last decades, The Netherlands still has one of the worst water qualities in Europe. In this strategic regional design we aim to improve the quality, quantity and distribution of water through the concept of sustainable land use. Industrial waste, waste water and increasing demand for land are the main pressures on our river water system. Considering the future risk of flood and drought there is also a big insecurity about our water quantity. There will be a need for more space and fair use of our water system. The goal is to restore the water quality in the North West European river delta and secure a sufficient water quantity for future use, flood protection and a fair distribution of the water available. In this report research by design is used to develop a spatial vision and spatial strategy that will create spatial water justice and with that a healthy river landscape. There are three themes related to spatial water justice: water quantity, quality and distribution. To meet sufficient standards for these three themes there are three important concepts: a circular industrial water system, decontamination and room for the rivers. All of them deal with the broader concept of sustainable land use. Sustainable land use is the fair and balanced distribution of land and environmental resources. This report shows a regenerative decontamination design for the North West European river delta. It is the development of a catalog of interventions that can be used in the whole river basin. Many small changes make a big difference in an interconnected system like a river basin. Every small intervention that will help clean the water, adds to the whole system and changes the whole system into a healthy and just river landscape. Together with this catalog there is a manual where the interventions are connected to spatial qualities of a specific location. The, AR2U086 R&D Studio – Spatial Strategies for the Global Metropolis, Architecture, Urbanism and Building Sciences | Urbanism

Reducing the computation time of model predictive control (MPC) is important, especially for systems constrained by many state constraints. In this paper, we propose a new online constraint removal framework for linear systems, for which we coin the term constraint-adaptive MPC (ca-MPC). In so-called exact ca-MPC, we adapt the imposed constraints by removing, at each time-step, a subset of the state constraints in order to reduce the computational complexity of the receding-horizon optimal control problem, while ensuring that the closed-loop behavior is {\em identical} to that of the original MPC law. We also propose an approximate ca-MPC scheme in which a further reduction of computation time can be accomplished by a tradeoff with closed-loop performance, while still preserving recursive feasibility, stability, and constraint satisfaction properties. The online constraint removal exploits fast backward and forward reachability computations combined with optimality properties.

Reducing the computation time of model predictive control (MPC) is important, especially for systems constrained by many state constraints. In this paper, we propose a new online constraint removal framework for linear systems, for which we coin the term constraint-adaptive MPC (ca-MPC). In so-called exact ca-MPC, we adapt the imposed constraints by removing, at each time-step, a subset of the state constraints in order to reduce the computational complexity of the receding-horizon optimal control problem, while ensuring that the closed-loop behavior is {\em identical} to that of the original MPC law. We also propose an approximate ca-MPC scheme in which a further reduction of computation time can be accomplished by a tradeoff with closed-loop performance, while still preserving recursive feasibility, stability, and constraint satisfaction properties. The online constraint removal exploits fast backward and forward reachability computations combined with optimality properties.

Willems' fundamental lemma has recently received an impressive amount of attention in the (data-driven) control community. In this paper, we formulate a frequency-domain equivalent of this lemma. In doing so, we bridge the gap between recent developments in data-driven analysis and control and the extensive knowledge on non-parametric frequency-domain identification that has accumulated, particularly in industry, through decades of working with classical (frequency-domain) control and identification techniques. Our formulation also allows for the combination of multiple data sets in the sense that, in the data, multiple input directions may be excited at the same frequency. We also illustrate the usefulness of our results by demonstrating how they can be applied to perform frequency-domain-data-driven simulation.

Background: During resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to measure temperature noninvasively in a volume during treatment. However, several studies have shown that this approach is not always sufficiently accurate for thermal dosimetry in areas with motion, such as the pelvic region. Model-based temperature estimation is a promising approach to correct and supplement 3D online temperature estimation in regions where MR thermometry is unreliable or cannot be measured. However, complete 3D temperature modeling of the pelvic region is too complex for online usage. Purpose: This study aimed to evaluate the use of proper orthogonal decomposition (POD) model reduction combined with Kalman filtering to improve temperature estimation using MR thermometry. Furthermore, we assessed the benefit of this method using data from hyperthermia treatment where there were limited and unreliable MR thermometry measurements. Methods: The performance of POD–Kalman filtering was evaluated in several heating experiments and for data from patients treated for locally advanced cervical cancer. For each method, we evaluated the mean absolute error (MAE) concerning the temperature measurements acquired by the thermal probes, and we assessed the reproducibility and consistency using the standard deviation of error (SDE). Furthermore, three patient groups were defined according to susceptibility artifacts caused by the level of intestinal gas motion to assess if the POD–Kalman filtering could compensate for missing and unreliable MR thermometry measurements. Results: First, we showed that this method is beneficial and reproducible in phantom experiments. Second, we demonstrated that the combined method improved the ma, RST/Applied Radiation & Isotopes

Background: During resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to measure temperature noninvasively in a volume during treatment. However, several studies have shown that this approach is not always sufficiently accurate for thermal dosimetry in areas with motion, such as the pelvic region. Model-based temperature estimation is a promising approach to correct and supplement 3D online temperature estimation in regions where MR thermometry is unreliable or cannot be measured. However, complete 3D temperature modeling of the pelvic region is too complex for online usage. Purpose: This study aimed to evaluate the use of proper orthogonal decomposition (POD) model reduction combined with Kalman filtering to improve temperature estimation using MR thermometry. Furthermore, we assessed the benefit of this method using data from hyperthermia treatment where there were limited and unreliable MR thermometry measurements. Methods: The performance of POD–Kalman filtering was evaluated in several heating experiments and for data from patients treated for locally advanced cervical cancer. For each method, we evaluated the mean absolute error (MAE) concerning the temperature measurements acquired by the thermal probes, and we assessed the reproducibility and consistency using the standard deviation of error (SDE). Furthermore, three patient groups were defined according to susceptibility artifacts caused by the level of intestinal gas motion to assess if the POD–Kalman filtering could compensate for missing and unreliable MR thermometry measurements. Results: First, we showed that this method is beneficial and reproducible in phantom experiments. Second, we demonstrated that the combined method improved the ma

Purpose: Hyperthermia treatments are successful adjuvants to conventional cancer therapies in which the tumor is sensitized by heating. To monitor and guide the hyperthermia treatment, measuring the tumor and healthy tissue temperature is important. The typical clinical practice heavily relies on intraluminal probe measurements that are uncomfortable for the patient and only provide spatially sparse temperature information. A solution may be offered through recent advances in magnetic resonance thermometry, which allows for three-dimensional internal temperature measurements. However, these measurements are not widely used in the pelvic region due to a low signal-to-noise ratio and presence of image artifacts. Methods: To advance the clinical integration of magnetic resonance-guided cancer treatments, we consider the problem of removing air-motion-induced image artifacts. Thereto, we propose a new combined thermal and magnetic susceptibility model-based temperature estimation scheme that uses temperature estimates to improve the removal of air-motion-induced image artifacts. The method is experimentally validated using a dedicated phantom that enables the controlled injection of air-motion artifacts and with in vivo thermometry from a clinical hyperthermia treatment. Results: We showed, using probe measurements in a heated phantom, that our method reduced the mean absolute error (MAE) by 58% compared to the state-of-the-art near a moving air volume. Moreover, with in vivo thermometry our method obtained a MAE reduction between 17% and 95% compared to the state-of-the-art. Conclusion: We expect that the combined thermal and magnetic susceptibility modeling used in model-based temperature estimation can significantly improve the monitoring in hyperthermia treatments and enable feedback strategies to further improve MR-guided hyperthermia cancer treatments.

The latest generation wafer scanners use extreme ultraviolet light to project a pattern of electronic connections onto a silicon wafer. A significant part of the projection light is absorbed by the mirrors in the projection system. This causes the mirrors to heat up and expand, which leads to a significant reduction in the imaging quality of the wafer scanner. The imaging quality can be improved by applying an additional actuation heat load to the mirrors. Because the wafer scanner can be used with a large number of different illumination settings (which lead to different load cases) and the number of thermal actuators is limited, designing an effective actuation heater layout is an important but nontrivial task. To assist this design process, this paper proposes a computational framework to optimize a small number of spatial actuation heat load shapes with their corresponding intensities for a large number of load cases. It is guaranteed that the obtained actuation heat load shapes can keep the steady-state temperature in the optical surface of the mirror sufficiently close to a desired temperature in all considered load cases. The proposed computational framework is applied to a representative three-dimensional finite element model of a mirror. The obtained actuation heat load shapes and their corresponding intensities provide insights for the design of a thermal actuation layout for mirror heating.

Purpose: Hyperthermia treatments are successful adjuvants to conventional cancer therapies in which the tumor is sensitized by heating. To monitor and guide the hyperthermia treatment, measuring the tumor and healthy tissue temperature is important. The typical clinical practice heavily relies on intraluminal probe measurements that are uncomfortable for the patient and only provide spatially sparse temperature information. A solution may be offered through recent advances in magnetic resonance thermometry, which allows for three-dimensional internal temperature measurements. However, these measurements are not widely used in the pelvic region due to a low signal-to-noise ratio and presence of image artifacts. Methods: To advance the clinical integration of magnetic resonance-guided cancer treatments, we consider the problem of removing air-motion-induced image artifacts. Thereto, we propose a new combined thermal and magnetic susceptibility model-based temperature estimation scheme that uses temperature estimates to improve the removal of air-motion-induced image artifacts. The method is experimentally validated using a dedicated phantom that enables the controlled injection of air-motion artifacts and with in vivo thermometry from a clinical hyperthermia treatment. Results: We showed, using probe measurements in a heated phantom, that our method reduced the mean absolute error (MAE) by 58% compared to the state-of-the-art near a moving air volume. Moreover, with in vivo thermometry our method obtained a MAE reduction between 17% and 95% compared to the state-of-the-art. Conclusion: We expect that the combined thermal and magnetic susceptibility modeling used in model-based temperature estimation can significantly improve the monitoring in hyperthermia treatments and enable feedback strategies to further improve MR-guided hyperthermia cancer treatments.

Introduction: In clinical practice at Erasmus MC, the target-to-hotspot-quotient (THQ) of the specific absorption rate (SAR) is used to optimize phases and amplitudes of the signals to be applied to the hyperthermia applicator [1]. Recent research showed that the ratio between tumor and healthy tissue temperatures can be increased when amplitudes and phases are time-multiplexed when applying SAR optimization [2]. However, direct temperature optimization achieves higher tumor temperatures when considering time-multiplexed antenna steering [3]. In this work, we investigated the benefit of time-multiplexed steering when applying temperature optimization in models of patients with tumors on the head and neck region. Methods: For five patients with a tumor in the head and neck region, a Sim4Life model was created and treatment planning was applied for the HyperCollar3D. A single distribution SAR based THQ optimization was performed for reference. A novel temperature optimization scheme was developed, which optimizes the tumor temperature for the first 15 minutes of the treatment. This results in higher tumor temperatures throughout the treatment by explicitly including the transient effects in the optimization. The evaluation was based on simulations of the full treatment time of 75 minutes, with the total power scaled to reach maximum 43°C in the tumor. Performance was evaluated by comparing T50 for both healthy and tumor tissue during treatment. Results: The ratio between T50 in the healthy and tumor tissue was improved when using the novel temperature-based optimization for time-multiplexed distributions (Figure 1C). The SAR THQ showed a lower ratio for the time-multiplexed solution, this is resolved in the temperature simulations (Figure 2). Conclusion/Discussion: The resulting T50 values, show that the temperatures during treatment might benefit from the temperature optimized with the multiplexed steering approach: either the temperature in the tumor t

Currently available European Alpine Altitude Climate Treatment (AACT) programs combine the physical characteristics of altitude with the avoidance of environmental triggers in the alpine climate and a personalized multidisciplinary pulmonary rehabilitation approach. The reduced barometric pressure, oxygen pressure, and air density, the relatively low temperature and humidity, and the increased UV radiation at moderate altitude induce several physiological and immunological adaptation responses. The environmental characteristics of the alpine climate include reduced aeroallergens such as house dust mites (HDM), pollen, fungi, and less air pollution. These combined factors seem to have immunomodulatory effects controlling pathogenic inflammatory responses and favoring less neuro-immune stress in patients with different asthma phenotypes. The extensive multidisciplinary treatment program may further contribute to the observed clinical improvement by AACT in asthma control and quality of life, fewer exacerbations and hospitalizations, reduced need for oral corticosteroids (OCS), improved lung function, decreased airway hyperresponsiveness (AHR), improved exercise tolerance, and improved sinonasal outcomes. Based on observational studies and expert opinion, AACT represents a valuable therapy for those patients irrespective of their asthma phenotype, who cannot achieve optimal control of their complex condition despite all the advances in medical science and treatment according to guidelines, and therefore run the risk of falling into a downward spiral of loss of physical and mental health. In the light of the observed rapid decrease in inflammation and immunomodulatory effects, AACT can be considered as a natural treatment that targets biological pathways.

Background During resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to measure temperature noninvasively in a volume during treatment. However, several studies have shown that this approach is not always sufficiently accurate for thermal dosimetry in areas with motion, such as the pelvic region. Model-based temperature estimation is a promising approach to correct and supplement 3D online temperature estimation in regions where MR thermometry is unreliable or cannot be measured. However, complete 3D temperature modeling of the pelvic region is too complex for online usage. Purpose This study aimed to evaluate the use of proper orthogonal decomposition (POD) model reduction combined with Kalman filtering to improve temperature estimation using MR thermometry. Furthermore, we assessed the benefit of this method using data from hyperthermia treatment where there were limited and unreliable MR thermometry measurements. Methods The performance of POD–Kalman filtering was evaluated in several heating experiments and for data from patients treated for locally advanced cervical cancer. For each method, we evaluated the mean absolute error (MAE) concerning the temperature measurements acquired by the thermal probes, and we assessed the reproducibility and consistency using the standard deviation of error (SDE). Furthermore, three patient groups were defined according to susceptibility artifacts caused by the level of intestinal gas motion to assess if the POD–Kalman filtering could compensate for missing and unreliable MR thermometry measurements. Results First, we showed that this method is beneficial and reproducible in phantom experiments. Second, we demonstrated that

Purpose: Hyperthermia treatments are successful adjuvants to conventional cancer therapies in which the tumor is sensitized by heating. To monitor and guide the hyperthermia treatment, measuring the tumor and healthy tissue temperature is important. The typical clinical practice heavily relies on intraluminal probe measurements that are uncomfortable for the patient and only provide spatially sparse temperature information. A solution may be offered through recent advances in magnetic resonance thermometry, which allows for three-dimensional internal temperature measurements. However, these measurements are not widely used in the pelvic region due to a low signal-to-noise ratio and presence of image artifacts. Methods: To advance the clinical integration of magnetic resonance-guided cancer treatments, we consider the problem of removing air-motion-induced image artifacts. Thereto, we propose a new combined thermal and magnetic susceptibility model-based temperature estimation scheme that uses temperature estimates to improve the removal of air-motion-induced image artifacts. The method is experimentally validated using a dedicated phantom that enables the controlled injection of air-motion artifacts and with in vivo thermometry from a clinical hyperthermia treatment. Results: We showed, using probe measurements in a heated phantom, that our method reduced the mean absolute error (MAE) by 58% compared to the state-of-the-art near a moving air volume. Moreover, with in vivo thermometry our method obtained a MAE reduction between 17% and 95% compared to the state-of-the-art. Conclusion: We expect that the combined thermal and magnetic susceptibility modeling used in model-based temperature estimation can significantly improve the monitoring in hyperthermia treatments and enable feedback strategies to further improve MR-guided hyperthermia cancer treatments.

The latest generation wafer scanners use extreme ultraviolet light to project a pattern of electronic connections onto a silicon wafer. A significant part of the projection light is absorbed by the mirrors in the projection system. This causes the mirrors to heat up and expand, which leads to a significant reduction in the imaging quality of the wafer scanner. The imaging quality can be improved by applying an additional actuation heat load to the mirrors. Because the wafer scanner can be used with a large number of different illumination settings (which lead to different load cases) and the number of thermal actuators is limited, designing an effective actuation heater layout is an important but nontrivial task. To assist this design process, this paper proposes a computational framework to optimize a small number of spatial actuation heat load shapes with their corresponding intensities for a large number of load cases. It is guaranteed that the obtained actuation heat load shapes can keep the steady-state temperature in the optical surface of the mirror sufficiently close to a desired temperature in all considered load cases. The proposed computational framework is applied to a representative three-dimensional finite element model of a mirror. The obtained actuation heat load shapes and their corresponding intensities provide insights for the design of a thermal actuation layout for mirror heating.

Background: During resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to measure temperature noninvasively in a volume during treatment. However, several studies have shown that this approach is not always sufficiently accurate for thermal dosimetry in areas with motion, such as the pelvic region. Model-based temperature estimation is a promising approach to correct and supplement 3D online temperature estimation in regions where MR thermometry is unreliable or cannot be measured. However, complete 3D temperature modeling of the pelvic region is too complex for online usage. Purpose: This study aimed to evaluate the use of proper orthogonal decomposition (POD) model reduction combined with Kalman filtering to improve temperature estimation using MR thermometry. Furthermore, we assessed the benefit of this method using data from hyperthermia treatment where there were limited and unreliable MR thermometry measurements. Methods: The performance of POD–Kalman filtering was evaluated in several heating experiments and for data from patients treated for locally advanced cervical cancer. For each method, we evaluated the mean absolute error (MAE) concerning the temperature measurements acquired by the thermal probes, and we assessed the reproducibility and consistency using the standard deviation of error (SDE). Furthermore, three patient groups were defined according to susceptibility artifacts caused by the level of intestinal gas motion to assess if the POD–Kalman filtering could compensate for missing and unreliable MR thermometry measurements. Results: First, we showed that this method is beneficial and reproducible in phantom experiments. Second, we demonstrated that the combined method improved the ma

Background: During resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to measure temperature noninvasively in a volume during treatment. However, several studies have shown that this approach is not always sufficiently accurate for thermal dosimetry in areas with motion, such as the pelvic region. Model-based temperature estimation is a promising approach to correct and supplement 3D online temperature estimation in regions where MR thermometry is unreliable or cannot be measured. However, complete 3D temperature modeling of the pelvic region is too complex for online usage. Purpose: This study aimed to evaluate the use of proper orthogonal decomposition (POD) model reduction combined with Kalman filtering to improve temperature estimation using MR thermometry. Furthermore, we assessed the benefit of this method using data from hyperthermia treatment where there were limited and unreliable MR thermometry measurements. Methods: The performance of POD–Kalman filtering was evaluated in several heating experiments and for data from patients treated for locally advanced cervical cancer. For each method, we evaluated the mean absolute error (MAE) concerning the temperature measurements acquired by the thermal probes, and we assessed the reproducibility and consistency using the standard deviation of error (SDE). Furthermore, three patient groups were defined according to susceptibility artifacts caused by the level of intestinal gas motion to assess if the POD–Kalman filtering could compensate for missing and unreliable MR thermometry measurements. Results: First, we showed that this method is beneficial and reproducible in phantom experiments. Second, we demonstrated that the combined method improved the ma, RST/Applied Radiation & Isotopes

New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data. This work analyses how the most prevalent CMP designs affect people's consent choices. We scraped the designs of the five most popular CMPs on the top 10,000 websites in the UK (n=680). We found that dark patterns and implied consent are ubiquitous; only 11.8% meet our minimal requirements based on European law. Second, we conducted a field experiment with 40 participants to investigate how the eight most common designs affect consent choices. We found that notification style (banner or barrier) has no effect; removing the opt-out button from the first page increases consent by 22-23 percentage points; and providing more granular controls on the first page decreases consent by 8-20 percentage points. This study provides an empirical basis for the necessary regulatory action to enforce the GDPR, in particular the possibility of focusing on the centralised, third-party CMP services as an effective way to increase compliance., NSF (Award 1639994)

Background: Asthma is a common chronic disease with various clinical presentations. Although most patients are able to reach good asthma control, some patients are not able to reach sufficient asthma control following the regular treatment guidelines and could be referred to high-altitude climate therapy (HACT). HACT includes environmental trigger avoidance in the alpine climate with multidisciplinary clinical treatment. Patients with severe and difficult-to-control asthma, who are unable to reach asthma control at sea level, can follow a 12-week lung rehabilitation program at 1600 m above sea level. Mobile health (mHealth) tools can be used to enhance self-management in these patients when they return home. For an mHealth system to be effective, it must meet the expectations of the end users. Objective: In this Davos@home study, we explored the attitudes toward mHealth aimed at supporting the self-management of patients with severe, difficult-to-control asthma who underwent HACT and asthma health care providers. Methods: In the first stage, interviews with referrers to HACT and focus groups with patients with asthma who participated in or completed HACT were conducted. The data were then analyzed thematically. On the basis of these results, a questionnaire was developed. In the second stage of the study, this questionnaire, combined with the Asthma Control Questionnaire and the Individual Innovativeness Questionnaire, was provided to patients who completed HACT. Results: In total, 11 interviews and 3 focus groups (n=18, age 47.6, SD 12.1 years, Asthma Control Questionnaire score 2.6, SD 1.0) were conducted. A total of 3 themes were identified: potential goals, useful measurements, and perceived barriers and facilitators. The questionnaire developed in stage 2 included items based on these results. The most agreed-upon goal among the 52 patients who completed the questionnaire was to increase their asthma control (45/52, 86% of the patients). Conclusions: Different

Model Predictive Control (MPC) is a successful control methodology, which is applied to increasingly complex systems. However, real-time feasibility of MPC can be challenging for complex systems, certainly when an (extremely) large number of constraints have to be adhered to. For such scenarios with a large number of state constraints, this paper proposes two novel MPC schemes for general nonlinear systems, which we call constraint-adaptive MPC. These novel schemes dynamically select at each time step a (varying) set of constraints that are included in the on-line optimization problem. Carefully selecting the included constraints can significantly reduce, as we will demonstrate, the computational complexity with often only a slight impact on the closed-loop performance. Although not all (state) constraints are imposed in the on-line optimization, the schemes still guarantee recursive feasibility and constraint satisfaction. A numerical case study illustrates the proposed MPC schemes and demonstrates the achieved computation time improvements exceeding two orders of magnitude without loss of performance.

Contains fulltext : 219577.pdf (Publisher’s version ) (Open Access), Background: Executive functions have been proposed to account for individual variation in reading comprehension beyond the contributions of decoding skills and language skills. However, insight into the direct and indirect effects of multiple executive functions on fifth-grade reading comprehension, while accounting for decoding and language skills, is limited. Aim: The present study investigated the direct and indirect effects of fourth-grade executive functions (i.e., working memory, inhibition, and planning) on fifth-grade reading comprehension, after accounting for decoding and language skills. Sample: The sample included 113 fourth-grade children (including 65 boys and 48 girls; Age M = 9.89; SD = .44 years). Methods: The participants were tested on their executive functions (working memory, inhibition and planning), and their decoding skills, language skills (vocabulary and syntax knowledge) and reading comprehension, one year later. Results: Using structural equation modelling, the results indicated direct effects of working memory and planning on reading comprehension, as well as indirect effects of working memory and inhibition via decoding (X2 = 2.46). Conclusions: The results of the present study highlight the importance of executive functions for reading comprehension after taking variance in decoding and language skills into account: Both working memory and planning uniquely contributed to reading comprehension. In addition, working memory and inhibition also supported decoding. As a practical implication, educational professionals should not only consider the decoding and language skills children bring into the classroom, but their executive functions as well.

BACKGROUND AND PURPOSE: Plaque ulceration is a marker of previous plaque rupture. We studied the association between atherosclerotic plaque composition at baseline and plaque ulceration at baseline and follow-up. MATERIALS AND METHODS: We included symptomatic patients with a carotid stenosis of ,70% who underwent MDCTA and MR imaging at baseline (n=180). MDCTA was repeated at 2 years (n=73). We assessed the presence of ulceration using MDCTA. Baseline MR imaging was used to assess the vessel wall volume and the presence and volume of plaque components (intraplaque hemorrhage, lipid-rich necrotic core, and calcifications) and the fibrous cap status. Associations at baseline were evaluated with binary logistic regression and reported with an OR and its 95% CI. Simple statistical testing was performed in the follow-up analysis. RESULTS: At baseline, the prevalence of plaque ulceration was 27% (49/180). Increased wall volume (OR = 12.1; 95% CI, 3.5-42.0), higher relative lipid-rich necrotic core (OR= 1.7; 95% CI, 1.3-2.2), higher relative intraplaque hemorrhage volume (OR= 1.7; 95% CI, 1.3-2.2), and a thin-or-ruptured fibrous cap (OR = 3.4; 95% CI, 1.7-6.7) were associated with the presence of ulcerations at baseline. In 8% (6/73) of the patients, a new ulcer developed. Plaques with a new ulceration at follow-up had at baseline a larger wall volume (1.04 cm3 [IQR, 0.97-1.16 cm3] versus 0.86 cm3 [IQR, 0.73-1.00 cm3]; P=.029), a larger relative lipid-rich necrotic core volume (23% [IQR, 13-31%] versus 2% [IQR, 0-14%]; P=.002), and a larger relative intraplaque hemorrhage volume (14% [IQR, 8-24%] versus 0% [IQR, 0-5%]; P<.001). CONCLUSIONS: Large atherosclerotic plaques and plaques with intraplaque hemorrhage and lipid-rich necrotic cores were associated with plaque ulcerations at baseline and follow-up.

Regarding investigations on sentence processing, research is often done using complex models that use various parameters, which leads to opacity of models of human language. In addition, this causes the models to be extremely difficult to compare with other models from earlier experiments. With the intention of finding alternative features for computational models and to encourage further research, we aim to find a correlation between memory recall and word similarity in sentence processing. To test this theory, the outlines of a similar research of Smith & Vasishth (2020) were implemented, to investigate if reading times were affected by different word similarities regarding presupposition resolution. Reading times were subtracted from a memory recall experiment carried out by Jan Winkowski (currently unpublished) to compare reading data to cosine similarities between certain words in each sentence. Results indicate an unforeseen effect of word similarity on reading times, since reading times increased when word similarities became greater.

Regarding investigations on sentence processing, research is often done using complex models that use various parameters, which leads to opacity of models of human language. In addition, this causes the models to be extremely difficult to compare with other models from earlier experiments. With the intention of finding alternative features for computational models and to encourage further research, we aim to find a correlation between memory recall and word similarity in sentence processing. To test this theory, the outlines of a similar research of Smith & Vasishth (2020) were implemented, to investigate if reading times were affected by different word similarities regarding presupposition resolution. Reading times were subtracted from a memory recall experiment carried out by Jan Winkowski (currently unpublished) to compare reading data to cosine similarities between certain words in each sentence. Results indicate an unforeseen effect of word similarity on reading times, since reading times increased when word similarities became greater.

Model Predictive Control (MPC) is a successful control methodology, which is applied to increasingly complex systems. However, real-time feasibility of MPC can be challenging for complex systems, certainly when an (extremely) large number of constraints have to be adhered to. For such scenarios with a large number of state constraints, this paper proposes two novel MPC schemes for general nonlinear systems, which we call constraint-adaptive MPC. These novel schemes dynamically select at each time step a (varying) set of constraints that are included in the on-line optimization problem. Carefully selecting the included constraints can significantly reduce, as we will demonstrate, the computational complexity with often only a slight impact on the closed-loop performance. Although not all (state) constraints are imposed in the on-line optimization, the schemes still guarantee recursive feasibility and constraint satisfaction. A numerical case study illustrates the proposed MPC schemes and demonstrates the achieved computation time improvements exceeding two orders of magnitude without loss of performance.

Cet ouvrage s'inscrit dans le cadre de la thématique " villes et territoires durables " portée par de nombreux acteurs en France, en Afrique et à l'international. Il s'adresse en premier lieu aux décideurs et responsables " Habitat " des ministères et des collectivités locales et territoriales concernés, mais aussi aux autres parties prenantes du secteur (artisans et entrepreneurs, organisations professionnelles, centres de formation académique et professionnelle, centres de recherche, ONGs, etc.). En se basant sur l'expérience de ces dernières décennies en la matière, il vise à promouvoir une approche Développement Durable pour répondre aux immenses besoins en matière de construction en Afrique, basée sur une utilisation rationnelle et durable des matériaux locaux. Pour cela, il présente de manière pédagogique et synthétique les avantages, les questions qui se posent et les conditions prérequises pour l'utilisation de ces matériaux. Outre des exemples de solutions techniques illustrés par un panorama des ressources potentielles des territoires (bio et géosourcés), il fournit des éléments d'analyse de l'impact des filières locales en " circuit court " et des éléments de méthodologie. Il met aussi en valeur la nécessaire adéquation entre conception architecturale et caractéristiques spécifiques des matériaux disponibles localement, ce qui se résume bien par: " le bon matériau au bon endroit ".

Contains fulltext : 219577.pdf (Publisher’s version ) (Open Access), Background: Executive functions have been proposed to account for individual variation in reading comprehension beyond the contributions of decoding skills and language skills. However, insight into the direct and indirect effects of multiple executive functions on fifth-grade reading comprehension, while accounting for decoding and language skills, is limited. Aim: The present study investigated the direct and indirect effects of fourth-grade executive functions (i.e., working memory, inhibition, and planning) on fifth-grade reading comprehension, after accounting for decoding and language skills. Sample: The sample included 113 fourth-grade children (including 65 boys and 48 girls; Age M = 9.89; SD = .44 years). Methods: The participants were tested on their executive functions (working memory, inhibition and planning), and their decoding skills, language skills (vocabulary and syntax knowledge) and reading comprehension, one year later. Results: Using structural equation modelling, the results indicated direct effects of working memory and planning on reading comprehension, as well as indirect effects of working memory and inhibition via decoding (X2 = 2.46). Conclusions: The results of the present study highlight the importance of executive functions for reading comprehension after taking variance in decoding and language skills into account: Both working memory and planning uniquely contributed to reading comprehension. In addition, working memory and inhibition also supported decoding. As a practical implication, educational professionals should not only consider the decoding and language skills children bring into the classroom, but their executive functions as well.

Contains fulltext : 219577.pdf (Publisher’s version ) (Open Access), Background: Executive functions have been proposed to account for individual variation in reading comprehension beyond the contributions of decoding skills and language skills. However, insight into the direct and indirect effects of multiple executive functions on fifth-grade reading comprehension, while accounting for decoding and language skills, is limited. Aim: The present study investigated the direct and indirect effects of fourth-grade executive functions (i.e., working memory, inhibition, and planning) on fifth-grade reading comprehension, after accounting for decoding and language skills. Sample: The sample included 113 fourth-grade children (including 65 boys and 48 girls; Age M = 9.89; SD = .44 years). Methods: The participants were tested on their executive functions (working memory, inhibition and planning), and their decoding skills, language skills (vocabulary and syntax knowledge) and reading comprehension, one year later. Results: Using structural equation modelling, the results indicated direct effects of working memory and planning on reading comprehension, as well as indirect effects of working memory and inhibition via decoding (X2 = 2.46). Conclusions: The results of the present study highlight the importance of executive functions for reading comprehension after taking variance in decoding and language skills into account: Both working memory and planning uniquely contributed to reading comprehension. In addition, working memory and inhibition also supported decoding. As a practical implication, educational professionals should not only consider the decoding and language skills children bring into the classroom, but their executive functions as well.