Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.Comment: Final author version, accepted for publication at 62nd IEEE Conference on Decision and Control, Singapore, 202

Navigating the political: An analysis of political calibration of integrated assessment modelling in light of the 1.5 °C goal

Some of the most influential explorations of low-carbon transformations are conducted with Integrated Assessment Models (IAMs). The recent attempts by the IPCC to look for pathways compatible with the 1.5 °C and 2 °C temperature goals are a case in point. Earlier scholarship indicates that model-based pathways are persuasive in bringing specific possible future alternatives into view and guiding policymaking. However, the process through which these shared imaginations of possible futures come about is not yet well understood. By closely examining the science-policy dynamics around the IPCC SR1.5, we observe a sequence of mutually legitimising interactions between modelling and policy making through which the 1.5 °C goal gradually gained traction in global climate politics. Our findings reveal a practice of ‘political calibration’, a continuous relational readjustment between modelling and the policy community. This political calibration is indicative of how modellers navigate climate politics to maintain policy relevance. However, this navigation also brings key dilemmas for modellers, between 1) requirements of the policy process and experts’ conviction of realism; 2) perceived political sensitivities and widening the range of mitigation options; and 3) circulating crisp storylines and avoiding policy-prescriptiveness. Overall, these findings call into question the political neutrality of IAMs in its current position in the science-policy interface and suggest a future orientation in which modellers aim to develop additional relations with a broader set of publics resulting in more diverse perspectives on plausible and desirable futures

Shear-stress and wall-stress regulation of vascular remodeling after balloon angioplasty: effect of matrix metalloproteinase inhibition

BACKGROUND: Constrictive vascular remodeling (VR) is the most significant component of restenosis after balloon angioplasty (PTA). Whereas in physiologi

Decisional Conflict after Deciding on Potential Participation in Early Phase Clinical Cancer Trials:Dependent on Global Health Status, Satisfaction with Communication, and Timing

SIMPLE SUMMARY: Early phase clinical trials are an essential part of modern drug development and thus the advance of anti-cancer therapies for patients. However, deciding whether to participate in such trials can be complex and patients have reported decisional conflict (i.e., unresolved decisional needs). The aim of our study was to untangle several factors that contribute to decisional conflict in patients with advanced cancer who have recently been asked to decide whether to participate in early phase clinical trials. We found that patients experienced less decisional conflict if they had a better global health status, higher satisfaction, and made their decision sooner. Other factors, such as the decision to (not) participate, did not prove to be the best indicators for decisional conflict. With these insights, we can start to build hypotheses on how to improve the decision-making process for patients with end-stage cancer, which can ultimately improve their quality of life. ABSTRACT: When standard treatment options are not available anymore, patients with advanced cancer may participate in early phase clinical trials. Improving this complex decision-making process may improve their quality of life. Therefore, this prospective multicenter study with questionnaires untangles several contributing factors to decisional conflict (which reflects the quality of decision-making) in patients with advanced cancer who recently decided upon early phase clinical trial participation (phase I or I/II). We hypothesized that health-related quality of life, health literacy, sense of hope, satisfaction with the consultation, timing of the decision, and the decision explain decisional conflict. Mean decisional conflict in 116 patients was 30.0 (SD = 16.9). Multivariate regression analysis showed that less decisional conflict was reported by patients with better global health status (β = −0.185, p = 0.018), higher satisfaction (β = −0.246, p = 0.002), and who made the decision before (β = −0.543, p < 0.001) or within a week after the consultation (β = −0.427, p < 0.001). These variables explained 37% of the variance in decisional conflict. Healthcare professionals should realize that patients with lower global health status and who need more time to decide may require additional support. Although altering such patient intrinsic characteristics is difficult, oncologists can impact the satisfaction with the consultation. Future research should verify whether effective patient-centered communication could prevent decisional conflict