Quality of diabetes care, diabetes knowledge and risk of severe hypoglycaemia one and four years after participation in a 5-day structured treatment and teaching programme for intensified insulin therapy

PURPOSE: Cardiopulmonary bypass (CPB) is characterized by translocation of intestinal endotoxin and subsequent endogenous production of the pro-inflammatory cytokine interleukin-6 (IL-6). Plasma lipid fractions, especially high density lipoproteins, bind and neutralize endotoxin and, therefore, inhibit endotoxin-induced macrophage cytokine production, including IL-6. Increased IL-6 plasma levels have been implicated in adverse consequences associated with CPB. Previous studies demonstrated large interpatient variability in IL-6 plasma levels after CPB. The purpose of this study was to evaluate the relationship between plasma lipid concentrations and the concentrations of IL-6 following CPB in humans. METHODS: In a prospective study, a group of 15 patients selected to exclude variables known to influence post-CPB plasma levels of IL-6 (preoperative left ventricular ejection fraction > 45%, similar durations of aortic cross clamping and total CPB time, similar temperature control during CPB, and avoidance of platelet transfusion and shed mediastinal blood re-infusion), IL-6 was measured at baseline, one and 24 hr post-CPB. RESULTS: Interleukin-6 plasma concentrations (mean +/- SD) increased at one (142 +/- 89 pg.ml-1, P < 0.05) and 24 (129 +/- 82 pg.ml-1, P < 0.05) hr post-CPB compared with baseline (1.5 +/- 1 pg.ml-1) concentrations. An inverse correlation was found between IL-6 plasma concentrations at one hour post-CPB and plasma cholesterol concentrations (r = -0.592, P = 0.02), high density lipoprotein (r = -0.595, P = 0.02), and low density lipoprotein (r = -0.656, P = 0.01). CONCLUSIONS: These results suggest that plasma lipids attenuate the production of IL-6 during CPB and may partly explain the variability of interpatient levels of IL-6 reported post-CPB by others.     

Prediction-based methods for teleoperation across delayed networks

The remote nature of telepresence scenarios can be seen as a strongpoint and also as a weakness. Although it enables the remote control of robots in dangerous or inaccessible environments, it necessarily involves some kind of communication mechanism for the transmission of control signals. This communication mechanism necessarily involves adverse network effects such as delay. Three mechanisms aimed at improving the effects of network delay are presented in this paper: (1) Motion prediction to partially compensate for network delays, (2) force prediction to learn a local force model, thereby reducing dependency on delayed force signals, and (3) haptic data compression to reduce the required bandwidth of high frequency data. The utilized motion prediction scheme was shown to improve operator performance, but had no influence on operator immersion. The force prediction provided haptic feedback through synchronous forces from the local model, thereby stabilizing the control loop. The developed haptic data compression scheme reduced the number of packets sent across the network by 90%, while improving the quality of the haptic feedback.     

An approach for robust PDE-constrained optimization with application to shape optimization of electrical engines and of dynamic elastic structures under uncertainty

We present a robust optimization framework that is applicable to general nonlinear programs (NLP) with uncertain parameters. We focus on design problems with partial differential equations (PDE), which involve high computational cost. Our framework addresses the uncertainty with a deterministic worst-case approach. Since the resulting min–max problem is computationally intractable, we propose an approximate robust formulation that employs quadratic models of the involved functions that can be handled efficiently with standard NLP solvers. We outline numerical methods to build the quadratic models, compute their derivatives, and deal with high-dimensional uncertainties. We apply the presented approach to the parametrized shape optimization of systems that are governed by different kinds of PDE and present numerical results.     

Sensors and control concept of a biped robot

The biped robot "Johnnie" is designed to achieve a dynamically stable gait pattern, allowing for high walking velocities. Very accurate and fast sensors were developed for the machine. In particular, the design of the three-dimensional-orientation sensor and the six-axes force-torque sensor are presented. The control scheme is based on the information from these sensors to deal with unstructured terrain and disturbances. Two different implementations are investigated: a computed torque approach and a trajectory control with adaptive trajectories. Walking speeds of 2.4 km/h have been achieved in experiments.