A novel mechatronic body weight support (BWS) system has been developed to provide precise body weight unloading for patients with neurological or other impairments during treadmill training. The system is composed of a passive elastic spring element to take over the main unloading force and an active closed-loop controlled electric drive to generate the exact desired force. Both force generating units, the passive spring and the active electric drive, act on the patient via a polyester rope connected to a harness worn by the patient. The length of the rope can be adjusted with an electric winch to adapt the system to different patient sizes. The system is fully computer controlled. At unloading loads of up to 60 kg and walking speeds of up to 3.2 km/h, the mean unloading error and the maximum unloading error of the presented BWS system was less than 1 and 3 kg, respectively. The performance was compared with those of two purely passive BWS systems currently being used by most other rehabilitation groups. This comprised counterweight systems and static BWS systems with fixed rope lengths. Counterweight systems reached mean and maximum unloading errors of up to 5.34 and 16.22 kg, respectively. The values for the static BWS were 11.02 kg and 27.67 kg, respectively. The novel mechatronic BWS system presented in this study adjusts desired unloading changes of up to 20 kg within less than 100 ms. Thus, not only constant BWS, but also gait cycle dependent or time variant oscillations of the desired force can be realized with high accuracy. Precise and constant unloading force is believed to be an important prerequisite for BWS gait therapy, where it is important to generate physiologically correct segmental dynamics and ground reaction forces. Thus, the novel BWS system presented in this paper is an important contribution to maximize the therapeutic outcome of human gait rehabilitation.

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The significant increase in traces of human activity in the environment worldwide provides evidence of the beginning of a new geological era, informally named the Anthropocene. The rate and variability of these human modifications at the local and global scale remain largely unknown, but new analytical methods such as high-resolution mass spectrometry (HRMS) can help to characterize chemical contamination. We therefore applied HRMS to investigate the contamination history of two lakes in Central Europe over the preceding 100 years. A hierarchical clustering analysis (HCA) of the collected time series data revealed more than 13 000 profiles of anthropogenic origin in both lakes, defining the beginning of large-scale human impacts during the 1950s. Our results show that the analysis of temporal patterns of nontarget contaminants is an effective method for characterizing the contamination pattern in the Anthropocene and an important step in prioritizing the identification of organic contaminants not yet successfully targeted by environmental regulation and pollution reduction initiatives. As proof of the concept, the success of the method was demonstrated with the identification of the pesticide imazalil, which probably originated from imported fruits. This new approach applicable to palaeoarchives can effectively be used to document the time and rate of change in contamination over time and provide additional information on the onset of the Anthropocene.

[1] A distributed hydrological model was used to simulate the distribution of fast runoff formation as a proxy for critical source areas for herbicide pollution in a small agricultural catchment in Switzerland. We tested to what degree predictions based on prior knowledge without local measurements could be improved upon relying on observed discharge. This learning process consisted of five steps: For the prior prediction (step 1), knowledge of the model parameters was coarse and predictions were fairly uncertain. In the second step, discharge data were used to update the prior parameter distribution. Effects of uncertainty in input data and model structure were accounted for by an autoregressive error model. This step decreased the width of the marginal distributions of parameters describing the lower boundary (percolation rates) but hardly affected soil hydraulic parameters. Residual analysis (step 3) revealed model structure deficits. We modified the model, and in the subsequent Bayesian updating (step 4) the widths of the posterior marginal distributions were reduced for most parameters compared to those of the prior. This incremental procedure led to a strong reduction in the uncertainty of the spatial prediction. Thus, despite only using spatially integrated data (discharge), the spatially distributed effect of the improved model structure can be expected to improve the spatially distributed predictions also. The fifth step consisted of a test with independent spatial data on herbicide losses and revealed ambiguous results. The comparison depended critically on the ratio of event to preevent water that was discharged. This ratio cannot be estimated from hydrological data only. The results demonstrate that the value of local data is strongly dependent on a correct model structure. An iterative procedure of Bayesian updating, model testing, and model modification is suggested.Citation: Frey, M. P., C. Stamm, M. K. Schneider, and P. Reichert (2011), Using discharge data to reduce structural deficits in a hydrological model with a Bayesian inference approach and the implications for the prediction of critical source areas, Water Resour.

In this work, the particular and combined influence of nonparabolicity and phonon scattering on the device characteristics of a triple-gate silicon nanowire is investigated. In addition, different approximations of the retarded self-energy for electron-phonon scattering are analyzed in terms of the electrostatics, current and computational cost.

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Simulation environments based on virtual reality technologies can support medical education and training. In this paper, the novel approach of an "interactive phantom" is presented that allows a realistic display of haptic contact information typically generated when touching and moving human organs or segments. The key idea of the haptic interface is to attach passive phantom objects to a mechanical actuator. The phantoms look and feel as real anatomical objects. Additional visualization of internal anatomical and physiological information and sound generated during the interaction with the phantom yield a multimodal approach that can increase performance, didactic value, and immersion into the virtual environment. Compared to classical approaches, this multimodal display is convenient to use, provides realistic tactile properties, and can be partly adjusted to different, e.g., pathological properties. The interactive phantom is exemplified by a virtual human knee joint that can support orthopedic education, especially for the training of clinical knee joint evaluation. It is suggested that the technical principle can be transferred to many other fields of medical education and training such as obstetrics and dentistry.