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Stretch-shortening type actions are characterized by lengthening of the pre-activated muscle-tendon unit (MTU) in the eccentric phase immediately followed by muscle shortening. Under 1 g, pre-activity before and muscle activity after ground contact, scale muscle stiffness, which is crucial for the recoil properties of the MTU in the subsequent push-off. This study aimed to examine the neuro-mechanical coupling of the stretch-shortening cycle in response to gravity levels ranging from 0.1 to 2 g. During parabolic flights, 17 subjects performed drop jumps while electromyography (EMG) of the lower limb muscles was combined with ultrasound images of the gastrocnemius medialis, 2D kinematics and kinetics to depict changes in energy management and performance. Neuro-mechanical coupling in 1 g was characterized by high magnitudes of pre-activity and eccentric muscle activity allowing an isometric muscle behavior during ground contact. EMG during pre-activity and the concentric phase systematically increased from 0.1 to 1 g. Below 1 g the EMG in the eccentric phase was diminished, leading to muscle lengthening and reduced MTU stretches. Kinetic energy at take-off and performance were decreased compared to 1 g. Above 1 g, reduced EMG in the eccentric phase was accompanied by large MTU and muscle stretch, increased joint flexion amplitudes, energy loss and reduced performance. The energy outcome function established by linear mixed model reveals that the central nervous system regulates the extensor muscles phase- and load-specifically. In conclusion, neuro-mechanical coupling appears to be optimized in 1 g. Below 1 g, the energy outcome is compromised by reduced muscle stiffness. Above 1 g, loading progressively induces muscle lengthening, thus facilitating energy dissipation.
The adsorption of fluor-carbon complexes on GaAs(110) studied by electron energy loss spectroscopy
(1986)
Purpose
The most commonly used mobility assessments for screening risk of falls among older adults are rating scales such as the Tinetti performance oriented mobility assessment (POMA). However, its correlation with falls is not always predictable and disadvantages of the scale include difficulty to assess many of the items on a 3-point scale and poor specificity. The purpose of this study was to describe the ability of the new Aachen Mobility and Balance Index (AMBI) to discriminate between subjects with a fall history and subjects without such events in comparison to the Tinetti POMA Scale.
Methods
For this prospective cohort study, 24 participants in the study group and 10 in the control group were selected from a population of patients in our hospital who had met the stringent inclusion criteria. Both groups completed the Tinetti POMA Scale (gait and balance component) and the AMBI (tandem stance, tandem walk, ten-meter-walk-test, sit-to-stand with five repetitions, 360° turns, timed-up-and-go-test and measurement of the dominant hand grip strength). A history of falls and hospitalization in the past year were evaluated retrospectively. The relationships among the mobility tests were examined with Bland–Altmananalysis. Receiver-operated characteristics curves, sensitivity and specificity were calculated.
Results
The study showed a strong negative correlation between the AMBI (17 points max., highest fall risk) and Tinetti POMA Scale (28 points max., lowest fall risk; r = −0.78, p < 0.001) with an excellent discrimination between community-dwelling older people and a younger control group. However, there were no differences in any of the mobility and balance measurements between participants with and without a fall history with equal characteristics in test comparison (AMBI vs. Tinetti POMA Scale: AUC 0.570 vs. 0.598; p = 0.762). The Tinetti POMA Scale (cut-off <20 points) showed a sensitivity of 0.45 and a specificity of 0.69, the AMBI a sensitivity of 0.64 and a specificity of 0.46 (cut-off >5 points).
Conclusion
The AMBI comprises mobility and balance tasks with increasing difficulty as well as a measurement of the dominant hand-grip strength. Its ability to identify fallers was comparable to the Tinetti POMA Scale. However, both measurement sets showed shortcomings in discrimination between fallers and non-fallers based on a self-reported retrospective falls-status.
Textile reinforced concrete. Part I: Process model for collaborative research and development
(2003)
Die Stadt Augsburg war eine der größten Textilstädte Europas. Seit 2010 präsentiert das Textil- und Industriemuseum (TIM) eine Vielzahl von Exponaten in der alten Kammgarnspinnerei im ehemaligen Augsburger Textilviertel und ist somit ein wichtiger Bestandteil der bayrischen Museumslandschaft und deutschen Textilgeschichte. Neben seiner besonderen Lage überzeugt das TIM vor allem mit einer großen Musterbuchsammlung. Im Fokus des in dieser Arbeit neugestalteten Erscheinungsbildes stehen die verschiedenen Muster, die den textilen Schwerpunkt in den Vordergrund rücken und dem Museum damit einen einprägsamen Wiedererkennungswert geben. Mithilfe des neuen Corporate Designs soll das Museum für regionale und überregionale Besucher:innen attraktiver gemacht und somit die einmalige geschichtliche Bedeutung vermittelt und erhalten werden.
Testing of a 10 kW diffusive micro-mix combustor for hydrogen-fuelled micro-scale gas turbines
(2007)
We discuss the testing problem of homogeneity of the marginal distributions of a continuous bivariate distribution based on a paired sample with possibly missing components (missing completely at random). Applying the well-known two-sample Crámer–von-Mises distance to the remaining data, we determine the limiting null distribution of our test statistic in this situation. It is seen that a new resampling approach is appropriate for the approximation of the unknown null distribution. We prove that the resulting test asymptotically reaches the significance level and is consistent. Properties of the test under local alternatives are pointed out as well. Simulations investigate the quality of the approximation and the power of the new approach in the finite sample case. As an illustration we apply the test to real data sets.