Kraftanlagen (Germany)

Background and Objectives: Meniscus injuries, particularly Grade I and II, are common knee injuries that can affect pain, joint function and quality of life, but the effectiveness of non-invasive treatments like Kinesiology taping (KT) in this population remains limited. This pilot randomized controlled trial aimed to explore the short-term effects of KT on pain, fear of movement, muscle strength, proprioceptive force sense, joint range of motion, joint position sense and quality of life in individuals with Grade I/II meniscus injuries. Materials and Methods: 26 participants diagnosed with Grade I-II meniscus injury were randomly assigned to two groups: the experimental group was applied ‘Y shaped’ kinesiology taping on quadriceps femoris muscle, based on facilitation technique with 25–50% tension. The control (placebo) group was applied a tape without tension, perpendicular to the quadriceps femoris muscle. Outcomes were evaluated before and 48–72 h after taping. Results: Between-group analysis demonstrated a significant improvement in joint position sense at 60° flexion with eyes closed in KT group compared with placebo (p = 0.002). Additionally, the KT group showed significantly greater improvements in the physical function (p = 0.006) and energy (p = 0.013) subdomains of the SF-36 quality of life scale. No significant between-group differences were observed for pain, fear of movement, muscle strength, proprioceptive force sense, or joint range of motion. Conclusions: In this pilot study, KT showed acute benefits in proprioception and quality of life in grade I-II meniscus injuries, but no advantage over placebo taping for pain, fear of movement, joint range of motion or muscle strength. Given the exploratory nature and limited sample size, these findings should be interpreted cautiously. Larger trials should confirm these results and determine the role of KT within multimodal rehabilitation programs.

This paper investigates the sensitivity of solutions to multicriteria optimal control (MOC) problems with respect to unexpected reductions of terminal time. A trajectory of a MOC problem with a fixed terminal time <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> is termed persistently nondominated on an interval [<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s, T$</tex>] if it remains Pareto optimal for all problems with terminal times within [<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s, T$</tex>]. Finding such trajectories may be particularly important in situations where the terminal time is free, uncertain, or subject to external influences, such as in flood control, epidemic management, or when mitigating adversarial behavior of players in non-cooperative differential games. Building on our earlier work, this paper provides a solution framework for stationary linear control systems with multiple linear-quadratic integral criteria. Our approach derives conditions that guarantee the Pareto optimality of a trajectory despite a reduction of the control period. We present a computational procedure to identify which optimal controls maximize the length of the subinterval of the control period over which the trajectory remains Pareto optimal. The problem of finding persistently nondominated trajectories can be viewed as a special case of anytime control problem for multicriteria trajectory optimization. The proposed approach is illustrated with a numerical example involving a water reservoir control problem, demonstrating its practical utility in real-world scenarios.

Predicting transformer degradation is essential for ensuring the reliability and efficiency of power systems. This study explores Bayesian inference as a robust alternative to traditional statistical and machine learning approaches for degradation modeling. Unlike conventional methods, Bayesian models explicitly quantify uncertainty, making them well-suited for predictive maintenance. The proposed framework incorporates Markov Chain Monte Carlo (MCMC) techniques to improve parameter estimation and model selection. A systematic evaluation is conducted using predictive accuracy, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and computational efficiency. Results demonstrate that Bayesian models outperform traditional and machine learning methods, with the MCMC Bayesian model achieving the highest predictive accuracy (92.1%) while providing superior uncertainty quantification. Future research directions include enhancing computational efficiency, integrating Bayesian models with real-time monitoring, and validating their effectiveness across diverse transformer systems. These findings contribute to more reliable predictive maintenance strategies, reducing operational risks and improving power network resilience.

A tudatos öregedés egy komplex, interdiszciplináris szemléletmód, amely öregedéssel kapcsolatos megközelítéseket, elméleteket, gyakorlatokat integrál, illetve új szempontokat azonosít. Alapvetően alulról szerveződő, mozgalom jellegű és a mindennapok realitásából gyökerezik. Egyéni, közösségi, társadalmi és ökológiai szintekre és a 21. század kihívásaira egyaránt reflektál.

In diesen Gedichten, die er niemand gezeigt hat, sprach der verschlossene Mann sein innerstes Gefühl aus. Immer wieder beschäftigt ihn der Tod der Gefallenen. Ein Ausdruck seines Wesens liegt in einer Strophe aus dem Jahre 1944: "Kommt außenher des Schicksals Macht, Nicht Betteln hilft und Fleh'n, Hab immer nur auf Eines acht: Von innen zu besteh'n."