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Decoding the cellular network interaction of neurons and glial cells are important in the development of new therapies for diseases of the central nervous system (CNS). Electrophysiological in vivo studies in mice will help to understand the highly complex network. In this paper, the optimization of epidural liquid crystal polymer (LCP) electrodes for different platinum electroplating parameters are presented and compared. Constant current and pulsed current electroplating varied in strength and duration was used to decrease the electrode impedance and to increase the charge storage capacity (CSCc). In best cases, both methods generated similar results with an impedance reduction of about 99%. However, electroplating with pulsed currents was less parameter-dependent than the electroplating with constant current. The use of ultrasound was essential to generate platinum coatings without plating defects. Electrode model parameters extracted from the electrode impedance reflected the increase in surface porosity due to the electroplating processes.
Intervention in the form of core-specific stability exercises is evident to improve trunk stability. The purpose was to assess the effect of an additional 6 weeks sensorimotor or resistance training on maximum isokinetic trunk strength and response to sudden dynamic trunk loading (STL) in highly trained adolescent athletes. The study was conducted as a single-blind, 3-armed randomized controlled trial. Twenty-four adolescent athletes (14f/10 m, 16 ± 1 yrs.;178 ± 10 cm; 67 ± 11 kg; training sessions/week 15±5; training h/week 22±8) were randomized into resistance training (RT; n=7), sensorimotor training (SMT; n = 10), and control group (CG; n = 7). Athletes were instructed to perform standardized, center-based training for 6 weeks, two times per week, with a duration of 1 h each session. SMT consisted of four different core-specific sensorimotor exercises using instable surfaces. RT consisted of four trunk strength exercises using strength training machines, as well as an isokinetic dynamometer. All participants in the CG received an unspecific heart frequency controlled, ergometer-based endurance training (50 min at max. heart frequency of 130HF). For each athlete, each training session was documented in an individual training diary (e.g., level of SMT exercise; 1RM for strength exercise, pain). At baseline (M1) and after 6 weeks of intervention (M2), participants’ maximum strength in trunk rotation (ROM:63°) and flexion/extension (ROM:55°) was tested on an isokinetic dynamometer (concentric/eccentric 30°/s). STL was assessed in eccentric (30°/s) mode with additional dynamometer-induced perturbation as a marker of core stability. Peak torque [Nm] was calculated as the main outcome. The primary outcome measurements (trunk rotation/extension peak torque: con, ecc, STL) were statistically analyzed by means of the two-factor repeated measures analysis of variance (α = 0.05). Out of 12 possible sessions, athletes participated between 8 and 9 sessions (SMT: 9 ± 3; RT: 8 ± 3; CG: 8 ± 4). Regarding main outcomes of trunk performance, experimental groups showed no significant pre–post difference for maximum trunk strength testing as well as for perturbation compensation (p > 0.05). It is concluded, that future interventions should exceed 6 weeks duration with at least 2 sessions per week to induce enhanced trunk strength or compensatory response to sudden, high-intensity trunk loading in already highly trained adolescent athletes, regardless of training regime.
Universities, as innovation drivers in science and technology worldwide, should attempt to become carbon-neutral institutions and should lead this transformation. Many universities have picked up the challenge and quantified their carbon footprints; however, up-to-date quantification is limited to use-phase emissions. So far, data on embodied impacts of university campus infrastructure are missing, which prevents us from evaluating their life cycle costs. In this paper, we quantify the embodied impacts of two university campuses of very different sizes and climate zones: the Umwelt-Campus Birkenfeld (UCB), Germany, and the Nanyang Technological University (NTU), Singapore. We also quantify the effects of switching to full renewable energy supply on the carbon footprint of a university campus based on the example of UCB. The embodied impacts amount to 13.7 (UCB) and 26.2 (NTU) kg CO2e/m2•y, respectively, equivalent to 59.2% (UCB), and 29.8% (NTU), respectively, of the building lifecycle impacts. As a consequence, embodied impacts can be dominating; thus, they should be quantified and reported. When adding additional use-phase impacts caused by the universities on top of the building lifecycle impacts (e.g., mobility impacts), both institutions happen to exhibit very similar emissions with 124.5–126.3 kg CO2e/m2•y despite their different sizes, structures, and locations. Embodied impacts comprise 11.0–20.8% of the total impacts at the two universities. In conclusion, efficient reduction in university carbon footprints requires a holistic approach, considering all impacts caused on and by a campus including upstream effects.
Abstract: This paper is about detecting the difference between fully-random and semi-random shuffleing data sets, with the use of unsupervised learning algorithms. Because of the limits of the k-means algorithm alone, a recurrent autoencoder is used for feature extraction to improve the results of k-means. In the next step the autoencoder alone is used for clustering.
Introduction: In the last years, machine learning has been used more and more in different areas and it is also appropriate for for pattern recognition in data. Random data is characterized through the missing of defined patterns. Permutations without repetitions have the highest amount of entropy for a sequence of its length, which is similar to random data according to Andrei Kolmogorov, who states that random data have the highest amount of information and can’t be compressed. Therefore, this paper analyses the difference between random permutations and good shuffled permutations, which have some remaining patterns left. This is done via a recurrent autoencoder.