Lompat Jauh dengan Menggunakan Modifikasi Kardus di Sdn 21 Sungai Ayak

This study aims to determine whether there is an increased jump cardboard game against the squat -style long jump results in Class IV SDN 21 Sungai Ayak Sekadau . research subjects are teachers collaborate with fourth grade students of SDN 21 Sungai Ayak Kabupaten Sekadau as many as 27 students . Learning the long jump with mediakardus provide convenience to the students to always be active and brave movement high jump is easy and fun give suasanan new that has never been done before , the positive impact of such students do not experience fear , feel good and appropriate to the capabilities students . In making value starts prasiklus , the first cycle ( 20 students ) and the second cycle ( 27 students ) have increased systematicall

TEASER: Early and Accurate Time Series Classification

Early time series classification (eTSC) is the problem of classifying a time series after as few measurements as possible with the highest possible accuracy. The most critical issue of any eTSC method is to decide when enough data of a time series has been seen to take a decision: Waiting for more data points usually makes the classification problem easier but delays the time in which a classification is made; in contrast, earlier classification has to cope with less input data, often leading to inferior accuracy. The state-of-the-art eTSC methods compute a fixed optimal decision time assuming that every times series has the same defined start time (like turning on a machine). However, in many real-life applications measurements start at arbitrary times (like measuring heartbeats of a patient), implying that the best time for taking a decision varies heavily between time series. We present TEASER, a novel algorithm that models eTSC as a two two-tier classification problem: In the first tier, a classifier periodically assesses the incoming time series to compute class probabilities. However, these class probabilities are only used as output label if a second-tier classifier decides that the predicted label is reliable enough, which can happen after a different number of measurements. In an evaluation using 45 benchmark datasets, TEASER is two to three times earlier at predictions than its competitors while reaching the same or an even higher classification accuracy. We further show TEASER's superior performance using real-life use cases, namely energy monitoring, and gait detection

S₃ flavor symmetry at the LHC

Discrete symmetries employed to explain neutrino mixing and mass hierarchies are often associated with an enlarged scalar sector which might lead to exotic Higgs decay modes. We explore such a possibility in a scenario with S3 flavor symmetry which requires three scalar SU(2) doublets. The spectrum is fixed by minimizing the scalar potential, and we observe that the symmetry of the model leads to tantalizing Higgs decay models potentially observable at the CERN Large Hadron Collider (LHC)

Lipase-catalyzed Reactions at Interfaces of Two-phase Systems and Microemulsions

This work describes the influence of two polar lipids, Sn-1/3 and Sn-2 monopalmitin, on the activity of lipase in biphasic systems and in microemulsions. In previous communications, we have shown that Sn-2 monoglycerides can replace Sn-1,3 regiospecific lipases at the oil-water interface, causing a drastically reduced rate of lipolysis. We here demonstrate that even if the lipase is expelled from the interface, it can catalyze esterification of the Sn-2 monoglyceride with fatty acids in both macroscopic oil-water systems and in microemulsions, leading to formation of di- and triglyceride

A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis

This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results

Fatigue Crack Closure Analysis Using Digital Image Correlation

Fatigue crack closure during crack growth testing is analyzed in order to evaluate the critieria of ASTM Standard E647 for measurement of fatigue crack growth rates. Of specific concern is remote closure, which occurs away from the crack tip and is a product of the load history during crack-driving-force-reduction fatigue crack growth testing. Crack closure behavior is characterized using relative displacements determined from a series of high-magnification digital images acquired as the crack is loaded. Changes in the relative displacements of features on opposite sides of the crack are used to generate crack closure data as a function of crack wake position. For the results presented in this paper, remote closure did not affect fatigue crack growth rate measurements when ASTM Standard E647 was strictly followed and only became a problem when testing parameters (e.g., load shed rate, initial crack driving force, etc.) greatly exceeded the guidelines of the accepted standard