Negative Effects of Mental Fatigue on Performance in the Yo-Yo Test, Loughborough Soccer Passing and Shooting Tests: A Meta-Analysis

We aimed to examine the effects of mental fatigue on the Yo-Yo test and Loughborough soccer passing and shooting tests performance using a meta-analysis. The search for studies was performed through eight bibliographic databases (Academic Search Elite, AUSPORT, Cochrane Library, PsycInfo, PubMed/MEDLINE, Scopus, SPORTDiscus, and Web of Science). The methodological quality of the included studies was assessed using the PEDro checklist. A random-effects meta-analysis was performed for data analysis. After reviewing 599 search results, seven studies with a total of ten groups were included in the review. All studies were classified as being of good methodological quality. Mental fatigue reduced the distance covered in the Yo-Yo test (Cohen’s d: −0.49; 95% confidence interval [CI]: −0.66, −0.32). In the Loughborough soccer passing test, mental fatigue increased the original time needed to complete the test (Cohen’s d: −0.24; 95% CI: −0.46, −0.03), increased penalty time (Cohen’s d: −0.39; 95% CI: −0.46, −0.31), and decreased performance time (Cohen’s d: −0.52; 95% CI: −0.80, −0.24). In the Loughborough soccer shooting test, mental fatigue decreased points per shot (Cohen’s d: −0.37; 95% CI: −0.70, −0.04) and shot speed (Cohen’s d: −0.35; 95% CI: −0.64, −0.06). Overall, the findings presented in this review demonstrated that mental fatigue negatively impacts endurance-based running performance as well as soccer passing and shooting skills.

The Empirical Adaptive Wavelet Decomposition (EAWD): An adaptive decomposition for the variability analysis of observation time series in atmospheric science

Most observational data sequences in geophysics can be interpreted as resulting from the interaction of several physical processes at several time and space scales. As a consequence, measurements time series have often characteristics of non-linearity and non-stationarity and thereby exhibit strong fluctuations at different time-scales. The variability analysis of a time series consists in decomposing it into several mode of variability, each mode representing the fluctuations of the original time series at a specific time-scale. Such a decomposition enables to obtain a time-frequency representation of the original time series and turns out to be very useful to estimate the dimensionality of the underlying dynamics. Decomposition techniques very well suited to non-linear and non-stationary time series have recently been developed in the literature. Among the most widely used of these technics are the empirical mode decomposition (EMD) and the empirical wavelet transformation (EWT). The purpose of this paper is to present a new adaptive filtering method that combines the advantages of the EMD and EWT technics, while remaining close to the dynamics of the original signal made of atmospheric observations, which means reconstructing as close as possible to the original time series, while preserving its variability at different time scales.

Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

To meet the basic assumption of classical Mann-Kendall (MK) trend analysis, which requires serially independent time series, a pre-whitening (PW) procedure is proposed to alleviate the serial correlation structure of a given hydro-meteorological time series records for application. The procedure is simply to take the lagged differences in a given time series in the hope that the new time series will have an independent serial correlation coefficient. The whole idea was originally based on the first-order autoregressive AR (1) process, but such a procedure has been documented to damage the trend component in the original time series. On the other hand, the over-whitening procedure (OW) proposes a white noise process superposition of the same length with zero mean and some standard deviation on the original time series to convert it into serially independent series without any damage to the trend component. The stationary white noise addition does not have any trend components. For trend identification, annual average temperature records in New Jersey and Istanbul are presented to show the difference between PW and OW procedures. It turned out that the OW procedure was superior to the PW procedure, which did not cause a loss in the original trend component.

Prediction of Transient NOx Emission from Diesel Vehicles Based on Deep-Learning Differentiation Model with Double Noise Reduction

For diesel engines, accurate prediction of NOx (Nitrogen Oxides) emission plays an essential role in virtual NOx sensor development and engine design under situations of actual road driving. However, due to the randomness and uncertainty in the driving process of diesel vehicles, it is difficult to make predictions about NOx emissions. In order to solve this problem, this paper proposes differential models for noise reductions of NOx emissions in time series. First, according to the internal fluctuation of time series, use SSA (Singular Spectrum Analysis) to reduce the noises of the original time series; second, use ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to decompose the noise-reducing data into several relatively stable subsequences; third, use the sample entropy to calculate the complexity of each subsequence, and divide the sequences into high-frequency ones and low-frequency ones; finally, use GRU (Gated Recurrent Unit) to complete the prediction of high-frequency sequences and SVR (Support Vector Regression) for the prediction of low-frequency sequences. To obtain the final models, integrate the prediction results of the subsequences. Make comparisons with five single models, SSA single-processing models, and ICEEMDAN single-processing models. The experimental results show that the proposed model can predict the instantaneous NOx emissions of diesel engines better than the single model and the model processed by SSA, and the differentiated model can effectively improve the execution speed of the model.

An Improved Adaptive IVMD-WPT-Based Noise Reduction Algorithm on GPS Height Time Series

To improve the reliability of Global Positioning System (GPS) signal extraction, the traditional variational mode decomposition (VMD) method cannot determine the number of intrinsic modal functions or the value of the penalty factor in the process of noise reduction, which leads to inadequate or over-decomposition in time series analysis and will cause problems. Therefore, in this paper, a new approach using improved variational mode decomposition and wavelet packet transform (IVMD-WPT) was proposed, which takes the energy entropy mutual information as the objective function and uses the grasshopper optimisation algorithm to optimise the objective function to adaptively determine the number of modal decompositions and the value of the penalty factor to verify the validity of the IVMD-WPT algorithm. We performed a test experiment with two groups of simulation time series and three indicators: root mean square error (RMSE), correlation coefficient (CC) and signal-to-noise ratio (SNR). These indicators were used to evaluate the noise reduction effect. The simulation results showed that IVMD-WPT was better than the traditional empirical mode decomposition and improved variational mode decomposition (IVMD) methods and that the RMSE decreased by 0.084 and 0.0715 mm; CC and SNR increased by 0.0005 and 0.0004 dB, and 862.28 and 6.17 dB, respectively. The simulation experiments verify the effectiveness of the proposed algorithm. Finally, we performed an analysis with 100 real GPS height time series from the Crustal Movement Observation Network of China (CMONOC). The results showed that the RMSE decreased by 11.4648 and 6.7322 mm, and CC and SNR increased by 0.1458 and 0.0588 dB, and 32.6773 and 26.3918 dB, respectively. In summary, the IVMD-WPT algorithm can adaptively determine the number of decomposition modal functions of VMD and the optimal combination of penalty factors; it helps to further extract effective information for noise and can perfectly retain useful information in the original time series.

A Fast Data Processing Technique for Continuous Gravitational Wave Searches

This article discusses the potential advantages of a data processing technique for continuous gravitational wave signals searches in the data measured by ground-based gravitational wave interferometers. Its main advantage over other techniques is that it does not need to search over the signal’s direction of propagation. Although it is a “coherent method” (i.e., it coherently processes year-long data), it is applied to a data set obtained by multiplying the original time-series with a (half-year) time-shifted copy of it. As a result, the phase modulation due to the interferometer motion around the Sun is automatically canceled in the signal of the synthesized time-series. Although the resulting signal-to-noise ratio is not as high as that of a coherent search, it equals that of current hierarchical methods. In addition, since the signal search is performed over a parameters space of smaller dimensionality, the associated false-alarm probability should be smaller than those characterizing hierarchical methods and result in an improved likelihood of detection.

A novel method for determining fixed running time in operating electric train tracking optimal speed profile

<span lang="EN-US">Tracking the optimal speed profile in electric train operation has been proposed as a potential solution for reducing energy consumption in electric train operation, at no cost to improve infrastructure of existing Metro lines as well. However, the optimal speed profile needs to meet fixed running time. Therefore, this paper focuses on a new method for determining the fixed running time complied with the scheduled timetable when trains track the optimal speed profile. The novel method to ensure the fixed running time is the numerical-analytical one. Calculating accelerating time ta, coasting time tc, braking time tb via values of holding speed vh, braking speed vb of optimal speed profile with the constraint condition: the running time equal to the demand time. The other hands, vh and vb are determined by solving nonlinear equations with constraint conditions. Additionally, changing running time suit for each operation stage of metro lines or lines starting to conduct schedules by the numerical-analytical method is quite easy. Simulation results obtained for two scenarios with data collected from electrified trains of Cat Linh-Ha Dong metro line, Vietnam show that running time complied with scheduled timetables, energy saving by tracking optimal speed profile for the entire route is up to 8.7%, if the running time is one second longer than original time, energy saving is about 11.96%.</span>

Rolling Element Bearing Fault Time Series Prediction Using Optimized MCKD-LSTM Model

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

Time to Conceive: a New Argument for the Possibility of Time Travel

<p>Ordinarily, philosophers arguing for the possibility of time travel restrict themselves to defending time travel against allegations of inconsistency and contradiction. These objections are usually based on particular theories about time and causality. I believe, however, that this way of arguing can be turned on its head. By using the conceivability thesis – that is, the thesis that if something is conceivable, then it is also possible – we can put forward a positive argument for the possibility of time travel, and then consider how this should inform our metaphysical views. I do this by assuming the truth of the conceivability thesis and then presenting a simple piece of time travel fiction. I argue for the conceivability of this story and, in doing so, attempt to show that the time travel journey it describes is logically possible. I then develop this argument by considering other more controversial cases of time travel. I make minor alterations to the original time travel story in order to show that there are a number of different kinds of time travel journeys (including cases of both forwards and backwards time travel) which are conceivable and, therefore, logically possible. Finally, I ask how the conceivability of different types of time travel should affect the metaphysical views we choose to adopt. I argue that since the conceivability of time travel entails the logical possibility of journeys to other times, any plausible theory of time must be able to accommodate such journeys. I also explain how the conceivability of time travel entails the logical possibility of two particularly unusual cases of causation. I argue that the conceivability of instantaneous time travel entails the logical possibility of causation at a distance, and that the conceivability of backwards time travel entails the logical possibility of backwards causation. Any plausible theory of causation must therefore be able to account for the possibility of these two types of causation. I consider one particular theory of causation which does not (namely, that put forward by D.H. Mellor), and then attempt to establish where it goes wrong.</p>

Time to Conceive: a New Argument for the Possibility of Time Travel

<p>Ordinarily, philosophers arguing for the possibility of time travel restrict themselves to defending time travel against allegations of inconsistency and contradiction. These objections are usually based on particular theories about time and causality. I believe, however, that this way of arguing can be turned on its head. By using the conceivability thesis – that is, the thesis that if something is conceivable, then it is also possible – we can put forward a positive argument for the possibility of time travel, and then consider how this should inform our metaphysical views. I do this by assuming the truth of the conceivability thesis and then presenting a simple piece of time travel fiction. I argue for the conceivability of this story and, in doing so, attempt to show that the time travel journey it describes is logically possible. I then develop this argument by considering other more controversial cases of time travel. I make minor alterations to the original time travel story in order to show that there are a number of different kinds of time travel journeys (including cases of both forwards and backwards time travel) which are conceivable and, therefore, logically possible. Finally, I ask how the conceivability of different types of time travel should affect the metaphysical views we choose to adopt. I argue that since the conceivability of time travel entails the logical possibility of journeys to other times, any plausible theory of time must be able to accommodate such journeys. I also explain how the conceivability of time travel entails the logical possibility of two particularly unusual cases of causation. I argue that the conceivability of instantaneous time travel entails the logical possibility of causation at a distance, and that the conceivability of backwards time travel entails the logical possibility of backwards causation. Any plausible theory of causation must therefore be able to account for the possibility of these two types of causation. I consider one particular theory of causation which does not (namely, that put forward by D.H. Mellor), and then attempt to establish where it goes wrong.</p>