Effectiveness and time-course adaptation of resistance training vs. plyometric training in prepubertal soccer players

Purpose:This study aimed to assess the effectiveness and time course for improvements in explosive actions through resistance training(RT)vs.plyometric training(PT)in prepubertal soccer players.Methods:Thirty-four male subjects were assigned to:a control group(n=11);an RT group(5 regular soccer training sessions per week,n=12);a PT group(3 soccer training sessions and 2 RT sessions per week,n=11).The outcome measures included tests for the assessment of muscle strength(e.g.,1 repetition maximum half-squat test),jump ability(e.g.,countermovement jump,squat jump,standing long jump,and multiple 5 bounds test),linear speed(e.g.,20m sprint test),and change of direction(e.g.,Illinois change of direction test).Results:The RTG showed an improvement in the half-squat(△=13.2%;d=1.3,p<0.001)and countermovement jump(△=9.4%;d=2.4,p<0.001)at Week 4,whereas improvements in the 20-m sprint(△=4.2%;d=1.1,p<0.01);change of direction(CoD)(△=3.8%;d=2.1,p<0.01);multiple 5 bounds(△=5.1%;d=1.5,p<0.05);standing long jump(△=7.2%;d=1.2,p<0.01);squat jump(△=19.6%;d=1.5,p<0.01);were evident at Week 8.The PTG showed improvements in CoD(△=2.1%;d=1.3,p<0.05);standing long jump(△=9.3%;d=1.1,p<0.01);countermovement jump(△=16.1%;d=1.2,p<0.01);and squat jump(△=16.7%;d=1.4,p<0.01);at Week 8 whereas improvements in the 20-m sprint(△=4.1%;d=1.3,p<0.01);and multiple 5 bounds(△=7.4%;d=2.4,p<0.001);were evident only after Week.The RT and PT groups showed improvements in all sprint,CoD,and jump tests(p<0.05)and in half-squat performance,for which improvement was only shown within the RTG(p<0.001).Conclusion:RT and PT conducted in combination with regular soccer training are safe and feasible interventions for prepubertal soccer players.In addition,these interventions were shown to be effective training tools to improve explosive actions with different time courses of improvements,which manifested earlier in the RTG than in the PTG.These outcomes may help coaches and fitness trainers set out clear and concise goals of training according to the specific time course of improvement difference between RT and PT on proxies of athletic performance of prepubertal soccer players.

A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Short-course or long-course radiation therapy as a part of a neoadjuvant regimen for stage Ⅱ & Ⅲ rectal adenocarcinoma?

The aim of this mini-review is to compare and contrast the pros and cons of short-course and long-course neoadjuvant chemoradiation therapy regimens for stageⅡ&Ⅲrectal adenocarcinoma.Multiple trials have demonstrated the equal efficacy and safety of short-course and long-course radiation therapy as a part of neoadjuvant regimens.Published data also shows that total neoadjuvant therapy could be more successful than neoadjuvant chemoradiation followed by adjuvant chemotherapy.This review points out future research directions for patients with locally advanced rectal adenocarcinoma such as comparing total neoadjuvant therapy that contains a short-course of radiation therapy to the standard of care,and evaluating how the sequence of short-course radiation therapy and chemotherapy in the total neoadjuvant therapy impacts the pathological complete response(pCR)rate,local control,and survival outcomes.

Mix design method of asphalt rubber open-graded friction course and its application in overlay

The mix design method of asphalt rubber open-graded friction course （AR-OGFC） by the Marshall test and key technologies of AR-OGFC overlay are studied. Based on the draindown test, the stripping test and the Marshall test by different beating times, the asphalt content estimating method and Marshall beat criterion are studied. It is found that the asphalt content of AR-OGFC can be estimated by the formula of Arizona, and the proper Marshall test criterion for AR-OGFC is 50 beats on both sides. By analyzing the influence of gradation on OGFC performance, the gradation of AR-OGFC is recommended, and the amount of aggregate passing 4. 75 mm should be minimized for AR-OGFC. Through investigation of AR-OGFC overlay projects, key technologies such as the treatment of underlying pavement and the selection of tack coat are advanced, and modified emulsified asphalt should be used in AR-OGFC overlay tack coat.

Origin of “Long time,no see”

"Long time no see" is a very interesting English expression used as a greeting by people who have not seen each other for a while. The essay shows how Chinese people and native English speaker think about "Long time no see". Meanwhile, it does research upon the historical appearances of the phrase.

Long-time Convergence of Numerical Approximations for Semilinear Parabolic Equations (Ⅱ)

In this article we extend ours framework of long time convergence for numeracal approximations of semilinear parabolic equations prorided in “Wu Haijun and Li Ronghua, Northeast. Math. J., 16(1)(2000), 1—28”, to the Gauss Ledendre full discretization. When apply the result to the Crank Nicholson finiteelement full discretization of the Navier Stokes equations, we can remore the grid ratio restriction of “Heywood, J. G. and Rannacher, R., SIAM J. Numer. Anal., 27(1990), 353—384”, and weaken the stability condition on the continuous solution.

Binaural Speech Separation Algorithm Based on Long and Short Time Memory Networks

Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.

Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.

Dynamics and Long Time Convergence of the Extended Fisher-Kolmogorov Equation under Numerical Discretization

We present a numerical study of the long time behavior of approxima- tion solution to the Extended Fisher-Kolmogorov equation with periodic boundary conditions. The unique solvability of numerical solution is shown. It is proved that there exists a global attractor of the discrete dynamical system. Furthermore, we obtain the long-time stability and convergence of the difference scheme and the upper semicontinuity d（Ah,τ, .A） → O. Our results show that the difference scheme can effectively simulate the infinite dimensional dynamical systems.

Source time functions of the 1999, Jiji (Chi-Chi) earthquake from GDSN long period waveform data using aftershocks as empirical Green's functions

A large earthquake (Mw=7.6) occurred in Jiji (Chi-Chi), Taiwan, China on September 20, 1999, and was followed by many moderate-size shocks in the following days. Two of the largest aftershocks with the magnitudes of Mw=6.1 and Mw=6.2, respectively, were used as empirical Green's functions (EGFs) to obtain the source time functions (STFs) of the main shock from long-period waveform data of the Global Digital Seismograph Network (GDSN) including IRIS, GEOSCOPE and CDSN. For the Mw=6.1 aftershock of September 22, there were 97 pairs of phases clear enough from 78 recordings of 26 stations; for the Mw=6.2 aftershock of September 25, there were 81 pairs of phases clear enough from 72 recordings of 24 stations. For each station, 2 types of STFs were retrieved, which are called P-STF and S-STF due to being from P and S phases, respectively. Totally, 178 STF individuals were obtained for source-process analysis of the main shock. It was noticed that, in general, STFs from most of the stations had similarities except that those in special azimuths looked different or odd due to the mechanism difference between the main shock and the aftershocks; and in detail, the shapes of the STFs varied with azimuth. Both of them reflected the stability and reliability of the retrieved STFs. The comprehensive analysis of those STFs suggested that this event consisted of two sub-events, the total duration time was about 26 s, and on the average, the second event was about 7 s later than the first one, and the moment-rate amplitude of the first event was about 15% larger than that of the second one.

Discussion of Influences on Snow Water Equivalent at Utah Snow Courses

Snow data collection systems in the western United States were originally designed to forecast water supply and may be subject to several sources of bias. In addition to climate change and weather modification effects, site-specific effects may be introduced from vegetation changes, site physical changes, measurement technique, and sensor changes. This paper examines changes in Utah＇s snowpack conditions over the past decade compared with all previous measurement years, focusing on the 15 snow courses with the longest observational record within the state of Utah. Although patterns in snowpack data consistent with those that would be expected due to temperature h as greater declines at lower elevations and latitudes--were not identified, snow water equivalent decreased at sites with significant increases in vegetation coverage. Additionally, we provide a list of 22 snow courses in Utah that are best-suited for long-term climate analysis.

Longtime Convergence of Numerical Approximations for Semilinear Parabolic Equations (Ⅰ)

The numerical approximations of the dynamical systems governed by semilinear parabolic equations are considered. An abstract framework for long time error estimates is established. When applied to reaction diffusion equation, Navier Stokes equations and Chan Hilliard equation, approximated by Galerkin and nonlinear Galerkin methods in space and by Runge Kutta method in time, our framework yields error estimates uniform in time.

RHEOLOGICAL BEHAVIOUR FOR AN ANHYDRITE SPECIMEN AND DETERMINATION OF ITS LONG TIME STRENGTH

Based on the method of torsional creep, the creep laws of ananhydrite specimen are studied in this paper. When a shearing stressapplied to the specimen is less than a value, only the primary stagetakes place. How- ever, when the shearing stress is more than anothervalue, all the three stages of a creep curve, i. e. primary, steady-state and accelerated are exhibited.

LONG-TIME CONVERGENCE OF GENERALIZED DIFFERENCE METHOD FOR NAVIER-STOKES EQUATIONS

In this paper, we first provide a generalized difference method for the 2-dimensional Navier-Stokes equations by combing the ideas of staggered scheme m and generalized upwind scheme in space, and by backward Euler time-stepping. Then we apply the abstract framework of to prove its long-time convergence. Finally, a numerical example for solving driven cavity flows is given.

Long-time limit behavior of the solution to an atom's master equation

We study the long-time limit behavior of the solution to an atom＇s master equation. For the first time we derive that the probability of the atom being in the α-th （α = j ＋ 1 -jz, j is the angular momentum quantum number, jz is the z-component of angular momentum） state is {（1 - K/G）/[1 - （K/G）2j＋1]}（K/G）^α-1 as t → ＋∞, which coincides with the fact that when K/G 〉 1, the larger the a is, the larger the probability of the atom being in the α-th state （the lower excited state） is. We also consider the case for some possible generaizations of the atomic master equation.

从“Long time no see”说起

前几天，我在20多年前教过的一位学生来看我。我们谈话时，他随手拿起我书桌上的一本英语口语教材翻看，他说：“呀，老师，教材里怎么有错句?”我拿起教材一看，是“Long time no see”。我笑了。“老师，我清楚地记得，我们在中学时，您不止一次地在黑板上纠正过类似的错句，怎么20年后它又成对的了?”我说：“你问得好。”接着我对此作了解释。

Time distribution characteristics of regional macroseismic activity in the Sichuan-Yunnan region and its significance to mid-long term prediction

The earthquakes with Ms≥6.0 are often gathered into belts or clusters and are roughly consistent with tectonic structure trends in the Sichuan-Yunnan (Chuan-Dian) region. The middle south part(98°-106°E, 21°-34°N) of South-North Seismic Zone can be zoned into seven small areas. There all were strong quakes with M_s≥7.0 historically in each small area. Ten earthquakes with M_s≥7.0 have occurred in this region since 1970 and they appeared in five small areas respectively. The relationships between occurrence-time and cumulative frequencies of strong quakes in these five areas are shown to be an exponential distribution or power function. By examining the inner coincidence it is indicated that these relationships are of definite significance to mid-long term macroseismic prediction of each area.

LONG-TIME BEHAVIOR OF TRANSIENT SOLUTIONS FOR CELLULAR NEURAL NETWORK SYSTEMS

By establishing concept an transient solutions of general nonlinear systems converging to its equilibrium set, long-time behavior of solutions for cellular neural network systems is studied. A stability condition in generalized sense is obtained. This result reported has an important guide to concrete neural network designs.

A Long Time Coming

South Sudan,Africa’s newest nation,carries much hope on its shoulders IF joy and happiness could be measured, then millions of South Sudanese would have pushed the meter off the charts when their country got its independence on July 9,2011. The foot-thumping,hip-swaying and shoulder-shaking crowd chanted and danced rhythmically to celebrate their new country,their own President Salva