Space–time correlations in turbulent flow: A review

This paper reviews some of the principal uses, over almost seven decades, of correlations, in both Eulerian and Lagrangian frames of reference, of properties of turbulent flows at variable spatial locations and variable time in- stants. Commonly called space-time correlations, they have been fundamental to theories and models of turbulence as well as for the analyses of experimental and direct numerical simulation turbulence data.

Curved Space-Time at the Planck Scale

This paper presents a physically plausible and somewhat illuminating first step in extending the fundamental principles of mechanical stress and strain to space-time. Here the geometry of space-time, encoded in the metric tensor, is considered to be made up of a dynamic lattice of extremely small, localized fields that form a perfectly elastic Lorentz symmetric space-time at the global (macroscopic) scale. This theoretical model of space-time at the Planck scale leads to a somewhat surprising result in which matter waves in curved space-time radiate thermal gravitational energy, as well as an equally intriguing relationship for the anomalous dispersion of light in a gravitational field.

VARIATIONAL ANALYSIS FOR THE MAXIMAL TIME FUNCTION IN NORMED SPACES

For a general normed vector space,a special optimal value function called a maximal time function is considered.This covers the farthest distance function as a special case,and has a close relationship with the smallest enclosing ball problem.Some properties of the maximal time function are proven,including the convexity,the lower semicontinuity,and the exact characterizations of its subdifferential formulas.

ESTIMATION OF AVERAGE DIFFERENTIAL ENTROPY FOR A STATIONARY ERGODIC SPACE-TIME RANDOM FIELD ON A BOUNDED AREA

In this paper,we mainly discuss a discrete estimation of the average differential entropy for a continuous time-stationary ergodic space-time random field.By estimating the probability value of a time-stationary random field in a small range,we give an entropy estimation and obtain the average entropy estimation formula in a certain bounded space region.It can be proven that the estimation of the average differential entropy converges to the theoretical value with a probability of 1.In addition,we also conducted numerical experiments for different parameters to verify the convergence result obtained in the theoretical proofs.

CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

Modified Lorentz Transformations and Space-Time Splitting According to the Inverse Relativity Model

Analysis of a four-dimensional displacement vector on the fabric of space-time in the special or general case into two Four-dimensional vectors, according to specific conditions leads to the splitting of the total fabric of space-time into a positive subspace-time that represents the space of causality and a negative subspace-time which represents a space without causality, thus, in the special case, we have new transformations for the coordinates of space and time modified from Lorentz transformations specific to each subspace, where the contraction of length disappears and the speed of light is no longer a universal constant. In the general case, we have new types of matric tensor, one for positive subspace-time and the other for negative subspace-time. We also find that the speed of the photon decreases in positive subspace-time until it reaches zero and increases in negative subspace-time until it reaches the speed of light when the photon reaches the Schwarzschild radius.

Mechanism Study on the Effect of Retarder on Polyurethane Setting Time Based on Molecular Simulation

This study identified castor oil and phosphate ester as effective retarders through setting time,tensile,and flexural tests,and determined their optimal dosages.The mechanism by which phosphate ester affects the setting time of polyurethane was further investigated using molecular dynamics simulations.Fourier transform infrared spectroscopy was also employed to systematically study the physical and chemical interactions between phosphate esters and polyurethane materials.The results demonstrate that a 1%concentration of phosphate ester provides the most effective retarding effect with minimal impact on the strength of polyurethane.When phosphate ester is added to the B component of the two-component polyurethane system,its interaction energy with component A decreases,as do the diffusion coefficient and aggregation degree of component B on the surface of component A.This reduction in interaction slows the setting time.Additionally,the addition of phosphate ester to polyurethane leads to the disappearance or weakening of functional groups,indicating competitive interactions within the phosphate ester components that inhibit the reaction rate.

DecMamba:Mamba Utilizing Series Decomposition for Multivariate Time Series Forecasting

Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.

IoT Empowered Early Warning of Transmission Line Galloping Based on Integrated Optical Fiber Sensing and Weather Forecast Time Series Data

Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.

Impact of time between meniscal injury and isolated meniscus repair on post-operative outcomes:A systematic review

BACKGROUND Meniscal tears are one of the most common knee injuries.After the diagnosis of a meniscal tear has been made,there are several factors physicians use to guide clinical decision-making.The influence of time between injury and isolated meniscus repair on patient outcomes is not well described.Assessing this relationship is important as it may influence clinical decision-making and can add to the preoperative patient education process.We hypothesized that increasing the time from injury to meniscus surgery would worsen postoperative outcomes.AIM To investigate the current literature for data on the relationship between time between meniscus injury and repair on patient outcomes.METHODS PubMed,Academic Search Complete,MEDLINE,CINAHL,and SPORTDiscus were searched for studies published between January 1,1995 and July 13,2023 on isolated meniscus repair.Exclusion criteria included concomitant ligament surgery,incomplete outcomes or time to surgery data,and meniscectomies.Patient demographics,time to injury,and postoperative outcomes from each study were abstracted and analyzed.RESULTS Five studies met all inclusion and exclusion criteria.There were 204(121 male,83 female)patients included.Three of five(60%)studies determined that time between injury and surgery was not statistically significant for postoperative Lysholm scores(P=0.62),Tegner scores(P=0.46),failure rate(P=0.45,P=0.86),and International Knee Documentation Committee scores(P=0.65).Two of five(40%)studies found a statistically significant increase in Lysholm scores with shorter time to surgery(P=0.03)and a statistically significant association between progression of medial meniscus extrusion ratio(P=0.01)and increasing time to surgery.CONCLUSION Our results do not support the hypothesis that increased time from injury to isolated meniscus surgery worsens postoperative outcomes.Decision-making primarily based on injury interval is thus not recommended.

Storage time affects the level and diagnostic efficacy of plasma biomarkers for neurodegenerative diseases

Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.

“Culture time”板块教学中跨文化意识培养的策略

在新课程改革的大环境中,文化意识成为英语学科核心素养的重要组成部分。语言是文化的载体,语言与文化是相互依赖、相互影响、相互制约的。学习语言的过程也是学习文化的过程。文章主要结合译林版英语五、六年级教材中的Culture time板块的教学案例,探讨如何在英语课堂教学实践中丰富文化素材、发掘其中的文化元素和领略中西方文化异同,以帮助学生有效学习文化知识,拓宽文化视野,培养文化意识。

Effect of pouring time on microstructure and mechanical properties of centrifugal cast Ti-46Al alloy tubes

The grain size of TiAl alloy castings prepared by traditional casting process is coarse,thus showing poor mechanical properties.In this study,a new type of high performance Ti-46Al alloy tube prepared by vacuum centrifugal casting technology was introduced.This research comprehensively examined the influence of pouring time on the microstructure and mechanical performance of the castings,employing both experimental approaches and ProCast simulation methodologies.The findings indicate that prolonging the pouring time facilitates a microstructural evolution from coarse columnar grains to refined equiaxed grains.Under the condition of pouring temperature of 1,600℃,rotation speed of 800 r·min^(-1) and pouring time of 6 s,the tensile strength of Ti-46Al alloy at room temperature reaches 650 MPa,and the tensile strength at 800℃ reaches 705 MPa,which is significantly higher than that of traditional as-cast Ti-Al alloy.

Viscoacoustic prestack reverse time migration based onthe optimal time-space domain high-order finite-difference method

Prestack reverse time migration （RTM） is an accurate imaging method ofsubsurface media. The viscoacoustic prestack RTM is of practical significance because itconsiders the viscosity of the subsurface media. One of the steps of RTM is solving thewave equation and extrapolating the wave field forward and backward; therefore, solvingaccurately and efficiently the wave equation affects the imaging results and the efficiencyof RTM. In this study, we use the optimal time-space domain dispersion high-order finite-difference （FD） method to solve the viscoacoustic wave equation. Dispersion analysis andnumerical simulations show that the optimal time-space domain FD method is more accurateand suppresses the numerical dispersion. We use hybrid absorbing boundary conditions tohandle the boundary reflection. We also use source-normalized cross-correlation imagingconditions for migration and apply Laplace filtering to remove the low-frequency noise.Numerical modeling suggests that the viscoacoustic wave equation RTM has higher imagingresolution than the acoustic wave equation RTM when the viscosity of the subsurface isconsidered. In addition, for the wave field extrapolation, we use the adaptive variable-lengthFD operator to calculate the spatial derivatives and improve the computational efficiencywithout compromising the accuracy of the numerical solution.

The space time CE/SE method for solving one-dimensional batch crystallization model with fines dissolution

This article is concerned with the numerical investigation of one-dimensional population balance models for batch crystallization process with fines dissolution.In batch crystallization,dissolution of smaller unwanted nuclei below some critical size is of vital importance as it improves the quality of product.The crystal growth rates for both size-independent and size-dependent cases are considered.A delay in recycle pipe is also included in the model.The space–time conservation element and solution element method,originally derived for non-reacting flows,is used to solve the model.This scheme has already been applied to a range of PDEs,mainly in the area of fluid mechanics.The numerical results are compared with those obtained from the Koren scheme,showing that the proposed scheme is more efficient.

基于space-time的二叉树表示动态环境中的路径规划

研究具有空间和时间的三维动态环境下的机器人路径规划分析了四叉树表示二维空间的搜索算法,在此基础上,提出采space-time,用二叉树表示二维空间的方法时间信息中增加加速度利用二叉树遍历方法和算法设计一个在动态障碍物环境下进行路径规划的新算法并,,A*,,在足球机器人系统中进行仿真,实现了较好的路径规划。

3D MERGE与3D SPACE STIR序列在腰椎间盘突出症检查中的应用比较

目的:对比三维多回波恢复梯度回波(3D MERGE)、三维可变反转角快速自旋回波(3D SPACE STIR)序列在腰椎间盘突出症(LDH)检查中的应用效果。方法:选择2020年1月~2022年11月收治的135例LDH患者,回顾性分析患者临床和磁共振成像(MRI)资料,所有患者均接受常规MRI扫描及3D MERGE、3D SPACE STIR序列扫描,对比3D MERGE、3D SPACE STIR序列测量神经根直径的一致性,评价两种序列的图像质量参数[信噪比(SNR)、对比噪声比(CNR)]、图像清晰度评分。结果:3D MERGE和3D SPACE STIR序列测量的L3~S1神经根直径比较差异无统计学意义(P>0.05),且两组序列测量的L3、L4、L5和S1直径均显示出较高相关性(r=0.957,0.986,0.975,0.972,P<0.05);3D MERGE序列的SNR及CNR均高于3D SPACE STIR序列,神经根显示分级、图像清晰度评分优于3D SPACE STIR序列,差异有统计学意义(P<0.05)。结论:3D MERGE、3D SPACE STIR序列在LDH神经根直径测量中具有极高一致性,3D MERGE序列较3D SPACE STIR序列能够更清晰显示神经跟的解剖形态,图像质量更好。

Protective Measures for Space Environment of Time-honored Brands——With Xi'an City as a Study Case

Based on the field survey and analysis of representative traditional time-honored shops in Xi'an City,specific measures for protecting their space environment were put forward,and the sustainable preservation and development pattern of such intangible cultural heritages in the future urban development was established.

TVAR Time-frequency Analysis for Non-stationary Vibration Signals of Spacecraft

Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.

我国研究生学术素养研究热点分析(2013—2023年)——基于Cite Space的可视化分析研究

从中国知网数据库检索2013—2023年底国内发表的研究生学术素养研究相关文献,共筛选出259篇相关文献。将文献纳入Cite Space中,分别对作者、机构、关键词等进行可视化分析,并通过Excel绘制年发文量折线图,发现年发文量总体呈现下降趋势。发文作者为教育学领域的居多,发文机构大部分来自于各学校研究所等。高频关键词有研究生、学术道德、学术不端、学术规范、学术素养,并形成了“研究生”“学术道德”“学术不端”“治理”“学术规范”“学术素养”“博士生”“道德理论”“中心和导向”9个聚类。研究者近年来对学术素养相关研究整体呈现下降状态;研究机构以及研究人员结构单一,国家机构对学术素养相关研究较少;但关于“研究生”“学术道德”“学术不端”以及“学术理论”的研究仍有较高热度。