《动物研究:体内实验报告》即ARRIVE 2.0指南的解释和阐述(五)

提高生物医学研究结果的可重复性是一项重大挑战,研究人员透明且准确地报告其研究过程有利于读者对该研究结果的可靠性进行评估,进而重复该实验或在该成果的基础上进一步探索。ARRIVE 2.0指南是英国国家3Rs中心(NC3Rs)于2019年组织发布的一份适用于任何与活体动物研究报告相关的指导性清单,用以提高动物体内实验设计、实验实施和实验报告的规范性,以及动物实验结果的可靠性、可重复性和临床转化率。ARRIVE 2.0指南的使用不仅可以丰富动物实验研究报告的细节,确保动物实验结果信息被充分评估和利用,还可以使读者准确且清晰地了解作者所表述的内容,促进基础研究评审过程的透明化和完整性。本文是在国际期刊遵循ARRIVE 2.0指南的最佳实践基础上,对2020年发表于PLoS Biology期刊上的ARRIVE 2.0指南完整解读版(https://arriveguidelines.org)第五部分包括“推荐11条”里的第6~11条:“动物照护和监测”、“解析/科学阐释”、“可推广性/转化”、“研究方案注册”、“数据获取”和“利益冲突声明”等内容进行了编译、解释和阐述,以期促进国内研究人员充分理解并使用ARRIVE 2.0指南,提高实验动物研究及报告的规范性,助推我国实验动物科技与比较医学研究的高质量发展。

Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application

Accurately picking P-and S-wave arrivals of microseismic(MS)signals in real-time directly influences the early warning of rock mass failure.A common contradiction between accuracy and computation exists in the current arrival picking methods.Thus,a real-time arrival picking method of MS signals is constructed based on a convolutional-recurrent neural network(CRNN).This method fully utilizes the advantages of convolutional layers and gated recurrent units(GRU)in extracting short-and long-term features,in order to create a precise and lightweight arrival picking structure.Then,the synthetic signals with field noises are used to evaluate the hyperparameters of the CRNN model and obtain an optimal CRNN model.The actual operation on various devices indicates that compared with the U-Net method,the CRNN method achieves faster arrival picking with less performance consumption.An application of large underground caverns in the Yebatan hydropower station(YBT)project shows that compared with the short-term average/long-term average(STA/LTA),Akaike information criterion(AIC)and U-Net methods,the CRNN method has the highest accuracy within four sampling points,which is 87.44%for P-wave and 91.29%for S-wave,respectively.The sum of mean absolute errors(MAESUM)of the CRNN method is 4.22 sampling points,which is lower than that of the other methods.Among the four methods,the MS sources location calculated based on the CRNN method shows the best consistency with the actual failure,which occurs at the junction of the shaft and the second gallery.Thus,the proposed method can pick up P-and S-arrival accurately and rapidly,providing a reference for rock failure analysis and evaluation in engineering applications.

Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone

Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.

Enhancing microseismic/acoustic emission source localization accuracy with an outlier-robust kernel density estimation approach

Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust localization method that integrates kernel density estimation(KDE)with damping linear correction to enhance the precision of microseismic/acoustic emission(MS/AE)source positioning.Our approach systematically addresses abnormal arrival times through a three-step process:initial location by 4-arrival combinations,elimination of outliers based on three-dimensional KDE,and refinement using a linear correction with an adaptive damping factor.We validate our method through lead-breaking experiments,demonstrating over a 23%improvement in positioning accuracy with a maximum error of 9.12 mm(relative error of 15.80%)—outperforming 4 existing methods.Simulations under various system errors,outlier scales,and ratios substantiate our method’s superior performance.Field blasting experiments also confirm the practical applicability,with an average positioning error of 11.71 m(relative error of 7.59%),compared to 23.56,66.09,16.95,and 28.52 m for other methods.This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies.It also provides a practical solution for real-world engineering and safety monitoring applications.

Localization in modified polar representation: hybrid measurements and closed-form solution

Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) unifies near-field and farfield models, alleviating the thresholding effect. Current localization methods in MPR based on the angle of arrival (AOA) and time difference of arrival (TDOA) measurements resort to semidefinite relaxation (SDR) and Gauss-Newton iteration, which are computationally complex and face the possible diverge problem. This paper formulates a pseudo linear equation between the measurements and the unknown MPR position,which leads to a closed-form solution for the hybrid TDOA-AOA localization problem, namely hybrid constrained optimization(HCO). HCO attains Cramér-Rao bound (CRB)-level accuracy for mild Gaussian noise. Compared with the existing closed-form solutions for the hybrid TDOA-AOA case, HCO provides comparable performance to the hybrid generalized trust region subproblem (HGTRS) solution and is better than the hybrid successive unconstrained minimization (HSUM) solution in large noise region. Its computational complexity is lower than that of HGTRS. Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator (MLE) attains if the noise is small, but the MLE deviates from CRB earlier.

Cyclic Beam Direction of Arrival Estimation Method for Ship Propeller Noise

In underwater acoustic applications,the conventional cyclic direction of arrival algorithm faces challenges,including a low signal-to-noise ratio and high bandwidth when compared with modulated frequencies.In response to these issues,this paper introduces a novel,robust,and broadband cyclic beamforming algorithm.The proposed method substitutes the conventional cyclic covariance matrix with the variance of the cyclic covariance matrix as its primary feature.Assuming that the same frequency band shares a common steering vector,the new algorithm achieves superior detection performance for targets with specific modulation frequencies while suppressing interference signals and background noise.Experimental results demonstrate a significant enhancement in the directibity index by 81%and 181%when compared with the traditional Capon beamforming algorithm and the traditional extended wideband spectral cyclic MUSIC(EWSCM)algorithm,respectively.Moreover,the proposed algorithm substantially reduces computational complexity to 1/40th of that of the EWSCM algorithm,employing frequency band statistical averaging and covariance matrix variance.

DOA estimation of high-dimensional signals based on Krylov subspace and weighted l_(1)-norm

With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.

Underdetermined direction of arrival estimation with nonuniform linear motion sampling based on a small unmanned aerial vehicle platform

Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.

A Novel CCA-NMF Whitening Method for Practical Machine Learning Based Underwater Direction of Arrival Estimation

Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.

基于子空间模型的稀疏贝叶斯DOA估计

为了提高相干信源条件下的离格波达方向(direction of arrival,DOA)估计精度,提出一种基于子空间模型的稀疏贝叶斯学习(sparse Bayesian learning,SBL)的DOA估计算法。该算法首先将子空间平滑(subspace smoothing,SS)技术与加权子空间拟合(weighted subspace fitting,WSF)技术结合,然后将此子空间模型应用于SBL算法,并将离散网格点视为动态参数,用期望最大化(expectation maximization,EM)算法迭代更新网格点位置。与传统稀疏恢复算法相比,该算法在估计误差及计算复杂度上均具有明显优势,并对信源数目的估计误差具有较强的鲁棒性。

Study on the pattern of train arrival headway time in high-speed railway

Purpose-The design goal for the tracking interval of high-speed railway trains in China is 3 min,but it is difficult to achieve,and it is widely believed that it is mainly limited by the tracking interval of train arrivals.If the train arrival tracking interval can be compressed,it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval.The goal of this article is to study how to compress the train arrival tracking interval.Design/methodologylapproach-By simulating the process of dense train groups arriving at the station and stopping,the headway between train arrivals at the station was calculated,and the pattern of train arrival headway was obtained,changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains.Findings-When the running speed of trains is high,the headway between trains is short,the length of the station approach throat area is considerable and frequent train arrivals at the station,the arrival headway for the first group or several groups of trains will exceed the headway,but the subsequent sets of trains will havea headway equal to the arrival headway.This convergence characteristic is obtained by appropriately increasing the running time.Originality/value-According to this pattern,there is no need to overly emphasize the impact of train arrival headway on the headway.This plays an important role in compressing train headway and improving high-speedrailwaycapacity.

Optimization study of station track utilization in high-speed railroad based on constraints of control in random origin and process

Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals.The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodologylapproach-To achieve this objective,the paper simulates actual train operations,incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station.The Monte Carlo simulation method is adopted to solve this problem.This approach transforms a nonlinear model,which includes constraints from probability distribution functions and is difficult to solve directly,into a linear programming model that is easier to handle.The method then linearly weights two objectives to optimize the solution.Findings-Through the application of Monte Carlo simulation,the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model.By continuously adjusting the weighting coefficients of the linear objectives,the method is able to optimize the Pareto solution.Notably,this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/value-The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times.The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement.Furthermore,the method's ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.

Comprehensive analysis of the impact of primary percutaneous coronary intervention on patients with ST-segment elevation myocardial infarction

This comprehensive analysis by Saeed and Faeq investigates the impact of primary percutaneous coronary intervention(pPCI)on mortality among patients with ST-segment elevation myocardial infarction(STEMI)at the Erbil Cardiac Center.Analyzing data from 96 consecutive STEMI patients,the study identified significant predictors of in-hospital mortality,emphasizing the critical impact of time of hospital arrival post-symptom onset on overall prognosis.Findings indicate that factors such as atypical presentation,cardiogenic shock,chronic kidney disease,and specific coronary complications are associated with higher mortality rates.The study underscores the necessity of prompt medical intervention for improving survival outcomes in STEMI patients,especially in the high-risk subgroup.This research offers valuable insights into optimizing STEMI management and enhancing patient survival rates through effective and timely pPCI.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

UWB无线传感器网络中基于匹配滤波检测的TOA估计

深入研究了UWB(ultra wideband)无线传感器网络中基于匹配滤波门限检测的TOA(time of arrival)估计算法.针对现有算法的不足,提出了一种三步TOA估计算法:先确定DP(direct path)搜索区域,然后使用门限检测确定DP的粗略位置,最后精确搜索到DP的中心.其中,用于计算检测门限的门限因子依据匹配滤波输出的峭度动态设置,设置模型独立于信道模式,其正确性通过与使用固定门限因子所获得的性能对比进行了验证.与其他算法的性能对比仿真结果表明,所提出的三步TOA估计算法在运算效率和TOA估计精度上取得了较好折衷,适合于当前实际应用.还通过对TOA估计误差的统计分析讨论了测距结果的可信度:依据峭度将测距结果划分为可信和不可信两个级别,并为各级别的TOA估计误差分别了建立概率密度模型.在定位模块中有效利用这些可信度信息,可进一步提高定位精度.

初至波层析成像的反演参数选取：以南海中央次海盆三维地震探测数据为例

FAST(first arrival seismic tomography)软件是获取复杂地区三维深部结构的最常用工具之一,而反演参数选取是快速有效地获得真实合理的地震结构的关键环节。以南海中央次海盆三维地震探测数据为基础,采用先固定其中一个参数、选取另外一个参数、再综合选取的控制变量法,详细地介绍了FAST反演参数组合的选取过程。选取结果表明,反演参数组合(阻尼因子λ=2,平滑度权重因子sz=1,反演次数I=4)为南海中央次海盆三维地震结构的最佳反演参数,由此获得的最佳初步速度模型表明中央次海盆具有典型慢速扩张的洋壳结构。此项反演参数选取工作不仅为珍贝-黄岩海山链精细三维地震结构的正式模拟奠定了基础,同时为FAST软件在其他地区的应用提供了丰富的经验与借鉴。

基于UWB的无线传感器网络中的两步TOA估计法

为了设计一种以较小运算量获得较高测距精度的TOA(time of arrival)估计算法以适合节点运算能力有限的UWB(ultra wideband)无线传感器网络,提出了一种结合能量检测与匹配滤波的两步TOA估计方法.分析了该方法的工作原理,指出了第1步中DP(direct path)块检测成功率及第2步中匹配滤波门限因子设置的重要性.通过仿真对影响DP块检测成功率的两个因素,即DP块检测算法的选用和能量积分周期的设置进行了讨论.提出了依据能量采样序列中DP块与最小块比值DMR(DP to minimum energy sample ratio)动态设置匹配滤波门限因子的思想,并为其建立了数学模型.仿真结果表明,两步TOA估计方法在运算量比单一的基于匹配滤波的相干算法小很多的情况下,获得了比单一的基于能量检测的非相干方法更好的TOA估计性能,从而更适合应用于有低复杂度、低能耗设计需求的传感器节点中.

基于短基线传感器网络的远场声源TDoA定位组合算法

针对远场声源定位问题,提出到达时间差(TDoA)定位的短基线传感器网络方案.通常有一类定位算法无需估计初始点,而另一类算法则依赖初始估计值,提出将这2类方法相结合,设计出几种组合定位算法.主要思想是扬长避短,由第1类定位算法实现粗定位和输出初始估计位置,将其作为初始点输入给定位精度高和依赖初始值的第2类算法.提出利用蒙特卡洛法和简化的几何配置案例,计算定位算法的概率误差圆,论证了这种性能评估准则的可行性.通过对组合算法的概率误差圆进行数值分析,得出球形插值法与最小二乘方程差法的有机组合具有最优性能的结论,并分析了这种组合算法的盒图特性.根据不同的距离和声程差标准偏差进行模拟,以及采用野外真实场景下获得的声源数据进行试验,验证了结论.

Shifted first arrival point travel time NMO inversion

Serious stretch appears in shallow long offsset signals after NMO correction. In this article we study the generation mechanism of NMO stretch, demonstrate that the conventional travel time equation cannot accurately describe the travel time of the samples within the same reflection wavelet. As a result, conventional NMO inversion based on the travel time of the wavelet＇s central point occurs with errors. In this article, a travel time equation for the samples within the same wavelet is reconstructed through our theoretical derivation （the shifted first arrival point travel time equation）, a new NMO inversion method based on the wavelet＇s first arrival point is proposed. While dealing with synthetic data, the semblance coefficient algorithm equation is modified so that wavelet first arrival points can be extracted. After that, NMO inversion based on the new velocity analysis is adopted on shot offset records. The precision of the results is significantly improved compared with the traditional method. Finally, the block move NMO correction based on the first arrival points travel times is adopted on long offset records and non-stretched results are achieved, which verify the proposed new equation.