Automatic Identification of Clear-Air Echoes Based on Millimeter-wave Cloud Radar Measurements

Millimeter-wave cloud radar(MMCR)provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds.Therefore,MMCR has been widely applied in cloud observations.However,due to the influence of non-meteorological factors such as insects,the cloud observations are often contaminated by non-meteorological echoes in the clear air,known as clear-air echoes.It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background.The characteristics of clear-air echoes are studied here by combining data from four devices:an MMCR,a laser-ceilometer,an L-band radiosonde,and an all-sky camera.In addition,a new algorithm,which includes feature extraction,feature selection,and classification,is proposed to achieve the automatic identification of clear-air echoes.The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions.The recognition accuracy can reach up to 95.86%for the simple cases when cloud echoes and clear-air echoes are separate,and 88.38%for the complicated cases when low cloud echoes and clear-air echoes are mixed.

应用ECHO模型评价药物治疗管理服务在儿童支气管哮喘治疗中的效果

目的探究药物治疗管理(MTM)服务对儿童哮喘治疗的应用效果。方法选取2022年7—12月在河北省人民医院咳喘药学门诊就诊的5~11岁哮喘患儿107例,随机分成对照组(50例)和干预组(52例)。对照组患儿首诊时给予单次吸入制剂教育服务;干预组患儿给予全程规范化MTM服务。应用经济、临床与人文结果(ECHO)模型,分别于干预前、干预3个月及干预6个月后,统计2组患儿在经济(药物成本、成本-效用比),临床(ACT评分、吸入制剂使用正确率、哮喘发作次数)和人文(EQ-5D-5L效用值、Morisky用药依从性、患者满意度)结果中的差异,评价MTM服务在哮喘患儿中的应用效果。结果干预前2组各项评分指标差异无统计学意义;干预后干预组患儿经济结果、临床结果、人文结果3方面均优于对照组,差异有统计学意义(P<0.05)。结论药师主导的MTM服务可使哮喘患儿在经济、临床、人文多维度获益,既使哮喘患儿疾病得到长期有效控制,又提升药师药学专科服务能力。

Exploring the associations between skeletal muscle echogenicity and physical function in aging adults:A systematic review with meta-analyses

Background:Assessment and quantification of skeletal muscle within the aging population is vital for diagnosis,treatment,and injury/disease prevention.The clinical availability of assessing muscle quality through diagnostic ultrasound presents an opportunity to be utilized as a screening tool for function-limiting diseases.However,relationships between muscle echogenicity and clinical functional assessments require authoritative analysis.Thus,we aimed to(a) synthesize the literature to assess the relationships between skeletal muscle echogenicity and physical function in older adults(≥60 years),(b) perform pooled analyses of relationships between skeletal muscle echogenicity and physical function,and(c) perform sub-analyses to determine between-muscle relationships.Methods:CINAHL,Embase,MEDLINE,PubMed,and Web of Science databases were systematically searched to identify articles relating skeletal muscle echogenicity to physical function in older adults.Risk-of-bias assessments were conducted along with funnel plot examination.Meta-analyses with and without sub-analyses for individual muscles were performed utilizing Fisher's Z transformation for the most common measures of physical function.Fisher's Z was back-transformed to Pearson's r for interpretation.Results:Fifty-one articles(n=5095,female=~2759,male=~2301,72.5± 5.8 years,mean±SD(1 study did not provide sex descriptors))were extracted for review,with previously unpublished data obtained from the authors of 13 studies.The rectus femoris(n=34) and isometric knee extension strength(n=22) were the most accessed muscle and physical qualities,respectively.The relationship between quadriceps echogenicity and knee extensor strength was moderate(n=2924,r=-0.36(95% confidence interval:-0.38 to-0.32),p <0.001),with all other meta-analyses(grip strength,walking speed,sit-to-stand,timed up-and-go) resulting in slightly weaker correlations(r:-0.34 to-0.23,all p <0.001).Sub-analyses determined minimal differences in predictive ability between muscle groups,although combining muscles(e.g.,rectus femoris+vastus lateralis) often re sulted in stronger correlations with maximal strength.Conclusion:While correlations are modest,the affordable,portable,and noninvasive ultrasonic assessment of muscle quality is a consistent predictor of physical function in older adults.Minimal between-muscle differences suggest that echogenicity estimates of muscle quality are systemic.Therefore,practitioners may be able to scan a single muscle to estimate full-body skeletal muscle quality/composition,while researchers should consider combining multiple muscles to strengthen the model.

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Research on Sleeve Grouting Density Detection Based on the Impact Echo Method

Grouting defects are an inherent challenge in construction practices,exerting a considerable impact on the operational structural integrity of connections.This investigation employed the impact-echo technique for the detection of grouting anomalies within connections,enhancing its precision through the integration of wavelet packet energy principles for damage identification purposes.A series of grouting completeness assessments were meticulously conducted,taking into account variables such as the divergent material properties of the sleeves and the configuration of adjacent reinforcement.The findings revealed that:(i)the energy distribution for the highstrength concrete cohort predominantly occupied the frequency bands 42,44,45,and 47,whereas for other groups,it was concentrated within the 37 to 40 frequency band;(ii)the delineation of empty sleeves was effectively discernible by examining the wavelet packet energy ratios across the spectrum of frequencies,albeit distinguishing between sleeves with 50%and full grouting density proved challenging;and(iii)the wavelet packet energy analysis yielded variable detection outcomes contingent on the material attributes of the sleeves,demonstrating heightened sensitivity when applied to ultrahigh-performance concrete matrices and GFRP-reinforced steel bars.

Enhanced air-coupled impact echo technique by phase analysis of signals from multiple sensors

This paper presents an air-coupled impact echo(IE)technique that relies on the phase spectrum of the collected data to find the frequencies corresponding to the reflections from delaminations.The proposed technique takes advantage of the fact that the IE compression wave is not a propagating wave,but it is the 1st order symmetrical(S1)mode Lamb wave at zero group velocity(S1-ZGV).Therefore,it searches the phase spectra of the data collected by multiple sensors to locate the frequency corresponding to the lowest phase difference.As a result,the technique reduces the effect of propagating waves,including the direct acoustic wave and ambient noise.It is named the Constant Phase IE(CPIE).The performance of the CPIE is experimentally compared with the regular amplitude spectrum-based IE technique and two other multisensor IE techniques.The CPIE shows a performance advantage,especially in a noisy environment.

Relationship of inflammatory indices with left atrial appendage thrombus or spontaneous echo contrast in patients with atrial fibrillation

BACKGROUND Inflammatory indices derived from complete blood tests have been reported to be associated with poor outcomes in patients with atrial fibrillation(AF).The data about the relationship between inflammatory indices and left atrial appendage thrombus(LAAT)or dense spontaneous echo contrast(SEC)are limited.AIM To explore the value of inflammatory indices for predicting the presence of LAAT or dense SEC in nonvalvular AF patients.METHODS A total of 406 patients with nonvalvular AF who underwent transesophageal echocardiography were included and divided into two groups based on the presence(study group)or absence(control group)of LAAT or dense SEC.Inflammatory indices,including the neutrophil-to-lymphocyte ratio(NLR),platelet–tolymphocyte ratio(PLR),and lymphocyte-to-monocyte ratio(LMR),were calculated from complete blood analysis.The associations of inflammatory indices RESULTS LAAT and dense SEC were detected in 11(2.7%)and 42(10.3%)patients,respectively.The PLR only showed an association with LAAT/dense SEC in the univariate model.Elevated NLR(odds ratio[OR]=1.48,95%confidence interval[CI]:1.11-1.98,P=0.007)and reduced LMR(OR=0.59,95%CI:0.41-0.83,P=0.003)were found to be independent risk factors for the presence of LAAT/dense SEC.The areas under the NLR and LMR curves for predicting LAAT/dense SEC were 0.73(95%CI:0.66-0.80,P<0.001)and 0.73(95%CI:0.65-0.81,P<0.001),respectively,while the cutoff values were 2.8(sensitivity:69.8%;specificity:64.0%)and 2.4(sensitivity:71.7%;specificity:60.6%),respectively.CONCLUSION Increased NLR and decreased LMR may predict LAAT/dense SEC in patients with nonvalvular AF.

CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather events.However,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous states.To address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar echoes.Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets.Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,etc.In particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

Comparison of the reaction of polar mesosphere winter echoes and polar mesosphere summer echoes to high-frequency heating in terms of modulated characteristics

In this work,for the first time,we have analyzed and compared the responses of polar mesosphere winter echoes(PMWE)and their summer counterpart,polar mesosphere summer echoes(PMSE),to high-frequency(HF)heating in terms of modulated characteristics(i.e.,backscatter intensity reduction,recovery,and overshoot).Both PMWE and PMSE observations were from the same site(Tromsφ,Norway;69.6°N,19.2°E)and radar(EISCAT[European Incoherent Scatter Scientific Association]very high frequency,224 MHz).The heating patterns of both PMWE and PMSE were found to be similar;however,PMSE was more greatly affected by HF heating.Polar mesosphere summer echoes showed recovery and overshoot more frequently than did PMWE.In addition,the mean recovery and overshoot of PMSE were greater than those of PMWE.The associated electron temperature enhancement was estimated for both PMWE and PMSE and showed that,compared with PMWE,the electron temperature enhancement was more significant in PMSE.The strong heating effects on PMSE may be due to the considerable increase in electron temperature.

Echo State Network With Probabilistic Regularization for Time Series Prediction

Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world tasks.This work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness guaranteed.Specifically,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the PRESN.Furthermore,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical analyses.Finally,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN.The source code is publicly available at https://github.com/LongJinlab/probabilistic-regularized-echo-state-network.

Gravitational wave echoes from strange quark stars in the equation of state with density-dependent quark masses

According to the recent studies,the gravitational wave(GW)echoes are expected to be generated by quark stars composed of ultrastiff quark matter.The ultrastiff equations of state(EOS)for quark matter were usually obtained either by a simple bag model with artificially assigned sound velocity or by employing interacting strange quark matter(SQM)depicted by simple reparameterization and rescaling.In this study,we investigate GW echoes with EOSs for SQM in the framework of the equivparticle model with density-dependent quark masses and pairing effects.We conclude that strange quark stars(SQSs)can be sufficiently compact to possess a photon sphere capable of generating GW echoes with frequencies in the range of approximately 20 kHz.However,SQSs cannot account for the observed 72 Hz signal in GW170817 event.Furthermore,we determined that quark-pairing effects play a crucial role in enabling SQSs to satisfy the necessary conditions for producing these types of echoes.

Analysis of echo signal modulation characteristic parameters on aerial and space targets

Based on the scattering characteristic,the comparison of RCS(radar cross-section)at different positions of a target in the same direction of incidence can be obtained first by extruding or deleting part of the entity.A simulation method of aerial&space targets echo characteristics(A&STEC)is proposed that is universal to aerial and space targets.We utilize a fixed-wing UAV(unmanned aerial vehicle)and typical missiles in simulation.The echo signal modulation characteristic parameters are calculated theoretically by the atmospheric attenuation model,the finite element method and a MUMPS solver.The verification simulations show that this method can analyze the influence of the target shape,incident direction,detection position and detection frequency on echo waveform,intensity and energy distribution.The results show that the profile of echo waveform can invert the general shape of the target.The relationship between time and intensity can determine whether the target is moving towards or away from the detector in addition.These conclusions can provide a reference for the ballistic missile target tracking and the defense against UVA intrusion in theory.

Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model

Weather radar echo extrapolation plays a crucial role in weather forecasting.However,traditional weather radar echo extrapolation methods are not very accurate and do not make full use of historical data.Deep learning algorithms based on Recurrent Neural Networks also have the problem of accumulating errors.Moreover,it is difficult to obtain higher accuracy by relying on a single historical radar echo observation.Therefore,in this study,we constructed the Fusion GRU module,which leverages a cascade structure to effectively combine radar echo data and mean wind data.We also designed the Top Connection so that the model can capture the global spatial relationship to construct constraints on the predictions.Based on the Jiangsu Province dataset,we compared some models.The results show that our proposed model,Cascade Fusion Spatiotemporal Network(CFSN),improved the critical success index(CSI)by 10.7%over the baseline at the threshold of 30 dBZ.Ablation experiments further validated the effectiveness of our model.Similarly,the CSI of the complete CFSN was 0.004 higher than the suboptimal solution without the cross-attention module at the threshold of 30 dBZ.

Echo state network based symbol detection in chaotic baseband wireless communication

The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting bits.However,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold calculation.The unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)performance.In contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using ESN.The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative operation.With this approach,the calculation complexity is reduced compared to the previous ESN-based approach.The proposed method achieves better BER performance compared to the previous method.The reason for this superior result is twofold.First,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was available.Second,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information estimation.Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.

空天目标雷达智能识别仿真系统设计与实现

雷达智能识别方法训练、测试与评估通常需要构建大样本的数据集,如何快速获得有效的数据集是亟待解决的问题。分析了空天目标雷达智能识别系统应用场景,并结合应用需求给出了空天目标雷达智能识别仿真系统建设思路;采用组件化、模块化设计方法,设计空天目标雷达智能识别仿真系统框架,构建空天目标智能识别仿真系统,快速准确模拟空天目标电磁特性和动态回波,具有可扩展、可重构能力。仿真结果和初步应用表明,该系统可用于不同应用场景下空天目标雷达智能识别方法的测试与评估。

应用脉冲回波的避雷线断线检测系统研究

为高效率、低成本地检测风力发电机组桨叶避雷线断线位置,设计基于脉冲回波的避雷线断线检测系统。该系统以单端行波法为原理,由信号发生模块,传感器模块,信号采集模块、控制器模块四部分构成。信号发生模块负责产生行波脉冲信号,传感器模块负责采集回波信号,信号采集模块负责将采集到的回波信号处理转化送给控制模块,控制模块负责分析回波信号,判断故障类型和故障点位置。该文对各个模块的电路和性能参数进行设计,搭建整体的检测系统。经实验验证,该系统可以精准的实现断线判断,并获得断线位置,测量误差精确到0.5 m以内。

社交媒体时代“回音室”与“过滤泡”之辨析

在国内研究对信息茧房、回音室、过滤泡三者概念常常混用的情况下,相较于信息茧房较高的关注度,国内对后两者的研究还远远不够,二者的异同也未得到细致辨析。经过对回音室、过滤泡相关研究进行文献计量分析,并从概念提出的背景、概念的内涵、操作化和研究结论方面深入辨析,发现二者概念的相同点在于:它们所描述的信息环境是相似的,即在网络技术的支持之下,一个由与个体喜好/倾向相匹配的同质化信息构成的信息环境。而不同之处在于:回音室强调受众主动选择与己具有相同倾向的个体进行交流互动,从而陷入只能听到自己声音的回音室中,其实质是观点同质化,所导致的后果是受众观点的极化,受众本身常被视为是主动性的;过滤泡强调算法推荐导致受众陷入同质化信息环境之中,其实质是信息接触单一化,导致的主要后果包括受众认知的窄化和观点的极化,受众往往被视为是被动性的。

凝视与想象:《回响》的回响——东西小说《回响》和电视剧《回响》读札

文学作品文本与影视艺术剧本的改编由来已久。可以说,中国现当代文学作品,始终为影视创作提供着不竭的文学、文化支撑。影视短剧《回响》以其超高的文本还原度,深度地反映当下的现实情境和时代症候,在想象与现实的缝隙中建构起能够触摸灵魂、心灵的场域,激发起警示人性的回响。本文通过对东西的长篇小说《回响》的影视化改编进行分析,从改编策略、主题哲思与心理关照等角度切入,探究在文学与影视交融中,在镜像呈现与文学想象之间,对于现实、人生和时代的多重反思和建构。

重大突发事件下网络舆情传播中回声室网络结构研究

[目的/意义]在社交网络中回声室对重大突发事件下的社交网络舆情演进具有重要作用,研究重大突发事件网络舆情中回声室网络结构可以为相关部门的舆情引导和管控提供指导和帮助。[方法/过程]基于网络结构理论,利用社会网络分析法和情感分析方法,进行用户回声室网络结构识别以及用户回声室网络结构表征及网络同质性检验。在此基础上,构建重大突发事件中回声室网络结构分析模型,并结合重大突发事件“3·21”东航客机事故中典型舆情话题进行实证研究。[结果/结论]重大突发事件网络舆情中存在回声室效应,由于微博评论与转发机制的不同,回声室网络结构存在明显的网络特征差异,且评论机制更有利于社交网络在重大突发事件下的情感分享,并可以凝聚群体观点和用户想法,从而促进意见领袖在群内和群外成员之间的互动。

裂纹对弹性波在混凝土中传播特性的影响研究

目的为研究混凝土中裂纹对弹性波传播特性的影响,方法采用COMSOL软件构建弹性波在不同形态混凝土裂纹中的传播数值模型,并开展带裂纹混凝土的弹性波传播试验,系统研究混凝土裂纹特征(裂纹角度、裂纹深度、裂纹距声发射源距离、裂纹数量等)与弹性波传播之间的关系。结果研究发现:数值模拟结果与试验现象吻合较好,验证了数值模型的可靠性。其中,裂纹数量和裂纹距声发射源距离的变化对回波峰值影响较大,距声发射源2 cm裂纹的回波峰值最大,为3.46×10^(6)Pa;对波速和信号损伤影响最大的因素为裂纹深度,60 mm深度裂纹损伤率高达62.14%、波速衰减至2590.674 m/s,其次为裂纹数量,信号损伤率达31.28%。基于不同裂纹特征的波速变化规律,对裂纹角度和深度的波速关系模型进行回归分析,回归曲线拟合优度较好,R^(2)均大于0.95。结论混凝土中存在的裂纹和裂纹特征对弹性波传播具有显著影响,本文研究结果为混凝土无损检测技术提供了理论支撑。