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5.8 years,mean±SD(1 study did not provide sex descriptors))were extracted for review,with previously unpublished data obtained from the authors of 13 studies.The rectus femoris(n=34) and isometric knee extension strength(n=22) were the most accessed muscle and physical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.

肌肉超声回声联合血浆炎性因子对ICUAW诊断及预后评估的价值研究

目的:探讨重症监护病房(intensive care unit,ICU)获得性肌无力(ICU acquired weakness,ICUAW)患者肌肉超声回声与血浆炎性因子的相关性,以及其对ICUAW的诊断价值和预后的预测价值。方法:选择重庆市急救医疗中心ICU住院患者,分别在第1、3、7天使用床旁超声检测患者肌肉回声,获得的总体肌肉回声评分(global muscle echogenicity score,GEM),测定血清白细胞介素-6(interleukin-6,IL-6)和降钙素原(procalcitonin,PCT)浓度,采用医学研究理事会肌力评分法(medical research council scales,MRC-ss)评估肌肉力量。根据患者入ICU第7天MRC-ss评分将患者分为ICUAW组和非ICUAW组,分析比较2组患者GEM、IL-6、PCT的差异及各指标的相关性。利用受试者工作特征(receiver operator characteristic,ROC)曲线分析以上参数对ICUAW诊断效能,分析GEM、IL-6、PCT对ICUAW患者的预测预后价值。结果:ICUAW组第3天GEM、第7天IL-6浓度、GEM高于非ICUAW组(P<0.05)。GEM与第7天IL-6水平呈正相关(r=0.221),第7天GEM与MRC-ss评分呈负相关(r=-0.581)。ROC曲线分析显示,第7天GEM对ICUAW有诊断预测价值,ROC曲线下面积(area under the curve,AUC)为0.838,使用GEM、IL-6、PCT联合诊断,AUC=0.885(P<0.05)。ICUAW组Barthel指数评分(Barthel index,BI)低于非ICUAW组,ICUAW组中总体肌肉超声回声评分(global muscle echogenicity score,GEM)高的患者BI低于GEM低的患者(P<0.05)。结论:ICU住院患者GEM与IL-6、PCT浓度相关,其对ICUAW具有一定的诊断价值,并能够预测ICUAW患者的预后。

随钻核磁共振仪器缩短回波间隔方法优化设计

核磁共振测井是一种直接探测储层孔隙流体性质的地球物理方法,随钻核磁共振技术是在钻井过程中实时获得油气藏评价参数的技术方法.面对复杂油气藏中随钻核磁共振测量问题,通过缩短脉冲序列的回波间隔,可以减少仪器测量时间,能够获得快速衰减的短弛豫组分,实现储层孔隙结构的精细表征.本文提出了一种多级能量泄放方法,能够逐级降低天线品质因数,缩短天线恢复的死区时间,在不改变回波采集信噪比的情况下,实现脉冲序列的短回波间隔采集系统设计,并在随钻核磁共振测井仪器实验样机上进行了测试验证.测试结果表明,仪器回波信号信噪比良好,振铃噪声与回波信号区分明显,测试结果与模拟仿真结果一致,能够实现短回波间隔数据采集,验证了方法的有效性和实用性.

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。

压力容器小径支管角焊缝相控阵检测埋藏缺陷回波特性研究

采用CIVA仿真软件对实际规格的压力容器小径接管角焊缝模型进行了仿真分析,研究了球孔、横通孔、未熔合、未焊透和裂纹等典型缺陷的回波响应规律及特征,结果表明,相对于端角结构信号,不同缺陷的回波强度不同,面积型缺陷与声束角度90°时回波强度最大,同一类型缺陷位于不同位置时的回波强度差距明显,最大达到了13 dB。设计加工了典型缺陷的模拟试件,对5种典型缺陷的检测结果开展了试验分析,结果表明,试件中的缺陷均可被有效检测。总结了不同缺陷的回波特性变化规律,可为现场检测中的缺陷定性提供参考和依据。

天气雷达空中生态监测

每年数以亿计的昆虫、候鸟和蝙蝠等空中动物在全球范围内远距离迁飞,显著影响空中生态系统的结构和功能。随着全球气候和环境的不断变化,空中生态环境也在迅速恶化,对人类的生命和健康构成了严重威胁。为了保护空中生态环境,首要任务是对其进行有效监测。天气雷达具有大范围、全天时、全天候监测能力,可覆盖百千千米空间尺度,是监测和研究宏观空中生态的有效工具。十余年来,天气雷达空中生态监测领域取得了突破性的进展,极大提高了对空中迁飞动物的探测能力,可有效弥补传统监测手段存在的覆盖范围小和时间分辨率低等技术局限。天气雷达已在全球范围内的生物多样性保护、迁飞性虫害监测、疫源疫病传播防控等重大空中生态议题中发挥着日益重要的作用,有效填补了宏观生态研究空白。目前,美国、欧洲和中国分别建立了全球三大天气雷达网,为空中生态研究提供了重要的基础设施和数据支持。本文详细阐述了当前全球天气雷达空中生态监测的科学研究进展,以及通过天气雷达揭示的空中动物迁飞科学规律。然后,对天气雷达生态监测中迁飞动物的电磁散射建模、雷达回波提取、群体参数反演、雷达观测验证实验,以及宏观迁飞模型等关键技术进行了梳理和总结,并详细介绍了中国天气雷达空中生态监测的进展情况。最后,对未来发展方向进行了展望,指出针对中国当前农业迁飞性害虫和鸟类宏观迁飞等重大监测需求,需要在扩维探测、多参反演、多源预测等方面深入研究和探索,以进一步解读重大动物迁飞事件、分析宏观迁飞规律、揭示空中生态对气候变化的响应。

相干测风激光雷达回波偏振态对径向风速精度影响

为了解决大气退偏效应造成激光回波与本振光偏振失配,导致相干测风激光雷达(LiDAR)外差效率降低的问题,基于相干多普勒激光测风雷达的系统组成和工作原理,采用数学建模法,仿真分析了回波偏振态对系统信噪比、探测距离和径向风速误差的影响;提出了偏振态修正途径,并利用琼斯矩阵建模讨论修正精度;用径向风速比对实验验证不同回波偏振态对径向风速精度的影响。结果表明,对于同一测试系统,在相同的天气条件下,与回波为圆偏振态相比,回波为椭圆偏振态时的最大探测距离下降46.7%,径向风速精度下降63%。此研究为相干多普勒测风激光雷达回波接收模块的优化提供了理论支撑。

M8激光雷达回波的非接触式发射率三维分布测量方法

针对目前发射率测量方法只是对某一单一物质进行接触式测量,难以获得复杂目标的发射率三维分布问题,提出一种基于M8激光雷达回波的非接触式发射率三维分布测量方法。首先基于激光雷达传输距离方程分析回波强度特性,通过M8激光雷达对95%标准漫反射板进行扫描,叠加多帧单线点云,得到具有反射光谱特性的雷达强度三维点云像;运用分段多项式模型拟合距离-强度及入射角-强度之间的关系,基于得到的分段多项式模型校正距离和入射角度影响下的回波强度,使不同距离与入射角情况下所测回波强度能够真实地反映目标的反射光谱特性,校正结果显示,标准漫反射板回波强度变异系数分别从0.2676和0.3438降低到了0.0420和0.0412,回波强度一致性分别提高了84.31%和88.02%,使雷达所测得强度值能直接反映目标材料的反射光谱特性,验证了强度校正模型的有效性;最后基于得到的分段多项式校正模型对带有反射率真值贴片的缩比卫星模型雷达强度三维点云像进行强度校正,利用校正后的回波强度的反射光谱特性,计算出目标表面的反射率,运用反射法进一步推导其发射率,得到缩比卫星模型发射率的三维分布,三组卫星模型表面贴片的发射率平均偏差均分别能控制在3.33%、4.84%和4.44%。为目标识别、低空可探测技术等提供技术支持。