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5.8 years,mean±SD(1 study did not provide sex descriptors))were extracted for review,with previously unpublished data obtained from the authors of 13 studies.The rectus femoris(n=34) and isometric knee extension strength(n=22) were the most accessed muscle and physical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

Lightweight Intrusion Detection Using Reservoir Computing

The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.

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.

Mesoscale and Microphysical Characteristics of a Double Rain Belt Event in South China on May 10–13,2022

A second rain belt sometimes occurs ahead of a frontal rain belt in the warm sector over coastal South China,leading to heavy precipitation.We examined the differences in the mesoscale characteristics and microphysics of the frontal and warm sector rain belts that occurred in South China on May 10–13,2022.The southern rain belt occurred in an environment with favorable mesoscale conditions but weak large-scale forcing.In contrast,the northern rain belt was related to low-level horizontal shear and the surface-level front.The interaction between the enhanced southeasterly winds and the rainfall-induced cold pool promoted the persistent growth of convection along the southern rain belt.The convective cell propagated east over the coastal area,where there was a large temperature gradient.The bow-shaped echo in this region may be closely related to the rear-inflow jet.By contrast,the initial convection of the northern rain belt was triggered along the front and the region of low-level horizontal shear,with mesoscale interactions between the enhanced warm-moist southeasterly airflow and the cold dome associated with the earlier rain.The terrain blocked the movement of the cold pool,resulting in the stagnation of the frontal convective cell at an early stage.Subsequently,a meso-γ-scale vortex formed during the rapid movement of the convective cell,corresponding to an enhancement of precipitation.The representative raindrop spectra for the southern rain belt were characterized by a greater number and higher density of raindrops than the northern rain belt,even though both resulted in comparable hourly rainfalls.These results help us better understand the characteristics of double rain belts over South China.

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.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

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.

Analysis of a Type Ⅱ Snowstorm Process in the Early Spring of 2022 in Ulanqab

Based on the observation data of meteorological stations,Doppler radar observation data of Ulanqab City,and ERA-5 reanalysis data,a snowstorm process in Ulanqab City from March 17 to 18,2022 was analyzed.The results show that this was a type Ⅱ snowstorm process generated under the joint influence of upper trough and ground low inverted trough and frontal cyclone.The main period of snowfall can be divided into two time stages,and the total snowfall was more in the south and less in the north,which was consistent with that of average specific humidity field.Water vapor conditions provided by strong water vapor transport and convergence,strong upward movement shown by large vertical velocity field,and the suction action of high-and low-layer divergence and convergence were the reasons for the hourly heavy snowfall on the 18^(th).During the process,radar echoes were mainly sheet-shaped,and composite reflectivity was 15-25 dBZ in most areas.The zero speed line in the first period was positively"S"-shaped,and there was warm advection and southwest wind.On the morning of the 18^(th),after the cold front transited the city,Ulanqab City was gradually controlled by northwest wind,and the snow tended to end.

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.

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.

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.

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.

Analysis of a Local Rainstorm in Central Inner Mongolia

Based on conventional observation data,satellite cloud image data and new generation Doppler radar data,the local rainstorm weather situation and physical quantities in Ulanqab City from 08:00 to 20:00 on June 24,2019 were analyzed by means of synoptic methods.The results show that the local rainstorm was caused by the forward trough system and the convergence of warm and cold air,and triggered by the low-level jet and the surface convergence line.The splitting and merging of cloud clusters in satellite cloud images and strong radar echoes had a good guiding effect on short-term heavy precipitation.

Hepatic MR imaging using IDEAL-IQ sequence:Will Gd-EOB-DTPA interfere with reproductivity of fat fraction quantification?

BACKGROUND Iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence(IDEAL-IQ)is based on chemical shift-based water and fat separation technique to get proton density fat fraction.Multiple studies have shown that using IDEAL-IQ to test the stability and repeatability of liver fat is acceptable and has high accuracy.AIM To explore whether Gadoxetate Disodium(Gd-EOB-DTPA)interferes with the measurement of the hepatic fat content quantified with the IDEAL-IQ and to evaluate the robustness of this technique.METHODS IDEAL-IQ was used to quantify the liver fat content at 3.0T in 65 patients injected with Gd-EOB-DTPA contrast.After injection,IDEAL-IQ was estimated four times,and the fat fraction(FF)and R2* were measured at the following time points:Precontrast,between the portal phase(70 s)and the late phase(180 s),the delayed phase(5 min)and the hepatobiliary phase(20 min).One-way repeated-measures analysis was conducted to evaluate the difference in the FFs between the four time points.Bland-Altman plots were adopted to assess the FF changes before and after injection of the contrast agent.P<0.05 was considered statistically significant.RESULTS The assessment of the FF at the four time points in the liver,spleen and spine showed no significant differences,and the measurements of hepatic FF yielded good consistency between T1 and T2[95%confidence interval:-0.6768%,0.6658%],T1 and T3(-0.3900%,0.3178%),and T1 and T4(-0.3750%,0.2825%).R2* of the liver,spleen and spine increased significantly after injection(P<0.0001).CONCLUSION Using the IDEAL-IQ sequence to measure the FF,we can obtain results that will not be affected by Gd-EOB-DTPA.The high reproducibility of the IDEAL-IQ sequence makes it available in the scanning interval to save time during multiphase examinations.

福建省2011年病毒性脑炎暴发病原Echo30分子流行病学分析

目的了解福建省2011年病毒性脑炎暴发的病原特点及福建省近10年Echo30分子流行病学特征。方法应用细胞培养法获得病毒株,微量血清中和试验或RT-PCR序列分析鉴定Echo30血清型,提取病毒RNA核酸,逆转录为cD-NA,分段扩增VP1基因序列全长,测定并分析扩增产物的序列。结果 2011年4月1日-6月2日,从福建省4个县(市)区病毒性脑炎病例标本中分离的病毒经鉴定均为Echo30;VP1基因序列分析显示,泉州市安溪和德化两地流行株高度同源(核苷酸与氨基酸序列同源性分别为99.0%和99.6%),同福安和厦门流行株(核苷酸与氨基酸序列同源性分别为98.5%和100%)核苷酸差异约9%,3个位点氨基酸出现了变异。近10年福建省Echo30分子流行病学分析显示,2011年泉州流行株并非由2005年当地流行株进化而来,而与2008年河南省分离株(HQ625646-Henan/04/2008)同源性最高;近10年福建省Echo30存在多基因型、多谱系的同时传播,并在2006年发现了两株第III新基因型病毒。结论造成2011年福建省病毒性脑炎暴发的病原为Echo30,且至少存在两条不同传播链病毒同时流行。Echo30福建株呈多基因型、多谱系分布流行特点。