The Correlation of Hospital Operational Efficiency and Average Length of Stay in China: A Study Based on Provincial Level Data

Objective: To measure the hospital operation efficiency, study the correlation between average length of stay and hospital operation efficiency, analyze the importance of shortening average length of stay to the improvement of the hospital operation efficiency and put forward relevant policy suggestion. Methods: Based on China provincial panel data from 2003 to 2012, the hospital operation efficiencies are calculated using Super Efficiency Data Envelopment Analysis model, and the correlation between average length of stay and hospital operation efficiency is tested using Spearman rank correlation coefficient test. Results: From 2003 to 2012, the average of national hospital operation efficiency was increasing slowly and the hospital operations were inefficient in most of the areas. The national hospital operation efficiency is negatively correlated to the average length of stay. Conclusion: Measures should be taken to set average length of stay in a scientific and reasonable way, improve social and economic benefits based on the improvement of efficiency.

Fast acquisition of L2C CL codes based on combination of hyper codes and averaging correlation

This paper provides a direct and fast acquisition algorithm of civilian long length（CL） codes in the L2 civil（L2C） signal. The proposed algorithm simultaneously reduces the number of fast Fourier transformation（FFT） correlation through hyper code technique and the amount of points in every FFT correlation by using an averaging correlation method. To validate the proposed acquisition performance, the paper applies this algorithm to the real L2C signal collected by the global positioning system（GPS） L2C intermediate frequency（IF） signal sampler—SIS100L2C. The acquisition results show that the proposed modified algorithm can acquire the code phase accurately with less calculation and its acquisition performance is better than the single hyper code method.

Study of modeling unsteady blade row interaction in a transonic compressor stage part 2:influence of deterministic correlations on time-averaged flow prediction

The average-passage equation system （APES） provides a rigorous mathematical framework for account- ing for the unsteady blade row interaction through multistage compressors in steady state environment by introducing de- terministic correlations （DC） that need to be modeled to close the equation system. The primary purpose of this study was to provide insight into the DC characteristics and the in- fluence of DC on the time-averaged flow field of the APES. In Part 2 of this two-part paper, the influence of DC on the time-averaged flow field was systematically studied; Several time-averaging computations boundary conditions and DC were conducted with various for the downstream stator in a transonic compressor stage, by employing the CFD solver developed in Part 1 of this two-part paper. These results were compared with the time-averaged unsteady flow field and the steady one. The study indicat;d that the circumferential- averaged DC can take into account major part of the unsteady effects on spanwise redistribution of flow fields in compres- sors. Furthermore, it demonstrated that both deterministic stresses and deterministic enthalpy fluxes are necessary to reproduce the time-averaged flow field.

Average Incremental Correlation Analysis Model and Its Application in Fault Diagnosis

The concept of average incremental correlation degree is put forward.It has been proved that the average incremental correlation model has such properties as parallelism,consistency,affine,affine transformation isotonicity and interference factors independence,and it will not lead to changes of the sequence order relation because of the data transformation.Therefore,the new model keeps good stability.Finally,the incremental average correlation model is applied to failure model analysis of equipment,and an ideal diagnostic effect is obtained.

Correlation Method of Average Plasma Velocity Measurement in Hall-Effect Thrusters

A new method to measure the average plasma velocity in a Hall-effect thruster is presented. The method is brought forward in virtue of the characteristics of low frequency oscillation induced by the propellant ionization in the channel and the oriented movement feature of the plasma density out of the channel. The method, equivalent to the correlation method generally used in the signal processing field, provides a solution to the problem of specific impulse measurement on a timescale of hundreds of microseconds and makes the time evolution of average plasma velocity clear. The comparison between the measured value and the calibrated value shows that the relative error is about 3%.

A correlation OPTS algorithm for reducing peak to average power ratio of FBMC-OQAM systems

A correlation overlapping partial transmit sequence(C-OPTS) algorithm is proposed to solve the issue of high complexity of overlapping partial transmit sequence(OPTS) algorithm in suppressing the peak to average power ratio(PAPR) of filter bank multicarrier-offset quadrature amplitude modulation(FBMC-OQAM) signals.The V subblocks in partial transmit sequence(PTS) are regrouped into U combinations according to the correlation coefficient p,and overlapping subblocks are allowed between adjacent groups.The search starts from the first group and sets the phase factors of the subsequent groups to 1.When the phase factors of the non-overlapping subblocks in the first group are determined,the subsequent groups are searched in turn to determine their respective phase factors.Starting from the second data block,the data overlapped with it should be taken into account when determining its optimal phase factor vector.Theoretical analysis and simulation results indicate that compared with the OPTS algorithm,the proposed algorithm can significantly reduce the computational complexity at the cost of slight deterioration of PAPR performance.Meanwhile,compared with the even-odd iterative double-layers OPTS(ID-OPTS) algorithm,it can further reduce the complexity and obtain a better PAPR suppression effect.

Study of modeling unsteady blade row interaction in a transonic compressor stage part 1:code development and deterministic correlation analysis

The average-passage equation system （APES） provides a rigorous mathematical framework for account- ing for the unsteady blade row interaction through multi- stage compressors in steady state environment by introduc- ing deterministic correlations （DC） that need to be modeled to close the equation system. The primary purpose of this study is to provide insight into the DC characteristics and the influence of DC on the time-averaged flow field of the APES. In Part 1 of this two-part paper, firstly a 3D viscous unsteady and time-averaging flow CFD solver is developed to investi- gate the APES technique. Then steady and unsteady simu- lations are conducted in a transonic compressor stage. The results from both simulations are compared to highlight the significance of the unsteady interactions. Furthermore, the distribution characteristics of DC are studied and the DC at the rotor/stator interface are compared with their spatial cor- relations （SC）. Lastly, steady and time-averaging （employing APES with DC） simulations for the downstream stator alone are conducted employing DC derived from the unsteady re- suits. The results from steady and time-averaging simula- tions are compared with the time-averaged unsteady results. The comparisons demonstrate that the simulation employing APES with DC can reproduce the time-averaged field and the 3D viscous time-averaging flow solver is validated.

Quality of correlation in quantum information

A strong and stable correlation in quantum information is of high quality for quantum information processing.We define two quantities,selective average correlation and ripple coefficient,to evaluate the quality of correlation in quantum information in a time interval.As a new communication channel,Heisenberg spin chains are widely investigated.We select a two-qubit Heisenberg XXZs pin chain with Dzyaloshinskii-Moriya interaction in an inhomogeneous magnetic field as an example,and use the two quantities to evaluate the qualities of the correlation in quantum information with different measures.The result shows that,if the time evolutions are similar,there needs only evaluating one of them to know when the correlation has high quality for quantum information processing.

基于新灰色关联与结构变动度的抑郁症患者次均住院费用分析

目的探讨某医院抑郁症患者次均住院费用结构及重要性排序,为改善患者住院费用结构及有效控费提供依据。方法收集2018—2023年广州市某三甲医院抑郁症患者住院费用资料,运用结构变动度和新灰色关联分析评价各项费用与次均住院费用的关联程度、构成比及变化情况。结果2018—2023年抑郁症患者次均住院费用先逐步上升后降低,诊断费、检查费占比持续上升,护理费、药品费占比持续下降。2018—2023年次均住院费用结构变动度为33.84%,年均结构变动度为5.64%。结构变动贡献率前3位是药品费(26.77%)、诊断费(23.43%)和医疗服务费(19.77%),累计贡献率达69.97%。新灰色关联分析结果显示,对抑郁症患者次均住院费用影响最大的为治疗费(0.939),其次为诊断费(0.883)、检查费(0.866)和医疗服务费(0.817)。结论治疗费、诊断费和检查费是影响抑郁症患者住院费用的主要因素,护理费和药品费收入持续降低。建议持续优化费用结构,建立合理稳定高效的控费方案。

强噪声干扰下采煤机行星齿轮故障诊断方法

采煤机摇臂截割部行星齿轮的健康状态直接影响截割效率。针对采煤机截割煤岩过程中受多重冲击引起的强噪声干扰、齿轮结构复杂且传递路径多变导致故障特征难以提取等特点,提出了一种基于频谱平均降噪和相关谱的采煤机行星齿轮故障诊断方法。根据信号频谱分布特征及噪声随机特性,采用频谱平均降噪方法抑制噪声对信号频谱的干扰,获得信号降噪频谱。构建相关谱以建立少样本降噪频谱和多样本降噪频谱的内在联系,减少频谱平均降噪对样本数量的需求。采用一维卷积神经网络(1D CNN)建立相关谱与故障类别之间的精确映射关系,以相关谱为输入、故障类别为输出,实现行星齿轮故障分类识别。在DDS传动系统故障诊断实验台对基于频谱平均降噪和相关谱的采煤机行星齿轮故障诊断方法进行实验验证,结果表明该方法能够增强表征故障特征的关键频率,对正常、断齿、磨损、缺齿和裂纹5种行星齿轮健康状态信号的整体识别率达96%,在信噪比不低于15 dB时可有效、准确地实现齿轮故障诊断。

湍流图像退化中的模糊与畸变

基于光线传播以及相位叠加原理,构建了湍流图像退化模型,获得了湍流下目标对应的点扩散函数,并通过调制传递函数面积以及等晕角验证了仿真模型。同时,通过平均相关系数定义了像方近似不变区域,像方近似不变区域有助于对点扩散函数变化程度的理解。通过光线在湍流中的传播过程,猜测图像退化的模糊与畸变的程度和湍流的分布有关。为验证以上猜测,设置了两种极端情况下的湍流分布情形进行仿真实验,并通过调制传递函数面积以及像方近似不变区域对仿真结果进行分析。结果表明在同等湍流强度情况下,靠近镜头端的湍流会对图像造成更多的模糊效应,靠近物体端的湍流会对图像造成更多的畸变效应。基于此,解释了在湍流图像退化模型中采用先畸变后模糊是更为合理的操作,并建议湍流复原算法应根据退化结果中的模糊与畸变的程度采取相应的复原措施。

A Practical Approach for Missing Wireless Sensor Networks Data Recovery

In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.

重型柴油车NO_(x)排放因子与其浓度相关性研究

重型柴油车排放的NO_(x)污染物对环境空气质量影响较大,受排放标准、排放控制技术水平、检测方法、驾驶工况等众多因素的影响,当前较难整体评估在用重型柴油车的实际排放状况.本研究首先通过发动机排放台架试验及实际道路排放(PEMS)循环试验,探究了重型柴油车NO_(x)排放因子与NO_(x)平均浓度之间的内在联系;其次,研究了远程监控有效数据筛选规则,提出了一种基于NO_(x)日均浓度评估远程监控重型柴油车NO_(x)排放因子的方法.结果表明:重型柴油车发动机排放台架、PEMS循环试验的NO_(x)排放因子均与NO_(x)平均浓度呈较强相关性,相关系数(R^(2))为0.99.通过远程监控平台监测数据验证了重型柴油车的NO_(x)排放因子与NO_(x)日均浓度有一定相关性,R^(2)高于0.9(p<0.01).因此,可用重型柴油车排放的NO_(x)日均浓度来表征整车污染物排放情况.研究显示,安装远程终端的第五和第六排放阶段的重型柴油车,分别按照NO_(x)日均浓度低于200×10^(−6)和50×10^(−6)来判定排放稳定达标重型柴油车,按照NO_(x)日均浓度高于900×10^(−6)和500×10^(−6)来判定高排放重型柴油车,监管部门和车辆生产企业可利用该指标快速筛选排放稳定达标以及高排放重型柴油车.

基于统计分析方法对古代玻璃制品的成分探究

利用斯皮尔曼相关系数和卡方检验分析了古代玻璃的成分,得出玻璃类型、纹饰、颜色与表面风化的相关性和差异性;对风化玻璃制品数据借助中位数、平均值、标准差等指标进行对比分析,通过加权平均值预测模型,预测风化前玻璃状态,得出玻璃类型的鉴别方法。利用敏感度系数公式,得到二氧化硅含量增减10%与氧化铅含量比值,说明氧化铅和二氧化硅含量的微量改变不会对类别产生影响。借助相关性分析和数据透视表得出高钾玻璃和铅钡玻璃二氧化硅含量较多,风化前氧化铝、氧化钙、氧化铜含量均较高,钾玻璃风化后化学成分关联性较弱,铅钡玻璃风化后化学成分关联性较强。

衍生高效用模式挖掘算法综述

在数据挖掘领域中,高效用模式挖掘任务具有较高的理论研究价值和广泛的实际应用场景。针对多变的应用场合,提出了一系列衍生高效用模式。首先从关键技术的角度对高平均效用模式挖掘算法进行了分类论述,主要包括基于先验、基于树、基于列表、基于投影和基于数据格式的方法。其次,分析讨论了基于全集、精简集以及融合模式的含有负效用的高效用模式挖掘算法。再次,从模糊高效用模式、相关高效用模式和其他新兴高效用模式三个方面概述和总结了扩展高效用模式算法。最后,针对现阶段研究方向的不足,给出下一步的研究方向。

北京市某精神专科医院次均住院费用及结构分析

目的分析北京市某精神专科医院住院费用构成及结构变动情况,为优化住院费用结构、合理控制医疗费用、促进精神卫生事业的高质量发展提供参考。方法采用结构变动度和灰色关联方法分析2018—2022年北京市某精神专科医院住院费用变动情况。结果2018—2022年,次均住院费用构成占比最高的依次是诊断类、治疗类和综合医疗服务类。西药类费用在所有年度区间均负向变动,治疗类费用变动均为正向变动且绝对值相对较大。治疗类费用的结构贡献率最大,其次是诊断类、综合医疗服务类和西药类。诊断类费用与次均住院费用关联度最大。结论受医疗改革的正向影响,费用结构日趋合理化。需重视检查类费用对次均住院费用的影响,可视情况丰富中医类康复类等治疗手段,增加特色优势,推动均衡发展。

利用健康因子的卫星电池容量估计方法

卫星在轨工作期间,准确估计电池容量对提高电源系统的安全性与可靠性有重要意义。为此,针对电池在工作过程中实际容量难以直接测量的问题,提出一种利用间接健康因子估计电池容量的方法。首先,从锂电池恒压充电模式下的电流数据中提取平均充电电流,将其作为反映电池容量变化的特征参数。在实现了锂电池健康状态表征的基础上,对比分析容量和平均充电电流随着充放电循环周期的变化曲线。然后,讨论了容量和平均充电电流之间的相关性,进而建立了容量估计模型。最后,通过从NASA锂电池数据集中提取容量和平均充电电流,划分训练集和测试集,验证了用该方法估计容量的有效性和准确性。

基于贝叶斯网络的差分隐私高维数据发布技术研究

在实现隐私保护的同时提高数据可用性是高维结构化数据发布研究中的挑战性问题,经典算法Priv Bayes针对该问题提供了一种解决方案。为进一步减少计算开销、提高数据可用性,提出基于贝叶斯网络的差分隐私数据发布算法ELPriv Bayes。分析贝叶斯网络结构学习阶段的理论计算开销,构建存储属性之间互信息的相关矩阵,避免结构学习算法迭代过程中互信息的冗余计算,降低了时间复杂度。基于平均互信息优化了节点进入贝叶斯网络的顺序,提高结构学习迭代过程中指数机制贡献的互信息期望值,进而提高生成数据集与原始数据集的统计近似度,并实证分析网络结构质量对首节点选择的低敏感性。在4个典型数据集上的实验结果表明,与经典算法Priv Bayes及其改进方案相比较,结构学习阶段的计算开销降低了97%~99%,基于指数机制捕获的互信息提高了14%~67%,生成数据集与原始数据集的平均变差距离降低了32%~40%,构建的支持向量机(SVM)分类器的准确率提高了4%~5%,并且当ε≤0.8时,采用ELPriv Bayes算法生成数据的可用性提升更为显著。

基于改进KCF的移动机器人视觉行人跟踪系统

针对视觉跟踪机器人在跟踪目标被大面积遮挡和短暂丢失的场景下容易跟踪失败的问题,提出了一种基于改进核相关滤波(kernelized correlation filter,KCF)的移动机器人视觉行人跟踪系统。在传统KCF算法的基础上引入平均峰值相关能量(average peak correla-tion energy,APCE),结合APCE响应值和最大响应值判断目标丢失情况,并加入了两级重检测机制。在OTB2013数据集的目标丢失(out of view,OV)场景中,改进算法的成功率和精度分别达到0.626和0.592像素,比传统KCF算法分别提升了8.7%和10.9%,有效提高了算法在目标遮挡和短暂丢失场景下的跟踪鲁棒性。在有行人干扰的场景中进行跟踪实验,实验结果表明所提视觉行人跟踪系统能够稳定跟踪目标行人。

一种带控制端的二次校正滤波器

提出了一种带控制端的二次校正滤波器,可在系统稳定和不稳定情况下实现自校正滤波.对于稳定系统,将其转化为自回归滑动平均(ARMA)模型进行自校正滤波;对于由于参数问题导致的不稳定系统,关键参数可分解为限定范围的游离参数1和固定或缓慢变化的游离参数2之和,即将系统转化为游离参数1作用下的稳定系统和可通过控制端进行二次校正的系统的组合.通过设定控制准则,将状态值和控制端联系起来,利用相关函数法估计控制端的控制系数,并将控制端二次校正值反馈至稳态滤波器(或预报器)中,实现一定范围内较稳定的自校正滤波.通过一个仿真例子验证了该方法的有效性.