The impact of the provincial reimbursement scheme on the use of targeted anticancer medications in Zhejiang,China:a controlled interrupted time-series analysis

To alleviate problems with access and affordability,six targeted anticancer medications(TAMs)were listed in the Provincial Reimbursement Drug List(PRDL)for the first time in Zhejiang,China in February 2015.In the present study,we aimed to evaluate the implementation of the PRDL policy on TAMs use.Using the pharmaceutical procurement data of these six listed TAMs(study group)and four unlisted TAMs(control group)from 22 tertiary hospitals in Zhejiang,China dated between January 2014 and February 2017,interrupted time-series analysis was adopted to examine differences in the average hospital purchasing volume(HPV)and the average hospital purchasing spending(HPS)between the two groups.The average daily cost of listed TAMs in the study group was decreased after April 2015.After enlistment,the average HPV per month was significantly increased by 34.6 defined daily doses(DDDs)(P<0.001),and the average HPS per month was significantly increased by USD 6614.9(P<0.001)for the listed TAMs in the study group(n=6).Neither the average HPV nor the average HPS changed significantly for the unlisted TAMs in the control group(n=4).The PRDL policy showed positive effects on improving patients’affordability and promoting access to TAMs in Zhejiang.The government should conduct further price negotiations and include more TAMs with clinical benefits into reimbursement schemes to relieve patients’financial burden and promote access.

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Does Monetary Support Increase the Number of Scientific Papers？ An Interrupted Time Series Analysis

Purpose： One of the main indicators of scientific production is the number of papers published in scholarly journals. Turkey ranks 18th place in the world based on the number of scholarly publications. The objective of this paper is to find out if the monetary support program initiated in 1993 by the Turkish Scientific and Technological Research Council （TUBITAK） to incentivize researchers and increase the number, impact, and quality of international publications has been effective in doing so.Design/methodology/approach： We analyzed some 390,000 publications with Turkish affiliations listed in the Web of Science （WoS） database between 1976 and 2015 along with about 157,000 supported ones between 1997 and 2015. We used the interrupted time series （ITS） analysis technique （also known as ＂quasi-experimental time series analysis＂ or ＂intervention analysis＂） to test if TOBITAK＇s support program helped increase the number of publications. We defined ARIMA （1,1,0） model for ITS data and observed the impact of TOBiTAK＇s support program in 1994, 1997, and 2003 （after one, four and 10 years of its start, respectively）. The majority of publications （93%） were full papers （articles）, which were used as the experimental group while other types of contributions functioned as the control group. We also carried out a multiple regression analysis.Findings： TUBITAK＇s support program has had negligible effect on the increase of the number of papers with Turkish affiliations. Yet, the number of other types of contributions continued to increase even though they were not well supported, suggesting that TUBITAK＇s support program is probably not the main factor causing the increase in the number of papers with Turkish affiliations. Research limitations： Interrupted time series analysis shows if the ＂intervention＂ has had any significant effect on the dependent variable but it does not explain what caused the increase in the number of papers if it was not the intervention. Moreover, except the＂intervention＂, other ＂event（s）＂ that might affect the time series data （e.g., increase in the number of research personnel over the years） should not occur during the period of analysis, a prerequisite that is beyond the control of the researcher. Practical implications： TUBITAK＇s ＂cash-for-publication＂ program did not seem to have direct impact on the increase of the number of papers published by Turkish authors, suggesting that small amounts of payments are not much of an incentive for authors to publish more. It might perhaps be a better strategy to concentrate limited resources on a few high impact projects rather than to disperse them to thousands of authors as ＂micropayments.＂ Originality/value： Based on 25 years＇ worth of payments data, this is perhaps one of the first large-scale studies showing that ＂cash-for-publication＂ policies or ＂piece rates＂ paid to researchers tend to have little or no effect on the increase of researchers＇ productivity. The main finding of this paper has some implications for countries wherein publication subsidies are used as an incentive to increase the number and quality of papers published in international journals. They should be prepared to consider reviewing their existing support programs （based usually on bibliometric measures such as journal impact factors） and revising their reward policies.

TIME-SERIES MODELI NG AND FAULT FORECAST STUDY ON SPECTRAL ANALYSIS OF LUBRICATING OIL

The application of ti me-series modeling and forecasting method to the spectral analysis for lubricat ing oil of mechanical equipment is discussed. The AR model is used to perform a time-series modeling and forecasting analysis for the spectral analysis data co llected from aero-engines. In the oil condition monitoring field of mechanical equipment, the use of the method of time-series analysis has rarely been report ed. As indicated in the satisfactory example, a practical method for condition m onitoring and fault forecasting of mechanical equipment has been achieved.

Time-series analysis of the characteristic pressure fluctuations in a conical fluidized bed with negative pressure

The negative pressure conical fluidized bed is widely used in the pharmaceutical industry.In this study,experiments based on the negative pressure conical fluidized bed are carried out by changing the material mass and particle size.The pressure fluctuation signals are analyzed by the time and the frequency domain methods.A method for absolutely characterizing the degree of the energy concentration at the main frequency is proposed,where the calculation is to divide the original power spectrum by the average signal power.A phenomenon where the gas velocity curve temporarily stops growing is observed when the material mass is light,and the particle size is small.The standard deviation and kurtosis both rapidly change at the minimum fluidization velocity and thus can be used to determine the flow regime,and the variation rule of the kurtosis is independent of both the material mass and particle size.In the initial fluidization stage,the dominant pressure signal comes from the material movement;with the increase in the gas velocity,the power of a 2.5 Hz signal continues to increase.A method of dividing the main frequency by the average cycle frequency can conveniently determine the fluidized state,and a novel concept called stable fluidized zone proposed in this paper can be obtained.Controlling the gas velocity within the stable fluidized zone ensures that the fluidized bed consistently remains in a stable fluidized state.

Analyses of unified congestion measures for interrupted traffic flow on urban roads

To study the congestion of interrupted flow on urban roads, a comprehensive evaluation method is proposed. First, based on the results of correlation analysis between different parameters of interrupted flow, the traffic parameters of interrupted traffic flow are divided into two categories： the basic parameters and the operation parameters. Polynomial regression is used to formulize the nonlinear relationships between the basic parameters and the operation parameters. Then, the congestion model incorporating both operational and volume characteristics of traffic flow is proposed. The inputs of the model are the basic parameters, while the output is a dimensionless index value between 0 and 1. Finally, the proposed methods are compared with existing evaluation measures of congestion. Results show that the proposed indices can capture the variation of both the basic parameters and the operation parameters, which is more balanced compared with the existing evaluation measures.

Using Interrupted Time Series Design to Analyze Changes in Hand, Foot, and Mouth Disease Incidence during the Declining Incidence Periods of 2008-2010 in China

Objective To identify patterns of hand, foot and mouth disease （HFMD） incidence in China during declining incidence periods of 2008, 2009, and 2010. Methods Reported HFMD cases over a period of 25 months were extracted from the National Disease Reporting System （NDRS） and analyzed. An interrupted time series （ITS） technique was used to detect changes in HFMD incidence rates in terms of level and slope between declining incidence periods of the three years. Results Over 3.58 million HFMD cases younger than 5 years were reported to the NDRS between May 1, 2008, and May 31, 2011. Males comprised 63.4% of the cases. ITS analyses demonstrated a significant increase in incidence rate level （P〈0.0001） when comparing the current period with the previous period. There were significant changes in declining slopes when comparing 2010 to 2009, and 2010 to 2008 （all P〈O.O05）, but not 2009 to 2008. Conclusion Incremental changes in incidence rate level during the declining incidence periods of 2009 and 2010 can potentially be attributed to a few factors. The more steeply declining slope in 2010 compared with previous years could be ascribed to the implementation of more effective interventions and preventive strategies in 2010. Further investigation is required to examine this possibility.

Rockfall seismic features analysis based on in situ tests:frequency, amplitude, and duration

In the seismic event classification,determining the seismic features of rockfall is significantly important for the automatic classification of seismic events because of the huge amount of raw data recorded by seismic stations in continuous monitoring. At the same time, the rockfall seismic features are still not completely understood.This study concentrates on the rockfall frequency content, amplitude(ground velocity), seismic waveform and duration analysis, of an artificial rockfall test at Torgiovannetto(a former quarry in Central Italy). A total of 90 blocks were released in the test, and their seismic signals and moving trajectories were recorded by four tri-axial seismic stations and four cameras, respectively. In the analysis processing,all the artificial rockfall signal traces were cut separately and the seismic features were extracted individually and automatically. In this study, the relationships between a) frequency content and impacted materials, b) frequency content and the distance between block releasing position and seismic station(source-receiver distance) were discussed. As a result, we found that the frequency content of rockfall focuses on 10-60 Hz and 80-90 Hz within a source-receiver distance of 200 m, and it is well correlated with impacted material and source-receiver distance. To evaluate the difference between earthquake and rockfall, 23 clear earthquake signals recorded in a seven month-long continuous seismic monitoring, carried out with the four seismic stations, were picked out, according to the Italian national earthquakes database(INGV). On these traces we performed the same analysis as in the artificial rockfall traces, and two parameters were defined to separate rockfall events from earthquake noise. The first one, the amplitude ratio, is related to the amplitude variation of rockfall between two stations and is greater than that of earthquakes, because of the higher attenuation occurring for rockfall events, which consists in high frequencies whereas for earthquakes it consists in low frequencies. The other parameter, the shape of waveform of signal trace, showed a significant difference between rockfall and earthquake and that could be a complementary feature to discriminate between both. This analysis of artificial rockfall is a first step helpful to understand the seismic characteristics of rockfall, and useful for rockfall seismic events classification in seismic monitoring of slope.

Uncertainty Analysis on Electric Power Consumption

The analysis of large time-series datasets has profoundly enhanced our ability to make accurate predictions in many fields.However,unpredictable phenomena,such as extreme weather events or the novel coronavirus 2019(COVID-19)outbreak,can greatly limit the ability of time-series analyses to establish reliable patterns.The present work addresses this issue by applying uncertainty analysis using a probability distribution function,and applies the proposed scheme within a preliminary study involving the prediction of power consumption for a single hotel in Seoul,South Korea based on an analysis of 53,567 data items collected by the Korea Electric Power Corporation using robotic process automation.We first apply Facebook Prophet for conducting time-series analysis.The results demonstrate that the COVID19 outbreak seriously compromised the reliability of the time-series analysis.Then,machine learning models are developed in the TensorFlow framework for conducting uncertainty analysis based on modeled relationships between electric power consumption and outdoor temperature.The benefits of the proposed uncertainty analysis for predicting the electricity consumption of the hotel building are demonstrated by comparing the results obtained when considering no uncertainty,aleatory uncertainty,epistemic uncertainty,and mixed aleatory and epistemic uncertainty.The minimum and maximum ranges of predicted electricity consumption are obtained when using mixed uncertainty.Accordingly,the application of uncertainty analysis using a probability distribution function greatly improved the predictive power of the analysis compared to time-series analysis.

A Semi Automated Method for Laminated Sediments Analysis

We developed a software performing laminae counting, thickness measurements, spectral and wavelet analysis of laminated sediments embedded signal. We validated the software on varved sediments. Varved laminae are automatically counted using an image analysis classification method based on K-Nearest Neighbors (KNN) algorithm. In a next step, the signal corresponding to varved black laminae thickness variation is retrieved. The obtained signal is a good proxy to study the paleoclimatic constraints controlling sedimentation. Finally, the use of spectral and wavelet analysis methods on the variation of black laminae thickness revealed the existence of frequencies and periods which can be linked to known paleoclimatic events.

Experimental Study of Heat Transfer to Flowing Air inside a Circular Tube with Longitudinal Continuous and Interrupted Fins

Experimental investigations have been performed to determine the detailed module-by-module pressure drop and heat transfer coefficient of turbulent flow inside a circular finned tube. The tubes are provided with longitudinal fins continuous or interrupted in the stream wise direction by arranging them both in a staggered and in-line manner. Experiments are carried out for two different fin geometries, with two numbers of fins (N = 6 and 12). All tested finned tubes have 16 modules each with length equal to the tube diameter (L = D = 30 mm). The thermal boundary condition considered here, is a uniform heat flux. The module-by-module heat transfer coefficient is found to vary only in the first modules, and then attained a constant thermally periodic fully developed value after eight to twelve modules. The results also showed that in the periodic hydrodynamic fully developed region, the value of the pressure drop along the tube with continuous fins is greater than that of the in-line arrangement, and lower than that of the staggered arrangement. Furthermore, the results showed that in the periodic fully developed region, the tube with continuous fins produces a greater value of the heat transfer coefficients than that the tube with interrupted fins, especially through a high range of Reynolds number (5 × 104 > Re > 2 × 104). The tube with Staggered arrangement of fins produces a greater value of the heat transfer coefficient than the tube with continuous fins and the in-line arrangement finned tube at low Reynolds number (Re < 1.2 × 104).). It was found that the fins efficiency is greater than 90 percent;in the worst case (maximum Reynolds number with continuous fins tube).

Time-series variation and attribution analysis of downward shortwave radiation over the Yunnan-Kweichow plateau from 1984 to 2018

The downward shortwave radiation(DSR)is a key input parameter for land surface models and climate models.Based on the daily averaged Global Land Surface Satellite downward shortwave radiation(GLASS-DSR)dataset over the Yunnan-Kweichow Plateau(YKP)from 1984 to 2018,this paper analyzes variation trend and breakpoints of DSR.The results show that:annual averaged DSR decreases at a decreasing rate of-1.84 W·m^(-2)·decade^(-1) over the YKP from 1984 to 2018;the overall distribution of interannual averaged DSR shows higher in the mid-west,and gradually decreasing from west to northeast over the YKP;the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon;monthly averaged DSR reaches its maximum in May and its minimum in December;breakpoints are found in the seasonal and trend components of daily averaged DSR.Eleven driving factors are examined for their effects on DSR variation,including annual average temperature,precipitation,10 m wind speed,aerosol optical thickness(AOT),total cloud cover,elevation,slope,aspect,longitude,latitude,and climate zones.According to thefindings,AOT predominates in the spatio-temporal distribution of DSR over the YKP.This study will contribute to studies related to climate change and highland radiation.

长期护理保险对失能人员医疗服务利用及费用的影响——基于中断时间序列分析

目的:研究长期护理保险政策对长期护理保险待遇享受人员医疗服务利用及费用的影响,探讨长期护理保险实施效果并提出建议。方法:采用卡方检验、中断时间序列分析,综合分析待遇享受人员的医疗服务利用及费用在长期护理保险实施前后的变化情况。结果:研究发现,对不同医疗保险类型、失能等级和年龄的失能人员选择不同的照护方式存在显著差异(P<0.05),待遇享受人员医疗服务利用频次及费用均有降低。结论:医疗保险类型、失能等级和年龄是照护方式选择的影响因素,待遇享受人员的医疗服务利用和费用显著降低,长期护理保险可能减少了“社会性住院”行为。政策建议:缩小城乡待遇差异、促进一体化发展,探索居家照护与机构上门结合、促进护理机构综合发展,健全照护服务体系、减轻家庭压力。

DRG支付对急性胰腺炎患者住院费用影响分析——基于中断时间序列模型研究

目的:本研究选取浙江省某三甲医院2019年至2021年的急性胰腺炎患者结算数据,评估DRG改革前后患者住院费用的变化,旨在为政策的持续优化提供实证依据。方法:采用中断时间序列模型分析政策执行前后住院费用变化趋势,并通过Spearman相关分析探讨住院总费用与各构成部分费用的关联度。结果:分析显示,改革前住院总费用每月上升1639.3元(P<0.001),改革后每月下降1924.1元(P<0.001),药品费和医疗服务费每月分别下降910.0元和480.3元(P<0.001)。改革后,医疗服务费与住院总费用的相关性最强(r=0.941,P<0.001)。结论:研究发现,随着DRG改革实施,样本医院急性胰腺炎患者的住院费用逐年下降,表明改革效果显著。医疗机构通过加强药品和耗材管控及规范医疗行为,有效体现了政策要求。为进一步提升DRG政策的调控作用,建议调整医疗服务价格以激励医务人员,同时建立动态调整机制,优化DRG分组方案,确保政策更好地适应临床需求和医疗实践。

基于间断时间序列分析的三明市公立医院DRG改革效果的综合评估

[目的]分析三明市C-DRG和CN-DRG医保支付方式改革对二级、三级公立医院医疗收入、服务效率和患者负担的影响。[方法]采用间断时间序列、灰色关联度和结构变动度分析方法对2015-2019年22家公立医院的医药收入等指标进行回顾性分析。[结果]三明市实施DRG医保支付方式改革后二级医院的医疗收入降低5.2%,对三级医院的医疗收入没有显著影响;二级、三级医院的平均住院天数和病床使用率无显著改变;三级医院的门急诊次均费用增长2%,出院例均费用未受显著影响;二级医院的出院例均费用降低10.8%后增长8.4%。[结论]在三明市的DRG改革下,公立医院情况稳中向好;DRG可以完善临床路径,促进医疗机构的成本管控,未来的医改进程中,应考虑不同等级医院特点,加强改革的监督管理。

脉内频率 时延捷变雷达抗间歇采样转发干扰方法

间歇采样转发干扰凭借快速准确的采样和转发手段,利用脉压雷达的匹配滤波特性形成假目标群,严重影响目标检测和跟踪性能。针对这一问题,结合波形设计和滤波思想,设计一种脉内频率时延捷变波形,并在此基础上提出一种基于时频分析的抗间歇采样转发干扰方法。首先,将线性调频信号划分为多个子脉冲,并在子脉冲之间增加随机时延。然后,提取未被干扰的子脉冲进行目标位置的准确判断,结合子脉冲的频率和随机时延等先验信息,利用迭代阈值分割算法去除强干扰信号。最后,通过构造时频域和时域带通滤波器分别进行子脉冲滤波和时域滤波处理。仿真实验结果表明,所提方法在不同干信比、信噪比和非同步采样情况下具有较好的抗间歇采样转发干扰性能。

基于“中心—多学科诊疗团队”架构的综合医院罕见病管理探索

目的对基于“中心—多学科诊疗团队”架构的综合医院罕见病管理模式进行初步研究。方法采用间断时间序列等分析方法,对样本医院2021—2023年出院的罕见病患者基本情况、相关医疗服务量进行了测量。结果2021—2023年,样本医院共为19825人次的罕见病患者提供住院服务,患者主诊断为罕见病的占比约为27%。经间断时间序列分析,2022年罕见病诊治中心成立当月,罕见病患者占比的预期值瞬时提高1.89个千分点,提示医院罕见病诊断能力显著提高。面向罕见病患者的生物信息学分析服务量呈现出增长趋势。结论基于“中心—多学科诊疗团队”架构的罕见病管理模式,可在不明显增加医院投入负担的条件下,提高医务人员对罕见病的关注程度,强化罕见病诊断能力,患者管理更加连续,学科合作更加高效。

COVID-19疫情前后28个国家生育率变化及影响因素研究:线性模型和中断时间序列分析

目的分析COVID-19疫情暴发前后不同国家经季节和日历调整后的生育率(seasonally and calendar adjusted fertility rate,SAFR)趋势的变化及其影响因素。方法使用国际人类生育力数据库(Human Fertility Database,HFD)中28个国家自2012年1月至2022年12月的月度SAFR数据,以2020年12月(2020年3月疫情暴发起点加9个月妊娠过程)为节点划分为疫情前(2012.1-2020.11)和疫情后(2020.12-2022.12)进行比较,使用中断时间序列方法分析各国疫情前后的SAFR趋势(短期波动和长期趋势)是否发生变化,使用秩和检验分析疫情前SAFR、人均GDP、公共卫生和社会措施(public health and social measures,PHSM)和失业率是否与SAFR趋势变化有关。结果疫情后28个国家中19个国家的SAFR出现短期下降,随后反弹。对于长期趋势,2个国家由下降趋势转为上升趋势,8个国家由上升趋势转为下降趋势,6个国家的SAFR保持不变。SAFR变化率下降主要集中在部分中欧国家以及地中海西岸的国家,而SAFR变化率增加的国家主要分布在北欧以及西欧地区。SAFR无短期波动的国家疫情前的SAFR低于有短期波动的国家(P=0.041),SAFR变化率下降国家的疫情前SAFR(P=0.005)与人均GDP(P=0.027)均低于SAFR变化率上升国家。未发现SAFR短期波动或长期趋势与PHSM严重程度指数或失业率存在关联。结论COVID-19疫情对28个国家的SAFR造成了不同的短期和长期影响,特别是经济水平和疫情前SAFR相对较低的国家可能更易遭到进一步打击。COVID-19疫情对各国人口的更长期影响值得进一步关注。