ESMO2023:恩扎卢胺联合雄激素剥夺治疗对比安慰剂联合雄激素剥夺治疗在中国转移性激素敏感性前列腺癌患者中的疗效和安全性(中国ARCHES)

2019年发表的全球ARCHES试验(NCT02677896)结果显示,与安慰剂(placebo,PBO)+雄激素剥夺治疗(androgen deprivation therapy,ADT)相比,恩扎卢胺+ADT延长了转移性激素敏感性前列腺癌(metastatic hormone-sensitive prostate cancer,mHSPC)患者的总生存期和放射影像学无进展生存期(radiographic progression-free survival,rPFS)^([1])。然而,该试验无中国患者入组。欧洲肿瘤内科学会(European Society for Medical Oncology,ESMO)2023年会议报道了中国ARCHES研究(NCT04076059)的初步结果,这是一项评估恩扎卢胺+ADTvs.PBO+ADT在中国m HSPC患者中的疗效和安全性的多中心、随机、双盲、PBO对照的Ⅲ期试验^([2])。

基于小波分解和ARIMA-GARCH-GRU组合模型的制造业PMI预测

制造业采购经理人指数(PMI)是反映国家经济运行情况的重要指标,而传统预测模型对该类时序数据预测精度不高。针对制造业PMI指数的非线性、波动性和数据量少的特点,提出一种基于一维离散小波变换进行数据预处理的组合模型。时序数据经过小波变换,由整合移动平均自回归–广义自回归条件异方差模型(ARIMA-GARCH)处理稳态低频数据,门控循环单元(GRU)处理波动性强的高频数据,将各频段预测结果进行融合得到最终预测结果。为验证模型有效性,选取一定数据量的PMI指数进行实验。结果表明,与其他常见模型对比,本文构建的组合模型具有较好的预测精度与性能,平均绝对误差(MAE)、均方根误差(RMSE)、平均绝对百分比误差(MAPE)分别达到0.00329、0.004162、0.65%。

Health diagnosis of ultrahigh arch dam performance using heterogeneous spatial panel vector model

Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and plastic complementary energy norm to assess the structural safety of arch dams.A comprehensive analysis was conducted,focusing on differences among conventional methods in characterizing the structural behavior of the Xiaowan arch dam in China.Subsequently,the spatiotemporal characteristics of the measured performance of the Xiaowan dam were explored,including periodicity,convergence,and time-effect characteristics.These findings revealed the governing mechanism of main factors.Furthermore,a heterogeneous spatial panel vector model was developed,considering both common factors and specific factors affecting the safety and performance of arch dams.This model aims to comprehensively illustrate spatial heterogeneity between the entire structure and local regions,introducing a specific effect quantity to characterize local deformation differences.Ultimately,the proposed model was applied to the Xiaowan arch dam,accurately quantifying the spatiotemporal heterogeneity of dam performance.Additionally,the spatiotemporal distri-bution characteristics of environmental load effects on different parts of the dam were reasonably interpreted.Validation of the model prediction enhances its credibility,leading to the formulation of health diagnosis criteria for future long-term operation of the Xiaowan dam.The findings not only enhance the predictive ability and timely control of ultrahigh arch dams'performance but also provide a crucial basis for assessing the effectiveness of engineering treatment measures.

Numerical Simulation Analysis of Welded Joints in Arch Ribs of Large Span Steel Pipe Arch Bridges

In order to study the residual stress distribution law of welded joints of arch ribs of large-span steel pipe concrete arch bridges,numerical simulation of temperature,stress and strain fields based on ABAQUS for welded joints of arch-ribbed steel tubes using 7-,8-and 9-layer welds is carried out and its accuracy is demonstrated.The steel pipe welding temperature changes,residual stress distribution,different processes residual stress changes in the law,the prediction of post-weld residual stress distribution and deformation are studied in this paper.The results show that the temperature field values and test results are more consistent with the accuracy of numerical simulation of welding,the welding process is mainly in the form of heat transfer;Residual high stresses are predominantly distributed in the Fusion zone(FZ)and Heat-affected zone(HAZ),with residual stress levels tending to decrease from the center of the weld along the axial path,the maximum stress appears in the FZ and HAZ junction;The number of welding layers has an effect on the residual stress distribution,the number of welding layers increases,the residual stress tends to decrease,while the FZ and HAZ high stress area range shrinks;Increasing the number of plies will increase the amount of residual distortion.

A Nonlinear Explicit Model of A Non-Circular Subsea Tunnel-Liner System with An FGM Inverted Arch Under Mechanical Loading and Fire Fields

This paper proposes an explicit scheme to analyze the failure of a subsea polyhedral tunnel-liner system with an inverted arch under mechanical loading and fire fields.The thin-walled liner is made of Functionally Graded Materials(FGMs),which may improve the stability behavior of the tunnel-liner system.Hydrostatic pressure is inevitable in the liner since underground water may penetrate the cracks of the tunnel,and reach the outer surface of the liner.In addition,an elevated temperature loading is taken into account,considering that fire may occur in the tunnel-liner system.Under the combination of mechanical loading and thermal loading,the liner deforms into a single-lobe shape,which is depicted by a trigonometric function.The total potential energy is expressed quantitatively after the energy approach and thin-walled shell theory are used.The minimum potential energy is obtained when the critical buckling occurs.The critical buckling pressure is calculated,which considers the effect of the thermal field.The present analytical prediction is subsequently compared precisely with other closed-form solutions.Finally,the effects of several parameters,such as the geometric shapes,temperature variations,and volume fraction indices,are discussed to further survey the buckling performance of the nonlinear buckling of an FGM polyhedral liner with an inverted arch.One may address a polyhedral liner with fewer polyhedral sides,and a lower volume fraction index is recommended to rehabilitate cracked tunnels in engineering applications.

Prediction model of surrounding rock deformation in doublecontinuous-arch tunnel based on the ABC-WNN

Purpose–The wavelet neural network(WNN)has the drawbacks of slow convergence speed and easy falling into local optima in data prediction.Although the artificial bee colony(ABC)algorithm has strong global optimization ability and fast convergence speed,it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.Design/methodology/approach–This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model.Based on the example of the Jinan Yuhan underground tunnel project,the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed,and the analysis results are compared with the actual detection amount.Findings–The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data,with a maximum relative error of only 4.73%.On this basis,the results show that the statistical features of ABC-WNN are the lowest,with the errors at 0.566 and 0.573,compared with the single back propagation(BP)neural network model and WNN model.Therefore,it can be derived that the ABC-WNN model has higher prediction accuracy,better computational stability and faster convergence speed for deformation.Originality/value–This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels.This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multiarch tunnels and small clearance tunnels.It can provide a new and effective way for deformation prediction in similar projects.

Effects of comprehensive nursing with cognitive behavioral therapy in orthodontic osteodilated arch treatment

BACKGROUND This work explored the effects of cognitive behavior therapy(CBT)-based comprehensive nursing intervention(CNI)mode in arch expansion to treat patients with orthodontic osteodilated arch(OOA).AIM To explore the application effect of CBT-based CNI model in orthodontic expansion arch treatment.METHODS Using convenient sampling method,81 patients with OOA were selected and rolled into a control group(Ctrl group,40 cases)and an observation group(Obs group,41 cases).During the treatment,patients in the Ctrl group received routine nursing intervention mode,and the those in the Obs group received CBT mode on the basis of this.Before and after intervention,the incidence of oral mucositis,the mastery rate of correct arch expansion method,self-rating anxiety scale score,soft scale index,and plaque index were compared for patients in different groups.In addition,satisfaction and complications were comparatively analyzed.RESULTS Incidence of oral mucositis in the Obs group was lower(14.6%vs 38.5%),and the mastery rate of correct arch expansion method was obviously higher(90.2%vs 55.0%)was obviously higher(all P<0.05).Meanwhile,the soft scale index and plaque index in the Obs group were much lower(P<0.05).The compliance(90.24%)and satisfaction(95.12%)in the Obs group were greatly higher(P<0.05).CONCLUSION The CBT-based CNI mode greatly improved the mastery rate of correct arch expansion method during arch expansion in treating patients with OOA and enhanced the therapeutic effect of arch expansion and the oral health of patients,improving the patient compliance.

基于SARIMA-ARCH的电量预测模型

电量预测是供电单位购电的重要依据,是电力经济稳定运行的根本基础。提出采用SARIMA-ARCH融合模型对某地区售电量进行预测。首先,采用季节时间序列分析方法(SARIMA)对月度电量时间序列进行建模,通过ACF和PACF图筛选确定出最佳模型阶数,得到季节性时间序列模型(SARIMA)基础预测模型;提取模型残差的波动性,建立自回归条件异方差(ARCH)模型;最后,将SARIMA-ARCH模型与常规SRIAM和ARIMA的预测值进行对比分析。结果表明,SARIMAARCH混合模型的预测精度较高。

基于ARCH族模型的股票超高频数据的波动性研究——以浦发银行为例

论文基于浦发银行股票的逐笔交易数据,利用ARCH族模型对其超高频数据中的波动性进行了研究。论文首先通过对数据的预处理和单位根检验,确保了数据的平稳性,随后应用多种ARCH族模型对波动性进行建模与比较,最终选择TGARCH(2,1,1)模型作为最优模型。该模型不仅能够捕捉数据中的波动集聚效应,还揭示了显著的波动非对称性。研究结果表明,浦发银行的股票收益波动性具有显著的持久性和杠杆效应,负面冲击对波动的影响大于正面冲击。基于这些发现,论文提出了加强高频交易监管、优化投资策略和强化风险管理等政策启示。

Effect of aggregation interval on vehicular traffic flow heteroscedasticity

The effect of the aggregation interval on vehicular traffic flow heteroscedasticity is investigated using real-world traffic flow data collected from the motorway system in the United Kingdom. 30 traffic flow series are generated using 30 aggregation intervals ranging from 1 to 30 min at 1 min increment, and autoregressive integrated moving average （AR/MA） models are constructed and applied in these series, generating 30 residual series. Through applying the portmanteau Q-test and the Lagrange multiplier （LM） test in the residual series from the ARIMA models, the heteroscedasticity in traffic flow series is investigated. Empirical results show that traffic flow is heteroscedastJc across these selected aggregation intervals, and longer aggregation intervals tend to cancel out the noise in the traffic flow data and hence reduce the heteroscedasticity in traffic flow series. The above findings can be utilized in the development of reliable and robust traffic management and control systems.

基于在线LASSO VAR和EGARCH模型的风场功率集成概率预测

由于风速波动性大,风力发电往往呈现一定的不确定性.传统风能预测模型以均值为0、方差固定的正态分布度量不确定性,但方差可能随时间变化,即具有异方差性.为提升预测精度,基于在线最小绝对收缩和选择算子的向量自回归(LASSO VAR)和指数自回归条件异方差(EGARCH)模型,提出一种考虑异方差性的风场级功率集成概率预测模型.首先使用在线LASSO VAR模型预测风力机的有功功率,再利用自回归条件异方差检验验证残差的异方差性,并利用信息冲击曲线和动态显著线评估正负残差对未来条件方差的不对称影响.然后针对异方差性和不对称性,使用EGARCH模型对单风力机有功功率的残差进行预测,得到有功功率的条件方差.最后,考虑各风力机有功功率的相关性,将风场中各风力机的有功功率求和,得到整个风场总有功功率的概率预测结果.将该方法应用于中国华东某地风场,验证了该模型能有效提高预测精度.

Comparison of the Length of Stay and Medical Expenditures among Japanese Hospitals for Type 2 Diabetes Treatments: The Box-Cox Transformation Model under Heteroscedasticity

In this paper, we analyzed length of stay (LOS) in hospitals and medical expenditures for type 2 diabetes patients. LOS was analyzed by the power Box-Cox transformation model when variances differed among hospitals. We proposed a new test and consistent estimator. We rejected the ho-moscedasticity of variances among hospitals, and then analyzed the LOS of 12,666 type 2 diabetes patients hospitalized for regular medical treatments collected from 60 general hospitals in Japan. The variables found to affect LOS were age, number of comorbidities and complications, introduced by another hospital, one-week hospitalization, 2010 revision, specific-hospitalization-period (SHP), and principal diseases E11.5, E11.6 and E11.7. There were surprisingly large differences in ALOS among hospitals even after eliminating the influence of characteristics and conditions of patients. We then analyzed daily medical expenditure (DME) by the ordinary least squares methods. The variables that affected DME were LOS, number of comorbidities and complications, acute hospitalization, hospital’s own outpatient, season, introduced by another hospital, one-week hospitalization, 2010 revision, SHP, time trend, and principal diseases E11.2, E11.4 and E117. The DME did not decrease after the SHP. After eliminating the influences of characteristics and conditions of patients, the differences among hospitals were relatively small, 12% of the overall average. LOS is the main determinant of medical expenditures, and new incentives to reduce LOS are needed to control Japanese medical expenditures. Since at least 99% of patients require medical care after leaving the hospital, systems that take proper care of patients for long periods of time after hospitalization are absolutely necessary for efficient treatment of diabetes.

Testing heteroscedasticity in nonparametric regression models based on residual analysis

The importance of detecting heteroscedasticity in regression analysis is widely recognized because efficient inference for the regression function requires that heteroscedasticity should be taken into account. In this paper, a simple test for heteroscedasticity is proposed in nonparametric regression based on residual analysis. Furthermore, some simulations with a comparison with Dette and Munk＇s method are conducted to evaluate the performance of the proposed test. The results demonstrate that the method in this paper performs quite satisfactorily and is much more powerful than Dette and Munk＇s method in some cases.

APPROXIMATE POWER OF HETEROSCEDASTICITY TEST IN NONLINEAR MODELS WITH ARIMA（0,1,0） ERRORS

This paper presents an approach for estimating power of the score test, based on an asymptotic approximation to the power of the score test under contiguous alternatives. The method is applied to the problem of power calculations for the score test of heteroscedasticity in European rabbit data （Ratkowsky, 1983）. Simulation studies are presented which indicate that the asymptotic approximation to the finite-sample situation is good over a wide range of parameter configurations.

Mechanical properties and influence mechanism of confined concrete arches in high-stress tunnels

Deep underground projects(e.g., coal mines), are often faced with complex conditions such as high stress and extremely soft rock. The strength and rigidity of the traditional support system are often insufficient,which makes it difficult to meet the requirements of ground control under complex conditions. As a new support form with high strength and rigidity, the confined concrete arch plays an important role in controlling the rock deformation under complex conditions. The section shape of the tunnel has an important impact on the mechanical properties and design of the support system. However, studies on the mechanical properties and influence mechanism of the new confined concrete arch are rarely reported. To this end, the mechanical properties of traditional U-shaped steel and new confined concrete arches are compared and comparative tests on arches of circular and straight-leg semicircular shapes in deep tunnels are conducted. A large mechanical testing system for underground engineering support structure is developed. The mechanical properties and influence mechanism of confined concrete arches with different section shapes under different loading modes and cross-section parameters are systematically studied. Test results show that the bearing capacity of the confined concrete arch is 2.10 times that of the U-shaped steel arch, and the bearing capacity of the circular confined concrete arch is 2.27 times that of the straight-leg semicircular arch. Among the various influencing factors and their engineering parameters,the lateral stress coefficient has the greatest impact on the bearing capacity of the confined concrete arch,followed by the steel pipe wall thickness, steel strength, and core concrete strength. Subsequently, the economic index of bearing capacity and cost is established, and the optimization design method for the confined concrete arch is proposed. Finally, this design method is applied to a high-stress tunnel under complex conditions, and the deformation of the surrounding rock is effectively controlled.

TESTING OF CORRELATION AND HETEROSCEDASTICITY IN NONLINEAR REGRESSION MODELS WITH DBL(p,q,1) RANDOM ERRORS

Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （Kantz and Schreiber （1997））. Tsai （1986） introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR（1） errors. Liu （2003） introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL（p, 0, 1） errors. Therefore, the important problems in regression model axe detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL（p, q, 1） random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedasticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai （1986）, Wei, et al （1995）, and Liu, et al （2003）.

Heteroscedasticity check in nonlinear semiparametric models based on nonparametric variance function

The assumption of homoscedasticity has received much attention in classical analysis of regression. Heteroscedasticity tests have been well studied in parametric and nonparametric regressions. The aim of this paper is to present a test of heteroscedasticity for nonlinear semiparametric regression models with nonparametric variance function. The validity of the proposed test is illustrated by two simulated examples and a real data example.

Effects of Multicollinearity on Type I Error of Some Methods of Detecting Heteroscedasticity in Linear Regression Model

Heteroscedasticity and multicollinearity are serious problems when they exist in econometrics data. These problems exist as a result of violating the assumptions of equal variance between the error terms and that of independence between the explanatory variables of the model. With these assumption violations, Ordinary Least Square Estimator (OLS) will not give best linear unbiased, efficient and consistent estimator. In practice, there are several structures of heteroscedasticity and several methods of heteroscedasticity detection. For better estimation result, best heteroscedasticity detection methods must be determined for any structure of heteroscedasticity in the presence of multicollinearity between the explanatory variables of the model. In this paper we examine the effects of multicollinearity on type I error rates of some methods of heteroscedasticity detection in linear regression model in other to determine the best method of heteroscedasticity detection to use when both problems exist in the model. Nine heteroscedasticity detection methods were considered with seven heteroscedasticity structures. Simulation study was done via a Monte Carlo experiment on a multiple linear regression model with 3 explanatory variables. This experiment was conducted 1000 times with linear model parameters of β0 = 4 , β1 = 0.4 , β2= 1.5 and β3 = 3.6. Five (5) levels of mulicollinearity are with seven (7) different sample sizes. The method’s performances were compared with the aids of set confidence interval (C.I.) criterion. Results showed that whenever multicollinearity exists in the model with any forms of heteroscedasticity structures, Breusch-Godfrey (BG) test is the best method to determine the existence of heteroscedasticity at all chosen levels of significance.

Conditional Heteroscedasticity in Streamflow Process: Paradox or Reality?

The various physical mechanisms governing the dynamics of streamflow processes act on a seemingly wide range of temporal and spatial scales;almost all the mechanisms involved present some degree of nonlinearity. Against the backdrop of these issues, in this paper, attempt was made to critically look at the subject of Autoregressive Conditional Heteroscedasticity (ARCH) or volatility of streamflow processes, a form of nonlinear phenomena. Towards this end, streamflow data (both daily and monthly) of the River Benue, Nigeria were used for the study. Results obtained from the analyses indicate that the existence of conditional heteroscedasticity in streamflow processes is no paradox. Too, ARCH effect is caused by seasonal variation in the variance for monthly flows and could partly explain same in the daily streamflow. It was also evident that the traditional seasonal Autoregressive Moving Average (ARMA) models are inadequate in describing ARCH effect in daily streamflow process though, robust for monthly streamflow;and can be removed if proper deseasonalisation pre-processing was done. Considering the findings, the potential for a hybrid Autoregressive Moving Average (ARMA) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH)type models should be further explored and probably embraced for modelling daily streamflow regime in view of the relevance of statistical modelling in hydrology.

Investigating Performances of Some Statistical Tests for Heteroscedasticity Assumption in Generalized Linear Model: A Monte Carlo Simulations Study

In a linear regression model, testing for uniformity of the variance of the residuals is a significant integral part of statistical analysis. This is a crucial assumption that requires statistical confirmation via the use of some statistical tests mostly before carrying out the Analysis of Variance (ANOVA) technique. Many academic researchers have published series of papers (articles) on some tests for detecting variance heterogeneity assumption in multiple linear regression models. So many comparisons on these tests have been made using various statistical techniques like biases, error rates as well as powers. Aside comparisons, modifications of some of these statistical tests for detecting variance heterogeneity have been reported in some literatures in recent years. In a multiple linear regression situation, much work has not been done on comparing some selected statistical tests for homoscedasticity assumption when linear, quadratic, square root, and exponential forms of heteroscedasticity are injected into the residuals. As a result of this fact, the present study intends to work extensively on all these areas of interest with a view to filling the gap. The paper aims at providing a comprehensive comparative analysis of asymptotic behaviour of some selected statistical tests for homoscedasticity assumption in order to hunt for the best statistical test for detecting heteroscedasticity in a multiple linear regression scenario with varying variances and levels of significance. In the literature, several tests for homoscedasticity are available but only nine: Breusch-Godfrey test, studentized Breusch-Pagan test, White’s test, Nonconstant Variance Score test, Park test, Spearman Rank, Glejser test, Goldfeld-Quandt test, Harrison-McCabe test were considered for this study;this is with a view to examining, by Monte Carlo simulations, their asymptotic behaviours. However, four different forms of heteroscedastic structures: exponential and linear (generalize of square-root and quadratic structures) were injected into the residual part of the multiple linear regression models at different categories of sample sizes: 30, 50, 100, 200, 500 and 1000. Evaluations of the performances were done within R environment. Among other findings, our investigations revealed that Glejser and Park tests returned the best test to employ to check for heteroscedasticity in EHS and LHS respectively also White and Harrison-McCabe tests returned the best test to employ to check for homoscedasticity in EHS and LHS respectively for sample size less than 50.