Assessment of Operational Performance in a Power Generation/Selling Integrated Company Using a Dynamic Proportional Adjustment Coefficient

Currently,the operational performance assessment system in the power market primarily focuses on power generation and electricity retail companies,lacking a system tailored to the operational characteristics of power generation/selling integrated companies.Therefore,this article proposes an assessment index system for assessing the operational performance of a power generation/selling integrated company,encompassing three dimensions:basic capacity,development potential,and external environment.A dynamic proportional adjustment coefficient is designed,along with a subjective and objective weighting model for assessment indexes based on a combined weightingmethod.Subsequently,the operational performance of an integrated company is assessed using extension theory.The results in the case study demonstrate the feasibility and effectiveness of the proposed dynamic proportional adjustment coefficient.

Comparison of Cox proportional hazards model,Cox proportional hazards with time-varying coefficients model,and lognormal accelerated failure time model:Application in time to event analysis of melioidosis patients

Objective:To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time(AFT),Cox proportional hazards(PH),and Cox PH with time-varying coefficient(TVC)models.Methods:A retrospective study was conducted from 2014 to 2019 among 453 patients who were admitted to Hospital Sultanah Bahiyah,Kedah and Hospital Tuanku Fauziah,Perlis in Northern Malaysia due to confirmed-cultured melioidosis.The prognostic factors of mortality from melioidosis were obtained from AFT survival analysis,and Cox’s models and the findings were compared by using the goodness of fit methods.The analyses were done by using Stata SE version 14.0.Results:A total of 242 patients(53.4%)survived.In this study,the median survival time of melioidosis patients was 30.0 days(95%CI 0.0-60.9).Six significant prognostic factors were identified in the Cox PH model and Cox PH-TVC model.In AFT survival analysis,a total of seven significant prognostic factors were identified.The results were found to be only a slight difference between the identified prognostic factors among the models.AFT survival showed better results compared to Cox's models,with the lowest Akaike information criteria and best fitted Cox-snell residuals.Conclusions:AFT survival analysis provides more reliable results and can be used as an alternative statistical analysis for determining the prognostic factors of mortality in melioidosis patients in certain situations.

ANALYSIS OF HUMAN PLACENTA BY ^(31)P NUCLEAR MAGNETIC RESONANCE AND THIN-LAYER CHROMATOGRAPHY SCANNING COMBINED WITH THE CORRECTIVE METHOD OF ABSORBANCE PROPORTIONAL COEFFICIENT

Five phospholipids in human placenta were determined by phosphorus 31 nuclear magnetic resonance(^(31)P NMR)spectroscopy and thin-layer chromatography(TLC) scanning combined with the corrective method of absorbance proportional coefficient. The NMR spectrometer used this investigation was a Bruker AM-500 spectrometer operating at 202.4 MHz for ^(31)P chemical shifts are relative to 85% phosphoric acid. TIC was carried out by silica gel H plate developed in chloroform-methanol-glacial acetic acid-ethanol-water(25:4:6:2:0.5),with Vaskovsky reagent as colour -developing agent of phospholipids.

GENERAL DECAY FOR A VISCOELASTIC EQUATION OF VARIABLE COEFFICIENTS WITH A TIME-VARYING DELAY IN THE BOUNDARY FEEDBACK AND ACOUSTIC BOUNDARY CONDITIONS

A variable coefficient viscoelastic equation with a time-varying delay in the boundary feedback and acoustic boundary conditions and nonlinear source term is considered.Under suitable assumptions, general decay results of the energy are established via suitable Lyapunov functionals and some properties of the convex functions. Our result is obtained without imposing any restrictive growth assumption on the damping term and the elements of the matrix A and the kernel function g.

ADDITIVE HAZARDS MODEL WITH TIME-VARYING REGRESSION COEFFICIENTS

This article discusses regression analysis of failure time under the additive hazards model, when the regression coefficients are time-varying. The regression coefficients are estimated locally based on the pseudo-score function [12] in a window around each time point. The proposed method can be easily implemented, and the resulting estimators are shown to be consistent and asymptotically normal with easily estimated variances. The simulation studies show that our estimation procedure is reliable and useful.

A Simulation Study on Comparing General Class of Semiparametric Transformation Models for Survival Outcome with Time-Varying Coefficients and Covariates

The consideration of the time-varying covariate and time-varying coefficient effect in survival models are plausible and robust techniques. Such kind of analysis can be carried out with a general class of semiparametric transformation models. The aim of this article is to develop modified estimating equations under semiparametric transformation models of survival time with time-varying coefficient effect and time-varying continuous covariates. For this, it is important to organize the data in a counting process style and transform the time with standard transformation classes which shall be applied in this article. In the situation when the effect of coefficient and covariates change over time, the widely used maximum likelihood estimation method becomes more complex and burdensome in estimating consistent estimates. To overcome this problem, alternatively, the modified estimating equations were applied to estimate the unknown parameters and unspecified monotone transformation functions. The estimating equations were modified to incorporate the time-varying effect in both coefficient and covariates. The performance of the proposed methods is tested through a simulation study. To sum up the study, the effect of possibly time-varying covariates and time-varying coefficients was evaluated in some special cases of semiparametric transformation models. Finally, the results have shown that the role of the time-varying covariate in the semiparametric transformation models was plausible and credible.

A High-Order Conservative Numerical Method for Gross-Pitaevskii Equation with Time-Varying Coefficients in Modeling BEC

We propose a high-order conservative method for the nonlinear Sehodinger/Gross-Pitaevskii equation with time- varying coefficients in modeling Bose Einstein condensation （BEC）. This scheme combined with the sixth-order compact finite difference method and the fourth-order average vector field method, finely describes the condensate wave function and physical characteristics in some small potential wells. Numerical experiments are presented to demonstrate that our numerical scheme is efficient by the comparison with the Fourier pseudo-spectral method. Moreover, it preserves several conservation laws well and even exactly under some specific conditions.

Partial Time-Varying Coefficient Regression and Autoregressive Mixed Model

Regression and autoregressive mixed models are classical models used to analyze the relationship between time series response variable and other covariates. The coefficients in traditional regression and autoregressive mixed models are constants. However, for complicated data, the coefficients of covariates may change with time. In this article, we propose a kind of partial time-varying coefficient regression and autoregressive mixed model and obtain the local weighted least-square estimators of coefficient functions by the local polynomial technique. The asymptotic normality properties of estimators are derived under regularity conditions, and simulation studies are conducted to empirically examine the finite-sample performances of the proposed estimators. Finally, we use real data about Lake Shasta inflow to illustrate the application of the proposed model.

Partial Time-Varying Coefficient Regression and Autoregressive Mixed Model

Regression and autoregressive mixed models are classical models used to analyze the relationship between time series response variable and other covariates. The coefficients in traditional regression and autoregressive mixed models are constants. However, for complicated data, the coefficients of covariates may change with time. In this article, we propose a kind of partial time-varying coefficient regression and autoregressive mixed model and obtain the local weighted least-square estimators of coefficient functions by the local polynomial technique. The asymptotic normality properties of estimators are derived under regularity conditions, and simulation studies are conducted to empirically examine the finite-sample performances of the proposed estimators. Finally, we use real data about Lake Shasta inflow to illustrate the application of the proposed model.

Methodology for expressing the flow coefficients of coupled throttling grooves in a proportional–directional valve

Calculating the flow coefficient of a spool-valve is complicated due to the coupling–throttling effect in the throttling grooves of a proportional–directional valve.In this paper,a methodology for expressing the flow coefficient of coupled throttling grooves is proposed to resolve that difficulty.With this purpose,an approach of a 3 D numerical simulation and an experimental bench were introduced based on the prototype of a commercial proportional valve.The results show consistency between the numerical simulation and the bench test.Based on that,the concept of‘saturation limit’is introduced to describe the value gap between the current and saturated flows,so that the flow-coefficient saturation limit of the prototype in the process can be deducted.Accordingly,an approximate flow coefficient suitable for coupled throttling grooves within finite variable space,which is based on three typical throttling structures(i.e.O-shape,U-shape,and C-shape)of the coupled throttling grooves,is obtained based on an orthogonal test.The model results are consistent with the numerical and experimental results,with maximum errors of less than 5.29%and 5.34%,respectively.This suggests that the proposed method is effective in approximating the flow coefficient.

Robust stability analysis for Markovian jumping stochastic neural networks with mode-dependent time-varying interval delay and multiplicative noise

This paper is concerned with the problem of robust stability for a class of Markovian jumping stochastic neural networks （MJSNNs） subject to mode-dependent time-varying interval delay and state-multiplicative noise. Based on the Lyapunov-Krasovskii functional and a stochastic analysis approach, some new delay-dependent sufficient conditions are obtained in the linear matrix inequality （LMI） format such that delayed MJSNNs are globally asymptotically stable in the mean-square sense for all admissible uncertainties. An important feature of the results is that the stability criteria are dependent on not only the lower bound and upper bound of delay for all modes but also the covariance matrix consisting of the correlation coefficient. Numerical examples are given to illustrate the effectiveness.

基于反向比例解析的近红外光谱定量模型快速构建方法

为解决光谱漂移问题,该研究设计了一种基于反向比例解析的近红外光谱定量模型方法。以烟叶近红外光谱和烟碱含量为研究对象,将数据划分为训练集和测试集。通过计算训练集光谱与测试集光谱的相关性并按照高低排序,选择前20%的光谱,运用约束规划的方法,计算测试集的拟合系数,得到测试集光谱的估计值。结果显示,使用反向比例解析法建立的模型的平均绝对误差为0.3466,预测标准偏差为0.4252,相关系数为0.7932,优于PLS模型。反向光谱比例解析可以有效解决光谱漂移问题,实现烟草中烟碱含量的准确预测,为烟碱的有效测量提供参考。

基于力学性能指标与耐久性能指标联合的夯土改性配比优化设计方法

素土强度低、耐久性差且各改性材料掺量往往由工匠凭经验大致确定,导致夯土表面时常容易开裂剥落,故应根据现场土质状况以及合理试验方法来设计夯土改性配比。现有研究大都局限于单一性能指标最优的配比,而现实中夯土既要考虑针对多项力学性能指标与耐久性能指标的同步优化,还要考虑不同地域与气候条件下各指标的重要性不同。另外,现有研究仅能确定若干预设配比中的最优组,而非改性材料预设掺量范围内的最优配比。为此,文章提出一种基于力学性能指标与耐久性能指标联合的夯土改性配比优化设计方法,其包括:(1)土料选用原则与土质鉴定方法;(2)考虑夯土多性能指标的综合赋权方法;(3)基于加权评分和回归分析的夯土改性配比综合比选与优化方法。文章方法通过调整指标权重筛选出适应指标重要性不同时的夯土改性配比,并通过优化设计获得各改性材料预设掺量范围内的最优配比。最后通过设计实例证明了文章方法的确可得到最优的夯土改性配比。

隧道火灾相向射流排烟系统烟气质量流率比例系数

为探究隧道相向射流排烟系统中火灾发生时烟气的流动特性,通过FDS数值模拟研究隧道内不同上、下游风速工况下的上、下游烟气质量流率变化。基于等效风速、理论分析和数值模拟结果得到上、下游风速与烟气质量流率比例系数的关系式。结果表明:当等效风速小于临界风速时,上、下游烟气质量流率比值和烟气质量流率比例系数随等效风速增加而减小,并且等效风速与上、下游烟气质量流率比值存在线性关系;当等效风速大于临界风速时,上、下游烟气质量流率比值和烟气质量流率比例系数为0。

基坑中软土的水平抗力系数几种确定方法探讨

该文围绕基坑中软土的的水平抗力系数的确定方法开展了探讨,分析了基坑规程中土的水平反力系数的比例系数m值的来源,分析了水平抗力系数确定的理论,罗列了现行规范中软土的水平抗力系数的经验值,最后通过案例分析,将3种土的水平抗力系数的计算方法(m值、k值和c值)的计算结果与实测进行了比较,发现按目前基坑规程公式计算的软土的水平反力系数的比例系数m值偏小的现象。

计及渗透系数的海绵城市路面透水沥青混合料配比设计优化

与普通城市路面不同,海绵城市路面对透水沥青混合料的渗透性能要求较高。为提高透水沥青混合料的路用性能,提出了计及渗透系数的海绵城市路面透水沥青混合料配比设计优化。以改性沥青、石灰石碎石、机制砂、矿粉填料、聚酯纤维和玄武岩纤维为原材料,在沥青混合料的3种设计级配下,根据海绵城市路面承载能力与渗水系数之间的主次关系确定目标孔隙率,实测发现设计级配3与目标孔隙率相符,并通过Schellenberg沥青渗流试验和Cantabro飞散试验确定了最佳沥青用量。结果表明,在设计级配3下,当孔隙率为18.058%、最佳沥青用量为4.95%时,沥青混合料的渗透系数在800 mL/15 s以上,可以满足海绵城市路面规范的要求。

基于电场传感器阵列的输电线路电压非接触式测量方法

电压作为表征线路运行状态的重要参量,实现其灵活准确的测量对保证电力系统稳定运行至关重要。传统的接触式电压测量方法,因电压互感器体积较大、会产生电磁谐振和高频振荡、额外测量需断电安装等问题,对于海量化测量并不便利。相比较而言,非接触式测量不接触导线,更加安全和便捷,将成为未来电压测量的发展方向。文中提出了基于电场传感器阵列的输电线路电压非接触式测量方法,该方法为了解决导线位置信息未知的问题,设计了与导线方向垂直排列的电场传感器阵列;求解了含导线未知信息量的方程组;构建了比例系数矩阵,反推出导线电压瞬时值;分析了电场传感器对测量的影响;最后,进行了10 kV电压等级电压瞬时值仿真验证,结果显示最大测量误差为2.219%。

宫颈鳞癌中PD-L1不同抗体免疫组织化学染色的一致性分析

目的探讨宫颈鳞癌中程序性死亡配体1(PD-L1)表达情况及22C3、SP263、E1L3N 3种抗体免疫组化染色的一致性。方法收集解放军总医院两个医学中心89例复发或转移性宫颈鳞癌样本,用PD-L13种克隆号抗体22C3、SP263、E1L3N进行染色,并采用联合阳性评分(CPS)、肿瘤细胞比例评分(TPS)两种评分标准,在不同阈值下对抗体的一致性进行统计学分析。结果宫颈鳞癌在CPS阳性阈值为1时,PD-L1阳性率从高到低依次为22C3(98.31%)、E1L3N(97.75%)、SP263(93.90%);CPS为10时,阳性率依次为E1L3N(76.40%)、22C3(72.88%)、SP263(69.51%);TPS阈值为1%时,阳性率依次为E1L3N(95.51%)、22C3(93.22%)、SP263(92.68%);TPS为10%时,阳性率依次E1L3N(65.17%)、22C3(64.41%)、SP263(60.98%)。当CPS为1或10时、TPS为1%或10%时,E1L3N与22C3、SP263与22C3、SP263与E1L3N均表现较好的一致性(Kappa=0.66~0.98)。无论使用CPS或TPS,组内相关系数检验显示3个克隆号抗体判读数据有高度一致性(ICC分别为0.987、0.984)。结论E1L3N、SP263与22C3之间具有较高的可互换性,为临床检测PD-L1在宫颈癌组织中的表达情况,提供了更多选择。

Stackelberg微分博弈下的鲁棒最优投资-再保险问题

考虑一个以模糊厌恶再保险公司为领导者,模糊中立保险公司为追随者的Stackelberg随机微分博弈问题.通过求解拓展的HJB(Hamilton-Jacobi-Bellman)方程组,给出时间一致性均值-方差准则下的鲁棒最优投资-再保险策略以及相应的值函数.最后,通过数值例子和敏感性分析说明最优策略与主要参数之间的关系.

基于MIC-EEMD-改进Informer的含高比例清洁能源与储能的电力市场短期电价多步预测

随着电力现货市场的开展,短期电价预测对于各市场主体的决策有着重要意义,而高比例清洁能源与储能的不断接入给短期电价预测带来很大挑战。提出一种基于最大信息系数法(maximum information coefficient,MIC)、集成经验模态分解(ensemble empirical mode decomposition,EEMD)和改进Informer的短期电价多步预测模型。首先,采用MIC分析出与电价相关性较高的几类因素作为模型原始输入序列;然后,将上述原始序列进行EEMD分解后得到多条本征模函数(intrinsic mode function,IMF)和一个残余项后输入改进Informer分别得到翌日24点多步预测结果,再对预测结果进行滤波;最后,将滤波后序列的预测结果叠加得到最终的预测值。以西班牙电力市场数据进行验证,实验结果证明该模型可以有效提高电力市场短期电价多步预测精度。