慢性病流行病学研究的带时依变量多状态Cox回归模型

目的 探讨带时依变量多状态Cox回归模型在慢性病流行病学研究中的推广应用。方法 介绍带时依变量多状态Cox回归模型的基本原理 ,并将它应用于 2型糖尿病不同发展阶段影响因素的探讨。结果 应用带时依变量多状态Cox回归模型探讨 2型糖尿病不同发展阶段的影响因素 ,取得了较为满意的效果。

基于状态转移回归的动态集成时序预测方法

最优模型子集的确定和集成权重的设置是组合预测中的两个重要问题,直接关系到组合模型的预测表现。为此,本文提出了一个基于状态转移回归的动态集成时序预测方法,首先基通过计算单体模型和原始数据之间的互信息确定最优模型子集;其次,通过状态转移回归实现单体模型的动态集成,并获得最终预测值。通过对9个国家的主权信用违约互换利差数据进行预测实验,本文发现所提出的状态转移组合预测模型表现良好,不仅优于一般单体预测模型和组合预测模型,还优于基于滑动窗口技术的动态组合预测模型。

State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression

The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR)to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs)derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration(NASA)LIB.The root mean square error(RMSE)is maintained within 0.20%,and the mean absolute error(MAE)is below 0.16%,illustrating the proposed approach’s excellent predictive accuracy and wide applicability.

特高压交流V型悬式绝缘子串空间位置确定的方法

针对特高压交流试验示范工程中所用特高压V型悬式绝缘子串的空间位置难以准确定位的问题,提出了特高压V型悬式绝缘子串中各片绝缘子空间位置的一种计算方法。首先分析了悬式绝缘子串、金具、扩径导线组合结构的受力模型;其次通过试验总结出扩径导线上下挂点荷载的分配规律,为特高压V型悬式绝缘子串的空间定位明确边界条件;然后分析了特高压V型悬式绝缘子串的竖向荷载对其空间定位的影响,采用状态方程回归得到各片绝缘子空间位置;最后通过与实际试验结果对比分析,验证了所提计算方法的准确性。该计算方法为设计院在后续特高压工程中进行均压环设计提供了准确的参数。

中医理念的现代阐释

中医学理论根植于中国古代哲学,其所使用的概念是哲学意义与医学意义混杂在一起的综合概念,往往表现出多义性和多质性,从而导致中医学术发展的滞后。实现中医理论的现代化研究,必须实现中医传统理论的4个转化:哲学理论向自然科学理论的转化,本体认识论向客体实证论的转化,意象思维向抽象思维的转化和语言术语范式的古今转化。据此构建中医学的6大核心理论,构建中医学的现代理论体系。

SOC estimation based on data driven exteaded Kalman filter algorithm for power battery of electric vehicle and plug-in electric vehicle

State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradiction problem between the exact requirements of EKF(extended Kalman filter)algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period,a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed.The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery.Through the combination of data-based and model-based SOC estimation method,the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods,and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment(such as in an EV or PHEV).A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.

基于RBF-AR的船舶变形极短期预报

统一空间基准是海上作战平台实现精准探测打击的重要保证,而船体角变形的存在将严重影响空间基准的建立;针对这一问题,提出一种基于状态相依自回归(state-dependent auto-regressive,SD-AR)与径向基(radial basis function,RBF)神经网络的极短期变形预报方法,实现船体角形变的实时预报,为后续角变形的补偿提供依据;不同于传统的时间序列预报方法,该模型用一组RBF网络来逼近SD-AR模型中的函数系数,并采用一种结构化的非线性参数优化方法 (structured nonlinear parameter optimization method,SNPOM)辨识该模型;基于该RBF-AR预报模型,给出了船舶变形预报算法设计并进行了仿真实验;实验结果表明,该方法在船体变形预测精度上优于传统时间序列预测方法,具有较好的应用前景。

A model to determining the remaining useful life of rotating equipment,based on a new approach to determining state of degradation

Condition assessment is one of the most significant techniques of the equipment’s health management.Also,in PHM methodology cycle,which is a developed form of CBM,condition assessment is the most important step of this cycle.In this paper,the remaining useful life of the equipment is calculated using the combination of sensor information,determination of degradation state and forecasting the proposed health index.The combination of sensor information has been carried out using a new approach to determining the probabilities in the Dempster-Shafer combination rules and fuzzy c-means clustering method.Using the simulation and forecasting of extracted vibration-based health index by autoregressive Markov regime switching(ARMRS)method,final health state is determined and the remaining useful life(RUL)is estimated.In order to evaluate the model,sensor data provided by FEMTO-ST Institute have been used.

The relationship between self-presentation concerns and pre-game affect among adolescent American football players

Background： The influence of self-presentation concerns on the adolescent sport experience has received scant empirical attention. The purpose of this investigation was to prospectively examine the relationship among self-presentational concerns and pre-game affective states among middle and high school aged football players. Methods： American football players （n = 112; mean age = 15.57 years） completed a measure of self-presentational concerns （SPSQ, McGowan, et al., 2008） a week prior to the measurement of selected pre-game affective states （i.e., attentiveness, self-assurance, serenity, and fear）. Results： Regression analyses revealed that concerns about appearing athletically untalented negatively contributed to the significant prediction （p 〈 0.001） of pre-game attentiveness, /3 = -0.43, Radj ^2 19.5% （p 〈 0.001）, and self-assurance, /3 = -0.38, R^dj = 11.9% （p 〈 0.01）. Conclusion： These findings highlight the importance of reducing self-presentational concerns in promoting positive pre-game mental states that likely impact the quality of athletes＇ competitive play and experience. Copyright @ 2012, Shanghai University of Sport. Production and hosting by Elsevier B.V. All rights reserved.

Prognostic Significance of Hormonal Receptor Status of Malignant Ovarian Tumors

The objective of this study is to investigate hormonal receptor status of MOT （malignant ovarian tumor） and to evaluate its clinical and prognostic significance. Retrospective analysis of the case reports of 284 patients with MOT of different histogenesis, stages I-IV, and immunohistochemical study of paraffin-embedded tissues were performed. Hormonal receptor status of tumors with different morphology genesis was studied and hormonal receptor phenotype of serous OC （ovarian cancer） was determined. The analysis of correlation between the expression of steroid hormone receptors （receptors to estrogens （ER）, progesterone （PR） and testosterone （TR）） in ovarian tumors, histological type of tumors and clinical morphological parameters were performed. Overall and relapse-free survival rates of the patients with serous OC depending on the hormonal receptor phenotype of the tumor were assessed. Presence of positive expression of steroid hormone receptors in serous OC （ER-66.4%, PR^53.4%, TR-53.0%）, mucinous OC （ER-88.0%, PR-84.0%, TR-60.0%） and in sex cord stromal tumors （ER-74.1%, PR and TR-77.8%） is proved by correlation of all steroid receptors expression with morphology type of ovarian tumors （ER - r = 0.4; PR - r = 0.4; TR - r = 0.3; p 〈 0.05）. Direct correlation between hormonal receptor phenotype of serous OC and the age period of the patients was established （r = 0.5; p = 0.002）： postmenopausal women patients reported the most increased frequency of serous OC with positive hormonal receptor tumor phenotypes （52.4%）, in particular during their late post-menopausal period （39.0%）. Significantly low overall survival among the patients with positive hormonal receptor phenotype of serous OC was recorded （29.5±3.4%） in comparison with the same score in the patients with negative phenotype of tumors （44.5±3.7%） （p 〈 0.05）. Multifactor analysis of Cox-regression model has defined that positive hormonal receptor phenotype of serous OC increases the risk of disease relapse （HR 1.4; 95.0% CI 1.1-1.7）, significantly decreases overall survival rates in the patients （HR 1.4; 95.0% CI 1.1-1.8）. Positive hormonal receptor status of MOT is an independent factor of unfavorable clinical progress of tumor process which can be regarded as the criterion for development of the methods of hormonal therapy application in complex treatment of the patients, and demands further large-scale multi-center studies in that direction.

Efficient estimation for additive hazards regression with bivariate current status data

This paper discusses efficient estimation for the additive hazards regression model when only bivariate current status data are available. Current status data occur in many fields including demographical studies and tumorigenicity experiments （Keiding, 1991; Sun, 2006） and several approaches have been proposed for the additive hazards model with univariate current status data （Linet M., 1998; Martinussen and Scheike, 2002）. For bivariate data, in addition to facing the same problems as those with univariate data, one needs to deal with the association or correlation between two related failure time variables of interest. For this, we employ the copula model and an efficient estimation procedure is developed for inference. Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations. An illustrative example is provided.