Dn-最优确切设计的改进单纯形构造法及其在焊接工艺中的应用

详细地研究了Dn 最优确切设计的数值构造法以及对称算法理论,对Evans的单纯形搜索来构造D 最优设计的方法进行了改进·应用改进的Fibonacci技巧来求新增设计点并引入负测度,采取双循环多点迭代的方法来构造多分量对数项混料模型的Dn 最优确切设计,提出了Dn 最优确切设计的改进单纯形构造法;并运用此新方法构造了多分量对数项混料模型的Dn 最优确切设计·将构造出的Dn 最优确切设计,应用在焊接工艺的配料比中,得到较好的预测与回归效果·从而有力地证明了该算法的有效性及Dn 最优确切设计的实际应用价值·     

土坯力学性能及受压本构模型研究

为获取传统土坯受压应力-应变特性,确定其数学表达式,针对湿制法和干打法制作的土坯开展了单轴抗压试验和三点抗折试验。分别从抗压、抗折强度、破坏机理以及应力-应变关系等方面分析了两类土坯的力学性能。结果表明,普通砌墙砖的抗压及抗折试验方法均适用于传统土坯,干打土坯的抗压强度为湿制土坯的3倍,抗折强度为湿制土坯的1.3倍,但湿制土坯的断裂能为干打土坯的2.5倍。受压初期,干打土坯的应力-应变曲线存在因土料压密而导致的下凹段,而湿制土坯未表现出该特性。基于两类土坯单轴受压状态下的应力-应变曲线特征,提出了土坯单轴受压本构模型,该本构模型与试验数据吻合较好,可用于土坯砌体结构的数值模拟研究。     

张衡和托勒密的地心说与中西方科学模式差异

在中西方科学史中都曾经形成了以地心说占主流的天文学体系,分别以同时代中国张衡的浑天说理论和古希腊托勒密的本轮-均轮说理论为代表.这两种体系的组成理论及天文模式完全不同,反映了中西方科学模式存在的差异.西方科学技术注重形式逻辑、定量描述和理性思维模式,古代中国则发展辩证逻辑,以定性描述为主,采用感性思维模式.     

数字普惠金融如何影响农村居民创业活动来自中国家庭追踪调查（CFPS）的证据

本研究基于中国家庭动态追踪调查数据和数字普惠金融发展指数,实证检验了数字普惠金融对农村居民创业活动的影响。结果表明：数字普惠金融可显著促进农村居民创业活动;互联网信息获取能力及电子商务重要性感知可作为解释机制;家庭创业榜样可调节电子商务重要性感知与农村居民创业活动之间的关系。随着信息科技的发展,数字普惠金融在融资等方面展现出独有优势,这就为促进农村居民创业、实现经济均衡发展提供了新的思路。     

Function approximation for incompletely specified regression models

This report extends earlier work by Brailovsky on regression theory and methodology, giving particular emphasis to function approximation for incompletely specified models. The interest here is with situations where the form of the regression relation is not known in advance. We discuss several difficulties that arise in using local approximation and linear regression methods, and propose ways to overcome these problems. To aid the data analyst in developing a suitable model, an illustrative table is derived for determining the number of initial explanatory functions justifiable for a given prespecified confidence level. The general approach formulated here is illustrated with an application to medical data. Relevance to classification and possible extensions are discussed.     

Unobserved components in ARCH models: An application to seasonal adjustment

The paper deals with unobserved components in ARIMA models with GARCH errors, in the context of an actual application, namely seasonal adjustment of the monthly Spanish money supply series. The series shows clear evidence of (moderate) non-linearity, which does not disappear with simple outlier correction. The GARCH structure explains reasonably well the non-linearity, and this explanation is robust with respect to the GARCH specification. We look at the time variation of the standard error of the adjusted series estimator and show how it can be measured. Next, we look at the implications this variation has on short-term monetary control. The non-linearity seems to have a small effect in practice. It is further seen that the conditional variance of the GARCH process may, in turn, be decomposed into components. In fact, the conditional variance of the money supply series is the sum of a weak linear trend, a strong non-linear seasonal component, and a moderate non-linear irregular component. This information has policy implications: for example, there are periods in the year when policy can be more assertive because information is more precise. Finally, looking at the non-linear components of the money supply it is seen how linear combinations of non-linear series can produce series that behave linearly.     

Forecasting the Antwerp maritime traffic flows using transformations and intervention models

This article uses univariate time-series models with data transformations and intervention models to forecast the volumes of twenty-two maritime traffic flows in the port of Antwerp which are expressed in tonnes. The models obtained produce forecasts that are a substantial improvement over those obtained with unadjusted data. The models also provide useful insight into the behaviour of maritime traffic flows during the period 1971–82.     

Existence of macroscopic spatial superpositions in collapse theories

It is shown that the collapse dynamics in the CSL model will entangle two independent systems under certain conditions, and their state after collapse may be an entangled superposition of spatially separated states. However, since the conditions can hardly be satisfied in reality, the occurrence of such superpositions is very improbable, and thus collapse theories still provide a promising solution to the measurement problem.     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

An ensemble of LSTM neural networks for high‐frequency stock market classification

We propose an ensemble of long–short‐term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large‐cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.