The metabolism of methadone by cultured mammalian cells.

Rat hepatoma tissue culture cells and mouse leukemic cells were found to metabolize [1-3H] methadone to at least 2 unidentified radioactive compounds. These results suggest that cultured cells may be useful models for studying methadone metabolism by specific cell types.     

改进果蝇优化算法优化广义回归神经网络的预测问题研究

为提高传统非线性预测模型的预测精度,提出一种基于改进果蝇优化算法优化广义回归神经网络的预测方法,将果蝇群体分两部分分别进行迭代寻优,从而改进了果蝇优化算法的寻优性能,进而避免了在寻优过程中陷入局部最优。该方法利用改进果蝇优化算法优化广义回归神经网络的径向基函数扩展参数,然后用训练好的广义回归神经网络预测模型进行预测,最后通过订单预测算例进行实证研究。实证研究结果显示,该方法在解决订单预测问题中与未改进的果蝇优化算法优化广义回归神经网络和传统的广义回归神经网络方法对比,具有更高的预测精度和更好的非线性拟合能力。     

Using social media mining technology to improve stock price forecast accuracy

Many stock investors make investment decisions based on stock-price-related chip indicators. However, in addition to quantified data, financial news often has a nonnegligible impact on stock price. Nowadays, as new reviews are posted daily on social media, there may be value in using web opinions to improve the performance of stock price prediction. To this end, we use logistic regression to screen the chip indicators and establish a basic stock price prediction model. Then, we employ text mining technology to quantify the unstructured data of social media opinions on stock-related news into sentiment scores, which are found to correlate significantly with the change extent of the stock price. Based on the findings that the higher the sentiment scores, the lower the prediction accuracy of the logistic regression model, we propose an improved prediction approach that integrates sentiment scores into the logistic regression model. Our results show that the proposed model can improve the prediction accuracy for stock prices, and can thus provide a new reference for investment strategies for stock investors.     

A semiparametric method for predicting bankruptcy

Bankruptcy prediction methods based on a semiparametric logit model are proposed for simple random (prospective) and case–control (choice‐based; retrospective) data. The unknown parameters and prediction probabilities in the model are estimated by the local likelihood approach, and the resulting estimators are analyzed through their asymptotic biases and variances. The semiparametric bankruptcy prediction methods using these two types of data are shown to be essentially equivalent. Thus our proposed prediction model can be directly applied to data sampled from the two important designs. One real data example and simulations confirm that our prediction method is more powerful than alternatives, in the sense of yielding smaller out‐of‐sample error rates. Copyright © 2007 John Wiley & Sons, Ltd.     

The metabolism of methadone by cultured mammalian cells

Summary Rat hepatoma tissue culture cells and mouse leukemic cells were found to metabolize [1-³H] methadone to at least 2 unidentified radioactive compounds. These results suggest that cultured cells may be useful models for studying methadone metabolism by specific cell types.This study was supported by United States Public Health Service Research Grant No. MH 15622. The illustrations were prepared by Diane J. Kastelic.     

基于改进神经网络的厚煤层采煤方法预测模型

针对厚煤层采煤方法选择多目标非线性的问题,在影响因素分析的基础上,建立了预测仿真模型,利用神经网络改进算法训练网络,通过早停的方式解决网络过拟合问题。通过计算机仿真结合现场应用表明,该模型给出了最优方案,可为厚煤层采煤方法的合理选择和工作面主要经济技术指标的预测提供一种新的研究思路,在煤矿开采中具有广阔的应用前景。     

How accurate are the U.S. Bureau of the Census projections of total live births

"The accuracy of total live birth forecasts issued by the U.S. Bureau of the Census was analysed. Forecast accuracy has not improved significantly since 1950. Further, the forecasts are not more accurate than several naive alternatives. Moving from a period methodology to a cohort methodology improved forecast accuracy for certain forecasts. [It is demonstrated that] the Bureau of the Census systematically underestimated total births in the upswing and overestimated in the downswing."     

Removing Forecasting Errors with White Gaussian Noise after Square Root Transformation

An analytical model has been developed in the present paper based on a square root transformation of white Gaussian noise. The mathematical expectation and variance of the new asymmetric distribution generated by white Gaussian noise after a square root transformation are analytically deduced from the preceding four terms of the Taylor expansion. The model was first evaluated against numerical experiments and a good agreement was obtained. The model was then used to predict time series of wind speeds and highway traffic flows. The simulation results from the new model indicate that the prediction accuracy could be improved by 0.1–1% by removing the mean errors. Further improvement could be obtained for non‐stationary time series, which had large trends. Copyright © 2016 John Wiley & Sons, Ltd.     

顺煤层水力割缝抽采煤层瓦斯渗流规律数值模拟

为得到顺煤层水力割缝抽采瓦斯渗流规律以及预测水力割缝后瓦斯气体抽采量,结合渗流力学和弹塑性力学理论,建立了水力割缝抽采煤层瓦斯的固流耦合数学模型,同时给出了相应初始条件及边界条件。利用Madab计算得到顺煤层水力割缝后煤体应力场、瓦斯压力场以及瓦斯抽采量变化规律。数值模拟结果显示：水力割缝后,煤体有效体积应力得到释放;沿割缝方向储层卸压效果明显;煤层内裂隙、裂缝数量增加,长度和张开度增大;煤体渗透性能增强;煤层气抽采量较普通钻孔有较大幅度的提高。模拟结果显示了顺煤层水力割缝抽采煤层气的优势。此模拟方法对煤层气增产工业中确定水力割缝工艺参数以及预测瓦斯产量具有重要的指导意义。     

A new insight into combining forecasts for elections: The role of social media

This study is devoted to gain insight into a timely, accurate, and relevant combining forecast by considering social media (Facebook), opinion polls, and prediction markets. We transformed each type of raw data into the possibility of victory as a forecasting model. Besides the four single forecasts, namely Facebook fans, Facebook “people talking about this” (PTAT) statistics, opinion polls, and prediction markets, we generated three combined forecasts by associating various combinations of the four components. Then, we examined the predictive performance of each forecast on vote shares and the elected/non‐elected outcome across the election period. Our findings, based on the evidence of Taiwan's 2018 county and city elections, showed that incorporating the Facebook PTAT statistic with polls and prediction markets generates the most powerful forecast. Moreover, we recognized the matter of the time horizons where the best proposed model has better accuracy gains in prediction—in the “late of election,” but not in “approaching election”. The patterns of the trend of accuracy across time for each forecasting model also differ from one another. We also highlighted the complementarity of various types of data in the paper because each forecast makes important contributions to forecasting elections.     

The protein - only theory and the yeast Saccharomyces cerevisiae: the prions and the propagons

The yeast prions represent a very attractive and tractable model for investigating the prion world. The more extensively studied yeast prion [PSI] leads to a propagation model that links auto-aggregation in amyloid formation and inactivation of the cellular function of the yeast 'prion protein' Sup35p. The other prion model, [URE3], appears to be similar in some genetic and biochemical properties. The characterisation of both Sup35p and Ure2p, the two 'prion proteins', mainly focusing on their aggregation properties, support this model. However, some important differences still exist that should be examined carefully. In particular, we have shown that Ure2p aggregation in vivo (monitored by fluorescence of Ure2-GFP fusion) does not necessarily give rise to a [URE3] phenotype. Comparisons of these two systems as well as more recent experiments are discussed in this review.     

Combining var estimation and state space model reduction for simple good predictions

A combination of VAR estimation and state space model reduction techniques are examined by Monte Carlo methods in order to find good, simple to use, procedures for determining models which have reasonable prediction properties. The presentation is largely graphical. This helps focus attention on the aspects of the model determination problem which are relatively important for forecasting. One surprising result is that, for prediction purposes, knowledge of the true structure of the model generating the data is not particularly useful unless parameter values are also known. This is because the difficulty in estimating parameters of the true model causes more prediction error than results from a more parsimonious approximate model.     

神经树在煤与瓦斯突出预测中应用研究

由于煤与瓦斯突出影响因素之间存在着复杂的非线性关系,为准确预测煤与瓦斯突出的危险性,本文提出了基于柔性神经树的煤与瓦斯突出预潮模型,其中利用多表达式编程和粒子群优化算法分别优化了自身的结构及相关参数,使得神经树具有强大的预测和分类能力,与传统神经网络相比具有更加灵活的自动优化能力.通过采用实测数据对算法进行了验证. 结果 表明与常规预测方法相比较,该模型的预测准确性高,具有良好的适应性和有效性.     

岷江源头区水土流失模拟计算与动态分析

通过对卫星遥感图片的解译以及ArcGIS软件的应用,借助已有水文数据和通用水土流失方程(USLE)模拟计算了岷江源头区1974年、1994年以及2002年的年水土流失总量。同时,按照国家水利部公布的水土流失强度标准,将该区域分为无流失区、轻度流失区、中度流失区和强度流失区四个水土流失等级。结果表明:(1)该区域年水土流失总量从1974年到2002年呈下降趋势,1994年~2002年减少的速度远远大于前20年。(2)研究区域的水土流失强度有从流失严重的等级向流失轻微的等级转化的趋势,从转化面积来看,主要是由轻度流失区向无流失区转化以及中度流失区向轻度流失区转化。从转化比例来看,强度流失区减少得最多,大多转化为中度流失区。     

Should fixed coefficients be re-estimated every period for extrapolation?

This paper demonstrates that forecast accuracy is not necessarily improved when fixed-coefficient models are sequentially re-estimated and used for prediction, after updating the database with the latest observation(s). It is argued that although sequential estimation may minimize the variances of predictors based on some classes of estimators, sequential estimation does not necessarily yield accurate predictions (i.e. predictions that are close to actual realizations). Minimizing the mean squared prediction error about the actual realization is a necessary condition for maximizing the probability that one predictor is more accurate than others. This minimization need not require, and may even exclude, the most recent data. It has been shown by an example that a prediction based on a nonsequential estimate of a stochastically varying coefficient model is superior to predictions based on several sequential estimates of the fixed-coefficient models, including a random walk model.     

Forecasting the Nikkei spot index with fractional cointegration

We investigate the forecast performance of the fractionally integrated error correction model against several competing models for the prediction of the Nikkei stock average index. The competing models include the martingale model, the vector autoregressive model and the conventional error correction model. We consider models with and without conditional heteroscedasticity. For forecast horizons of over twenty days, the best forecasting performance is obtained for the model when fractional cointegration is combined with conditional heteroscedasticity. Our results reinforce the notion that cointegration and fractional cointegration are important for long‐horizon prediction. Copyright © 1999 John Wiley & Sons, Ltd.     

A Bayesian nonlinear support vector machine error correction model

The use of linear error correction models based on stationarity and cointegration analysis, typically estimated with least squares regression, is a common technique for financial time series prediction. In this paper, the same formulation is extended to a nonlinear error correction model using the idea of a kernel‐based implicit nonlinear mapping to a high‐dimensional feature space in which linear model formulations are specified. Practical expressions for the nonlinear regression are obtained in terms of the positive definite kernel function by solving a linear system. The nonlinear least squares support vector machine model is designed within the Bayesian evidence framework that allows us to find appropriate trade‐offs between model complexity and in‐sample model accuracy. From straightforward primal–dual reasoning, the Bayesian framework allows us to derive error bars on the prediction in a similar way as for linear models and to perform hyperparameter and input selection. Starting from the results of the linear modelling analysis, the Bayesian kernel‐based prediction is successfully applied to out‐of‐sample prediction of an aggregated equity price index for the European chemical sector. Copyright © 2006 John Wiley & Sons, Ltd.     

Evaluation of the predictive performance of biased regression estimators

Regression models are widely used in forecasting, either directly as prediction equations, or indirectly as the basis of other procedures. The predictive performance of a regression model can be adversely affected by both multicollinearity and high-leverage data points. Although biased estimation procedures have been proposed as an alternative to least squares, there has been little analysis of the predictive performance of the resulting equations. This paper discusses the predictive performance of various biased estimators, emphasizing the concept that the predictive region, as well as the strength of the multicollinearity, dictates the choice of appropriate coefficient estimators.     

Comparison of the effects of imidazo[1,2-a]pyridine-2-carbamates and benzimidazole-2-carbamates on the development of Hymenolepis nana in Tribolium confusum

The anthelmintic properties of several imidazo[1,2-a]pyridine carbamates and benzimidazole carbamates against Hymenolepis nana are compared. The results of this study, coupled with previous work, indicate that methyl 6-(trichloroethenyl)-imidazo[1,2-a]pyridine-2-carbamate has the potential of being a broad spectrum anthelmintic effective against both nematodes and cestodes.     

A predictive model of train delays on a railway line

Delay prediction is an important issue associated with train timetabling and dispatching. Based on real-world operation records, accurate forecasting of delays is of immense significance in train operation and decisions of dispatchers. In this study, we established a model that illustrates the interaction between train delays and their affecting factors via train describer records on a Dutch railway line. Based on the main factors that affect train delay and the time series trend, we determined the independent and dependent variables. A long short-term memory (LSTM) prediction model in which the actual delay time corresponded to the dependent variable was established via Python. Finally, the prediction accuracy of the random forest model and artificial neural network model was compared. The results indicated that the LSTM model outperformed the other two models.