A limit approaching pop-up message reduces gambling expenditures,except among players with a financially focused self-concept

Responsible gambling (RG) tools that guide electronic gaming machine (EGM) players to set a pre-set money limit on their gambling expenditures are known to reduce excessive gambling. However, not all EGM players who use a limit-setting RG tool will adhere to their limit. We hypothesized that limit adherence is facilitated by informing players that their limit is approaching (and when their limit is reached), but undermined by a financially focused self-concept (FFS). Accordingly, EGM players (N = 88) were provided seed funds to gamble with on a slot machine in a simulated virtual reality casino. They were randomly assigned to receive a limit reminder both when their limit was approaching and again when their limit was reached (experimental condition) or just when their limit was reached (control condition). Players in the experimental condition were more likely to stop playing before reaching their money limit compared to players in the control condition. However, this was observed among players who are low, but not high, in FFS. Unexpectedly, condition (control vs experimental) was unrelated to playing beyond the money limit and FFS did not moderate this relation. Results suggest that individual difference factors, like FFS, can undermine the utility of RG tools.     

Negotiating With the Growth Machine: Community Benefits Agreements and Value‐Conscious Growth

Scholars have extended, challenged, and molded growth machine theory to examine growth—in terms of population, basic industry, labor force participation, commerce, financial activity, and land development—in a variety of contexts. The theory's core, however, has remained the same: cities are conceptualized as growth machines, which consist of unified and powerful growth coalitions. These coalitions pursue a pro‐growth agenda, seeking to enhance the exchange value of local land and property. They often face opposition from local residents, who are more oriented toward use values of land. Resident opposition, however, tends to be unsuccessful in the face of large‐scale commercial development. Aware of this, communities across the country are pursuing new strategies to address development projects in their backyards. In particular, some have formed coalitions to negotiate for benefits from developers through legally binding community benefits agreements (CBAs). Drawing from a case study of Pittsburgh's first CBA, this article analyzes the implications of CBAs for pro‐growth agendas. Pittsburgh's CBA surrounded the construction of a professional sports facility, a development project that presents an ideal example of growth processes in today's cities. Ultimately, CBAs can achieve “value‐conscious” growth, but they do not fundamentally alter dominant standards of growth or growth machine processes.     

新型注射机控制系统设计

介绍了用S5－115U型PC对注射机实行闭环控制的原理,软件、硬件组成等内容,用本系统控制的注射机具有控制功能多、精度多、可靠性强、灵活方便等特点。     

我国英语翻译教学研究:问题与对策

对近年来我国翻译教学研究的相关文献调查分析后发现,目前我国英语翻译教学研究在教学研究内容、研究途径和方法以及教学指导思想等四个方面存在着一些不足,即翻译教学研究学科单一化,翻译教学研究手段单一化,翻译教学研究定位笼统化和翻译教学研究内容技能化。作者在分析了相关的原因背景基础之上简要概述了相应的对策。     

Switching nonparametric regression models

We propose a methodology to analyse data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z. The states form a stochastic process and their possible values are j=1,?…?, J. If z equals j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. We develop an Expectation–Maximisation algorithm to estimate the parameters of the latent state process and the functions corresponding to the J states. We also obtain standard errors for the parameter estimates of the state process. We conduct simulation studies to analyse the frequentist properties of our estimates. We also apply the proposed methodology to the well-known motorcycle dataset treating the data as coming from more than one simulated accident run with unobserved run labels.     

Data Mining and Knowledge Discovery in Databases – An Overview

Data mining seeks to extract useful, but previously unknown, information from typically massive collections of non-experimental, sometimes non-traditional data. From the perspective of statisticians, this paper surveys techniques used and contributions from fields such as data warehousing, machine learning from artificial intelligence, and visualization as well as statistics. It concludes that statistical thinking and design of analysis, as exemplified by achievements in clinical epidemiology, may fit well with the emerging activities of data mining and 'knowledge discovery in databases' (DM&KDD).     

MML clustering of multi-state,Poisson, von Mises circular and Gaussian distributions

Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also statistically consistent and efficient. We provide a brief overview of MML inductive inference (Wallace C.S. and Boulton D.M. 1968. Computer Journal, 11: 185–194; Wallace C.S. and Freeman P.R. 1987. J. Royal Statistical Society (Series B), 49: 240–252; Wallace C.S. and Dowe D.L. (1999). Computer Journal), and how it has both an information-theoretic and a Bayesian interpretation. We then outline how MML is used for statistical parameter estimation, and how the MML mixture modelling program, Snob (Wallace C.S. and Boulton D.M. 1968. Computer Journal, 11: 185–194; Wallace C.S. 1986. In: Proceedings of the Nineteenth Australian Computer Science Conference (ACSC-9), Vol. 8, Monash University, Australia, pp. 357–366; Wallace C.S. and Dowe D.L. 1994b. In: Zhang C. et al. (Eds.), Proc. 7th Australian Joint Conf. on Artif. Intelligence. World Scientific, Singapore, pp. 37–44. See http://www.csse.monash.edu.au/-dld/Snob.html) uses the message lengths from various parameter estimates to enable it to combine parameter estimation with selection of the number of components and estimation of the relative abundances of the components. The message length is (to within a constant) the logarithm of the posterior probability (not a posterior density) of the theory. So, the MML theory can also be regarded as the theory with the highest posterior probability. Snob currently assumes that variables are uncorrelated within each component, and permits multi-variate data from Gaussian, discrete multi-category (or multi-state or multinomial), Poisson and von Mises circular distributions, as well as missing data. Additionally, Snob can do fully-parameterised mixture modelling, estimating the latent class assignments in addition to estimating the number of components, the relative abundances of the parameters and the component parameters. We also report on extensions of Snob for data which has sequential or spatial correlations between observations, or correlations between attributes.     

Monotone support vector quantile regression

Quantile regression (QR) models have received a great deal of attention in both the theoretical and applied statistical literature. In this paper we propose support vector quantile regression (SVQR) with monotonicity restriction, which is easily obtained via the dual formulation of the optimization problem. We also provide the generalized approximate cross validation method for choosing the hyperparameters which affect the performance of the proposed SVQR. The experimental results for the synthetic and real data sets confirm the successful performance of the proposed model.     

机床导轨液压爬行的原因及解决措施

分析了机床导轨爬行运动的危害 ,产生爬行运动的原因及解决措施     

贵金属期货价格预测方法及实证研究

本文融合了二次分解与极限学习机的优势,提出了VMD-Res.-EEMD-ELM贵金属期货价格预测模型,选择变分模态分解(VMD)作为主要的分解技术,生成模态分量序列(VMFi)和残差序列(Res.),采用集合经验模态分解(EEMD)对残差序列进行二次分解,并使用具有良好泛化能力的极限学习机(ELM)对各分量进行预测,最后叠加各模态分量和残差的预测值形成收益率的最终预测结果。所提出的模型不仅充分发挥了二次分解技术的优势,而且解决了传统变分模态分解组合预测模型未考虑残差影响因素的问题。实证研究表明,本文所提出的组合模型能够全面捕捉黄金、白银期货价格日收益率序列的特征,方向性预测准确率分别为83.33%和93.33%,误差指标MAE分别为0.15和0.11,经比较本文所提出的模型具有良好的预测性能。