制造系统生产单元碳排放核算模型

制造业是我国国民经济的支柱产业，但也是环境污染物排放的主要来源、能源消耗主体和温室气体排放大户。制造系统高能耗、高物耗、高碳排放的加工过程是造成制造业碳排放量大的重要原因。生产单元作为制造系统的加工的主体，其碳排放量的核算是确定整个制造系统乃至制造业碳排放总量的关键。本文首先对生产单元的原材料、电能、辅助物料及废屑处理所引起的碳排放进行分析，确定生产单元的碳排放源；其次，产品合格率的不同会造成生产单元输入及输出的成品/半成品数量的差异，进而影响生产单元单位产品的碳排放量，在此基础上，综合考虑了原材料、电能、辅助物料及废屑处理的碳排放情况，构建了给定工艺流程下生产单元碳排放核算模型。最后，结合一汽车排气装置加工实例，分别核算加工过程中九个生产单元的碳排放量，验证了模型的可行性。     

初次收入分配对我国居民旅游消费影响研究

文章选取初次分配工资分配率、城镇居民基尼系数、各收入阶层旅游消费占比作为分析指标，经分析研究所得结论为：低、中收入阶层旅游消费占比与分配率负相关，分配率所代表的初次收入分配总量性影响对中收入阶层旅游消费影响较大，中、高收入阶层旅游消费占比都与城镇居民基尼系数正相关，其中城镇居民基尼系数代表的初次收入分配结构性影响对高收入阶层旅游消费影响较大；并针对各阶层的特殊性对各收入阶层旅游消费发展提出相应政策建议.     

Living with the parents: the purpose of young graduates’ return to the parental home in England

Young graduates in England often return to the parental home after a period of living away during their university studies. Little is known, however, about why they return and how coresidence with parents fits within a life trajectory. This paper reports upon an in-depth cross-sectional qualitative study of young graduates’ coresidence with their parents. It identifies a five-part typology of the purpose of coresidence as perceived by the graduates: a base camp for exploration before settling into adulthood; a launch pad for careers; a savings bank, in particular for future property purchases; a refuge for respite and reflection; and a preferred residence, whether on account of comfort, cultural practice or to support parents. The paper further explores how far these purposes were associated in young adults’ accounts with social structures, individual agency or some combination of these. It concludes that the default understanding of graduates’ return and coresidence as a residual function when other options fail is insufficient. Such a generalisation obscures the different purposes which the return can enable; it overplays some notion of a broken biography rather than the positive contribution of coresidence to graduates’ trajectories towards adulthood and to their life experiences.     

中国城镇居民家庭储蓄水平队列效应分析

利用中国家庭收入调查（ CHIP）数据对城镇居民家庭的储蓄率进行了队列效应的实证研究。分析结果表明，属于不同队列的城镇家庭在储蓄水平上都呈现出相似的“U”型年龄分布，即中年家庭（40－50岁）储蓄率低，年轻家庭（20－40岁）和退休家庭储蓄率较高，这一现象与其他国家的实证研究结果有显著不同。本文认为，中国城镇居民储蓄率的“U”型年龄分布主要是由人口与经济的高速增长带来的：人口高速增长带来的队列规模效应使得面对更大竞争压力的年轻人口储蓄率较高，而经济水平持续增长带来的消费水平增长预期使得接近退休家庭储蓄水平重新上升。     

Machine Learning Vasicek Model Calibration with Gaussian Processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.     

A new generalization of the Weibull-geometric distribution with bathtub failure rate

In this paper, the researchers attempt to introduce a new generalization of the Weibull-geometric distribution. The failure rate function of the new model is found to be increasing, decreasing, upside-down bathtub, and bathtub-shaped. The researchers obtained the new model by compounding Weibull distribution and discrete generalized exponential distribution of a second type, which is a generalization of the geometric distribution. The new introduced model contains some previously known lifetime distributions as well as a new one. Some basic distributional properties and moments of the new model are discussed. Estimation of the parameters is illustrated and the model with two known real data sets is examined.     

Aggregating classifiers via Rademacher–Walsh polynomials

Methods are proposed to combine several individual classifiers in order to develop more accurate classification rules. The proposed algorithm uses Rademacher–Walsh polynomials to combine M (≥2) individual classifiers in a nonlinear way. The resulting classifier is optimal in the sense that its misclassification error rate is always less than, or equal to, that of each constituent classifier. A number of numerical examples (based on both real and simulated data) are also given. These examples demonstrate some new, and far-reaching, benefits of working with combined classifiers.     

On analysis of binary response data in longitudinal factorial studies

Binary data are commonly used as responses to assess the effects of independent variables in longitudinal factorial studies. Such effects can be assessed in terms of the rate difference (RD), the odds ratio (OR), or the rate ratio (RR). Traditionally, the logistic regression seems always a recommended method with statistical comparisons made in terms of the OR. Statistical inference in terms of the RD and RR can then be derived using the delta method. However, this approach is hard to realize when repeated measures occur. To obtain statistical inference in longitudinal factorial studies, the current article shows that the mixed-effects model for repeated measures, the logistic regression for repeated measures, the log-transformed regression for repeated measures, and the rank-based methods are all valid methods that lead to inference in terms of the RD, OR, and RR, respectively. Asymptotic linear relationships between the estimators of the regression coefficients of these models are derived when the weight (working covariance) matrix is an identity matrix. Conditions for the Wald-type tests to be asymptotically equivalent in these models are provided and powers were compared using simulation studies. A phase III clinical trial is used to illustrate the investigated methods with corresponding SAS® code supplied.     

SROI in the pay for success context: Are they at odds?

The Pay For Success (PFS) and Social Impact Bond (SIB) movements to date have focused heavily on shorter-term outcomes that can be monetized and show clear savings to government entities. In part, this focus derives from the need to specify contract payments based on a narrow set of well measured outcomes (e.g., avoided days in jail and foster care, decreased use of behavioral health services). Meanwhile efforts to measure the social return on investment (SROI) of interventions have sought to expand the view of relevant outcomes to include domains that lend themselves less clearly to monetization. This paper explores the intersection between these two movements with illustrations from a SIB initiative underway focused on homeless families with children in foster care. Challenges and potential for SROI in a third-party payor environment will be discussed as well as opportunities to better leverage the strengths of both types of initiative.     

On additive-multiplicative hazards model

In survival analysis and reliability theory, a fundamental problem is the study of lifetime properties of a live organism or system. In this regard, there have been considered and studied several models based on different concepts of ageing such as hazard rate and mean residual life. In this paper, we consider an additive-multiplicative hazard model (AMHM) and study some reliability and ageing properties of the proposed model. We then specify the bivariate models whose conditionals satisfy AMHM. Several properties of the proposed bivariate model are investigated and adequacy of the model is evaluated based on a real data set.