“无意识”与“有选择”:大学生注意力分布样态及机制——基于大学生关注的热点话题分析

随着移动互联网技术的发展和应用,社会注意力的供需主体均发生了深刻变化,大学生群体在这一过程中表现出新的显著特征。本研究从大学生日常关注的热点话题入手,结合多轮次问卷调查和深度访谈结果,研究探讨大学生注意力分布的样态和机制。研究发现,当前大学生注意力在主观能动和媒介导向的双重互构下,呈现出“无意识”与“有选择”相互交织的现实样态。建议进一步提升大学生注意力管理的自主自觉意识,积极关注回应大学生的心理需求和偏好,提升大学生的价值辨别能力和媒介素养。

1939～1944年西康省康属各县宗教样态的社会学分析

西康正式建省于1939年元旦,民国时期存续10年。1955年撤销西康省建制。文章依托文献史料,以社会学视角和社会学方法研究民国时期西康正式建省后的第一个五年(1939～1944年)期间的宗教样态。研究表明,1939～1944年间,西康省康属各县宗教样态如下:第一,各县域间寺庙分布不平衡,僧侣规模不平衡;第二,藏传佛教寺庙的分布和教派成分的构成,呈现明显的地域性;第三,各县寺庙总量和僧徒规模之间具不可逆性的相关关系。

美国在线高等教育发展规模的样态分布、影响因素及启示——基于2016和2017年在线教育联盟调查报告的分析

在线教育联盟2016和2017年调查报告显示,美国在线高等教育自2012年进入拐点后,已然从爆发式增长过渡到稳定性增长的发展模式,具体表现为总体规模增速减缓、校际分布高度集中、学历层次本科为主、生源跨区流动较低等。导致美国在线高等教育发展规模下滑的影响因素主要来自政府层面的资助政策、专业层面的价值判断和市场层面的经济环境。美国在线高等教育十五年来发展的路径和经验可为我国提供有益借鉴。

THE RESEARCH OF GRADATION FUSION ALGORITHM BASED ON MULTISENSOR ASYNCHRONOUS SAMPLING SYSTEM

This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.

Climate Change Adaptation Planning for Biodiversity Conservation:A Review

Climate change has been linked to well-documented changes in physiology, phenology, species distributions, and in some cases, extinction. Projections of future change point to dramatic shifts in the states of many ecosystems. Aceommodating these shifts to effectively conserve biodiversity in the context of uncertain climate regimes represents one of the most difficult challenges faced by conservation planners. A number of adaptation strategies have been proposed for managing species and ecosystems in a changing climate. However, there has been little guidance available on integrating climate change adaptation strategies into contemporary conservation planning frameworks. The paper reviews the different approaches being used to integrate climate change adaptation into conservation planning, broadly categorizing strategies as continuing and extending on ＂best practice＂ principles and those that integrate species vulnerability assessments into conservation planning. We describe the characteristics of a good adaptation strategy emphasizing the importance of incorporating clear principles of flexibility and efficiency, accounting for uncertainty, integrating human response to climate change and understanding trade-offs.

Checking for normality in linear mixed models

Linear mixed models are popularly used to fit continuous longitudinal data, and the random effects are commonly assumed to have normal distribution. However, this assumption needs to be tested so that further analysis can be proceeded well. In this paper, we consider the Baringhaus-Henze-Epps-Pulley （BHEP） tests, which are based on an empirical characteristic function. Differing from their case, we consider the normality checking for the random effects which are unobservable and the test should be based on their predictors. The test is consistent against global alternatives, and is sensitive to the local alternatives converging to the null at a certain rate arbitrarily close to 1/V~ where n is sample size. ^-hlrthermore, to overcome the problem that the limiting null distribution of the test is not tractable, we suggest a new method： use a conditional Monte Carlo test （CMCT） to approximate the null distribution, and then to simulate p-values. The test is compared with existing methods, the power is examined, and several examples are applied to illustrate the usefulness of our test in the analysis of longitudinal data.

Corrected-loss estimation for Error-in-Variable partially linear model

We consider an Error-in-Variable partially linear model where the covariates of linear part are measured with error which follows a normal distribution with a known covariance matrix. We propose a corrected-loss estimation of the covariate effect. The proposed estimator is asymptotically normal. Simulation studies are presented to show that the proposed method performs well with finite samples, and the proposed method is applied to a real data set.

A robust new metric of phenotypic distance to estimate and compare multiple trait differences among populations

Whereas a rich literature exists for estimating population genetic divergence, metrics of phenotypic trait divergence are lacking, particularly for comparing multiple traits among three or more populations. Here, we review and analyze via simula- tion Hedges＇ g, a widely used parametric estimate of effect size. Our analyses indicate that g is sensitive to a combination of unequal trait variances and unequal sample sizes among populations and to changes in the scale of measurement. We then go on to derive and explain a new, non-parametric distance measure, ＇Aft＇, which is caiculated based upon a joint cumulative distribution function （CDF） from all populations under study. More precisely, distances are measured in terms of the percentiles in this CDF at which each population＇s median lies. Ap combines many desirable features of other distance metrics into a single metric; namely, compared to other metrics, p is relatively insensitive to unequal variances and sample sizes among the populations sam- pied. Furthermore, a key feature of Ap--and our main motivation for developing it--is that it easily accommodates simultaneous comparisons of any number of traits across any number of populations. To exemplify its utility, we employ Ap to address a ques- tion related to the role of sexual selection in speciation： are sexual signals more divergent than ecological traits in closely related taxa？ Using traits of known function in closely related populations, we show that traits predictive of reproductive performance are indeed, more divergent and more sexually dimorphic than traits related to ecological adaptation [Current Zoology 58 （3）： 426-439 2012].

Asymptotic properties of plug-in level set estimators for right censored data

We assume T1,..., Tn are i.i.d. data sampled from distribution function F with density function f and C1,...,Cn are i.i.d. data sampled from distribution function G. Observed data consists of pairs （Xi, δi）, em= 1,..., n, where Xi = min{Ti,Ci}, δi = I（Ti 6 Ci）, I（A） denotes the indicator function of the set A. Based on the right censored data {Xi, δi}, em=1,..., n, we consider the problem of estimating the level set {f 〉 c} of an unknown one-dimensional density function f and study the asymptotic behavior of the plug-in level set estimators. Under some regularity conditions, we establish the asymptotic normality and the exact convergence rate of the λg-measure of the symmetric difference between the level set {f ≥ c} and its plug-in estimator {fn ≥ c}, where f is the density function of F, and fn is a kernel-type density estimator of f. Simulation studies demonstrate that the proposed method is feasible. Illustration with a real data example is also provided.