外语能力与科学研究的密切联系——周立伟院士访谈

近日对我国著名电子光学和光电子成像专家周立伟院士进行了访谈。访谈中，周院士首先分享了自己学习英语和俄语的经历，强调了自主学习能力的重要性；其次，周院士详细论述了外语教学，尤其是英语教学的必要性，指出英语能力与科学研究紧密相关，英语教学不可或缺；之后周院士分析了研究生在英语学习和科学研究方面存在的问题，并讨论了大学中开设人文课程的迫切性；最后，周院士对上理工师生提出了建议与期望。     

治理的发轫与嬗变：中国历史视野下的考察

国内学术界一般以西方学术发展史作为背景，把治理作为一种社会互动来进行研究。中国历史视野下的治理，发轫于中国社会生活，其内涵最初主要是为了获取生存资料，即有收成才能够形成秩序也就是治理。从“乂”这个象形文字来看，乂就是治理，其原意为“收割”。不过，围绕生存资料的生产和分配逐渐产生了更加丰富的社会权力关系，而巩固和拓展这种权力关系则使得构筑更加严格的秩序成为必然。神秘感是构筑秩序的重要依据，而赋予仪式以神秘感的则是觋、巫等知识群体，他们将天、帝、王、人通过仪式联系在一起，建构起一套以礼为基础的秩序。但是，西周末年，觋、巫的神秘感被世俗的君权摧毁以后，“君臣秩序”以及由此构筑起来的社会秩序都被颠覆，从而导致“君臣之礼既坏”和“礼坏乐崩”的情形。“以智力相雄长”不仅表现在王室内部、诸侯之间、社会层面上，而且也表现在知识分子身上。从此以后，士绝大多数以追逐爵禄而骄天下为荣，以至于两千多年来一直存在“士贱而君肆”的情形。“君士互动”的治理模式，正是随着神秘主义的礼的崩溃而崩溃的。     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

局部平稳部分线性面板数据模型的统计推断

部分线性模型是一类非常重要的半参数回归模型,由于它既含有参数部分又含有非参数部分,与常规的线性模型相比具有更强的适应性和解释能力。文章研究带有局部平稳协变量的固定效应部分线性面板数据模型的统计推断。首先提出一个两阶段估计方法得到模型中未知参数和非参数函数的估计,并证明估计量的渐近性质,然后运用不变原理构造出非参数函数的一致置信带,最后通过数值模拟研究和实例分析验证了该方法的有效性。     

工业劳动力流动、资本创造与经济地理空间均衡

资本创造模型（CC模型）忽视了要素流动对产业空间分布的影响。而发展的新的资本创造模型则认为资本集聚的过程必然伴随着工业劳动力的流动过程。另外，是资本的实际收益而不是名义收益决定资本是否创造。研究结果表明，随着贸易自由度、工业品支出份额及资本贴现率的变大，替代弹性及资本折旧率的变小，将降低对称结构的稳定性，而提高中心-外围结构的稳定性；经济地理空间的产业均衡是集聚力和分散力相互作用的结果。当企业生产工业品的规模报酬递增程度足够显著，或者工业品支出份额很高时，市场拥挤效应将彻底消失，并转化成为促进产业集聚的动力；突破点与持续点的大小比较可以形成不同的关系，这意味着随着贸易自由度的变化，本文发展的资本创造模型可以体现出多样化的产业空间动态演化行为。     

Consolidation and fragmentation in environmental scanning: A review and research agenda

Environmental scanning is a broadly defined concept, having first received attention from scholars in the late 1960s. Over the years a number of similar and overlapping constructs have emerged in management literature. The aim of this study, via a systematic review and thematic analysis of relevant empirical research, is to consolidate foundation environmental scanning knowledge, demonstrate how scanning research has developed and fragmented over time, and propose an agenda for future research. The first contribution of our review is a new typology of environmental scanning research made up of five discrete research views, which provides a more comprehensive and contemporary overview of the field than previous studies. The second is a proposed agenda for future research that explicitly acknowledges the role of technology, an area that is presently underdeveloped in foundation scanning literature. The third contribution is to signpost future directions for research on scanning and organisational performance using a number of theoretical perspectives. The overall outcome of our review is to move scanning research on from increasingly incremental contributions concerned with context to a place where the changing role of technology and the mechanisms through which environmental scanning contributes to competitive advantage can be more thoroughly understood.     

Midwives’ views of caseload midwifery – comparing the caseload and non-caseload midwives’ opinions. A cross-sectional survey of Australian midwives.

BackgroundMidwife-led continuity of care has substantial benefits for women and infants and positive outcomes for midwives, yet access to these models remains limited. Caseload midwifery is associated with professional satisfaction and lower burnout, but also impacts on work-life boundaries. Few studies have explored caseload midwifery from the perspective of midwives working in caseload models compared to those in standard care models, understanding this is critical to sustainability and upscaling.AimTo compare views of caseload midwifery – those working in caseload models and those in standard care models in hospitals with and without caseload.MethodsA national cross-sectional survey of midwives working in Australian public hospitals providing birthing services.FindingsResponses were received from 542/3850 (14%) midwives from 111 hospitals – 20% worked in caseload, 39% worked in hospitals with caseload but did not work in the model, and 41% worked in hospitals without caseload. Regardless of exposure, midwives expressed support for caseload models, and for increased access to all women regardless of risk. Fifty percent of midwives not working in caseload expressed willingness to work in the model in the future. Flexibility, autonomy and building relationships were positive influencing factors, with on-call work the most common reason midwives did not want to work in caseload.ConclusionsThere was widespread support for and willingness to work in caseload. The findings suggest that the workforce could support increasing access to caseload models at existing and new caseload sites. Exposure to the model provides insight into understanding how the model works, which can positively or negatively influence midwives’ views.     

Approximate composite marginal likelihood inference in spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.     

Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.