造纸废水处理过程的高斯过程回归软测量建模

针对造纸废水处理系统的时变性、非线性和复杂性等特点,本文提出一种基于高斯过程回归的软测量模型.基于平方指数协方差、线性协方差和周期性协方差函数组合构建了7种高斯过程回归模型,分别对出水化学需氧量和出水悬浮固形物浓度进行回归预测.此外,还对比了多元线性回归模型、主成分回归模型、偏最小二乘模型、人工神经网络模型和高斯回归模型的预测效果.对比计算结果表明无论是对输出变量的训练拟合还是预测,高斯过程回归模型的拟合效果均优于非高斯过程回归模型.高斯过程回归模型的预测结果表明：对于出水化学需氧量,线性协方差函数与周期性协方差函数的组合模型可以取得最好的预测结果;对于出水悬浮固形物,平方指数协方差函数与线性协方差函数组合模型可以取得最好的预测结果.     

Thriving at work: A meta‐analysis

Thriving at work refers to a positive psychological state characterized by a joint sense of vitality and learning. On the basis of Spreitzer and colleagues' model, we present a comprehensive meta‐analysis of antecedents and outcomes of thriving at work (K = 73 independent samples, N = 21,739 employees). Results showed that thriving at work is associated with individual characteristics, such as psychological capital (r_c = .47), proactive personality (r_c = .58), positive affect (r_c = .52), and work engagement (r_c = .64). Positive associations were also found between thriving at work and relational characteristics, including supportive coworker behavior (r_c = .42), supportive leadership behavior (r_c = .44), and perceived organizational support (r_c = .63). Moreover, thriving at work is related to important employee outcomes, including health‐related outcomes such as burnout (r_c = ?.53), attitudinal outcomes such as commitment (r_c = .65), and performance‐related outcomes such as task performance (r_c = .35). The results of relative weights analyses suggest that thriving exhibits small, albeit incremental predictive validity above and beyond positive affect and work engagement, for task performance, job satisfaction, subjective health, and burnout. Overall, the findings of this meta‐analysis support Spreitzer and colleagues' model and underscore the importance of thriving in the work context.     

基于机器学习方法的安全帽佩戴行为检测

施工现场作业人员是否佩戴安全帽主要依靠人工检查,存在监管效率低、时效性差等问题,为了实时自动监管作业人员是否佩戴安全帽,提出1种基于机器学习的安全帽佩戴行为检测方法。首先利用深度学习YOLOv3算法检测出现场视频中的施工人员脸部位置,根据安全帽与人脸的关系估算出安全帽潜在区域;然后对安全帽潜在区域图像进行增强处理,使用HOG（方向梯度直方图）提取样本的特征向量;再利用SVM（机器学习的支持向量机）分类器对脸部上方是否有安全帽进行判断,进而实现对施工人员安全帽佩戴行为的实时检测与预警。以某高铁站施工现场为例进行验证,研究数据表明在施工通道和塔吊作业区域,该方法可实时有效检测出工人未佩戴安全帽的行为,识别率达90%。     

基于智能手机采集数据的施工活动识别

施工人员的活动监控对施工安全管理及预防职业疾病至关重要。为提升施工安全的智能化管理水平,对楼板钢筋工程施工中的8个主要活动进行识别。为弥补单一传感技术采集维度不足的缺陷和集成传感技术对系统灵活性的限制,采用智能手机内置加速度传感器和陀螺仪采集试验人员模拟施工人员活动时的加速度和倾角数据,并提取平均值、标准差、协方差、四分位距(IQR)为活动的特征矢量。通过决策树中的CART算法建立分类训练模型,采用“交叉验证法”对模型进行评估和验证。测试结果表明:对于样本个体的平均分类准确率为95.28%,预测准确率为92.86%;样本总体的分类准确率为89.67%,预测准确率为94.82%。研究表明,基于智能手机采集数据的决策树模型可以用于施工人员的活动识别。     

Knowledge Production and Learning for Sustainable Landscapes: Seven Steps Using Social–Ecological Systems as Laboratories

There are multiple challenges regarding use and governance of landscapes’ goods, functions and intangible values for ecosystem health and human well-being. One group of challenges is to measure and assess principal sustainability dimensions through performance targets, so stakeholders have transparent information about states and trends. Another group is to develop adaptive governance at multiple levels, and management of larger geographical areas across scales. Addressing these challenges, we present a framework for transdisciplinary research using multiple landscapes as place-based case studies that integrates multiple research disciplines and non-academic actors: (1) identify a suite of landscapes, and for each (2) review landscape history, (3) map stakeholders, use and non-use values, products and land use, (4) analyze institutions, policies and the system of governance, (5) measure ecological, economic, social and cultural sustainability, (6) assess sustainability dimensions and governance, and finally (7) make comparisons and synthesize. Collaboration, communication and dissemination are additional core features. We discuss barriers bridges and bridges for applying this approach.     

Eigenbehaviors: identifying structure in routine

Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James). An erratum to this article can be found at     

Developing a theory of change for a community‐based response to illegal wildlife trade

The escalating illegal wildlife trade (IWT) is one of the most high‐profile conservation challenges today. The crisis has attracted over US$350 million in donor and government funding in recent years, primarily directed at increased enforcement. There is growing recognition among practitioners and policy makers of the need to engage rural communities that neighbor or live with wildlife as key partners in tackling IWT. However, a framework to guide such community engagement is lacking. We developed a theory of change (ToC) to guide policy makers, donors, and practitioners in partnering with communities to combat IWT. We identified 4 pathways for community‐level actions: strengthen disincentives for illegal behavior, increase incentives for wildlife stewardship, decrease costs of living with wildlife, and support livelihoods that are not related to wildlife. To succeed the pathways, all require strengthening of enabling conditions, including capacity building, and of governance. Our ToC serves to guide actions to tackle IWT and to inform the evaluation of policies. Moreover, it can be used to foster dialogue among IWT stakeholders, from local communities to governments and international donors, to develop a more effective, holistic, and sustainable community‐based response to the IWT crisis.     

Evolution of disaster relief law under multiple transformations: Progressive learning or walking in a circle?

The aim of this paper is to examine the association between double-loop disaster learning and transformations of political systems. The particular question is how disasters increase the rapidity and complexity, with which the transforming regimes face the unsuitability and inadequacy of the past forms and processes of disaster governance. The centennial evolution of the disaster relief law at a country scale (Czech Republic, resp. Czechoslovakia, 1918-present) is used as an example. The thorough search in digital repository of Czech and Slovak Parliamentary Library was performed to obtain legislative norms and protocols from parliamentary debates, whereas major disasters that induced new laws (1927 flash flood, 1947 drought, 1997 and 2002 floods) were identified from existing databases and from detailed reconstructions of historical disastrous events. The described cases enabled to add new empirical evidence contesting (i) the process scheme of disaster politics during critical junctures and institutional transformations, and (ii) the progressive nature of the double-loop learning approach related to disasters. It is shown that the double-loop learning from disasters and its benefits should be considered as time-dependent and related to permissive and productive conditions that facilitated the adoption of the new disaster relief law (i.e. the learning process). Finally, the implications for the research agenda (historical disaster research) and for disaster policy (evaluation of disaster relief effectiveness) are summarized.     

Modulation of predator-prey interactions by the Allee effect

An Allee effect arising from density-dependent mating success can have significant impacts at the ecosystem level when considered in the context of predator-prey interactions. These are captured by a mathematical model for the exchange of biomass between a structured predator population (continuous weight distribution) and a resource. Because the predator’s mating success affects the amount of resources required for the production of offsprings and their future growth into mature organisms, it influences the flux of biomass between trophic levels. Under simple assumptions, the equations can be reduced to an equivalent unstructured predator-prey model in which the Allee effect modulates the predation rate: the mating probability multiplies the rate of predator growth as well as the rate of resource depletion. Implications of the Allee effect for the bifurcation structure and equilibrium densities are examined. The model is compared to a modified version in which the Allee effect instead modulates the assimilation efficiency, hence the mating probability does not appear in the dynamical equation for the resource density. Both models exhibit qualitatively similar dynamics. However, compared to the model in which the Allee effect modulates predation, the model in which the Allee effect modulates assimilation efficiency predicts (i) unrealistically inefficient resource assimilation when predator density is low, (ii) a higher risk of catastrophic extinction resulting from a change in the parameter controlling the strength of the Allee effect, and (iii) no possibility of an increase in population size when the density dependence is enhanced.     

Active learning climate and employee errors: The moderating effects of personality traits

This study developed and tested a cross‐level model of the relationship between two components of active learning climate—enactive exploration and guided learning—and employee errors, and the moderating roles of the individual's traits of openness to experience and conscientiousness. The study examined the medical errors made by 142 resident physicians in 22 hospital departments. Results demonstrated significant interactions between climate and traits: when an individual had a low level of the trait of openness to experience, there was a curvilinear relationship between enactive exploration climate and number of errors. With a high level of openness to experience, the enactive exploration climate was positively associated with the number of errors up to a certain point. In addition, when an individual had a low level of conscientiousness, there was a negative association between guided learning climate and number of errors; with a high level of conscientiousness, there was a positive association between guided learning climate and number of errors. Thus, individual performance was better when the climate complemented the individual's lack of a trait. When the level of the individual's trait was high, the climate intensified the already pronounced trait up to an extent that impaired the individual's performance. Copyright © 2015 John Wiley & Sons, Ltd.