基于组织协同系统的创客组织发展稳定性实证分析

创客组织是实现组织协同创新的重要载体。创客位于组织协同系统的核心层，创客入驻创客组织的创新状态决定了协同创新发展状态；创客组织位于组织协同系统的中间层，起组织协调作用，其发展稳定性影响着整个系统；协同组织位于外部层，其参与程度决定了整个系统的组织协同和协同创新吸引能力。基于波动系数计量模型，对240家创客组织的12项评价指标进行分析，结果显示，以高校主导的创客组织衡量指标波动系数为标准，创客组织在整体上处于发展不稳定状态；分类别分析结果表明，高校和科研院所主导的创客组织发展稳定性明显优于企业和个人主导的创客组织。研究结果可以为完善创新政策、促进创客组织稳健发展提供参考。     

基于因子分析的森林康养空间评价 指标体系研究

森林康养空间是开展康养活动、建立森林康养基地 的基本单元。通过4组轮回专家问卷与因子分析法，收集229 份问卷，筛选并构建森林康养空间评价指标体系。结果表明： 1)以康养功能为核心、森林康养空间为目标的评价指标体系 包含3个一级指标、6个二级指标和22个三级指标；2)森林环 境资源与森林空间特征是评价森林康养空间的决定性条件； 3)森林康养空间特征主要体现在环境舒适性、设施便捷性与 空间规模性3方面；4)改善空气负离子浓度、空间可达性、设 施保健功能适宜性、道路联通性、设施服务半径覆盖度和森林 气候舒适度等指标可以显著提升森林康养空间康养效果。本研 究成果能为森林康养空间规划设计及康养效果优化提供一定的 理论支撑。     

Rural development and the role of game farming in the Eastern Cape,South Africa

The analysis of game farming is set in the Eastern Cape, South Africa. Game farming reorders the use, meaning and value of land and animal species. However, what it means for rural development processes in the immediate region and beyond is not well accounted for. We perceive game farming as an assemblage that brings together new actors, new forms of land use and new discourses. We argue that although game farming has generated new opportunities and new forms of added value to the available resources (e.g. eco-tourism, trophy hunting, game-meat production), situated in the history and contemporary context of the Eastern Cape, it is a contested, and from a development point of view, problematic land-use practice. We argue that game farming constrains land and agrarian reforms: the distribution of land and income remains skewed; ‘poaching’ occurs and game farms do not, or only minimally, generate new and badly needed employment opportunities. The game farm has emerged as an exclusive, globally well-connected space. The nature of the relationships this space maintains with the surrounding communities is, however, such that the overall contribution to rural development in South Africa is questionable.     

Tradespace exploration of in-space communications network architectures

Making architectural decisions in long lifecycle systems is challenging because the time between system definition and end of operations can span multiple decades, resulting in shifts in stakeholder needs and major advances in technologies. Space-based communications using relay satellite constellations is one such example, requiring substantial up-front planning to define capabilities and size capacity due to the large investment of time and resources. Additionally, there are numerous viable system architectures. In this paper, we build on existing methods to develop a graph-based decision method to assess and explore architectural flexibility in the future evolution of long lifecycle systems. The tradespace graph defines edges between similar architectures, quantifies the switching cost between architectures, using graphs to analyse the potential system evolution pathways. In a test case on NASA communication satellites, we find that hosting government communications payloads, in particular optical payloads, on commercial satellites could reduce cost and increase flexibility of the NASA network.     

基于特征增量的新类识别方法研究

在分类应用的过程中,经常会出现新的类别,导致数据分布发生显著变化,使得原分类模型不再适用。如何识别新的类别使分类模型能适应其出现已经成为一个亟需解决的问题。本文提出基于特征增量的SVDD(支持向量数据描述)新类识别方法。该方法在SVDD算法的基础上,通过增加新特征,扩大特征空间维度从而提高模型对于新类的识别能力。在多个数据集上的实验结果表明,该方法能有效识别新类,使更新后的模型具有更高的准确度。     

Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we significantly expand the data coverage by providing democracy indices for 186 countries in the period from 1919 to 2019.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

Exploring the relationship between multilevel highway networks and local development patterns—a case study of Taiwan

The relationship between local development patterns and highway networks is complicated, as suggested by studies using various analytical methods. The spatial effects of each hierarchical highway network on local development patterns remains unclear. This study combines space syntax and the hierarchical linear model (HLM) within the research framework in a case study of Taiwan. The former is employed to model the topological accessibility of multilevel highway networks, and the latter is applied to handle the hierarchical relationship between multilevel highway networks and local development patterns. Results indicate a high R-squared (R² = 0.803) value, and statistically significant cross-level interactions are observed. The results show the different spatial effects of various highway networks on the scale of local development patterns, and confirm consideration of highway networks with classification in the research design. The implications of the findings may be appropriate for substantive planning and future research approaches.     

要素禀赋、政策支持与金融资源配置:产业转移的承接模式研究

本文以商务部认定的湖南、江西省四个加工贸易梯度转移重点承接地为样本,对比分析要素禀赋、政策支持和金融资源配置的异同及其影响,在总结归纳各自承接模式的特点的基础上,提出了"精品园区+核心企业+直接融资+金融助推"的较优模式,认为政策支持是影响承接产业转移的直接因素;要素禀赋的影响力有弱化趋势;金融资源配置是影响承接力和产业结构优化升级的重要因素;对待处于不同生命周期的产业转移企业,承接的工作重点应各有侧重.因此,承接产业转移要与"转方式、调结构"的战略方向一致.注重与本地的产业互补相结合,并需要金融部门的主动参与和服务创新.