环境史中的欧洲特殊道路问题

欧洲为什么率先进行工业革命是一个永恒的问题,从东西方环境史比较的角度可能会对此得出独特的解释。当然,这种比较必须建立在坚实的实地研究的基础上。本文要分析的十个问题是：谨慎对待精神观点,环境史的制度分析,耕地与牧场的平衡与失衡,西欧的婚姻模式,延续、持续性和自给自足,森林和权力,欧洲多中心主义的优势,魏特夫“亚细亚生产方式”的绿色复兴,欧洲殖民主义的环境影响,环境史上是否存在一个“欧洲奇迹”。从这十方面的比较中会对“环境史中的欧洲特殊道路问题”有更清楚的把握,进而揭示出一个辩证的历史逻辑,即“长期的成功恰恰加剧了危机”。这或许就是东方前现代的繁荣昭示给现在西方文明的前景。     

The prescription as literature

In the medical writings of antiquity the recipe, a basic form of scientific writing, was considered a literary genre with conventions of its own. In texttype-linguistics, however, the recipe plays only a minor role; in studies on the history of literary short texts it is hardly ever mentioned and, furthermore, it has come to the conclusion that the recipe's function has lain solely in the curing of the sick. The present study focuses on its multifunctional character. A collection of widely scattered and hardly accessible recipes shows that the recipe adopted functions beyond giving technical directives. Its multifunctionality in the German literature of early modern times is manifest in medical and cooking jocular recipes, cooking recipes in minnesang, mock recipes aimed at physicians and astrologers, recipes of spiritual-religious content, recipes containing art and literary criticism as well as recipes in a moral-satirical or a political-agitative vein.     

Prediction of Bike-sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm

Regression models are commonly applied in the analysis of transportation data. This research aims at broadening the range of methods used for this task by modeling the spatial distribution of bike-sharing trips in Cologne, Germany, applying both parametric regression models and a modified machine learning approach while incorporating measures to account for spatial autocorrelation. Independent variables included in the models consist of land use types, elements of the transport system and sociodemographic characteristics. Out of several regression models with different underlying distributions, a Tweedie generalized additive model is chosen by its values for AIC, RMSE, and sMAPE to be compared to an XGBoost model. To consider spatial relationships, spatial splines are included in the Tweedie model, while the estimations of the XGBoost model are modified using a geographically weighted regression. Both methods entail certain advantages: while XGBoost leads to far better values regarding RMSE and sMAPE and therefore to a better model fit, the Tweedie model allows an easier interpretation of the influence of the independent variables including spatial effects.