The importance of agricultural yield elasticity for indirect land use change: a Bayesian network analysis for robust uncertainty quantification

ABSTRACT

A major barrier to realising biofuels’ climate change mitigation potential is uncertainty concerning carbon emissions from indirect land use change (ILUC). Central to this uncertainty is the extent to which yields can respond dynamically to increased demand for agricultural commodities. This study examines the elasticity of soybean and corn yields in the USA for 1990–2017 using Bayesian network models to robustly quantify uncertainty. The central finding is that a single parameter value for yield elasticity does not adequately represent the effects of technology, policy and price pressures through time. The models demonstrate the limiting role of technological progress as well as farmers’ capital investment in response to system shocks. Results suggest evaluation of parameter uncertainty alone is unlikely to capture a full range of future ILUC scenarios and reiterate the need for ILUC studies to use probabilistic approaches as standard to robustly inform climate change mitigation policies.     

Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system

A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Niño southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models.     

基于神经网络的活性污泥系统建模及其控制

针对松江污水厂污水处理活性污泥系统,采用神经网络技术进行建模试验研究,在对实际运行数据剔除异常数据后,将样本数据随机分成训练样本、检验样本和测试样本.用试凑法确定合理的神经网络隐层节点数,用检验样本实时监控训练过程从而避免"过训练"现象,用多次改变网络初始连接权值求得全局极小点,从而建立了泛化能力较好的基于神经网络的活性污泥系统数学模型.利用建立的神经网络模型,对活性污泥系统运行情况的仿真与控制进行了分析研究.示例研究表明:神经网络技术能较好地应用于活性污泥系统的建模与控制,有很好的理论与实践意义.     

无锡新区生态工业链网设计探讨

生态工业园区是按照工业生态学原理，通过模拟自然生态系统，建立工业系统中“生产者-消费者-分解者”的循环途径，实现物质闭路循环和能量多级利用。文中介绍了生态工业园区的概念、分类和中国工业生态园区的建设进展。结合无锡新区生态工业园区的建设现状，提出新区的生态工业链网设计思路，通过园区成员企业之间的协同管理，以取得资源、环境与经济、社会效益的增长最大化。     

Short-term Model of the Production of Construction Aggregates in Taiwan Based on Artificial Neural Networks

BACKGROUND: Taiwan's geography and limited stock of sandstone have caused sandstone resources to gradually decline to the point of exhaustion after long-term excavation. Moreover, the Taiwanese government has continuously increased the amount of land area near rivers that cannot be excavated to facilitate riverbed remediation and promote conservation of water resources. Accordingly, predicting and managing the annual production of construction aggregates in future construction projects, and dealing appropriately with some thorny problems, for instance, demand that excess supply, excessive excavation, unregulated excavation, and the consequent environmental damage, will significantly affect the efficient use of natural resources in a manner that accords with the national policy of Sustainable Development (SD). METHODS:. This study establishes an empirical model for forecasting the annual production of future construction aggregates using Artificial Neural Networks (ANN), based on 15 relevant socio-economic indicators, such as indicator of annual consumption of cement. A sensitivity analysis is then performed on these indicators. RESULTS AND DISCUSSION: This work applies ANN to estimate the annual production of construction aggregates; the estimates, the verification of the model and the sensitivity analysis are all acceptable. Furthermore, sensitivity analysis results indicate that the annual consumption of cement is the indicator that most strongly influences the production of construction aggregates, as well as whether construction waste can be recycled and steel structures can be used in buildings, helping to reduce the future production of construction aggregates in Taiwan. CONCLUSIONS: The elaborate prediction methodology presented in this study avoids some of the weaknesses or limitations of conventional linear statistics, linear programming or system dynamics. Additionally, the results not only provide a short-term prediction of the production of construction aggregates in Taiwan, but also provide a viable and flexible means of verifying quality certification of the production data of construction aggregates in the future by incorporating those relevant socio-economic indicators. RECOMMENDATIONS AND OUTLOOK: The continuity and quality of the database of relevant indicators used in this study should be closely scrutinized in order to ensure the SD means of exploiting resources.     

深层过滤理论与技术的研究进展

深层过滤是水处理工艺中的重要单元操作。近年来 ,在滤料、过滤工艺的研究开发及过滤理论研究方面有了新的发展。本文评述了一些主要研究进展     

Isomorphic chain graphs for modeling spatial dependence in ecological data

Graphical models (alternatively, Bayesian belief networks, path analysis models) are increasingly used for modeling complex ecological systems (e.g., Lee, In: Ferson S, Burgman M(eds) Quantative methods for conservation biology. Springer, Berlin Heilin Heideslperk New York, pp.127–147, 2000; Borsuk et al., J Water Res Plann Manage 129:271–282, 2003). Their implementation in this context leverages their utility in modeling interrelationships in multivariate systems, and in a Bayesian implementation, their intuitive appeal of yielding easily interpretable posterior probability estimates. However, methods for incorporating correlational structure to account for observations collected through time and/or space—features of most ecological data—have not been widely studied; Haas et al. (AI Appl 8:15–27, 1994) is one exception. In this paper, an “isomorphic” chain graph (ICG) model is introduced to account for correlation between samples by linking site-specific Bayes network models. Several results show that the ICG preserves many of the Markov properties (conditional and marginal dependencies) of the site-specific models. The ICG model is compared with a model that does not account for spatial correlation. Data from several stream networks in the Willamette River valley, Oregon (USA) are used. Significant correlation between sites within the same stream network is shown with an ICG model.     

网上银行的发展与创新

传统银行一般是单方面开发业务品种,向客户推销产品和服务,客户只能在规定的业务范围内选择自己需要的银行服务,而因特网向银行服务提供了交互式的沟通渠道.网络银行的出现,使传统银行经受了一场技术革命,传统银行业务将受到挑战,网络银行具有灵活强大的业务创新能力,不仅可延伸改造传统的业务,而且会不断设计业务新品种,创新业务方式,满足客户多样化的需求.     

Designing an efficient adaptive cluster sample

The choice of neighborhood definition and critical value in adaptive cluster sampling is critical for designing an efficient survey. In designing an efficient adaptive cluster sample one should aim for a small difference between the initial and final sample size, and a small difference between the within-network and population variances. However, the two aims can be at odds with each other because small differences between initial and final sample size usually means small within-network variance. One way to help in designing an efficient survey is to think in terms of small network sizes since the network size is a function of both critical value and neighborhood definition. One should aim for networks that are small enough to ensure the final sample size is not excessively large compared with the initial sample size but large enough to ensure the within-network variance is a reasonable fraction of the population variance. In this study surveys that had networks that were two to four units in size were the most efficient.     

网络音乐作品著作权的保护的几点建议

在数字作品著作权越来越频繁受到侵犯的今天,如何对其进行有效的保护,维护著作权人的利益,给法律提出了更高的要求.因此,来阐述与著作权保护相关的几个理论问题,提出对网络音乐作品保护的几点建议,以形成一个以保护音乐作品著作权人利益为宗旨的保护体制.