Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.     

科技创新投入对环境全要素生产率的影响机制

使用2004~2015年的中国280个地级市的面板数据,对科技创新投入与环境全要素生产率间的非线性关系、内部影响机理和空间异质性进行分析,结果显示：科技创新投入与环境全要素生产率之间呈现倒N型关系,两个拐点的位置分别为7.722（2257.47万元）和9.610（14913.17万元）;在外部资本进入、污染治理、市场规模效应3种影响路径中,科技创新投入影响下的外部资本进入对环境全要素生产率依然存在污染避难所的负向效应,科技创新投入与外部资本间效应为0.1363,外部资本与环境全要素生产率间效应为-0.0065;科技创新投入能够增强企业的污染治理技术并提高环境全要素生产率,三者间前后效应分别为-0.0277和-0.0311;科技创新的投入与高效益增强了市场规模效应,有效促进生产结构的转型进而提高环境全要素生产率,三者间前后效应为0.0186和0.4346.空间异质性中,外部资本进入与溢出效应带来的污染避难所负效应在中部地区显著,在西部和东北部地区不显著,而污染天堂正效应在东部地区存在但不显著;污染创新治理投入的技术正溢出效应在东部和西部地区效应显著,在中部和东北部不显著;科技创新投入与市场需求规模效应在空间区域无差异且显著为正.建议依据科技创新投入的不同影响路径来实施空间差异化策略.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

河流枯水流量特征研究

本文以泾、洛、渭三河流干流在陕西境内的主要控制断面为例,分析了１４个时段枯水流量的概率分布特征,选定了理论分布线型,揭示了各枯水序列的时段平均流量均值、变差系数和偏态系数的变化规律,建立了推求不同频率不同时段的枯水流量的经验公式。绘制了相应的关系曲线,应用十分方便。     

水域悬浮固体含量的遥感定量研究

该文提出了反射率与悬浮固体含量之间的理论关系式——负指数关系式,并确定了其与经验公式的关系。理论分析和实际应用的结果表明,该式精度高于原有公式的精度,是目前遥感水中悬浮固体含量的最佳公式。将该式应用于杭州湾水域悬浮固体含量调查中取得了成功。     

A New Modus Operandi for the Agricultural Economics Profession

Agricultural economics has, until the 1990s, enjoyeda reputation for relevance and usefulness to theagri-food industry and policy-makers. Thatreputation has been jeopardized by a growinginfatuation with models and quantification, and aconcomitant underemphasis placed on many complexproblems and issues of society. An illustrativeexample is explored, using agriculturalactivity-related damage to the natural resourcebase, environment and ecology. Agriculturaleconomists are urged to respond by broadening theirterms of reference and joining forces with otherdisciplines.     

可视化灾害数字仿真重构理论及实证研究(Ⅱ)--理论框架与概念化范式

可视化灾害数字仿真重构理论研究,不仅可以实现对灾害空间数据进行有效的集成管理和时空分析,而且为灾害的防治、应急管理和工程论证等提供可靠的依据,是解决灾害实验的危险性和不可重复性的最佳途径.本文提出可视化灾害数字仿真重构理论的概念、研究对象和方法,综述该理论所需要涉及多种跨学科的技术支持,提出灾害数字仿真重构理论的实施流程,按照致灾因子孕育、发生以及作用于承灾体形成灾害的过程为线索,建立灾害数字仿真模型的概念化范式.上述工作共同构成灾害数字仿真重构的完整理论体系.     

地震应急的影响因素分析

本文在理论分析、国内外历史震例和救灾案例研究的基础上,同时参照我国的相关政策和法律法规文件,对影响地震应急(包括震前准备和震后应急行动)的因素进行了初步分类,并系统讨论了这些因素影响地震应急的方式、途径和程度.以期为地震应急的理论研究和地震应急的实际工作提供部分补充和指导.     

论高职高专英语教学改革——我院英语分级教学探索

根据目前我院英语教学状况,英语公共课有必要改革"单一目标"的教学模式,采用多目标个性化分层教学模式,提高学生英语素质.文中具体探讨了多目标个性化分级教学模式的内涵、实施的前提条件、依据及策略.     

A Guide to Understanding Social Science Research for Natural Scientists

Natural scientists are increasingly interested in social research because they recognize that conservation problems are commonly social problems. Interpreting social research, however, requires at least a basic understanding of the philosophical principles and theoretical assumptions of the discipline, which are embedded in the design of social research. Natural scientists who engage in social science but are unfamiliar with these principles and assumptions can misinterpret their results. We developed a guide to assist natural scientists in understanding the philosophical basis of social science to support the meaningful interpretation of social research outcomes. The 3 fundamental elements of research are ontology, what exists in the human world that researchers can acquire knowledge about; epistemology, how knowledge is created; and philosophical perspective, the philosophical orientation of the researcher that guides her or his action. Many elements of the guide also apply to the natural sciences. Natural scientists can use the guide to assist them in interpreting social science research to determine how the ontological position of the researcher can influence the nature of the research; how the epistemological position can be used to support the legitimacy of different types of knowledge; and how philosophical perspective can shape the researcher's choice of methods and affect interpretation, communication, and application of results. The use of this guide can also support and promote the effective integration of the natural and social sciences to generate more insightful and relevant conservation research outcomes. Una Guía para Entender la Investigación de Ciencias Sociales para las Ciencias Naturales Katie Moon