人工智能技术对长江流域水污染治理的思考

随着经济的快速发展和城市化进程的不断加速,促使水污染严重的长江流域需从污染物去除过程的建模与优化、污水处理过程的优化控制、水污染监测系统的构建开展水污染治理研究.传统的水污染处理技术存在污染物去除效率预测精度较低、污水优化控制成本较高、水污染监测滞后效应严重的问题.人工智能技术能够有效克服上述问题,因此通过梳理国内外学者利用人工智能技术在污水污染物去除过程的建模与优化、污水处理过程的优化控制及水污染监测系统的构建等方面的研究成果,为全面加强长江流域水污染治理能力提供科学可靠的技术指导.结果表明:①利用人工神经网络技术（径向基神经网络、多层前馈网络-人工神经网络、多层感知器神经网络）对污水污染物去除过程进行建模与优化,为精确预测长江流域重金属（Cr、Cu）、营养盐（TN、TP）、持久性有机污染物〔PBDEs（多溴二苯醚）、HCH（六氯环己烷）〕的去除率提供重要参考价值.②采用污水处理的自动控制技术与人工智能技术（递归神经网络、支持向量机、模糊神经网络等）构建污水智能控制系统,为长江流域实现高效节能的污水优化控制提供重要的技术指导.③利用在线监测仪器和人工智能技术（小波神经网络、多元线性回归-人工神经网络、叠层去噪自动编码器等）建立水污染智能监测系统,为解决长江流域水污染监测响应滞后问题提供有力的技术支持.因此,人工智能技术对长江流域提高污水污染物去除率,降低污水优化控制成本,提升水污染监测时效性具有重要的推广价值.      

基于神经网络的东北地区秸秆焚烧预测

选取农作物秸秆露天燃烧严重的东北地区,采用人工神经网络的方法,结合卫星火点和气象数据,开展秸秆露天燃烧预测研究.结果表明：人工神经网络预测模型成功验证了松嫩平原地区2015年10月25日~11月15日的秸秆露天燃烧情况,其准确度为67.1%,经过多次试验,在神经网络建模与验证数据配比为80：20时,预测准确度最高,可达69.7%,同时该模型的稳定性较好.而对不同区域,不同时间段的预测研究表明,人工神经网络较适用于长时间序列的预测.就影响因素而言,相对湿度是影响秸秆露天燃烧的最重要因素.本研究结果可为空气质量模式提供火点预测数据,提高其预报预警能力,为区域联防联控政策的制定提供科技支持.     

Land User Participation in Developing a Computerised Decision Support System for Combating Desertification

Combating desertification in natural rangelands has recently become a priority in large parts of southern Africa. Rangeland managers, farmers, scientists, conservationists and land users have been applying a variety of restoration technologies to address this problem. Bush encroachment, as part of the desertification process, involves the natural replacement of the herbaceous plant cover by undesirable problem woody species. The active and passive restoration technologies that are applied, are mainly based on indigenous knowledge and include the chemical, mechanical or manual reclamation of unproductive rangelands, as well as the combating of woody and alien species encroachment. Indigenous practices and knowledge play a major role in the effectiveness and success rate of these technologies. This project faces the challenge of bringing together both local and scientific knowledge in a single user-friendly, computerised Decision Support System (DSS) which is directly accessible by land users to support them in the process of decision making, concerning the combating of desertification. Case studies from central and northern Namibia were used to combine qualitative and quantitative data to develop this Decision Support System. The DSS currently consists of two databases and an expert system, which evaluates the results of land users’ management practices, and provides easily accessible information and advice for participants in the system, based on the incorporated data. The DSS is also linked to national and international web sites and databases to offer a wider range of information on technologies concerning agricultural and conservation practices.     

餐饮泔水油及废动植物油下脚料深加工利用大有可为：介绍《国家级科技成果重点推广计划》高效益环保项目“利用餐饮泔水油及废动植物油生产油酸、甘油、硬脂酸”新工艺

综述了我国的餐饮泔水油及废油脂资源浪费、破坏环境现状，对比介绍了新工艺显著的经济及环保效益。     

人工智能在金属塑性加工领域中的应用

概述了人工智能及其 2个主要分支 (专家系统和人工神经网络 ) ,讨论了遗传算法与专家系统集成以及神经网络与模糊逻辑、专家系统集成的必要性和集成方法 ,分别介绍了专家系统和人工神经网络在塑性加工领域中的应用现状。     

现代智力观对教学的启示

现代智力扩充了传统智力的内涵。从现代智力出发,提出了学校教育要注意开发学生的 多元智力。改革教育方法。贯彻因材施教,培养学生的创新能力。     

Ethological farm programs and the “market” for animal welfare

Ethological farm programs as they exist in Switzerland are compared with environmental farm programs in respect of demand and supply. Because animal welfare is not a public good but rather a relation that causes psychological externalities, the demand for animal welfare has a different standing in economic theory than the demand for a clean environment. The supply of animal welfare by farmers, however, largely follows the patterns known from the delivery of environmental goods. Farm size, age and education, and also family size and capital intensity are influencing variables. The paper concludes that the design of ethological farm programs should be based on broad public discussions as described by deliberation theorists rather than willingness-to-pay studies.     

Pyrolysis of waste animal fats in a fixed-bed reactor: Production and characterization of bio-oil and bio-char

Several animal (lamb, poultry and swine) fatty wastes were pyrolyzed under nitrogen, in a laboratory scale fixed-bed reactor and the main products (liquid bio-oil, solid bio-char and syngas) were obtained. The purpose of this study is to produce and characterize bio-oil and bio-char obtained from pyrolysis of animal fatty wastes. The maximum production of bio-oil was achieved at a pyrolysis temperature of 500 °C and a heating rate of 5 °C/min. The chemical (GC–MS analyses) and spectroscopic analyses (FTIR analyses) of bio-oil showed that it is a complex mixture consisting of different classes of organic compounds, i.e., hydrocarbons (alkanes, alkenes, cyclic compounds…etc.), carboxylic acids, aldehydes, ketones, esters,…etc. According to fuel properties, produced bio-oils showed good properties, suitable for its use as an engine fuel or as a potential source for synthetic fuels and chemical feedstock. Obtained bio-chars had low carbon content and high ash content which make them unattractive for as renewable source energy.     

Exploring the role of emotional intelligence in behavior-based safety coaching

INTRODUCTION: Safety coaching is an applied behavior analysis technique that involves interpersonal interaction to understand and manipulate environmental conditions that are directing (i.e., antecedent to) and motivating (i.e., consequences of) safety-related behavior. A safety coach must be skilled in interacting with others so as to understand their perspectives, communicate a point clearly, and be persuasive with behavior-based feedback. METHOD: This article discusses the evidence-based "ability model" of emotional intelligence and its relevance to the interpersonal aspect of the safety coaching process. RESULTS: Emotional intelligence has potential for improving safety-related efforts and other aspects of individuals' work and personal lives. Safety researchers and practitioners are therefore encouraged to gain an understanding of emotional intelligence and conduct and support research applying this construct toward injury prevention.     

Impact on sward composition and stock performance of grazing Molinia-dominant grassland

A comparative experiment was conducted to test the extent to which grazing of rank Molinia caerulea-dominated grassland increases botanical diversity and improves animal performance. The study was carried out on an area of under-grazed Molinia-dominant pasture that had not been stocked for 20 years. Three treatments were compared from 2001 until 2008: (1) no grazing, (2) grazing by Welsh Mountain sheep and (3) grazing by Welsh Black heifers. Patterns of change in sward surface composition across the grazing season were consistent with greater consumption of Molinia by the cattle, but there was little evidence of management regime influencing changes in sward composition year-on-year. There was a significant effect of year on animal performance (P < 0.001). Although the growth rates of the heifers improved over the course of the first three years of the experiment, this performance was not sustained. In contrast, any weight changes for the sheep were positive. The results highlight the slow pace of change within upland swards, and the need to develop site-specific grazing guidelines.