Development pattern and enhancing system of automotive components remanufacturing industry in China

Along with the increasing number of automotive output and End-of-Life vehicles (ELVs) in China, resources shortage and environmental pollution are aggravating, so the tremendous need to reuse automotive components gives birth to the industry. The Chinese government becomes to realize that it is necessary to build a conservation-oriented and environment-friendly harmonious society. As the ultimate form of recycling, remanufacturing will be an effective method to promote the development of Chinese circular economy. The automotive remanufacturing industry in China is just at the preliminary stage, this paper presents some problems before remanufacturing, during remanufacturing and after remanufacturing, and then it points out several barriers, such as restrictive policies and regulations, consumer acceptance, scarcity of technologies, etc. Like many other developed countries, Chinese remanufacturing industry will also experience the primary stage, growth stage and developed stage. while the emphasis of resources input will not be the same. By analyzing the resources input characteristics at different development stages, three development patterns, such as the Government Incenting Pattern, Technology Driving Pattern and Market Leading Pattern, are put forward. At present, the cooperation of the government, industry, universities and research institutes, etc, will jointly construct the enhancing system of automotive components remanufacturing industry in China. Finally, it concludes with a summary and some suggestions in the field.     

The Integration of a Genetic Programming-Based Feature Optimizer With Fisher Criterion and Pattern Recognition Techniques to Non-Intrusive Load Monitoring for Load Identification

Identification of electricity energy consumption on individual household appliances used in a smart house is the first important step for making the use and conservation of electricity energy more efficient. In the past, Non-Intrusive Load Monitoring (NILM) techniques, which are part of smart grid techniques realized to improve electricity energy usage efficiency, have been developed to identify individual appliances with avoiding installing many smart meters for appliances in a field. In this paper, a new NILM technique that integrates an efficient Genetic Programming (GP)-based feature optimizer with pattern recognition techniques is proposed to identify which appliance is being turned on or off. The proposed GP-based feature optimizer with Fisher criterion is used to generate a more efficient feature than original potential transient features extracted from captured transient response of household appliances through analysis of NILM. The new feature generated by GP is used by pattern recognition techniques as load identifiers for load identification. The load identifiers used and compared in this paper include k-Nearest-Neighbor Rule, Back-Propagation Artificial Neural Network, and Learning Vector Quantization. Experiments are conducted under different single-load and multiple-load operation circumstances at different actual experimental environments with small disturbances. As shown from the experimental results, the proposed is confirmed to be feasible and usable.     

煤炭自燃发火危险程度识别研究

对影响煤炭自燃发火危险程度的主要因素进行主观判断，运用逐步聚类分析法，对开采煤层煤炭自燃发火的危险程度进行识别，为判定煤炭自燃发火危险程度提供了一种理论方法     

矿山灾害学的研究模式、特征与方法

矿山灾害学主要研究矿山灾害成因动力学、矿山灾害运动学、矿山灾害预测学和矿山灾害防治对策学等 4个内容 ,其研究模型可分为工程技术研究、技术科学研究和基础科学研究三大层次 ,具有学科交叉与综合、生态环境、社会参与、安全教育和工程科学研究五大特征 ,研究方法须以科学的系统论思想为指导 ,采用逻辑辩证与历史唯物相统一法 ,既从微观上研究单一矿山灾种事故的防治技术与方法 ,又从宏观上以不同学科、不同层次、不同方位切入 ,对矿山灾害进行系统的通盘研究 ,与此同时 ,还必须大力开展多层次、多领域、多方位的国际协作与研究。因此 ,矿山灾害学的研究与发展 ,不但拓宽了矿山灾害科学研究的领域 ,而且对现代“灾害学”的建立和发展也具有十分重大的科学价值     

Creation and preservation of vegetation patterns by grazing

Structural patterns of tall stands (“tussock”) and short stands (“lawn”) are observed in grazed vegetation throughout the world. Such structural vegetation diversity influences plant and animal diversity. A possible mechanism for the creation and preservation of such patterns is a positive feedback between grazing and plant palatability. Although some theoretical studies have addressed this point in a non-spatial setting, the spatial consequences of this feedback mechanism on the stability and spatial characteristics of vegetation structure patterns have not been studied.We addressed this issue by analyzing a spatially explicit individual-based plant-grazer simulation model, based on published empirical relations and the assumption of optimal foraging.In the model, the selection by the grazer of short stands (that have a higher energy content and are more palatable) is affected by traveling costs and the spatial organization of swards. Nevertheless, the most selected biomass in this type of short stands was the optimal biomass predicted by cropping and digestion constraints. As a result of the optimal foraging strategy, the grazers displayed Lévy-flight traveling behavior during the simulations with characteristic exponent μ ≈ 2.Patterns of short and tall stands created by grazing were preserved for at least a decade. Even in seasonal habitat, the spatial organization of the patterns remained relatively constant, despite fluctuations in the area of short stands. Heterogeneity of initial vegetation increased heterogeneity of the grazing-induced pattern, but did not affect its stability.The area of short stands that was preserved by grazing scaled with the herbivore mass to the power 0.4 and with the carrying capacity of the vegetation to the power −0.75.Patterns of tall and short stands can be created and perpetuated by optimally grazing ruminants, irrespective of possible underlying soil patterns. The simulations generate predictions for the stability and spatial characteristics of such structural vegetation patterns.     

Pattern formation and individual-based models: The importance of understanding individual-based movement

This paper demonstrates that while pattern formation can stabilize individual-based models of predator–prey systems, the same individual-based models also allow for stabilization by alternate mechanisms, particularly localized consumption or diffusion limitation. The movement rules of the simulation are the critical feature which determines which of these mechanisms stabilizes any particular predator–prey individual-based model. In particular, systems from well-connected subpopulations, in each of which a predator can attack any prey, generally exhibit stabilization by pattern formation. In contrast, when restricted movement within a (sub-)population limits the ability of predators to consume prey, localized consumption or diffusion limitation can stabilize the system. Thus while the conclusions from differential equations on the role of pattern formation for stability may apply to discrete and noisy systems, it will take a detailed understanding of movement and scales of interaction to examine the role of pattern formation in real systems. Additionally, it will be important to link an understanding of both foraging and inter-patch movement, since by analogy to the models, both would be critical for understanding how real systems are stabilized by being discrete and spatial.     

城市景观格局对热环境影响遥感研究

为揭示城市景观格局对热环境的影响,在遥感及景观理论的支持下,对长春市1993年、2005年的城市热环境与景观格局进行分析。研究发现,在清理围墙院落等科学、合理的城市规划影响下,研究区"封闭式"的景观状态得以改变,向着离散、多样、均匀的方向发展;热环境也有所改善,特别是高温区面积减少了6.2%;城市热环境不仅与下垫面的景观类型密切联系,还与景观类型的空间组合格局有关系。对该区地表温度与景观多样性指数进行分析,发现二者具有明显的二次抛物线相关关系。     

Identifying landscape scale patterns from individual scale processes

Extrapolating across scales is a critical problem in ecology. Explicit mechanistic models of ecological systems provide a bridge from measurements of processes at small and short scales to larger scales; spatial patterns at large scales can be used to test the outcomes of these models. However, it is necessary to identify patterns that are not dependent on initial conditions, because small scale initial conditions will not normally be measured at large scales. We examined one possible pattern that could meet these conditions, the relationship between mean and variance in abundance of a parasitic tick in an individual based model of a lizard tick interaction. We scaled discrepancies between the observed and simulated patterns with a transformation of the variance–covariance matrix of the observed pattern to objectively identify patterns that are “close”.     

Pattern recognition and classification of sediments according to their metal content using chemometric tools. A case study: the estuary of Nerbioi-Ibaizabal River (Bilbao, Basque Country)

Chemometrics are increasingly used in environmental monitoring studies, but are still far from being accepted as routine tools by field specialists. The multivariate character of usually highly correlated environmental data recommends the use of advanced chemometrics as part of the analytical methodology in order to get information on the basic structure of data. In this work, we have applied a battery of non-supervised (Principal Component Analysis (PCA)) and supervised (k-Nearest Neighbour (k-NN), Soft Independent Modelling of Class Analogies (SIMCA), Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs)) multivariate techniques on a specific environmental dataset. The dataset consists on the concentration of 14 elements (Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V and Zn) in 95 sediments collected at eight different locations of the estuary of the Nerbioi-Ibaizabal River (Bilbao, Basque Country) during 12 sampling campaigns conducted every 3 months between 2005 and 2008. The study aims to present a simple methodology of general applicability which may result in a flexible and practical tool to assess chemical pollution in sediments of a given specific site.