Verification of the Contribution of Vehicular Traffic to the Total NMVOC Emissions in Germany and the Importance of the NO3 Chemistry in the City Air

In 1997 and 1998 several field campaigns for monitoring non-methane volatile organic compounds (NMVOCs) and nitrogen oxides (NO_x) were carried out in a road traffic tunnel and in the city center of Wuppertal, Germany. C₂–C₁₀ aliphatic and aromatic hydrocarbons were monitored using a compact GC instrument. DOAS White and long path systems were used to measure aromatic hydrocarbons and oxygenated aromatic compounds. A formaldehyde monitor was used to measure formaldehyde. Chemiluminescence NO analysers with NO₂ converter were used for measuring NO and NO₂. The high mixing ratios of the NMVOCs observed in the road traffic tunnel, especially 2.9 ppbv phenol, 1.5ppbv para-cresol and 4.4 ppbv benzaldehyde, in comparison with themeasured background concentration clearly indicate that these compounds were directly emitted from road traffic. Para-Cresol was for the first timeselectively detected as primary pollutant from traffic. From the measured data a NMVOC profile of the tunnel air and the city air, normalised to benzene (ppbC/ppbC), was derived. For most compounds the observed city air NMVOC profile is almost identical with that obtained in the traffic tunnel. Since benzene originates mainly from road traffic emission, the comparison of the normalised emission ratios indicate that the road traffic emissions in Wuppertal have still the largest impact on the city air composition, which is in contrast to the German emission inventory. In both NMVOC profiles, aromatic compounds have remarkably large contributions of more than 40 ppbC%. In addtion, total NMVOC/NO_x ratios from 0.6 up to 3.0ppbC/ppb in the traffic tunnel air and 3.4± 0.5 in the city air of Wuppertal were obtained. From the observed para-cresol/toluene and ortho-cresol/toluene ratios in the city air, evidence was found thatalso during daytime NO₃ radical reactions play an important role in urban air.     

Intelligent technology-based control of motion and vibration using MR dampers

Due to their intrinsically nonlinear characteristics, development of control strategies that are implementable and can fully utilize the capabilities of semiactive control devices is an important and challenging task. In this study, two control strategies are proposed for protecting buildings against dynamic hazards, such as severe earthquakes and strong winds, using one of the most promising semiactive control devices, the magnetorheological (MR) damper. The first control strategy is implemented by introducing an inverse neural network (NN) model of the MR damper. These NN models provide direct estimation of the voltage that is required to produce a target control force calculated from some optimal control algorithms. The major objective of this research is to provide an effective means for implementation of the MR damper with existing control algorithms. The second control strategy involves the design of a fuzzy controller and an adaptation law. The control objective is to minimize the difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper. The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the control strategy based on the combination of the fuzzy controller and the adaptation law doesn’t require a prior knowledge of the combined building-damper system, this approach provides a robust control strategy that can be used to protect nonlinear or uncertain structures subjected to random loads. Supported by: Hong Kong Research Grant Council Competitive Earmarked Research Grant HKUST 6218 / 99E and by the National Science Foundation under grant CMS 99-00234.     

广义回归神经网络预测加筋土支挡结构高度

土工合成材料加筋支挡结构（Geosythetics-Reinforced Retaining Wall, 简称GRW）设计方法主要是建立在似粘聚力理论基础之上的半经验设计法。由于土性及加筋机理的复杂性，常常要对它们进行人为假定，导致计算结果差强人意。神经网络方法与传统方法的不同之处在于不需要主观假定，而是模拟人脑思维，通过数据样本的学习来获得预测结果。引入神经网络技术来预测加筋土支挡结构的设计高度是一种新尝试。由于本问题具有样本容量非常有限、影响因素复杂多样的特点。因此，采用适用于稀土样本数据的广义回归网络（General Regression Neural Network)来预测加筋土支挡结构设计高度。基于MATLAB神经网络工具箱及文献[1]的挡墙离心模型试验结果，建立了一个可用于加筋支挡结构设计高度预测的GRNN网络。通过对足尺试验，实际工程及模型试验结果的检验，表明网络的学习是成功的，具有一定指导意义。     

河南平原绿化发展研究

文章根据河南省近期（1998年）森林资源清查结构和1993－2000年年度平原绿化复查情况，分析了河南平原绿化的发展现状和存在问题，提出河南平原绿化应以建立持续，高效的平原林业生态体系为目标，坚持生态，社会，经济效益相统一，高标准建设农田林网，农林间作，城镇村庄和通道绿化，合理调整林种，树种结构，积极发展速生丰产用材林和名特优新经济林。并对河南平原绿化建设布局，内容及实现手段和策略进行了讨论。     

Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing

The transformation of land cover, in particular coniferous forest, constitutes one of the most notable agents of regional-to-global-scale environmental change. Remote sensing provides an excellent opportunity for providing forest cover information at appropriate spatial and temporal scales. The optimal exploitation of remote sensing relies on the link between known forest cover and the remotely sensed dataset. This paper explores the accuracy of three methods – vegetation indices, regression analysis and neural networks – for estimating coniferous forest cover across the United States Pacific Northwest. All methods achieved a similar accuracy of forest cover estimation. However, in view of the benefits and limitations of each, the neural network approach is recommended for future consideration.     

基于神经网络的GPS周跳探测的新方法

为了用GPS获得高精度的定位结果,周跳必须在数据处理过程中被检测和修复。通过对周跳产生原因和特点的分析,提出神经网络方法,并与传统方法进行比较。用神经网络建立模型对周跳进行预测。通过对比预测值的不同来确定周跳的大小,从而实现周跳的修复。此外,还利用实测的相位数据,验证方法的可行性与有效性。     

山区道路工程建设用地地质灾害危险性评估技术探讨

地质灾害对建设项目造成破坏,因此,在项目建设前进行建设用地地质灾害危险性评估就显得特别重要。在评估过程中,只有对灾害体类型、规模、影响范围、稳定性、危险性和危害程度做出准确的评估,评估结论才能成为拟建项目后续工作的科学依据。文章以某山区道路改造项目为例,探讨了山区崩塌危岩、泥石流灾种的地质灾害危险性评估的定性、半定量评价方法,并根据评估结果,提出了相应的治理方案。本文提出的评估方法在类似项目地质灾害危险性评估中具有参考和借鉴价值。     

Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

The complexity of hydrological processes and lack of data for modeling require the use of specific tools for non-linear natural phenomenon. In this paper, an effort has been made to develop a conjunction model – wavelet transformation, data-driven models, and genetic algorithm (GA) – for forecasting the daily flow of a river in northern Algeria using the time series of runoff. This catchment has a semi-arid climate and strong variability in runoff. The original time series was decomposed into multi-frequency time series by wavelet transform algorithm and used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Several factors must be optimized to determine the best model structures. Wavelet-based data-driven models using a GA are designed to optimize model structure. The performances of wavelet-based data-driven models (i.e. WANFIS and WANN) were superior to those of conventional models. WANFIS (RMSE = 12.15 m³/s, EC = 87.32%, R = .934) and WANN (RMSE = 15.73 m³/s, EC = 78.83%, R = .888) models improved the performances of ANFIS (RMSE = 23.13 m³/s, EC = 54.11%, R = .748) and ANN (RMSE = 22.43 m³/s, EC = 56.90%, R = .755) during the test period.     

Community Cohesion: Social and Economic Ties in the Personal Networks of Fisherfolk

Social ties influence access to knowledge and cooperation in natural resource management, with the sharing of certain characteristics thought to be positive for social cohesion and participatory forms of management. In this article, a holistic characterisation of fisherfolk personal networks is developed, disaggregating results by the main occupational groups within the fisheries studied, to provide a more nuanced understanding of the personal networks of types of fisherfolk. Links are then made between the characteristics of personal networks and evidence on how fisherfolk benefit from their networks, interpreted as contributing to social cohesion within the communities. The personal networks of fisherfolk (boat crew, boat owners and traders/processors) of Kenya, Tanzania and Uganda bordering Lake Victoria were investigated using personal network analysis, with fisherfolk asked who they discuss their fisheries activities with. The analysis found that networks based on the same occupation were more characteristic of fish traders/processors networks than those of boat crew and boat owners and that shared ethnicity, gender and location were characteristic of boat owner and boat crew networks, though shared ethnicity may reflect the composition of the communities rather than choice. Social and economic interactions within the networks were based on provision of credit, social support and advice, suggesting that these form the basis of social cohesion and should be taken into consideration in working with fishing communities in both development interventions and designing collaborative management approaches.     

城市道路网面域层次结构特征的识别与表达

层次性是道路网一个重要的空间结构特征,文中提出一种城市道路网面域层次结构特征的识别与表达方法。首先以路链为基本单元对道路网数据进行重新组织,选取连通度、接近度、中介度和长度对路链的结构特征重要性进行评价;然后以道路网眼作为表达道路网面域层次结构特征的基本单元,依据路链的属性等级、结构特征重要性以及视觉对称性原则,依次选取路链对道路网最大包络区域进行层次剖分从而获得其包含的各级层次道路网眼;最后利用二叉树模型对道路网眼之间的层次关系和邻接关系进行结构化表达。实验结果表明,所提方法能够完整提取出道路网包含的全部道路网眼并准确建立它们之间的空间关系,其结果能较好地反映道路网面域的层次结构特征。