Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks: A case study in China

In this study, we applied Bayesian networks to prioritize the factors that influence hazardous material (Hazmat) transportation accidents. The Bayesian network structure was built based on expert knowledge using Dempster-Shafer evidence theory, and the structure was modified based on a test for conditional independence. We collected and analyzed 94 cases of Chinese Hazmat transportation accidents to compute the posterior probability of each factor using the expectation-maximization learning algorithm. We found that the three most influential factors in Hazmat transportation accidents were human factors, the transport vehicle and facilities, and packing and loading of the Hazmat. These findings provide an empirically supported theoretical basis for Hazmat transportation corporations to take corrective and preventative measures to reduce the risk of accidents.     

基于FXLMS算法的矿用有源耳罩的研究

基于矿井中低频噪声突出,存在时间长、危害大,尤其周期性噪声比重较大,而传统降噪技术对其降噪效果并不明显,提出了基于fxlms 算法的自适应有源降噪耳罩降低矿井中的低频周期性信号,并采用离线建模方法建立了次级通道模型,利用MATLAB8.0拟合了64阶的滤波器,对 矿井中具有代表性的1kHz低频周期噪声和伴有随机噪声的1kHz低频周期性噪声进行了降噪仿真研究。结果表明:1kHz低频周期性噪声的降噪量 达到了30dB。而且在本模型下,随机噪声对低频周期性噪声的降噪效果几乎没有影响。     

基于铀尾矿库核素污染监测WSN分簇路由协议

用WSN对铀尾矿库核素污染监测优于传统有线布置,为解决无线传输路由能耗的问题,提出了一种基于蚁群算法的前向传输分簇路由协议。该协议以分簇的方式来减少数据发送量与寻优开销,在下一跳接收节点选择阶段,以前向传输区域为切入点,利用优化的蚁群算法进行多路径搜索最后选出满足条件的最优路径。仿真实验表明,CFTA协议的网络生存周期有效延长,并减小了能耗。     

基于LSSVM的石灰石浆液制取电耗优化

提出了基于最小二乘支持向量机(LSSVM)构造以球磨机制浆速度作为目标优化模型,并采用遗传算法实现了给料量、给水量、漩流器入口压力等运行参数的寻优,获得了优化制浆速度的调整方式。以此方式的相应参数作为当前负荷下的基准值,能很好地解决制浆工况下运行参数基准的确定问题。通过现场试验证明:按此基准值运行,可以提高制浆速度,降低电耗。     

UASB-MBR组合工艺处理模拟黄连素废水

采用升流式厌氧污泥床-膜生物反应器(UASB-MBR)组合工艺处理模拟黄连素废水,模拟废水中有机污染物由葡萄糖和黄连素配制,以葡萄糖作为初级能源物质,通过微生物协同降解作用去除废水中的黄连素.在水力停留时间(HRT)为24 h,进水ρ(COD_Cr),ρ(NH₄+-N)和ρ(黄连素)分别为1 717～4 393,91.8～158.7和64.4～276.8 mg/L,废水中黄连素的ρ(COD_Cr)贡献率为7.5%～25.0%的条件下,组合工艺可实现ρ(COD_Cr),ρ(NH₄+-N)和ρ(黄连素)的去除率分别为92.5%～95.9%,67.0%～98.9%和99%以上,废水中黄连素主要通过UASB去除,去除率为95.2%～98.9%.在进水COD_Cr负荷为0.54～1.88 kg/(m3·d),黄连素负荷为0.71～12.42 g/(m3·d)的条件下,MBR可保证出水ρ(COD_Cr),ρ(黄连素)和ρ(NH₄+-N)分别低于50,1.0和2.0 mg/L;随着MBR进水ρ(黄连素)升至3.45～12.42 mg/L,在黄连素的微生物毒性胁迫作用下,MBR中污泥呈由分散态向聚集态的转变.      

Modeling Habitat Suitability for Complex Species Distributions by Environmental-Distance Geometric Mean

This paper presents a new habitat suitability modeling method whose main properties are as follows: (1) It is based on the density of observation points in the environmental space, which enables it to fit complex distributions (e.g. nongaussian, bimodal, asymmetrical, etc.). (2) This density is modeled by computing the geometric mean to all observation points, which we show to be a good trade-off between goodness of fit and prediction power. (3) It does not need any absence information, which is generally difficult to collect and of dubious reliability. (4) The environmental space is represented either by an expert-selection of standardized variables or the axes of a factor analysis [in this paper we used the Ecological Niche Factor Analysis (ENFA)].We first explain the details of the geometric mean algorithm and then we apply it to the bearded vulture (Gypaetus barbatus) habitat in the Swiss Alps. The results are compared to those obtained by the median algorithm and tested by jack-knife cross-validation. We also discuss other related algorithms (BIOCLIM, HABITAT, and DOMAIN). All these analyses were implemented into and performed with the ecology-oriented GIS software BIOMAPPER 2.0.The results show the geometric mean to perform better than the median algorithm, as it produces a tighter fit to the bimodal distribution of the bearded vulture in the environmental space. However, the median algorithm being quicker, it could be preferred when modeling more usual distribution.     

Automatic Calibration of Hydrologic Models With Multi‐Objective Evolutionary Algorithm and Pareto Optimization1

Abstract: In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade‐offs between these objectives. A Pareto optimal solution set is achieved when a solution cannot be improved upon without degrading at least one of its objective criteria. This study investigated the application of multi‐objective evolutionary algorithm (MOEA) and Pareto ordering optimization in the automatic calibration of the Soil and Water Assessment Tool (SWAT), a process‐based, semi‐distributed, and continuous hydrologic model. The nondominated sorting genetic algorithm II (NSGA‐II), a fast and recent MOEA, and SWAT were called in FORTRAN from a parallel genetic algorithm library (PGAPACK) to determine the Pareto optimal set. A total of 139 parameter values were simultaneously and explicitly optimized in the calibration. The calibrated SWAT model simulated well the daily streamflow of the Calapooia watershed for a 3‐year period. The daily Nash‐Sutcliffe coefficients were 0.86 at calibration and 0.81 at validation. Automatic multi‐objective calibration of a complex watershed model was successfully implemented using Pareto ordering and MOEA. Future studies include simultaneous automatic calibration of water quality and quantity parameters and the application of Pareto optimization in decision and policy‐making problems related to conflicting objectives of economics and environmental quality.     

MODELING AND SENSITIVITY ANALYSIS FOR PLANNING DECISIONS IN WATER RESOURCES EXPANSION1

ABSTRACT. Individuals and organizations concerned with the expansion of the facilities of a river basin (such as a river basin authority) need to determine optimal strategies of operation and capital investment. They also need to examine the sensitivity of whatever planning decisions are contemplated. This paper extends the applicability of an algorithm that had been previously applied to the deterministic river-basin expansion problem to include the feature of a sensitivity analysis. The algorithm, containing a partial enumeration search technique and a network analysis code, gave a construction sequence of reservoirs, canals, and treatment plants, and an operating policy that maximized the present value of net earnings consistent with certain underlying assumptions. A river basin was chosen that had an existing configuration of unregulated streams and rivers, reservoirs, canals and treatment plants, and sites for future additional facilities. A series of representative synthetic flow sequences, future demand profiles, interest rates and reservoir costs that served as inputs to or parameters in the system were each perturbed by various factors (for a total of 24 cases). The sensitivity studies showed that the immediate planning decision of what facility to construct next was insensitive to variations in future demands and costs and independent of later decisions. Thus, decision-making was adaptive in the sense that by always making the optimal proximate decision, the management of the river basin is optimized.     

OPTIMAL CAPITAL INVESTMENT IN THE EXPANSION OF AN EXISTING WATER RESOURCES SYSTEMS1

The study of the optimal expansion of existing water resources systems is of continuing importance because of the rising demand and limited supply of water in many areas of the world, particularly in the southwestern part of the United States of America. This study is concerned with the investigation of the optimal expansion of a realistic water resources system to meet an increasing demand for municipal and industrial use, irrigation, energy, and recreation over a planning horizon of T years. A number of possible dam sites are available for the further regulation of river (canal) flows in the basin and/or the regulation of imported waters into the basin. To maximize, over the set of alternative projects, the sum of discounted present value of net earnings subject to the demands and various institutional, physical and budgetary limits, an optimization problem (Problem I) was formed as a 0-1 mixed integer programming problem and was decomposed into the set of all feasible combinations (Problem II). The economic return was determined for each combination (Problem III). Problem II was solved by a branch and bound procedure which selected each feasible combination of dams while the optimal return for each such combination (Problem III) was found by a network analysis code.     

门限自回归模型在海洋冰情预测中的应用

给出了建立门限自回归模型（ＴＡＲ） 的一套实用方法, 用作者提出的改进遗传算法可同时优化门限值和自回归系数,成功地解决了ＴＡＲ建模过程所涉及的大量复杂寻优工作这一难题。实例计算的结果说明了, 这套方法在海洋冰情预测中是可行的和有效的, 在各种自然灾害非线性时序动态预测中具有重要的理论意义和广泛的实用价值。