环境空气质量评价的普适指数公式

在适当设定空气污染物的基准值和用相对于基准值的空气污染物的“相对”值情况下,采用S型曲线表示空气污染物对环境空气质量的危害程度,并应用遗传算法优化公式中的参数,得出了一个对多种空气污染物通用的环境空气质量评价的普适指数公式。借助于折衷型激励功效函数将单基污染物分指数加权为综合指数。该公式形式简单,容量计算、评价结果精度较高,且具有可比性。     

Colony fission affects kinship in a social insect

Establishment of new groups is an important step in the life history of a social species. Fissioning is a common mode not only in group proliferation, for instance, as a regular part of the life cycle in the honey bee, but also when multiple females reproduce in the same group, as in multiple-queen ant societies. We studied the genetic consequences of fissioning in the ant Proformica longiseta, based on DNA microsatellites. In P. longiseta, new nests arise by fissioning from the old ones when they grow large, and the daughter nests consist of workers and queens or queen pupae but never both. Our results show that fissioning is not entirely random with respect to kinship. Workers tend to segregate along kin lines, but only when the initial relatedness in the parental nests is low. Workers in a daughter nest also tend to be associated with closely related adult queens, whereas such an association is not detected between workers and queen pupae. Most queens and workers are carried to the daughter nest by a specialized group of transporting workers, suggesting active kin discrimination by them. Fissioning pattern in P. longiseta is different from that found in other social insects with regular fission (e.g., the honey bee, swarm-founding wasps), where no fissioning along kin lines has been found. It does, however, resemble fissioning in another group of social animals: primates.     

应急供水规划调控方案优化模型的研究

目前应急供水方案通常是根据经验进行确定,缺乏对于各种方案及其成本的比较。本文以水源突发性事件造成的供水短缺为前提,将应急供水系统的方案分成备用水源供水、污染水的应急处理、水量的压缩3个方面,建立了符合实际的应急供水系统方案优化模型。运用遗传算法,得到新建应急供水系统的成本并求得其最优值。将该模型用于东莞市应急供水规划调控方案,可以为实际应急供水方案提供依据和指导。     

Monitoring and estimating the flow conditions and fish presence probability under various flow conditions at reach scale using genetic algorithms and kriging methods

The combination of current velocity and water depth influences stream flow conditions, and fish activities prefer particular flow conditions. This study develops a novel optimal flow classification method for identifying types of stream flow based on the current velocity and the water depth using a genetic algorithm. It is applied to the Datuan stream in northern Taiwan. Fish were sampled and their habitat investigated at the study site during the spring, summer, fall and winter of 2008-2009. The current velocity, water depth and maps of the presence probability of fish were estimated by ordinary and indicator kriging. The optimal classification results were compared with the classification results obtained using the Froude number and empirical methods. The flow classification results demonstrate that the proposed optimal flow classification method that considers depth-velocity and optimally identified criteria for classifying flow types, yields a current velocity and water depth of 0.32 (m/s) and 0.29 (m), respectively, and classifies the flow conditions in the study area as pool, run, riffle and slack. The variography results of the current velocity and the water depth data reveal that seasonal flows are not spatially stationary among seasons in the study area. Kriging methods and a two-dimensional hydrodynamic model (River 2D) with empirical and optimal flow classification methods are more effective than the Froude number method in classifying flow conditions in the study area. The flow condition classifications and probability maps were generated by River 2D, ordinary kriging and indicator kriging, to quantify the flow conditions preferred by Sicyopterus japonicus in the study area. However, the proposed optimal classification method with kriging and River 2D is an effective alternative method for mapping flow conditions and determining the relationship between flow and the presence probability of target fish in support of stream restoration.     

基于改进遗传算法率定水质参数的研究

提出一种新的优化准则函数,结合改进的加速遗传算法,能够求得全局最优的水质参数。实例计算结果表明,新算法具有较高的精确度和良好的优化性能。     

Real-time leak detection in oil pipelines using an Inverse Transient Analysis model

Based on Inverse Transient Analysis (ITA) method, a real-time leak detection method is proposed to capture leak location and the associated leak rate in oil pipe conveyance systems. In the proposed approach, location and flow rate of leak (if any), the fluid properties, as well as physical parameters of the system, are calculated in consecutive periods through minimizing the discrepancy between the calculated and measured flow parameters of the system. The method of characteristics is employed to numerically calculate the transient responses of the system and the genetic algorithm is utilized as the optimization engine. The proposed approach was applied to several real pipeline systems in which the required transient flow data are either directly collected from the field or fabricated with a third-party numerical software. Extensive numerical explorations were conducted to investigate the performance of the proposed method in real-time leak detection and to determine the extent to which field data errors, stemming from Supervisory Control and Data Acquisition (SCADA) systems and measurement equipment, affect the leak flow rate and location detectability of the proposed approach. The results show that the proposed approach provides promising results under a variety of transient and steady-state flow conditions even in the case with small leak flow rate of around 2% of the line rate. The results also reveal that the noises in the measurement data and the errors originated from SCADA systems do not significantly compromise the leak detectability of the proposed approach, confirming that the proposed approach can be utilized in practice.     

Chromosome damage in individuals exposed to heavy metals†

Testing the mutagenic activity of environmental pollutants has become an important area of modern environmental science and prophylactic medicine. The most suitable method for short‐term mutagenicity testing on man, at present, are chromosome studies on somatic cells of exposed individuals. Mutation types analyzed by such studies are of high practical relevance as indicator system of genetic damage induced in man under in vivo conditions. A rather large series of such studies has been dedicated to the action of heavy metals on individuals contacted with these metals under therapeutic, ecological or occupational conditions or by intoxication. Lead, cadmium, chromium, nickel, mercury, zinc and other metals as well as their compounds have been under study. Analyses of that kind, of course, are hampered by difficulties with the distinct estimation of the actual load as well as unclear conditions of exposition, e.g. simultaneous exposition to different metals.

Results obtained till now arouse some suspicion of a direct or indirect mutagenic activity in man by certain chromium and platinum compounds, arsenic, mercury, and combinations of lead with other heavy metals (cadmium, zinc, arsenic, antimony, etc.). Life style, above all smoking habits, well may act comutagenic. In most cases, however, mutagenic activity of metals and metal compounds apparently is clearly superposed by their toxic activity. In specific cases, chromosome studies also may contribute to discover sources of ecological exposition and to monitor occupational load by heavy metals.     

Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks’ maximum tangential stress σ_θ, rocks’ uniaxial compressive strength σ_c, rocks’ uniaxial tensile strength σ_t, stress coefficient σ_θ/σ_c, rock brittleness coefficient σ_c/σ_t and elastic energy index W_et. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.