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.     

Elevated ozone affects the genetic composition of Plantago lanceolata L. populations

The genetic composition and diversity of Plantago lanceolata L. populations were analysed using amplified fragment length polymorphism (AFLP) as well as simple sequence repeat (SSR) markers to test for differences in an old semi-natural grassland after five years of treatment with ambient or elevated ozone (O3) using a free-air fumigation system. Genetic diversity in populations exposed to elevated O3 was slightly higher than in populations sampled from control plots. This effect was significant for AFLP-based measures of diversity and for SSR markers based on observed heterozygosity. Also, a small but significant difference in genetic composition between O3 treatments was detected by analysis of molecular variance and redundancy analysis. The results show that micro-evolutionary processes could take place in response to long-term elevated O3 exposure in highly diverse populations of outbreeding plant species.     

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.     

基于马尔科夫链-多目标模型的应急供应链决策优化研究*

为应对洪涝、新冠肺炎疫情等突发灾难,提出供应物资满足率最大、供应时间最短、供应成本最低的离散时间马尔科夫链-多目标规划模型（DTMC-MOP）,动态地识别、分析、应对应急供应链风险;采用改进自适应NSGA-Ⅱ算法求解优化模型,并通过标准测试函数进行测试与评价,验证模型的可行性和有效性;通过算例分析,获得精度更高、分布更均匀的Pareto最优前沿。研究结果表明:决策者可以依据应急管理核心目标或不同偏好选择相适应的应急方案,研究结果可为应急供应链决策优化提供1种科学方法,对保障灾民生命安全、维护社会和谐稳定具有积极意义。     

基于可信性理论的水资源优化配置模型

针对城市水资源与水环境系统中存在的不确定性与复杂性,提出了模糊环境下基于可信性理论的CFCCP模型(可信性模糊机会约束规划模型),并将其应用于衡水市水资源优化配置模型的研究中. CFCCP模型以衡水市经济、社会与环境效益最大化为目标,以供需水量、区域协调发展等为不确定性约束,以各水源在各子区不同部门间的分配为决策变量,并且设计了基于模糊模拟的猴群算法进行求解. CFCCP模型可反映不确定性因素对系统收益的影响,计算了各规划水平年置信水平为08时的水资源配置风险-收益权衡方案,得到2015年和2025年的区域经济效益分别为198 36530×104和266 22730×104元,缺水量分别为25 29936×104和20 99096×104 m3,污染物排放量分别为12 80847和13 00044 t. 同时,通过与传统优化算法的比较,证明了基于模糊模拟的猴群算法在求解多维优化问题时的有效性.      

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.     

Determining the community structure of the coral Seriatopora hystrix from hydrodynamic and genetic networks

The exchange of genetic information between coral reefs through the transport of larvae can be described in terms of networks that capture the linkages between distant populations. A key question arising from these networks is the determination of the highly connected modules (communities). Communities can be defined using genetic similarity or distance statistics between multiple samples but due to limited specimen sampling capacity the boundaries of the communities for the known coral reefs in the seascape remain unresolved. In this study we use the microsatellite composition of individual corals to compare sample populations using a genetic dissimilarity measure (F_ST) which is then used to create a complex network. This network involved sampling 1025 colonies from 22 collection sites and examining 10 microsatellites loci. The links between each sampling site were given a strength that was created from the pair wise F_ST values. The result is an undirected weighted network describing the genetic dissimilarity between each sampled population. From this network we then determined the community structure using a leading eigenvector algorithm within graph theory. However, given the relatively limited sampling conducted, the representation of the regional genetic structure was incomplete. To assist with defining the boundaries of the genetically based communities we also integrated the communities derived from a hydrodynamic and distance based networks. The hydrodynamic network, though more comprehensive, was of smaller spatial extent than our genetic sampling. A Bayesian Belief network was developed to integrate the overlapping communities. The results indicate the genetic population structure of the Great Barrier Reef and provide guidance on where future genetic sampling should take place to complete the genetic diversity mapping.