Modelling Replicated Weed Growth Data using Spatially-varying Growth Curves

Weed growth in agricultural fields constitutes a major deterrent to the growth of crops, often resulting in low productivity and huge losses for the farmers. Therefore, proper understanding of patterns in weed growth is vital to agricultural research. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data, which enable more sophisticated spatial analysis. Our current application concerns the development of statistical models for conducting spatial analysis of growth patterns in weeds. Our data comes from an experiment conducted in Waseca, Minnesota, that recorded growth of the weed Setariaspp. We capture the spatial variation in Setaria spp. growth using spatially-varying growth curves. An added challenge is that these designs are spatially replicated, with each plot being a lattice of sub-plots. Therefore, spatial variation may exist at different resolutions – a macro level variation between the plots and micro level variation between the sub-plots nested within each plot. We develop a Bayesian hierarchical framework for this setting. Flexible classes of models result which are fitted using simulation-based methods.     

单面受火的方中空夹层钢管混凝土短柱耐火极限

为研究单面受火的方中空夹层钢管混凝土短柱耐火极限,在合理选择材料本构关系和边界条件的基础上,利用ABAQUS软件建立了高温下方中空夹层钢管混凝土短柱温度场和力学场分析模型,通过与试验数据对比验证,结果吻合良好。基于此模型,对不同的混凝土强度、钢管强度、内钢管厚度、空心率、荷载比进行耐火极限参数分析,并定量给出计算此类构件耐火极限的简化公式。研究结果表明:荷载比是构件耐火极限的主要影响参数。荷载比越大,构件的耐火极限越小。     

火灾后内配H型钢的矩形钢管混凝土柱剩余承载力有限元分析

该文采用ABAQUS有限元软件建立了火灾后内配H型钢的矩形钢管混凝土柱模型,计算了常温下与火灾后该构件的轴向荷载-位移关系曲线,对火灾后的受力性能进行了分析;此外与相同含钢率的钢管混凝土柱构件火灾后剩余承载力进行了对比,发现内配型钢钢管混凝土柱剩余承载力较钢管混凝土柱得以增强,且构件延性优于钢管混凝土;同时对比了强、弱轴两种加载方式下该类构件火灾后剩余承载力,最后对影响火灾后该类构件剩余承载力的主要因素进行了参数分析,发现升温时间、截面周长和长细比对其影响较为显著。     

Generality of Models that Predict the Distribution of Species: Conservation Activity and Reduction of Model Transferability for a Threatened Bustard

Abstract:  Predictive models can help clarify the distribution of poorly known species but should display strong transferability when applied to independent data. Nevertheless, model transferability for threatened tropical species is poorly studied. We built models predicting the incidence of the critically endangered Bengal Florican ( Houbaropsis bengalensis ) within the Tonle Sap (TLS) floodplain, Cambodia. Separate models were constructed with soil, land-use, and landscape data and species incidence sampled over the entire floodplain (12,000 km²) and from the Kompong Thom (KT) province (4000 km²). In each case, the probability of Bengal Florican presence within randomly selected 1 × 1 km squares was modeled by binary logistic regression with multimodel inference. We assessed the transferability of the KT model by comparing predictions with observed incidence elsewhere in the floodplain. In terms of standard model-validation statistics, the KT model showed good spatial transferability. Nevertheless, it overpredicted florican presence outside the KT calibration region, classifying 491 km² as suitable habitat compared with 237 km² predicted as suitable by the TLS model. This resulted from higher species incidence within the calibration region, probably owing to a program of conservation education and enforcement that has reduced persecution there. Because both research and conservation activity frequently focus on areas with higher density, such effects could be widespread, reducing transferability of predictive distribution models.     

A flexible intelligent algorithm for identification of optimum mix of demographic variables for integrated HSEE-ISO systems: The case of a gas transmission refinery

This study proposes a flexible intelligent algorithm for assessment and optimization of demographic features on integrated health, safety, and environment and ergonomics (HSEE)-ISO systems among operators of a gas transmission refinery. To achieve the objectives of this study, standard questionnaires with respect to HSEE and ISO standards are completed by 80 operators. Demographic features include age, education, gender, weight, stature, marital status, and work type. The average results for each category of HSEE are used as inputs and effectiveness of ISO systems (ISO 18000, ISO 14000, and ISO 9000) are used as output for the intelligent algorithm. Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) in addition to conventional regression are used in this paper. Result shows the applicability and superiority of the flexible intelligent algorithm over conventional methods through mean absolute percentage error (MAPE). Computational results show that the proposed ANN performs better than ANFIS and conventional regressions based on its relative error. Finally, the optimum mix of demographic variables from viewpoint of HSEE and ISO are identified. This is the first study that proposes a flexible intelligent algorithm for assessment of optimum mix of demographic features for HSEE and ISO systems in a complex system such as a gas transmission refinery.     

摩托车齿轮类零件的工艺规程自动生成系统的研制

此文介绍了摩托车齿轮类零件的工艺规程自动生成系统。该系统直接从CAD系统中获取零件的有关信息,根据此文所建立的特征体素描述方法,自动生成零件的特征编码,采用零件自动识别的方法实现CAD/CAPP的信息集成,利用工艺知识库,通过工艺决策模型的分级推理,实现齿轮类零件的工艺过程设计的自动化。     

Application of an evolutionary algorithm to the inverse parameter estimation of an individual-based model

Inverse parameter estimation of individual-based models (IBMs) is a research area which is still in its infancy, in a context where conventional statistical methods are not well suited to confront this type of models with data. In this paper, we propose an original evolutionary algorithm which is designed for the calibration of complex IBMs, i.e. characterized by high stochasticity, parameter uncertainty and numerous non-linear interactions between parameters and model output. Our algorithm corresponds to a variant of the population-based incremental learning (PBIL) genetic algorithm, with a specific “optimal individual” operator. The method is presented in detail and applied to the individual-based model OSMOSE. The performance of the algorithm is evaluated and estimated parameters are compared with an independent manual calibration. The results show that automated and convergent methods for inverse parameter estimation are a significant improvement to existing ad hoc methods for the calibration of IBMs.     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

基于模糊神经网络的A2/O工艺出水氨氮在线预测模型

采用厌氧/缺氧/好氧污水处理系统(A2/O)对人工合成污水进行处理,并利用人工神经网络(ANN)模型和自适应模糊人工神经网络(ANFIS)模型对A2/O处理污水的过程进行仿真模拟.在MATLAB环境下,选取可在线监测的水力停留时间(HRT)、进水pH值(pH)、好氧池溶解氧(DO)和混合液回流比(r)作为输入参量,系统出水氨氮浓度(NH4+eff)为输出量,建立在线预测模型.结合自适应模糊C均值聚类算法,确定ANFIS模型的模糊规则数及最优运行参数,对实验数据进行仿真预测.结果表明,与ANN模型相比,ANFIS模型的仿真输出值与实际值的拟合程度更高,相对误差在6.45%之内,平均绝对百分比误差(MAPE)为2.8%,均方根误差(RMSE)为0.1209,相关系数(R)达0.9956.模型训练过程中所得到的三维曲面图,可直观的反映各因素与出水氨氮浓度之间的非线性函数关系,为A2/O系统的高效稳定运行提供指导.