基于GPR-TOPSIS方法的充填配比多目标多属性优化

为获得某金矿尾砂胶结充填材料最优配比,基于试验结果,以海水比例、灰砂比和料浆质量浓度为输入参数,以充填体强度、塌落度及泌水率为输出参数,建立了充填配比与其响应量的高斯过程回归模型,分析了不同因素对充填性能的影响程度;采用遗传算法对高斯过程回归模型进行多目标参数优化,获得了Pareto非劣解,在此基础上,引入多属性决策的TOPSIS法对Pareto非劣解进行方案优选,确定了充填最优配比。研究结果表明:高斯过程回归模型相对误差值均小于6%,可靠性高;灰砂比及料浆质量浓度对充填性能影响较为显著,采用海水作为充填水源将降低充填体的强度;经优化后的充填配比与试验结果相符。     

Disentangling horizontal pay dispersion: Experimental evidence

Although horizontal pay dispersion has been explored extensively using cross‐sectional field methods, it has received little attention using the control available through experimental designs. Many of the questions relevant to pay dispersion research can be addressed by taking an individual‐level experimental approach because this allows for clean separation of pay policies and individual effects. In this paper, we hypothesize both the motivation‐based and affect‐based effects of pay dispersion policies and test our hypotheses with a sample of over 400 participants in a real‐pay, real‐effort experiment. Results of the experiment provide support that two pay dispersion‐related pay policies, performance‐based pay dispersion and allocation criteria, have unique effects. Whereas motivation and performance effects are direct, pay satisfaction and interest in continuing work effects are the result of interactions, incorporating the performance level of workers. In follow‐up analyses, we find evidence that the temporal nature of responses to pay dispersion should be incorporated into future studies of pay dispersion.     

蔬菜施不同肥料对产量和土壤肥力的贡献

为控制施肥对蔬菜和土壤的负面影响，充分利用施肥对土壤肥力的保护功能，探讨蔬菜合理的有机肥施用量，在不同质地土壤上，田间试验小白菜、苦瓜、豇豆三种蔬菜施不同量有机肥，收获时测定其产量、品质及试验前后的土壤有机质质量分数。结果表明，(1)在施适量无机NPK的条件下，每造蔬菜的有机肥(禽畜粪便，下同)施用量，砂土上小白菜为4500kg／hm^2、苦瓜和豇豆为3000kg/hm^2；在壤土上，小白菜和苦瓜均宜施3000kg/hm^2，豇豆则宜施4500kg／hm^2；在粘土上，小白菜每造只宜施1500～3000kg／hm^2的有机肥。(2)有机肥必须与无机肥合理配合施用，才能获得最佳的产量和品质效果。(3)施有机肥3000-4500kg/hm^2 适量无机肥可使土壤有机质水平保持在种植蔬菜前的质量分数水平，该施肥量和方式不仅可使蔬菜高产、优质，还有利于保护土壤肥力。(4)对蔬菜产量的贡献，有机肥和化肥配合施用的处理对蔬菜产量的贡献最大，来自土壤的效应由小到大依次为有机肥和化肥配合施用→化肥→有机肥，进一步反映合理施肥有利于保护土壤肥力。     

环境质量的模糊综合评判—灰色关联分析

本文简要分析了环境质量评价中目前广泛应用的各种评价方法与模型。在此基础上提出环境质量综合评判－灰色关联优势分析复合模型，应用于黄山风景区景观生态环境质量的综合评价，取得了满意的结果。     

钾对龙眼产量和品质的影响

为了提高龙眼产量，提高钾肥的施肥效益，于1993～1995年进行龙眼果园不同钾肥用量的施肥试验．结果表明，龙眼在施用氮碗肥及补充土壤缺乏的中微量元素养分的基础上，增施钾肥可增大果型、提高单果重，显著增加果实产量，并可改善果实品质；龙眼增施钾肥有明显的经济效益．提出龙眼适宜的施钾量为：每株每产50kg鲜果树面年施K2O1.2kg．     

基于BP神经网络算法的密云水库水质参数反演研究

密云水库是北京市重要的地表饮用水源地,在保障首都水源安全方面起着重要作用,而密云水库水质参数的区域监测已成为当前亟待解决的问题。为了实现密云水库水质参数大范围、实时获取,该文基于遥感和GIS技术,采用BP神经网络算法,结合地面监测数据和Landsat 8遥感影像,分别建立了反演总磷、总氮、氨氮和COD(化学需氧量)4个水质参数的BP神经网络模型,并反演了密云水库2013-2018年非结冰期主要水质参数,分析了密云水库主要水质参数的年际变化特征、季节变化特征和空间分异特征。结果表明,(1)水质参数的Landsat 8敏感波段分别为:总氮为1、4波段,氨氮为1-7波段,总磷为1、3-7波段,COD为2-5波段。(2)密云水库主要水质参数在2013-2018年总体呈下降趋势,氨氮和COD为Ⅰ类水质,总磷为Ⅱ类水质,总氮为Ⅲ类水质。(3)4个水质参数指标春季最高、秋季次之、夏季最低,总氮、总磷、氨氮和COD的春季值分别是夏季值的1.08、1.36、1.6、1.45倍。(4)密云水库不同水质参数的空间差异性较大,总体来看,水库北部和东部的4个水质参数含量相对较高,这种分布与北部和东部村庄密集以及密云水库两大入库河流有关。综上所述,基于BP神经网络算法的密云水库水质反演研究是可行的,且得到了较为可信的研究结果,该研究可为密云水库水质管理与政策制定提供重要的科学依据。     

论环境、资源与循环经济

根据质量守恒定律推导出环境与资源的数学关系,从而揭示了环境污染产生的根本原因,找到了解决环境和资源问题的根本途径,为发展循环经济,实现环境与资源可持续发展提供了科学的依据。     

天空蓝色饱和程度与城市空气质量等级的相关性

以大气散射理论为基础,结合颜色学、大气环境监测及城市空气质量日报等相关原理,从理论分析与实证研究2个方面证实了天空蓝色饱和程度与城市空气质量等级之间存在着显著的负相关性,即:天空蓝色饱和程度越低,空气污染等级越高,空气质量状况越差;天空蓝色饱和程度越高,空气污染等级较低,空气质量状况越好;通过相关及回归分析与检验,制得“城市空气质量等级目测色卡”,适用于晴朗天气肉眼观察天空颜色,进而目测估计空气质量等级状况,以采取必要防护措施。     

Examination of model predictions at different horizontal grid resolutions

While fluctuations in meteorological and air quality variables occur on a continuum of spatial scales, the horizontal grid spacing of coupled meteorological and photochemical models sets a lower limit on the spatial scales that they can resolve. However, both computational costs and data requirements increase significantly with increasing grid resolution. Therefore, it is important to examine, for any given application, whether the expected benefit of increased grid resolution justifies the extra costs. In this study, we examine temperature and ozone observations and model predictions for three high ozone episodes that occurred over the northeastern United States during the summer of 1995. In the first set of simulations, the meteorological model RAMS4a was run with three two-way nested grids of 108/36/12 km grid spacing covering the United States and the photochemical model UAM-V was run with two grids of 36/12 km grid spacing covering the eastern United States. In the second set of simulations, RAMS4a was run with four two-way nested grids of 108/36/12/4 km grid spacing and UAM-V was run with three grids of 36/12/4 km grid spacing with the finest resolution covering the northeastern United States. Our analysis focuses on the comparison of model predictions for the finest grid domain of the simulations, namely, the region overlapping the 12 km and 4 km domains. A comparison of 12 km versus 4 km fields shows that the increased grid resolution leads to finer texture in the model predictions; however, comparisons of model predictions with observations do not reveal the expected improvement in the predictions. While high-resolution modeling has scientific merit and potential uses, the currently available monitoring networks, in conjunction with the scarceness of highly resolved spatial input data and the limitations of model formulation, do not allow confirmation of the expected superiority of the high-resolution model predictions.The U.S. Governments right to retain a non-exclusive royalty-free licence in and to any copyright is acknowledged.     

Probability distributions and statistical inference for axial data

Observations on axes which lack information on the direction of propagation are referred to as axial data. Such data are often encountered in enviromental sciences, e.g. observations on propagations of cracks or on faults in mining walls. Even though such observations are recorded as angles, circular probability models are inappropriate for such data since the constraint that observations lie only in [0, π) needs to be enforced. Probability models for such axial data are argued here to have a general structure stemming from that of wrapping a circular distribution on a semi-circle. In particular, we consider the most popular circular model, the von Mises or circular normal distribution, and derive the corresponding axial normal distribution. Certain properties of this distribution are established. Maximum likelihood estimation of its parameters are shown to be surprisingly, in contrast to trigonometric moment estimation, numerically quite appealing. Finally we illustrate our results by several real life axial data sets. Received: September 2004/ Revised: December 2004