考虑空气影响的干旱区土壤水分运移模式研究

在前人研究的基础上,根据石河子垦区的实际情况,确定模型特定的边界条件及参数,建立适合该区的包气带-饱和带水、气二相流的质热传输模型,模拟土壤水分、温度的动态变化,并利用实测土壤温度、土壤含水率数据进行模型检验。结果表明:模拟的土壤温度和含水率能够较好地反映实测值的变化规律,各土层模拟精度评价指标表明考虑空气影响的模拟结果更接近实测结果。对于9.7 mm日降水量的入渗深度,耦合模型可影响20 cm深土壤含水率,单相模型可影响到30 cm深左右。降水当日考虑空气模拟的蒸发速率比不考虑空气模拟的蒸发速率大7.7%;降水后1 d,不考虑空气影响导致蒸发速率比考虑空气的小3.19%。计算模拟期2种模型土壤总蒸发量,相差不大。根据2种模型水量平衡分析结果对比蒸发量的计算值与模拟值,发现考虑空气影响的模型模拟结果更可靠。     

大功率拖拉机强制润滑的仿真和试验

为了研究大功率拖拉机强制润滑系统的流量分配特性,基于AMESim分析平台建立了某系统的热液压流体模型,仿真得出强制润滑系统的流量分配特性,并用试验证明了热液压仿真结果的正确性,为液压润滑系统流量分配的改进设计提供依据。     

可追溯农产品的消费者购买意愿调查--以乌鲁木齐市为例

2013年在乌鲁木齐市对消费者进行了包括消费者的基本特征及其对可追溯农产品的认知程度和购买意愿的实证调查,并且运用二元Logistic模型分析了影响消费者购买意愿的主要因素。结果表明：婚姻状况、民族、文化程度、职业、价格等因素对消费者购买可追溯农产品的购买意愿有显著影响。     

添加配重的五杆式丹参移栽机构动力学优化设计与试验

配重法能降低五杆式丹参移栽机构的振动,改善其动力学性能.为获取所添加配重的最优参数,建立五杆式丹参移栽机构的动力学模型,在动力学模型的基础上建立移栽机构的动力学优化模型,运用多目标函数获取机构不同转速下所添加配重的优化参数,通过动力学分析,确定曲柄转速为60 r/min时所对应的配重参数为最优参数组合:曲柄AB添加配重...     

悬链线和抛物线理论在金枪鱼延绳钓渔业中的应用

本文依次用悬链线和抛物线理论分析推导了金枪鱼延绳钓具的两种投放方法以及在渔场各水层相同流态下钓具充分沉降后所有钓钩位置包括深度和相互间水平间距的计算公式 ,可用于作业参考。     

杭州湾南岸滩涂贝类7种养殖模式的比较研究

在杭州湾南岸滩涂,设计7种贝类养殖模式:平涂单养泥螺(Ⅰ)、平涂单养彩虹明樱蛤(Ⅱ)、蓄水单养青蛤(Ⅲ)、平涂混轮养泥螺和彩虹明樱蛤(Ⅳ)、平涂混养缢蛏和彩虹明樱蛤(Ⅴ)、蓄水混养缢蛏和斑节对虾(Ⅵ)和蓄水混养缢蛏和青蛤(Ⅶ),经养殖试验对它们的养殖效益作了比较。结果表明,7种养殖模式的产值/投入比值差异不显著,在1.43~1.55之间;而其效益(利润)差异较大,在2.94~21.04万元/hm2,其中以模式Ⅵ最高,是模式Ⅰ的7倍多和模式Ⅲ的4倍,模式Ⅳ比Ⅰ高86.1%,模式Ⅴ比Ⅱ高62.66%,模式Ⅲ比Ⅰ或Ⅱ高70%左右,模式Ⅳ比Ⅴ高9.81%,模式Ⅰ与Ⅱ、模式Ⅶ与Ⅲ等效益相差不大。按生态及经济效益评价这7种滩涂贝类放养模式的结果揭示,以蓄水混轮养不同栖息习性和不同食性种类(贝虾混养),充分利用空间生态位和营养生态位者为最佳;对无法蓄水的平涂以螺蛤或螺蛏混轮养为好。     

Catch per unit effort standardization of the eastern Bering Sea walleye pollock (Theragra chalcogramma) fleet

A general linear model (GLM) was used to standardize catch per unit effort (CPUE) data for Alaska walleye pollock (Theragra chalcogramma) from the Bering Sea fleet for the years 1995–1999. Data were stratified temporally by year and season and spatially by area using either Alaska Department of Fish and Game (ADF&G) or National Marine Fisheries Service (NMFS) reporting areas. Four factors were used: vessel identification (ID) number, vessel speed, percentage of pollock by weight in the haul (a measure of targeting), and whether most of the haul took place before or after sunset. At least 29 combinations of main effects, quadratic covariates, and interactions were tested for each year/area/season stratum. GLM models explained from 31 to 48% of the total sums of squares. Vessel identification number was included in all models and explained the most variability. Of the remaining factors, the square of the percentage of pollock in the haul was included in most models, following an F-test to determine parsimony. Analysis of the vessel identification number coefficients indicated that larger vessels tended to have higher CPUEs; and that this relationship differed between dedicated catcher vessels and offshore catcher processors. Coefficient estimates and response surfaces generally indicated increased CPUEs with the percentage of pollock in the haul and showed mixed results with vessel speed. The vessel identification number incorporated most vessel characteristics, leaving vessel speed primarily as a fitting variable with less biological meaning. The year/area/season stratification procedure was found to be necessary due to the unbalanced design, which otherwise would have factor levels with no data in a large combined model. In addition, the stratification procedure reduced the variability in CPUE substantially.     

A generalized linear mixed model analysis of a multi-vessel fishery resource survey

A generalized linear mixed model (GLMM) that treats year and spatial cell as fixed effects while treating vessel as a random effect is used to examine fishing power among chartered industry-based vessels and a research trawler, the FRV Miller Freeman, for bottom trawl surveys on the upper continental slope of U.S. West coast. A Bernoulli distribution is used to model the probability of a non-zero haul and the gamma distribution to model the non-zero catch rates of four groundfish species. The use of vessel as a random effect allows the data for the various vessels to be combined and a single continuous time-series of biomass indices to be developed for stock assessment purposes. The GLMMs fit the data reasonably well. Among the different models examined, the GLMM incorporating a random vessel × year effect had the smallest ΔAIC and was thus chosen as the best model. Also, estimated random effects coefficients associated with the industry-based vessels and the FRV Miller Freeman for each year suggests that these vessels can be assumed to be from a common random effects distribution. These results suggest that combining data from the chartered industry-based vessels and from the research trawler may be appropriate to develop indices of abundance for stock assessment purposes. Finally, an evaluation of variances associated with abundance indices from the different models indicate that analyzing these data as a fixed effect GLM may underestimate the level of variability due to ignoring the grouped nature of tows within vessels. As such, use of a mixed model approach with vessel as a random effect is a reasonable approach to developing abundance indices and their variances.