单栋塑料温室内多因子综合CFD稳态模拟分析

为分析单栋塑料温室内的综合环境:气流场、温度场、湿度场、CO2浓度场,建立了包括温室内外空间、室内作物和土壤层等的温室环境几何模型。将温室内的湿空气看作水蒸气、CO2和干空气的混合气体,在分析温室中太阳辐射、作物与环境的质热交换,动量及质能传递过程的基础上,对单栋塑料温室内的环境因子进行了稳态模拟。温室内热辐射传递过程采用蒙特卡罗法模拟方法;将室内作物简化为连续固体换热模型,采用剪应力输运模型(SST)表述温室内的空气紊流。结果显示:温室通风对温度、湿度和CO2分布的影响很大,温室内部上风向温度低,湿度小,同时CO2浓度也不高;温室下风向作物冠层的环境未达到优化状态;模型的预测值低于实测值,但变化规律相似,温度、湿度、CO2含量的预测相对误差分别低于8%、6%和7%。     

差分生长模型预测误差的分析

差分模型是一种特殊随机参数模型,仅有一个参数为随机参数。对于未参与抽样建模的林分,差分模型首先对应变量在林龄Aij0时的期望函数求解关于随机参数的表达式,然后用非随机参数的估计值和应变量在Aij0时的观测值Yij0分别取代对应参数和数学期望E(Yij0)来估计随机参数。显而易见,Yij0相当于E(Yij0)的估计值。由于这种特有的统计特征,经典非线性回归模型不能准确地估计差分模型预测误差的方差。针对这一不足,依据非线性回归模型预测误差的方差估计量的推导过程,导出了一个适用于差分模型的预测误差的方差估计量,并给出一个应用示例。所提出的估计量充分地考虑了重复观测数据的自相关性和Yij0对预测的影响作用。结果表明,该估计量能够描述未抽样林分预测误差的方差及其构成分量的变化趋势,而对于抽样建模的林分应该使用非线性回归模型的估计量进行预测误差分析。      

新一轮最低收购价政策调整对稻谷生产的影响研究——基于广义合成控制法的分析

在当前世界百年未有之大变局和中华民族伟大复兴战略全局两个“大局”下,国内粮食支持保护政策面临新的要求和挑战。自2015年国家开启新一轮最低收购价政策改革以来,政策连续调整对我国稻谷生产及粮食安全带来的影响值得关注。本文聚焦新发展阶段最低收购价政策调整的背景,基于2004—2019年全国省级面板数据,采用广义合成控制法,分析新一轮政策调整对稻谷播种面积、单产及产量影响的作用机理与政策效应。研究结果表明：新一轮政策调整对稻谷播种面积有显著的负向影响,且政策作用效果具有明显的区域异质性,对稻谷产量波动产生一定的风险。进一步分析发现,在最低收购价格不再连续上调的压力下,稻谷播种面积出现明显调减,产量增加主要依赖于单产提升。因此,需关注本轮政策调整的影响及风险,在保持国内最低收购价政策基本框架稳定的前提下,坚持循序渐进主原则和市场调节主方向,进一步推动政策改革深化,同时完善配套支持政策,加强生物育种和科技创新,牢牢守住国家粮食安全的底线。     

长江近口段鳗苗捕捞量的时间格局及其与生态因子的GAM模型分析

为研究长江口鳗苗捕捞量与生态因子的相互关系,于2012年汛期对长江靖江段鳗苗的捕捞量进行了监测,采用广义可加模型(GAM)对日捕捞量与水温、潮差、气压、浑浊度等生态因子之间的相关性作了分析。结果显示,靖江段鳗苗汛期为1月下旬—4月上旬,单船总捕捞量为221~443尾,平均(344.8±83.4)尾。1月均值仅0.4尾/d,且空网率高达90.9%;4月为旺汛期,均值10.4尾/d,空网率仅为10.0%。GAM模型显示,潮汐周期—月份交互项、水温和潮差对鳗苗日捕捞量的影响显著,而气压、浊度和月相周期对鳗苗日捕捞量的影响不显著。潮汐周期—月份交互项、水温和潮差对鳗苗日捕捞量的偏差解释率分别为42.4%、19.1%和13.1%,均呈现正相关关系。统计也显示,日捕捞量表现出上、下弦月较低、新月或满月前后较高的半月周期波动。鳗苗捕获的最低水温为6.3℃,而10~15℃为适宜捕捞水温。高潮期和低潮期分别占总捕捞量的76.8%和23.2%。研究表明,长江口鳗苗在借助潮汐流而快速溯河的过程中,部分在口门水域即被捕获,部分滞留在了长江河口段,而影响鳗苗溯河的重要生态因子是潮汐和水温。     

Spatio‐temporal trends of sailfish,Istiophorus platypterus catch rates in relation to spawning ground and environmental factors in the equatorial and southwestern Atlantic Ocean

Spatial and temporal trends of sailfish catch rates in the southwestern and equatorial Atlantic Ocean in relation to environmental variables were investigated using generalized additive models and fishery‐dependent data. Two generalized additive models were fit: (i) ‘spatio‐temporal’, including only latitude, longitude, month, and year; and (ii) ‘oceanographic’, including sea surface temperature (SST), chlorophyll‐a concentration, wind velocity, bottom depth, and depth of mixed layer and year. The spatio‐temporal model explained more (average ~40%) of the variability in catch rates than the oceanographic model (average ~30%). Modeled catch rate predictions showed that sailfish tend to aggregate off the southeast coast of Brazil during the peak of the spawning season (November to February). Sailfish also seem to aggregate for feeding in two different areas, one located in the mid‐west Atlantic to the south of ~15°S and another area off the north coast of Brazil. The oceanographic model revealed that wind velocity and chlorophyll‐a concentration were the most important variables describing catch rate variability. The results presented herein may help to understand sailfish movements in the Atlantic Ocean and the relationship of these movements with environmental effects.     

约束形式下广义最大元Nash均衡的存在性

运用广义最大元方法在非传递性偏好下给出了博弈均衡的存在性定理，推广了一些经典的博弈均衡存在性定理.在文中介绍策略式博弈的Nash均衡具有宽泛的条件，在微观经济理论中有广泛的应用.     

中籼稻穗部性状遗传力研究

本文研究了中籼稻穗部性状的广义遗传力和变异系数。其分别为:一次枝梗71.46％,36.08％,千粒重67.79％,9.67％;着粒密度60.77％,23.9％;二次枝梗59.41％,23.22％;穗粒数56.67％,23.38％;结实率54.3,13.39％;穗粒重48.02％,25.64％;穗长38.15％,11.26％。其中穗长的遗传力在不同组合间有较大的差异,最低为10.76％,高的达60.02％,相差6倍;变异系数一次枝梗在不同组合中也有较大的差异,最低为13.66％,高达50.31％,相差4倍。因此在杂交亲本和杂种后代选择时应注意穗长和一次枝梗这两个性状。     

多品种（系）试验中简化广义格子设计的探讨

根据多品种（系）田间试验的特点，在Ｐａｔｔｅｒｓｏｎ等广义格子设计基础上提出简化广义格子设计（简称ＳＧＬ设计）。ＳＧＬ设计基于格子设计的正交性原理，对于可用区组容量整除的任何供试品种数、２－４次重复，均可构成不完全区组设计。且构成方法简便，设计效率因子高，具有可解析性，连通性，ＳＧＬ设计的参数采用修饰极大似然法估计。     

巴拿赫空间中广义向量F的隐互补类问题

在一些恰当的假设条件下,利用Fan-KKM定理研究了巴拿赫空间中一类新的广义向量F的隐互补类问题及与其相关的一类广义向量F的隐变分类不等式问题,证明了在巴拿赫空间中这两类问题在一定条件下的等价性,利用这一等价性也证明了解的存在性定理.     

Predicting summer fin whale distribution in the Pelagos Sanctuary (north-western Mediterranean Sea) to identify dynamic whale–vessel collision risk areas

1. Mediterranean fin whales aggregating in the Pelagos Sanctuary in summer to feed are exposed to vessel collision risk, particularly from high-speed ferries.
2. This study developed models to predict summer fin whale distribution using a generalized additive model (GAM) and MaxEnt, with the aim of providing a tool to identify potential high whale–ferry collision risk areas along ferry routes within the Pelagos Sanctuary during summertime.
3. Models were trained using sightings data collected in the summer months of 2009–2018 on board ferries crossing the central area of the Pelagos Sanctuary. Environmental predictors were bathymetry and mean sea surface chlorophyll concentration of the annual spring bloom period.
4. The predictive ability of GAM and MaxEnt was assessed using existing knowledge of summer fin whale distribution in the region. GAM (deviance explained = 20.2%) predictions matched documented distributions more closely than that of MaxEnt, with highest predicted fin whale occurrence in deep offshore waters (>2000 m) encompassing the central north-western and western regions, and in the south-eastern region, consistent with known fin whale habitats within the Pelagos Sanctuary. Inter-annual variability was evident, influencing collision risk areas.
5. Collision risk was estimated as a function of the overlap between the predicted probability of fin whale occurrence and ferry density estimated from Automated Identification System data. Ferry routes that cross the northern and eastern regions of the Pelagos Sanctuary presented relatively higher collision risk.
6. Areas with changes in risk intensity between the years were temporally and spatially dynamic: some appeared intermittently throughout the study period while others persisted over consecutive years or recurred in different years.
7. Due to the vastness of the Pelagos Sanctuary, vessel speed reduction maybe a more practical measure to manage collision risk than re-routing shipping lanes. A combination of Seasonal Management Areas and Dynamic Management Areas approaches could be adopted for high-risk areas.