农田黑线姬鼠发生规律与防治技术

１９８５￣１９９５年１０年监测结果表明,黑线姬鼠为浙江农田优势种,每年４￣５月和９￣１０月为繁殖高峰期,６月和１０￣１１月为数量高峰。冬前冬后密度基数,种群年龄结构,繁殖力,温度,降水,天敌和药剂防治是影响种群数量变动的主要因子及预测的依据。根据害鼠密度与稻麦为类损失率及经济允许水平,提出主害期控制的鼠密度指标为：大小麦２．７％￣４．４％,早稻４．６％￣６．７％,晚稻６．９％￣８．４％,防治指标为     

新亚欧大陆桥新疆段受损区段的灰色预测

在对新亚欧大陆桥新疆段受损区段、受损长度及受损频率和受损密度详尽分析的基础上，揭示了陆桥新疆段受损区段的时空分布规律。并依据灾害的时序分布特征，运用灰色原理和方法，根据受损区段数确立阈值和相应的年份序列，分别建立GM（1，1）模型，求出年份序列的序号响应式，并对实际值和理论预测结果进行了拟合检验，模型群的原点误差范围为0.198-1.314之间，平均误差为0.823-1.917，并同时预测了陆桥新疆段2001－2005年间可能的受损区段数。     

反刍动物采食量的概念与研究方法

本研究对反刍家畜采食量的研究发展与现状进行了回顾与总结,采食量及其相关术语的定义主要基于饲草料或家畜,其测定方法和精确含义有差异;反刍家畜采食是由生物和非生物因素相互作用共同影响的复杂动态过程,采食量影响因子主要分为家畜的(胃肠蠕动力、选择性采食等)、饲草料的(粗纤维含量、抗营养成分等)和饲养条件的(草层结构、草地饮水点分布等)因素3个方面;采食量的测定方法有基于牧草测定的直接法和基于家畜测定的间接法,后者较多地应用于放牧家畜;预测方法有基于数学函数和生物学原理的模型法,基于消化试验的经验法和结合家畜属性的改进经验法。舍饲条件下可准确地测定和预测家畜的采食量,但对放牧家畜还没有精确的方法。通过测定与饲草水平密切相关的家畜尿液、血液或粪便中的代谢物水平将成为预测放牧家畜采食量的准确方法。     

水产食品特定腐败菌与货架期的预测和延长

Fresh fish and lightly preserved fish products are welcome by the global market, however, they are also among the most perishable food products. The research on specific spoilage organisms (SSO) reveals the spoilage process of aquatic product. This paper reviews the current knowledge (past ten years) on SSO of fresh fish and lightly preserved fish products with particular emphasis on characteristics of SSO and how to apply this concept to determine, predict and extend the shelf life of aquatic product. During storage, the microflora changes owing to different abilities of the microorganisms to tolerate the preservation conditions. SSO is defined as special microorganisms which can increase rapidly during preservation and has the ability to produce off- odours and off- flavours associated with spoilage, and spoilage metabolites. Identification of an SSO relies on comparison of the sensory and chemical characteristics of spoiled product with those of isolates from the spoilage microflora. Generally, the SSO of fresh fish may be a single species or genus, but the ones of lightly preserved fish products will be more complex. One exciting area for use of SSO aims to obtain quantitative knowledge about probable behavior of SSO and their function during the progression of spoilage. Thus mathematical models on the growth of SSO are established to evaluate the quality lost degree of product, which provide a sound information for the rational development of devices to monitor loss of products shelf life. Models for the growth of Pseudomonas spp, S. putrefaciens, P. phosphoreum have been established, and validated for shelf life prediction of seafood successfully. Another application field of SSO intends to develop the techniques to prolong the shelf life of food products by inhibiting SSO targetedly. Targeted inhibition of spoilage bacteria during preservation reduces their growth and results in a significant extension of shelf life in despite of the activity of non - spoilage organisms has not been influenced. Such techniques have been applied in perversion field of fresh fish and lightly preserved fish.     

河北省小麦白粉病发生程度的中期预测

根据全省１０个县（市）９ａ的历史资料进行归纳总结，提出小麦白粉病流行结构模型，利用３月下旬至４月中旬的气象因子（降雨量、雨日、平均相对湿度）实况值，用判别分析的方法，对当年小麦白粉病发生程度进行中期预测，经１９９３～１９９４年的应用检验，预测结果与各监测县及全省实况吻合     

利用广义线性模型预测家畜离散性状育种值

将广义线性混合模型(GLMM)引入动物离散性状的遗传分析及个体的遗传评定,初步比较了GLMM方法与一般线性方法(LM)的估计效果。模拟研究的性状为单阈值二项分类性状,选用的连接函数为对数连接μi=eη/(1+eη),方差函数为V(μi)=μ(i1-μi)/n,试验设计为全同胞-半同胞混合家系,参数估计采用Fisher迹法。结果表明:GLMM方法能较准确地估计公畜的个体育种值,在个体的遗传评定效果方面要明显优于常规的线性方法,其预测的育种值排序结果与真实育种值的排序之间存在极显著的相关性(P<0.001)。     

近50 a中国降水变化的准周期性特征及未来的变化趋势

通过分区分析了1961-2006年近50 a中国降水周期分量的年际变化特征,并对未来20 a（2006-2026年）降水的可能变化趋势做了预测。结果表明：就年际变化而言,大部分气候区的降水年际变化存在着显著的周期变化,并以高频（准3～4 a）变化为主,且具有相对的稳定性。过去的50 a,各气候区的降水呈现出下降趋势。周期叠加外推的结果表明,未来20 a,除西部干旱（半干旱）区的降水会出现少许的增多外,其余各气候区的降水量会出现弱的减少趋势。     

Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

There is an increasing interest in using whole‐genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole‐genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole‐genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non‐synonymous and non‐coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non‐synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.     

基于布谷鸟搜索算法优化LSTM的大坝变形预测

对大坝变形进行合理分析和准确预测是确保大坝安全运行的重要手段.大坝变形监测数据具有趋势性、周期性、随机性和非线性等特性,现有的机器学习模型大都基于大坝变形监测数据的非线性特点进行构建,而忽略了监测数据还具有趋势性和周期性的线性特征.提出了一种大坝变形预测模型,通过采用布谷鸟搜索算法(CS)对长短期记忆人工神经网络(LSTM)进行优化,再基于物联网传感器的实时监测数据,使用局部加权回归的周期趋势分解方法(STL)将数据分解成趋势分量、周期分量和余项分量,采用优化后的LSTM模型对趋势分量和余项分量分别进行预测,并通过简单周期估计方法进行计算,将3个分量的预测结果求和后得到最终变形预测结果.试验选取浙江利山水库开展变形预测研究,结果表明：STL-CS-LSTM模型的水平和沉降变形预测精度都高于其他模型,水平位移预测精度由高到低依次为LSTM模型、支持向量回归模型SVR和人工神经网络模型ANN,沉降预测精度由高到低依次为ANN模型、LSTM模型、SVR模型.     

四川桤木经营技术、收获模型及效益分析研究报告

系统总结了铜鼓县经营四川桤木的管理经验 ,研制了四川桤木生长收获模型 ,并对经营效益进行了客观的预估