基于WSN的油菜生长环境数据采集系统

针对传统作物生长环境数据获取手段实时性差、劳动强度大以及部署微型自动气象站和商用Zigbee产品成本高、开放性较差等问题，设计并实现了一种基于WSN的油菜生长环境数据采集系统。提出了轻量级的能量感知路由协议CLFP，并给出了软硬件的相关设计方法。仿真和大田试验结果表明，系统温度采集精度最高可达0.01 ℃，测湿精度达±5%，光强采集范围为1~65 535 lx，可并发的数据传输达到36路，可满足农业现场环境数据的较高测量要求。在标称电源供电情况下，系统实际有效生存周期超过142 d，由于采用AT89C51和nRF2401作为基础硬件平台，成本低廉，有助于大规模部署和应用。     

猪戊型肝炎病毒ORF3蛋白影响HepG2细胞核黄素代谢的lncRNA-mRNA调控网络的鉴定

为初步鉴定并挖掘出猪戊型肝炎病毒(Hepatitis E virus,HEV)ORF3蛋白影响HepG2细胞核黄素代谢信号通路的lncRNA-mRNA调控网络,本试验通过构建腺病毒过表达载体,制备高滴度过表达腺病毒,介导猪 HEV-ORF3在HepG2细胞中实现过表达。Western blotting检测ORF3蛋白过表达成功后,运用lncRNA高通量组学测序,筛选出差异表达的lncRNA并进行靶向差异基因预测,对lncRNA靶向差异基因进行GO功能和KEGG通路富集分析,初步鉴定出与核黄素代谢信号通路相关的lncRNA-mRNA调控网络。Western blotting结果显示,在约为12 ku处出现目的条带,说明成功实现腺病毒介导猪HEV-ORF3在HepG2细胞中过表达。lncRNA高通量组学测序结果显示,共发现102个显著差异表达lncRNAs表达量上调,80个显著差异表达lncRNAs表达量下调。GO功能和KEGG通路富集分析显示,初步挖掘出lncRNA(MSTRG.13995.2)和lncRNA(MSTRG.1960.1)可能是与核黄素代谢信号通路相关的显著差异表达lncRNA,分别通过顺式调控其靶向基因APC-5和FLAD1来影响核黄素代谢信号通路。本试验初步鉴定出lncRNA(MSTRG.13995.2)-APC5和lncRNA(MSTRG.1960.1)-FLAD1可能是影响HepG2细胞核黄素代谢信号通路的lncRNA-mRNA调控网络。     

LM-BP神经网络水质评价模型应用研究——以新乡市卫河为例

介绍了LM-BP神经网络模型的原理及算法和模型的优点。针对实际水质评价问题,利用随机内插方法在地表水环境质量分级标准阈值间生成训练样本和检验样本,建立了新乡市卫河地面水环境质量综合评价的LM-BP神经网络模型,将模型应用于卫河2011年3月份、9月份的水质评价,并与单因子评价法、模糊综合评价法进行了比较分析。实验结果表明该模型设计合理,泛化能力强,收敛速度快,算法稳定,推导严谨,有较充分的理论依据,应用于水质评价具有其合理性、实用性和有效性,适用于作深入的水环境质量分析。     

基于神经网络红枣红外辐射干燥预测模型建立

针对红枣红外辐射干燥含水率的变化具有非线性和时变性、很难利用现有的模型构造一个数学模型来描述其变化规律的问题,利用Mat Lab神经网络工具箱和红枣红外辐射干燥特性试验数据建立了神经网络预测模型。通过对实测值和模型预测值进行分析研究,得出利用BP神经网络可以较快速、准确地建立模型来描述含水率的变化规律,且模型的预测值与试验测试值误差较小,能很好地实现在线预测的效果。     

基于无线传感器网络的水质监测系统研究

针对国内外水质环境研究中水质样品异地监测分析存在的巨大缺陷,研究了水质自动监测系统。文章介绍了基于无线通讯传感器网络的简易水质自动监测系统,实现水质数据自动采集和实时上传,可以实现对钱塘江源头水质（衢州水质）进行更好的监测及保护。     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

桃的电特性及新鲜度识别

为了解采后果品电特性变化机理，并探索基于电特性识别果品品质的方法，研究了桃采后电特性和生理特性的变化。结果表明，随着桃新鲜度下降，相对介电常数呈余弦规律变化，损耗角正切逐渐减小，呼吸高峰时相对介电常数最大。分析了电特性变化的原因，进而建立了采后桃电特性与生理特性之间的关系模型。以相对介电常数和损耗角正切为输入特征参数，应用BP神经网络技术识别桃的新鲜度等级，平均识别率为82％。     

稀燃汽油机瞬态空燃比的滑模-神经网络控制

提出了稀燃汽油机空燃比滑模-神经网络控制方案,采用滑模变结构控制对稀燃工况的空燃比进行反馈控制,采用神经网络实现对进气量的精确预测和油膜动态特性的前馈补偿,进而实现对稀燃发动机瞬态空燃比的精确控制。采用自行开发的电控系统,在一台稀燃发动机上进行了瞬态空燃比实验。实验结果表明:节气门急速变化时空燃比超调可以控制在1个空燃比单位以内,调整时间不超过3s;转速越低,节气门变化越剧烈;空燃比超调越大,调整时间越长。     

Fault Section Diagnosis Algorithm of Distribution Networks Based On Topology Identification

An algorithm of fault section diagnosis based on topology identification for distribution networks is presented. By decomposing the topologic matrix which describing the distribution network into two parts,one part only contain the complex coupling factor ,and the other ignore the complex coupling factor. Using this method, the fault zones in distribution network can be identified and isolated efficiently, and the vertexes of the zones can also be identified automatically. This approach adapts to the changefully network structures.     

NETWORK FEOW APPROACH TO RELIABILITY EVALUATION IN IARGE SCALE NETWORKS BY MIXING INCREASING ELOW WITH DECREASING FLOW

Based on the maximum flow principle about networks, a network flow approach to reliability evaluation in large scale netwerks ty mixing inereasing flow with decreasing flow is proposed in this paper.This approach gets the initial flow of each branch in a r.etwork associated with a state of the retwork by decreasing flows of some branches correlative to another state of the network.As a result, the number of flow compute required can be reduced enormously.The numerical example shows that the approach is powerful. The comparison of this approach with the minimal cutset one is also given in this paper.