民勤沙区胡杨林健康序列及其影响因子分析

运用50个胡杨林样地调查资料对民勤沙区的胡杨林健康状况进行模糊判别分类,并以其中19个样地的土壤取样测定数据,分析胡杨健康指标及伴生植物健康指标与土壤水分、土壤pH值、土壤有机质、土壤全N、土壤速效P以及土壤粒度的关系。模糊判别分类结果表明,健康序列1的胡杨无枯枝枯梢且更新苗相对最多;健康序列2的胡杨植株最高且龄级数最全,更新苗次多;健康序列5的胡杨过熟株、成熟株、中龄株和幼龄株均有枯枝枯梢,且样地上无更新苗。胡杨林健康指标与土壤水分、土壤pH值、土壤养分及土壤粒度的相关分析结果表明,越是靠近农田水分条件好的地块胡杨林的健康状况相对越好;土壤pH值在8.0左右时胡杨林的健康状况相对较好,土壤pH值>8.5胡杨林健康状况最差;胡杨林的健康状况与土壤有机质和全N及速效P含量正相关;健康状况较好的胡杨林地土壤中细砂、粉粒和粘粒含量较高。在民勤沙区,土壤干旱缺水是导致胡杨林退化的主要原因,土壤pH值在8.0左右的微碱性细沙地上的胡杨林相对较健康。对目前退化的胡杨林应适当补充人工灌溉,使土壤含水率保持在5%以上。营造人工胡杨林要选择靠近农田边缘水分条件较好的地块。0~60 cm的土壤速效P和0~20 cm的土壤细砂含量较多的土壤胡杨更新苗相对较多,可作为选定胡杨造林地的依据之一。

民勤荒漠草场退化植被健康序列的数值分析

民勤沙区退化荒漠植被的健康度分析结果表明,基于模糊判别方法的健康度分析方法完全可以运用于退化植被健康度的排序。评判指标的选择是决定评判结果是否准确可靠的关键。将退化荒漠植被的判别指标分为优势种(或灌木)和伴生种(或草本)两类指标,更适合植被群落的自然属性,且可以通过一级判别分别优势种植物和伴生植物研究其健康状况,能更准确地揭示植被群落的影响因素。分析结果表明,同一植物群落在半固定沙丘和流动沙丘上的健康状况优于固定沙丘;群落健康度受优势种健康状况影响明显。健康序列的灵敏度分析既可以用来检验分析结果的可靠性,同时还可以进一步揭示各类指标所代表的对象在植被群落中所处的作用和地位。

生态系统健康评价方法在退化梭梭群落分析中的应用

健康序列评价指标的确定是健康序列分析的关键。本文运用模糊判别方法对民勤沙区梭梭群落的健康状况进行了判别分析,建立的退化植物群落健康评价指标体系包括了植物的状态指标、病态指标、多样性指标和自然更新能力指标等4个方面,分析结果较好地反映了不同群落的健康状况,且反映了优势种植物和伴生植物与群落健康序列的关系。一般来说,对于多个研究对象的健康状况进行比较排序要比只对一个研究对象的健康状况做出评价容易得多。对于荒漠植物群落应该分别乔木、灌木和草本植物进行一级判别和二级判别,对二级判别则必须进行灵敏度分析。

基于深度SVDD的发动机外涵静子叶片故障预警

外涵静子叶片是大涵道比涡扇发动机气路的核心部件之一,外涵静子脱出是一种较为严重的故障模式,此故障可能会导致飞机或其他发动机部件损伤,进而造成灾难性事故。对外涵静子叶片脱出故障的预警是一项重要的工作。但因其早期特征不明显,现有的方法较难对此类故障进行有效的预警。因此,针对该问题,基于监控数据提出一种深度特征提取的支持向量数据域描述(Support vector data description,SVDD)的故障预警方法,以实现对外涵静子叶片脱出故障的早期预警。首先,采用基于发动机气路性能辨识的建模方法,建立发动机特定性能参数的观测模型对气路参数进行深度特征提取,以真实状态量与模型观测量的差值作为航空发动机是否发生故障的特征;然后利用SVDD算法建立决策边界,实现故障数据的自动划分,决策边界生成的阈值可在故障发生之前的一定时间之内给出告警;最后,经过多次计算,结果表明,在故障早期直至故障发生的区间内,表征其健康状态的性能参数都与观测量有较大的偏移,表明了所选特征的有效性。使用数据增强方法生成故障仿真数据与真实数据进行对比验证,预警时间比故障真实发生时间预警模型平均提前3.14 h。

Feature extraction and damage alarming using time series analysis

Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis is presented. The monitoring data were first modeled as ARMA models, while a principalcomponent matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobisdistance criterion functions. Then, a new damage-sensitive feature index DDSF is proposed. A hypothesis test involving the t-test method is further applied to obtain a decision of damage alarming as the mean value of DDSF had significantly changed after damage. The numerical results of a three-span-girder model shows that the defined index is sensitive to subtle structural damage, and the proposed algorithm can be applied to the on-line damage alarming in SHM.

Characterization and in-vivo evaluation of potential probiotics of the bacterial flora within the water column of a healthy shrimp larviculture system

A thorough understanding of the normal bacterial flora associated with shrimp larviculture systems contributes to probiotic screening and disease control. The bacterial community of the water column over a commercial Litopenaeus vannamei larval rearing run was characterized with both culture-dependent and culture-independent methods. A total of 27 phylotypes at the species level were isolated and identified based on 16S rDNA sequence analysis. Denaturing gradient gel electrophoresis （DGGE） analysis of the V3-V5 region of 16S rRNA genes showed a dynamic bacterial community with major changes occurred from stages zoea to mysis during the rearing run. The sequences retrieved were affiliated to four phyla, Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes, with the family Rhodobacteraceae being the most frequently recovered one. Subsequently, 13 representative strains conferred higher larval survival than the control when evaluated in the in-vivo experiments; in particular, three candidates, assigned to Phaeobacter sp., Arthrobacter sp., and Microbacteriurn sp., significantly improved larval survival （P〈0.05）. Therefore, the healthy shrimp larviculture system harbored a diverse and favorable bacterial flora, which contribute to larval development and are of great importance in exploiting novel probiotics.

基于ARIMA模型的动力电池不一致性预警研究

单体电压的不一致性影响动力电池使用寿命和行车安全。为了实现动力电池不一致性的及时预警,文章基于车联网平台海量历史数据,构建时间维度反应不一致性退化趋势的健康因子,并基于荷电状态分段区间进行健康因子时间序列提取;考虑到健康因子长时间范围内波动且难以预测,引入ARIMA模型进行相邻放电工况内健康因子时间序列的小样本预测。预测结果表明,该方法对训练样本数量和硬件资源配置要求低,且整体预测准确性不低于90%,可以满足实用需求。

Genetic diversity of coronaviruses in Miniopterus fuliginosus bats

Coronaviruses, such as severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus, pose significant public health threats. Bats have been suggested to act as natural reservoirs for both these viruses, and periodic monitoring of coronaviruses in bats may thus provide important clues about emergent infectious viruses. The Eastern bent-wing bat Miniopterus fuliginosus is distributed extensively throughout China. We therefore analyzed the genetic diversity of coronaviruses in samples of M. fuliginosus collected from nine Chinese provinces during 2011–2013. The only coronavirus genus found was Alphacoronavirus. We established six complete and five partial genomic sequences of alphacoronaviruses, which revealed that they could be divided into two distinct lineages, with close relationships to coronaviruses in Miniopterus magnater and Miniopterus pusillus. Recombination was confirmed by detecting putative breakpoints of Lineage 1 coronaviruses in M. fuliginosus and M. pusillus(Wu et al., 2015), which supported the results of topological and phylogenetic analyses. The established alphacoronavirus genome sequences showed high similarity to other alphacoronaviruses found in other Miniopterus species, suggesting that their transmission in different Miniopterus species may provide opportunities for recombination with different alphacoronaviruses. The genetic information for these novel alphacoronaviruses will improve our understanding of the evolution and genetic diversity of coronaviruses, with potentially important implications for the transmission of human diseases.