应用神经网络和多元回归技术预测森林产量

应用传统统计技术常会因样本小和测量数据不符某种分布而受到限制。本文评价一种前馈型神经网络算法以预测落叶阔叶林产量。另外,还介绍一种由定性变为定量的数据变换方法,以用相对小的样本建立多元回归预测模型。数据变换方法有助于改善多元回归模型的预测效果。在本实验的条件下,研究结果表明神经网络技术能够产生最好的预测效果.     

荧光原位杂交技术及其在环境微生物生态学中的应用研究

荧光原位杂交技术是一种能够同时对微生物进行定性、定量和研究微生物群落空间分布情况的有力工具。简要介绍了荧光原位杂交技术的方法,并对其在人为创制环境和自然环境中特征性微生物种群及群落生态学中的应用研究进行了讨论,指出了该种技术在应用中存在的问题与缺陷,最后对荧光原位杂交技术在堆肥微生物生态中的应用及与其他方法的组合应用进行了展望。     

生物细胞相位显微技术研究进展

相位显微技术,作为一种非侵入式无损伤的生物细胞检测及研究工具,受到了广泛的关注.从定性和定量两个方面综述了生物细胞相位显微技术的研究现状,具体介绍了定量全场相位显微技术的新进展.在此基础上,指出了现有生物细胞相位显微技术有待改进的一些共性问题,并预测了该技术的发展趋势.     

植物多酚的定量分析方法和生态作用研究进展

植物多酚是植物体内最重要的次生代谢物质.由于其特殊的结构特征和生物学活性,植物体中多酚潜在的生态学意义受到广泛关注.本文综述了植物多酚在生态领域的研究进展,并对其未来的研究方向进行了展望和预测;通过分析各种植物多酚的定性定量方法的优缺点,试图为研究植物多酚的非化学专业研究人员提供一些简单通用的分析方法.     

一种用高效液相色谱-电喷雾/串联质谱(HPLC-ESI/MSn)定量检测植物组织中微量1-氨基环丙烷-1-羧酸(ACC)含量的方法

介绍一种用高效液相色谱碳18柱(HPLC-C18)分离.电喷雾串联质谱(ESI-MSn)鉴定和定量检测植物组织中微量1-氨基环丙烷-1-羧酸(ACC)含量的方法,其最低检测限达0.7 pmol,标准曲线线性符合系数为0.9992.建立了从20～100 mg微量植物样品中提取ACC和固相萃取(SPE)预纯化的方法,加样回收率为95.1%±4.2%.此种提取方法结合HPLC-ESI/MSn分析与定性定量检测苹果'2001富士'中ACC含量为306.6 ng·g-1(FW),表明此法适合于定性定量检测植物样品中的微量ACC.     

阪崎肠杆菌标准品制备中冻干工艺的优化

本研究对阪崎肠杆菌(Enterobacter sakazakii)冻干工艺进行优化,旨在为E.sakazaki定性标准品和定量标准品的制备提供技术基础,同时为其他肠杆菌科标准品的制备工艺提供理论指导.实验结果表明:最佳冻干保护剂组合为海藻糖3%,脱脂奶粉8%,谷氨酸钠1.5%;最佳预冻温度和预冻时间分别为-20℃,4 ...     

对虾白斑综合征病毒基因组快速定性定量方法的建立

对于对虾白斑综合征病毒(white spot syndrome virus,WSSV)全基因组样品,使用限制性内切酶酶切以及荧光绝对定量的方法进行分析,建立了WSSV全基因组快速定性定量的方法。定性实验通过对GenBank中WSSV基因组序列深度分析,选择确定合适的限制性内切酶BamHI对基因组进行酶切,通过比对实际酶切结果和软件模拟酶切结果,以定性待测样品。定量实验使用荧光定量试剂盒,通过建立标准曲线的方法对未知浓度的WSSV基因组样品进行绝对定量。实验结果表明,结合使用酶切分析和荧光定量的方法可以准确、快速、方便、经济地对WSSV基因组样品进行定性定量分析,为进一步深入研究WSSV基因组奠定坚实基础。     

人体正常菌群微生物定量技术的研究进展

20世纪生物医学发展的主要特点之一是对生命现象和疾病本质的认识逐渐向分子水平深入.人类对感染性疾病的研究不仅满足于粗略的细菌定性诊断,医学工作者们开展了细菌定量技术的研究,为临床诊断和治疗开辟了更广阔的空间.随着医疗卫生条件的提高,更有效抗生素的应用,细菌感染性疾病已经越来越易于控制,改变医学研究方向的正常菌群的研究应时而生.以细菌定量技术为基础的菌群定量技术飞速发展.     

游离DNA研究进展及标准化的必要性

游离DNA (cell free DNA,cfDNA)作为一种新型分子标志物在疾病预测、肿瘤防治等方面的作用日益突出.但在实现其广泛的临床应用前仍存在诸多障碍,主要是cfDNA相关的标准较少,导致目前的研究报道缺乏可比性,检测结果缺乏稳定的可重复性,从而降低了临床应用的可靠性.本综述集中阐述了近年来cfDNA的相关研究进展,包括cfDNA的样本来源、样本保存、样本提取、定性定量检测和应用,并分析比较了cfDNA研究的各个环节采用的方法及其优缺点,对其中存在的问题进行了剖析和总结,指明了cfDNA相关标准制定的迫切性.     

河北省棉铃虫(Helicoverpa armigera Hübner)种群数量的气象综合预报方法的研究

为了客观地反映棉铃虫种群数量变化与气候背景的关系规律,创建了棉铃虫气象多时段综合因子的因子组建方法;建立了贡献度权重修正的气象距离指标方法和模型;匹配以传统的多元回归模型,进行棉铃虫种群数量的气象监测和预报;将其自身生物潜能和气象条件影响相结合,建立了棉铃虫灾害的生物气象定量综合预报模型.在棉铃虫发生的气象条件评价和发生程度预测预报应用中,趋势准确率达到93%.     

农作物产量预报模型研究与实践

选取玉米、大豆、小麦三种作物,建立产量的定性和定量预报模型,即年景趋势预报模型、逐步回归周期分量预报模型和多层递阶预报模型。经过检验和预报实践检验,表明所得到的预测模型具有一定的实用价值。     

Operational seasonal forecasting of crop performance

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.     

Ultra-short-term wind speed prediction based on multi-scale predictability analysis

Accurate prediction of ultra-short-term wind speed is important in many applications. Because of the different patterns of wind speed, researchers have indicated that a multi-scale prediction method based on wavelet algorithms could improve forecasting. However, traditional multi-scale methods that directly synthetize results of different-scale components may reduce forecasting accuracy because of error accumulation. In this paper, a multi-scale synthesis strategy that takes into account the predictability of different scales is proposed. As the correlation length of each frequency sub-series is different, prediction models are constructed for each frequency sub-series using different numbers of prediction steps. Finally, based on two wind farms in different regions of China and different basic forecast models, eight experiments are performed using real-world wind speed data. Experimental results show that the proposed multi-scale synthesis method has better performance than traditional multi-scale forecasting methods that use direct synthetizing strategies, indicating its utility in wind engineering applications.     

Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review

Over the last few years, Deep learning (DL) approaches have been shown to outperform state-of-the-art machine learning (ML) techniques in many applications such as vegetation forecasting, sales forecast, weather conditions, crop yield prediction, landslides detection and even COVID-19 spread predictions. Several DL algorithms have been employed to facilitate vegetation forecasting research using Remotely Sensed (RS) data. Vegetation is an extremely important component of our global ecosystem and a necessary indicator of land cover dynamics and productivity. Vegetation phenology is influenced by lifecycle patterns, seasonality and weather conditions, leading to changes in their spectral reflectance. Various relevant information, such as vegetation indices (VIs), can be extracted from RS data for vegetation forecasting. Therefore, the Normalized Difference Vegetation Index (NDVI) is known as one of the most widely recognized indices for vegetation related studies. This paper reviews the related works on DL-based spatio-temporal vegetation forecasting using RS data over the period between 2015 and 2021. In this review, we present several DL-based studies and discuss DL algorithms and various sources of data that have been used in these studies. The purpose of this work is to highlight the open challenges such as spatio-temporal prediction issues, spatial and temporal non-stationarity, fusion data, hybrid approaches, deep transfer learning and large parameter requirements. We also attempt to figure out the future directions and limits of DL for vegetation forecasting.     

物候模式识别在生态动力预报中的应用

以物候资料和数值天气预报模式输出图为基础,应用模式识别和数理逻辑判断的自动化技术,阐述制作生态动力预报的原理、方法和步骤.生态动力预报技术使传统的物候学在气象学和自动化技术支持下,扩展应用到生态预报业务领域,使物候预报从单站预报阶段发展到区域预报阶段,同时促进了农业气象预报方法从定性、统计阶段向动力预报阶段发展.该方法在农作物播种、长势、灌溉与施肥、病虫害防治等方面具有广阔的应用前景.     

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.     

樱花始花期预报方法

根据对1981—2016年36年武汉大学樱园日本樱花始花期的记录资料及同期气象资料的研究分析表明:(1)樱花始花期提前,但变化趋势不明显,变率特别大,平均始花期为3月14至15日(闰年为13至14日);(2)为改进始花期预报方程,计算1月1日及2月1日至开花前期2月25日、2月底、3月5日、3月10日、3月15日的活动积温,发现积温与始花期相关性显著,可作为樱花始花期预报方程的因子;(3)分析始花期与1月1日及2月1日至开花前期2月25日、2月底、3月5日、3月10日、3月15日累计日照时数关系,发现始花期与累计日照时数呈负相关;(4)用活动积温作为预报因子改进始花期预报方程预报始花期,有效地提高了预报准确率。     

树木花期预报的花芽形态测量法研究——以大山樱花期预报为例

树木花期预报在林果、养蜂、园林和旅游业等方面有很大的实用价值。该文以大山樱(Prunus sargentii)为例,探讨通过花芽形态测量进行花期预报的新方法。通过1998~2000年对北京玉渊潭公园大山樱进行的数据采集和处理,建立了线性和指数两种预报模型。2002年的试报检验表明,采用3株的观测数据,并利用3日滑动平均的方法,对观测数据进行处理后所作的预报,误差在3 d以内的预报达80%以上;2003年连续测报的平均误差,模型1为1.6 d,模型2为2.1 d。这一树木花期预报的物候学新方法,简便易行、建模周期短、预报精度高,在春季芽膨大后,直至露瓣期之前,可以逐日连续发布预报。     

Forecasting the 2013–2014 Influenza Season Using Wikipedia

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.