Modeling and Fault Monitoring of Bioprocess Using Generalized Additive Models （GAMs） and Bootstrap

差错监视 bioprocess 是重要的保证一个反应堆的安全并且维持产品的高质量。为 bioprocess 造一个精确机械学的模型是困难的，因此差错基于富有的历史或联机的数据库监视是一个有效方法。一组数据基于自举方法能随机地是采样，改善归纳能力当模特儿。在这份报纸，在网上指责与结合的概括添加剂模型(鲸鱼群) 监视自举为 glutamate 被建议发酵过程。鲸鱼群并且自举首先被用来从 glutamate 发酵实验基于联机、离线的正常取样的数据决定信心间隔。然后，鲸鱼群被用来在网上指责在时间，溶解的氧，氧举起率，和二氧化碳进化监视率。方法能在网上提供精确差错警报并且是有用的为在发酵移开差错和反常现象提供有用信息。

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Simulating Potential Distribution of Tamarix chinensis in Yellow River Delta by Generalized Additive Models

There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.

Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model

In this study, the horizontal and vertical distribution of primary production(PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model(GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP(calculated by carbon) ranged from 48.03 to 390.56 mg/(m~2·h),with an annual average of 182.77 mg/(m~2·h). The highest PP was observed in May and the lowest in November.Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation(PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a(Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes.

环境卫生与医院感染的时间序列研究:基于广义相加模型(GAM)

目的定量分析环境卫生对医院感染发生的影响。方法收集某三甲医院2018年1月—2022年12月医院感染与环境卫生学监测资料,采用时间序列的广义相加模型分析环境检出菌落形成单位(CFU)对医院感染发生的影响。结果单污染模型显示,医院感染与工作人员手细菌菌落数之间存在显著正相关性(β1=0.009,P=0.012),工作人员手月度平均菌落形成单位(MCFU/Dish)每升高1个四分位数间距(IQR),医院感染发生率增加13.28%(95%CI:2.82%~24.81%);亚组分析与滞后效应分析显示,工作人员手月度MCFU/Dish(卫生手消毒后)升高1个IQR,当月(lag0)医院感染超额风险(ER)为16.26%(95%CI:15.45%~17.09%)。多污染模型中,物体表面污染与医院感染的相关性同样具有统计学意义。结论医院环境卫生与医院感染之间存在显著相关性。

Modeling the effect of stand and site characteristics on the probability of mistletoe infestation in Scots pine stands using remote sensing data

Over the past decade,the presence of mistletoe(Viscum album ssp.austriacum)in Scots pine stands has increased in many European countries.Understanding the factors that influence the occurrence of mistletoe in stands is key to making appropriate forest management decisions to limit damage and prevent the spread of mistletoe in the future.Therefore,the main objective of this study was to determine the probability of mistletoe occurrence in Scots pine stands in relation to stand-related endogenous factors such as age,top height,and stand density,as well as topographic and edaphic factors.We used unmanned aerial vehicle(UAV)imagery from 2,247 stands to detect mistletoe in Scots pine stands,while majority stand and site characteristics were calculated from airborne laser scanning(ALS)data.Information on stand age and site type from the State Forest database were also used.We found that mistletoe infestation in Scots pine stands is influenced by stand and site characteristics.We documented that the densest,tallest,and oldest stands were more susceptible to mistletoe infestation.Site type and specific microsite conditions associated with topography were also important factors driving mistletoe occurrence.In addition,climatic water balance was a significant factor in increasing the probability of mistletoe occurrence,which is important in the context of predicted temperature increases associated with climate change.Our results are important for better understanding patterns of mistletoe infestation and ecosystem functioning under climate change.In an era of climate change and technological development,the use of remote sensing methods to determine the risk of mistletoe infestation can be a very useful tool for managing forest ecosystems to maintain forest sustainability and prevent forest disturbance.

Inference Procedures on the Generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric Models for Analysis of Covariance (ANCOVA) of Count Data

Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.

High-resolution peak demand estimation using generalized additive models and deep neural networks

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.

基于GAM的喀斯特植被覆盖与驱动因素非线性关系分析

利用Theil-Sen斜率估计和Mann-Kendall显著性检验对2000~2018年西南五省的归一化植被指数(NDVI)进行研究,探究植被覆盖的时空变化特征.用MK(Mann-Kendall)突变检验寻找NDVI与夜间灯光数据的突变时间点.借助广义可加模型(GAM)对社会经济驱动因素,自然驱动因素分别拟合植被覆盖的响应曲线,探究喀斯特地区与非喀斯特地区的非线性响应关系的差异.结果表明:研究区中部、东部的植被恢复效果整体比西部地区好;利用夜间灯光数据作为表征城镇化的替代变量,城镇化与植被覆盖都在2009~2010年之间发生了突变,城镇化的突变时间早于植被覆盖的突变;喀斯特地区有88.54%的植被覆盖增加,其中有48.15%显著增加;非喀斯特地区有80.08%的植被覆盖增加,其中显著增加的占32.34%.喀斯特地区的植被恢复整体要比非喀斯特地区好.植被指数与国内生产总值(GDP)、路网密度、建成区面积等人类影响因素呈现不同的非线性响应关系.整体而言,非喀斯特地区受到的气温影响要比喀斯特地区的大,降雨、土层厚度对喀斯特地区植被恢复的影响更大一些.对比评价喀斯特与非喀斯特地区的植被恢复效果,分区探索植被覆盖变化背后驱动机制的非线性关系,对生态恢复规划等具有启示意义.

Polar Vortex Response to Pacific Ocean Warming and Its Additive Nonlinearity with the Indian Ocean

A previous modeling study about Pacific Ocean warming derived polar vortex response signals, by subtracting those in the Indian Ocean warming experiments from those in the Indo-Pacific. This approach questions the resemblance of such an indirectly derived response to one directly forced by Pacific Ocean warming. This is relevant to the additive nonlinearity of atmospheric responses to separated Indian and Pacific Ocean forcing. In the present study, an additional set of ensemble experiments are performed by prescribing isolated SST forcing in the tropical Pacific Ocean to address this issue. The results suggest a qualitative resemblance between responses in the derived and additional experiments. Thus, previous findings about the impact of Indian and Pacific Ocean warming are robust. This study has important implications for future climate change projections, considering the non-unanimous warming rates in tropical oceans in the 21st century. Nevertheless, a comparison of present direct-forced experiments with previous indirect-forced experiments suggests a significant additive nonlinearity between the Indian and Pacific Ocean warmings. Further diagnosis suggests that the nonlinearity may originate from the thermodynamic processes over the tropics.

基于GAM模型的昆明地面臭氧与气象要素拟合分析

基于昆明2018-2021年O_(3)日最大8 h滑动平均值[ρ(O_(3)-8 h)]和气象要素数据,采用广义相加模型(GAM)中的平滑样条函数拟合单要素、交互项的平滑回归函数拟合多要素与ρ(O_(3)-8 h)的影响关系。引入相对危险度概念,用分布滞后非线性模型(DLNM)分析气象要素和ρ(O_(3)-8 h)的滞后效应。构造滞后项和交互项的GAM模型,进行ρ(O_(3)-8 h)拟合预测。结果表明:当地面气压>818 hPa或平均风速<2.0 m/s时,ρ(O_(3)-8 h)出现1~3 d的滞后效应;GAM模型的交互项平滑回归函数优于单要素平滑样条函数的效果;干冷、湿热、低压大风、高压小风天气以及适当的气温和适中的水汽压有利于ρ(O_(3)-8 h)的增加;纳入交互项(包含滞后项)的GAM模型的拟合效果好于其他模型。该模型的判定系数达到0.672,广义交叉验证得分为352,拟合误差为13.7μg/m^(3),准确率达71.1%,特别在拟合因变量峰值和谷值时优势明显。

基于曲线拟合误差估计的阻抗优化识别方法

在雷达系统、舰船系统、数据中心等大型复杂系统中,供配电系统变流器端口阻抗信息的完整、快速、准确获取,是研判系统可靠性的重要基础。现有研究大都使用分段线性拟合值方法进行阻抗识别,不仅需要大量阻抗测量才能获得完整的阻抗特性,并且可能因过度测量而对系统的正常运行造成干扰。针对这些问题,从阻抗特性曲线的非线性拟合入手,提出一种“非线性曲线拟合、全局误差估计、优选测量点频率”的阻抗识别方法,实现基于曲线拟合误差估计的测量点优化选择,以较少阻抗测量代价获取宽频带阻抗特性。与现有的方法相比,所提方法更有助于测量点的合理选择、阻抗谐振峰的准确识别,从而更高效地实现宽频带阻抗的识别,为复杂供配电系统运行状况的研判提供可靠依据。算例仿真证实了所提方法的有效性。

Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.

佳芦河流域设计年径流和设计洪水的广义可加模型

受气象要素变化和人类活动的影响,径流序列的一致性常遭到破坏,需要直接针对非一致性水文序列进行研究。本文以黄土高原区佳芦河流域的年径流量和年最大洪峰流量为研究对象,基于广义可加模型(GAMLSS),优选气温、降水和库坝控制下的流域有效产流面积作为协变量进行模拟分析,并采用平均设计寿命水平法(ADLL)与等效可靠性法(ER)估算非平稳序列的设计年径流及洪峰流量。结果表明:两个水文序列均具有非一致性,所构建的多协变量年径流模型的低分位数曲线和高分位数曲线的拟合效果较好,洪峰流量模型整体模拟效果均较好;GAMLSS模型及相应的ADLL和ER方法在特定重现期或概率区间能提供更高精度的洪峰流量及年径流量设计值,且较好地体现了水文序列变化趋势对设计值的影响。

Effect of climate factors on the incidence of hand, foot, and mouth disease in Malaysia: A generalized additive mixed model

Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents,including malaria,cholera,dengue fever,hand,foot,and mouth disease(HFMD),and the recent Corona-virus pandemic.HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s.The outbreaks may result from a combination of rapid population growth,climate change,socioeconomic changes,and other lifestyle changes.However,the modeling of climate variability and HFMD remains unclear,particularly in statistical theory development.The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling.When dealing with time-series data with clustered variables such as HFMD with clustered states,the independence principle of both modeling approaches may be violated.Thus,a Generalized Additive Mixed Model(GAMM)is used to investigate the relationship between HFMD and climate factors in Malaysia.The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect.This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation.The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia.The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm.Besides,a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances.The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon.The study's outcomes can be used by public health officials and the general public to raise awareness,and thus,implement effective preventive measures.

Modeling deformation resistance for hot rolling based on generalized additive model

A model of deformation resistance during hot strip rolling was established based on generalized additive model.Firstly,a data modeling method based on generalized additive model was given.It included the selection of dependent variable and independent variables of the model,the link function of dependent variable and smoothing functional form of each independent variable,estimating process of the link function and smooth functions,and the last model modification.Then,the practical modeling test was carried out based on a large amount of hot rolling process data.An integrated variable was proposed to reflect the effects of different chemical compositions such as carbon,silicon,manganese,nickel,chromium,niobium,etc.The integrated chemical composition,strain,strain rate and rolling temperature were selected as independent variables and the cubic spline as the smooth function for them.The modeling process of deformation resistance was realized by SAS software,and the influence curves of the independent variables on deformation resistance were obtained by local scoring algorithm.Some interesting phenomena were found,for example,there is a critical value of strain rate,and the deformation resistance increases before this value and then decreases.The results confirm that the new model has higher prediction accuracy than traditional ones and is suitable for carbon steel,microalloyed steel,alloyed steel and other steel grades.

应用地基激光雷达三维点云数据构建长白落叶松树干削度方程

使用地基激光雷达(TLS)三维点云数据提取的落叶松干形数据,构建树干削度方程,为落叶松干形精准预测提供依据。以吉林省一面山林场和杨木林林场落叶松人工林为研究对象,获取71株落叶松点云信息,并提取树干干形数据。选择简单、可变指数、三角函数和分段函数等9个基础削度方程进行比较,利用分位数回归和广义加性模型方法构建削度方程。结果表明:在9个基础削度方程中,Bi(2000)削度方程的拟合效果最好,多重共线性指标条件数也小于100;Bi(2000)基础削度方程构建的分位数回归模型,在9个分位点(τ=0.1、0.2、0.3、0.4、0.5、0.6、0.7、0.8、0.9)处均能收敛,其中在τ=0.5的分位点处的拟合效果最好,略优于非线性回归的拟合结果。在以相对直径为因变量,以相对高的平方根、胸径的平方和树高为自变量的广义加性削度方程中,6种光滑样条函数(三次回归样条函数(CR)、B-样条函数(BS)、薄板回归样条函数(TP)、P-样条函数(PS)、Duchon样条函数(DS)和高斯过程平滑样条函数(GP))的拟合效果相差不大,但广义加性削度方程使用(DS+CR)光滑样条函数比一种光滑样条函数的拟合效果好(相对误差4.407、均方根误差1.158、确定系数0.966),广义加性削度方程的各检验统计量均优于基础削度方程和分位数回归削度方程,且在树干相对高度10%~80%,广义加性削度方程也表现最优(相对误差4.534、均方根误差1.191、确定系数0.964)。因此,(DS+CR)组合光滑样条函数的广义加性削度方程预测精度最高,可用于该区域的落叶松干形预测。

中西太平洋海山特征对延绳钓渔业和围网渔业黄鳍金枪鱼CPUE的影响

海山是海底重要的生物栖息地类型之一,是研究海洋生物多样性的热点区域。黄鳍金枪鱼(Thunnus albacares)广泛分布于中西太平洋,具有极高的生态和经济价值,然而,鲜有关于海山及其相关特征对黄鳍金枪鱼资源丰度和分布影响的研究。基于2010—2021年中西太平洋渔业委员会(Western and Central Pacific Fisheries Commission,WCPFC)汇总的延绳钓和围网渔业数据结合海山特征数据,采用广义加性模型(Generalized additive model,GAM)分析两种不同捕捞方式的黄鳍金枪鱼单位捕捞努力量渔获量(Catch per unit effort,CPUE)与海山相关特征之间的关系。结果表明,中西太平洋两种渔业方式的黄鳍金枪鱼渔获量主要来源于海山区域,海山特征对两种渔业黄鳍金枪鱼的CPUE均产生了极显著性影响(P<0.001)。在延绳钓渔业中,较高的CPUE出现在山顶深度、粗糙度、底面积和海山密度较小、坡度较缓的区域;而在围网渔业中,较高的CPUE则出现在粗糙度较小、山顶深度较大、底面积较大、较陡峭且密集的海山区域。研究探讨了中西太平洋海山特征对黄鳍金枪鱼不同群体的影响机制,为今后进一步探索黄鳍金枪鱼种群分布和资源丰度变化与海洋环境的关系提供了参考与新思路。

黑河上游天然草地蝗虫物种丰富度与地形关系的GAM分析

地形差异性导致的环境异质性为小尺度范围内生物空间格局的形成与维持提供了一种重要机制,是形成物种丰富度差异性的前提条件。借助GIS和S-PLUS软件,利用广义可加模型(GAM)于7-8月对影响蝗虫分布的地形因子进行了研究,在定量分析黑河上游祁连山区北坡地形的海拔分异特征的基础上研究了该区域蝗虫的丰富度与地形复杂度的关系。结果表明:在36个样方中共采集蝗虫3149头,隶属于3科10属13种;蝗虫丰富度受地形因子影响的顺序为海拔>坡向>坡度>剖面曲率>平面曲率>坡位;蝗虫的分布在平面曲率和剖面曲率各个梯度上的分布比较均衡,在海拔、坡向以及坡位的每个梯度上呈二次抛物线分布,坡度上呈递减趋势;从分布的区域上来看,蝗虫在整个区域都有较高的丰富度,但主要分布在海拔2600～2700m区域,坡向上则主要集中在西北坡和西坡,与实际观测情况相一致。蝗虫丰富度与地形因子之间的相互关系以及分布状态,反映了地形因子对水热条件的重分配使蝗虫分布格局出现多元化以及破碎化。