Subgroup Analysis for Longitudinal Data via Semiparametric Additive Mixed Effects Model

This paper proposed a general framework based on semiparametric additive mixed effects model to identify subgroups on each covariate and estimate the corresponding regression functions simultaneously for longitudinal data,thus it could reveal which covariate contributes to the existence of subgroups among population.A backfitting combined with k-means algorithm was developed to detect subgroup structure on each covariate and estimate each semiparametric additive component across subgroups.A Bayesian information criterion is employed to estimate the actual number of groups.The efficacy and accuracy of the proposed procedure in identifying the subgroups and estimating the regression functions are illustrated through numerical studies.In addition,the authors demonstrate the usefulness of the proposed method with applications to PBC data and Industrial Portfolio's Return data and provide meaningful partitions of the populations.

Effects of Nitrogen Addition on the Mixed Litter Decomposition in Stipa baicalensis Steppe in Inner Mongolia

During the past two centuries, global changes (i.e., enhanced nitrogen deposition) have exerted profound effects on ecological processes of steppe ecosystems. We used litterbag method and mixed litters of three different plant species tissues (Stipa baicalensis: Sb, Leymus chinensis: Lc and Artemisia frigid: Af), endemic to Stipa baicalensis Steppe, and measured the mass loss of mixtures over 417 days under the N addition treatment. We studied the effect of N addition (N0: no N addition;N15: 1.5 g N/m2·a;N30: 3.0 g N/m2·a;N50: 5.0 g N/m2·a;N100: 10.0 g N/m2·a;N150: 15.0 g N/m2·a) on the rate of mixed litter decomposition and nutrient dynamics change. The decomposition constant (k) of leaf mixtures was higher than that of root mixtures. The k values of leaf mixed combinations were 0.880 (Sb + Lc), 1.231 (Lc + Af), 1.027 (Sb + Lc + Af), respectively. The k value of stem was 0.806 (Lc + Af) and the root mixed combinations were 0.665 (Sb + Lc), 0.979 (Lc + Af) and 1.164 (Sb + Lc + Af), respectively. The results indicated that N addition had significantly effect on the mixed litter decomposition and nutrient releasing. The rate of plant tissues litter decomposition had different response to N addition. In the context of N addition, litter decomposition rate and nutrient dynamics were changed by synthetic effect of decaying time, specie types and N addition dose. Our findings suggested that prairie plants may adapt to environmental change by adjusting litter quality, thus retaining the stability of the steppe ecosystem.

Can a multistage approach improve individual tree mortality predictions across the complex mixed-species and managed forests of eastern North America?

Tree mortality plays a fundamental role in the dynamics of forest ecosystems,yet it is one of the most difficult phenomena to accurately predict.Various modeling strategies have been developed to improve individual tree mortality predictions.One less explored strategy is the use of a multistage modeling approach.Potential improvements from this approach have remained largely unknown.In this study,we developed a novel multistage approach and compared its performance in individual tree mortality predictions with a more conventional approach using an identical individual tree mortality model formulation.Extensive permanent plot data(n=9442)covering the Acadian Region of North America and over multiple decades(1965–2014)were used in this study.Our results indicated that the model behavior with the multistage approach better depicted the observed mortality and showed a notable improvement over the conventional approach.The difference between the observed and predicted numbers of dead trees using the multistage approach was much smaller when compared with the conventional approach.In addition,tree survival probabilities predicted by the multistage approach generally were not significantly different from the observations,whereas the conventional approach consistently underestimated mortality across species and overestimated tree survival probabilities over the large range of DBH in the data.The new multistage approach also predictions of zero mortality in individual plots,a result not possible in conventional models.Finally,the new approach was more tolerant of modeling errors because it based estimates on ranked tree mortality rather than error-prone predicted values.Overall,this new multistage approach deserves to be considered and tested in future studies.

Mixed-field effect at the hyperfine level of^(127)I^(79)Br in its rovibronic ground state:Toward field manipulation of cold molecules

The mixed-field effect at the hyperfine level of the rovibronic ground state of the^(127)I^(79)Br(X^(1)Σ,v=0,J=0)molecule is computed on the J-I uncoupled basis of|JM_(J)I_(1)M_(1)I_(2)M_(2)>,where J is the molecular total angular momentum excluding nuclear spin,M_J is the projection number of J,I_(1) and I_(2) are the nuclear spins of the iodine and bromine atoms,and M_(1) and M_(2) are the projection numbers of I_(1) and I_(2),respectively.When the two applied electric and magnetic fields are parallel,the perturbations are rare and only one perturbation is observed in a relatively large field regime in our computation range.However,when the two fields are off-parallel,the perturbations increase significantly and some sublevels show the Feshbach-like resonance phenomenon.Therefore,such sublevels transit between weak-field seeking and strong-field seeking repeatedly,which can be utilized to enhance or suppress cold molecular collision and chemical reaction rates.Such behavior of the molecular hyperfine structure in the mixed off-parallel fields may also be utilized to construct an electric-field-assisted anti-Helmholtz magnetic trap for cold molecules and to realize evaporative cooling of cold molecules(sub-mK)into the ultracold regime(μK).

Additive mixed models to study the effect of tree age and climatic factors on stem radial growth of Eucalyptus trees

The effect of tree age and climatic variables on stem radial growth of two hybrid clones of Eucalyptus was determined using longitudinal data from eastern South Africa.The stem radius of was measured weekly as the response variable.In addition to tree age,average weekly temperature,solar radiation,relative humidity and wind speed were simultaneously recorded with total rainfall at the site.An additive mixed effects model that incorporates a non-parametric smooth function was used.The results of the analysis indicate that the relationship between stem radius and each of the covariates can be explained by nonlinear functions.Models that account for the effect of clone and season together with their interaction in the parametric part of the additive mixed model were also fitted.The interaction between clone and season was not significant in all cases.For analyzing the joint effect all the covariates,additive mixed models that included two or more covariates were fitted.A significant effect of tree age was found in all cases.Although tree age was the key determinant of stem radial growth,weather variables also had a significant effect that was dependent on season.

Competitive effect, but not competitive response, varies along a climatic gradient depending on tree species identity

Background: Understanding the role of species identity in interactions among individuals is crucial for assessing the productivity and stability of mixed forests over time. However, there is limited knowledge concerning the variation in competitive effect and response of different species along climatic gradients. In this study, we investigated the importance of climate, tree size, and competition on the growth of three tree species: spruce(Picea abies), fir(Abies alba), and beech(Fagus sylvatica), and examined their competitive response and effect along a climatic gradient.Methods: We selected 39 plots distributed across the European mountains with records of the position and growth of 5,759 individuals. For each target species, models relating tree growth to tree size, climate and competition were proposed. Competition was modelled using a neighbourhood competition index that considered the effects of inter-and intraspecific competition on target trees. Competitive responses and effects were related to climate.Likelihood methods and information theory were used to select the best model.Results: Our findings revealed that competition had a greater impact on target species growth than tree size or climate. Climate did influence the competitive effects of neighbouring species, but it did not affect the target species? response to competition. The strength of competitive effects varied along the gradient, contingent on the identity of the interacting species. When the target species exhibited an intermediate competitive effect relative to neighbouring species, both higher inter-than intraspecific competitive effects and competition reduction occurred along the gradient. Notably, species competitive effects were most pronounced when the target species' growth was at its peak and weakest when growing conditions were far from their maximum.Conclusions: Climate modulates the effects of competition from neighbouring trees on the target tree and not the susceptibility of the target tree to competition. The modelling approach should be useful in future research to expand our knowledge of how competition modulates forest communities across environmental gradients.

Toward managing mixed-species stands： from parametrization to prescription

A better understanding and a more quantitative design of mixed-species stands will contribute to more integrative and goal-oriented research in mixed-species forests. Much recent work has indicated that the structure and growth of mixed species forests may fundamentally differ from monocultures. Here we suggest how to progress from the present accumulation of phenomenological findings to a design of mixed-species stands and advanced silvicultural prescriptions by means of modelling. First, the knowledge of mixing effects on the structure and growth at the stand, species, and individual tree level is reviewed, with a focus on those findings that are most essential for suitable modelling and silvicultural designs and the regulation of mixed stands as opposed to monocultures. Then, the key role of growth models, stand simulators, and scenario assessments for designing mixed species stands is discussed The next section illustrates that existing forest stand growth models require some fundamental modifications to become suitable for both monocultures and mixed-species stands. We then explore how silvicultural prescriptions derived from scenario runs would need to be both quantified and simplified for transfer to forest management and demonstrated in training plots. Finally, we address the main remaining knowledge gaps that could be remedied through empirical research.

Electrical Relaxation in Mixed Alkali Bi_2O_3-K_2O-Li_2O-Fe_2O_3 Glasses

A new glass system （Bi2O3）50（Fe2O3）10（Li2O）x（K2O）40-x, where x changes in steps of 5 mole fraction between 0 and 40, was selected to study the electrical relaxation and the mixed alkali effect （MAE） phenomena. Measurements of ac conductivity σac, dielectric permittivity ε′ and loss factor tanδ in the frequency range of 0.12～10^2 kHz and in the temperature range of 300～650 K were carried out. The temperature dependence of the ac conductivity shows a slow increasing rate at low temperature and high frequency and a rapid increase at high temperature and low frequency. At constant temperature, the ac conductivity is found to be proportional to ω^8, where s is the frequency exponent, which is less than 1. Analysis of the conductivity data and the frequency exponent shows that the overlapping large polaron tunnelling （OLPT） model of ions is the most favorable mechanism for the ac conduction in the present glass system. The ac response, the dc conductivity and dielectric relaxation have the same activation energy and they originate from the same basic transport mechanism. The results of the dielectric permittivity show no maximum peak in the temperature and frequency range studied. This absence of maximum peak is an indication of non-ferroelectric behavior of all the studied samples. The MAE has been detected in the ac conductivity, which is the same as the classical MAE in the dc conductivity. The electrical parameters such as dielectric permittivity ε′ and real dielectric modulus M′ show a typical minimum deviation from linearity by about two orders of magnitude. The loss factor tanδ and the imaginary dielectric modulus M″ are insignificantly dependent on composition even at the same transition temperature Tg.

ROBUST ESTIMATION IN PARTIAL LINEAR MIXED MODEL FOR LONGITUDINAL DATA

In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.

Influence of alkali mixed effect on the mixing enthalpy in 0.75B_2O_3-0.25[xNa_2O-(1-x)K_2O]glass system

The mixed alkali effect was investigated in the glass system 0.75B2O3-0.25[xNa2O-（1 -x）K20] through thermodynamic properties. The calorimetric measurements were performed in HF solution calorimetry at 298 K. The mixing enthalpy values show non-linear behaviour upon substitution of one alkali ion by another. This thermodynamic non-ideality is caused by the slight variations of distance between metallic cations, the macromolecular structure being unchanged. It can be explained, at least qualitatively, using electrolyte theory based on the Coulombic interactions of charged species originally developed by Debye and Hückel.

Posterior propriety in nonparametric mixed efects model

It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparametric mixed effects model under this class of partially informative normal prior for fixed effect with inverse gamma priors on the variance compo- nents and hierarchical priors for covariance matrix of random effect, then we explore the Gibbs sampling procedure.

Covalent bonding and J–J mixing effects on the EPR parameters of Er^(3+) ions in GaN crystal

The EPR parameters of trivalent Er（3＋） ions doped in hexagonal Ga N crystal have been studied by diagonalizing the 364×364 complete energy matrices. The results indicate that the resonance ground states may be derived from the Kramers doublet Γ6. The EPR g-factors may be ascribed to the stronger covalent bonding and nephelauxetic effects compared with other rare-earth doped complexes, as a result of the mismatch of ionic radii of the impurity Er（3＋）ion and the replaced Ga（3＋） ion apart from the intrinsic covalency of host Ga N. Furthermore, the J–J mixing effects on the EPR parameters from the high-lying manifolds have been evaluated. It is found that the dominant J–J mixing contribution is from the manifold 2K（15/2）, which accounts for about 2.5%. The next important J–J contribution arises from the crystal–field mixture between the ground state 4I（15/2） and the first excited state4I（13/2）, and is usually less than 0.2%. The contributions from the rest states may be ignored.

Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

Objective: Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. Methods: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. Results: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality.

基于卫星遥感AOD的华北地区2003—2014年PM2.5浓度时空分布特征

华北及周边地区PM2.5造成的污染,近十年来引起了社会的广泛关注,也是科学研究的重要领域。利用2003—2014年的卫星遥感MODIS AOD数据和2014—2015年的地面观测PM2.5浓度数据,采用聚类分析、混合效应模型、EOF分解等统计分析方法,反演了2003—2014年华北及周边地区PM2.5浓度,分析其时空分布特征。主要结论如下:(1)卫星遥感MODIS AOD与地面观测PM2.5值有较高的相关系数,可利用MODIS卫星遥感AOD对地面观测的PM2.5浓度进行反演;(2)华北地区PM2.5浓度呈现出明显的空间分布特征:太行山脉是污染强弱明确的分界线,山脉东南部的污染显著高于西部,且在地势变化的地方出现明显的突变;河北南部、河南北部和山东西北部分区域是污染最严重的地区;(3)2004年、2009年以及2013年后都是污染浓度比较低的年份。

New Approaches and Markers for Identifying Secondary Biogenic Coalbed Gas

According to the adsorption-desorption characteristics of coalbed gas and analysis of various experimental data, this paper proposes that the generation of secondary biogenic gas （SBG） and its mixing of with the residual thermogenic gas at an early stage inevitably lead to secondary changes of the thermogenic gas and various geochemical additive effects. Experimental results also show that the fractionation of the carbon isotope of methane of coal core desorption gas changes very little; the δ13C1 value of the mixed gas of biogenic and thermogenic gases is between the δ13C1 values of the two ＂original＂ gases, and the value is determined by the carbon isotopic compositions and mixing proportions of the two ＂original＂ methanes. Therefore this paper proposes that the study on the secondary changes of the thermogenic gas and various additive effects is a new effective way to study and identify SBG. Herein, a systematic example of research on the coalbed gas （Huainan coalbed gas） is further conducted, revealing a series of secondary changes and additive effects, the main characteristics and markers of which are： （1） the contents of CO2 and heavy-hydrocarbons decrease significantly; （2） the content of CH4 increases and the gas becomes drier; （3） the δ13C and δD values of methane decrease significantly and tend to have biogenetic characteristics; and （4） the values of 513C2 and δ13Cc02 grow higher. These isotopic values also change with the degradation degrees by microbes and mixing proportions of the two kinds of gases in different locations. There exists a negative correlation between the △13C1 It＇S δ13Cco2 values. The δ13Cc2-c1 values obviously become higher. The distributions of the △δ^13Cco2-C1 values are within certain limits and show regularity. There exist a positive correlation between the N2 versus Ar contents, and a negative correlation between the N2 versus CH4 contents, indicating the down forward infiltration of the surface water containing air. These are important markers of the generation and existence of SBG .

Efficient Shrinkage Estimation about the Partially Linear Varying Coefficient Model with Random Effect for Longitudinal Data

In this paper, an efficient shrinkage estimation procedure for the partially linear varying coefficient model (PLVC) with random effect is considered. By selecting the significant variable and estimating the nonzero coefficient, the model structure specification is accomplished by introducing a novel penalized estimating equation. Under some mild conditions, the asymptotic properties for the proposed model selection and estimation results, such as the sparsity and oracle property, are established. Some numerical simulation studies and a real data analysis are presented to examine the finite sample performance of the procedure.

A Robust Segmented Mixed Effect Regression Model for Baseline Electricity Consumption Forecasting

Renewable energy production has been surging around the world in recent years.To mitigate the increasing uncertainty and intermittency of the renewable generation,proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system operation.One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately.In this paper,we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California,USA,by combining the ideas of random effect regression model,segmented regression model,and the least trimmed squares estimate.Since the log-likelihood of the considered model is not differentiable at breakpoints,we propose a new backfitting algorithm to estimate the unknown parameters.The estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

Mixed State and High Effective Utilization of Pilbara Blending Iron Ore Powder

Pilbara blending iron ore powder （PB powder） is blending ores with good and poor quality iron ores, so how to use PB power effectively is a problem. The self-characteristics of PB powder and its single-components were studied respectively such as the macroscopic properties, microscopic properties, and high-temperature properties the behavior and effect in the sintering were mastered. Then based on the new ore-proportioning idea of iron ores sintering characteristics complementary, the principles on the effective use of PB powder were discussed, and was fur ther validated through the sintering pot test and industrial production. The results show that PB powder is composed of three kinds of iron ore, and the sintering characteristics of different iron ores are obviously discrepant. With the ore-proportioning optimization based on the iron ores sintering characteristics complementary, the proportion of PB iron ore powder can be increased to more than 45 %.

Robust inference in linear mixed model with skew normal-symmetric error

Linear mixed effects models with general skew normal-symmetric （SNS） error are considered and several properties of the SNS distributions are obtained. Under the SNS settings, ANOVA-type estimates of variance components in the model are unbiased, the ANOVA-type F-tests are exact F-tests in SNS setting, and the exact confidence intervals for fixed effects are constructed. Also the power of ANOVA-type F-tests for components are free of the skewing function if the random effects normally distributed. For illustration of the main results, simulation studies on the robustness of the models are given by comparisons of multivariate skew-normal, multivariate skew normal-Laplace, multivariate skew normal-uniform, multivariate skew normal-symmetric, and multivariate normal distributed errors. A real example is provided for the illustration of the proposed method.