The improved artificial bee colony algorithm for mixed additive and multiplicative random error model and the bootstrap method for its precision estimation

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model.The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation.The experimental results show that based on the weighted least squares criterion,the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation.The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods,which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

Improved cat swarm optimization for parameter estimation of mixed additive and multiplicative random error model

To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.

Ridge estimation iterative solution of ill-posed mixed additive and multiplicative random error model with equality constraints

The reasonable prior information between the parameters in the adjustment processing can significantly improve the precision of the parameter solution. Based on the principle of equality constraints, we establish the mixed additive and multiplicative random error model with equality constraints and derive the weighted least squares iterative solution of the model. In addition, aiming at the ill-posed problem of the coefficient matrix, we also propose the ridge estimation iterative solution of ill-posed mixed additive and multiplicative random error model with equality constraints based on the principle of ridge estimation method and derive the U-curve method to determine the ridge parameter. The experimental results show that the weighted least squares iterative solution can obtain more reasonable parameter estimation and precision information than existing solutions, verifying the feasibility of applying the equality constraints to the mixed additive and multiplicative random error model. Furthermore, the ridge estimation iterative solution can obtain more accurate parameter estimation and precision information than the weighted least squares iterative solution.

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.

Infl uence of altitude and tree class on climate-growth relationships in a larch plantation in subtropical China

Precise quantifi cation of climate-growth relationships can make a major contribution to scientifi c forest management.However,whether diff erences in the response of growth to climate at diff erent altitudes remains unclear.To answer this,264 trees of Larix kaempferi from 88 plots,representing diff erent altitudinal ranges(1000-2100 m)and tree classes were sampled and used to develop tree-ring chronologies.Tree-ring growth(TRG)was either positively(dominant)or negatively(intermediate and suppressed)correlated with climate in diff erent tree classes at diff erent altitudes.TRG was strongly correlated with growing season at low altitudes,but was less sensitive to climate at middle altitudes.It was mainly limited by precipitation and was highly sensitive to climate at low altitudes.Climate-growth relationships at high altitudes were opposite compared to those at low altitudes.TRG of dominant trees was more sensitive to climate change compared to intermediate and suppressed trees.Climate factors(annual temperatures;moisture,the number of frost-free days)had diff erent eff ects on tree-ring growth of diff erent tree classes along altitudinal gradients.It was concluded that the increase in summer temperatures decreased water availability,resulting in a signifi cant decline in growth rates after 2005 at lower altitudes.L.kaempferi is suitable for planting in middle altitudes and dominant trees were the best sampling choice for accurately assessing climate-growth relationships.

Interannual variability of dimethylsulfide in the Yellow Sea

The 22-year(1998-2019)surface seawater dimethylsulfi de(DMS)concentrations in the Yellow Sea(YS)were hindcasted based on satellite sea surface temperature(SST)and chlorophyll-a(Chl-a)data using a generalized additive mixed model(GAMM).A continuous monthly dataset of DMS concentration in the YS was obtained after using the data interpolation empirical orthogonal function(DINEOF)to reconstruct missing information in the dataset.Then,the interannual DMS variability in the YS was analyzed.The results indicated that the monthly climatological DMS concentration in the YS was 3.61 nmol/L.DMS concentrations in the spring and summer were signifi cantly higher than those in the autumn and winter.DMS concentrations were highest in coastal YS waters and lowest primarily in off shore YS waters.Interannual DMS variability between 1998 and 2019 was subdivided into two inverse phases:with the exception of the central YS,DMS increased before the turning point and decreased after.The turning point in interannual DMS variation was earlier in the inshore YS as compared to the central YS.Spectrum analysis identifi ed some signifi cant patterns of interannual variation in the DMS anomaly in the YS.Chl a appeared to be the main factor infl uencing interannual trends in DMS in the YS.Interannual DMS variability was under the joint control of Chl a and SST.However,short-term interannual DMS variation(2-3 years)was primarily related to SST,while longer term interannual DMS variation(6-8 years)was signifi cantly correlated with Chl a and SST.

Mixed H_2/H_∞ State Feedback Attitude Control of Microsatellite Based on Extended LMI Method

For the appearance of the additive perturbation of controller gain when the controller parameter has minute adjustment at the initial running stage of system,to avoid the adverse effects,this paper investigates the mixed H_2/H_∞ state feedback attitude control problem of microsatellite based on extended LMI method.Firstly,the microsatellite attitude control system is established and transformed into corresponding state space form.Then,without the equivalence restriction of the two Lyapunov variables of H_2 and H∞performance,this paper introduces additional variables to design the mixed H_2/H_∞ control method based on LMI which can also reduce the conservatives.Finally,numerical simulations are analyzed to show that the proposed method can make the satellite stable within 20 s whether there is additive perturbation of the controller gain or not.The comparative analysis of the simulation results between extended LMI method and traditional LMI method also demonstrates the effectiveness and feasibility of the proposed method in this paper.

The Additive Hazard Mixing Models

This paper is concerned with the aging and dependence properties in the additive hazard mixing models including some stochastic comparisons. Further, some useful bounds of reliability functions in additive hazard mixing models are obtained.

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.

Identification of parameter to assess cracking resistance of asphalt mixtures subjected to aging and moisture conditioning

This study comprehensively evaluated different parameters based on tensile strength testing to assess the cracking resistance of asphalt mixtures subjected to aging and moisture conditioning.For this purpose,two sources of aggregates were selected to produce hot and warm mix asphalt mixtures.Asphalt mixtures were subjected to short term and long term aging,three levels of moisture conditioning(freeze thaw cycles),and tested at two temperatures(15℃and 25℃).The load-displacement data was used to determine the fracture work density,fracture energy,toughness index,cracking resistance index,cracking tolerance index,and rate dependent cracking index.It was noticed that moisture conditioning increased the variability of the different parameters.The cracking tolerance index and rate dependent cracking index parameter had a much higher coefficient of variation(Co V)with a maximum value close to 50%.Indirect tensile strength,fracture energy,and fracture work density appropriately captured the effect of moisture on cracking resistance of mixtures.The cracking resistance index,cracking tolerance index,and rate dependent cracking index increased with an increase in the moisture conditioning level.The Statistical analysis showed that tensile strength,fracture work density,and fracture energy were significantly influenced by different aging and moisture conditions evaluated.Fracture energy showed better association with fatigue life of asphalt mixtures subjected to three freeze-thaw cycles compared to tensile strength.Further,the fatigue life prediction models showed that both indirect tensile strength and fracture energy significantly influence the fatigue life of asphalt mixtures subjected to aging and moisture conditioning.

Adopting the margin of stability for space–time landslide prediction–A data-driven approach for generating spatial dynamic thresholds

Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition factors.While data-driven methods for assessing landslide susceptibility or for establishing rainfall-triggering thresholds are prevalent,integrating spatiotemporal information for dynamic large-area landslide prediction remains a challenge.The main aim of this research is to generate a dynamic spatial landslide initiation model that operates at a daily scale and explicitly counteracts potential errors in the available landslide data.Unlike previous studies focusing on space–time landslide modelling,it places a strong emphasis on reducing the propagation of landslide data errors into the modelling results,while ensuring interpretable outcomes.It introduces also other noteworthy innovations,such as visualizing the final predictions as dynamic spatial thresholds linked to true positive rates and false alarm rates and by using animations for highlighting its application potential for hindcasting and scenario-building.The initial step involves the creation of a spatio-temporally representative sample of landslide presence and absence observations for the study area of South Tyrol,Italy(7400 km2)within well-investigated terrain.Model setup entails integrating landslide controls that operate on various temporal scales through a binomial Generalized Additive Mixed Model.Model relationships are then interpreted based on variable importance and partial effect plots,while predictive performance is evaluated through various crossvalidation techniques.Optimal and user-defined probability cutpoints are used to establish quantitative thresholds that reflect both,the true positive rate(correctly predicted landslides)and the false positive rate(precipitation periods misclassified as landslide-inducing conditions).The resulting dynamic maps directly visualize landslide threshold exceedance.The model demonstrates high predictive performance while revealing geomorphologically plausible prediction patterns largely consistent with current process knowledge.Notably,the model also shows that generally drier hillslopes exhibit a greater sensitivity to certain precipitation events than regions adapted to wetter conditions.The practical applicability of the approach is demonstrated in a hindcasting and scenario-building context.In the currently evolving field of space–time landslide modelling,we recommend focusing on data error handling,model interpretability,and geomorphic plausibility,rather than allocating excessive resources to algorithm and case study comparisons.

Associations of short-term ambient temperature exposure with lung function in middle-aged and elderly people:A longitudinal study in China

The short-term associations of ambient temperature exposure with lung function in middle-aged and elderly Chinese remain obscure.The study included 19,128 participants from the Dongfeng-Tongji cohort's first(2013)and second(2018)follow-ups.The lung function for each subject was determined between April and December 2013 and re-assessed in 2018,with three parameters(forced vital capacity[FVC],forced expiratory volume in 1 s[FEV1],and peak expiratory flow[PEF])selected.The China Meteorological Data Sharing Service Center provided temperature data during the study period.In the two follow-ups,a total of 25,511 records(average age:first,64.57;second,65.80)were evaluated,including 10,604 males(41.57%).The inversely J-shaped associations between moving average temperatures(lag01–lag07)and FVC,FEV1,and PEF were observed,and the optimum temperatures at lag04 were 16.5
C,18.7
C,and 16.2
C,respectively.At lag04,every 1
C increase in temperature was associated with 14.07 mL,9.78 mL,and 62.72 mL/s increase in FVC,FEV1,and PEF in the lowtemperature zone(<the optimum temperatures),whereas 5.72 mL,2.01 mL,and 11.64 mL/s decrease in the high-temperature zone(the optimum temperatures),respectively(all P<0.05).We observed significant effect modifications of gender,age,body mass index,body surface area,smoking status,drinking status,and physical activity on the associations(all Pmodification<0.05).Non-optimal temperatures may cause lung function decline.Several individual characters and lifestyles have effect modification on the temperature effects.