In view of the many scenes of unmanned aerial vehicle(UAV)detection,a third-party signal source is used to design a receiver to monitor the UAV.It is of great significance to understand the reflection of the signal il...In view of the many scenes of unmanned aerial vehicle(UAV)detection,a third-party signal source is used to design a receiver to monitor the UAV.It is of great significance to understand the reflection of the signal illuminating the UAV.Taking the communication base station(BS)signal as the third-party signal source,and considering the complete transmission link,reflection changes and loss fading of the communication signal,this study conducts model fitting for irregular UAV targets,simplifying complex targets into a combination of simple targets.Furthermore,the influence of the dielectric constant of the target surface and the signal irradiation angle on the signal reflection is analyzed.The analysis shows that the simulation results of this model fitting method are consistent with the results of other literature,which provides theoretical support for the detection of low and slow small targets such as UAVs.展开更多
Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time.Bayesian spatio-temporal regression models fit with Markov chain Monte ...Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time.Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo(MCMC)methods are commonly used for such data.When the spatio-temporal support of the model is large,implementing an MCMC algorithm becomes a significant computational burden.This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatiotemporal mixed effects binomial regression model.We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach.Both methods are used to produce monthly forecasts for Lyme disease,anaplasmosis,ehrlichiosis,and heartworm disease in domestic dogs for the contiguous United States.The data have a spatial support of 3108 counties and a temporal support of 108e138 months with 71e135 million test results.The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm,with a similar mean absolute prediction error.展开更多
Biomass models to estimate carbon stocks in arid environment are very limited. This study employed destructive sampling to develop a new biomass model for Vachellia tortilis, a widely known species in the Sultanate of...Biomass models to estimate carbon stocks in arid environment are very limited. This study employed destructive sampling to develop a new biomass model for Vachellia tortilis, a widely known species in the Sultanate of Oman. Twenty trees with a diameter at stump height (DSH) ranging from 18.5 cm to 150 cm were selected based on DSH and height variations for destructive sampling in As Saleel Natural Park Reserve (SNPR) in Al Sharqiyah governorate, South of Oman. Each tree was excavated and cut into three parts: Stems, Branches, twigs, and leaves. The total fresh weight of each tree was obtained in the field using a 300 balance. Sub-samples (250 - 300 grams) were taken from each part of the tree and transferred to the laboratory for dry weight determination. Linear multiple regression analysis was done using SPSS software between the three variables, DSH, H, CA (x) and the total dry biomass (y). Five models were tested for the best-fit model based on R-Square and Mean Square Error (MSE). Model 5 was the best-fit model, including the LOG of DSH and the LOG of CA (R2 = 0.97, MSE = 0.114). The models developed in this research fill a critical gap in estimating the AGB of terrestrial native species in Oman and other countries with similar ecological and climate conditions.展开更多
This article describes the development and application of a streamlined air control and response modeling system with a novel response surface modeling-linear coupled fitting method and a new module to provide streaml...This article describes the development and application of a streamlined air control and response modeling system with a novel response surface modeling-linear coupled fitting method and a new module to provide streamlined model data for PM_（2.5） attainment assessment in China.This method is capable of significantly reducing the dimensions required to establish a response surface model,as well as capturing more realistic response of PM_（2.5） to emission changes with a limited number of model simulations.The newly developed module establishes a data link between the system and the Software for Model Attainment Test—Community Edition（SMAT-CE）,and has the ability to rapidly provide model responses to emission control scenarios for SMAT-CE using a simple interface.The performance of this streamlined system is demonstrated through a case study of the Yangtze River Delta（YRD） in China.Our results show that this system is capable of reproducing the Community Multi-Scale Air Quality（CMAQ） model simulation results with maximum mean normalized error 〈 3.5%.It is also demonstrated that primary emissions make a major contribution to ambient levels of PM_（2.5） in January and August（e.g.,more than50%contributed by primary emissions in Shanghai）,and Shanghai needs to have regional emission control both locally and in its neighboring provinces to meet China＇s annual PM_（2.5）National Ambient Air Quality Standard.The streamlined system provides a real-time control/response assessment to identify the contributions of major emission sources to ambient PM_（2.5）（and potentially O_3 as well） and streamline air quality data for SMAT-CE to perform attainment assessments.展开更多
The Chapman-Richards Function and its two exception cases in applications were discussed and compared with the Schnute model in stand growth studies. Compared from all perspective, it was found that the Schnute model ...The Chapman-Richards Function and its two exception cases in applications were discussed and compared with the Schnute model in stand growth studies. Compared from all perspective, it was found that the Schnute model commonly used in foreitry was identical to the Chapman-Richards function. If some parameter in the Chapman-Richdrds Function was unconstraint, the function could also be very versatile to fit some exceptional growth curves, the fitted function should be identical to that the Schnute model.展开更多
In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1<sup>st</sup> March 2020 up to 25<sup>th</sup> December 2020, using sever</span><span>&...In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1<sup>st</sup> March 2020 up to 25<sup>th</sup> December 2020, using sever</span><span><span style="font-family:Verdana;">al copies of a Susceptible-Exposed-Infectious-Recovered (<i></span><i><span style="font-family:Verdana;">SEIR</span></i><span style="font-family:Verdana;"></i>) compart</span></span><span style="font-family:Verdana;">mental model, and compare it to </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">a </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">detailed publicly available dataset. We split the data into 10 time intervals and fit the models on the consecutive intervals to the cumulative number of confirmed positive cases on each interval. Using the fitted parameter estimates, we also provide estimates of the reproduction number.</span><span style="font-family:Verdana;"> We also discuss the limitations and possible extensions of the employed model.展开更多
Wood density(WD)is an important quality and functional trait of wood.However,despite the relationships between WD and abiotic factors being important to model or predict spatial distributions of functional traits,as w...Wood density(WD)is an important quality and functional trait of wood.However,despite the relationships between WD and abiotic factors being important to model or predict spatial distributions of functional traits,as well as responses of vegetation to climate changes,in current Earth system models or dynamic global vegetation models(ESMs/DGVMs),WD is often oversimplified,being defined as a globally uniform constant either for all plant functional types(PFTs)or for each individual PFT.Such oversimplifications may lead to simulation biases in the morphology of woody PFTs,as well as ecosystem transition and vegetation-atmosphere interactions.Moreover,existing conclusions about the relationships between WD and abiotic factors drawn from field observations remain mixed,making model parameterization improvements difficult.This study systematically investigated the influences of climate and soil factors on WD across various PFTs.Optimal fitting models for predicting WD within each PFT were then constructed by utilizing our collated global database of 138604 observations.For WDs of tree PFTs,climate emerges as a more influential factor than soil characteristics,whereas for shrub PFTs the effects of climate and soil are of equivalent significance.Across all six PFTs,correlation coefficients between predictions by fitting models and observed WD range from 0.49 to 0.93.The predicted and observed WD exhibit good agreement across climate space.It is expected that the incorporation of our research findings into DGVMs will improve the simulation of tree height and forest fractional coverage,particularly in the central forest areas and forest transition zones.展开更多
Regression estimates are biased when potential confounders are omitted or when there are other similar risks to validity.The instrumental variable(IV)method can be used instead to obtain less biased estimates or to st...Regression estimates are biased when potential confounders are omitted or when there are other similar risks to validity.The instrumental variable(IV)method can be used instead to obtain less biased estimates or to strengthen causal inferences.One key assumption critical to the validity of the IV method is the exclusion assumption,which requires instruments to be correlated with the outcome variable only through endogenous predictors.The chi-square test of model fit is widely used as a diagnostic test for this assumption.Previous simulation studies assessed the power of this diagnostic test only in situations with strong violations of the exclusion assumption.However,low to moderate levels of assumption violation are not uncommon in reality,especially when the exclusion assumption is violated indirectly.In this study,we showed through Monte Carlo simulations that the chi-square model fit test suffered from a severe lack of power(<30%)to detect violations of the exclusion assumption when the level of violation was of typical size,and the IV causal inferences were severely inaccurate and misleading in this case.We thus advise using the IV method with caution unless there is a chance for thorough assumption diagnostics,like in meta-analyses or experiments.展开更多
Bayesian structural equation model(BSEM)integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances.Prev...Bayesian structural equation model(BSEM)integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances.Previous studies have shown that prior specifications in BSEM influence model parameter estimation,but the impact on model fit indices is yet unknown and requires more research.As a result,two simulation studies were carried out.Normal distribution priors were specified for factor loadings,while inverse Wishart distribution priors and separation strategy priors were applied for the variance-covariance matrix of latent factors.Conditions included five sample sizes and 24 prior distribution settings.Simulation Study 1 examined the model-fitting performance of BCFI,BTLI,and BRMSEA proposed by Garnier-Villarreal and Jorgensen(Psychol Method 25(1):46-70,2020)and the PPp value.Simulation Study 2 compared the performance of BCFI,BTLI,BRMSEA,and DIC in model selection between three data generation models and three fitting models.The findings demonstrated that prior settings would affect Bayesian model fit indices in evaluating model fitting and selecting models,especially in small sample sizes.Even under a large sample size,the highly improper factor loading priors resulted in poor performance of the Bayesian model fit indices.BCFI and BTLI were less likely to reject the correct model than BRMSEA and PPp value under different prior specifications.For model selection,different prior settings would affect DIC on selecting the wrong model,and BRMSEA preferred the parsimonious model.Our results indicate that the Bayesian approximate fit indices perform better when evaluating model fitting and choosing models under the BSEM framework.展开更多
In recently years,high-performance wearable strain sensors have attracted great attention in academic and industrial.Herein,a conductive polymer composite of electrospun thermoplastic polyurethane(TPU)fibrous film mat...In recently years,high-performance wearable strain sensors have attracted great attention in academic and industrial.Herein,a conductive polymer composite of electrospun thermoplastic polyurethane(TPU)fibrous film matrix-embedded carbon black(CB)particles with adjustable scaffold network was fabricated for high-sensitive strain sensor.This work indicated the influence of stereoscopic scaffold network structure built under various rotating speeds of collection device in electrospinning process on the electrical response of TPU/CB strain sensor.This structure makes the sensor exhibit combined characters of high sensitivity under stretching strain(gauge factor of 8962.7 at 155%strain),fast response time(60 ms),outstanding stability and durability(>10,000 cycles)and a widely workable stretching range(0–160%).This high-performance,wearable,flexible strain sensor has a broad vision of application such as intelligent terminals,electrical skins,voice measurement and human motion monitoring.Moreover,a theoretical approach was used to analyze mechanical property and a model based on tunneling theory was modified to describe the relative change of resistance upon the applied strain.Meanwhile,two equations based from this model were first proposed and offered an effective but simple approach to analyze the change of number of conductive paths and distance of adjacent conductive particles.展开更多
In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segme...In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.展开更多
A 41-wk growth trial was conducted to evaluate the effects of dietary protein levels on the long-term growth response and fitting growth models of gibel carp(Carassius auratus gibelio) with an initial body weight of 1...A 41-wk growth trial was conducted to evaluate the effects of dietary protein levels on the long-term growth response and fitting growth models of gibel carp(Carassius auratus gibelio) with an initial body weight of 1.85 ± 0.17 g. The dietary protein levels were designed at 320(P32), 360(P36). 400(P40).and 440 g/kg(P44), respectively. The growth curves of the gibel carp for each group were fitted and analyzed with four nonlinear regression models(Gompertz. logistic. von Bertalanffy and Richards). The final body weights(mean ± SD) of the fish were 226 ± 6.231 ± 7.242 ± 2, and 236 ± 2 g for P32, P36, P40,and P44. respectively. Feed conversion ratio of P40 and P44 groups was significantly lower than that of P32 and P36 groups(P < 0.05). Productive protein value of P44 group was significantly lower than that of P32 and P36 groups, but not different from that of P40 group(P > 0.05). The growth response of the gibel carp for each group was the best fitted by Richards model with the lowest Chi^2, residual sum of squares and residual variance, then Gompertz and von Bertalanffy growth models, but the logistic model did not fit the data well justified by Chi^2 values. The optimal protein level(400 g/kg) prolonged the stage of fast growth and predicted the highest asymptotic weight, which was close to the harvest size in practice.展开更多
Based on the biological hypothesis of tree growth, the generalized Korf growth equation, was derived theoretically. From a standpoint of applications, the equation can be used in two ways associated with the power exp...Based on the biological hypothesis of tree growth, the generalized Korf growth equation, was derived theoretically. From a standpoint of applications, the equation can be used in two ways associated with the power exponent ofp, and two types of growth equations: the Korf-A (p>1) and the Korf-B (O<p<1) were developed and between them, there is the Gompertz equation (p=1) to separate each other. All of the three types of equations are independent. It was concluded that the Korf-A equation could be used to describe the growth of trees, of which inflection point is between 0 andA/e, while the Korf-B equation with the inflection point betweenA/e andA could be applied to describe the biological population growth. It was found that the Korf-A equation had a better property in describing the growth process of a tree or a stand and its applications to predicting height growth and stand self-thinning showed general good fitness.展开更多
This paper presents a Markov random field （MRP） approach to estimating and sampling the probability distribution in populations of solutions. The approach is used to define a class of algorithms under the general he...This paper presents a Markov random field （MRP） approach to estimating and sampling the probability distribution in populations of solutions. The approach is used to define a class of algorithms under the general heading distribution estimation using Markov random fields （DEUM）. DEUM is a subclass of estimation of distribution algorithms （EDAs） where interaction between solution variables is represented as an undirected graph and the joint probability of a solution is factorized as a Gibbs distribution derived from the structure of the graph. The focus of this paper will be on describing the three main characteristics of DEUM framework, which distinguishes it from the traditional EDA. They are： 1） use of MRF models, 2） fitness modeling approach to estimating the parameter of the model and 3） Monte Carlo approach to sampling from the model.展开更多
The objective of the present study was to develop a computer software for simulating the temporal development of plant disease epidemics using Richards, logistic, Gompertz, monomolecular, and exponential functions, re...The objective of the present study was to develop a computer software for simulating the temporal development of plant disease epidemics using Richards, logistic, Gompertz, monomolecular, and exponential functions, respectively, and for predicting disease with a fitted model. The software was programmed using Visual Basic （VB6.0） and packaged with the Wise Installation System. The Fibonacci （＇0.618＇） section strategy was used to find out the most appropriate value for the shape parameter （m） in Richards function simulation through looping procedures. The software program was repeatedly tested, debugged and edited until it was run through favorably and produced ideal outputs. It was named Epitimulator based on the phrase ＇epidemic time simulator＇ and has been registered by the National Copyright Department of China （Reg. no. 2007SR18489）. It can be installed and run on personal computers with all versions of Windows operational systems. Data of disease index and survey time are keyed in or imported from Access files. The output of fitted models and related data of parameters can be pasted into Microsoft Excel worksheet or into Word document for editing as required and the simulated disease progress curves can be stored in separate graphic files. After being finally tested and completed, Epitimulator was applied to simulate the epidemic progress of corn northern leaf blight （Exserohilum turcicum） with recorded data from field surveys of corn crops and the fitted models were output. Comparison of the simulation results showed that the disease progress was always best described by Richards function, which resulted in the most accurate simulation model. Result also showed that forecast of northern leaf blight development was highly accurate by using the computed progress model from Richards function.展开更多
Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a produc...Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.展开更多
This study presents the deep removal of copper (Ⅱ) from the simulated cobalt electrolyte using fabricated polystyrene-supported 2-aminomethylpyridine chelating resin (PS-AMP) in a fixed-bed.The effects of bed height ...This study presents the deep removal of copper (Ⅱ) from the simulated cobalt electrolyte using fabricated polystyrene-supported 2-aminomethylpyridine chelating resin (PS-AMP) in a fixed-bed.The effects of bed height (7.0–14.0 cm),feed flow rate (4.5–9.0 mL/min),initial copper (Ⅱ) concentration of the feed (250–1000 mg/L),feed temperature (25–40 ℃) and the value of pH (2.0–4.0) on the adsorption process of the PS-AMP resin were investigated.The experimental data showed that the PS-AMP resin can deeply eliminate copper (Ⅱ) from the simulated cobalt electrolyte.The bed height,feed flow rate,initial copper (Ⅱ) concentration of the feed,feed temperature and feed pH value which corresponded to the highest removal of copper (Ⅱ) were 7.0 cm with 35 mm of the column diameter,4.5 mL/min,40℃,1000 mg/L and 4.0,respectively.The breakthrough capacity,the saturated capacity of the column and the mass ratio of Cu/Co (g/g) in the saturated resin were correspondingly 16.51 mg/g dry resin,61.72 mg/g dry resin and 37.67 under the optimal experimental conditions.The copper (Ⅱ) breakthrough curves were fitted by the empirical models of Thomas,Yoon-Nelson and Adam-Bohart,respectively.The Thomas model was found to be the most suitable one for predicting how the concentration of copper (Ⅱ) in the effluent changes with the adsorption time.展开更多
In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in paral...In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.展开更多
We introduce Tsallis mapping in Bianconi-Barabgsi （B-B） fitness model of growing networks. This mapping addresses the dynamical behavior of the fitness model within the framework of nonextensive statistics mechanics...We introduce Tsallis mapping in Bianconi-Barabgsi （B-B） fitness model of growing networks. This mapping addresses the dynamical behavior of the fitness model within the framework of nonextensive statistics mechanics, which is characterized by a dimensionless nonextensivity parameter q. It is found that this new phenomenological parameter plays an important role in the evolution of networks： the underlying evolving networks may undergo a different phases depending on the q exponents, comparing to the original B-B fitness model, and the corresponding critical transition temperature Tc is also identified.展开更多
The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic inf...The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic infections as one of the silent drivers of the epidemic. An accurate estimation of the asymptomatic cases and the understanding of their contribution to the spread of the disease could enhance the effectiveness of current control strategies, mainly based on the symptom onset, to curb transmission. We investigate the dynamics of the COVID-19 epidemic in Northern Ireland during the period 1st March 25th to December 2020 to estimate the proportion of the asymptomatic infections in the country. We extended our previous model to include the stage of the asymptomatic infection, and we implement the corresponding deterministic model using a publicly available dataset. We partition the data into 11 sets over the period of study and fit the model parameters on the consecutive intervals using the cumulative number of confirmed positive cases for each interval. Moreover, we assess numerically the impacts of uncertainty in testing and we provide estimates of the reproduction numbers using the fitted parameters. We found that the proportion of asymptomatically infectious subpopulations, in Northern Ireland during the period of study, ranged between 5% and 25% of exposed individuals. Also, the estimate of the basic reproduction number, R<sub>0</sub>, is 3.3089. The lower and upper estimates for herd immunity are (0.6181, 0.7243) suggesting that around 70% of the population of Northern Ireland should acquire immunity via infection or vaccination, which is in line with estimates reported in other studies.展开更多
基金supported by the State Major Research and Development Project(2018YFB1802004)the State Key Laboratory of Air Traffic Management System and Technology(SKLATM201807)。
文摘In view of the many scenes of unmanned aerial vehicle(UAV)detection,a third-party signal source is used to design a receiver to monitor the UAV.It is of great significance to understand the reflection of the signal illuminating the UAV.Taking the communication base station(BS)signal as the third-party signal source,and considering the complete transmission link,reflection changes and loss fading of the communication signal,this study conducts model fitting for irregular UAV targets,simplifying complex targets into a combination of simple targets.Furthermore,the influence of the dielectric constant of the target surface and the signal irradiation angle on the signal reflection is analyzed.The analysis shows that the simulation results of this model fitting method are consistent with the results of other literature,which provides theoretical support for the detection of low and slow small targets such as UAVs.
基金RH and SS were supported in part or in full by the Companion Animal Parasite Council.SSAM were supported in part by the Research Center for Child Well-Being[NIGMS P20GM130420].
文摘Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time.Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo(MCMC)methods are commonly used for such data.When the spatio-temporal support of the model is large,implementing an MCMC algorithm becomes a significant computational burden.This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatiotemporal mixed effects binomial regression model.We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach.Both methods are used to produce monthly forecasts for Lyme disease,anaplasmosis,ehrlichiosis,and heartworm disease in domestic dogs for the contiguous United States.The data have a spatial support of 3108 counties and a temporal support of 108e138 months with 71e135 million test results.The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm,with a similar mean absolute prediction error.
文摘Biomass models to estimate carbon stocks in arid environment are very limited. This study employed destructive sampling to develop a new biomass model for Vachellia tortilis, a widely known species in the Sultanate of Oman. Twenty trees with a diameter at stump height (DSH) ranging from 18.5 cm to 150 cm were selected based on DSH and height variations for destructive sampling in As Saleel Natural Park Reserve (SNPR) in Al Sharqiyah governorate, South of Oman. Each tree was excavated and cut into three parts: Stems, Branches, twigs, and leaves. The total fresh weight of each tree was obtained in the field using a 300 balance. Sub-samples (250 - 300 grams) were taken from each part of the tree and transferred to the laboratory for dry weight determination. Linear multiple regression analysis was done using SPSS software between the three variables, DSH, H, CA (x) and the total dry biomass (y). Five models were tested for the best-fit model based on R-Square and Mean Square Error (MSE). Model 5 was the best-fit model, including the LOG of DSH and the LOG of CA (R2 = 0.97, MSE = 0.114). The models developed in this research fill a critical gap in estimating the AGB of terrestrial native species in Oman and other countries with similar ecological and climate conditions.
基金Financial support and data source for this work is provided by the US Environmental Protection Agency(No.OR13810-001.04 A10-0223-S001-A02)Guangzhou Environmental Protection Bureau(No.x2hj B2150020)+4 种基金the project of an integrated modeling and filed observational verification on the deposition of typical industrial point-source mercury emissions in the Pearl River Deltapartly supported by the funding of Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control(No.2011A060901011)the project of Atmospheric Haze Collaboration Control Technology Design(No.XDB05030400)from the Chinese Academy of Sciencesthe Ministry of Environmental Protection's Special Funds for Research on Public Welfare(No.201409002)Partly financial support is also provided by the Guangdong Provincial Department of Science and Technology,the project of demonstration research of air quality management cost-benefit analysis and attainment assessments technology(No.2014A050503019)
文摘This article describes the development and application of a streamlined air control and response modeling system with a novel response surface modeling-linear coupled fitting method and a new module to provide streamlined model data for PM_（2.5） attainment assessment in China.This method is capable of significantly reducing the dimensions required to establish a response surface model,as well as capturing more realistic response of PM_（2.5） to emission changes with a limited number of model simulations.The newly developed module establishes a data link between the system and the Software for Model Attainment Test—Community Edition（SMAT-CE）,and has the ability to rapidly provide model responses to emission control scenarios for SMAT-CE using a simple interface.The performance of this streamlined system is demonstrated through a case study of the Yangtze River Delta（YRD） in China.Our results show that this system is capable of reproducing the Community Multi-Scale Air Quality（CMAQ） model simulation results with maximum mean normalized error 〈 3.5%.It is also demonstrated that primary emissions make a major contribution to ambient levels of PM_（2.5） in January and August（e.g.,more than50%contributed by primary emissions in Shanghai）,and Shanghai needs to have regional emission control both locally and in its neighboring provinces to meet China＇s annual PM_（2.5）National Ambient Air Quality Standard.The streamlined system provides a real-time control/response assessment to identify the contributions of major emission sources to ambient PM_（2.5）（and potentially O_3 as well） and streamline air quality data for SMAT-CE to perform attainment assessments.
文摘The Chapman-Richards Function and its two exception cases in applications were discussed and compared with the Schnute model in stand growth studies. Compared from all perspective, it was found that the Schnute model commonly used in foreitry was identical to the Chapman-Richards function. If some parameter in the Chapman-Richdrds Function was unconstraint, the function could also be very versatile to fit some exceptional growth curves, the fitted function should be identical to that the Schnute model.
文摘In this study, we investigate the dynamics of the COVID-19 epidemic in Northern Ireland from 1<sup>st</sup> March 2020 up to 25<sup>th</sup> December 2020, using sever</span><span><span style="font-family:Verdana;">al copies of a Susceptible-Exposed-Infectious-Recovered (<i></span><i><span style="font-family:Verdana;">SEIR</span></i><span style="font-family:Verdana;"></i>) compart</span></span><span style="font-family:Verdana;">mental model, and compare it to </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">a </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">detailed publicly available dataset. We split the data into 10 time intervals and fit the models on the consecutive intervals to the cumulative number of confirmed positive cases on each interval. Using the fitted parameter estimates, we also provide estimates of the reproduction number.</span><span style="font-family:Verdana;"> We also discuss the limitations and possible extensions of the employed model.
基金supported by the National Natural Science Foundation of China(Grant No.42275177)the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2021B0301030007)the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Wood density(WD)is an important quality and functional trait of wood.However,despite the relationships between WD and abiotic factors being important to model or predict spatial distributions of functional traits,as well as responses of vegetation to climate changes,in current Earth system models or dynamic global vegetation models(ESMs/DGVMs),WD is often oversimplified,being defined as a globally uniform constant either for all plant functional types(PFTs)or for each individual PFT.Such oversimplifications may lead to simulation biases in the morphology of woody PFTs,as well as ecosystem transition and vegetation-atmosphere interactions.Moreover,existing conclusions about the relationships between WD and abiotic factors drawn from field observations remain mixed,making model parameterization improvements difficult.This study systematically investigated the influences of climate and soil factors on WD across various PFTs.Optimal fitting models for predicting WD within each PFT were then constructed by utilizing our collated global database of 138604 observations.For WDs of tree PFTs,climate emerges as a more influential factor than soil characteristics,whereas for shrub PFTs the effects of climate and soil are of equivalent significance.Across all six PFTs,correlation coefficients between predictions by fitting models and observed WD range from 0.49 to 0.93.The predicted and observed WD exhibit good agreement across climate space.It is expected that the incorporation of our research findings into DGVMs will improve the simulation of tree height and forest fractional coverage,particularly in the central forest areas and forest transition zones.
基金supported by Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515011986)National Natural Science Foundation of China(Grant No.31700986).
文摘Regression estimates are biased when potential confounders are omitted or when there are other similar risks to validity.The instrumental variable(IV)method can be used instead to obtain less biased estimates or to strengthen causal inferences.One key assumption critical to the validity of the IV method is the exclusion assumption,which requires instruments to be correlated with the outcome variable only through endogenous predictors.The chi-square test of model fit is widely used as a diagnostic test for this assumption.Previous simulation studies assessed the power of this diagnostic test only in situations with strong violations of the exclusion assumption.However,low to moderate levels of assumption violation are not uncommon in reality,especially when the exclusion assumption is violated indirectly.In this study,we showed through Monte Carlo simulations that the chi-square model fit test suffered from a severe lack of power(<30%)to detect violations of the exclusion assumption when the level of violation was of typical size,and the IV causal inferences were severely inaccurate and misleading in this case.We thus advise using the IV method with caution unless there is a chance for thorough assumption diagnostics,like in meta-analyses or experiments.
基金supported by the MOE(Ministry of Education)Project of Humanities and Social Science of China[23YJA190007]the Natural Science Foundation of Guangdong Province[2022A1515010367]the Key Research and Development Plan of Yunnan Province,China[202203AC100003].
文摘Bayesian structural equation model(BSEM)integrates the advantages of the Bayesian methods into the framework of structural equation modeling and ensures the identification by assigning priors with small variances.Previous studies have shown that prior specifications in BSEM influence model parameter estimation,but the impact on model fit indices is yet unknown and requires more research.As a result,two simulation studies were carried out.Normal distribution priors were specified for factor loadings,while inverse Wishart distribution priors and separation strategy priors were applied for the variance-covariance matrix of latent factors.Conditions included five sample sizes and 24 prior distribution settings.Simulation Study 1 examined the model-fitting performance of BCFI,BTLI,and BRMSEA proposed by Garnier-Villarreal and Jorgensen(Psychol Method 25(1):46-70,2020)and the PPp value.Simulation Study 2 compared the performance of BCFI,BTLI,BRMSEA,and DIC in model selection between three data generation models and three fitting models.The findings demonstrated that prior settings would affect Bayesian model fit indices in evaluating model fitting and selecting models,especially in small sample sizes.Even under a large sample size,the highly improper factor loading priors resulted in poor performance of the Bayesian model fit indices.BCFI and BTLI were less likely to reject the correct model than BRMSEA and PPp value under different prior specifications.For model selection,different prior settings would affect DIC on selecting the wrong model,and BRMSEA preferred the parsimonious model.Our results indicate that the Bayesian approximate fit indices perform better when evaluating model fitting and choosing models under the BSEM framework.
文摘In recently years,high-performance wearable strain sensors have attracted great attention in academic and industrial.Herein,a conductive polymer composite of electrospun thermoplastic polyurethane(TPU)fibrous film matrix-embedded carbon black(CB)particles with adjustable scaffold network was fabricated for high-sensitive strain sensor.This work indicated the influence of stereoscopic scaffold network structure built under various rotating speeds of collection device in electrospinning process on the electrical response of TPU/CB strain sensor.This structure makes the sensor exhibit combined characters of high sensitivity under stretching strain(gauge factor of 8962.7 at 155%strain),fast response time(60 ms),outstanding stability and durability(>10,000 cycles)and a widely workable stretching range(0–160%).This high-performance,wearable,flexible strain sensor has a broad vision of application such as intelligent terminals,electrical skins,voice measurement and human motion monitoring.Moreover,a theoretical approach was used to analyze mechanical property and a model based on tunneling theory was modified to describe the relative change of resistance upon the applied strain.Meanwhile,two equations based from this model were first proposed and offered an effective but simple approach to analyze the change of number of conductive paths and distance of adjacent conductive particles.
基金supported by the National Natural Science Foundation of China (31501229)the Chinese Academy of Agricultural Sciences Innovation Project (CAAS-ASTIP2017-AII)the Special Research Funds for Basic Scientific Research in Central Public Welfare Research Institutes, China (JBYW-AII-2017-05)
文摘In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.
基金Financial support was provided by the Special Fund for AgroScientific Research in the Public Interest(201203015201003020)+2 种基金the National Natural Science Foundation of China Project No.3110190731372539the National Basic Research Program of China(2014CB138600)
文摘A 41-wk growth trial was conducted to evaluate the effects of dietary protein levels on the long-term growth response and fitting growth models of gibel carp(Carassius auratus gibelio) with an initial body weight of 1.85 ± 0.17 g. The dietary protein levels were designed at 320(P32), 360(P36). 400(P40).and 440 g/kg(P44), respectively. The growth curves of the gibel carp for each group were fitted and analyzed with four nonlinear regression models(Gompertz. logistic. von Bertalanffy and Richards). The final body weights(mean ± SD) of the fish were 226 ± 6.231 ± 7.242 ± 2, and 236 ± 2 g for P32, P36, P40,and P44. respectively. Feed conversion ratio of P40 and P44 groups was significantly lower than that of P32 and P36 groups(P < 0.05). Productive protein value of P44 group was significantly lower than that of P32 and P36 groups, but not different from that of P40 group(P > 0.05). The growth response of the gibel carp for each group was the best fitted by Richards model with the lowest Chi^2, residual sum of squares and residual variance, then Gompertz and von Bertalanffy growth models, but the logistic model did not fit the data well justified by Chi^2 values. The optimal protein level(400 g/kg) prolonged the stage of fast growth and predicted the highest asymptotic weight, which was close to the harvest size in practice.
文摘Based on the biological hypothesis of tree growth, the generalized Korf growth equation, was derived theoretically. From a standpoint of applications, the equation can be used in two ways associated with the power exponent ofp, and two types of growth equations: the Korf-A (p>1) and the Korf-B (O<p<1) were developed and between them, there is the Gompertz equation (p=1) to separate each other. All of the three types of equations are independent. It was concluded that the Korf-A equation could be used to describe the growth of trees, of which inflection point is between 0 andA/e, while the Korf-B equation with the inflection point betweenA/e andA could be applied to describe the biological population growth. It was found that the Korf-A equation had a better property in describing the growth process of a tree or a stand and its applications to predicting height growth and stand self-thinning showed general good fitness.
文摘This paper presents a Markov random field （MRP） approach to estimating and sampling the probability distribution in populations of solutions. The approach is used to define a class of algorithms under the general heading distribution estimation using Markov random fields （DEUM）. DEUM is a subclass of estimation of distribution algorithms （EDAs） where interaction between solution variables is represented as an undirected graph and the joint probability of a solution is factorized as a Gibbs distribution derived from the structure of the graph. The focus of this paper will be on describing the three main characteristics of DEUM framework, which distinguishes it from the traditional EDA. They are： 1） use of MRF models, 2） fitness modeling approach to estimating the parameter of the model and 3） Monte Carlo approach to sampling from the model.
基金supported by the National Programs of Public-Beneficiary Sectors Funds,Ministryof Science and Technology,China(200803024)
文摘The objective of the present study was to develop a computer software for simulating the temporal development of plant disease epidemics using Richards, logistic, Gompertz, monomolecular, and exponential functions, respectively, and for predicting disease with a fitted model. The software was programmed using Visual Basic （VB6.0） and packaged with the Wise Installation System. The Fibonacci （＇0.618＇） section strategy was used to find out the most appropriate value for the shape parameter （m） in Richards function simulation through looping procedures. The software program was repeatedly tested, debugged and edited until it was run through favorably and produced ideal outputs. It was named Epitimulator based on the phrase ＇epidemic time simulator＇ and has been registered by the National Copyright Department of China （Reg. no. 2007SR18489）. It can be installed and run on personal computers with all versions of Windows operational systems. Data of disease index and survey time are keyed in or imported from Access files. The output of fitted models and related data of parameters can be pasted into Microsoft Excel worksheet or into Word document for editing as required and the simulated disease progress curves can be stored in separate graphic files. After being finally tested and completed, Epitimulator was applied to simulate the epidemic progress of corn northern leaf blight （Exserohilum turcicum） with recorded data from field surveys of corn crops and the fitted models were output. Comparison of the simulation results showed that the disease progress was always best described by Richards function, which resulted in the most accurate simulation model. Result also showed that forecast of northern leaf blight development was highly accurate by using the computed progress model from Richards function.
文摘Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.
基金Project(2014CB643401)supported by the National Basic Research Program of ChinaProjects(51134007,51474256)supported by the National Natural Science Foundation of ChinaProject(2017TP1001)supported by the Hunan Provincial Science and Technology Plan Project,China
文摘This study presents the deep removal of copper (Ⅱ) from the simulated cobalt electrolyte using fabricated polystyrene-supported 2-aminomethylpyridine chelating resin (PS-AMP) in a fixed-bed.The effects of bed height (7.0–14.0 cm),feed flow rate (4.5–9.0 mL/min),initial copper (Ⅱ) concentration of the feed (250–1000 mg/L),feed temperature (25–40 ℃) and the value of pH (2.0–4.0) on the adsorption process of the PS-AMP resin were investigated.The experimental data showed that the PS-AMP resin can deeply eliminate copper (Ⅱ) from the simulated cobalt electrolyte.The bed height,feed flow rate,initial copper (Ⅱ) concentration of the feed,feed temperature and feed pH value which corresponded to the highest removal of copper (Ⅱ) were 7.0 cm with 35 mm of the column diameter,4.5 mL/min,40℃,1000 mg/L and 4.0,respectively.The breakthrough capacity,the saturated capacity of the column and the mass ratio of Cu/Co (g/g) in the saturated resin were correspondingly 16.51 mg/g dry resin,61.72 mg/g dry resin and 37.67 under the optimal experimental conditions.The copper (Ⅱ) breakthrough curves were fitted by the empirical models of Thomas,Yoon-Nelson and Adam-Bohart,respectively.The Thomas model was found to be the most suitable one for predicting how the concentration of copper (Ⅱ) in the effluent changes with the adsorption time.
基金Project(2009CB320603)supported by the National Basic Research Program of ChinaProject(IRT0712)supported by Program for Changjiang Scholars and Innovative Research Team in University+1 种基金Project(B504)supported by the Shanghai Leading Academic Discipline ProgramProject(61174118)supported by the National Natural Science Foundation of China
文摘In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.
基金Supported by the National Natural Science Foundation of China under Grant No. 10875058the Initiative Plan of Shanghai Education Committee under Grant No. 10YZ76the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry (SRF for ROCS,SEM)
文摘We introduce Tsallis mapping in Bianconi-Barabgsi （B-B） fitness model of growing networks. This mapping addresses the dynamical behavior of the fitness model within the framework of nonextensive statistics mechanics, which is characterized by a dimensionless nonextensivity parameter q. It is found that this new phenomenological parameter plays an important role in the evolution of networks： the underlying evolving networks may undergo a different phases depending on the q exponents, comparing to the original B-B fitness model, and the corresponding critical transition temperature Tc is also identified.
文摘The identification and understanding of COVID-19 potential routes of transmission are fundamental to informing policies and strategies to successfully control the outbreak. Various studies highlighted asymptomatic infections as one of the silent drivers of the epidemic. An accurate estimation of the asymptomatic cases and the understanding of their contribution to the spread of the disease could enhance the effectiveness of current control strategies, mainly based on the symptom onset, to curb transmission. We investigate the dynamics of the COVID-19 epidemic in Northern Ireland during the period 1st March 25th to December 2020 to estimate the proportion of the asymptomatic infections in the country. We extended our previous model to include the stage of the asymptomatic infection, and we implement the corresponding deterministic model using a publicly available dataset. We partition the data into 11 sets over the period of study and fit the model parameters on the consecutive intervals using the cumulative number of confirmed positive cases for each interval. Moreover, we assess numerically the impacts of uncertainty in testing and we provide estimates of the reproduction numbers using the fitted parameters. We found that the proportion of asymptomatically infectious subpopulations, in Northern Ireland during the period of study, ranged between 5% and 25% of exposed individuals. Also, the estimate of the basic reproduction number, R<sub>0</sub>, is 3.3089. The lower and upper estimates for herd immunity are (0.6181, 0.7243) suggesting that around 70% of the population of Northern Ireland should acquire immunity via infection or vaccination, which is in line with estimates reported in other studies.
