基于Deep Forest算法的对虾急性肝胰腺坏死病(AHPND)预警数学模型构建

为预报池塘养殖凡纳对虾(Penaeus vannamei)急性肝胰腺坏死病(AHPND)的发生,自2020年开始,笔者对凡纳对虾养殖区开展了连续监测工作,包括与疾病发生相关的环境理化因子、微生物因子、虾体自身健康状况等18个候选预警因子指标,通过数据标准化处理后分析病原、宿主与环境之间的相关性,对候选预警因子进行筛选,基于Python语言编程结合Deep Forest、Light GBM、XGBoost算法进行数据建模和预测性能评判,仿真环境为Python2.7,以预警因子指标作为输入样本(即警兆),以对虾是否发病指标作为输出结果(即警情),根据输入样本和输出结果各自建立输入数据矩阵和目标数据矩阵,利用原始数据矩阵对输入样本进行初始化,结合函数方程进行拟合,拟合的源代码能利用已知环境、病原及对虾免疫指标数据对目标警情进行预测。最终建立了基于Deep Forest算法的虾体(肝胰腺内)细菌总数、虾体弧菌(Vibrio)占比、水体细菌总数和盐度的4维向量预警预报模型,准确率达89.00%。本研究将人工智能算法应用到对虾AHPND发生的预测预报,相关研究结果为对虾AHPND疾病预警预报建立了预警数学模型,并为对虾健康养殖和疾病防控提供了技术支撑和有力保障。

Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,Model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,Model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.

Generalized Height-Diameter Models for Pinus montezumae Lamb. and Pinus pseudostrobus Lindl. Plantations in Michoacan, Mexico

Tree height (H) in a natural stand or forest plantation is a fundamental variable in management, and the use of mathematical expressions that estimate H as a function of diameter at breast height (d) or variables at the stand level is a valuable support tool in forest inventories. The objective was to fit and propose a generalized H-d model for Pinus montezumae and Pinus pseudostrobus established in forest plantations of Nuevo San Juan Parangaricutiro, Michoacan, Mexico. Using nonlinear least squares (NLS), 10 generalized H-d models were fitted to 883 and 1226 pairs of H-d data from Pinus montezumae and Pinus pseudostrobus, respectively. The best model was refitted with the maximum likelihood mixed effects model (MEM) approach by including the site as a classification variable and a known variance structure. The Wang and Tang equation was selected as the best model with NLS;the MEM with an additive effect on two of its parameters and an exponential variance function improved the fit statistics for Pinus montezumae and Pinus pseudostrobus, respectively. The model validation showed equality of means among the estimates for both species and an independent subsample. The calibration of the MEM at the plot level was efficient and might increase the applicability of these results. The inclusion of dominant height in the MEM approach helped to reduce bias in the estimates and also to better explain the variability among plots.

The Altitudinal Belts of Subalpine Virgin Forest on Mt.Gongga Simulated by a Succession Model

How to accurately simulate the distribution of forest species based upon their biological attributes has been a traditional biogeographical issue.Forest gap models are very useful tools for examining the dynamics of forest succession and revealing the species structure of vegetation.In the present study,the GFSM(Gongga Forest Succession Model) was developed and applied to simulate the distribution,composition and succession process of forests in 100 m elevation intervals.The results indicate that the simulated results of the tree species,quantities of the different types of trees,tree age and differences in DBH(diameter at breast height) composition were in line with the actual situation from 1400 to 3700 MASL(meters above sea level) on the eastern slope of Mt.Gongga.Moreover,the dominant species in the simulated results were the same as those in the surveyed database.Thus,the GFSM model can best simulate the features of forest dynamics and structure in the natural conditions of Mt.Gongga.The work provides a new approach to studying the structure and distribution characteristics of mountain ecosystems in varied elevations.Moreover,the results of this study suggest that the biogeochemistry mechanism model should be combined with the forestsuccession model to facilitate the ecological model in simulating the physical and chemical processes involved.

Modelling potential distribution of a pine bark beetle in Mexican temperate forests using forecast data and spatial analysis tools

Accurate and reliable predictions of pest species distributions in forest ecosystems are urgently needed by forest managers to develop management plans and monitor new areas of potential establishment.Presence-only species distribution models are commonly used in these evaluations.The maximum entropy algorithm(MaxEnt)has gained popularity for modelling species distribution.Here,MaxEnt was used to model the spatial distribution of the Mexican pine bark beetle(Dendroctonus mexicanus)in a daily fashion by using forecast data from the Weather Research and Forecasting model.This study aimed to exploit freely available geographic and environmental data and software and thus provide a pathway to overcome the lack of costly data and technical guidance that are a challenge to implementing national monitoring and management strategies in developing countries.Our results showed overall agreement values between 60 and 87%.The results of this research can be used for D.mexicanus monitoring and management and may aid as a model to monitor similar species.

A system dynamics model for billion trees tsunami afforestation project of Khyber Pakhtunkhwa in Pakistan:Model application to afforestation activities

As part of the global effort to plant billion trees,an afforestation project is launched in Pakistan in Khyber Pakhtunkhwa(KP)province to conserve existing forests and to increase area under forest cover.The present study is designed to build a Systems'model by incorporating major activities of the Billion Tree Tsunami Afforestation Project(BTTAP)with special focus on afforestation activities to estimate the growth in forest area of KP.Availability of complete dataset was a challenge.To fix the model,the raw data taken from the project office has been utilized.Planning Commission Form 1-Phase I&II helped us with additional information.We relied on the data available for one and half period of the project as rest of the data is subject to the completion of the project.Our results show that the project target to enhance area under forest differs from the target to afforest area under the project.The system dynamics'model projection shows that the forest area of KP would be 23.59 million hectares at the end of the BTTA project,thus having an increase of 3.29%instead of 2%that has been initially proposed.However,the results show that the progress to meet the target in some afforestation classes is slow as compared to other categories.Farm forestry,plantation on communal lands and owners'plantation need special focus of the authority.Deforestation would affect 0.02 million hectares area of the project.The model under study may be used as a reference model that can be replicated to other areas where billion tree campaigns are going on.

Desertification status mapping in MuttumaWatershed by using Random Forest Model

Potential of the Random Forest Model on mapping of different desertification processes was studied in Muttuma watershed of mid-Murrumbidgee river region of New South Wales,Australia.Desertification vulnerability index was developed using climate,terrain,vegetation,soil and land quality indices to identify environmentally sensitive areas for desertification.Random Forest Model(RFM)was used to predict the different desertification processes such as soil erosion,salinization and waterlogging in the watershed and the information needed to train classification algorithms was obtained from satellite imagery interpretation and ground truth data.Climatic factors(evaporation,rainfall,temperature),terrain factors(aspect,slope,slope length,steepness,and wetness index),soil properties(pH,organic carbon,clay and sand content)and vulnerability indices were used as an explanatory variable.Classification accuracy and kappa index were calculated for training and testing datasets.We recorded an overall accuracy rate of 87.7%and 72.1%for training and testing sites,respectively.We found larger discrepancies between overall accuracy rate and kappa index for testing datasets(72.2%and 27.5%,respectively)suggesting that all the classes are not predicted well.The prediction of soil erosion and no desertification process was good and poor for salinization and water-logging process.Overall,the results observed give a new idea of using the knowledge of desertification process in training areas that can be used to predict the desertification processes at unvisited areas.

Unsuspected implications arising from assumptions in simulations: insights from recasting a forest growth model in system dynamics

Background:Familiarity with a simulation platform can seduce modellers into accepting untested assumptions for convenience of implementation.These assumptions may have consequences greater than commonly suspected,and it is important that modellers remain mindful of assumptions and remain diligent with sensitivity testing.Methods:Familiarity with a technique can lead to complacency,and alternative approaches and software can reveal untested assumptions.Visual modelling environments based on system dynamics may help to make critical assumptions more evident by offering an accessible visual overview and empowering a focus on representational rather than computational efficiency.This capacity is illustrated using a cohort-based forest growth model developed for mixed species forest.Results:The alternative model implementation revealed that untested assumptions in the original model could have substantial influence on simulated outcomes.Conclusions:An important implication is that modellers should remain conscious of all assumptions,consider alternative implementations that reveal assumptions more clearly,and conduct sensitivity tests to inform decisions.

Estimating the carbon fluxes of forests with an individual-based forest model

Background： Capturing the response of forest ecosystems to inter-annual climate variability is a great challenge.In this study, we tested the capability of an individual-based forest gap model to display carbon fluxes at yearly and daily time scales.The forest model was applied to a spruce forest to simulate the gross primary production（GPP）, respiration and net ecosystem exchange（NEE）.We analyzed how the variability in climate affected simulated carbon fluxes at the scale of the forest model.Results： Six years were simulated at a daily time scale and compared to the observed eddy covariance（EC） data.In general, the seasonal cycle of the individual carbon fluxes was correctly described by the forest model.However, the estimated GPP differed from the observed data on the days of extreme climatic conditions.Two new parameterizations were developed： one resulting from a numerical calibration, and the other resulting from a filtering method.We suggest new parameter values and even a new function for the temperature limitation of photosynthesis.Conclusions： The forest model reproduced the observed carbon fluxes of a forest ecosystem quite wel.Of the three parameterizations, the calibrated model version performed best.However, the filtering approach showed that calibrated parameter values do not necessarily correctly display the individual functional relations.The concept of simulating forest dynamics at the individual base is a valuable tool for simulating the NEE, GPP and respiration of forest ecosystems.

The carbon fluxes in different successional stages:modelling the dynamics of tropical montane forests in South Ecuador

Background： Tropical forests play an important role in the global carbon（C） cycle.However, tropical montane forests have been studied less than tropical lowland forests, and their role in carbon storage is not well understood.Montane forests are highly endangered due to logging, land-use and climate change.Our objective was to analyse how the carbon balance changes during forest succession.Methods： In this study, we used a method to estimate local carbon balances that combined forest inventory data with process-based forest models.We utilised such a forest model to study the carbon balance of a tropical montane forest in South Ecuador, comparing two topographical slope positions（ravines and lower slopes vs upper slopes and ridges）.Results： The simulation results showed that the forest acts as a carbon sink with a maximum net ecosystem exchange（NEE） of 9.3 Mg C？（ha？yr）-1during its early successional stage（0–100 years）.In the late successional stage, the simulated NEE fluctuated around zero and had a variation of 0.77 Mg C？（ha？yr）–1.The simulated variability of the NEE was within the range of the field data.We discovered several forest attributes（e.g., basal area or the relative amount of pioneer trees） that can serve as predictors for NEE for young forest stands（0–100 years） but not for those in the late successional stage（500–1,000 years）.In case of young forest stands these correlations are high, especially between stand basal area and NEE.Conclusion： In this study, we used an Ecuadorian study site as an example of how to successfully link a forest model with forest inventory data, for estimating stem-diameter distributions, biomass and aboveground net primary productivity.To conclude, this study shows that process-based forest models can be used to investigate the carbon balance of tropical montane forests.With this model it is possible to find hidden relationships between forest attributes and forest carbon fluxes.These relationships promote a better understanding of the role of tropical montane forests in the context of global carbon cycle, which in future will become more relevant to a society under global change.

Observations and Modeling of Incoming Longwave Radiation to Snow Beneath Forest Canopies in the West Tianshan Mountains, China

Forest canopy reduces shortwave radiation and increases the incoming longwave radiation to snowpacks beneath forest canopies. Furthermore, the effect of forest canopy may be changed by complex topography. In this paper, we measured and simulated the incoming longwave radiation to snow beneath forest at different canopy openness in the west Tianshan Mountains, China(43°16'N, 84°24'E) during spring 2013. A sensitivity study was conducted to explore the way that terrain influenced the incoming longwave radiation to snow beneath forest canopies. In the simulation model, measurement datasets, including air temperature, incoming shortwave radiation above canopy, and longwave radiation enhanced by adjacent terrain, were applied to calculate the incoming longwave radiation to snow beneath forest canopy. The simulation results were consistent with the measurements on hourly scale and daily scale. The effect of longwave radiation enhanced by terrain was important than that of shortwave radiation above forest canopy with different openness except the 20% canopy openness. The longwave radiation enhanced due to adjacent terrain increases with the slope increase and temperature rise. When air temperature(or slope) is relatively low, thelongwave radiation enhanced by adjacent terrain is not sensitive to slope(or air temperature), but the sensitivity increases with the decrease of snow cover area on sunny slope. The effect of longwave radiation is especially sensitive when the snow cover on sunny slope melts completely. The effect of incoming shortwave radiation reflected by adjacent terrain on incoming longwave radiation to snow beneath forest canopies is more slight than that of the enhanced longwave radiation.

Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus

BACKGROUND Type 2 diabetes mellitus(T2DM)is associated with periodontitis.Currently,there are few studies proposing predictive models for periodontitis in patients with T2DM.AIM To determine the factors influencing periodontitis in patients with T2DM by constructing logistic regression and random forest models.METHODS In this a retrospective study,300 patients with T2DM who were hospitalized at the First People’s Hospital of Wenling from January 2022 to June 2022 were selected for inclusion,and their data were collected from hospital records.We used logistic regression to analyze factors associated with periodontitis in patients with T2DM,and random forest and logistic regression prediction models were established.The prediction efficiency of the models was compared using the area under the receiver operating characteristic curve(AUC).RESULTS Of 300 patients with T2DM,224 had periodontitis,with an incidence of 74.67%.Logistic regression analysis showed that age[odds ratio(OR)=1.047,95%confidence interval(CI):1.017-1.078],teeth brushing frequency(OR=4.303,95%CI:2.154-8.599),education level(OR=0.528,95%CI:0.348-0.800),glycosylated hemoglobin(HbA1c)(OR=2.545,95%CI:1.770-3.661),total cholesterol(TC)(OR=2.872,95%CI:1.725-4.781),and triglyceride(TG)(OR=3.306,95%CI:1.019-10.723)influenced the occurrence of periodontitis(P<0.05).The random forest model showed that the most influential variable was HbA1c followed by age,TC,TG, education level, brushing frequency, and sex. Comparison of the prediction effects of the two models showedthat in the training dataset, the AUC of the random forest model was higher than that of the logistic regressionmodel (AUC = 1.000 vs AUC = 0.851;P < 0.05). In the validation dataset, there was no significant difference in AUCbetween the random forest and logistic regression models (AUC = 0.946 vs AUC = 0.915;P > 0.05).CONCLUSION Both random forest and logistic regression models have good predictive value and can accurately predict the riskof periodontitis in patients with T2DM.

Identification model of geochemical anomaly based on isolation forest algorithm

The methods for geochemical anomaly detection are usually based on statistical models, and it needs to assume that the sample population satisfies a specific distribution, which may reduce the performance of geochemical anomaly detection. In this paper, the isolation forest model is used to detect geochemical anomalies and it does not require geochemical data to satisfy a particular distribution. By constructing a tree to traverse the average path length of all data, anomaly scores are used to characterize the anomaly and background fields, and the optimal threshold is selected to identify geochemical anomalies. Taking 1∶200 000 geochemical exploration data of Fusong area in Jilin Province, NE China as an example, Fe2O3 and Pb were selected as the indicator elements to identify geochemical anomalies, and the results were compared with traditional statistical methods. The results show that the isolation forest model can effectively identify univariate geochemical anomalies, and the identified anomalies results have significant spatial correlation with known mine locations. Moreover, it can identify both high value anomalies and weak anomalies.

A New Function for Modelling Diameter Frequency Distribution in the Tropical Rain Forest of Xishuangbanna,Southwest of China

Permanent plots in the montane tropical rain forests in Xishuangbanna, southwest China, were established, and different empirical models, based on observation data of these plots in 1992, were built to model diameter frequency distributions. The focus of this study is on predicting accuracy of stem number in the larger diameter classes, which is much more important than that of the smaller trees, from the view of forest management, and must be adequately considered in the modelling and estimate. There exist 3 traditional ways of modelling the diameter frequency distribution: the negative exponential function model, limiting line function model, and Weibull distribution model. In this study, a new model, named as the logarithmic J-shape function, together with the others, was experimented and was found as a more suitable model for modelling works in the tropical forests.

Establishment and evaluation of a risk prediction model for gestational diabetes mellitus

BACKGROUND Gestational diabetes mellitus(GDM)is a condition characterized by high blood sugar levels during pregnancy.The prevalence of GDM is on the rise globally,and this trend is particularly evident in China,which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses.Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses.Therefore,this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin,blood glucose,and body mass index(BMI)on the occurrence of GDM.AIM To develop a risk prediction model to analyze factors leading to GDM,and evaluate its efficiency for early prevention.METHODS The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed.According to whether GDM occurred,they were divided into two groups to analyze the related factors affecting GDM.Then,according to the weight of the relevant risk factors,the training set and the verification set were divided at a ratio of 7:3.Subsequently,a risk prediction model was established using logistic regression and random forest models,and the model was evaluated and verified.RESULTS Pre-pregnancy BMI,previous history of GDM or macrosomia,hypertension,hemoglobin(Hb)level,triglyceride level,family history of diabetes,serum ferritin,and fasting blood glucose levels during early pregnancy were determined.These factors were found to have a significant impact on the development of GDM(P<0.05).According to the nomogram model’s prediction of GDM in pregnancy,the area under the curve(AUC)was determined to be 0.883[95%confidence interval(CI):0.846-0.921],and the sensitivity and specificity were 74.1%and 87.6%,respectively.The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin,fasting blood glucose in early pregnancy,pre-pregnancy BMI,Hb level and triglyceride level.The random forest model achieved an AUC of 0.950(95%CI:0.927-0.973),the sensitivity was 84.8%,and the specificity was 91.4%.The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model(P<0.05).CONCLUSION The random forest model is superior to the nomogram model in predicting the risk of GDM.This method is helpful for early diagnosis and appropriate intervention of GDM.

Modeling susceptibility to deforestation of remaining ecosystems in North Central Mexico with logistic regression

Determining underlying factors that foster deforestation and delineating forest areas by levels of susceptibility are of the main challenges when defining policies for forest management and planning at regional scale. The susceptibility to deforestation of remaining forest ecosystems （shrubland, temperate forest and rainforest） was conducted in the state of San Luis Potosi, located in north central Mexico. Spatial analysis techniques were used to detect the deforested areas in the study area during 1993-2007. Logistic regression was used to relate explana- tory variables （such as social, investment, forest production, biophysical and proximity factors） with susceptibility to deforestation to construct predictive models with two focuses： general and by biogeographical zone In all models, deforestation has positive correlation with distance to rainfed agriculture, and negative correlation with slope, distance to roads and distance to towns. Other variables were significant in some cases, but in others they had dual relationships, which varied in each biogeographi- cal zone. The results show that the remaining rainforest of Huasteca region is highly susceptible to deforestation. Both approaches show that more than 70% of the current rainforest area has high and very high levels of susceptibility to deforestation. The values represent a serious concern with global warming whether tree carbon is released to atmos- phere. However, after some considerations, encouraging forest environ- mental services appears to be the best alternative to achieve sustainableforest management.

Mixed-effects modeling for tree height prediction models of Oriental beech in the Hyrcanian forests

Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech（Fagus orientalis Lipsky） in the Hyrcanian Forest in Iran.The predictive performance of these models was first assessed by different evaluation criteria： adjusted R^2（R^2_（adj））,root mean square error（RMSE）,relative RMSE（%RMSE）,bias,and relative bias（%bias） criteria.The best model was selected for use as the base mixed-effects model.Random parameters for test plots were estimated with different tree selection options.Results show that the Chapman–Richards model had better predictive ability in terms of adj R^2（0.81）,RMSE（3.7 m）,%RMSE（12.9）,bias（0.8）,%Bias（2.79） than the other models.Furthermore,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.

Development of Hydrological Model in the "Forestwater" System for Example in the Juniper Forests of Southern Kyrgyzstan

The article considers the issues of methodology and development of optimal model adapted for the ＂forest-water＂ system, for forecasting the rate of stream flow and for preventing mudflows, flood flows and soil flows in juniper forests of Kyrgyzstan, and also shows the dynamics of ecosystems＇ progress.

A succession and silviculture model for the broad-leaved Korean pine forests of Changbai Mountain Area

A succession and silviculture model (ZELIG.CBA) for broad-leaved Korean pine forest of Changbai Moutain Area was developed based on the framework of ZELIG model and characteristics of Broad-leaved Korean pine forests of Changbai area. ZELIG.CBA model consists four sub-models: growth model simulating annual increment of individual tree in forest, regeneration model simulating annual establishment of different tree species, mortality model simulating annual agerelated and stress-related morality; and silviculture model simulating the forest response to different silviculture scenario. The validation of the ZELIG.CBA showed that the accuracy of the model for the forest growth was more than 95%. The succession from clear cutting site simulating showed that the ZELIG.CBA model was stable for long term simulation. And selective cutting experiment showed that the optimal scenario for broad-leaved Korean pine forests was removal volume 30% combining with 30a rotation.

Application of site-specific biomass models to quantify spatial distribution of stocks and historical emissions from deforestation in a tropical forest ecosystem

Allometric equations developed for the Lama forest, located in southern Benin, West Africa, were applied to estimate carbon stocks of three vegetation types：undisturbed forest, degraded forest, and fallow. Carbon stock of the undisturbed forest was 2.7 times higher than that in the degraded forest and 3.4 times higher than that in fallow. The structure of the forest suggests that the individual species were generally concentrated in lower diameter classes. Carbon stock was positively correlated to basal area and negatively related to tree density, suggesting that trees in higher diameter classes contributed significantly to the total carbon stock. The study demonstrated that large trees constitute an important component to include in the sampling approach to achieve accurate carbon quantification in forestry. Historical emissions from deforestation that converted more than 30% of the Lama forest into cropland between the years 1946 and 1987 amounted to 260,563.17 tons of carbon per year（t CO2/year） for the biomass pool only. The study explained the application of biomass models and ground truth data to estimate reference carbon stock of forests.