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.

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.

Modeling the Spatial Distribution of Soil Heavy Metals Using Random Forest Model—A Case Study of Nairobi and Thirirka Rivers’ Confluence

Modeling the spatial distribution of soil heavy metals is important in determining the safety of contaminated soils for agricultural use. This study utilized 60 topsoil samples (0 - 30 cm), multispectral images (Sentinel-2), spectral indices, and ancillary data to model the spatial distribution of heavy metals in the soils along the Nairobi River. The model was generated using the Random Forest package in R. Using R2 to assess the prediction accuracy, the Random Forest model generated satisfactory results for all the elements. It also ranked the variables in order of their importance in the overall prediction. Spectral indices were the most important variables within the rankings. From the predicted topsoil maps, there were high concentrations of Cadmium on the easterly end of the river. Cadmium is an impurity in detergents, and this section is in close proximity to the Nairobi water sewerage plant, which could be a direct source of Cadmium. Some farms had Zinc levels which were above the World Health Organization recommended limit. The Random Forest model performed satisfactorily. However, the predictions can be improved further if the spatial resolutions of the various variables are increased and through the addition of more predictor variables.

Experimental study of population density using an optimized random forest model

Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population.We took Shijiazhuang as the research area,with comprehensive zoning based on endowments as the modeling unit,conducted stratified sampling on a hectare grid cell,and systematically carried out incremental selection experiments of population density impact factors,optimizing the population density random forest model throughout the process(zonal modeling,stratified sampling,factor selection,weighted output).The results are as follows:(1)Zonal modeling addresses the issue of confusion in population distribution laws caused by a single model.Sampling on a grid cell not only ensures the quality of training data by avoiding the modifiable areal unit problem(MAUP)but also attempts to mitigate the adverse effects of the ecological fallacy.Stratified sampling ensures the stability of population density label values(target variable)in the training sample.(2)Zonal selection experiments on population density impact factors help identify suitable combinations of factors,leading to a significant improvement in the goodness of fit(R^(2))of the zonal models.(3)Weighted combination output of the population density prediction dataset substantially enhances the model's robustness.(4)The population density dataset exhibits multi-scale superposition characteristics.On a large scale,the population density in plains is higher than that in mountainous areas,while on a small scale,urban areas have higher density compared to rural areas.The optimization scheme for the population density random forest model that we propose offers a unified technical framework for uncovering local population distribution law and the impact mechanisms.

Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.

A Data-Driven Car-Following Model Based on the Random Forest

The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re- presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em- ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car- following behavior with better performance under multiple performance indicators.

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.

Linear and Nonlinear Trading Models with Gradient Boosted Random Forests and Application to Singapore Stock Market

This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow three modes of trades, namely, buy, sell or stand by, and the stand-by case is important as it caters to the market conditions where a model does not produce a strong signal of buy or sell. Linear trading models are firstly developed with the scoring technique which weights higher on successful indicators, as well as with the Least Squares technique which tries to match the past perfect trades with its weights. The linear models are then made adaptive by using the forgetting factor to address market changes. Because stock markets could be highly nonlinear sometimes, the Random Forest is adopted as a nonlinear trading model, and improved with Gradient Boosting to form a new technique—Gradient Boosted Random Forest. All the models are trained and evaluated on nine stocks and one index, and statistical tests such as randomness, linear and nonlinear correlations are conducted on the data to check the statistical significance of the inputs and their relation with the output before a model is trained. Our empirical results show that the proposed trading methods are able to generate excess returns compared with the buy-and-hold strategy.

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.

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.

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.

新型城镇化对共同富裕的影响研究——基于Random Forests模型和Loess模型的分析

基于2010—2021年31个省份的相关数据,运用综合评分法测度我国的新型城镇化水平和共同富裕程度,结合Random Forests模型和Loess模型分析新型城镇化对共同富裕的影响效应及其成因。研究发现,新型城镇化总体上可以促进共同富裕,但存在维度异质性与空间异质性。各维度对共同富裕的影响力从高至低排序依次为经济城镇化、人口城镇化、社会城镇化和生态城镇化,各区域按相关性从大到小排序依次为东北、东部、中部和西部地区。不同维度的城镇化对不同区域的共同富裕影响程度不同,对东部、中部和东北地区影响最大的是经济城镇化,对西部地区影响最大的是人口城镇化。基于此,提出以人的全面发展为目标、以经济城镇化为重点、以差异化发展策略为前提、以公共服务均等化为突破口推进共同富裕的政策建议。

中国农田磷流失风险评价及其关键驱动因素

农田面源磷流失是农业面源污染的重要原因之一,识别流域内农田磷流失风险的关键源区及其影响因子是面源污染控制的重要手段。基于磷指数模型开展2000—2020年中国农田磷流失风险评估,选取土壤有效磷含量、磷肥施用量为源因子,土壤侵蚀模数、年径流深、农田和水体间归一化距离指数为迁移因子,结合GIS技术评估了中国农田磷流失的关键源区;在此基础上,利用随机森林法分析影响中国农田磷流失的关键因子,并通过结构方程模型揭示了农田磷流失风险指数与各因子的关系。结果表明:1)2000—2020年中国农田的磷流失的低、中、高、极高风险面积分别占农田总面积的43.8%、40.5%、13.4%、2.4%。2)中国农田磷流失在2000、2005、2010、2015、2020年高风险和极高风险总面积的年平均占比从大到小依次为:淮河流域、长江流域、珠江流域、东南诸河流域、松辽河流域、西南诸河流域、黄河流域、内陆河流域、海河流域。3)影响农田磷流失风险的关键源因子和迁移因子分别为土壤有效磷含量和归一化距离指数,其重要性特征值分别为129.53和65.12,土壤有效磷含量是农田磷流失最主要影响因子。4)磷流失风险指数与源因子指数、迁移因子指数呈极显著正相关,选取的14个指标对磷指数的解释度达0.62,其中源因子和迁移因子对磷指数的贡献率分别为0.77、0.19(P <0.001)。研究结果可为中国农田磷流失风险评估提供科学参考,对中国农业面源污染的宏观防控及战略决策具有重要意义。

Modelling the dead fuel moisture content in a grassland of Ergun City,China

The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.

基于随机森林和XGBoost算法构建心脏骤停患者自主循环恢复后神经功能预后不良的风险预测模型

目的 利用机器学习算法构建预测心脏骤停(CA)患者自主循环恢复(ROSC)后神经功能预后不良的预测模型,探索结局相关因子。方法 回顾性收集2016年1月至2024年1月沧州市中心医院收治的CA行心肺复苏(CPR)后ROSC的患者481例为研究对象。收集患者临床资料,根据患者转出重症监护病房(ICU)时的格拉斯哥-匹兹堡脑功能表现分级(CPC)评分,将其分为预后良好组(GNO,n=158)和预后不良组(PNO,n=323)。481例患者按7∶3随机分为训练集(n=338)和测试集(n=143),训练集用于构建模型,测试集用评价模型效能。利用极端梯度提升(XGBoost)和随机森林(RF)两种机器学习算法构建患者神经功能预后不良的预测模型,分别得出影响患者神经功能预后的变量,应用SHAP进行XGBoost模型可解释性分析。将XGBoost和RF算法得出的变量取交集,再将交集变量进行多因素Logistic回归分析,得到差异有统计学意义的变量,进而构建决策树模型。在训练集和测试集上利用受试者工作特征(ROC)曲线和曲线下面积(AUC)评估决策树模型的预测性能。结果 通过XGBoost模型得到与神经功能预后不良相关的变量15个,RF模型得到与神经功能预后不良相关的变量14个,两种模型取交集得到11个与神经功能预后不良相关的交集变量[视神经鞘直径(ONSD)变化率、神经元特异性烯醇化酶(NSE)、入ICU第3天ONSD(ONSD day3)、心脏骤停至心肺复苏(CA-CPR)时间、ROSC时间、急性生理学与慢性健康状况评价Ⅱ(APACHEⅡ)评分、血肌酐、白蛋白、住ICU时间、血乳酸及年龄]。将这11个交集变量进行多因素Logistic回归分析,结果显示,PNO组与GNO组ONSD变化率、NSE、ONSD day3、ROSC时间及年龄这5个变量差异有统计学意义(P<0.05)。用这5个重要变量构建决策树模型,得出3个与患者神经功能预后不良最相关的变量(NSE、ROSC时间及ONSD变化率),在训练集上的决策树模型预测CA行CPR后ROSC患者神经功能预后不良的AUC为0.857(95%CI 0.809~0.903,P<0.001),在测试集上的AUC为0.834 (95%CI 0.761~0.906,P<0.001)。结论 基于XGBoost和RF这2种机器学习方法构建的决策树模型能够更准确地评估CA患者ROSC后神经功能的不良预后,且评价指标可能简化为NSE、ROSC时间及ONSD变化率。

基于森林资源清查资料的盈江县森林生物量和生长量分析

为确定盈江县森林碳储量和碳汇潜力的变化特征及其影响因子,以便更好地分析盈江县森林生物量和生长量。基于盈江县2012年和2017年森林资源清查数据,利用生物量换算因子连续函数法和异速生长方程,评估盈江县森林碳储量和碳汇潜力的变化特征及其影响因子。结果显示:(1)盈江县森林生物量储量丰富,达85.55 t/hm^(2)。其中:栎类林最大,为168.3 t/hm^(2);核桃林最低,为7.10 t/hm^(2)。(2)不同林龄林分生物量差异较大,近熟林最高,其次分别为中龄林、成熟林、过熟林、幼龄林。(3)常绿阔叶林的林分生长量最大,其次是落叶阔叶林和针叶林。结果表明:盈江县森林生物量储量丰富且以阔叶林为主;林龄和年均气温是影响林分生长和生物量的主要因素。

近40年海南岛国土空间格局演化的生态效应及其影响因素

科学认知国土空间格局演化与生态服务的关联作用及主导影响因素,可为区域国土空间开发和生态环境保护提供决策支持。采用空间转移矩阵、InVEST模型分析1980—2020年海南岛国土空间格局及生态系统服务的时空演化特征,并在此基础上引入综合生态服务指数(CESI)和国土空间转型生态贡献率评估国土空间格局演化的生态效应,最后运用随机森林模型探究生态系统服务演化的主导因素及作用机制。结果表明:(1)近40年海南岛城镇空间持续扩张,农业空间不断缩减,生态空间先增后减,全域国土空间交叉转化总规模达到2717.11km^(2),其中以生态空间与农业空间的动态转换最为显著,生态空间和农业空间向城镇空间转换次之,城镇空间向生态空间和农业空间的转换最弱。(2)生态系统服务功能中产水量分布呈现“东北部高、西南低”的分布特征,土壤保持、碳储量、生境质量及综合生态服务指数分布则表现为“中部高、四周低”,40年间各类生态系统服务功能均呈现不同的程度的下降趋势,这是海南岛生态系统服务功能恶化趋势大于改善趋势的结果,其中生态空间向农业空间和城镇空间的转换是区域生态服务功能恶化的重要原因。(3)海南岛生态服务功能受到自然地理、交通区位、社会经济和政策的综合作用,交通区位、社会经济以及自然地理类因素影响作用显著,政策类因素影响相对较弱,且各影响因素与生态系统服务功能间呈现非线性相关关系。

基于荧光光镊与机器学习的单细胞血液分类方法

利用物种间血液成分的差异来识别物种,对生物医学、医疗健康、海关、刑侦、食品安全、野生动物保护等工作十分重要。但目前的研究都是针对群体细胞展开,忽略了单细胞的异质性,开展基于单细胞的血液光谱分类方法研究非常迫切。在此提出了一种基于荧光光镊和机器学习的单细胞血液分类方法,利用光镊实现了单细胞捕获,通过荧光光谱检测系统获得了单细胞荧光光谱数据,并基于机器学习方法实现了准确分类。首先,设计并搭建了一套荧光光镊系统,实现了单细胞捕获和荧光光谱检测。然后,制备了马、猪、犬、鸡四种动物的红细胞稀释液,以440 nm激光作为荧光激发光源,获得了四个物种每种100条、共计400条荧光光谱数据,并进行了背景去除、平滑、归一化的预处理,消除了信号中的噪声干扰。随后,建立了随机森林分类模型,分析了当抽取特征数k=20时,模型中树的棵数与预测准确率之间的关系,当决策树m=500时,分类正确率趋于稳定,有很高的分类正确率和运行效率。进一步地,设定样本数据的30%作为测试集、70%为训练集,计算不同波长与特征重要性之间的关系,得到了10个分类准确率,并取平均值作为模型分类的准确率,测试集最终准确率达到93.1%,方差为0.31%。最后,计算了混淆矩阵,对模型预测精度进行了评价,鸡的分类正确率最高,马的分类正确率最低。分析表明,对分类有重要贡献的物质分别是卟啉类物质、血红素和黄素腺嘌呤二核苷酸。总之,研究表明,将荧光光镊与机器学习方法相结合,可实现单细胞水平的血液分类,较高的分类正确率验证了这种方法的可行性和有效性。同时,该方法不需要过多样品就能满足建模需求,避免了因浓度低带来的荧光自吸收强度过低等问题,具有快速、准确分类的优点,具有非常重要的潜在应用价值。

基于随机森林模型分析共情心理对大学生心理健康的影响

目的运用随机森林算法分析共情心理对大学生心理健康的影响。方法采取方便抽样的方法,以问卷调查的方式于2022年8月对河北省某大学的1383名学生进行在线调查,内容包括调查对象一般情况以及基本共情量表和症状自评量表。使用最小二乘法回归模型分析共情心理是否为大学生心理健康的影响因素,构建随机森林模型分析共情心理对大学生心理健康的重要性。结果共回收问卷1383份,有效问卷1348份,问卷有效率为97.5%。回归模型结果表明,共情心理对大学生心理健康水平的影响具有统计学意义(t=-2.051,P<0.05)。随机森林模型的结果显示,大学生心理健康重要性排名前3位的解释变量分别为共情心理、睡眠质量以及学业压力。结论共情心理是影响大学生心理健康的重要因素。建议合理开展提升共情心理能力的活动,关心学生的睡眠时长,适当减轻学生的学业压力。

冻融循环下玄武岩纤维混凝土冲击力学性能预测模型

冻融循环下纤维混凝土的劣化规律是寒区服役混凝土工程安全性和耐久性评价的重要依据,现有服役混凝土工程的安全性和耐久性评价的研究具有工作量大、成本高、周期长等特点,构建基于机器学习的高精度力学性能预测模型已成为本领域研究热点。为探究冻融循环后玄武岩纤维混凝土冲击力学性能的高精度预测模型,采用SHPB装置对冻融循环后BFRC开展动态冲击压缩力学性能试验,并构建机器学习-Optuna混合预测模型,对60组以玄武岩纤维体积掺量、冻融循环次数、动荷载冲击速度为影响因素建立的动态峰值应力样本数据集进行预测。结果表明:k近邻、Lasso、多层感知机、极度梯度提升树和随机森林5种经典机器学习模型的预测准确度均较高,说明机器学习算法对于冻融循环后BFRC动态力学性能预测具有良好的预测效果,其中随机森林算法为最优预测算法;RF-Optuna混合预测模型显示出0.9754的拟合优度,具有良好的预测精度;非数据集工况预测表明,该混合模型对于各影响因素均具有良好泛化能力。研究成果可为冻融循环条件下BFRC动态力学性能的快捷精准预测提供参考。