Mapping species assemblages of tropical forests at different hierarchical levels based on multivariate regression trees

Background: Vegetation distribution maps are of great significance for nature protection and management. In diverse tropical forests, accurate spatial mapping of vegetation types is challenging;the high species diversity and abundance of rare species challenge classification concepts, while remote sensing signals may not vary systematically with species composition, complicating the technical capability for delineating vegetation types in the landscape.Methods: We used a combination of field-based compositional data and their relations to environmental variables to predict the distribution of forest types in the Wuzhishan National Natural Reserve(WNNR), Hainan Island,China, using multivariate regression trees(MRT). The MRT was based on arboreal vegetation composition in 132plots of 20 m×20 m with a regular spacing of 1 km. Apart from the MRT, non-metric multidimensional scaling(NMDS) was used to evaluate vegetation-environment relationships.Results: The MRT model worked best when using 14 key environmental variables including topography, climate,latitude and soil, although the difference with the simpler model including only topographical variables was small. The full model classified the 132 plots into 3 vegetation types, 6 formation groups, 20 formations and 65associations at different hierarchical syntaxonomic levels. This model was the basis for forest vegetation maps for the WNNR. MRT and NMDS showed that elevation was the main driving force for the distribution of vegetation types and formation groups. Climate, latitude, and soil(especially available P), together with topographic variables, all influenced the distribution of formations and associations.Conclusions: While elevation determines forest-type distributions, lower-level syntaxonomic forest classes respond to the topographic diversity typical for mountains. Apart from providing the first detailed forest vegetation map for any part of WNNR, we show how, in spite of limitations, MRT with existing environmental data can be a useful method for mapping diverse and remote tropical forests.

Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods.Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas,including investment analysis,image identification,and population genetic structure analysis.However,these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency.Therefore,in this article,we introduce the reduced rank regression method and its extensions,sparse reduced rank regression and subspace assisted regression with row sparsity,which hold potential to meet the above demands and thus improve the interpretability of regression models.We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods.For different application scenarios,we also provide selection suggestions based on predictive ability and variable selection accuracy.Finally,to demonstrate the practical value of these methods in the field of microbiome research,we applied our chosen method to real population-level microbiome data,the results of which validated our method.Our method extensions provide valuable guidelines for future omics research,especially with respect to multivariate regression,and could pave the way for novel discoveries in microbiome and related research fields.

COX MULTIVARIATE REGRESSION ANALYSIS OF RECURRENCE FACTORS FOR COLONIC CARCINOMA

Objective: To determine the independent prognostic factors in the recurrence of colonic carcinoma after curative resection. Methods: Two hundred and one patients undergoing curative resections for colonic carcinoma were investigated by univariate and Cox multivariate regression analyses. Ten factors contributed to the rate were analyzed. Results: Dukes stages, obstruction, postoperative chemotherapy as well as the growth manner of the tumor were significantly associated with the recurrence rate of colonic carcinoma (P<0.05) by univariate analysis, while Dukes stages, obstruction, and postoperative chemotherapy were significant factors by the multivariate analysis. Conclusion: Dukes stages, obstruction, and postoperative chemotherapy are independent prognostic factors in the recurrence of colonic carcinoma.

Multivariate adaptive regression splines and neural network models for prediction of pile drivability

Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical solution with regard to the phenomena involved.In situations where measured data or numerical hypothetical results are available, neural networks stand out in mapping the nonlinear interactions and relationships between the system’s predictors and dependent responses. In addition, unlike most computational tools, no mathematical relationship assumption between the dependent and independent variables has to be made. Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configuration is not known a priori. This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines（MARS）, as an alternative to neural networks, to approximate the relationship between the inputs and dependent response, and to mathematically interpret the relationship between the various parameters. In this paper, the Back propagation neural network（BPNN） and MARS models are developed for assessing pile drivability in relation to the prediction of the Maximum compressive stresses（MCS）, Maximum tensile stresses（MTS）, and Blow per foot（BPF）. A database of more than four thousand piles is utilized for model development and comparative performance between BPNN and MARS predictions.

Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines

This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.

Using multivariate adaptive regression splines to develop relationship between rock quality designation and permeability

The assessment of in situ permeability of rock mass is challenging for large-scale projects such as reservoirs created by dams,where water tightness issues are of prime importance.The in situ permeability is strongly related to the frequency and distribution of discontinuities in the rock mass and quantified by rock quality designation(RQD).This paper analyzes the data of hydraulic conductivity and discontinuities sampled at different depths during the borehole investigations in the limestone and sandstone formations for the construction of hydraulic structures in Oman.Cores recovered from boreholes provide RQD data,and in situ Lugeon tests elucidate the permeability.A modern technique of multivariate adaptive regression splines(MARS)assisted in correlating permeability and RQD along with the depth.In situ permeability shows a declining trend with increasing RQD,and the depth of investigation is within 50 m.This type of relationship can be developed based on detailed initial investigations at the site where the hydraulic conductivity of discontinuous rocks is required to be delineated.The relationship can approximate the permeability by only measuring the RQD in later investigations on the same site,thus saving the time and cost of the site investigations.The applicability of the relationship developed in this study to another location requires a lithological similarity of the rock mass that can be verified through preliminary investigation at the site.

Mountain permafrost distribution modeling using Multivariate Adaptive Regression Spline (MARS) in the Wenquan area over the Qinghai-Tibet Plateau

In high mountainous areas, the development and distribution of alpine permafrost is greatly affected by macro- and mi- cro-topographic factors. The effects of latitude, altitude, slope, and aspect on the distribution of permafrost were studied to under- stand the dislribution patterns of permafrost in Wenquan on the Qinghai-Tibet Plateau. Cluster and correlation analysis were per- formed based on 30 m Global Digital Elevation Model （GDEM） data and field data obtained using geophysical exploration and borehole drilling methods. A Multivariate Adaptive Regression Spline model （MARS） was developed to simulate permafrost spa- tial distribution over the studied area. A validation was followed by comparing to 201 geophysical exploration sites, as well as by comparing to two other models, i.e., a binary logistic regression model and the Mean Annual Ground Temperature model （IVlAGT）. The MARS model provides a better simulation than the other two models. Besides the control effect of elevation on permafrost distribution, the MARS model also takes into account the impact of direct solar radiation on permafrost distribution.

Multivariate adaptive regression splines based simulation optimization using move-limit strategy

This paper makes an approach to the approximate optimum in structural design,which combines the global response surface（GRS） based multivariate adaptive regression splines（MARS） with Move-Limit strategy（MLS）.MARS is an adaptive regression process,which fits in with the multidimensional problems.It adopts a modified recursive partitioning strategy to simplify high-dimensional problems into smaller highly accurate models.MLS for moving and resizing the search sub-regions is employed in the space of design variables.The quality of the approximation functions and the convergence history of the optimization process are reflected in MLS.The disadvantages of the conventional response surface method（RSM） have been avoided,specifically,highly nonlinear high-dimensional problems.The GRS/MARS with MLS is applied to a high-dimensional test function and an engineering problem to demonstrate its feasibility and convergence,and compared with quadratic response surface（QRS） models in terms of computational efficiency and accuracy.

Multivariate Aggregated NOMA for Resource Aware Wireless Network Communication Security

NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.

Combining otolith elemental signatures with multivariate analytical models to verify the migratory pattern of Japanese Spanish mackerel(Scomberomorus niphonius) in the southern Yellow Sea

Japanese Spanish mackerel,Scomberomorus niphonius,is a commercially important,highly migratory species that is widely distributed throughout the northwestern Pacific region.However,its life history and migratory patterns are only partially understood.This study used otolith chemistry to investigate the migratory pattern of S.niphonius in the southern Yellow Sea,an important fishing ground.Transverse sections of otoliths from 15 age-1 spawning or spent individuals,comprising up to one complete migration cycle,were analyzed from the core to the margin by using laser ablation inductively coupled plasma mass spectrometry.The ratios of the element to Ca were integrated with microstructural analysis to produce age-related elemental profiles.Combining multielemental analysis of otolith composition with multivariate analytical models,we quantified structural changes in otolith chemistry profiles.Results revealed there were diverse changing patterns of otolith chemistry profiles for detected elements and the elements of Na,Mg,Sr and Ba were important for the chronological signal.Five clusters were identified through chronological clustering,representing the five life stages from the early stage to the spawning stage.Variation of Ba:Ca ratio was most informative,showing a step-decreasing pattern in the first four stages and a rebound in the spawning stage.These results support the hypothesized migratory pattern of S.niphonius:hatching and spending their early life in the coastal sandy ridges system of the southern Yellow Sea,migrating northeastward and offshore for feeding during juvenile stage,aggregating in early October and migrating outward to the Jeju Island for wintering,and returning to the coastal waters for spawning.This study demonstrated the value of life-history related otolith chemistry profiles combined with multivariate analytical models was a means to verify the migration patterns of S.niphonius at regional scales with potential application in fisheries assessment and management.

Boosting the partial least square algorithm for regression modelling

Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.

Risk factors and care of early surgical site infection after primary posterior lumbar interbody fusion

Objectives:To explore the risk factors and nursing measures of early surgical site infection(SSI)after posterior lumbar interbody fusion(PLIF).Methods:A total of 468 patients who received PLIF in our hospital from January 2017 to June 2020 were enrolled into this study.According to the occurrence of early SSI,the patients were divided into two groups,and the general data were analyzed by univariate analysis.Multivariate logistic regression analysis was conducted with the dichotomous variable of whether early SSI occurred and other factors as independent variables to identify the risk factors of early SSI and put forward targeted prevention and nursing measures.Results:Among 468 patients with PLIF,18 patients developed early SSI(3.85%).The proportion of female,age,diabetes mellitus and urinary tract infection(UTI),operation segment,operation time,post-operative drainage volume,and drainage time were significantly higher than those in the uninfected group,with statistical significance(P<0.05),whereas the preoperative albumin and hemoglobin in the infected group were significantly lower than those in the uninfected group,with statistical significance(P<0.05).There was no significant difference between the two groups in the American Society of Anesthesiologists(ASA)grading,body mass index(BMI),complications including cardiovascular and cerebrovascular diseases or hypertension(P>0.05).Logistic regression analysis showed that preoperative diabetes mellitus(OR=2.109,P=0.012)/UTI(OR=1.526,P=0.035),prolonged drainage time(OR=1.639,P=0.029)were risk factors for early SSI.Men(OR=0.736,P=0.027)and albumin level(OR=0.526,P=0.004)were protective factors in reducing early SSI.Conclusions:Women,preoperative diabetes/UTI,hypoproteinemia,and prolonged drainage time are risk factors for early SSI after PLIF.Clinical effective preventive measures should be taken in combination with targeted nursing intervention to reduce the risk of early SSI.

Data-driven intelligent modeling framework for the steam cracking process

Steam cracking is the dominant technology for producing light olefins,which are believed to be the foundation of the chemical industry.Predictive models of the cracking process can boost production efficiency and profit margin.Rapid advancements in machine learning research have recently enabled data-driven solutions to usher in a new era of process modeling.Meanwhile,its practical application to steam cracking is still hindered by the trade-off between prediction accuracy and computational speed.This research presents a framework for data-driven intelligent modeling of the steam cracking process.Industrial data preparation and feature engineering techniques provide computational-ready datasets for the framework,and feedstock similarities are exploited using k-means clustering.We propose LArge-Residuals-Deletion Multivariate Adaptive Regression Spline(LARD-MARS),a modeling approach that explicitly generates output formulas and eliminates potentially outlying instances.The framework is validated further by the presentation of clustering results,the explanation of variable importance,and the testing and comparison of model performance.

Physics-based and data-driven modeling for stability evaluation of buried structures in natural clays

This study presents a hybrid framework to predict stability solutions of buried structures under active trapdoor conditions in natural clays with anisotropy and heterogeneity by combining physics-based and data-driven modeling.Finite-element limit analysis(FELA)with a newly developed anisotropic undrained shear(AUS)failure criterion is used to identify the underlying active failure mechanisms as well as to develop a numerical(physics-based)database of stability numbers for both planar and circular trapdoors.Practical considerations are given for natural clays to three linearly increasing shear strengths in compression,extension,and direct simple shear in the AUS material model.The obtained numerical solutions are compared and validated with published solutions in the literature.A multivariate adaptive regression splines(MARS)algorithm is further utilized to learn the numerical solutions to act as fast FELA data-driven surrogates for stability evaluation.The current MARS-based modeling provides both relative importance index and accurate design equations that can be used with confidence by practitioners.

Relationship of Body Mass Index, Waist Circumference and Cardiovascular Risk Factors in Chinese Adult

Objective To compare the relative risk of waist circumference （WC） and/or BMI on cardiovascular risk factors. Methods A cross-sectional data of 41 087 adults （19 567 male and 21 520 female） from the 2002 China National Nutrition and Health Survey were examined. According to the obesity definition of WGOC （BMI, 24 kg/m^2 and 28 kg/m^2; WC, male 85 cm and 95 cm for male, 80 cm and 90 cm for female）, the study population were divided into 9 groups. The prevalence and odds ratio （ORs） of cardiovascular disease （CVD） risk factors （hypertension, high fasting plasma glucose and dyslipidemia） were compared among these 9 groups. Stepwise linear regression analyses were used to compare the likelihood of BMI and/or WC on CVD risk factors. Results Both the indexes levels and the odds ratios of CVD risk factors were significantly increased （decreased for HDL-C levels） along with the increase of WC and/or BMI, even when the effect of age, sex, income, education, sedentary activity and dietary factors were adjusted. The variances （R2） in CVD risk factors explained by WC only and BMI only were quite similar, but a little bit larger when WC and BMI were combined. The standard fl was higher of BMI when predicting systolic BP and was higher of WC when predicting TG, TC and HDL. Conclusions BMI and WC had independent effects on CVD risk factors and combination of BMI and WC would be more predictive. Findings from the present study provided substantive evidence for the WGOC recommendation of a combined use of BMI and WC classifications.

Studies on Heavy Metal Pollution in Soil-Plant System:A Review

Heavy metal pollution in soil-plant system is of major environmental concern on a world scale and in China in particular with the rapid development of industry. The heavy metal pollution status in soil-plant system in China, the research progress on the bioavailability of heavy metals (affecting factors, extraction methods, free-ion activity model, adsorption model, multivariate regression model, Q-I relationship, and compound pollution), and soil remediation are reviewed in the paper. Future research and monitoring is also discussed.

Maternal risk factors for low birth weight for term births in a developed region in China:a hospital-based study of 55,633 pregnancies

Low birth weight （LBW） is an important risk factor for neonatal and infant mortality and morbidity in adults.. How- ever, no large scale study on the prevalence of LBW and related maternal risk factors in China has been published. To explore the effects of maternal factors on LBW for term birth in China, we conducted a hospital-based retrospective study of 55, 633 Chinese pregnancy cases between 2001 and 2008. Maternal sociodemographic data, history of infer- tility and contraceptive use were obtained. Their medical status and diseases during pre-pregnancy were examined by physical examination at the first antenatal care visit. Maternal medical status before childbirth and pregnancy outcomes, including body weight, infant gender, multiple pregnancy and congenital anomalies, were recorded. Univariate and multivariate logistic regression, and linear regression were used to investigate the relationship be- tween maternal factors and term LBW. The general incidence of term LBW was 1.70% in the developed area of China. After preliminary analysis using the univariate model, low primary education, anemia, hypertensive disor- ders, placental previa, oligohydramnios and premature rupture of membrane were predicted as independent factors of term LBW in the multivariate model. Furthermore, the decrease in annual frquencies of these risk factors were major causes of gradual decline in the incidence of LBW （from 2.43% in 2001 to 1.21% in 2008）. The study dem- onstrated that among maternal factors, primary education, anemia and hypertensive disorders could contribute to LBW for term birth even in the most developed area of China.

Modified shock index and mortality rate of emergency patients

BACKGROUND:This study aimed to determine whether modified shock index(MSI)is associated with mortality that is superior to heart rate,blood pressure,or the shock index(SI).in emergency patients.METHODS:A retrospective database review was performed on 22 161 patients who presented to Peking Union Medical College Hospital Emergency Department and received intravenous fluids from January 1 to December 31,2009.We gathered data of the patients on age,gender,vital signs,levels of consciousness,presenting complaints,and SI and MSI were calculated for all patients.RESULTS:Multivariate regression analysis was performed to determine the correlation between risk factors and outcome.There is a significant correlation between emergency patient mortality rate and patient's vital signs obtained at the triage desk(HR>120 beats/min,systolic BP<90 mmHg,diastolic BP<60 mmHg).MSI is a stronger predictor of emergency patient mortality compared to heart rate and blood pressure alone,whereas SI does not have a significant correlation with emergency patient mortality rate.CONCLUSION:MSI is a clinically significant predictor of mortality in emergency patients.It may be better than using heart rate and blood pressure alone.SI is not significantly correlated with the mortality rate of the emergency patient.

Three Practical Methods for Analyzing Slope Stability

Since the environmental capacity and the arable as well as the inhabitant lands have actually reached a full balance, the slopes are becoming the more and more important options for various engineering constructions. Because of the geological complexity of the slope, the design and the decision-making of a slope-based engineering is still not practical to rely solely on the theoretical analysis and numerical calculation, but mainly on the experience of the experts. Therefore, it has important practical significance to turn some successful experience into mathematic equations. Based upon the abundant typical slope engineering construction cases in Yunnan, Southwestern China, 3 methods for analyzing the slope stability have been developed in this paper. First of all, the corresponded analogous mathematic equation for analyzing slope stability has been established through case studies. Then, artificial neural network and multivariate regression analysis have also been set up when 7 main influencing factors are adopted.

Variable screening in multivariate linear regression with high-dimensional covariates

We propose two variable selection methods in multivariate linear regression with highdimensional covariates.The first method uses a multiple correlation coefficient to fast reduce the dimension of the relevant predictors to a moderate or low level.The second method extends the univariate forward regression of Wang[(2009).Forward regression for ultra-high dimensional variable screening.Journal of the American Statistical Association,104(488),1512–1524.https://doi.org/10.1198/jasa.2008.tm08516]in a unified way such that the variable selection and model estimation can be obtained simultaneously.We establish the sure screening property for both methods.Simulation and real data applications are presented to show the finite sample performance of the proposed methods in comparison with some naive method.