Human induced dryland degradation in Ordos Plateau,China,revealed by multilevel statistical modeling of normalized difference vegetation index and rainfall time-series

Land degradation causes serious environmental problems in many regions of the world, and although it can be effectively assessed and monitored using a time series of rainfall and a normalized difference vegetation index （NDVI） from remotely-sensed imagery, dividing human-induced land degradation from vegetation dynamics due to climate change is not a trivial task. This paper presented a multilevel statistical modeling of the NDVI-rainfall relationship to detect human-induced land degradation at local and landscape scales in the Ordos Plateau of Inner Mongolia, China, and recognized that anthropogenic activities result in either positive （land restoration and re-vegetation） or negative （degradation） trends. Linear regressions were used to assess the accuracy of the multi- level statistical model. The results show that： （1） land restoration was the dominant process in the Ordos Plateau between 1998 and 2012; （2） the effect of the statistical removal of precipitation revealed areas of human-induced land degradation and improvement, the latter reflecting successful restoration projects and changes in land man- agement in many parts of the Ordos; （3） compared to a simple linear regression, multilevel statistical modeling could be used to analyze the relationship between the NDVI and rainfall and improve the accuracy of detecting the effect of human activities. Additional factors should be included when analyzing the NDVI-rainfall relationship and detecting human-induced loss of vegetation cover in drylands to improve the accuracy of the approach and elimi- nate some observed non-significant residual trends.

Multilevel security model for ad hoc networks

Modern battlefield doctrine is based on mobility, flexibility, and rapid response to changing situations. As is well known, mobile ad hoc network systems are among the best utilities for battlefield activity. Although much research has been done on secure routing, security issues have largely been ignored in applying mobile ad hoc network theory to computer technology. An ad hoc network is usually assumed to be homogeneous, which is an irrational assumption for armies. It is clear that soldiers, commanders, and commanders-in-chief should have different security levels and computation powers as they have access to asymmetric resources. Imitating basic military rank levels in battlefield situations, how multilevel security can be introduced into ad hoc networks is indicated, thereby controlling restricted classified information flows among nodes that have different security levels.

The Refinement Algorithm Consideration in Text Clustering Scheme Based on Multilevel Graph

To construct a high efficient text clustering algorithm the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed. The model is applied to text clustering. The performance of clustering algorithm has to be improved with the refinement algorithm application. The experiment result demonstrated that the multilevel graph text clustering algorithm is available. Key words text clustering - multilevel coarsen graph model - refinement algorithm - high-dimensional clustering CLC number TP301 Foundation item: Supported by the National Natural Science Foundation of China (60173051)Biography: CHEN Jian-bin(1970-), male, Associate professor, Ph. D., research direction: data mining.

Peer Pressure and Family Smoking Habits Influence Smoking Uptake in Teenage Boys Attending School: Multilevel Modeling of Survey Data

Introduction: Among young teens, about one in five smokes worldwide. Adolescents spend a considerable amount of their time in school, and the school environment is therefore important for child health practices and outcomes. Objectives: We aimed to investigate the impact on smoking behavior of the school environment and the personal characteristics of male teenage students attending schools in Pakistan, taking into account the survey sampling structure. Methods: A two-stage cluster sampling with stratification was employed, and we interviewed 772 male secondary school students. We adopted random effect and generalizing estimating equation models. Results: Peer pressure in particular had a strong influence on adolescents smoking;those whose friends smoked were up to 6 times more likely to smoke. Family smoking was also significantly associated with adolescents smoking, but those students whose mother was educated were 50% less likely to smoke. The fitted random effect model indicated that the between school variability was significant (p-value < 0.01), indicating differences in smoking habits between schools. A random coefficient model showed that variability among schools was not significantly different for public and private schools. Conclusion: Public health campaigns for smoking cessation should target not only the individual but also the families of adolescents attending schools.

A Regional Spectral Nested Multilevel Primitive Equation Model

By means of vertical normal modes a regional nested multilevel primitive equation model can be reduced to several sets of shallow water equations characterized by various equivalent depths. Therefore, time integration of the model in spectral form can be performed in the manner similar to those used in the spectral nested shallow water equation model case.

What Influences The Family Migration Decision of China’s New Generation Rural-urban Migrants? A Multilevel Logistic Regression Analysis

The massive scale of new-generation rural-urban migrants in China has attracted extensive scholarly attention in recent years.While previous studies on China’s rural migrant workers focus on migrants’settlement intentions,migrants’family migration decision-making and the intergenerational differences between the old-generation migrants and new-generation migrants are underexplored.Based on the data of the 2017 China Migrants Dynamic Survey,this paper adopts a multilevel logistic regression approach to explore family and destination factors influencing the family migration decisions of China’s new generation of rural migrant workers.The empirical results reveal that both the migrants’family and destination attributes significantly influence their family migration decision.The demographic and socioeconomic characteristics of the family have been pivotal factors underlying the family migration decision of China’s new generation rural-urban migrants,while 16.9%of the chances are explained by between-destination differences.Self-employed migrants with housing properties in host cities,long migration duration and high-income levels are more likely to migrate with their family members.Yet,the possibility of family migration is found to be significantly and negatively correlated with the age,education level,number of children and inter-provincial mobility of the new generation of migrant workers.In addition,new-generation rural-urban migrants’family migration is more likely to be found in cities with service-oriented industry structures,better environmental quality,and higher hukou barriers which is possibly related to more job opportunities.These research findings not only complement the existing literature on China’s new generation of rural urban migrants,but also have important policy implications for reforming the hukou system and enhancing social integration of the rural-to-urban migrant population.

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

Numerical Simulation of Tidal Current in a Bay

Based on the multilevel model, numerical calculations of tidal current affected by the M-2 tide in the Tokyo Bay have been carried out. The results of calculation are compared with the data observed in the Tokyo Bay and the result calculated by an approximate formula as the Tokyo Bay is regarded as a rectangular bay, and good agreement is found. It is proved that the mathematical model and the calculation method are correct and useable.

The association between drinking water salinity and hypertension in coastal Bangladesh

Aims:This study aims to explore the association between drinking water salinity and hypertension in three coastal sub-districts of Bangladesh.Methods:The study uses complete data on 6,296 individuals extracted from the latest Bangladesh Poverty and Groundwater Salinity Survey and a mixed-effects logistic regression model as the analytical tool.Results:Mixed-effects logistic regression analysis shows a significant association of medium or higherlevel salinity with hypertension(adjusted odds ratio[AOR]1.650,95%confidence interval[CI]:1.101—2.473).Other variables significantly associated with hypertension are age,sex,education status,water source,and geographical location.A sizable proportion of the total individual-level variance in the probability of being hypertensive was at household-level(20%)and cluster-level(8%).Conclusion:The findings from this study suggest that greater salinity in potable water common in coastal areas in Bangladesh is associated with increased risk of hypertension.The study refrains from asserting causality but seeks to stimulate public health and policy interventions to address the increased risk.

Study on Mandatory Access Control in a Secure Database Management System

This paper proposes a security policy model for mandatory access control in class B1 database management system whose level of labeling is tuple. The relation hierarchical data model is extended to multilevel relation hierarchical data model. Based on the multilevel relation hierarchical data model, the concept of upper lower layer relational integrity is presented after we analyze and eliminate the covert channels caused by the database integrity. Two SQL statements are extended to process polyinstantiation in the multilevel secure environment. The system is based on the multilevel relation hierarchical data model and is capable of integratively storing and manipulating multilevel complicated objects ( e.g., multilevel spatial data) and multilevel conventional data ( e.g., integer, real number and character string).

PLANNED LEAD TIME OPTIMIZATION IN MATERIAL REQUIREMENT PLANNING ENVIRONMENT FOR MULTILEVEL PRODUCTION SYSTEMS

This paper deals with the problem of planned lead time calculation in a Material Requirement Planning （MRP） environment under stochastic lead times. The objective is to minimize the sum of holding and backlogging costs. The proposed approach is based on discrete time inventory control where the decision variables are integer. Two types of systems are considered： multi-level serial-production and assembly systems. For the serial production systems （one type of component at each level）, a mathematical model is suggested. Then, it is proven that this model is equivalent to the well known discrete Newsboy Model. This directly provides the optimal values for the planned lead times. For multilevel assembly systems, a dedicated model is proposed and some properties of the decision variables and objective function are proven. These properties are used to calculate lower and upper limits on the decision variables and lower and upper bounds on the objective function. The obtained limits and bounds open the possibility to develop an efficient optimization algorithm using, for example, a Branch and Bound approach. The paper presents the proposed models in detail with corresponding proofs and se＇~eral numerical examples. Some advantages of the suggested models and perspectives of this research are discussed.

Modification of laser-induced state in atomic attosecond transient absorption by the XUV pulse pair

Attosecond transient absorption(ATA)has been developed as an all-optical technique for probing electron dynamics in matter.Here we present a scheme that can modify the laserinduced state and the corresponding ATA spectrum via excitation by a pair of XUV attosecond pulses and by a time-delayed mid-infrared(MIR)laser probe.Different from the scheme of the electronic excitation by a single XUV attosecond pulse,the application of a pair of XUV pulses provides extra degrees of freedom,such as the time delay and the intensity ratio between two XUV pulses,which make it possible to adjust the pump process,resulting in the modification of the ATA spectrum.We show that by varying the time delay between the two XUV pulses,the population of the dark state and the ATA spectrum of the laser-induced state have periodic modulations.We also demonstrate that the peak of the ATA spectrum of the laser-induced state appears at a fixed time delay between the XUV pair and the MIR laser when the intensity ratio is large,and it changes with the time delay when the intensity ratio is small,which can be related to either one of two peaks in the population of the dark state.

Neighborhood Effects and Regional Poverty Traps in Rural China

The present paper investigates how neighborhood effects are connected to chronic poverty. We examine a large sample of groups of households and find that neighborhood effects are significant in a majority of groups, especially in the poorest groups. People living in poor communities tend to suffer from poverty over time. It is of theoretical and empirical importance to explore how neighborhood effects are interrelated with chronic poverty and the channels through which this occurs. Unlike other econometric analyses, we establish a multilevel econometric model to show that： （i） it is difficult for an individual living in a neighborhood with a high proportion of agricultural labor, low education levels, and poor transport and telecommunication infrastructure to escape from poverty traps; （ii） neighborhood effects dominate in poor communities; and （iii） although poverty is affected by group-level factors, individual factors still play a dominant role in regards to escaping poverty when income surpasses a threshold level Therefore, policy priority should be given to providing social protection and public services, especially in poor rural areas.

Formidable females redux:male social integration into female networks and the value of dynamic multilayer networks

The development of multilayer network techniques is a boon for researchers who wish to understand how different interaction layers might influence each other,and how these in turn might influence group dynamics.Here,we investigate how integration between male and female grooming and aggression interaction networks influences male power trajectories in vervet monkeys Chlorocebus pygerythrus.Our previous analyses of this phenomenon used a monolayer approach,and our aim here is to extend these analyses using a dynamic multilayer approach.To do so,we constructed a temporal series of male and female interaction layers.We then used a multivariate multilevel autoregression model to compare cross-lagged associations between a male's centrality in the female grooming layer and changes in male Elo ratings.Our results confirmed our original findings:changes in male centrality within the female grooming network were weakly but positively tied to changes in their Elo ratings.However,the multilayer network approach offered additional insights into this social process,identifying how changes in a male's centrality cascade through the other network layers.This dynamic view indicates that the changes in Elo ratings are likely to be short-lived,but that male centrality within the female network had a much stronger impact throughout the multilayer network as a whole,especially on reducing intermale aggression(i.e.,aggression directed by males toward other males).We suggest that multilayer social network approaches can take advantage of increased amounts of social data that are more commonly collected these days,using a variety of methods.Such data are inherently multilevel and multilayered,and thus offer the ability to quantify more precisely the dynamics of animal social behaviors.

Operational method for determining bottom hole pressure in mechanized oil producing wells,based on the application of multivariate regression analysis

One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure.The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps,which makes it difficult to take direct bottom-hole flowing pressure measurements.The bottomhole filtration pressure(BHFP)in these wells is very often determined by recalculating the parameters measured at the well mouth(annulus pressure,dynamic fluid level depth).The recalculation is done by procedures based on analytically determining the characteristics of the gas-liquid mixture in the wellbore,which is very inconsistent to perform due to the mixture's complex behavior.This article proposes an essentially different approach to BHFP measurements that relies on the mathematical processing of the findings of more than 4000 parallel mouth and deep investigations of the oil production wells of a large oil-production region.As a result,multivariate mathematical models are elaborated that allow reliably determining the BHFP of oil-production wells in operation.

Composite Hierachical Linear Quantile Regression

Multilevel （hierarchical） modeling is a generalization of linear and generalized linear modeling in which regression coefficients are modeled through a model, whose parameters are also estimated from data. Multilevel model fails to fit well typically by the use of the EM algorithm once one of level error variance （like Cauchy distribution） tends to infinity. This paper proposes a composite multilevel to combine the nested structure of multilevel data and the robustness of the composite quantile regression, which greatly improves the efficiency and precision of the estimation. The new approach, which is based on the Gauss-Seidel iteration and takes a full advantage of the composite quantile regression and multilevel models, still works well when the error variance tends to infinity, We show that even the error distribution is normal, the MSE of the estimation of composite multilevel quantile regression models nearly equals to mean regression. When the error distribution is not normal, our method still enjoys great advantages in terms of estimation efficiency.