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 New Perspective to Evaluate Doppler Vascular Impedance in Hypertensive Disorders Complicating Pregnancy: Multilevel Modeling Established in a Case Control Study

Background: There have been researches on the evaluation of Doppler vascular impedance in hypertensive disorders complicating pregnancy (HDCP). With respect to the method of analysis used and the conclusions drawn in previous studies, different vessels were usually viewed separately and independently. This study was designed to evaluate Doppler vascular impedance changes in HDCP from a new perspective, with original thought and insight into an ordinary issue. Methods: 273 pregnant women (110 hypertensive pregnancies and 163 normotensive pregnancies) were randomly included in a grouping case-control study conducted from February 10, 2011 to April 30, 2013. All women in the study underwent Doppler measurements of six different vessels including the umbilical artery, the uterine arteries, the placental bed spiral artery, the fetal middle cerebral artery and the fetal renal artery. Doppler vascular impedance was presented as pulsatility index (PI), resistance index (RI), and systolic and diastolic ratio (S/D). Doppler changes in the hypertensive and normotensive groups were assessed by the multilevel modeling approach with univariate and multivariate-adjusted analyses. Results: According to multilevel modeling approach with multivariate-adjusted analysis, a relatively average evaluation on Doppler vascular impedance was provided. Hypertension was significantly associated with positive effects on PI, RI and S/D values (coefficients were 0.10, 0.03 and 0.08, respectively;95% CIs were 0.06 - 0.14, 0.02 - 0.04 and 0.04 - 0.11, respectively;P values were all less than 0.001) in comparison with normotensive group. Conclusion: According to an overall evaluation, Doppler vascular impedance in hypertensive disorders complicating pregnancy was higher than in normotensive pregnancy. The novel thought and approach applied in this research may bring about inspirations for better understanding and assessment of the disease.

Applying Multivariate Multilevel Models to Explore Arable Land Quality in Sub-Saharan Africa: A Case Study in Kenya

Controversy exists on the magnitude and variability of farm nutrient balances and quality of arable land in sub-Saharan Africa with Kenya among those affected negatively. This study investigates quality of arable land by fitting multivariate multilevel model to farm nutrient balance data collected from five agro-climatic zones of Kenya (arable lands). Objectives of the study were to investigate the magnitude and variability of Nitrogen, Phosphorus and Potassium (NPK) farm nutrient balances in arable lands of Kenya, study effects of agro-climatic zones on nutrient balances and to determine effects of household resource endowments on NPK nutrient balances. The study concludes that agro-climatic zones differ with respect to farm nutrient balances;that livestock resource endowments and hired labour have positive effects on the magnitude and direction of farm nutrient balances;and that household ownership of large capital resources do not guarantee a positive effect on farm nutrient balances. The study recommends integration of sound livestock practices and application of agro-climatic zone differentiated interventions in future strategies for addressing farm nutrient balances and arable land quality, and the use of large sample sizes and relevant factors/covariates in future analysis to shed additional insights on farm nutrient balances and on how arable land quality can be improved.

Correlates of Parental Choice of Child Discipline Methods in Ghana: A Multilevel Modeling Approach

This study applied multilevel modeling to investigate the impact of observed predictors and different levels or groups that households belong, on parents’ choice of discipline methods using data from 8156 households derived from a nationwide survey by the Ghana Statistical Service (GSS) in 2011. The aim of the study is to provide in-depth information on why parents choose particular discipline methods as corrective measures to reduce unwanted child behaviour in the present and to increase desirable ones in the future. The results of the study show that, religion and age-group of household heads have significant effect on household’s likelihood to choose physical discipline methods whereas the wealth index of a household and ethnicity of the household head, have significant effect on households’ likelihood to choose non-physical and psychological aggression methods. The results further show significant contextual effect on the differences in choices of parents at the household and regional levels. The choice of physical discipline methods by parents was consistent across households and regional levels unlike non-physical and psychological aggression methods whose application varied across the regions. Households in the Northern, Eastern and Volta regions mostly chose to apply physical discipline methods whereas in the Upper West, Western and Northern regions the most chosen discipline methods were non-physical discipline methods. Psychological aggression discipline methods were predominantly applied in the Upper East, Central and Northern regions of the country.

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.

Study of visualized simulation and analysis of nuclear fuel cycle system based on multilevel flow model

Complex energy and environment system, especially nuclear fuel cycle system recently raised socialconcerns about the issues of economic competitiveness, environmental effect and nuclear proliferation. Only underthe condition that those conflicting issues are gotten a consensus between stakeholders with different knowledgebackground, can nuclear power industry be continuingly developed. In this paper, a new analysis platform has beendeveloped to help stakeholders to recognize and analyze various socio-technical issues in the nuclear fuel cycle systembased on the functional modeling method named Multilevel Flow Models (MFM) according to the cognition theoryof human being. Its character is that MFM models define a set of mass, energy and information flow structures onmultiple levels of abstraction to describe the functional structure of a process system and its graphical symbol representationand the means-end and part-whole hierarchical flow structure to make the represented process easy to beunderstood. Based upon this methodology, a micro-process and a macro-process of nuclear fuel cycle system wereselected to be simulated and some analysis processes such as economics analysis, environmental analysis and energybalance analysis related to those flows were also integrated to help stakeholders to understand the process of decision-making with the introduction of some new functions for the improved Multilevel Flow Models Studio, and finallythe simple simulation such as spent fuel management process simulation and money flow of nuclear fuel cycleand its levelised cost analysis will be represented as feasible examples.

SPICE modeling of memristors with multilevel resistance states

With CMOS technologies approaching the scaling ceiling, novel memory technologies have thrived in recent years, among which the memristor is a rather promising candidate for future resistive memory （RRAM）. Memristor＇s potential to store multiple bits of information as different resistance levels allows its application in multilevel cell （MCL） tech- nology, which can significantly increase the memory capacity. However, most existing memristor models are built for binary or continuous memristance switching. In this paper, we propose the simulation program with integrated circuits emphasis （SPICE） modeling of charge-controlled and flux-controlled memristors with multilevel resistance states based on the memristance versus state map. In our model, the memristance switches abruptly between neighboring resistance states. The proposed model allows users to easily set the number of the resistance levels as parameters, and provides the predictability of resistance switching time if the input current/voltage waveform is given. The functionality of our models has been validated in HSPICE. The models can be used in multilevel RRAM modeling as well as in artificial neural network simulations.

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.

Support Operator’s Fault Diagnosis of Complex Plant Systemthrough Multilevel Flow Model and Ecological Interface Design

The paper describes a new human-interface system design method by combining the conception of Multilevel Flow Model and Ecological Interface Design to support operators’ fault diagnosis in the complex plant system. Modern man-made systems are always achieving many complex automatic and intelligent tasks so that they are becoming more and more complex and can be hardly understood by operators, who should be the primary role in system operating. This situation presents a big challenge to the operating support system that it should present the complex system in a direct and clear way to operators to and make operators understand the internal interaction of the system especially in the abnormal status to ensure the operating safety. The Multilevel Flow Model based on the idea of ”Abstraction Hierarchy”, aiming at decompressing a system by means-end and part-whole way, can be used to represent a complex system in a standard way and perform intelligent operating tasks such as fault diagnosis and process control. Ecological Interface Design, which based on the human cognitive properties, can present the internal interaction of the system in a direct way. This paper combines this two interface design conceptions to achieve two aspects, intelligent fault diagnosis and direct presentation of causal relationship of operating parameters, to support operators’ fault diagnosis in complex plant system. The design method is applied to a PWR power plant in this paper as an application example.

Effect of socio-demographic status on dental caries in pupils by using a multilevel hurdle model

Introduction: Dental caries is the most prevalent chronic disease among schoolchildren. We aimed to investigate the association between dental caries index and socio-demographic status in schoolchildren by using a multilevel hurdle model. Materials and Methods: A cross-sectional epidemiological study was carried out on 906 primary school pupils in Kerman, Iran in 2012. The subjects were selected through a stratified cluster random sampling. At first, the whole target area was stratified according to two geographic areas, the north and south area. Then each area was stratified according to gender. In the next step, several schools were considered as clusters, which were selected randomly in each geographic area and gender strata. All the schoolchildren in these schools were included in this study as samples. Twelve-year-old pupils were examined for dental caries. The dependent variable in this study was the dmft/DMFT index. Eight variables, including socio-demographic information, were collected. Multilevel negative binomial hurdle model was employed for data analysis. Results: The prevalence of caries-free pupils was 30.1% and the mean dmft/DMFT was 3.6 ± 2.2. Negative binomial part of the adjusted model showed that the dmft/DMFT adjusted rate for females was 1.36 (95% CI: 1.10-1.79) times higher than males. Also, the dmft/DMFT adjusted rate for overweight pupils was 0.86 (95% CI: 0.74-0.98) times lower than those with normal weight. Logistic part of the adjusted model showed that the posibility of caries-free state in overweight pupils was 1.95 (95% CI: 1.22-3.11) times higher than those with normal weight. In addition, pupils whose fathers and mothers were workers and housewives, respectively, and those with a high maternal age were at a greater risk for caries experience than others (p < 0.05). Conclusion: BMI, gender, parent’s job and mother’s age at delivery were factors effecting on dental caries in schoolchildren. These pupils need more attention to dental care.

Multiple-Attribute and Multilevel Synthetic Evaluation Model and Its Application

On the basis of analyzing the flaws of present multiple-attribute and multilevel evaluation methods, concerning the problem of characteristics of the multiple-attribute and multilevel system s appraisal and the particular emphasis on the respective attributes in the evaluation process, as well as its relevance to the environment, an optimal attribute system is proposed, and the multiplicative analytic hierarchy process (MAHP) is used to obtain subjective weight coefficients and the objective weight coefficient evaluating method is given. The deviation between the index value of each level and the values of the same index of other levels are formulated, and an optimal model is gained, thus establishing the weight coefficients of the whole optimal attribute system. Furthermore, the detailed implementation procedure of this method is introduced. Besides, favorable results have been gained by applying the model to the practical problems of economic evaluation.

Effects of Differential Item Discriminations between Individual-Level and Cluster-Level under the Multilevel Item Response Theory Model

This study attempted to interpret differential item discriminations between individual and cluster levels by focusing on patterns and magnitudes of item discriminations under 2PL multilevel IRT model through a set of variety simulation conditions. The consistency between the mean of individual-level ability estimates and cluster-level ability estimates was evaluated by the correlations between them. As a result, it was found that they were highly correlated if the patterns of item discriminations were the same for both individual and cluster levels. The magnitudes of item discriminations themselves did not affect much on correlations, as far as the patterns were the same at the two levels. However, it was found that the correlation became lower when the patterns of item discriminations were different between the individual and cluster levels. Also, it was revealed that the mean of the estimated individual-level abilities would not be necessarily a good representation of the cluster-level ability, if the patterns were different at the two levels.

A Visual Integrated Analysis Platform for Nuclear Fuel Cycle System Based on Multilevel Flow Model

Complex Nuclear Fuel Cycle (NFC) system faces many socio-technical issues that need to obtain the consensus between stakeholders of different knowledge background. In this paper, a visualized analysis platform based on graphical functional modeling method, Multilevel Flow Model (MFM), is proposed to help those stakeholders to recognize and analyze various socio-technical issues in NFC system. Some new functions, such as “Reaction Function", “Switch Function" and “Conversion Function", are introduced to fulfill new simulation tasks for NFC system. Based upon this methodology, a micro-process and a macro-process of NFC system are simulated and meanwhile some key analysis variables, such as CO2 emission and cost flow, required by some analysis methods are deducted and displayed in the platform. And finally a sample simulation analysis is conducted based on MFM.

State Space Modeling and Implementation of a New Transformer Based Multilevel Inverter Topology with Reduced Switch Count

This paper presents a new transformer based multilevel inverter, with a novel pulse width modulation scheme to achieve seven-level inverter output voltage. The proposed inverter switching pattern consists of three fundamental frequency sinusoidal reference signals with an offset value, and one high frequency triangular carrier signal. This switching scheme has been implemented using an 8-bit Xilinx SPARTAN-3E field programmable gate array based controller. In addition, the state space model of the proposed inverter is developed. The significant features of the proposed topology are: reduction of the power switch count and the gate drive power supply unit, the provision of a galvanic isolation between load and sources by a centre tap transformer. An exhaustive comparison has been made of the existing multilevel inverter topologies and the proposed topology. The performances of the proposed topology with resistive, resistive-inductive loads are simulated in a MATLAB environment and validated experimentally on a laboratory prototype.

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%.

MULTILEVEL INDEX STRUCTURE FOR INFORMATION FILTERING BASED ON USER CHARACTERISTIC

Aimed at information overload and personalized characteristic of user information requirement, this letter presents a type of multilevel index structure and algorithm which is applied to large scale information filtering system and has better performance and stronger scalability.

Community effects on public health in India: A hierarchical model

The data on any aspect of public health, including that on infant mortality, has inbuilt hierarchical structure. Using traditional regression approach in data analysis, i.e., ignoring hierarchical structure, either at micro (individual) or at macro (community) level will be avoiding desired assumption related to independence of records. Accordingly, this may result into distortion in the results due to probable underestimation of standard error of the regression coefficients. To be more specific, an irrelevant co-variate may emerge as an important covariate leading to inappropriate public health implications. To overcome this problem, the objective of the present work was to deal with multilevel analysis of the data on infant mortality available under second round of National family Health Survey and notify changes in results under traditional regression analysis that ignores hierarchical structure of data. This method provides more accurate results leading to meaningful public health implications. In addition, estimation of variability at different levels and their covariance are also obtained. The results indicate that the community (e.g., state) level characteristics still have major role regarding infant mortality in India. Further, if computational facilities are available, multilevel analysis may be preferred in dealing with data involving hierarchical structure leading to accurate results having meaningful public health implications.

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.

A multilevel pan-cancer map links gene mutations to cancer hallmarks

Background:A central challenge in cancer research is to create models that bridge the gap between the molecular level on which interventions can be designed and the cellular and tissue levels on which the disease phenotypes are manifested.This study was undertaken to construct such a model from functional annotations and explore its use when integrated with large-scale cancer genomics data.Methods:We created a map that connects genes to cancer hallmarks via signaling pathways.We projected gene mutation and focal copy number data from various cancer types onto this map.We performed statistical analyses to uncover mutually exclusive and co-occurring oncogenic aberrations within this topology.Results:Our analysis showed that although the genetic fingerprint of tumor types could be very different,there were less variations at the level of hallmarks,consistent with the idea that different genetic alterations have similar functional outcomes.Additionally,we showed how the multilevel map could help to clarify the role of infrequently mutated genes,and we demonstrated that mutually exclusive gene mutations were more prevalent in pathways,whereas many co-occurring gene mutations were associated with hallmark characteristics.Conclusions:Overlaying this map with gene mutation and focal copy number data from various cancer types makes it possible to investigate the similarities and differences between tumor samples systematically at the levels of not only genes but also pathways and hallmarks.