Hierarchical Data Structure Research Articles

Is the global prevalence rate of adult mental illness increasing? Systematic review and meta-analysis.

The question whether mental illness prevalence rates are increasing is a controversially debated topic. Epidemiological articles and review publications that look into this research issue are often compromised by methodological problems. The present study aimed at using a meta-analysis technique that is usually applied for the analysis of intervention studies to achieve more transparency and statistical precision. We searched PubMed, PsycINFO, CINAHL, Google Scholar and reference lists for repeated cross-sectional population studies on prevalence rates of adult mental illness based on ICD- or DSM-based diagnoses, symptom scales and distress scales that used the same methodological approach at least twice in the same geographical region. The study is registered with PROSPERO (CRD42018090959). We included 44 samples from 42 publications, representing 1035697 primary observations for the first time point and 783897 primary observations for the second and last time point. Studies were conducted between 1978 and 2015. Controlling for a hierarchical data structure, we found an overall global prevalence increase in odds ratio of 1.179 (95%-CI: 1.065-1.305). A multivariate meta-regression suggested relevant associations with methodological characteristics of included studies. We conclude that the prevalence increase in adult mental illness is small, and we assume that this increase is mainly related to demographic changes.

OMiCroN – Oblique Multipass Hierarchy Creation while Navigating

Rendering large point clouds ordinarily requires building a hierarchical data structure for accessing the points that best represent the object for a given viewing frustum and level-of-detail. The building of such data structures frequently represents a large portion of the cost of the rendering pipeline both in terms of time and space complexity, especially when rendering is done for inspection purposes only. This problem has been addressed in the past by incremental construction approaches, but these either result in low quality hierarchies or in longer construction times. In this work we present OMiCroN – Oblique Multipass Hierarchy Creation while Navigating – which is the first algorithm capable of immediately displaying partial renders of the geometry, provided the cloud is made available sorted in Morton order. OMiCroN is fast, being capable of building the entire data structure in memory spending an amount of time that is comparable to that of just reading the cloud from disk. Thus, there is no need for storing an expensive hierarchy, nor for delaying the rendering until the whole hierarchy is read from disk. In fact, a pipeline coupling OMiCroN with an incremental sorting algorithm running in parallel can start rendering as soon as the first sorted prefix is produced, making this setup very convenient for streamed viewing.

Template-Based 3D Road Modeling for Generating Large-Scale Virtual Road Network Environment

The 3D road network scene helps to simulate the distribution of road infrastructure and the corresponding traffic conditions. However, the existing road modeling methods have limitations such as inflexibility in different types of road construction, inferior quality in visual effects and poor efficiency for large-scale model rendering. To tackle these challenges, a template-based 3D road modeling method is proposed in this paper. In this method, the road GIS data is first pre-processed before modeling. The road centerlines are analyzed to extract topology information and resampled to improve path accuracy and match the terrain. Meanwhile, the road network is segmented and organized using a hierarchical block data structure. Road elements, including roadbeds, road facilities and moving vehicles are then designed based on templates. These templates define the geometric and semantic information of elements along both the cross-section and road centerline. Finally, the road network scene is built by the construction algorithms, where roads, at-grade intersections, grade separated areas and moving vehicles are modeled and simulated separately. The proposed method is tested by generating large-scale virtual road network scenes in the World Wind, an open source software package. The experimental results demonstrate that the method is flexible and can be used to develop different types of road models and efficiently simulate large-scale road network environments.

Collaborative Recommendations using Hierarchical Clustering based on K-d Trees and Quadtrees

Majority of the e-commerce sites implement Recommender Systems (RS) to help users navigate through the large search space and assist their decision making process by suggesting products that the user may like. Collaborative Filtering (CF) is the most successful and widely used algorithm in the domain of RS. However, due to the exponential growth of the web in terms of both content and number of users, CF based RS face serious scalability issues. To alleviate this problem, we propose a clustering based CF approach using two hierarchical space partitioning data structures — K-d tree and Quadtree. We cluster or partition the users’ space of the system on the basis of user location and then use the resultant clusters for predicting ratings of a target user. Since the CF based recommendation algorithm is applied separately to the clusters and not on the entire rating data, it helps in bringing down the runtime of the algorithm substantially. We further measure spatial autocorrelation indices in the clusters to justify our clustering method. However, our objective is not only to reduce the runtime but also to maintain an acceptable recommendation quality. This requirement is rightly addressed by the proposed method which assures scalability, by processing very large datasets using the same computing resource. Moreover our proposed clustering scheme is oblivious of the underlying CF algorithm. Results from the extensive experiments conducted, show that our hierarchical clustering based recommendation approach reduces runtime of the standard CF algorithms by about 88%, 82%, 79% and 85% for MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor data respectively, while maintaining good recommendation quality.

An Improved Hierarchical Datastructure for Nearest Neighbor Search

Nearest neighbor search is a fundamental computational tool and has wide applications. In past decades, many datastructures have been developed to speed up this operation. In this paper, we propose a novel hierarchical datastructure for nearest neighbor search in moderately high dimension. Our proposed method maintains good run time guarantees, and it outperforms several state-of-the-art methods in practice.

Ray Classification for Accelerated BVH Traversal

AbstractFor ray tracing based methods, traversing a hierarchical acceleration data structure takes up a substantial portion of the total rendering time. We propose an additional data structure which cuts off large parts of the hierarchical traversal. We use the idea of ray classification combined with the hierarchical scene representation provided by a bounding volume hierarchy. We precompute short arrays of indices to subtrees inside the hierarchy and use them to initiate the traversal for a given ray class. This arrangement is compact enough to be cache‐friendly, preventing the method from negating its traversal gains by excessive memory traffic. The method is easy to use with existing renderers which we demonstrate by integrating it to the PBRT renderer. The proposed technique reduces the number of traversal steps by 42% on average, saving around 15% of time of finding ray‐scene intersection on average.

Hierarchical Data Structures and Multilevel Modeling

Hierarchical Data Structures and Multilevel Modeling

Introducing a new category of activity cliffs with chemical modifications at multiple sites and rationalizing contributions of individual substitutions

Activity cliffs (ACs) are formed by structurally similar active compounds with large potency differences. In medicinal chemistry, ACs are of high interest because they reveal structure-activity relationship (SAR) information and SAR determinants. Herein, we introduce a new type of ACs that consist of analog pairs with different substitutions at multiple sites (multi-site ACs; msACs). A systematic search for msACs across different classes of bioactive compounds identified more than 4000 of such ACs, most of which had substitutions at two sites (dual-site ACs; dsACs). A hierarchical analog data structure was designed to analyze contributions of individual substitutions to AC formation. Single substitutions were frequently found to determine potency differences captured by dsACs. Hence, in such cases, there was redundancy of AC information. In instances where both substitutions made significant contributions to dsACs, additive, synergistic, and compensatory effects were observed. Taken together, the results of our analysis revealed the prevalence of single-site ACs (ssACs) in analog series, followed by dsACs, which reveal different ways in which paired substitutions contribute to the formation of ACs and modulate SARs.

Suicide and Types of Agriculture: A Time-Series Analysis in Japan.

ObjectiveIn recent years, rural areas have reported higher suicide rates than urban areas worldwide. Although agricultural activity is a key characteristic of many rural areas, rurality may also have heterogeneous qualities based on the type of agriculture pursued. However, to date, no study has examined potential linkages between suicide rate and types of agriculture.MethodIn this study, we used 1983–2007 annual time‐series data of the standardized mortality ratio (SMR) of suicide and product‐specific agricultural outputs in Japanese municipalities to shed light on this phenomenon. We conducted a multilevel linear regression analysis, taking into account a hierarchical structure of the time‐series data, limiting our analysis to municipalities where agricultural land use was high.ResultsOur multilevel analysis showed that the animal husbandry output was positively associated with suicide SMR in both women and men, with a stronger relationship among women, whereas no association was observed in agricultural crop output. Temporal analysis showed that the association could be observed consistently throughout the period between 1983 and 2007.ConclusionsThis study raises the possibility that the industrial and cultural characteristics of communities that rely on animal husbandry may be associated with an increased risk of suicide.

Cosmic voids, spatial algorithms and data structures

Cosmic voids are regions with low density in the large scale structure of the universe. These cosmic structures are discovered from simulated or observational galaxy catalogs. In this work, we expose a slight overview of the importance of cosmic voids in cosmology and the type of algorithms used for its discovery in data sets. The octrees, hierarchical spatial data structures, are proposed as an available option to find cosmic voids in galaxy catalogs.

Using the beta distribution to analyse plant cover data

Abstract Most plant species are spatially aggregated. Local demographic and ecological processes (e.g. vegetative growth and limited seed dispersal) result in a clustered spatial pattern within an environmentally homogenous area. Spatial aggregation should be considered when modelling plant abundance data. Commonly, plant abundance is quantified by measuring cover within multiple areal plots or along multiple lines randomly placed within a study area. A common practice for analysing plant cover is to use statistical methods that rely on the normal distribution for quantifying uncertainty. This is problematic because plant cover data tend to be left‐skewed (J‐shaped), right skewed (L‐shaped) or U‐shaped and, therefore, commonly violate classic statistical assumptions, such as normality. We outline statistical analyses that explicitly account for spatial aggregation by assuming that plant cover is beta‐distributed. The beta distribution is a flexible choice because within the open unit interval it can take on a wide range of shapes (L, J, U, or a bell‐shaped). We discuss and introduce extensions to the beta distribution that address common analysis issues encountered in plant cover datasets, such as (a) the treatment of zero and one cover values, (b) hierarchical data structures and (c) observations errors. For heuristic purposes, we focus on single species analyses, but we demonstrate how the outlined methods can be generalized to more species. The assumption that plant cover is beta‐distributed allows us to estimate the degree of spatial aggregation, and the ecological significance of this new knowledge is discussed. We provide a summary of available software for analyses (emphasizing standard R packages) and include worked examples and a simulation study comparing analysis options as supplemental information. Synthesis. Previously, the state of the statistical software made it practically difficult for empirical plant ecologists to analyze their cover data correctly, but new theory and R‐packages have been developed, and this difficulty no longer exists. We recommend that empirical plant ecologists embrace the new statistical possibilities for exploring the exciting ecological features in spatial variation of plant cover.

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Individual tree growth and yield models precisely describe tree growth irrespective of stand complexity and are capable of simulating various silvicultural alternatives in the stands with diverse structure, species composition, and management history. We developed both age dependent and age independent diameter increment models using long-term research sample plot data collected from both monospecific and mixed stands of European beech (Fagus sylvatica L.) in the Slovak Republic. We used diameter at breast height (DBH) as a main predictor and other characteristics describing site quality (site index), stand development stage (dominant height and stand age), stand density or competition (ratio of individual tree DBH to quadratic mean diameter), species mixture (basal area proportion of a species of interest), and dummy variable describing stand management regimes as covariate predictors to develop the models. We evaluated eight versatile growth functions in the first stage using DBH as a single predictor and selected the most suitable one, i.e., Chapman-Richards function for further analysis through the inclusion of covariate predictors. We introduced the random components describing sample plot-level random effects and stochastic variations on the diameter increment, into the models through the mixed-effects modelling. The autocorrelation caused by hierarchical data-structure, which is assumed to be partially reduced by mixed-effects modelling, was removed through the inclusion of the parameter accounting for the autoregressive error-structures. The models described about two-third parts of a total variation in the diameter increment without significant trends in the residuals. Compared to the age independent mixed-effects model (conditional coefficient of determination, R c 2 = 0.6566; root mean square error, RMSE = 0.1196), the age dependent model described a significantly larger proportion of the variations in diameter increment ( R c 2 = 0.6796, RMSE = 0.1141). Diameter increment was significantly influenced differently by covariate predictors included into the models. Diameter increment decreased with the advancement of stand development stage (increased dominant height and stand age), increasing intraspecific competition (increased basal area proportion of European beech per sample plot), and diameter increment increased with increasing site quality (increased site index) and decreased competition (increased ratio of DBH to quadratic mean diameter). Our mixed-effects models, which can be easily localized with the random effects estimated from prior measurement of diameter increments of four randomly selected trees per sample plot, will provide high prediction accuracies. Our models may be used for simulating growth of European beech irrespective of its stand structural complexity, as these models have included various covariate variables describing both tree-and stand-level characteristics, thinning regimes, except the climate characteristics. Together with other forest models, our models will be used as inputs to the growth simulator to be developed in the future, which is important for decision-making in forestry.

Кваліметричні моделі ступеню придатності навчально-бойового літака до використання в базовій навчальній льотній підготовці курсантів

The process of determination of the property of combat-capable trainer aircraft is object of research. Creation of qualimetrical models of combat-capable trainer aircraft is the purpose of research. The methods of research are include methods of systems analysis, method of analysis of hierarchical structures and method of group experts testing. A number of qualimetrical models of combat-capable trainer aircraft was created for determination of property index of basis of flight training of cadets. The qualimetrical model of combat-capable trainer aircraft is mathematical dependence of date index of property of purpose from dates of performance of aircraft. Index of property of purpose of combat-capable trainer aircraft is a vector. This vector includes two main components: index for prime flight training of cadets and index for basis of flight training of cadets. On the basis of flight training of cadets in air military high school analysis hierarchical data structure of combat-capable trainer aircraft characteristics was constructed. This structure describes the adaptation rate of aircraft to flight instruction solution. The elements of flight instruction solutions was grouping in the three groups for each program of flight training of cadets (prime and basis): training of takeoff, training of pilot missions and training of landing aircraft. Factor-space for qualimetric modeling of aircraft was created by means of detected links of the main characteristics analysis. Data of coefficients of factors of the model was determined after analysis of results of expert testing. A group of expert was included thirty-three pilots – instructors of military aviation school. The results of research was creation the six parts qualimetrical models (training of takeoff, training of pilot missions and training of landing aircraft for every programs of flight training of cadets) and two global qualimetrical models - for every programs of flight training of cadets (prime and basis programs).

Visualization approaches for understanding uncertainty in flow diagrams

Abstract Business Intelligence applications often handle data sets that contain uncertain values. In this contribution, we focus on product costing, which deals with the average costs of product components – that vary significantly based on many factors such as inflation, exchange rates, and commodity prices. After experts estimate the uncertainty information for single items, decision makers need to quickly ascertain the cost uncertainties within the hierarchical data structure of the complete product. We propose that only a holistic visualization containing both data and uncertainty can provide this kind of quick overview. Such a visualization must be able to visualize tree data structures associated with value attributes. After conducting interviews with product costing experts, we focused on Flow diagrams, which fulfill this basic requirement. However, they need to be extended in order to directly incorporate uncertainty information. We investigated three visualization techniques applicable to the ribbons of Flow diagrams to convey uncertainty information: Color-code, Gradient, and Margin. Moreover, we designed five visual approaches to show the uncertainty on nodes of Flow diagrams that we evaluated with visualization experts. The approaches add different geometries to the nodes such as triangles, blocks, or forks. The preferred solutions for the nodes was adding forks or filled blocks. With regards to the ribbons, we contribute a user study involving the solution of different product costing tasks using the three different visualizations. Although Gradient was considered an intuitive choice to show uncertainty, it yielded the highest error rates. In contrast, Color-code and Margin were superior depending on the performed task. Based on these findings and the subjective feedback, we designed an integrated approach that combines elements from all three distinct techniques and applied it to Sankey diagrams and Parallel sets.

Spatial-temporal effect of household solid waste on illegal dumping

Illegal dumping is an increasingly costly problem with profoundly negative consequences for the livability and sustainability of our communities. The problem of illegal dumping is particularly acute in the developing world. While the literature is rich in descriptive studies on illegal dumping, few studies leverage large-scale spatial-temporal data through innovative analytical tools to study the actual dynamics of household illegal waste dumping. Our study aims to fill this gap by developing a multilevel theoretical model with which to illustrate the impact of illegal dumping. We explore the spatial-temporal distribution of illegal dumping cases using data mining. Next, we integrate datasets reflecting different levels into a hierarchical data structure organized by membership function. We then use a hierarchical generalized linear model to validate our multilevel model. The results indicate that the spatial factors have a significant relationship with illegal dumping, whereas the direct influence of temporal and community-level factors on illegal dumping is insignificant. Furthermore, the moderating effect of management level and public order on the relationship between spatial features and illegal dumping is significant. Based on our results, we offer several suggestions for preventing illegal dumping.

Perceived Sustainable Urbanization Based on Geographically Hierarchical Data Structures in Nanjing, China

Concentrating on geographically hierarchical data structures and using large-scale satisfaction survey data in Nanjing, this study employs Bayesian spatial multilevel model (MLM) to evaluate Nanjing’s perceived sustainable urbanization. In this study, we consider the geographically hierarchical data structures and the city’s individual perceptions of sustainable urbanization to explore the effect of environment and self-rated health on perceived sustainable urbanization, controlling for individual sociodemographic attributes and household. Through clarifying the spatial dependence and heterogeneity, this paper provides a flexible framework for assessing sustainable urbanization and dealing with the geographical hierarchical data. In particular, by drawing on existing studies, our questionnaire is more representative of the overall characteristics of Nanjing’s population than census data, which can be helpful for understanding whether urbanization is sustainable from individual perspective and further for correcting practices. Based on a survey of 10,077 questionnaires, this paper finds the geographically hierarchical data structures have significantly influenced the evaluation of sustainable urbanization, and the Bayesian spatial MLM is an effective tool for evaluating China’s sustainable urbanization. In particular, this paper takes spatial effects into consideration and compares the geographically hierarchical data. Results show that spatial patterns significantly influence the assessment of sustainable urbanization, and perceived pollution, age, education level, and income are the four key factors influencing individual perceived sustainable urbanization.

Multilevel Structural Equation Modeling for Cross-National Comparative Research

This contribution focuses on a model that is gaining currency in cross-national research, namely multilevel structural equation modelling (MSEM). Similarly to standard multilevel modelling (MLM), this model distinguishes between various levels of analysis (e. g., individuals nested within countries) and, in doing so, takes the hierarchical structure of cross-national data into account. However, MSEM incorporates a latent-variable approach into the multilevel framework, making it possible to assess the measurement quality of latent constructs. As such, MSEM is a synthesis of structural equation modeling (SEM) and MLM that combines the best of both worlds. The MSEM approach makes it possible to model multilevel mediations and group-level outcomes, and therefore provides a more complete representation of Coleman’s bathtub model. This contribution presents the statistical and conceptual background of MSEM in a formal but accessible manner. The paper discusses applications of MSEM that are particularly useful for cross-national comparative research (CNCR), namely two-level confirmatory factor analysis (CFA), multilevel mediation models, and models for group-level outcomes. A practical step-by-step strategy on how MSEM can be used for applied research is provided and illustrated by means of a didactical example.

Validating process variables of sourcing in an assessment of multiple document comprehension.

BackgroundWith digital technologies, competence assessments can provide process data, such as mouse clicks with corresponding timestamps, as additional information about the skills and strategies of test takers. However, in order to use variables generated from process data sensibly for educational purposes, their interpretation needs to be validated with regard to their intended meaning.AimsThis study seeks to demonstrate how process data from an assessment of multiple document comprehension can be used to represent sourcing, which summarizes activities for the consideration of the origin and intention of documents. The investigated process variables were created according to theoretical assumptions about sourcing, and systematically tested for differences between persons, units (i.e., documents and items), and properties of the test administration.SampleThe sample included 310 German university students (79.4% female), enrolled in several bachelor's or master's programmes of the social sciences and humanities.MethodsRegarding the hierarchical data structure, the hypotheses were analysed with generalized linear mixed models (GLMM).ResultsThe results mostly revealed expected differences between individuals and units. However, unexpected effects of the administered order of units and documents were detected.ConclusionsThe study demonstrates the theory‐informed construction of process variables from log‐files and an approach for empirical validation of their interpretation. The results suggest that students apply sourcing for different reasons, but also stress the need of further validation studies and refinements in the operationalization of the indicators investigated.

Exploring the Effects of Vehicular and Operational Characteristics on Bus Roadworthiness: An Application of the Multilevel Modeling Approach

This paper examines the effects of vehicular and operational characteristics on bus roadworthiness. The analysis was based on annual bus inspection data in Victoria, Australia, between 2014 and 2017, consisting of 17,630 inspections of 6,447 vehicles run by 252 operators. A multilevel modeling approach was employed to account for the hierarchical data structure where inspections are nested within vehicles and vehicles within operators. The results offered insights into the effects on bus roadworthiness of characteristics attributable to inspections, vehicles, and operators. The probability of failing an inspection was found to be positively associated with vehicle age and odometer reading. Vehicle make played an important role in roadworthiness outcome, with the performance of different makes varying significantly. Small operators carried the highest risk of failure and large operators the lowest, irrespective of the location of operation. The multilevel analysis revealed that 28.9% of the variation in inspection outcomes occurred across operators and 5.2% across vehicles, which verified the presence of the hierarchical structure. The findings from this study provide safety regulators with solid research evidence to formulate policies aimed at enhancing bus roadworthiness.

Bootstrap ICC estimators in analysis of small clustered binary data

Survey data are often obtained through a multilevel structure and, as such, require hierarchical modeling. While large sample approximation provides a mechanism to construct confidence intervals for the intraclass correlation coefficients (ICCs) in large datasets, challenges arise when we are faced with small-size clusters and binary outcomes. In this paper, we examine two bootstrapping methods, cluster bootstrapping and split bootstrapping. We use these methods to construct the confidence intervals for the ICCs (based on a latent variable approach) for small binary data obtained through a three-level or higher hierarchical data structure. We use 26 scenarios in our simulation study with the two bootstrapping methods. We find that the latent variable method performs well in terms of coverage. The split bootstrapping method provides confidence intervals close to the nominal coverage when the ratio of the ICC for the primary cluster to the ICC for the secondary cluster is small. While the cluster bootstrapping is preferred when the cluster size is larger and the ratio of the ICCs is larger. A numerical example based on teacher effectiveness is assessed.