Hierarchical Data Structure Research Articles

Adherence to IMCI guidelines for key symptoms in Ethiopian children: A 2021-2022 national service provision survey.

The Health Services Provision Assessment in Ethiopia (SPA-ET) is a survey that generates data on the availability and quality of health services in Ethiopia. Despite the presence of integrated management of childhood illness guidelines in healthcare settings, there has been inadequate exploration or assessment of how effectively and consistently health professionals follow the guidelines. This study aims to identify factors influencing healthcare worker adherence to the integrated management of childhood illness guidelines to identify spatial clusters. The data for this study were gathered from the Service Provision Assessment (SPA) survey in Ethiopia, which was conducted nationwide from August 11, 2021, to February 4, 2022. It included a total of 788 health professionals who assessed sick children experiencing at least one of the three main childhood illness symptoms: fever, cough, or diarrhea. We employed STATA version 16 for data analysis, utilizing cross-tabulations to explore relationships between variables and logistic regression modeling to identify factors influencing adherence. To account for the hierarchical structure of the health survey data, we employed multilevel logistic regression. Model selection was based on comparison parameters including the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC). We computed adjusted odds ratios with 95% confidence intervals, and statistical significance was determined at a significance level of p < 0.05. The rate of adherence to the integrated management of childhood illness guideline was 33% (95% CI: 29.70%, 36.26%). The analysis revealed several factors influencing adherence to IMCI protocols. child's age (being ≥24 months) [aOR = 0.66, 95% CI: (0.45, 0.87)], facility type (health center) [aOR = 2.61, 95% CI: (1.84, 3.37)], place of residency (being rural) [aOR = 0.54, 95% CI: (0.38, 0.77)], and care provider's qualification (health officer) [aOR = 1.71, 95% CI: (1.18, 2.48)] were all statistically significant. Moreover, the primary cluster is situated in the west Oromia region, with a central focus at coordinates (7.982108 N, 36.203355 E) and extends to a radius of 78.28 km. This study confirms a low adherence rate (33%) among health professionals in Ethiopia to the IMCI guideline for assessing the three main symptoms of sick children. The study identified child's age, facility type, academic qualification, and place of residence as crucial factors correlated with adherence rate. Furthermore, 5 secondary clusters (hotspot areas) were identified using SaTScan software. To address the higher protocol refusal, interventional plan needs to be based on academic qualification of care provider, facility type, age of child and place of residency. Moreover, interventions to reduce non-adherence to IMCI guidelines should be location-tailored based on identified hotspot areas to restore guidelines adherence equality.

The relational modeling of hierarchical data in biodiversity databases.

The unifying element of all biodiversity data is the issue of taxon hierarchy modeling. We compared 25 existing databases in terms of handling taxa hierarchy and presentation of this data. We used documentation or demo installations of databases as a source of information and next in line was the analysis of structures using R packages provided by inspected platforms. If neither of these was available, we used the public interface of individual databases. For almost half (12) of the databases analyzed, we did not find any formalized taxa hierarchy data structure, providing only biological information about taxon membership in higher ranks, which is not fully formalizable and thus not generally usable. The least effective Adjacency List model (storing parentId of a taxon) dominates among the remaining providers. This study demonstrates the lack of attention paid by current biodiversity databases to modeling taxon hierarchy, particularly to making it available to researchers in the form of a hierarchical data structure within the data provided. For biodiversity relational databases, the Closure Table type is the most suitable of the known data models, which also corresponds to the ontology concept. However, its use is rather sporadic within the biodiversity databases ecosystem.

Real-Time Exposure to Negative News Media and Suicidal Ideation Intensity Among LGBTQ+ Young Adults

With a recent surge in anti-lesbian, gay, bisexual, transgender, or queer (LGBTQ+) policies and associated news and media coverage, there is a need to study the association of LGBTQ+ focused news or media exposure and short-term changes in suicidal ideation (SI) among LGBTQ+ youth. To examine within-person direct and indirect associations between exposure to general and LGBTQ+ negative news or media and SI intensity through expectations of rejection. This intensive longitudinal cohort study used a smartphone-based ecological momentary assessment (EMA) protocol wherein participants responded to EMAs 3 times per day for 28 consecutive days. Young adults aged 18 to 24 years who self-identified as LGBTQ+, resided in Tennessee, and had past-year SI and at least mild depression (defined as a score ≥5 on the Patient Health Questionnaire-9) were eligible for inclusion. Participants were recruited through social media advertisements, LGBTQ+ community organizations, and mental and behavioral health clinics. Participants were recruited between March 30, 2023, and August 23, 2023, and data analyses were conducted from August 28, 2023, through April 20, 2024. At each EMA, participants reported on recent exposure to negative news or media. A 3-level categorical exposure variable denoted (1) no news or media exposure (reference); (2) exposure to general negative news or media; and (3) exposure to LGBTQ+ negative news or media. Each EMA also assessed expectations of rejection due to LGBTQ+ identity on a scale of 0 (not at all) through 10 (very much). Three primary outcomes assessed current (ie, "right now") intensity of active SI, passive SI, and self-harm ideation, each measured on a scale from 0 (not at all) to 10 (very strong). Multilevel modeling approaches were used to account for the hierarchical structure of EMA data, with assessments (level 1) nested within people (level 2). Linear mixed models and multilevel mediation models were used to examine within-person associations between exposure to negative news or media and the 3 primary outcomes, as well as the mediating role of expectations of rejection. Of 31 total participants, 22 were assigned female sex at birth (71%), and 16 (52%) self-identified as transgender or gender diverse. The median (IQR) participant age was 21 (18-22) years, and a total of 2189 EMAs were completed, with a median (range) compliance of 90.5% (41.7%-100%). At the within-person level, recent exposure to LGBTQ+ negative news or media was significantly associated with increased active SI (estimate [b], 0.14; 95% CI, 0.04-0.25; P = .009), passive SI (b, 0.23; 95% CI, 0.04-0.41; P = .02), and self-harm ideation (b, 0.13; 95% CI, 0.02-0.23; P = .02). No statistically significant associations were detected for exposure to general negative news or media. In multilevel mediation models, heightened expectations of rejection explained some of the total effect of exposure to LGBTQ+ negative news or media on active SI (23%) and passive SI (37%). This intensive longitudinal cohort study found that SI intensity modestly increased in the hours immediately following exposure to LGBTQ+ negative news or media among LGBTQ+ young adults. These findings have timely implications for research and intervention, particularly within sociopolitical and geographic contexts where news or media coverage about LGBTQ+ topics is intensified.

Asymmetry index for data and its verification in dimensionality reduction and data visualization

We propose an asymmetry index as a measure of degree of asymmetry of a given dataset. It provides an additional information on a dataset allowing to guide and improve any further analysis. The index reflects the intensity of the asymmetric relationships among data resulting from hierarchical data structure. Using the information retrieved by our asymmetry index, one obtains a justification and explanation of the effectiveness of the subsequent asymmetric data analysis methods, as well as helpful preparation to asymmetrizing the tools for the further analysis. The asymmetry index is based on the k-nearest neighbors graph representing the considered data. Therefore, it uses the intrinsic geometry-based information on the data, in this way, providing an insight into the data structure. Our experiments on real data are designed to verify the usefulness of the asymmetry index and the correctness of its theoretical fundamentals. In our empirical validation, we employ the symmetric and asymmetric dimensionality reduction algorithms and evaluate their results on the basis of clustering in the 2-dimensional visualization space. We test, whether our index indeed predicts the level of superiority of the asymmetric methods over their symmetric counterparts.

Vai trò của vốn nhân lực trong hoạt động xuất khẩu của doanh nghiệp: Bằng chứng từ khu vực ASEAN

This study focuses on the impact of human capital, specifically employee education level and employee training, on enterprise export activities in the context of the ASEAN region. The study used World Bank Enterprise Survey data in 2015 and 2016 with 3,335 businesses from Indonesia, Malaysia, Philippines, Vietnam, Laos, Cambodia, and Thailand. The study uses the multilevel mixed-effects logit model to solve the hierarchical data structure (multilevel) of the data set used in the study. Research results show that employee education level and employee training both have a positive impact on the export performance of businesses in the ASEAN region. From the above results, the study provides a perspective for businesses on how to improve efficiency in export activities in international markets through enhancing the human capital of the workforce.

Jointly exploring mathematics ability and speed in large-scale computer-based testing

AbstractIn large-scale tests, the implementation of computer-based testing (CBT) allows to automatically collect data not only on the students’ response accuracy (RA) based on item responses of the test, but also on their response time (RT). RTs can provide a more comprehensive view of a test-taker’s performance beyond just what is obtainable based on correct responses alone. In this paper a joint approach is considered to improve the estimation of ability scores involving complex data coming from computer-based test administration. The study focuses on analysing the data of Italian grade 10 mathematics national assessment administered by the National Institute for the Evaluation of the Education and Training System (INVALSI). In addition, a bivariate multilevel regression with speed and ability estimates, obtained by joint model, is developed including individual covariates to evaluate the contribution of individual and contextual variables in predicting test-taking speed and ability. Overall, the main results indicate that mathematics ability and speed are significantly and negatively correlated, and that the hierarchical data structure (students nested into classes) should be taken into account when explaining the dependency of ability and speed on explanatory variables, such as prior achievement, test anxiety, sociodemographic covariates, class compositional variables, school tracks and geographical area.

Control of Frequentist type I error rates in hierarchical linear models for multiregional clinical trials using a Bayesian method

A multiregional clinical trial is a clinical trial conducted simultaneously in multiple regions under a single protocol. It is a new drug development strategy that has advantages in many ways. A significant part of these advantages comes from the hierarchical data structure of the multiregional clinical trials. Based on the recent work of Kim and Kang (2020) concerning hierarchical linear models to better reflect the structure, we address the problem of controlling the Frequentist type I error rate when the number of regions is small using a Bayesian method. More precisely, we present a strategy for controlling the type I error rate by setting a prespecified Bayesian credible level. Simulation results show that our proposed method controls the type I error rate reliably and well. Through sensitivity analysis, we also investigate the effect on the results when accurate information on between-region variability is given and when several different types of prior distributions are used.

Spatio-Temporal Hierarchical Data Structure Based on Sparse Voxel Octrees for Representation of the Geometry of Time-Variable Three-Dimensional Scenes

Abstract The paper deals with the problematics of representation of the geometry of three-dimensional scenes in computer graphics using domain-specific hierarchical data structures based on octant trees or directed acyclic graphs as the means of space saving binary representation of this information. Several hierarchical data structures were introduced in this field in past decades. In this paper we are moving this problematic to the field of the representation of three-dimensional scenes which are rather dynamic in time, so their geometry is changing on the regular basis. We are investigating the influence of this time-variability on the size of the related hierarchical data structure on binary level. We are comparing the approach in which each time step is considered as the stand-alone static three-dimensional scene and therefore is represented by its own hierarchical data structure with the approach when this time-variable scene is considered as the four dimensional space and spatio-temporal sparse voxel octree hierarchical data structure is used for the representation of the geometry of this scene. We tested both approaches on three different scenes which were obtained by voxelization into two different resolutions of polygonal surface models represented originally as the mesh of triangles in Wavefront Object file format. Results of the tests show that spatio-temporal sparse voxel octree is better solution, when we obtained from 1.73 to 2.08 more compact binary representation, in comparison to the solution when time frames were encoded separately as the set of sparse voxel octrees.

The working alliance inventory – short version: psychometric properties of the patient and therapist form in youth mental health and addiction care

The therapeutic alliance is considered to play an important role in youth treatment. The commonly used versions of the Working Alliance Inventory (WAI) are based on Bordin’s three-dimensional alliance model. However, previous psychometric studies of the WAI did not find this three-dimensional structure in youth psychotherapy. These earlier findings may indicate different perceptions of the alliance by adolescent versus adult patients, but may also be due to methodological shortcomings. The current study aims to address previous study limitations by evaluating the factor structure of the short version of the WAI (WAI-S) in youth treatment in multilevel analysis to address the hierarchical structure of the alliance data. We examined the psychometric properties of the patient (n = 203) and therapist (n = 62) versions of the WAI-S in youth mental health and addiction care and tested four multilevel models of alliance at start of treatment and 2-month follow-up. Our results suggests a two-factor model for youth and a three-dimensional model for their therapist at both time points. Since this is the first study that finds a best fit for a two-dimensional construct of alliance in youth, more research is needed to clarify whether the differences in alliance dimensions are due to measurement differences between the WAI-S for youth and therapists or whether youth and their therapists truly differ in their perceptions of the concept of alliance.

DETERMINANTS OF POOR HOUSEHOLDS IN SOUTH SUMATRA USING A MULTILEVEL LOGISTIC MODEL

Poverty is still one of the problems experienced by all countries, including Indonesia. According to BPS, in March 2021, the poverty rate in Indonesia was 10.14 percent. South Sumatra is a province with a poverty rate, which is the tenth-highest nationwide and the third-highest on Sumatra Island. This poverty rate is accompanied by a contraction in economic growth in 2021 by 3.58 percent. This condition indicates a contradiction and suggests that poverty still needs to be resolved. Moreover, the disparity in social and economic aspects across regions could potentially make the poverty rate high. This research aims to see the individual and regional or contextual factors affecting poor households. To simultaneously capture the effects of individual and regional level, we perform a Multilevel Logistic model using hierarchical structured poverty data from SUSENAS. The result shows that 4 (four) variables at individual level, which are the number of household members, the status of residence building, health insurance, and saving ownership, had a significant effect on the poor household. The region with high unemployment rate tend to have a high percentage of poor households. This result indicates that local government need to have policies that can affect poor households directly, such as socializing more about health insurance program and family planning program, as well as supervision in social aid distribution. Moreover, they need to create a program that can employ more people in order to decrease the percentage of poor households in such regions.

The effect of dietary diversity on anemia levels among children 6-23 months in sub-Saharan Africa: A multilevel ordinal logistic regression model.

Anemia is the most common hematologic disorder of children worldwide. Since dietary diversity is a main requirement of children is to get all the essential nutrients, it can thus use as one of the basic indicator when assessing the child's anemia. Although dietary diversity plays a major role in anemia among children in sub-Saharan Africa, there is little evidence of an association between the dietary diversity and anemia level to identified potential strategies for prevention of anemia level in sub-Saharan Africa. To examine the association between dietary diversity and anemia levels among children aged 6-23 months in sub-Saharan Africa. The most recent Demographic and Health Surveys from 32 countries in SSA were considered for this study, which used pooled data from those surveys. In this study, a total weighted sample of 52,180 children aged 6-23 months was included. The diversity of the diet given to children was assessed using the minimum dietary diversity (MDD), which considers only four of the seven food groups. A multilevel ordinal logistic regression model was applied due to the DHS data's hierarchical structure and the ordinal nature of anemia. With a p-value of 0.08, the Brant test found that the proportional odds assumption was satisfied. In addition, model comparisons were done using deviance. In the bi-variable analysis, variables having a p-value ≤0.2 were taken into account for multivariable analysis. The Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) was presented for potential determinants of levels of anemia in the multivariable multilevel proportional odds model. The overall prevalence of minimum dietary diversity and anemia among children aged 6-23 months were 43% [95% CI: 42.6%, 43.4%] and 72.0% [95% CI: 70.9%, 72.9%] respectively. Of which, 26.2% had mild anemia, 43.4% had moderate anemia, and 2.4% had severe anemia. MDD, being female child, being 18-23 months age, born from mothers aged ≥25, taking drugs for the intestinal parasite, higher level of maternal education, number of ANC visits, middle and richer household wealth status, distance of health facility and being born in Central and Southern Africa were significantly associated with the lower odds of levels of anemia. Contrarily, being 9-11- and 12-17-months age, size of child, having fever and diarrhea in the last two weeks, higher birth order, stunting, wasting, and underweight and being in West Africa were significantly associated with higher odds of levels of anemia. Anemia was a significant public health issue among children aged 6-23 months in sub-Saharan Africa. Minimum dietary diversity intake is associated with reduced anemia in children aged 6 to 23 months in sub-Saharan Africa. Children should be fed a variety of foods to improve their anemia status. Reducing anemia in children aged 6-23 months can be achieved by raising mother education levels, treating febrile illnesses, and improve the family's financial situation. Finally, iron fortification or vitamin supplementation could help to better reduce the risk of anemia and raise children's hemoglobin levels in order to treat anemia.

Path guiding for wavefront path tracing: A memory efficient approach for GPU path tracers

We propose a path-guiding algorithm to be incorporated into the wavefront style of path tracers (WFPTs). As WFPTs are primarily implemented on graphics processing units (GPUs), the proposed method aims to leverage the capabilities of the GPUs and reduce the hierarchical data structure and memory usage typically required for such techniques. To achieve this, our algorithm only stores the radiant exitance on a single global sparse voxel octree (SVO) data structure. Probability density functions required to guide the rays are generated on-the-fly using this data structure. The proposed approach reduces the scene-related persistent memory requirements compared to other path-guiding techniques while producing similar or better results depending on scene characteristics. To our knowledge, our algorithm is the first one that incorporates path guiding into a WFPT.

Contextual factors associated with diarrhea among under-five children in the Gambia: a multi-level analysis of population-based data

BackgroundDiarrhea poses a significant threat to the lives of children in The Gambia, accounting for approximately 9% of all deaths among children under the age of five. Addressing and reducing child mortality from diarrhea diseases is crucial for achieving the Sustainable Development Goal (SDG) 3, specifically target 3.2, which aims to eliminate preventable deaths in newborns and children under the age of five by 2030. Thus, this research aims to assess the prevalence and contextual factors associated with diarrhea among under-five children in The Gambia.MethodsThis research employed secondary data from the 2019/20 Gambia Demographic Health Survey (GDHS). The study initially involved 8,362 women aged between 15 and 49 years. Of these, 6,929 women with children under five were included in this analysis. Data were analyzed using STATA with cross-tabulation and model fitting. Multilevel logistic regression was applied to accommodate the hierarchical structure of the demographic health survey data. The model comparison parameters were BIC, AIC, deviance, and LLR. Variables with a p-value less than 0.05 were selected for multivariable analysis. The statistical significance of the factors was determined using an adjusted odds ratio with a 95% confidence interval (CI) and a p-value of less than 0.05.ResultsThe prevalence of diarrhea in under-five children was 53.2% in males and 46.8% in females. In the final model, Kerewan (aOR = 0.58; 95% CI = 0.33–0.98) and Basse (aOR = 0.59; 95% CI = 0.35–0.98) have significantly lower odds of childhood diarrhea compared to Banjul, female children show slightly lower, yet significant, odds of diarrhea compared to males (aOR = 0.96; 95% CI = 0.86–0.98), deliveries at government health centers are associated with higher odds of childhood diarrhea compared to home births (aOR = 1.24; 95% CI = 1.01–1.52). Mothers with post-secondary education had significantly lower odds of having children with diarrhea than those without any education (aOR = 0.50; 95% CI = 0.26–0.99) after controlling for confounders.ConclusionThe study findings indicate that several factors significantly impact the risk of childhood diarrhea in The Gambia. These factors include region of residence, sex of the child, place of delivery, and education level of the mother. The study suggests that existing interventions aimed at improving child health outcomes in the country should take into consideration these influential factors. Addressing these modifiable factors can enhance the effectiveness of interventions and promote better health outcomes for children in Gambia.

A dynamic data access control scheme for hierarchical structures in big data

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a promising solution to address the issues of data leakage and sharing in big data. When multiple files are shared, they often have hierarchical access relationships. However, existing hierarchical data CP-ABE schemes can only support simple hierarchical access structures. The limitation arises, necessitating the generation of multiple access structures to meet hierarchical access requirements, resulting in the wastage of computational and storage resources. Therefore, in this paper, we carefully design a dynamic hierarchy access control that allows its level nodes to flexibly participate in the decryption of other nodes, enabling the realization of complex hierarchical data relationships. Building upon this, we construct the Attribute-based Dynamic Hierarchical data Access Control (ADHAC) scheme, achieving efficient and dynamic access control for multiple datasets. Subsequently, we apply our scheme in distributed computing, which enables efficient access control for individuals and institutions over multiple data sets and fine-grained computation. Security analysis indicates that our scheme can resist chosen plaintext attack (CPA). Both theoretical and experimental analyses demonstrate that our scheme has impressive advantages in terms of computational efficiency and storage costs compared to existing schemes.

Целостность и иерархичность структур данных в жизненном цикле объектов малоэтажного жилищного строительства

The subject area of the article is construction facilities life cycle management, where the object-oriented automated technologies of low-rise housing construction are the subject of consideration. The purpose of this study is to describe the meta-structure of such technologies, consisting of a set of hierarchical structures of the finite elements (premises, structures, engineering equipment, etc.) and for this meta-structure definition the concept of low-rise housing estate construction facilities integrity is introduced. The object of the article's research is hierarchical data structures, on the basis of which object-oriented databases and knowledge bases are formed, serving automated technologies for construction facilities integrity generation within all stages of the life cycle. The article uses the method of decomposition of the low-rise housing estate construction facilities integrity into a variety of automated technologies and their constituent hierarchical data structures, which allows us to identify all subject areas of objects of manor low-rise housing estate construction for computational justifications of the limiting states of finite elements (objects) of architecture, building structures, engineering equipment. Hierarchical structures create a standardized basis for the organization of finite element information models for managing the life cycle of manor low-rise estate housing construction. Decision-makers should prepare hierarchical data structures and algorithms for calculating the justifications for finite elements. The article presents examples of hierarchical data structures schemes for the design stage tasks solving. It is concluded that all information models of finite elements on a variety of subject areas of manor low-rise estate housing construction objects arise during the execution of constructive (algorithmized) processes in the decision-making layers of the corresponding stages of the life cycle of construction objects. It is concluded that all information models of finite elements on a variety of subject areas of manor low-rise estate housing construction objects arise during the execution of constructive (algorithmized) processes in the decision-making layers of the corresponding stages of the life cycle of construction objects. A structuring condition is also proposed: if the main link of the life cycle management technology is the information model of the object, then the fundamental condition for object orientation and meaningful filling of hierarchical data structures is the construction system used in this construction object, as a set of load-bearing and enclosing structures, its components and materials used. The proposed approach will make it possible to create and maintain a shared data environment on a regular and ongoing basis as part of a set of hierarchical data structures of the construction projects under consideration. In the development, it becomes possible to qualitatively improve the technological conditions for decision-making according to various criteria, including criteria for environmental friendliness, energy efficiency, economy, comfort for low-rise residential buildings using neural network data structures and the use of artificial intelligence algorithms. The above together is the basis for the formation of a modern innovative technological order for the objects of estate low-rise housing construction.

On the Expected Cost of Partial Match Queries in Random Quad-K-d Trees

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They provide a unified framework for the analysis of associative queries, and they are specially suitable to investigate the trade-offs between the cost of different operations and the memory needs (each node x of a quad-K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K$$\\end{document}-d tree has arity 2m(x)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2^{m(x)}$$\\end{document} for some m(x), 1≤m(x)≤K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\\le m(x)\\le K$$\\end{document}). Indeed, we consider here partial match—one of the fundamental associative queries for several families of quad-K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K$$\\end{document}-d trees including, among others, relaxed K-d trees and quadtrees. In particular, we prove that the expected cost P^n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hat{P}_{n}$$\\end{document} of a random partial match query that has s out of K specified coordinates in a random quad-K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K$$\\end{document}-d tree of size n is P^n∼β·nα\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hat{P}_{n}\\sim \\beta \\cdot n^\\alpha $$\\end{document}, where α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document} and β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document} are constants given in terms of K and s as well as additional parameters that characterize the specific family of quad-K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K$$\\end{document}-d trees under consideration. Additionally, we derive a precise asymptotic estimate for the main order term of the expected cost Pn,q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{n,\ extbf{q}}$$\\end{document} of a fixed partial match with query q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{q}$$\\end{document} in a random quad-K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K$$\\end{document}-d tree of size n. The techniques used to derive the mentioned costs are those already applied successfully to derive analogous results in quadtrees and relaxed K-d trees; our results show that the previous results are just particular cases and prove the validity of the conjecture made in Duch et al. (In: Proceedings of the 12th Latin American Theoretical Informatics Conference (LATIN). Lecture Notes in Computer Science, vol. 9644, pp. 376–389, 2016) for a wider variety of multidimensional data structures.

Fuzzy similarity based hierarchical clustering for communities in twitter social networks

The complexity of the Internet has significantly increased along with its expansions. In particular, social networking has resulted in user clusters pertaining to many communities at different levels. Studies of online communities are a growing area of research user preferences can be categorized including dangerous groups. This work uses pre-processing techniques including stemming, stop words removals, and tokenizations of data using unigram, bigram, and 1–3 g representations. TF-IDFs (Term Frequency-Inverse Document Frequencies) and Word Embeds are used in feature Extractions. Perspectives generate hierarchical data structures which are then combined using consensus matrices followed by computations of dissimilarities between observed sets. This work employs FS-HC (fuzzy similarity based Hierarchical Clustering) to generate dendrograms for views. Finally, consensus matrices are generated by integrating several hierarchical agglomerations using transitive consensus matrix generations. They contain representative data of generated dendrograms. Performance metrics including precisions, recalls, f-measures, accuracies, clustering coefficients, conductance, and contractions are used to evaluate outcomes of benchmarked clustering algorithms. The proposed approach attains higher accuracy value of about 92 % when compared to other existing algorithms. The required data for community discoveries in social media were gathered from Twitter.

Generalized Estimating Equations Boosting (GEEB) machine for correlated data

AbstractRapid development in data science enables machine learning and artificial intelligence to be the most popular research tools across various disciplines. While numerous articles have shown decent predictive ability, little research has examined the impact of complex correlated data. We aim to develop a more accurate model under repeated measures or hierarchical data structures. Therefore, this study proposes a novel algorithm, the Generalized Estimating Equations Boosting (GEEB) machine, to integrate the gradient boosting technique into the benchmark statistical approach that deals with the correlated data, the generalized Estimating Equations (GEE). Unlike the previous gradient boosting utilizing all input features, we randomly select some input features when building the model to reduce predictive errors. The simulation study evaluates the predictive performance of the GEEB, GEE, eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) across several hierarchical structures with different sample sizes. Results suggest that the new strategy GEEB outperforms the GEE and demonstrates superior predictive accuracy than the SVM and XGBoost in most situations. An application to a real-world dataset, the Forest Fire Data, also revealed that the GEEB reduced mean squared errors by 4.5% to 25% compared to GEE, XGBoost, and SVM. This research also provides a freely available R function that could implement the GEEB machine effortlessly for longitudinal or hierarchical data.

Factors influencing forestland enrollment in Georgia's preferential property tax programs

More than two-thirds of Georgia's private forestland (or 14.6 million acres) are enrolled in one of the state's preferential property tax programs. Besides alleviating property tax burdens for rural landowners, the purposes of these incentive programs have gradually evolved from ensuring a sustainable supply of agricultural and timber products to promoting the conservation of rural open space and provision of ecosystem services. This study examined the factors associated with forestland enrollment in Georgia's major current-use tax programs using a combination of parcel-level spatial data, property tax roll, and county-level economic data for all private forestland in the study counties. A multilevel logistic regression model was used to control the effects of hierarchical data structure. A forest parcel's size, tax savings, neighboring parcel enrollment, land value, land productivity, landowner's residence status in relation to the property, development potential (e.g., distance to cities, county population density) as well as distance to lakes and identified conservation areas are important predictors of its likelihood of being enrolled in the property tax incentive programs. Larger forest parcels, located close to identified potential conservation areas, characterized by high soil productivity, held by non-absentee owners, and adjacent to enrolled parcels were more likely to be enrolled in the current-use tax programs.

Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition.

Audio-visual video recognition (AVVR) integrates audio and visual cues to accurately categorize videos. While current methods using provided datasets achieve satisfactory results, they face challenges in retaining historical class knowledge when new classes appear in real-world situations. There are no dedicated methods to address this issue, prompting this paper to explore Class Incremental Audio-Visual Video Recognition (CIAVVR). CIAVVR aims to preserve historical knowledge contained in stored data and learned models to prevent catastrophic forgetting. Audio-visual data and models inherently have hierarchical structures, where the model contains both low-level and high-level semantic information, and data includes snippet-level, video-level, and distribution-level spatial information. It is crucial to fully exploit these hierarchical structures for data knowledge preservation and model knowledge preservation. However, existing image class incremental learning methods do not explicitly consider these hierarchical structures. Therefore, we introduce Hierarchical Augmentation and Distillation (HAD), which includes the Hierarchical Augmentation Module (HAM) and Hierarchical Distillation Module (HDM). These modules efficiently utilize the hierarchical structure of data and models. Specifically, HAM uses a novel augmentation strategy, segmental feature augmentation, to preserve hierarchical model knowledge. Simultaneously, HDM employs newly designed hierarchical logical distillation (video-distribution) and hierarchical correlative distillation (snippet-video) to maintain intra-sample and inter-sample hierarchical knowledge. Evaluations on four benchmarks (AVE, AVK-100, AVK-200, and AVK-400) show that HAD effectively captures hierarchical information, enhancing the preservation of historical class knowledge and performance. We also provide a theoretical analysis to support the segmental feature augmentation strategy.