Hierarchical Schema Research Articles

KAE: A property-based method for knowledge graph alignment and extension

A common solution to the semantic heterogeneity problem is to perform knowledge graph (KG) extension exploiting the information encoded in one or more candidate KGs, where the alignment between the reference KG and candidate KGs is considered the critical procedure. However, existing KG alignment methods mainly rely on entity type (etype) label matching as a prerequisite, which is poorly performing in practice or not applicable in some cases. In this paper, we design a machine learning-based framework for KG extension, including an alternative novel property-based alignment approach that allows aligning etypes on the basis of the properties used to define them. The main intuition is that it is properties that intentionally define the etype, and this definition is independent of the specific label used to name an etype, and of the specific hierarchical schema of KGs. Compared with the state-of-the-art, the experimental results show the validity of the KG alignment approach and the superiority of the proposed KG extension framework, both quantitatively and qualitatively.

Motivations and Tools Relevant to Personalized Workspaces in VR Environments

In this paper, we propose a new virtual reality (VR) concept referred to as ‘context control’, which we use to describe VR workspaces that are dynamically reconfigurable based on the task at hand and the user’s individual learning and working style. To demonstrate the viability of the concept as well as how it could be applied in practical applications, we present an implementation framework that, at its foundations, relies on Kolb’s learning styles taxonomy, consisting of Assimilators, Accommodators, Convergers and Divergers. We propose a layout schema for each of these categories of learning style, and validate them based on an experiment involving 52 university students, showing that the test subjects preferred content layouts that represent cognitive profiles matching their own to a greater extent. We also propose a hierarchical schema template language with which the schemas can be formalized and made amenable to further dynamic customization.

Digital transformation of technological IP portfolio analysis for complex domain of satellite communication innovations

The latest mobile network protocols, currently adopted by most of global telecom-services, are at the level of fifth generation (5G) technological standards. However, 5G speed and bandwidth will not be likely to catch up with the advanced AI- and IoT-based applications, which require very low latency with wide coverage. Thus, industry is looking ahead beyond 5G (B5G) communication enabling technologies. Further, in order to provide global communications without disconnections due to geographical barriers (e.g., oceans and high mountains lacking base stations), low earth orbit (LEO) satellites become the ideal solution for connecting entire global network with terrestrial and non-terrestrial links. In this research, the integration of LDA topic modeling, clustering algorithm, technology-function matrix, and s-curve modeling is proposed to analyze complex IP portfolio for B5G satellite technologies. The domain ontology, which describes the transdisciplinary features of the B5G satellite technology taxonomy, is derived and refined in a hierarchical schema after thorough literature review and 2357 domain patents being collected and analyzed. This research aims to discover the main trends and the potentials of satellite communication technologies through novel tech-mining techniques in both macro- and micro-levels. During macro-analyses, trends of global inventions, the leading assignees, and the distribution of technology categories are depicted. We identify the leading patent assignees with their domain patents (published from 2012 to 2021) and distributions under sub-technical categories. Further, the micro-analyses discover the innovations of major technological themes, showing key R&D hot-spots, cold-spots and lifecycle maturities of sub-technical categories. Finally, the research strategies for technical-savvy countries (e.g., Taiwan, with advances in its semiconductor and ICT industries) are presented highlighting its strategic innovations related to the complex communication domain.

Inheritance of gene expression throughout fruit development in chili pepper

Gene expression is the primary molecular phenotype and can be estimated in specific organs or tissues at particular times. Here we analyzed genome-wide inheritance of gene expression in fruits of chili pepper (Capsicum annuum L.) in reciprocal crosses between a domesticated and a wild accession, estimating this parameter during fruit development. We defined a general hierarchical schema to classify gene expression inheritance which can be employed for any quantitative trait. We found that inheritance of gene expression is affected by both, the time of fruit development as well as the direction of the cross, and propose that such variations could be common in many developmental processes. We conclude that classification of inheritance patterns is important to have a better understanding of the mechanisms underlying gene expression regulation, and demonstrate that sets of genes with specific inheritance pattern at particular times of fruit development are enriched in different biological processes, molecular functions and cell components. All curated data and functions for analysis and visualization are publicly available as an R package.

Grasslands on Coastal Headlands in New South Wales, south eastern Australia

Aims: To use unsupervised techniques to produce a hierarchical classification of grasslands on coastal headlands of New South Wales in eastern Australia.Methods: A dataset of 520 vegetation plots scored on cover and placed across grasslands on coastal headlands (ca. 2000 km of coastline). Vegetation assemblages were identified with the aid of a clustering method based on group averaging and tested using similarity profile analysis (SIMPROF) using Bray-Curtis similarity. A hierarchical schema was developed based on EcoVeg hierarchy and was circumscribed using positive and negative diagnostic taxa via similarity percentage analysis (SIMPER) and importance based on summed cover scores and frequency. Mapping the occurrences grasslands was initially constructed using remote sensing which was verified and modified with on ground observations.Results: One groupThemeda – Pultenaea – Zoysia – Cynodongrasslands and heathy grasslands was defined to include all coastal headland grassland vegetation of the New South Wales, and within this, three alliances and ten associations. Only one of the circumscribed associations is represented within the current state classification schema. In total 107 ha were mapped of which 68 ha occurred within secure conservation tenure.Conclusions: A number of unique and rare grassland assemblages on coastal headlands have to date gone undescribed. The most common alliance constitutes approximately 87% of extant grassland occurrences but is currently the only type listed as endangered and afforded protection. AlthoughPoaspp. are listed as a threat toThemedadominated assemblages on headlands data from this study suggest that this is unlikely to be the case.Taxonomic reference: PlantNET (http://plantnet/10rbgsyd.nsw.gov.au/; accessed June 2019).Abbreviations: BC Act = Biodiversity Conservation Act; NMDS = non-metric multidimensional scaling; NSW = New South Wales; PCT = Plant Community Type; SIMPER = similarity percentage analysis; SIMPROF = Similarity profile analysis.

Montane mire vegetation of the New England Tablelands Bioregion of Eastern Australia

Aims: To use unsupervised techniques to produce a hierarchical classification of montane mires of the study region. Study area: New England Tablelands Bioregion (NETB) of eastern Australia. Methods: A dataset of 280 vascular floristic survey plots placed across the variation in montane mires of the NETB was collated. Vegetation types were identified with the aid of a clustering method based on group averaging and tested using similarity profile analysis (SIMPROF) and through ordinations using Bray-Curtis similarity and non-metric multidimensional scaling (NMDS). A hierarchical schema was developed based on EcoVeg hierarchy and was circumscribed using positive and negative diagnostic taxa via similarity percentage analysis (SIMPER) and importance based on summed cover scores and frequency. Results: We defined one macrogroup to include all montane mire vegetation of the NETB and within these two groups and twelve alliances. Conclusions: Our study re-enforced the separation of bogs from other montane mire systems and confirmed the separation of fens and wet meadows, a distinction that previously had not been independently tested. Based on our results many existing montane mire communities of the NETB have been ill-defined at multiple hierarchical levels, leading to confusion in threat status and mapping. Additionally, nearly half of the alliances we recognise were found to have no correlates within current classification systems, which necessarily has implications for the effectiveness of current conservation planning. Taxonomic reference: PlantNET (http://plantnet.rbgsyd.nsw.gov.au/, accessed June 2016). Abbreviations: BC Act = Biodiversity Conservation Act; EPBC Act = Environmental Protection and Biodiversity Act; NETB = New England Tablelands Bioregion; NMDS = non-metric multidimensional scaling; PCT = plant community type; RE = regional ecosystem; SIMPER = similarity percentage analysis; SIMPROF = similarity profile analysis.

Deep Learning for Classification of Radiology Reports with a Hierarchical Schema

Abstract Radiological reports are a valuable source of textual information, which can be exploited to improve clinical care and to support research. Such information can be extracted and put into a structured form using machine learning techniques. Some of them rely not only on the classification labels but also on the manual annotation of relevant snippets, which is a time consuming job and requires domain experts. In this paper, we apply deep learning techniques and in particular Long Short Term Memory (LSTM) networks to perform such a task relying only on the classification labels. We focus on the classification of chest computed tomography reports in Italian according to a classification schema proposed for this task by the radiologists of Spedali Civili di Brescia. Each report is classified according to such schema using a combination of neural network classifiers. The resulting system is a novel classification system, which we compare to a previous system based on standard machine learning techniques which used annotations of relevant snippets.

Cardiovascular Risk Reduction with Icosapent Ethyl for Hypertriglyceridemia

BackgroundPatients with elevated triglyceride levels are at increased risk for ischemic events. Icosapent ethyl, a highly purified eicosapentaenoic acid ethyl ester, lowers triglyceride levels, but data are needed to determine its effects on ischemic events.MethodsWe performed a multicenter, randomized, double-blind, placebo-controlled trial involving patients with established cardiovascular disease or with diabetes and other risk factors, who had been receiving statin therapy and who had a fasting triglyceride level of 135 to 499 mg per deciliter (1.52 to 5.63 mmol per liter) and a low-density lipoprotein cholesterol level of 41 to 100 mg per deciliter (1.06 to 2.59 mmol per liter). The patients were randomly assigned to receive 2 g of icosapent ethyl twice daily (total daily dose, 4 g) or placebo. The primary end point was a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, or unstable angina. The key secondary end point was a composite of cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke.ResultsA total of 8179 patients were enrolled (70.7% for secondary prevention of cardiovascular events) and were followed for a median of 4.9 years. A primary end-point event occurred in 17.2% of the patients in the icosapent ethyl group, as compared with 22.0% of the patients in the placebo group (hazard ratio, 0.75; 95% confidence interval [CI], 0.68 to 0.83; P<0.001); the corresponding rates of the key secondary end point were 11.2% and 14.8% (hazard ratio, 0.74; 95% CI, 0.65 to 0.83; P<0.001). The rates of additional ischemic end points, as assessed according to a prespecified hierarchical schema, were significantly lower in the icosapent ethyl group than in the placebo group, including the rate of cardiovascular death (4.3% vs. 5.2%; hazard ratio, 0.80; 95% CI, 0.66 to 0.98; P=0.03). A larger percentage of patients in the icosapent ethyl group than in the placebo group were hospitalized for atrial fibrillation or flutter (3.1% vs. 2.1%, P=0.004). Serious bleeding events occurred in 2.7% of the patients in the icosapent ethyl group and in 2.1% in the placebo group (P=0.06).ConclusionsAmong patients with elevated triglyceride levels despite the use of statins, the risk of ischemic events, including cardiovascular death, was significantly lower among those who received 2 g of icosapent ethyl twice daily than among those who received placebo. (Funded by Amarin Pharma; REDUCE-IT ClinicalTrials.gov number, NCT01492361.)

Hierarchical Schema Representation for Text-to-SQL Parsing With Decomposing Decoding

Most of existing studies on parsing natural language (NL) for constructing structured query language (SQL) do not consider the complex structure of database schema and the gap between NL and SQL query. In this paper, we propose a schema-aware neural network with decomposing architecture, namely HSRNet, which aims to address the complex and cross-domain Text-to-SQL generation task. The HSRNet models the relationship of the database schema with a hierarchical schema graph and employs a graph network to encode the information into sentence representation. Instead of end-to-end generation, the HSRNet decomposes the generation process into three phases. Given an input question and schema, we first choose the column candidates and generate the sketch grammar of the SQL query. Then, a detail completion module fills the details based on the column candidates and the corresponding sketch. We demonstrate the effectiveness of our hierarchical schema representation by incorporating the information into different baselines. We further show that the decomposing architecture significantly improves the performance of our model. Evaluation of Spider benchmark shows that the hierarchical schema representation and decomposing architecture improves our parser result by 14.5% and 4.3% respectively.

Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform

The objective of this study was to test the effectiveness of mapping the canopies of Firmiana danxiaensis (FD), a rare and endangered plant species in China, from remotely sensed images acquired by a customized imaging system mounted on an unmanned aerial vehicle (UAV). The work was conducted in an experiment site (approximately 10 km2) at the foot of Danxia Mountain in Guangdong Province, China. The study was conducted as an experimental task for a to-be-launched large-scale FD surveying on Danxia Mountain (about 200 km2 in area) by remote sensing on UAV platforms. First, field-based spectra were collected through hand-held hyperspectral spectroradiometer and then analyzed to help design a classification schema which was capable of differentiating the targeted plant species in the study site. Second, remote-sensed images for the experiment site were acquired and calibrated through a variety of preprocessing steps. Orthoimages and a digital surface model (DSM) were generated as input data from the calibrated UAV images. The spectra and geometry features were used to segment the preprocessed UAV imagery into homogeneous patches. Lastly, a hierarchical classification, combined with a support vector machine (SVM), was proposed to identify FD canopies from the segmented patches. The effectiveness of the classification was evaluated by on-site GPS recordings. The result illustrated that the proposed hierarchical classification schema with a SVM classifier on the remote sensing imagery collected by the imaging system on UAV provided a promising method for mapping of the spatial distribution of the FD canopies, which serves as a replacement for field surveys in the attempt to realize a wide-scale plant survey by the local governments.

A hierarchical anatomical classification schema for prediction of phenotypic side effects.

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a ‘hierarchical anatomical classification schema’ which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.

An extra spatial hierarchical schema in key-value store

The rapid growth of positioning technologies has resulted in an explosion of spatial data, and how to manage and retrieve such data has become a challenge. To solve this problem, many researchers pay attention to build spatial index in key-value store. Nevertheless, this will generate questions regarding the spatial index update and management. Furthermore, the efficiency of spatial query operations would decrease because it will generate much more request on network. Besides some scholars adopt space filling curve to carry out spatial query with primary key index, however it can bring out the questions of “Edge-Case Problem” and “Z-Order Problem” which is caused by space filling curve. To solve these questions, scholars resort to spatial index again. Nevertheless, we deem that the questions can be resolved without building spatial index. So this paper advocates an extra spatial hierarchical schema inspired by geohash, and design spatial query method based on primary keys index. Finally, to test the query accuracy and efficiency of the spatial hierarchical schema, we adopt Z-ordering, Hilbert, Row and Gray into the process of primary key encoding and conduct range query and k-NN queries in HBase. Experiment evaluation shows that the efficiency of the spatial queries good based on this schema even without the help of a spatial index.

Work adjustment of cancer survivors: An organisational support framework

Orientation: Medical advancements increase incidents of cancer survivors returning to work. Work adjustment of cancer survivors is essential for job satisfaction and productivity and should be supported and facilitated by the organisation. Research purpose: The purpose of the study was to explore cancer survivors’ return to work experience in order to explicate organisational support needed to facilitate their successful work adjustment. Motivation for the study: Despite the growing awareness of cancer survivorship, the challenges, expectations and management of the return to work process remain under researched. Research approach, design and method: Heidegger’s hermeneutic phenomenology formed the methodological foundation to the study. Purposive sampling was used to select eight participants from an oncology unit and cancer support organisation in the Southern Cape and Little Karoo regions. Participants, diagnosed with various types of cancer, were regarded as cancer survivors as they completed treatment and have returned to work. Data were collected using unstructured interviews and analysed through thematic analysis based on Ricoeur’s hermeneutic phenomenological theory of interpretation. Main findings: Results highlight four themes representing cancer survivors’ needs for organisational support. The support needs are presented in the context of the theory of work adjustment in a hierarchical schema that evolves from a basic need for emotion-focussed support to the need for knowledge and for practical guidance. Support needs culminate in the need for self-actualisation through meaning-making. An organisational support framework is proposed consisting of four integrated functions aimed at addressing the needs that emerged from the data. Practical and managerial implications: The organisational support framework provides guidance to develop an organisational policy and intervention strategy aimed at managing the successful work adjustment of the cancer survivor. Contribution or value-add: The study presents original research, contributing to the body of knowledge regarding cancer survivors returning to work, and introduces a new area of research in the discipline of industrial and organisational psychology.

Self-Perceived Viral Load and Sexual Risk Behavior Among Known HIV-Positive MSM in San Francisco, 2014.

Self-perceived viral suppression status among men who have sex with men (MSM) may impact HIV risk transmission behaviors. We conducted a 2014 cross-sectional survey of MSM in San Francisco and assessed differences in sexual risk behavior among known HIV-positive MSM based on viral suppression of HIV. We collected demographics, self-perceived viral load status, and sexual risk behavior and tested for viral load levels through laboratory assays. Men were categorized in a hierarchical schema of sexual risk behavior categories based on responses to questions regarding recent partners' HIV status, condom use, and sexual positioning. We used Fisher exact tests to assess for differences based on self-perceived viral load status. Out of a sample of 96 known HIV-positive men, 59 men self-reported an undetectable HIV viral load and 9 men self-reported a detectable viral load consented to confirmatory laboratory testing. The sample of self-reported undetectable men had gradually larger proportions of higher-risk sexual practices, whereas the sample of detectable men was evenly distributed across sexual practices. This association was not statistically significant (P = 0.91). Self-perceived viral suppression may influence sexual practices of known HIV-positive MSM, but small sample size, especially within the detectable category, hinders our ability to determine statistical significance. More research is necessary to assess how HIV-positive men account for viral load in sexual decision-making practices, and this research may inform resource allocation and clinical recommendations to maintain the health of MSM populations.

Utilizing Nested Normal Form to Design Redundancy Free JSON Schemas

JSON (JavaScript Object Notation) is a lightweight data-interchange format for the Internet. JSON is built on two structures: (1) a collection of name/value pairs and (2) an ordered list of values (http://www.json.org/). Because of this simple approach, JSON is easy to use and it has the potential to be the data interchange format of choice for the Internet. Similar to XML, JSON schemas allow nested structures to model hierarchical data. As data interchange over the Internet increases exponentially due to cloud computing or otherwise, redundancy free JSON data are an attractive form of communication because they improve the quality of data communication through eliminating update anomaly. Nested Normal Form, a normal form for hierarchical data, is a precise characterization of redundancy. A nested table, or a hierarchical schema, is in Nested Normal Form if and only if it is free of redundancy caused by multivalued and functional dependencies. Using Nested Normal Form as a guide, this paper introduces a JSON schema design methodology that begins with UML use case diagrams, communication diagrams and class diagrams that model a system under study. Based on the use cases’ execution frequencies and the data passed between involved parties in the communication diagrams, the proposed methodology selects classes from the class diagrams to be the roots of JSON scheme trees and repeatedly adds classes from the class diagram to the scheme trees as long as the schemas satisfy Nested Normal Form. This process continues until all of the classes in the class diagram have been added to some JSON scheme trees.

Hierarchical Schema for Identifying FocalElectrical Sources During HumanAtrial Fibrillation: Implications for Catheter-Based Atrial Substrate Ablation.

The study sought to localize focal sources (FS) during atrial fibrillation (AF) using periodic component analysis (PiCA) and QS unipolar electrogram (EGM) morphology based on the assumption that periodic activation with centrifugal propagation is inherent to a FS. The localization of FS maintaining AF remains challenging, due to limitations in conventional time-frequency domain analysis. This is relevant to identifying targets for AF substrate ablation. In 41 patients (age 56 ± 9 years, 76% persistent AF), bipolar EGMs were recorded in the left atrium (LA) during AF with a roving 20-pole catheter. Bipolar EGMs with periodicity were determined using PiCA. FS were defined as periodic sites with predominantly QS unipolar EGM morphology. For each patient, 456 ± 109 bipolar EGMs were recorded, of which 261 ± 15 (60%) demonstrated periodicity. FS were identified in 63% of patients (pulmonary vein [PV] 1.5 ± 1.5; extra-PV 2.6 ± 2.3). After PV antral ablation and follow-up of 14 ± 9 months, 37% of patients had symptomatic AF recurrence. Mean global LA periodicity cycle length was shorter in patients with AF recurrence compared to those without (143 ± 20 ms vs. 154 ± 9 ms; p= 0.02). Among 12 (29%) patients with FS exclusively in the PV, only 1 (8%) had AF recurrence. AF recurrence was significantly higher (50%;p= 0.01) in 14 (34%) patients with extra-PV FS. Our novel hierarchical analysis schema, incorporating PiCA and unipolar EGM morphology, detected a smallnumber of FS in patients with predominantly persistent AF. FS in the PV was associated with successful PV antral ablation. Further prospective studies are required to determine whether these FS maintain AF and represent ablation targets.

A Framework for Ontology-Based Top-K Global Schema Generation

Global schema generation is the problem of generating a unified and merged schema based on existing heterogeneous local schemas and a set of correspondences which are generated by a schema-matching algorithm. The existing schema integration approaches only target the flat schema structure, but cannot handle hierarchical schema structure well, and may produce redundant information in the global schema due to the weak entities in local schemas. To deal with these kinds of problems, a framework for ontology-based top-k global schema generation is proposed in this paper. The framework employs ontology as a base-merging model to create the global schema with higher quality but less user involvement, because ontology can provide semantic and detailed constraints and develop ways to preserve the hierarchical structure and remove redundant entities. The proposed approach consists of three contiguous steps: (1) local schemas are converted to local ontologies; (2) local ontologies are merged and top-k global ontologies are generated; (3) the user chooses one merged global ontology, which is converted automatically back to the global schema. After comparing the proposed approach with an existing state-of-the-art schema integration approach, the proposed approach better preserves the hierarchical structure and removes the redundant information. As a result, the quality of the generated global schema is higher.

Rape Perpetrators on Trial: The Effect of Sexual Assault–Related Schemas on Attributions of Blame

Research has consistently shown that jurors are influenced by multiple schemas in cases of alleged sexual assault, including offense stereotypes and victim stereotypes. These schemas appear to be organized in a hierarchy, as victim stereotypicality seems to matter most in acquaintance assaults (counter-stereotypical offense). However, despite numerous studies demonstrating the impact of defendant stereotypes on juror perceptions of guilt for other crimes, to date, the impact of stereotypes about defendants (perpetrators) in cases involving sexual violence have been overlooked. As such, the current research aimed to build on the existing hierarchical schema model by systematically examining the influence of perpetrator stereotypes. Following pilot work, mock jurors' ( N = 163) read a rape scenario that varied in terms of offense stereotypicality (stereotypical, counter-stereotypical), victim stereotypicality (stereotypical, counter-stereotypical), and perpetrator stereotypicality (stereotypical, counter-stereotypical). Broadly consistent effects of offense stereotypicality and victim stereotypicality were observed across the outcome measures, such that the victim was perceived more positively and the perpetrator more negatively when the victim was described as being stereotypical and when the offense was described as stereotypical. However, contrary to past findings, the effect of victim stereotypicality did not differ as a function of offense stereotypicality. Furthermore, perpetrator stereotypicality did not influence perceptions in the stereotypical offense scenario. These findings suggest that contrary to the assertions of previous research, there is not a series of specific, individual stereotypes that impact attributions of blame, rather, there may be one underlying schema about consent that influences perceptions. These findings have important implications for how we address the effect of juror-held schemas on attributions of blame in cases of sexual assault.

Answering Reachability Queries on Incrementally Updated Graphs by Hierarchical Labeling Schema

We proposed a novel solution schema called the Hierarchical Labeling Schema (HLS) to answer reachability queries in directed graphs. Different from many existing approaches that focus on static directed acyclic graphs (DAGs), our schema focuses on directed cyclic graphs (DCGs) where vertices or arcs could be added to a graph incrementally. Unlike many of the traditional approaches, HLS does not require the graph to be acyclic in constructing its index. Therefore, it could, in fact, be applied to both DAGs and DCGs. When vertices or arcs are added to a graph, the HLS is capable of updating the index incrementally instead of re-computing the index from the scratch each time, making it more efficient than many other approaches in the practice. The basic idea of HLS is to create a tree for each vertex in a graph and link the trees together so that whenever two vertices are given, we can immediately know whether there is a path between them by referring to the appropriate trees. We conducted extensive experiments on both real-world datasets and synthesized datasets. We compared the performance of HLS, in terms of index construction time, query processing time and space consumption, with two state-of-the-art methodologies, the path-tree method and the 3-hop method. We also conducted simulations to model the situation when a graph is updated incrementally. The performance comparison of different algorithms against HLS on static graphs has also been studied. Our results show that HLS is highly competitive in the practice and is particularly useful in the cases where the graphs are updated frequently.

Hierarchical decomposition of burn body diagram based on cutaneous functional units and its utility.

A burn body diagram (BBD) is a common feature used in the delivery of burn care for estimating the TBSA burn as well as calculating fluid resuscitation and nutritional requirements, wound healing, and rehabilitation intervention. However, little change has occurred for over seven decades in the configuration of the BBD. The purpose of this project was to develop a computerized model using hierarchical decomposition (HD) to more precisely determine the percentage burn within a BBD based on cutaneous functional units (CFUs). HD is a process by which a system is degraded into smaller parts that are more precise in their use. CFUs were previously identified fields of the skin involved in the range of motion. A standard Lund/Browder (LB) BBD template was used as the starting point to apply the CFU segments. LB body divisions were parceled down into smaller body area divisions through a HD process based on the CFU concept. A numerical pattern schema was used to label the various segments in a cephalo/caudal, anterior/posterior, medial/lateral manner. Hand/fingers were divided based on anatomical landmarks and known cutaneokinematic function. The face was considered using aesthetic units. Computer code was written to apply the numeric hierarchical schema to CFUs and applied within the context of the surface area graphic evaluation BBD program. Each segmented CFU was coded to express 100% of itself. The CFU/HD method refined the standard LB diagram from 13 body segments and 33 subdivisions into 182 isolated CFUs. Associated CFUs were reconstituted into 219 various surface area combinations totaling 401 possible surface segments. The CFU/HD schema of the body surface mapping is applicable to measuring and calculating percent wound healing in a more precise manner. It eliminates subjective assessment of the percentage wound healing and the need for additional devices such as planimetry. The development of CFU/HD body mapping schema has rendered a technologically advanced system to depict body burns. The process has led to a more precise estimation of the segmented body areas while preserving the overall TBSA information. Clinical application to date has demonstrated its worthwhile utility.