Modeling of field singularities at dielectric edges using grid based methods

Electric field singularities at sharp metallic edges or at a dielectric contact line can be described analytically by asymptotic expressions. The a priori known form of the field distribution in the vicinity of these edges can be used to construct numerical methods with improved accuracy. This contribution focuses on a modified Finite Integration Technique and on a Discontinuous Galerkin Method with singular approximation functions. Both methods are able to handle field singularities at perfectly electric conducting as well as at dielectric edges. The numerical accuracy of these methods is investigated in a number of simulation examples including static and dynamic field problems

Great Sumatra Earthquake Registers on Electrostatic Sensor

Strong electrical signals that correspond to the Mw = 9.3 earthquake of 26 December 2004, which occurred at 0058:50.7 UTC off the west coast of northern Sumatra, Indonesia, were recorded by an electrostatic sensor (a device that detects short-term variations in Earth’s electrostatic fi eld) at a seismic station in Italy, which had been installed to study the infl uence of local earthquakes on a new landslide monitoring system. Electrical signals arrived at the station practically instantaneously and were detected up to several hours before the onset of the Sumatra earthquake (Figure 1) as well as before local quakes. The corresponding seismic signals (p-waves) arrived 740 seconds after the start of the earthquake. Because the electrical signals travel at the speed of light, electrical monitoring for the global detection of very strong earthquakes could be an important tool in signifi cantly increasing the hazard alert window

First steps in the logic-based assessment of post-composed phenotypic descriptions

In this paper we present a preliminary logic-based evaluation of the integration of post-composed phenotypic descriptions with domain ontologies. The evaluation has been performed using a description logic reasoner together with scalable techniques: ontology modularization and approximations of the logical difference between ontologies

Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images

Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments according to the Kellgren and Lawrence grading scheme; a five-point ordinal scale (0-4). In this study, we used Elastic Net (EN) and Random Forests (RF) to build predictive models using patient assessment data (i.e. signs and symptoms of both knees and medication use) and a convolution neural network (CNN) trained using X-ray images only. Linear mixed effect models (LMM) were used to model the within subject correlation between the two knees. The root mean squared error for the CNN, EN, and RF models was 0.77, 0.97 and 0.94 respectively. The LMM shows similar overall prediction accuracy as the EN regression but correctly accounted for the hierarchical structure of the data resulting in more reliable inference. Useful explanatory variables were identified that could be used for patient monitoring before X-ray imaging. Our analyses suggest that the models trained for predicting the KOA severity levels achieve comparable results when modeling X-ray images and patient data. The subjectivity in the KL grade is still a primary concern

Exploring and linking biomedical resources through multidimensional semantic spaces

Background The semantic integration of biomedical resources is still a challenging issue which is required for effective information processing and data analysis. The availability of comprehensive knowledge resources such as biomedical ontologies and integrated thesauri greatly facilitates this integration effort by means of semantic annotation, which allows disparate data formats and contents to be expressed under a common semantic space. In this paper, we propose a multidimensional representation for such a semantic space, where dimensions regard the different perspectives in biomedical research (e.g., population, disease, anatomy and protein/genes). Results This paper presents a novel method for building multidimensional semantic spaces from semantically annotated biomedical data collections. This method consists of two main processes: knowledge and data normalization. The former one arranges the concepts provided by a reference knowledge resource (e.g., biomedical ontologies and thesauri) into a set of hierarchical dimensions for analysis purposes. The latter one reduces the annotation set associated to each collection item into a set of points of the multidimensional space. Additionally, we have developed a visual tool, called 3D-Browser, which implements OLAP-like operators over the generated multidimensional space. The method and the tool have been tested and evaluated in the context of the Health-e-Child (HeC) project. Automatic semantic annotation was applied to tag three collections of abstracts taken from PubMed, one for each target disease of the project, the Uniprot database, and the HeC patient record database. We adopted the UMLS Meta-thesaurus 2010AA as the reference knowledge resource. Conclusions Current knowledge resources and semantic-aware technology make possible the integration of biomedical resources. Such an integration is performed through semantic annotation of the intended biomedical data resources. This paper shows how these annotations can be exploited for integration, exploration, and analysis tasks. Results over a real scenario demonstrate the viability and usefulness of the approach, as well as the quality of the generated multidimensional semantic spaces

The CALBC Silver Standard Corpus for Biomedical Named Entities - A Study in Harmonizing the Contributions from Four Independent Named Entity Taggers

The production of gold standard corpora is time-consuming and costly. We propose an alternative: the 'silver standard corpus' (SSC), a corpus that has been generated by the harmonisation of the annotations that have been delivered from a selection of annotation systems. The systems have to share the type system for the annotations and the harmonisation solution has use a suitable similarity measure for the pair-wise comparison of the annotations. The annotation systems have been evaluated against the harmonised set (630.324 sentences, 15, 956, 841 tokens). We can demonstrate that the annotation of proteins and genes shows higher diversity across all used annotation solutions leading to a lower agreement against the harmonised set in comparison to the annotations of diseases and species. An analysis of the most frequent annotations from all systems shows that a high agreement amongst systems leads to the selection of terms that are suitable to be kept in the harmonised set. This is the first large-scale approach to generate an annotated corpus from automated annotation systems. Further research is required to understand, how the annotations from different systems have to be combined to produce the best annotation result for a harmonised corpus

Ontology Clustering with OWL2Vec*

In this work we present an exploratory study to apply OWL2Vec* to drive the clustering of ontology entities (i.e., ontology clustering). OWL2Vec* is a state-of-the-art system that creates embeddings, capturing the semantics of both entities and tokens that appear in the ontology

Biodiversity of the Colorado State University lands

Prepared for: Colorado State University Facilities Management.June 2022.Includes bibliographical references.During the academic year of 2021-2022, the Colorado Natural Heritage Program (CNHP) was contracted by CSU Facilities Management to complete a biodiversity survey of the CSU lands. This assessment will be used by Facilities Management to self-report on the Biodiversity component of the Operations category in the STARS (Sustainability, Tracking, Assessment, & Ratings System) report. This report assesses endangered and vulnerable species (including migratory species) on CSU-owned and managed lands and areas of biodiversity importance on CSU-owned and managed lands. An additional aim of this project was to include students in the geospatial analysis, research, and field data collection efforts, thereby lowering project costs and providing mentorship and experience to the students. Biodiversity was assessed through a geospatial environmental review of the properties which includes documented and potential occurrences of regulatory species and other species of concern within the property and a 1-mile buffer, assessment of the conservation areas adjacent to the property and within a buffer, and the diversity and acreage of wetlands and other ecosystem types. The conservation value of each property, based on a Return-on-Investment report, is presented. Geospatial data area used to evaluate climate resiliency and landscape disturbance. Further research into species on the largest and most well-studied properties is presented, along with results of field work. Colorado State University holds 32 individual properties, spanning 14 counties across Colorado, covering a total of 3,943 hectares. Properties held by CSU had 303 documented occurrences of regulatory species and other species of concern within 1 mile returned in the environmental review; additionally, potential habitat was returned for another 2210 regulatory and other species of concern from a combination of range maps, general precision CNHP element occurrence records, and models. Through the many metrics of biodiversity assessed, several properties stood out; these included the Eastern Colorado Research Center, the Mountain Campus, Foothills, Horsetooth, and the Environmental Learning Center. At the Eastern Colorado Research Center, a combination of research field work recorded 187 species as visual observations and/or within a modeled area. At the Mountain Campus, student research and field work recorded a total of 1,044 species as visual observations and/or within a modeled area, with 754 Animalia species, 273 Plantae species and 17 Fungi species. Along with providing information on the biodiversity on the CSU lands, this project provided educational value to CSU students and facilitated the creation of a storymap to showcase the biodiversity of CSU lands to the public and stakeholders. The biodiversity assessment suggests several actions which could be taken to protect, enhance, or restore the biodiversity found on CSU lands and identified properties with possible conservation gains through enhancement and restoration.June 2022