Quantitative Methods for Similarity in Description Logics

Description Logics (DLs) are a family of logic-based knowledge representation languages used to describe the knowledge of an application domain and reason about it in formally well-defined way. They allow users to describe the important notions and classes of the knowledge domain as concepts, which formalize the necessary and sufficient conditions for individual objects to belong to that concept. A variety of different DLs exist, differing in the set of properties one can use to express concepts, the so-called concept constructors, as well as the set of axioms available to describe the relations between concepts or individuals. However, all classical DLs have in common that they can only express exact knowledge, and correspondingly only allow exact inferences. Either we can infer that some individual belongs to a concept, or we can't, there is no in-between. In practice though, knowledge is rarely exact. Many definitions have their exceptions or are vaguely formulated in the first place, and people might not only be interested in exact answers, but also in alternatives that are "close enough". This thesis is aimed at tackling how to express that something "close enough", and how to integrate this notion into the formalism of Description Logics. To this end, we will use the notion of similarity and dissimilarity measures as a way to quantify how close exactly two concepts are. We will look at how useful measures can be defined in the context of DLs, and how they can be incorporated into the formal framework in order to generalize it. In particular, we will look closer at two applications of thus measures to DLs: Relaxed instance queries will incorporate a similarity measure in order to not just give the exact answer to some query, but all answers that are reasonably similar. Prototypical definitions on the other hand use a measure of dissimilarity or distance between concepts in order to allow the definitions of and reasoning with concepts that capture not just those individuals that satisfy exactly the stated properties, but also those that are "close enough"

Similarity Measures for Computing Relaxed Instances w.r.t. General EL-TBoxes

The notion of concept similarity is central to several ontology tasks and can be employed to realize relaxed versions of classical reasoning services. In this paper we investigate the reasoning service of answering instance queries in a relaxed fashion, where the query concept is relaxed by means of a concept similarity measure (CSM). To this end we investigate CSMs that assess the similarity of EL-concepts defined w.r.t. a general EL-TBox. We derive such a family of CSMs from a family of similarity measures for finite interpretations and show in both cases that the resulting measures enjoy a collection of formal properties. These properties allow us to devise an algorithm for computing relaxed instances w.r.t. general EL-TBoxes, where users can specify the „appropriate“ notion of similarity by instanciating our CSM appropriately

Structure and formation of trivalent chromium conversion coatings containing cobalt on zinc plated steel

The present study intends to elucidate the effect of treatment solution composition on the formation and structure of Trivalent Chromium-based Conversion (TCC) coatings containing cobalt on zinc substrates. Model solutions with two different complexing agents, viz. fluoride and oxalate, with and without cobalt were applied to zinc plated steel. The scanning electron microscopy and atomic force microscopy images revealed a morphology with microstructural defects that can be improved to a more uniform and adherent structure by adding cobalt to the passivating bath. The elemental composition of the layer was investigated by auger electron spectroscopy (AES). Furthermore, the amounts of Cr and Co in the coatings were also measured with the aid of inductively coupled plasma optical emission spectroscopy (ICP-OES). In good agreement with AES, cobalt was also detected in the layers via ICP-OES measurement. The results of accelerated corrosion tests suggested that the formation of a densely packed layer is crucial for a good corrosion resistance of the coating

Alphacoronavirus in a Daubenton's Myotis Bat (Myotis daubentonii) in Sweden

The ongoing COVID-19 pandemic has stimulated a search for reservoirs and species potentially involved in back and forth transmission. Studies have postulated bats as one of the key reservoirs of coronaviruses (CoVs), and different CoVs have been detected in bats. So far, CoVs have not been found in bats in Sweden and we therefore tested whether they carry CoVs. In summer 2020, we sampled a total of 77 adult bats comprising 74 Myotis daubentonii, 2 Pipistrellus pygmaeus, and 1 M. mystacinus bats in southern Sweden. Blood, saliva and feces were sampled, processed and subjected to a virus next-generation sequencing target enrichment protocol. An Alphacoronavirus was detected and sequenced from feces of a M. daubentonii adult female bat. Phylogenetic analysis of the almost complete virus genome revealed a close relationship with Finnish and Danish strains. This was the first finding of a CoV in bats in Sweden, and bats may play a role in the transmission cycle of CoVs in Sweden. Focused and targeted surveillance of CoVs in bats is warranted, with consideration of potential conflicts between public health and nature conservation required as many bat species in Europe are threatened and protected

UAV-Based forest health monitoring : a systematic review

CITATION: Ecke, S. et al. 2022. UAV-Based forest health monitoring : a systematic review. Remote Sensing, 14(13):3205, doi:10.3390/rs14133205.The original publication is available at https://www.mdpi.comIn recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.https://www.mdpi.com/2072-4292/14/13/3205Publisher's versio

Statistical Properties of Turbulence: An Overview

We present an introductory overview of several challenging problems in the statistical characterisation of turbulence. We provide examples from fluid turbulence in three and two dimensions, from the turbulent advection of passive scalars, turbulence in the one-dimensional Burgers equation, and fluid turbulence in the presence of polymer additives.Comment: 34 pages, 31 figure

Nomograms including the UBC® Rapid test to detect primary bladder cancer based on a multicentre dataset

Objectives: To evaluate the clinical utility of the urinary bladder cancer antigen test UBC Rapid for the diagnosis of bladder cancer (BC) and to develop and validate nomograms to identify patients at high risk of primary BC. Patients and Methods: Data from 1787 patients from 13 participating centres, who were tested between 2012 and 2020, including 763 patients with BC, were analysed. Urine samples were analysed with the UBC Rapid test. The nomograms were developed using data from 320 patients and externally validated using data from 274 patients. The diagnostic accuracy of the UBC Rapid test was evaluated using receiver-operating characteristic curve analysis. Brier scores and calibration curves were chosen for the validation. Biopsy-proven BC was predicted using multivariate logistic regression. Results: The sensitivity, specificity, and area under the curve for the UBC Rapid test were 46.4%, 75.5% and 0.61 (95% confidence interval [CI] 0.58–0.64) for low-grade (LG) BC, and 70.5%, 75.5% and 0.73 (95% CI 0.70–0.76) for high-grade (HG) BC, respectively. Age, UBC Rapid test results, smoking status and haematuria were identified as independent predictors of primary BC. After external validation, nomograms based on these predictors resulted in areas under the curve of 0.79 (95% CI 0.72–0.87) and 0.95 (95% CI: 0.92–0.98) for predicting LG-BC and HG-BC, respectively, showing excellent calibration associated with a higher net benefit than the UBC Rapid test alone for low and medium risk levels in decision curve analysis. The R Shiny app allows the results to be explored interactively and can be accessed at www.blucab-index.net. Conclusion: The UBC Rapid test alone has limited clinical utility for predicting the presence of BC. However, its combined use with BC risk factors including age, smoking status and haematuria provides a fast, highly accurate and non-invasive tool for screening patients for primary LG-BC and especially primary HG-BC

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Quantitative Methods for Similarity in Description Logics

Description Logics (DLs) are a family of logic-based knowledge representation languages used to describe the knowledge of an application domain and reason about it in formally well-defined way. They allow users to describe the important notions and classes of the knowledge domain as concepts, which formalize the necessary and sufficient conditions for individual objects to belong to that concept. A variety of different DLs exist, differing in the set of properties one can use to express concepts, the so-called concept constructors, as well as the set of axioms available to describe the relations between concepts or individuals. However, all classical DLs have in common that they can only express exact knowledge, and correspondingly only allow exact inferences. Either we can infer that some individual belongs to a concept, or we can't, there is no in-between. In practice though, knowledge is rarely exact. Many definitions have their exceptions or are vaguely formulated in the first place, and people might not only be interested in exact answers, but also in alternatives that are "close enough". This thesis is aimed at tackling how to express that something "close enough", and how to integrate this notion into the formalism of Description Logics. To this end, we will use the notion of similarity and dissimilarity measures as a way to quantify how close exactly two concepts are. We will look at how useful measures can be defined in the context of DLs, and how they can be incorporated into the formal framework in order to generalize it. In particular, we will look closer at two applications of thus measures to DLs: Relaxed instance queries will incorporate a similarity measure in order to not just give the exact answer to some query, but all answers that are reasonably similar. Prototypical definitions on the other hand use a measure of dissimilarity or distance between concepts in order to allow the definitions of and reasoning with concepts that capture not just those individuals that satisfy exactly the stated properties, but also those that are "close enough"

Quantitative Methods for Similarity in Description Logics

Description Logics (DLs) are a family of logic-based knowledge representation languages used to describe the knowledge of an application domain and reason about it in formally well-defined way. They allow users to describe the important notions and classes of the knowledge domain as concepts, which formalize the necessary and sufficient conditions for individual objects to belong to that concept. A variety of different DLs exist, differing in the set of properties one can use to express concepts, the so-called concept constructors, as well as the set of axioms available to describe the relations between concepts or individuals. However, all classical DLs have in common that they can only express exact knowledge, and correspondingly only allow exact inferences. Either we can infer that some individual belongs to a concept, or we can't, there is no in-between. In practice though, knowledge is rarely exact. Many definitions have their exceptions or are vaguely formulated in the first place, and people might not only be interested in exact answers, but also in alternatives that are "close enough". This thesis is aimed at tackling how to express that something "close enough", and how to integrate this notion into the formalism of Description Logics. To this end, we will use the notion of similarity and dissimilarity measures as a way to quantify how close exactly two concepts are. We will look at how useful measures can be defined in the context of DLs, and how they can be incorporated into the formal framework in order to generalize it. In particular, we will look closer at two applications of thus measures to DLs: Relaxed instance queries will incorporate a similarity measure in order to not just give the exact answer to some query, but all answers that are reasonably similar. Prototypical definitions on the other hand use a measure of dissimilarity or distance between concepts in order to allow the definitions of and reasoning with concepts that capture not just those individuals that satisfy exactly the stated properties, but also those that are "close enough"