Semantic Data Pre-Processing for Machine Learning Based Bankruptcy Prediction Computational Model

This paper studies a Bankruptcy Prediction Computational Model (BPCM model) – a comprehensive methodology of evaluating companies’ bankruptcy level, which combines storing, structuring and pre-processing of raw financial data using semantic methods with machine learning analysis techniques. Raw financial data are interconnected, diverse, often potentially inconsistent, and open to duplication. The main goal of our research is to develop data pre-processing techniques, where ontologies play a central role. We show how ontologies are used to extract and integrate information from different sources, prepare data for further processing, and enable communication in natural language. Using ontology, we give meaning to the disparate and raw business data, build logical relationships between data in various formats and sources and establish relevant context. Our Ontology of Bankruptcy Prediction (OBP Ontology) which provides a conceptual framework for companies’ financial analysis, is built in the widely established Prote ́ge ́ environment. An OBP Ontology can be effectively described with a graph database. Graph database expands the capabilities of traditional databases tackling the interconnected nature of economic data and providing graph-based structures to store information allowing the effective selection of the most relevant input features for the machine learning algorithm. To create and manage the BPCM Graph Database (Graph DB), we use the Neo4j environment and Neo4j query language, Cypher, to perform feature selection of the structured data. Selected key features are used for the Machine Learning Engine – supervised MLP Neural Network with Sigmoid activation function. The programming of this component is performed in Python. We illustrate the approach and advantages of semantic data pre-processing applying it to a representative use case

Live Demonstration of the PITHIA e-Science Centre

PITHIA-NRF (Plasmasphere Ionosphere Thermosphere Integrated Research Environment and Access services: a Network of Research Facilities) is a four-year project funded by the European Commission’s H2020 programme to integrate data, models and physical observing facilities for further advancing European research capacity in this area. A central point of PITHIA-NRF is the PITHIA e-Science Centre (PeSC), a science gateway that provides access to distributed data sources and prediction models to support scientific discovery. As the project reached its half-way point in March 2023, the first official prototype of the e-Science Centre was released. This live demonstration will provide an overview of the current status and capabilities of the PeSC, highlighting the underlying ontology and metadata structure, the registration process for models and datasets, the ontology-based search functionalities and the interaction methods for executing models and processing data. One of the main objectives of the PeSC is to enable scientists to register their Data Collections, that can be both raw or higher-level datasets and prediction models, using a standard metadata format and a domain ontology. For these purposes, PITHIA builds on the results of the ESPAS FP7 project by adopting and modifying its ontology and metadata specification. The project utilises the ISO 19156 standard on Observations and Measurements (O&M) to describe Data Collections in an XML format that is widely used within the research community. Following the standard, Data Collections are referring to other XML documents, such as Computations that a model used to derive the results, Acquisitions describing how the data was collected, Instruments that were used during the data collection process, or Projects that were responsible for the data/model. Within the XML documents, specific keywords of the Space Physics ontology can be used to describe the various elements. For example, Observed Property can be Field, Particle, Wave, or Mixed, at the top level. When preparing the XML metadata file, only these values are accepted for validation. Once described in XML format, Data Collections can be published in the PeSC and searched using the ontology-based search engine. Besides large and typically changing/growing Data Collections, PeSC also supports the registration of Catalogues. These are smaller sets of data, originating from a Data Collection and related to specific events, e.g. volcano eruptions. Catalogue Data Subsets can be assigned DOIs to be referenced in publications and provide a permanent set of data for reproducibility. Additionally, to publication and search, the PeSC also provides several mechanisms for interacting with Data Collections, e.g. executing a model or downloading subsets of the data. In the current version two of the four planned interaction methods are implemented: accessing the Data Collection by a direct link and interacting with it via an API and an automatically generated GUI. Data Collections can either be hosted by the local provider or can be deployed on EGI cloud computing resources. The development of the PeSC is still work in progress. Authentication and authorisation are currently being implemented using EGI Checkin and the PERUN Attribute Management System. Further interaction mechanisms enabling local execution and dynamic deployment in the cloud will also be added in the near future. The main screen of the PeSC is illustrated on Figure 1. The source code is open and available in GitHub

EnAbled: A Psychology Profile based Academic Compass to Build and Navigate Students' Learning Paths

Inthe moderneducational environmentstudents are faced with a plethora of different options in their learning journey during the University years. To help them to make optimal choices among all these options,that best correspond to their individual-ity, we have conducted a research project “Enabled: Educational Network Amplifying Learning Experience” (EnAbled). The project aims at “mapping” these choices to per-sonal preferences and individual learning styles. We allow students to either self-assess their profiles or usethe Lumina Psychological Traits of Behavioral Preferencestests.We argue that this approach will be beneficial not only to the students but also to the academics assisting them in the preparation and delivery of modules and providing them with more insight into what and how teaching is delivered

Science Gateways with Embedded Ontology-based E-learning Support

Science gateways are widely utilised in a range of scientific disciplines to provide user-friendly access to complex distributed computing infrastructures. The traditional approach in science gateway development is to concentrate on this simplified resource access and provide scientists with a graphical user interface to conduct their experiments and visualise the results. However, as user communities behind these gateways are growing and opening their doors to less experienced scientists or even to the general public as “citizen scientists”, there is an emerging need to extend these gateways with training and learning support capabilities. This paper describes a novel approach showing how science gateways can be extended with embedded e-learning support using an ontology-based learning environment called Knowledge Repository Exchange and Learning (KREL). The paper also presents a prototype implementation of a science gateway for analysing earthquake data and demonstrates how the KREL can extend this gateway with ontology-based embedded e-learning support

Methodological considerations in quantification of oncological FDG PET studies

Contains fulltext : 87741.pdf (publisher's version ) (Closed access) Contains fulltext : 87741-1.pdf (postprint version ) (Open Access)PURPOSE: This review aims to provide insight into the factors that influence quantification of glucose metabolism by FDG PET images in oncology as well as their influence on repeated measures studies (i.e. treatment response assessment), offering improved understanding both for clinical practice and research. METHODS: Structural PubMed searches have been performed for the many factors affecting quantification of glucose metabolism by FDG PET. Review articles and references lists have been used to supplement the search findings. RESULTS: Biological factors such as fasting blood glucose level, FDG uptake period, FDG distribution and clearance, patient motion (breathing) and patient discomfort (stress) all influence quantification. Acquisition parameters should be adjusted to maximize the signal to noise ratio without exposing the patient to a higher than strictly necessary radiation dose. This is especially challenging in pharmacokinetic analysis, where the temporal resolution is of significant importance. The literature is reviewed on the influence of attenuation correction on parameters for glucose metabolism, the effect of motion, metal artefacts and contrast agents on quantification of CT attenuation-corrected images. Reconstruction settings (analytical versus iterative reconstruction, post-reconstruction filtering and image matrix size) all potentially influence quantification due to artefacts, noise levels and lesion size dependency. Many region of interest definitions are available, but increased complexity does not necessarily result in improved performance. Different methods for the quantification of the tissue of interest can introduce systematic and random inaccuracy. CONCLUSIONS: This review provides an up-to-date overview of the many factors that influence quantification of glucose metabolism by FDG PET.01 juli 201

The provision of adult intensive care in Northern Ireland with reference to the role of high dependency care.

In 1991 an audit of Intensive Care Services was carried out by the Northern Ireland Intensive Care Group. In conjunction with this regional overview, all patients in the Regional Intensive Care Unit, (RICU) in the Royal Victoria Hospital were assessed daily, over a 10 month period in 1990-91 and classified as conforming to either intensive care or high dependency status. These data were then used to compare adult intensive care service in Northern Ireland with recent national and international recommendations on intensive care. Ten units in Northern Ireland were surveyed. In regard to national or international guidelines, all ten were deficient to some degree. Four units had significant deficiencies; small patient numbers, lack of 'dedicated' 24 hr medical cover and or deficiencies in the provision of appropriate monitoring and or equipment. There was a large diversity in casemix among the ten units surveyed which suggested differing admission criteria. The bed occupancy of RICU was 100%. Refused admissions constituted a further 13% of unresourced workload. The lack of physically separate, dedicated high dependency unit facilities meant that 26% of bed days were devoted to HDU care (usually for "improved" intensive care unit patients not yet ready for discharge to a general ward. Achieving nationally recommended intensive care standards (on a regional basis) is probably only possible if a number of the smaller intensive care units are redesignated as high dependency units, and patients requiring intensive care are concentrated in a smaller number of larger ICUs. This will increase the frequency of interhospital transfer of critically ill patients

Automorphisms of Real 4 Dimensional Lie Algebras and the Invariant Characterization of Homogeneous 4-Spaces

The automorphisms of all 4-dimensional, real Lie Algebras are presented in a comprehensive way. Their action on the space of $4\times 4$, real, symmetric and positive definite, matrices, defines equivalence classes which are used for the invariant characterization of the 4-dimensional homogeneous spaces which possess an invariant basis.Comment: LaTeX2e, 23 pages, 2 Tables. To appear in Journal of Physics A: Mathematical & Genera

The signalling effect of eco-labels in modern coastal tourism

As the demand for environmentally sustainable tourism grows, eco-labels are becoming increasingly popular as a signal of environmental quality. However, the existence of a causal link between awarding a seaside eco-label and the increase in tourism flows is still under discussion in the literature. In this article, we gauge the signalling impact of a specific eco-label, the Blue Flag award, using detailed data on tourism flows to seaside Italian destinations during the period 2008-2012. We adopt a recent econometric modelling strategy - the synthetic control method - in shaping estimation results and testing the sensitivity and robustness of our results. We find that being awarded the Blue Flag increases the flow of domestic tourists for up to three seasons after assignment. However, we find no effect for the flow of international tourists. Investigating the mechanisms driving the results, we find that the award of a Blue Flag only positively affects the flow of domestic tourists when it is used as a driver of organisation, coordination and integrated management of the tourism supply

The oxidation-reduction potentials of parsley ferredoxin and its selenium-containing homolog

We have measured the oxidation-reduction potential of parsley ferredoxin and its derivative in which the two atoms of labile sulfide have been replaced by selenide. The values are -0.416 V (25[deg], pH 7.94) and -0.378 V (25[deg], pH 8.14) for the sulfur and selenium derivatives, respectively. Both values show a slight negative dependence on pH.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/33583/1/0000086.pd