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

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

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

Multidimensional Cosmology: Spatially Homogeneous models of dimension 4+1

In this paper we classify all 4+1 cosmological models where the spatial hypersurfaces are connected and simply connected homogeneous Riemannian manifolds. These models come in two categories, multiply transitive and simply transitive models. There are in all five different multiply transitive models which cannot be considered as a special case of a simply transitive model. The classification of simply transitive models, relies heavily upon the classification of the four dimensional (real) Lie algebras. For the orthogonal case, we derive all the equations of motion and give some examples of exact solutions. Also the problem of how these models can be compactified in context with the Kaluza-Klein mechanism, is addressed.Comment: 24 pages, no figures; Refs added, typos corrected. To appear in CQ

Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate

Perfectionism and self-conscious emotions in British and Japanese students: Predicting pride and embarrassment after success and failure

Regarding self-conscious emotions, studies have shown that different forms of perfectionism show different relationships with pride, shame, and embarrassment depending on success and failure. What is unknown is whether these relationships also show cultural variations. Therefore, we conducted a study investigating how self-oriented and socially prescribed perfectionism predicted pride and embarrassment after success and failure comparing 363 British and 352 Japanese students. Students were asked to respond to a set of scenarios where they imagined achieving either perfect (success) or flawed results (failure). In both British and Japanese students, self-oriented perfectionism positively predicted pride after success and embarrassment after failure whereas socially prescribed perfectionism predicted embarrassment after success and failure. Moreover, in Japanese students, socially prescribed perfectionism positively predicted pride after success and self-oriented perfectionism negatively predicted pride after failure. The findings have implications for our understanding of perfectionism indicating that the perfectionism–pride relationship not only varies between perfectionism dimensions, but may also show cultural variations

Involvement of protein kinase A in ethanol-induced locomotor activity and sensitization

Mutant mice lacking the RIIβ subunit of protein kinase A (regulatory subunit II beta−/−) show increased ethanol preference. Recent evidence suggests a relationship between heightened ethanol preference and susceptibility to ethanol-induced locomotor sensitization. It is currently unknown if protein kinase A signaling modulates the stimulant effects and/or behavioral sensitization caused by ethanol administration. To address this question, we examined the effects of repeated ethanol administration on locomotor activity RIIβ−/− and littermate wild-type (RIIβ+/+) mice on multiple genetic backgrounds

Control of primary productivity and the significance of photosynthetic bacteria in a meromictic kettle lake.

During 1986 planktonic primary production and controlling factors were investigated in a small (A0 = 11.8 · 103 m2, Zmax = 11.5 m) meromictic kettle lake (Mittlerer Buchensee). Annual phytoplankton productivity was estimated to ca 120 gC · m–2 · a–1 (1,42 tC · lake–1 · a–1). The marked thermal stratification of the lake led to irregular vertical distributions of chlorophylla concentrations (Chla) and, to a minor extent, of photosynthesis (Az). Between the depths of 0 to 6 m low Chla concentrations (< 7 mg · m–3) and comparatively high background light attenuation (kw = 0,525 m–1, 77% of total attenuation due to gelbstoff and abioseston) was found. As a consequence, light absorption by algae was low (mean value 17,4%) and self-shading was absent. Because of the small seasonal variation of Chla concentrations, no significant correlation between Chla and areal photosynthesis (A) was observed. Only in early summer (June–July) biomass appears to influence the vertical distribution of photosynthesis on a bigger scale. Around 8 m depth, low-light adapted algae and phototrophic bacteria formed dense layers. Due to low ambient irradiances, the contribution of these organisms to total primary productivity was small. Primary production and incident irradiance were significantly correlated with each other (r2 = 0.68). Although the maximum assimilation number (Popt) showed a clear dependence upon water temperature (Q10 = 2.31), the latter was of minor importance to areal photosynthesis

CV15014

This report provides the main results and findings of the fourteenth annual underwater television on the Aran, Galway Bay and Slyne head Nephrops grounds, ICES assessment area; Functional Unit 17. The survey was multi-disciplinary in nature collecting UWTV, fishing, CTD and other ecosystem data. In 2015 a total of 44 UWTV stations were successfully completed, 34 on the Aran Grounds and 5 on each of the Slyne Head and Galway Bay patches. The mean burrow density observed in 2015, adjusted for edge effect, was medium at 0.38 burrows/m². The final krigged burrow abundance estimate for the Aran Grounds was 480 million burrows with a CV (or relative standard error) of 6 %. The final abundance estimate for Galway Bay and Slyne Head was 56 and 20 million burrows with CVs of 15% and 4% respectively. The total abundance estimates have fluctuated considerably over the time series. The 2015 abundance estimate was 42% higher than in 2014 and at 556 million and is just above to the new MSY Btrigger (540 million). Using the 2015 abundance estimate and updated stock data implies catch of 991 tonnes and landings of 915 tonnes in 2016 fishing at Fmsy (assuming all catch is landed). Virgilaria mirabilis was the most common of the two sea-pen species observed on the UWTV footage. Pennatula phosphorea was observed at one station on the Slyne Head Nephrops ground. Key words: Nephrops norvegicus, stock assessment, geostatistics, underwater television (UWTV), benthos, CTD. Suggested citation:Funder: Marine Institut