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Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe. [ABSTRACT FROM AUTHOR]

The article discusses the integration of ocean carbonate chemistry data by the European Network of Excellence for Ocean Ecosystems Analysis (EUR-OCEANS) and the European Project on Ocean Acidification (EPOCA). It states that only few papers have provided details regarding carbonate chemistry and complementary observations on the impact of ocean acidification on marine organisms and its communities. It mentions the efforts of gathering data, transforming information, and making data available.

Coronavirus disease 2019 (COVID-19) has resulted in approximately 5 million deaths around the world with unprecedented consequences in people's daily routines and in the global economy. Despite vast increases in time and money spent on COVID-19-related research, there is still limited information about the factors at the country level that affected COVID-19 transmission and fatality in EU. The paper focuses on the identification of these risk factors using a machine learning (ML) predictive pipeline and an associated explainability analysis. To achieve this, a hybrid dataset was created employing publicly available sources comprising heterogeneous parameters from the majority of EU countries, e.g., mobility measures, policy responses, vaccinations, and demographics/generic country-level parameters. Data pre-processing and data exploration techniques were initially applied to normalize the available data and decrease the feature dimensionality of the data problem considered. Then, a linear ε-Support Vector Machine (ε-SVM) model was employed to implement the regression task of predicting the number of deaths for each one of the three first pandemic waves (with mean square error of 0.027 for wave 1 and less than 0.02 for waves 2 and 3). Post hoc explainability analysis was finally applied to uncover the rationale behind the decision-making mechanisms of the ML pipeline and thus enhance our understanding with respect to the contribution of the selected country-level parameters to the prediction of COVID-19 deaths in EU. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze the current temporal and spatial trajectories in nanotechnology research in order to display the worldwide patterns of scientific domains across main geo-economic players. Current trends of nanotechnology have been growing in chemistry and medicine because of technological applications of new nanomaterials mainly in Chemical Engineering, Biochemistry, Genetics and Molecular Biology. In addition, the results show that the laboratories of Europe and North America have intensive scientific collaborations with foreign scholars in nanotechnology studies. This paper can provide vital findings to support new research and innovation policies in order to foster the development of these driving converging technologies for future patterns of economic growth. [ABSTRACT FROM AUTHOR]

The Single Europe Sky Air Traffic Management Research (SESAR) program develops and implements innovative technological and operational solutions to modernize European air traffic management and to eliminate the negative environmental impacts of aviation activity. This article presents our developments within the SESAR Solution "Safety Support Tools for Avoiding Runway Excursions". This SESAR Solution aims to mitigate the risk of runway excursion, to optimize airport operation management by decreasing the number of runway inspections, to make chemical treatment effective with respect to the environment, and to increase resilience, efficiency and safety in adverse weather situations. The proposed approach is based on the enhancement of runway surface condition awareness by integrating data from various sources. Dangerous windy conditions based on Lidar measurements are also discussed as another relevant factor in relation to runway excursions. The paper aims to explore four different data mining methods to obtain runway conditions from the available input data sources, examines their performance and discusses their pros and cons in comparison with a rule-based algorithm approach. The output of the SESAR Solution is developed in compliance with the new Global Reporting Format of the International Civil Aviation Organization for runway condition description to be valid from 2020. This standard is expected to provide concerned stakeholders with more precise information to enhance flight safety and environmental protection., Competing Interests: The authors declare no conflict of interest.

Background: In recent years, the development and application of emerging information technologies, such as artificial intelligence, cloud computing, Internet of Things, and wearable devices, has expanded the content of electronic health (e-health). Electronic health has become a research focus, but few studies have explored its knowledge structure from a global perspective., Methods: To detect the evolution track, knowledge base and research hotspots of e-health, we conducted a series of bibliometric analyses on the retrieved 3,085 papers from the Web of Science core database in 1992-2017. We used several bibliometric tools, such as HistCite, CiteSpace, NetDraw, and NEViewer, to describe the evolution process, time-and-space knowledge map, and hotspots in e-health., Results: The research results are as follows. (a) The number of publications has been obviously increasing after 2005 and according to the trend line it is expected to continue increase exponentially in the future. (b) Countries/regions conducting e-health research have close cooperative relationship, among which European countries have the closest cooperation. (c) Electronic health records, mobile health and health information technology are research hotspots in electronic health. Moreover, scholars also pay attention to the hot issues such as privacy, security, and quality improvement., Conclusions: Electronic health is a large and growing field with quite a number of research articles in medical journals. This study provides a comprehensive knowledge structure of electronic health for scholars in the healthcare informatics field, which can help them quickly grasp research hotspots and choose future research projects., (Copyright © 2019 Elsevier B.V. All rights reserved.)

This paper aims to bridge a gap in the literature by investigating a continuous audit case for anticorruption. The evolution of technology can offer valuable opportunities to integrate legality checks and business processes that are consistent with the growing call to fight corruption at the institutional level. Since the last decade, researchers have proposed conceptual frameworks demonstrating the visible advantages of continuous auditing and data mining, but significant difficulties still exist in practice. This process has been implemented in only a few cases, and the lack of empirical studies implies a need for additional research on this topic. To fill this gap, we adopt the framework proposed by Chan and Vasarhelyi (2011) and identify success factors for the implementation of continuous auditing. For the analysis, we use the research methodology of a single case study and focus on the Italian company Acciai Speciali Terni Spa (AST), the only steelwork company in Europe that has been ISO 37001 certified. This study demonstrates the practical impact of continuous auditing and data mining on strategic risk control by empirically testing the Chan and Vasarhelyi (2011) framework for the specific issue of anticorruption. The results show that effective continuous auditing is centered on an integrative and change management approach and that strategic vision, risk mapping, and a no‐corruption culture are among the most influential factors. The AST case demonstrates that technology is currently essential for supporting strategic risk control but only if it is integrated with consistent growth in organizational and managerial capabilities. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles. [ABSTRACT FROM AUTHOR]

Background: Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998.Objectives: To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future.Methods: We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers.Results: We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication.Conclusions: There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited. [ABSTRACT FROM AUTHOR]

The Europe Media Monitor (EMM) family of applications is a set of multilingual tools that gather, cluster and classify news in currently fifty languages and that extract named entities and quotations (reported speech) from twenty languages. In this paper, we describe the recent effort of adding the African Bantu language Swahili to EMM. EMM is designed in an entirely modular way, allowing plugging in a new language by providing the language-specific resources for that language. We thus describe the type of language-specific resources needed, the effort involved, and ways of boot-strapping the generation of these resources in order to keep the effort of adding a new language to a minimum. The text analysis applications pursued in our efforts include clustering, classification, recognition and disambiguation of named entities (persons, organisations and locations), recognition and normalisation of date expressions, as well as the identification of reported speech quotations by and about people. [ABSTRACT FROM AUTHOR]

lthough business aviation has been popular in the USA, Europe, and South America, however, top economies in East Asia, including Japan, Korea, and Taiwan, have been more conservative and lag behind in the development of business aviation. In this paper, we hope to discover possible trends and needs of business aviation for supporting the government to make decision in anticipation of eventual deregulation in the near future. We adopt knowledge-discovery tools based on rough set to analyze the potential for business aviation through an empirical study. Although our empirical study uses data from Taiwan, we are optimistic that our proposed method can be similarly applied in other countries to help governments there make decisions about a deregulated market in the future. [ABSTRACT FROM AUTHOR]

Sustainable development as a concept that aims to enhance the quality of life without affecting environment has been in focus in European policies since the last decade of 20th century. The objectives of EU Sustainable Development Strategy (EU SDS) are grouped into ten thematic areas, where almost each theme has at least one headline and several operational sustainable development indicators (SDIs). A large number of SDIs is needed to evaluate all EU SDS goals, which imposes the use of multivariate data mining techniques. This paper presents an empirical study carried out to assess the sustainability performance of European countries using the differential multi criteria analysis (DMCA) technique. The PROMETHEE (Preference Ranking Organization MeTHod for Enrichment Evaluations) was applied on 38 headline and operational SDIs defined under the EU SDS. This DMCA was applied to 30 European countries over a 10-year period (2004–2014) with the aim to determine the theme specific, as well as, overall sustainability progress. The DMCA reveals that the majority of European countries have made progress in sustainability in the studied period, where Czech Republic, Germany, Hungary and Sweden, have enhanced their sustainability performance concerning all themes. There are only two countries, namely Greece and Ireland, which have not made overall progress in this period. Also, above the average progress has been made concerning social inclusion, sustainable transport, and climate change and energy, while in all other themes additional efforts should be made in order to ensure sustainable performance progress in future years. Although the progress in reducing uneven development between EU members has been made, after 2009 a positive trend can be observed only in a limited number of SDS themes. [ABSTRACT FROM AUTHOR]

The co-occurrence of plant species is a fundamental aspect of plant ecology that contributes to understanding ecological processes, including the establishment of ecological communities and its applications in biological conservation. A priori algorithms can be used to measure the co-occurrence of species in a spatial distribution given by coordinates. We used 17 species of the genus Brachypodium, downloaded from the Global Biodiversity Information Facility data repository or obtained from bibliographical sources, to test an algorithm with the spatial points process technique used by Silva et al. (2016), generating association rules for co-occurrence analysis. Brachypodium spp. has emerged as an effective model for monocot species, growing in different environments, latitudes, and elevations; thereby, representing a wide range of biotic and abiotic conditions that may be associated with adaptive natural genetic variation. We created seven datasets of two, three, four, six, seven, 15, and 17 species in order to test the algorithm with four different distances (1, 5, 10, and 20 km). Several measurements (support, confidence, lift, Chi-square, and p-value) were used to evaluate the quality of the results generated by the algorithm. No negative association rules were created in the datasets, while 95 positive co-occurrences rules were found for datasets with six, seven, 15, and 17 species. Using 20 km in the dataset with 17 species, we found 16 positive co-occurrences involving five species, suggesting that these species are coexisting. These findings are corroborated by the results obtained in the dataset with 15 species, where two species with broad range distributions present in the previous dataset are eliminated, obtaining seven positive co-occurrences. We found that B. sylvaticum has co-occurrence relations with several species, such as B. pinnatum, B. rupestre, B. retusum, and B. phoenicoides, due to its wide distribution in Europe, Asia, and north of Africa. We demonstrate the utility of the algorithm implemented for the analysis of co-occurrence of 17 species of the genus Brachypodium, agreeing with distributions existing in nature. Data mining has been applied in the field of biological sciences, where a great amount of complex and noisy data of unseen proportion has been generated in recent years. Particularly, ecological data analysis represents an opportunity to explore and comprehend biological systems with data mining and bioinformatics tools. [ABSTRACT FROM AUTHOR]

In 2012, the author and colleagues surveyed eight publishers that had been involved with the Publishing and the Ecology of European Research (PEER) project to learn about the state of hybrid journal publishing. At the same time, one of the key questions asked to a panel of librarians at the International Association of Scientific, Technical & Medical Publishers May 2012 Meeting was what role librarians would play if scholarly publishing shortly went open access (OA) across the board? From the survey of the market, and the rapid OA developments in the UK and EU that include hybrid OA, a picture has begun to emerge of what roles librarians can play with regard to supporting hybrid OA publishing at their institutions. This article focuses on developing new partnerships within a given institution, looks at new budgetary models and the tracking of local scholarship creation. Current pertinent standards are highlighted. [ABSTRACT FROM AUTHOR]

The terms of digital economy and e-readiness are introduced and presented in the present area of evolution of economy stage within Europe and not only. The main aspects treated in the European University Institute ranking for e-readiness are briefly covered. An open source convertor for excel to arff file format is presented and used in the purpose of input transformation for using Weka Data Mining, an open source tool. The framework of ExceltoArff tool is demonstrated in a practical use, the one of 70 countries registrations of EUI Ranking. [ABSTRACT FROM AUTHOR]