The Centre for Research and Technology, Hellas

The recognition of playing styles in soccer has been established as highly significant in the performance analysis of the sport. The aim of this research was to clarify the terms used by authors to express this specific concept and to identify all recognized playing styles, examining their relationships, thereby creating a comprehensive framework. We employed a qualitative study design using a Grounded Theory approach. A rigorous process of open, axial, and selective coding was applied, involving nine researchers to ensure the reliability of the findings. Qualitative research data were obtained from documents found on Scopus and Google Scholar. After applying specific criteria, 205 documents were deemed suitable, with 22 of them necessary to achieve theoretical saturation, the point where no new properties , dimensions, or relationships emerge during analysis. The 22 documents were analyzed using Atlas.ti.23, identifying 84 codes, 40 of which were utilized as categories and 44 as subcategories. The set of codes categorized into six thematic folders. The analysis led to the identification of terms used to express the concept of style in the international literature and the recognition of playing styles used to characterize a team a) regardless of the game phases, b) in specific phases of the game, c) in specific sub-phases of the attack, d) based on the game phases that teams rely on for their tactics, and e) based on the teams' physical performance. By synthesizing existing literature, we proposed a Grounded Theory that serves as a consensus point for researchers and coaches. This theory managed to overcome the limitations of individual studies and can serve as the foundation for effective communication within the soccer community, thus being a useful tool for future research, as well as for coaches, analysts, and scouts of the teams.

Background/Objectives: Populations in Mediterranean countries are abandoning the traditional Mediterranean diet (MD) and lifestyle, shifting towards unhealthier habits due to profound cultural and socioeconomic changes. The SWITCHtoHEALTHY project aims to demonstrate the effectiveness of a multi-component nutritional intervention to improve the adherence of families to the MD in three Mediterranean countries, thus prompting a dietary behavior change. Methods: A parallel, randomized, single-blinded, and controlled multicentric nutritional intervention study will be conducted over 3 months in 480 families with children and adolescents aged 3–17 years from Spain, Morocco, and Turkey. The multi-component intervention will combine digital interactive tools, hands-on educational materials, and easy-to-eat healthy snacks developed for this study. Through the developed SWITCHtoHEALTHY app, families will receive personalized weekly meal plans, which also consider what children eat at school. The engagement of all family members will be prompted by using a life simulation game. In addition, a set of activities and educational materials for adolescents based on a learning-through-playing approach will be codesigned. Innovative and sustainable plant-based snacks will be developed and introduced into the children’s dietary plan as healthy alternatives for between meals. By using a full-factorial design, families will be randomized into eight groups (one control and seven interventions) to test the independent and combined effects of each component (application and/or educational materials and/or snacks). The impact of the intervention on diet quality, economy, and the environment, as well as on classical anthropometric parameters and vital signs, will be assessed in three different visits. The COM-B behavioral model will be used to assess essential factors driving the behavior change. The main outcome will be adherence to the MD assessed through MEDAS in adults and KIDMED in children and adolescents. Conclusions: SWITCHtoHEALTHY will provide new insights into the use of sustained models for inducing dietary and lifestyle behavior changes in the family setting. It will facilitate generating, boosting, and maintaining the switch to a healthier MD dietary pattern across the Mediterranean area. Registered Trial, National Institutes of Health, ClinicalTrials.gov (NCT06057324).

Blockchain technology advancements have made it feasible to build secure, decentralized networks with numerous use cases in various industries, including banking, education, government, and health. In higher education, there is a significant need for certification and credential verification through digital means, as fake certificates are common in the labor market. The BlockAdemiC system presented in this paper falls into solutions for such issues. BlockAdemiC is a blockchain-based educational platform that makes use of cutting-edge features in blockchain technology to ensure the required degree of trust both at the individual user and institutional level. Specifically, BlockAdemiC represents a digital distributed security system for the certification and verification of educational activities, degrees, and skills in higher education and lifelong learning, producing an immutable educational passport. Information distributed using blockchain enhances trust in the exchange of information between institutions, agencies, companies, and alumni and introduces a trustworthy mechanism for content verification and authentication. For self-sovereign identity and identification management, the system utilizes distributed ledger technology via Hyperledger frameworks and offers users authority over their identities and credentials, removing the need for the involvement of central authorities to validate and verify user identities. Smart contracts are additionally supported, which provide the connection for storing student activities that take place on educational platforms on the EOSIO blockchain. The BlockAdemiC was evaluated via a technological feasibility study focusing on the system's performance. The initial findings show that the system can efficiently process a high load of queries in parallel similar to the real-world situation and can be further considered for large-scale deployment and use.

Introduction Bike tourism is one of the fast-developing alternative forms of tourism since it can satisfy the main pillars of sustainability (economic, social, and environmental). The current study is part of a larger funded project (GoBike) and aims to profile bike tourists in Greece, examine the motives and constraints to tourism participation, and show the value of using technology as a means of promoting bike tourism. Methods The data was collected through a quantitative study, with one hundred and five individuals who had experience with bike tourism activities, with the use of an online questionnaire. Items were used to measure socio-demographics, motives, constraints, involvement, and attitudes toward a smartphone application. Results The results indicated that “Nature”, “Health”, “Bike eco-friendly place” and “Interesting places” were the most important motives. On the other hand, the lack of “Guides”, “Appropriate Routes” “Bike tourism Programs” and “Limited Information” were reported as the most important barriers. The bikers reported that technology can facilitate their decision to do bike tourism activities. Discussion A smartphone application should include several attributes the most important of which are the “Elevation difference”, the “warnings of obstacles/risks”, “the level of difficulty”, “the bike distance” and the “condition of the routes”.

The comparative analysis of homologous enzymes is a valuable approach for elucidating enzymes’ structure–function relationships. Glutathione transferases (GSTs, EC. 2.5.1.18) are crucial enzymes in maintaining the homeostatic stability of plant cells by performing various metabolic, regulatory, and detoxifying functions. They are promiscuous enzymes that catalyze a broad range of reactions that involve the nucleophilic attack of the activated thiolate of glutathione (GSH) to electrophilic compounds. In the present work, three highly homologous (96–98%) GSTs from ryegrass Lolium perenne (LpGSTs) were identified by in silico homology searches and their full-length cDNAs were isolated, cloned, and expressed in E. coli cells. The recombinant enzymes were purified by affinity chromatography and their substrate specificity and kinetic parameters were determined. LpGSTs belong to the tau class of the GST superfamily, and despite their high sequence homology, their substrate specificity displays remarkable differences. High catalytic activity was determined towards hydroxyperoxides and alkenals, suggesting a detoxification role towards oxidative stress metabolites. The prediction of the structure of the most active LpGST by molecular modeling allowed the identification of a non-conserved residue (Phe215) with key structural and functional roles. Site-saturation mutagenesis at position 215 and the characterization of eight mutant enzymes revealed that this site plays pleiotropic roles, affecting the affinity of the enzyme for the substrates, catalytic constant, and structural stability. The results of the work have improved our understanding of the GST family in L. perenne, a significant threat to agriculture, sustainable food production, and safety worldwide.

Air quality intensifies climate change through global pollution, particularly affecting lower and middle-income countries that lack local ground pollutant monitoring networks. While atmospheric pollutant satellite measurements offer a broad view, their coarse spatial resolution limits detailed air pollution insights, resulting in unmonitored regions and information gaps. Furthermore, satellites generate extensive data that is often challenging to directly correlate with ground air pollution stations. To overcome this, we propose a multimodal self-supervised approach that learns from diverse satellite sources for air pollution monitoring. More specifically, aside from incorporating a combination of multi-spectral and spectral modalities (Sentinel-2 & 5P products), we also leverage tabular land cover data. Their integration into multimodal self-supervised learning is highlighted, employing a novel augmentation scheme that results in more resilient embeddings. The proposed approach integrates a self-supervised redundancy reduction loss in a multi-modal fashion, capturing both inter-modal and intra-modal correspondences. Furthermore, an adaptive loss weighting mechanism is introduced to effectively combine different multi-modal losses. Our approach’s efficacy is showcased in the air pollution prediction task, exhibiting a noteworthy improvement of up to 17% compared to existing methods. Furthermore, in our experiments, the applicability of our approach in other environmental tasks is also exhibited.

In the previous two decades, knowledge graphs (KGs) have evolved significantly, inspiring developers to build ever-more context-related KGs. Due to this development, artificial intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, a framework that depicts functional design for indoor workspaces and urban adaptive design, in order to help architects, artists, and interior designers for the design and construction of an urban or indoor workspace, based on the emotions of human individuals, is introduced. For the creation of online adaptive environments, the framework may incorporate emotional, physiological, visual, and textual measures. Additionally, an information retrieval mechanism that extracts critical information from the framework in order to assist the architects, artists, and the interior designers is presented. The framework provides access to commonsense knowledge about the (re-)design of an urban area and an indoor workspace, by suggesting objects that need to be placed, and other modifications that can be applied to the location, in order to achieve positive emotions. The emotions referred reflect to the emotions experienced by an individual when being in the indoor or urban area, which are pointers for the functionality, the memorability, and the admiration of the location. The framework also performs semantic matching between entities from the web KG ConceptNet, using semantic knowledge from ConceptNet and WordNet, with the ones existing in the KG of the framework. The paper provides a set of predefined SPARQL templates that specifically handle the ontology upon which the knowledge retrieval system is based. The framework has an additional argumentation function that allows users to challenge the knowledge retrieval component findings. In the event that the user prevails in the reasoning, the framework will learn new knowledge.

Thorough examination of clonotypic B-cell receptor immunoglobulin (BcR IG) gene rearrangement sequences in patients with mature B-cell malignancies has revealed significant repertoire restrictions, leading to the identification of subsets of patients expressing highly similar, stereotyped BcR IG. This discovery strongly suggests selection by common epitopes or classes of structurally similar epitopes in the development of these tumors. Initially observed in chronic lymphocytic leukemia (CLL), where the stereotyped fraction accounts for a substantial fraction of patients, stereotyped BcR IGs have also been identified in other mature B-cell malignancies, including mantle cell lymphoma (MCL) and splenic marginal zone lymphoma (SMZL). Further comparisons across different entities have indicated that stereotyped IGs are predominantly “disease-biased,” indicating distinct immune pathogenetic trajectories. Notably, accumulating evidence suggests that molecular subclassification of mature B-cell malignancies based on BcR IG stereotypy holds biological and clinical relevance. Particularly in CLL, patients belonging to the same subset due to the expression of a specific stereotyped BcR IG exhibit consistent biological backgrounds and clinical courses, especially for major and extensively studied subsets. Therefore, robust assignment to stereotyped subsets may aid in uncovering mechanisms underlying disease initiation and progression, as well as refining patient risk stratification. In this chapter, we offer an overview of recent studies on BcR IG stereotypy in mature B-cell malignancies and delineate past and present methodological approaches utilized for the identification of stereotyped BcR IG.

Unraveling the complex patterns embedded in trajectory data offers profound insights into various applications, from urban planning to environmental impact assessments. This paper introduces a self-supervised trajectory clustering framework that synthesizes the strengths of deep learning models with advanced loss functions to address the nuances of spatial-temporal relationships. Our model integrates the Sinkhorn Distance to leverage the geometric nature of trajectories, enabling the optimal transport of mass in embedding space. Further, we employ a modified Generalized End-to-End (GE2E) loss, typically used in speaker verification, to fine-tune the latent space for distinct clustering results without reliance on explicit labels. Our results significantly outperform existing deep clustering methods, underscoring the potential of our approach in capturing the intricate characteristics of trajectory data for high-quality clustering outcomes and practical environmental applications, by identifying movement patterns.

Olive (Olea europea L.) is one of the most economically important tree crops worldwide, especially for the countries in the Mediterranean basin. Given the economic and nutritional importance of the crop for olive oil and drupe production, we generated transcriptional atlases for the Greek olive cultivars “Chondorlia Chalkidikis” and “Koroneiki,” which have contrasting characteristics in terms of fruit development, oil production properties, and use. Our analysis involved 14 different organs, tissue types, and developmental stages, including young and mature leaves, young and mature shoots, open and closed flowers, young and mature fruits (epicarp plus mesocarp), young and mature endocarps, stalks, as well as roots. The developed gene expression atlases and the associated resources offer a comprehensive insight into comparative gene expression patterns across several organs and tissue types between significant olive tree cultivars. The comparative analyses presented in this work between the “Koroneiki” cultivar, which performs better in olive oil production, and the “Chondorlia Chalkidikis,” which grows larger fruits, will be essential for understanding the molecular mechanisms underlying olive oil production and fruit shape and size development. The developed resource is also expected to support functional genomics and molecular breeding efforts to enhance crop quality and productivity in olive trees. Outline of data resources generated The transcriptome data were generated using paired-end Illumina Next-Generation Sequencing technologies. The sequencing yielded approximately 13 million reads per sample for "Chondrolia Chalkidikis" and around 24 million reads per sample for "Koroneiki." The transcriptomes were comparatively analyzed to reveal tissue-specific and differentially expressed genes and co-expression gene modules within and between cultivars. Summary of key results The comparative analysis unveiled tissue-specific and differentially expressed genes within and between cultivars. Hierarchical gene clustering revealed intra- and inter-cultivar expression patterns, particularly for the endocarp and fruit tissues relevant to olive oil production and fruit development. Additionally, genes associated with oil production and fruit size/shape development, including those in fatty acid metabolism and developmental regulation, were identified. Broader utility of the resource To facilitate accessibility, the GrOlivedb (www.GrOlivedb.com) database was developed, housing the comprehensive transcriptomic data for all of the analyzed organs and tissue types per cultivar. This resource will be a useful molecular tool for future breeding studies in olive oil production and fruit development and a valuable resource for crop improvement.

The advent of Industry 5.0 as a defining concept for the future, which advocates a human-centric coalescence of humans and technology or software, renders the skilled workforce the most important asset in any organization or business. The society is 'forced' to adapt itself to technological change and progress for setting the necessary skillsets for the workforce. In order to follow the digital transformation, it is necessary to evoke the reshaping, evolution, or replacement of traditional and possibly obsolete processes at intra-or inter-organizational levels in multiple aspects, introducing innovative ways of redefining the workforce. To do so, the key piece are data. In this context various platform collect and organize data, also exploiting the new era of Data Spaces (DS). SKILLAB will act as a smart tool for handling, honing, and widening the competencies of the personnel of companies, forecasting future skill gaps and providing European citizens with a tool for upskilling and reskilling, exploiting DS.

The integrity and resilience of Critical Infrastructures (CIs) are fundamental to the security, well-being and economic prosperity of Europe. However, the increasing complexity and interconnectedness of CIs pose new challenges, as disruptions in one CI can have cascading effects across multiple sectors and countries. ATLANTIS addresses these challenges by evaluating and addressing systemic risks against major natural hazards and complex attacks that could disrupt vital functions of European society. The project focuses on improving the resilience of interconnected CIs exposed to large-scale, combined Cyber-Physical-Human (CPH) threats and hazards. By providing sustainable security solutions, ATLANTIS aims to ensure the continuity of vital operations while minimising cascading effects and enhancing the protection of the involved population and the environment. ATLANTIS will be validated and demonstrated in three large-scale cross-border and cross-sector pilots (LSPs), with a focus on improving the security of the information exchange at different levels of operation: inside individual CIs, across CIs in a national security environment and across borders between CI operators.

This work proposes a framework integrating AI technologies and tools to assist micro and small hosting service providers (HSPs) in adhering to the terrorist content online (TCO) Regulation. The framework encompasses: (i) a suite of AI tools for the automated detection and removal of TCO, (ii) a federated learning infrastructure for (re-)training and testing the underlying AI models, (iii) a secure shared hash repository to facilitate the automated prevention of uploading subversive content to HSP platforms, and (iv) a unified reporting mechanism to support the submission of removal orders by competent authorities. The proposed framework is being developed in the context of the ALLIES project in a user-driven manner based on the requirements of HSP and law enforcement agency (LEA) personnel. In addition, its development is guided by a desktop and empirical study on the landscape of online radicalisation, extremism conducted by domain experts. Overall, this framework is envisaged to support micro and small HSPs and protect their platforms from terrorist abuse in an efficient and cost-effective way.

Artificial intelligence (AI) technology has greatly impacted various aspects of modern society and economy. Recent technological advancements have significantly improved decision-making and support systems, as well as autonomous processes, by utilising different types of data, such as text, visual content, and video footage. To provide accurate outcomes, AI models require collection and preparation of a number of representative data that leads to a costly and timely process, in terms of hardware and human resources. Though, their adoption in defence systems is severely affected by the lack of appropriate data either in terms of size or due to their limited classification level. Current AI trends can contribute to overcoming such limitations and improve their overall utilisation in the defence domain hence to modernise current warfare. To this end, the proposed framework aims to comprise an AI package that involves numerous models and addresses the core challenges of a defence system. This research study relies on the development of five, interconnected research pillars and is expected to impact their systems. The ambition of the so-called ‘FaRADAI framework’ is to produce relevant research advances in the development of new technologies which will be successfully applied in the context of AI for defence applications, covering all stages of a military operation supply chain, from planning, to execution, C2, decision-making, and mission adaptation.

Terrorist financing (TF) poses a significant threat to the security and stability of the European Union (EU) and its member states. To combat TF in an efficient and timely manner, advanced technologies are required. The EU-funded Cut the Cord project (CTC) seeks to strengthen the EU’s capability to understand and counter TF by exploring innovative technologies and developing artificial intelligence (AI)-based solutions. Moreover, it pioneers the development of a blockchain-based chain-of-custody system that ensures a secure, transparent, and auditable exchange of intelligence among stakeholders, thus providing an immutable record of transactions and contributing to the robustness of evidence that could be presented in a court of law. This paper outlines CTC’s objectives, proposed solutions, and potential impacts, sparking discussion on novel approaches to detect and fight TF.

Forest fires, exacerbated by the increasing frequency and intensity of extreme weather events, remain a pressing concern. Developing effective forest fire emergency management systems is paramount to mitigate the impacts of future events. The SAFERS project (Structured Approaches for Forest fire Emergencies in Resilient Societies) addresses this challenge by proposing a modular and comprehensive Emergency Management System (EMS) encompassing all phases of the emergency management cycle. The main backbone of SAFERS is composed of Intelligent Services (ISs) linked with a web-based platform allowing data visualization and end-user interactions. These services integrate diverse data sources, including Earth Observation data, meteorological forecasts, and crowdsourced information. Through advanced technologies such as Artificial Intelligence (AI) algorithms, the ISs enable early warnings, rapid fire detection, propagation prediction, postevent assessment, and monitoring of soil recovery. This work focuses on these services and on the methodologies applied within SAFERS to enhance decision-making capabilities, facilitate effective response strategies, and promote resilience in the face of forest fire emergencies.