Hellenic Open University

Mystakidis S.

The Metaverse is the post-reality universe, a perpetual and persistent multiuser environment merging physical reality with digital virtuality. It is based on the convergence of technologies that enable multisensory interactions with virtual environments, digital objects and people such as virtual reality (VR) and augmented reality (AR). Hence, the Metaverse is an interconnected web of social, networked immersive environments in persistent multiuser platforms. It enables seamless embodied user communication in real-time and dynamic interactions with digital artifacts. Its first iteration was a web of virtual worlds where avatars were able to teleport among them. The contemporary iteration of the Metaverse features social, immersive VR platforms compatible with massive multiplayer online video games, open game worlds and AR collaborative spaces.

Aad G., Abbott B., Abbott D.C., Abud A.A., Abeling K., Abhayasinghe D.K., Abidi S.H., AbouZeid O.S., Abraham N.L., Abramowicz H., Abreu H., Abulaiti Y., Acharya B.S., Achkar B., Adam L., et. al.

AbstractThis article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 $$\hbox {fb}^{-1}$$ fb - 1 of pp collision data at $$\sqrt{s}=13$$ s = 13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of $$Z\rightarrow \mu \mu $$ Z → μ μ and $$J/\psi \rightarrow \mu \mu $$ J / ψ → μ μ decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of $$|\eta |<2.7$$ | η | < 2.7 .

Lampropoulos G., Keramopoulos E., Diamantaras K., Evangelidis G.

This study scrutinizes the existing literature regarding the use of augmented reality and gamification in education to establish its theoretical basis. A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was conducted. To provide complete and valid information, all types of related studies for all educational stages and subjects throughout the years were investigated. In total, 670 articles from 5 databases (Scopus, Web of Science, Google Scholar, IEEE, and ERIC) were examined. Based on the results, using augmented reality and gamification in education can yield several benefits for students, assist educators, improve the educational process, and facilitate the transition toward technology-enhanced learning when used in a student-centered manner, following proper educational approaches and strategies and taking students’ knowledge, interests, unique characteristics, and personality traits into consideration. Students demonstrated positive behavioral, attitudinal, and psychological changes and increased engagement, motivation, active participation, knowledge acquisition, focus, curiosity, interest, enjoyment, academic performance, and learning outcomes. Teachers also assessed them positively. Virtual rewards were crucial for improving learning motivation. The need to develop appropriate validation tools, design techniques, and theories was apparent. Finally, their potential to create collaborative and personalized learning experiences and to promote and enhance students’ cognitive and social–emotional development was evident.

Aad G., Abbott B., Abbott D.C., Abud A.A., Abeling K., Abhayasinghe D.K., Abidi S.H., AbouZeid O.S., Abraham N.L., Abramowicz H., Abreu H., Abulaiti Y., Acharya B.S., Achkar B., Adam L., et. al.

AbstractJet energy scale and resolution measurements with their associated uncertainties are reported for jets using 36–81 fb$$^{-1}$$ - 1 of proton–proton collision data with a centre-of-mass energy of $$\sqrt{s}=13$$ s = 13  $${\text {Te}}{\text {V}}$$ TeV collected by the ATLAS detector at the LHC. Jets are reconstructed using two different input types: topo-clusters formed from energy deposits in calorimeter cells, as well as an algorithmic combination of charged-particle tracks with those topo-clusters, referred to as the ATLAS particle-flow reconstruction method. The anti-$$k_t$$ k t jet algorithm with radius parameter $$R=0.4$$ R = 0.4 is the primary jet definition used for both jet types. This result presents new jet energy scale and resolution measurements in the high pile-up conditions of late LHC Run 2 as well as a full calibration of particle-flow jets in ATLAS. Jets are initially calibrated using a sequence of simulation-based corrections. Next, several in situ techniques are employed to correct for differences between data and simulation and to measure the resolution of jets. The systematic uncertainties in the jet energy scale for central jets ($$|\eta |<1.2$$ | η | < 1.2 ) vary from 1% for a wide range of high-$$p_{{\text {T}}}$$ p T jets ($$250<p_{{\text {T}}} <2000~{\text {Ge}}{\text {V}}$$ 250 < p T < 2000 GeV ), to 5% at very low $$p_{{\text {T}}}$$ p T ($$20~{\text {Ge}}{\text {V}}$$ 20 GeV ) and 3.5% at very high $$p_{{\text {T}}}$$ p T ($$>2.5~{\text {Te}}{\text {V}}$$ > 2.5 TeV ). The relative jet energy resolution is measured and ranges from ($$24 \pm 1.5$$ 24 ± 1.5 )% at 20 $${\text {Ge}}{\text {V}}$$ GeV to ($$6 \pm 0.5$$ 6 ± 0.5 )% at 300 $${\text {Ge}}{\text {V}}$$ GeV .

Lioutas E.D., Charatsari C., De Rosa M.

The high potential of digital applications for agriculture generates enthusiasm about the future of food production. Some view the intelligence offered by digital tools as a way to solve the still pressing food problem. Nevertheless, social, ethical, political, cultural, and environmental concerns associated with digitalization loom large. In this article, using as a framework the artificial trolley dilemma – a situation in which one has to decide about the rightness of putting at risk someone trying to avoid some obvious and imminent noxious effects to a larger group of people – we present the promises and perils of agricultural digitalization. We also point out the need to develop new trajectories for the digital agricultural revolution that ensure the increase of food production without severe negative societal impacts. Although digitalization is sometimes viewed as a panacea for boosting the transition of agrifood production towards more sustainable paradigms, the trade-off may be complicated and requires in-depth analyses excavating the multiple rationales in digitalizing farming. Today, there is a need for science to prove that digitalization does have society-wide positive impacts. On the other side of the coin, the assessment and the estimation of the magnitude of the externalities that digital technologies may have require the development and application of robust evaluation methods. Finally, policies must emphasize the need to provide inclusive and open spaces for agricultural digitalization. • We still don't know what and at what cost digitalization can offer to food systems. • Agricultural digitalization can contribute to the fight against the food problem. • Digitalization has ethical, social, political, cultural, and environmental impacts. • Research must prove that agricultural digitalization has society-wide benefits. • Policy should take actions to prevent potential negative impacts of digitalization.

Dasaklis T.K., Voutsinas T.G., Tsoulfas G.T., Casino F.

In recent years, traceability systems have been developed as practical tools for improving supply chain (SC) transparency and visibility, especially in health and safety-sensitive sectors like food and pharmaceuticals. Blockchain-related SC traceability research has received significant attention during the last several years, and arguably blockchain is currently the most promising technology for providing traceability-related services in SC networks. This paper provides a systematic literature review of the various technical implementation aspects of blockchain-enabled SC traceability systems. We apply different drivers for classifying the selected literature, such as (a) the various domains of the available blockchain-enabled SC traceability systems and relevant methodologies applied; (b) the implementation maturity of these traceability systems along with technical implementation details; and (c) the sustainability perspective (economic, environmental, social) prevalent to these implementations. We provide key takeaways regarding the open issues and challenges of current blockchain traceability implementations and fruitful future research areas. Despite the significant volume and plethora of blockchain-enabled SC traceability systems, academia has so far focused on unstructured experimentation of blockchain-associated SC traceability solutions, and there is a clear need for developing and testing real-life traceability solutions, especially taking into account feasibility and cost-related SC aspects.

Xiong K., Adhikari B.R., Stamatopoulos C.A., Zhan Y., Wu S., Dong Z., Di B.

Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards.

Aad G., Abbott B., Abbott D.C., Abed Abud A., Abeling K., Abhayasinghe D.K., Abidi S.H., AbouZeid O.S., Abraham N.L., Abramowicz H., Abreu H., Abulaiti Y., Acharya B.S., Achkar B., Adachi S., et. al.

Abstract A search for new resonances decaying into a pair of jets is reported using the dataset of proton-proton collisions recorded at $$ \sqrt{s} $$ s = 13 TeV with the ATLAS detector at the Large Hadron Collider between 2015 and 2018, corresponding to an integrated luminosity of 139 fb−1. The distribution of the invariant mass of the two leading jets is examined for local excesses above a data-derived estimate of the Standard Model background. In addition to an inclusive dijet search, events with jets identified as containing b-hadrons are examined specifically. No significant excess of events above the smoothly falling background spectra is observed. The results are used to set cross-section upper limits at 95% confidence level on a range of new physics scenarios. Model-independent limits on Gaussian-shaped signals are also reported. The analysis looking at jets containing b-hadrons benefits from improvements in the jet flavour identification at high transverse momentum, which increases its sensitivity relative to the previous analysis beyond that expected from the higher integrated luminosity.

Price D.C., Enriquez J.E., Brzycki B., Croft S., Czech D., DeBoer D., DeMarines J., Foster G., Gajjar V., Gizani N., Hellbourg G., Isaacson H., Lacki B., Lebofsky M., MacMahon D.H., et. al.

Breakthrough Listen (BL) is a ten-year initiative to search for signatures of technologically capable life beyond Earth via radio and optical observations of the local Universe. A core part of the BL program is a comprehensive survey of 1702 nearby stars at radio wavelengths (1-10 GHz). Here, we report on observations with the 64-m CSIRO Parkes radio telescope in New South Wales, Australia, and the 100-m Robert C. Byrd Green Bank radio telescope in West Virginia, USA. Over 2016 January to 2019 March, a sample of 1138 stars was observed at Green Bank using the 1.10-1.90 GHz and 1.80-2.80 GHz receivers, and 189 stars were observed with Parkes over 2.60-3.45 GHz. We searched these data for the presence of engineered signals with Doppler-acceleration drift rates between -4 to 4 Hz/s. Here, we detail our data analysis techniques and provide examples of detected events. After excluding events with characteristics consistent with terrestrial radio interference, we are left with zero candidates. Given the sensitivity of our observations, we can put an upper limit on the power of potential radio transmitters at these frequencies at 2x10^12 W, and 9x10^12 W for GBT and Parkes respectively. These observations constitute the most comprehensive search over 1.10-3.45 GHz for technosignatures to date. All data products, totalling ~219 TB, are available for download as part of the first BL data release (DR1), as described in a companion paper (Lebofsky et. al., 2019)

Feretzakis G., Loupelis E., Sakagianni A., Kalles D., Martsoukou M., Lada M., Skarmoutsou N., Christopoulos C., Valakis K., Velentza A., Petropoulou S., Michelidou S., Alexiou K.

Hospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. Time is critical to identifying bacteria and their resistance to antibiotics due to the critical health status of patients in the intensive care unit (ICU). As common antibiotic resistance tests require more than 24 h after the sample is collected to determine sensitivity in specific antibiotics, we suggest applying machine learning (ML) techniques to assist the clinician in determining whether bacteria are resistant to individual antimicrobials by knowing only a sample’s Gram stain, site of infection, and patient demographics. In our single center study, we compared the performance of eight machine learning algorithms to assess antibiotic susceptibility predictions. The demographic characteristics of the patients are considered for this study, as well as data from cultures and susceptibility testing. Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, solely from the Microbiology Laboratory without any of the patient’s clinical data, even in resource-limited hospital settings, can provide informative antibiotic susceptibility predictions to aid clinicians in selecting appropriate empirical antibiotic therapy. These strategies, when used as a decision support tool, have the potential to improve empiric therapy selection and reduce the antimicrobial resistance burden.