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The Council of Europe's (CoE) model on Digital Citizenship Education (DCE) has at its basis the 'Competencies of Democratic Culture' model, which considers being an active and responsible citizen and implies the development of a set of lifelong competencies both online and offline at various levels, as well. Taking the 'Competencies of Democratic Culture' the CoE experts elaborated on the DCE domains, which are the appropriate way to develop democratic culture competences in the digital environment. Georgia, as a member country, signed the agreement on DCE to be applied at the K-12 curriculum level and joined the DCE project of the CoE in July 2020. Thus, as an educational concept, DCE is relatively new to the education system of Georgia. The aim of the study was to describe the current situation concerning DCE in Georgia, namely on awareness of the concept and its foundations, as well as identifying self-reported DCE competences by teachers, students and parents, in the light of the Ribble's and the CoE's models. Data was collected in five schools in 2020. The issue was studied using the quantitative method, an online survey, 1954 respondents were involved in the research. It was demonstrated that most of the respondents had a lack of awareness about information-communication technologies. It can be concluded that communication among school teachers, students and parents about the issues of digital security has not started yet. The correlation between the geographic location of a school and teachers' digital competences is not confirmed.

Adaptation to distance learning, which is one of the most effective ways of fighting against the COVID-19 pandemic, presented numerous challenges to society and the economy. The study aims to assess the consequences of distance learning as a result of the pandemic from the perspective of students and teachers. Quantitative research was conducted. The students (n = 417) and teachers (n = 47) of all disciplines from Tbilisi universities (Republic of Georgia) participated in the research. Non-probability convenience sampling technique was used for the study. Respondents evaluated the process of distance learning positively since they had the opportunity to attend lectures from any location, thus, saving costs, learning new skills, gaining valuable experience, and having more free time left than before. Using a Likert scale, the distance learning process was positively assessed by students (3.2 points out of 5) and teachers (3 points out of 5). The majority of students (n = 288, 69%) preferred the synthesis of distance and auditory learning methods as they consider the student's own choices in a superior way. Respondents pointed out some deficiencies, such as limited communication, technical access difficulties, low quality and malfunction of internet access, an inconvenient environment, students' involvement process and complicated social relationships. The crisis caused by the COVID-19 epidemic has identified the need to advance the methods of high-quality acquisition of knowledge. It is preferable to equip university auditoriums with the necessary technical capabilities and to develop curricula that allow students to decide whether to attend lectures in the classroom or to participate online.

The project 'Digital Citizenship in General Education Schools in Georgia: Challenges and Ways of Implementation' aimed to understand to what extent were teachers, students and parents aware of the Digital Citizenship Education (DCE) concept, whether teachers felt competent to implement it in the classroom and what DCE activities were carried out there. Data were collected from 1954 individuals, among teachers (205), students (972), parents and guardians (777), following an exploratory sequential design (qualitative followed by quantitative), and data analysis exposed that even half of the school society members claim to be aware of the DCE concept, they lack the right competences to apply them in their daily practice. Considering the project, two guides were created, one for teachers and one for parents, both aligned with the Georgian curriculum. Both documents aim to raise awareness of DCE and become key resources in training teachers and other educators.

Swapping visual perspective in virtual reality (VR) provides a unique means for embodying different virtual bodies and for self-distancing. Moreover, this technology is a powerful tool for experiential learning and for simulating realistic scenarios, with broad potential in the training of soft skills. However, there is scarce knowledge on how perspective swapping in VR might benefit the training of soft skills such as those required in a job interview. This article investigates the impact of virtual body swapping on the self-assessment of verbal and nonverbal communication skills, emotional states, and embodiment in a simulated job interview context. Three main conditions were compared: a baseline condition in which the participants practiced a job interview from the first-person perspective of a virtual interviewee (no swap condition); an external point of view condition where, first, the participants answered questions from the interviewee perspective, but then swap visual perspective to re-experience their responses from a nonembodied point of view (out of body condition); and a condition in which, after answering questions from the interviewee perspective, the participants re-experienced their responses from the embodied perspective of the virtual recruiter (recruiter condition). The experimental results indicated that the effectiveness of the out of body and recruiter conditions was superior to the no swap condition to self-assess the communication styles used during a job interview. Moreover, all the conditions led to a high level of embodiment toward the interviewee avatar when seen from the first-person perspective; in the case of the recruiter condition, the participants also felt embodied in the recruiter avatar. No differences in emotional states were found among conditions, with all sharing a positive valence.

Abstract Sedentary behaviours in adults with intellectual disabilities (ID) negatively impact health-related determinants and increase the risk of physical dysfunction or chronic health issues. Inclusive sport practice can enhance social inclusion and fitness, benefiting overall person development and life quality. This study aimed to assess fitness level variation in athletes with and without ID before and after a 9-month inclusive (INC) and non-inclusive (N_INC) basketball training. 38 athletes with ID and 14 athletes without ID belonged to INC group, 38 athletes with ID belonged to N_INC group and 23 participants with ID belonged to the control group (CG). Before and after the intervention period, all participants performed anthropometric (body weight, body mass index, body fat percentage) and fitness tests (muscle strength and power, cardiovascular endurance, balance, flexibility, agility, movement speed, and coordination). ID and non-ID athletes of both INC and N_INC groups significantly improved in most of the tests (weight, balance, flexibility, muscle strength, endurance, agility, movement speed and coordination) while CG group significantly worsened weight, muscle endurance, flexibility, agility, movement speed and coordination, after the intervention (p

Abstract Variational quantum algorithms represent a powerful approach for solving optimization problems on noisy quantum computers, with a broad spectrum of potential applications ranging from chemistry to machine learning. However, their performances in practical implementations crucially depend on the effectiveness of quantum circuit training, which can be severely limited by phenomena such as barren plateaus. While, in general, dissipation is detrimental for quantum algorithms, and noise itself can actually induce barren plateaus, here we describe how the inclusion of properly engineered Markovian losses after each unitary quantum circuit layer allows for the trainability of quantum models. We identify the required form of the dissipation processes and establish that their optimization is efficient. We benchmark the generality of our proposal in both a synthetic and a practical quantum chemistry example, demonstrating its effectiveness and potential impact across different domains.

During the fieldworks conducted within the CaBOL project in 2020–2023, 14 new species of Heteroptera belonging to seven families, and a new species of Auchenorrhyncha were collected in Georgia for the first time: Trypetimorpha occidentalis Huang & Bourgoin, 1993 (Tropiduchidae), Dimorphopterus blissoides (Baerensprung, 1859) (Blissidae), Europiella decolor (Uhler, 1893), Tuponia mixticolor (A. Costa, 1862), Deraeocoris ventralis Reuter, 1904, Orthocephalus brevis (Panzer, 1798) (Miridae), Alloeorhynchus flavipes (Fieber, 1836) (Nabidae), Holcogaster fibulata (Germar, 1831), Vilpianus galii Wolff, 1802 (Pentatomidae), Holotrichius bergrothi Reuter, 1891, Reduvius ciliatus Jakovlev, 1879, Oncocephalus squalidus (Rossi, 1790) (Reduviidae), Pterotmetus staphyliniformis (Germar, 1831), Diomphalus hispidulus Fieber, 1864 (Rhyparochromidae), and Psacasta neglecta (Herrich-Schäffer, 1837) (Scutellaridae). The present study provides distributional, genetic, and collecting information on the newly recorded species followed by remarks.

Abstract Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various analytical perspectives. Specifically, it focused on three distinct classes of performance metrics used to evaluate ML imputation methods: post-imputation bias of regression estimates, post-imputation predictive accuracy, and substantive model-free metrics. As an illustration, we applied data from a real-world breast cancer survival study. This comprehensive approach aimed to provide a thorough assessment of the effectiveness and reliability of ML imputation methods across various analytical perspectives. A simulated dataset with 30% Missing At Random (MAR) values was used. A number of single imputation (SI) methods - specifically KNN, missMDA, CART, missForest, missRanger, missCforest - and multiple imputation (MI) methods - specifically miceCART and miceRF - were evaluated. The performance metrics used were Gower’s distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores. The analysis revealed that in terms of Gower’s distance, CART and missForest were the most accurate, while missMDA and CART excelled for binary covariates; missForest and miceCART were superior for continuous covariates. When assessing bias and accuracy in regression estimates, miceCART and miceRF exhibited the least bias. Overall, the various imputation methods demonstrated greater efficiency than complete-case analysis (CCA), with MICE methods providing optimal confidence interval coverage. In terms of predictive accuracy for Cox models, missMDA and missForest had superior AUC and C-index scores. Despite offering better predictive accuracy, the study found that SI methods introduced more bias into the regression coefficients compared to MI methods. This study underlines the importance of selecting appropriate imputation methods based on study goals and data types in time-to-event research. The varying effectiveness of methods across the different performance metrics studied highlights the value of using advanced machine learning algorithms within a multiple imputation framework to enhance research integrity and the robustness of findings.

Contrast enhancement resolution induced by corticosteroids is a phenomenon primarily associated with primary central nervous system lymphoma, while malignant brain gliomas usually maintain a consistent radiological appearance during systemic steroid treatment.Although rare, a few primary and metastatic intracranial lesions have shown similar radiographic changes following corticosteroid therapy. In the case of glioblastomas, corticosteroid therapy is commonly used to alleviate pressure effects from peritumoral edema, but its impact on contrast enhancement is not well-established.A few reported cases in the literature describe reduced contrast enhancement in glioblastomas after corticosteroid treatment.We present a case of corticosteroid-induced regression on imaging of glioblastoma evaluated at our institutionwith the intention to explore the pathogenesis of this response and discuss the therapeutic and prognostic implications of this discovery.

Abstract Salmonella Typhi is a human-restricted pathogen that is transmitted by the faecal–oral route and causative organism of typhoid fever. Using health facility data from 2016 to 2020, this study focuses on modelling the spatial variation in typhoid risk in Ndirande township in Blantyre. To pursue this objective, we developed a marked inhomogeneous Poisson process model that allows us to incorporate both individual-level and environmental risk factors. The results from our analysis indicate that typhoid cases are spatially clustered, with the incidence decreasing by 54% for a unit increase in the water, sanitation, and hygiene (WASH) score. Typhoid intensity was also higher in children aged below 18 years than in adults. However, our results did not show evidence of a strong temporal variation in typhoid incidence. We also discuss the inferential benefits of using point pattern models to characterise the spatial variation in typhoid risk and outline possible extensions of the proposed modelling framework.