Search

Immersive virtual reality (IVR) and Metaverse applications are gaining attention in the educational field, showing potentials in transforming traditional learning methods by supporting active and experiential forms of learning. Our study, conducted within the context of an Italian university course, employs the Extended Expectation-Confirmation Model (EECM) as a theoretical framework to explore the key aspects of students' acceptance and continued intention to use IVR and Metaverse integrated learning environments in educational settings. The EECM, which bridges the gap between pre-adoption expectations and post-adoption experiences, provides a comprehensive perspective for exploring technology adoption in education. Students' attitudes were assessed before and after they completed an elective course offered by the university that delved into IVR and Metaverse applications. During the course, students explored the theoretical and practical applications of these technologies, engaging in a variety of experiences, from immersive relaxation exercises to immersive educational platforms in the emerging Metaverse. Contrary to common assumptions, pre-adoption factors like performance and effort expectancy had limited impact on expectancy confirmation. However, when students' initial expectations matched their experiences, their perceptions of the technology's usefulness, satisfaction, and confidence in its use were positively enhanced, influencing their continued intention to integrate these tools in education.

The large-scale diffusion of tablets and smartphones in the last decades offered new opportunities to broaden educational strategies. In particular, the Mobile-Assisted Language Learning (MALL) emerged as a promising approach leveraging the widespread availability of portable devices. In this study we investigated the feasibility and efficacy of a self-administered MALL homework training based on immersive 360° videos, compared to a training based on not-immersive standard videos showing the same visual content and enriched by an auditory description of the environment. The knowledge of target words was assessed before and after the training. In addition, students' attitudes toward the technology were assessed before the training. Results indicated that students with a more positive attitude toward technology watched the videos more than those with a less positive attitude. Furthermore, students who underwent the training with 360°videos learned more words than students belonging to the control group, even after controlling for the number of videos views.

Despite research having highlighted the positive outcomes of the use of videogames to learn, their integration into school contexts remains rare. Literature recognizes the importance of user's technology acceptance to predict the intention to use and the adoption of new technology in several settings. So far, investigations have primarily focused on students' and teachers' opinions, while only a few studies have considered the role of parents. This study aimed to investigate the role of the dimensions of the technology acceptance model on parental preference (254 Italian parents) for the adoption of videogames in primary or secondary schools. Results revealed that 71% of the variance of parental acceptance of videogames is explained by perceived usefulness, perceived ease of use, learning opportunities, and parental game expertise. Initiatives aimed to increase parental understanding of videogames as educational tools are encouraged.

The literature on university–industry (U–I) links has revealed many barriers that impede U–I technology transfer. A growing number of intermediary organizations, such as Technology Transfer Offices (TTOs), University Incubators (UIs), and Collaborative Research Centres (CRCs) have been established to mitigate such barriers. While the activities and effects of such intermediaries are frequently studied, conceptual understandings of how these organizations facilitate technology transfer are lacking. Our case study of nine Italian intermediary organizations shows that different types of intermediary organizations address the same fundamental issue of bridging the different logics of academia and industry in different ways. Based on a proximity approach, we develop a theoretical framework explaining how intermediary organizations can reduce cognitive, geographical, organizational, and social distance in U–I collaborations. Intermediary organizations address different proximity dimensions depending on the prior experience of academic and industrial actors and the nature of the knowledge that is transferred. In particular, TTOs focus more on improving cognitive and organizational dimensions, whereas UIs and CRCs attempt to reduce social and geographical distance. [ABSTRACT FROM AUTHOR]

A numerical study on the 2009 Mw 6.3 L'Aquila earthquake is here presented using a high-performance spectral element (SE) code to clarify the issues regarding the generation of physics-based deterministic ground-motion scenarios in near-fault conditions and over a broad range of frequencies. Numerical simulations provide reliable ground-motion scenarios up to about 2.5 Hz in the epicentral region of the L'Aquila earthquake, as confirmed by the good agreement between the simulated peak ground velocity (PGV) wave field and the observed distribution of damage. Moreover, the synthetic waveforms fit reasonably well the strong-motion recordings within the town of L'Aquila and the surrounding areas. Nevertheless, some discrepancies are found in the upper Aterno Valley, associated to a considerable extent with the details of the rupture process. Improvements on the source modeling are obtained through the definition of stochastically (but realistically) varying kinematic source parameters that may play a relevant role in propagating high-frequency (HF) components of ground motion, poorly controlled by numerical approaches. Finally, the proposed hybrid scheme to generate broadband (BB) synthetic waveforms, by combining the low-frequency (LF) synthetics from SE simulations (f < 2.5 Hz) with HF stochastic components, appears as a powerful tool to generate ground-shaking scenarios over the whole frequency range of interest for engineering applications. [ABSTRACT FROM AUTHOR]

Range-bounding normal faults can present significant challenges for seismic exploration. This is the case of the fault system bounding the Vallo di Diano, the largest intermountain basin in the southern Apennines seismic belt. Industry reflection profiles define the large-scale structure of the basin but barely image the shallow fault system due to unfavorable topographic and near-surface conditions along the foothills of the eastern range. We present two high-resolution (HR) wide-aperture profiles recorded at the eastern margin of the basin across unreported scarps that affect Middle-Late Pleistocene alluvial fans and slope debris. The survey is aimed at identifying possible recent faulting across these challenging terrains and at understanding the relationship between shallow structures and the master range-bounding fault at depth. Common depth point processing of wide-aperture reflection data and first-arrival travel-time tomography provide detailed images of the upper 200-300 m and sounding evidence of recent activity along previously unknown splays of the fault system. These splays dissect the Mesozoic limestone bedrock and alluvial-fan sequences, affecting their depositional pattern. Very high resolution VP and reflectivity images also give hints of possible coseismic surface faulting in Holocene colluvia. These results have relevant implications for the evaluation of the seismogenic potential of the range-bounding fault system and for seismic hazard assessment of the densely urbanized Vallo di Diano basin. [ABSTRACT FROM AUTHOR]

Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling.Objective: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia.Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori.Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively.Conclusions: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia. [ABSTRACT FROM AUTHOR]

Reproductive toxicity, with its many targets and mechanisms, is a complex area of toxicology; thus, the screening and identification of reproductive toxicants is a main scientific challenge for the safety assessment of chemicals, including the European Regulation on Chemicals (REACH). Regulatory agencies recommend the implementation of the 3Rs principle (refinement, reduction, replacement) as well as of intelligent testing strategies, through the development of in vitro methods and the use of mechanistic information in the hazard identification and characterization steps of the risk assessment process. The EU Integrated Project ReProTect (6th Framework Programme) implemented an array of in vitro tests to study different building blocks of the mammalian reproductive cycle: methodological developments and results on male and female germ cells, prostate and placenta are presented.

Aim: To evaluate the incidence and duration of late-onset neutropenia (defined as an absolute neutrophil count (ANC) <1500 mm(-3) at a postnatal age of >3 wk) in a population of infants with birthweight <2000 g, and to determine whether copper deficiency, a possible cause of both anemia and neutropenia, may be associated with this complication., Methods: Complete blood cell count and differential were assessed in 247 low (LBW) and very low birthweight (VLBW) infants who were discharged after 3 wk of life. In neutropenic infants plasma copper and ceruloplasmin levels were also measured., Results: Late-onset neutropenia was detected in 11 out of 147 VLBW infants (7.5%) and in 7 out of 127 LBW infants (5.5%). A neutrophil count of <1000 mm(-3) was observed in 14 infants (5.1%). A significantly lower gestational age was found in neutropenic infants compared with non-neutropenic infants. In neutropenic infants ANCs were significantly correlated with hemoglobin and hematocrit. In addition, a significant negative correlation was found between neutrophil and reticulocyte counts. Plasma copper concentration was significantly correlated with birthweight. Oral copper sulfate was administered to infants with plasma copper concentration <50 microg dl(-1), and did not seem to affect ANC, hemoglobin, hematocrit or reticulocyte counts., Conclusion: Late-onset neutropenia appears to be a benign condition that is not associated with any particular complication and does not require specific treatment. Reference ranges after the early neonatal period and during the first few months of life in LBW and VLBW infants should probably be set at lower values.