Archivio Istituzionale della Ricerca- Università degli Studi di Foggia
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Book of abstract - ICS 2025
No full textThe Book of Abstracts of the International Conference “ICS Exchange 2025” brings together the scientific contributions presented during the homonymous international event dedicated to educational innovation, the digital transformation of teaching, and the promotion of inclusive practices within learning environments.
The studies presented address, through an interdisciplinary and comparative approach, highly relevant topics such as the use of artificial intelligence in education, the application of augmented and virtual reality, gamification and serious games as learning tools, the development of new digital and socio-emotional competences, educational inclusion, and accessibility in school and university contexts.
The volume reflects the commitment of the international scientific community to defining innovative pedagogical models, sustainable training policies, and technological instruments that support the quality of teaching and learning. The Book of Abstracts ICS Exchange 2025 thus represents an academic and institutional publication of significant relevance, aimed at fostering the dissemination of best practices and the construction of shared knowledge on the evolving landscape of education in the digital age
A Framework for Data Lifecycle Model Selection
No full textThe selection of Data Lifecycle Models (DLMs) in complex data management scenarios necessitates finding a balance between quantitative and qualitative characteristics to ensure regulation, improve performance, and maintain governance requirements. In this context, an interactive web application based on AHP-Express has been developed as a user-friendly tool to facilitate decision-making processes related to DLM. The application facilitates customized decision matrices, organizes various expert interviews with distinct weights, calculates local and global priorities, and delivers final DLM rankings by consolidating sub-criteria scores into weighted macro-category values, accompanied by graphical representations. Key functions encompass consistency checks, sensitivity analysis for macro-category weight variations, and graphical representations (bar charts, radar maps, sensitivity charts) that emphasize strengths, shortcomings, and the robustness of rankings. In a suggested application for sensor-based artifact monitoring at the Museo del Carbone, the tool swiftly selected the most appropriate DLM as the leading contender, exhibiting consistent performance across diverse weight scenarios. The results of the Museo del Carbone case validate that AHP-Express facilitates rapid, transparent, and reproducible DLM selection, reducing cognitive load while maintaining scientific rigor. The tool’s modular architecture and visualization features enable educated decision making for various data management issues
Oltre le false credenze sull’emancipazione femminile. Lavoro, indipendenza e alfabetizzazione finanziaria: gli immaginari di ragazzi e ragazze
No full textQuality of life after ICU: 1-year follow-up in patients with and without COVID
No full textBackground: The purpose of this study was to perform a 1-year follow-up after ICU discharge and evaluate post-intensive care syndrome (PICS) in both COVID (GroupCov) and NON COVID (GroupNCov) patients. Methods: All consecutive patients discharged from our Intensive Care Unit (ICU) from June to December 2022 were prospectively screened. Scheduled in-person visits were carried on 3, 6, and 12 months after ICU discharge to evaluate physical, cognitive, and mental health status using different scale evaluations (SF-36, Barthel Index, ISI score, PCL-5 score, MNA-sf score, Fatigue Severity Score, MoCA Test, HADS and GDS) by means of standardized questionnaires. Results: Eighty patients (50 GroupCov vs 30 GroupNCov) were initially included, but some patients did not attend all follow-up visits. At 1-year follow-up, 60 patients (30 COVID-19 and 30 non-COVID) completed all evaluations. Both groups showed PICS, but GroupCov had a better nutritional status, better outcomes in physical evaluations, and a better perception of Quality of Life (QoL) and mental health status, but a worse cognitive assessment in the MoCA Test. Moreover, heterogeneity analysis showed that GroupNCov patients had the same trend during follow-up, while in GroupCov different trends were observed over time, especially a worse nutritional state, often found in older patients, that was related to a longer hospital stay and worse psychophysical outcomes. Conclusions: This study shows that PICS in SARS-COV2 patients is not always homogeneous, and that different clusters of psychophysical patterns may develop over time. Although our study was only observational, it seems from our preliminary results that performing a follow-up could be the basis for a secondary prevention and to develop new therapeutic strategies after patients discharge from ICU
Interconnection between emotion and cognition: implications for human and environmnetal well-being
No full textThis dissertation explores the interconnection between emotion and cognition, a central
theme in contemporary neuroscience and psychology. The overarching framework of the
thesis weaves together state-of-the-art research with experimental studies conducted during
three years of doctoral work, offering an integrated analysis that bridges theoretical and
applied approaches. The first chapter provides a comprehensive theoretical discussion of the
constructs of emotion and cognition. It concludes with a pioneering study that characterized
the doctoral research, investigating the associations between fluid and crystallized
intelligence, emotional recognition, and measures of psychological well-being. The findings
highlight the complexity of interactions between cognitive abilities, emotional competencies,
and their impact on individual well-being, offering new perspectives for the analysis of socio-
emotional skills.
The second chapter focuses on the critical role of emotional regulation as a foundation for
psychological well-being. This section includes a literature review published by Bonvino et
al. (2023), which examines the impact of the COVID-19 pandemic on emotional regulation in
Italian children. The synthesized findings underscore the vital importance of emotions and
regulation strategies during crises, contributing to a deeper understanding of psychological
vulnerabilities and resources during developmental stages.
The third chapter presents the central research study of this dissertation, which investigates
the effect of regret anticipation on promoting pro-environmental behaviors. Through an
innovative experimental design, the research demonstrates that the anticipatory activation of
emotions such as regret can be an effective tool for fostering ecologically sustainable choices,
with significant implications for behavioral change initiatives.
In the concluding chapters, the thesis reflects on the broader implications of the findings and
outlines future research directions, advocating for a paradigm shift in the understanding of the
emotion-cognition relationship. Specifically, it proposes moving beyond the rationalist
perspective of cogito, ergo sum to an integrated approach encapsulated by sentio et cogito,
ergo sum. This paradigm shift emphasizes the centrality of emotions as an essential and
complementary component of human thought, paving the way for new avenues in the
research and practice of psychological and cognitive sciences for the promotion of individual and environmental well-being in an ecosystemic logic
CNN-AutoMIC: Combining convolutional neural network and autoencoder to learn non-linear features for KNN-based malware image classification
No full textMalware refers to malicious software or a component of software intended for malicious purposes. The manual analysis and detection of malicious software is challenging due to its complexity. Thus, several automated solutions have become popular for real-time malware detection. A spread-out approach consists of generating images from the samples bytecode and giving them to convolutional neural networks (CNNs), which are used either as classifiers or feature extractors for further classification algorithms. These systems perform extremely well when trained and tested on partitions of the same dataset. However, cross-dataset tests and malware detection verification on emerging real-world samples are required in the real-world context. This is a crucial challenge when probing the robustness of the systems and models. This paper proposes CNN-AutoMIC,a robust automated approach to extract features from malware images. CNN-AutoMIC employs a specific CNN architecture to extract features, followed by an autoencoder-based compressor that reduces features to two fundamental components. The two-dimensional projection of these components is the basis of the predictions performed by the K-nearest neighbors (K-NN) algorithm. Moreover, the observable placement of new samples on the obtained scatter plot makes it possible to explain why the AI-based system produced a certain prediction. It was benchmarked against several CNN-based models and a Vision Transformer. They were trained on the Malevis dataset and cross-dataset evaluated on four different real-world datasets. CNN-AutoMIC outperformed the competitors for each classification performance metric, while requiring a reasonable training and prediction time. In addition, it achieves a promising Akaike information criterion (AIC) score, indicating its efficiency in terms of model complexity
Salinity tolerance in the halophyte species Cakile maritima from the Apulia region, southern Italy.
No full textIntroduction: Cakile maritima is a succulent halophyte from the Brassicaceae
family, commonly found along sandy coasts. Understanding its response
mechanisms to sodium excess is crucial for its exploitation under sustainable
biosaline farming.
Methods: For the first time, this research investigated the pinnatifid C. maritima
population from the Apulia region (Italy) grown under varying levels of NaCl (0
-T0, 100 -T100 and 400 -T400 mM NaCl).
Results: The T100 plants showed higher leaf area (LA) and specific leaf area (SLA)
compared to T0, with a slight reduction in succulence index (SI). In T400 plants, a
reduction in shoot and root fresh weight, water content (WC), leaf dry weight, LA,
and SLA was observed, alongside an increase in SI and dry matter concentration.
No changes were detected in leaf Na and Cl concentrations, whereas T400
stems accumulated Na. Leaf K, Mg, and Ca concentrations remained stable. The
operating efficiency of PSII (FPSII) was similar across treatments. In salt-exposed
plants, the decrease of Fv’/Fm’ was counteracted by an improvement of qP, with
carotenoids and anthocyanins appearing to be involved in photoprotection. Saltexposed
plants maintained stomatal opening (gs), allowing a higher CO2
assimilation rate (An), especially in T100. Despite unimpaired An, T400 plants
exhibited reduced canopy-level photosynthesis due to lower LA, leading to
reduced shoot biomass. Among antioxidants, ascorbic acid and anthocyanins
were effective in improving the antioxidative defence of T400 plants.
Discussion: The results indicate that C. maritima employs a complex protective
strategy involving morphological adjustments, selective ion accumulation,
efficient photoprotection, maintained gas exchange, and a potent antioxidant
system to mitigate salinity stress, demonstrating its strong potential for
biosaline agriculture
