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Expanding the Frontiers: Revolutionizing Quantitative Data Analysis in Social Sciences with AI
No full textThis chapter aims to discuss and explore the idea of using conversational artificial intelligence (AI)’s data analysis capabilities as a new way of data analysis and interpretation, effectively substituting both statistical package software and programming for statistical analysis. It seeks to provide a comprehensive overview of how AI tools can transform the landscape of data analysis in social sciences, making advanced statistical techniques more accessible and user-friendly. The chapter delves into the practicalities of conversational AI and its applications in social science research by showing some practical examples. Moreover, it compares AI tools with traditional toolkits for statistical analysis in terms of efficiency, accuracy and ease of use. Furthermore, the chapter addresses the potential challenges and limitations of this approach, ensuring a balanced perspective while offering guidance on how social scientists can integrate AI tools into their research workflow, including tested examples that demonstrate the practical benefits and drawbacks of this technology
Exploring Sentinel-1 Radar Polarisation and Landsat Series Data to Detect Forest Disturbance from Dust Events: A Case Study of the Paphos Forest in Cyprus
No full textMonitoring forest health has become essential due to increasing pressures caused by climate change and dust events, particularly in semi-arid regions. This study investigates the impact of dust events on forest vegetation in Paphos forest in Cyprus, which is a semi-arid area prone to frequent dust storms. Using multispectral and radar satellite data from Sentinel-1 and Landsat series, vegetation responses to eight documented dust events between 2015 and 2019 were analysed, employing BFAST (Breaks For Additive Season and Trend) algorithms to detect abrupt changes in vegetation indices and radar backscatter. The outcomes showed that radar data were particularly effective in identifying only the most significant dust events (PM10 > 100 μg/m3, PM2.5 > 30 μg/m3), indicating that SAR (Synthetic Aperture Radar) is more responsive to pronounced dust deposition, where backscatter changes reflect more substantial vegetation stress. Conversely, optical data were sensitive to a wider range of events, capturing responses even at lower dust concentrations (PM10 > 50 μg/m3, PM2.5 > 20 μg/m3) and detecting minor vegetation stress through indices like SAVI, EVI, and AVI. The analysis highlighted that successful detection relies on multiple factors beyond sensor type, such as rainfall timing and imagery availability close to the dust events. This study highlights the importance of an integrated remote sensing approach for effective forest health monitoring in regions prone to dust events.This work was funded by the EXCELSIOR Teaming project (Grant Agreement No. 857510,
www.excelsior2020.eu, accessed on 30 January 2025)
Evaluation of an immersive virtual reality application for the development of critical thinking and problem-solving skills in nursing students
No full textThis study investigates the effectiveness of an immersive virtual reality (VR) application, ViReTrain, in fostering critical thinking and problem-solving skills among third-year and final-year nursing students enrolled in a palliative care class. As nursing education evolves, innovative teaching methods such as VR-based training are being explored to enhance traditional learning approaches. The study employed a one-group pretest-posttest research, with 13 undergraduate nursing students, who were selected using a convenience sample, and completed pre- and post-test questionnaires before and after engaging with the VR simulation. The study assessed the impact of the VR simulation on students’ critical thinking and problem-solving skills. Two research questions guided the study: (1) Can immersive VR improve nursing students’ critical thinking across the five steps of the nursing process? and (2) What are students’ perceptions of engagement, immersion, and usability after interacting with the VR application? Two data sources were used: pre- and post-tests measuring self-assessed critical thinking using the nursing process framework, and a questionnaire evaluating engagement, immersion, and usability after the VR experience. The findings revealed a general increase of mean scores across all five steps of the nursing process (assessment, diagnosis, planning, intervention, and evaluation). However, the only result that showed a statistically significant improvement was the 'Assessment' (t(12) = - 2.46, p = 0.03) step of the nursing process, while other steps showed a slight (but not significant) increase. Questionnaire results indicated high emotional engagement (M = 6.11/7) but low usability (M = 4.42/7), suggesting technical barriers may affect learning outcomes. The findings contribute to a deeper understanding of how immersive VR technologies can be integrated into nursing education to better prepare students in developing specific aspects of critical thinking, particularly, asessment skills, while also fostering student engagement and emotional involvement, and complex clinical situations. The study also highlights the potential of VR-enhanced training as a complementary tool for developing essential competencies in future healthcare professionals.Complete
Autonomous Vehicle Adoption: A Persona-Based Approach to Commuter Behavior and Integration in Urban Mobility
No full textA Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring
No full textThis review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years.The authors would like to acknowledge the support of the ‘ERATOSTHENES: Excellence Research Centre for Earth Surveillance and SpaceBased Monitoring of the Environment-‘EXCELSIOR’ project (https://excelsior2020.eu/, accessed on 11 October 2021), which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857510 (Call: WIDESPREAD-01-2018-2019 Teaming Phase 2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development. Also, the authors acknowledge the framework of the AI-OBSERVER project (https://ai-observer.eu/, (accessed on 10 January 2025)) titled “Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence”, which has received funding from the European Union’s Horizon Europe Framework Programme HORIZON-WIDERA-2021-ACCESS-03 (Twinning) under the Grant Agreement No. 101079468
Synthesis of PRISMA data from Landsat 8 or 9 data using machine learning tools
No full textThe PRISMA mission, operated by Italian Space Agency (ASI), provides a relatively new hyperspectral imaging sensor that captures a wide range of narrow spectral bands. With the potential to acquire detailed hyperspectral images, increasing the availability of such data could significantly benefit various environmental monitoring fields, including forestry, agriculture, and climatology. This study aims to exploit multispectral Landsat 8/9 data, which are temporally and spatially aligned with PRISMA data, to train a Neural Network (NN) model that will then be used to generate synthetic hyperspectral PRISMA data from multispectral Landsat 8/9 images. The focus is on reflectance in the VNIR range of the spectrum, with Landsat 8/9 providing 5 bands and PRISMA offering more than 50 bands. The approach explores a traditional Deep Learning technique, where PRISMA synthetic data is generated after training a NN, specifically a Multi-Layer Sequential Model with optimized parameters to directly predict PRISMA band values from the corresponding Landsat 8/9 input. Additionally, the study explores the use of advanced technology as Generative Adversarial Networks (GANs) to simulate spectral band values. Best results are found with GAN models with training R2 score around 0.8, while test scores fluctuate between 0.68-0.81. The methodology outlined in this work can serve as a benchmark for future research exploring also alternative techniques for generating synthetic hyperspectral data
Σχεδιασμός διαδρομής εσωτερικού χώρου και πλοήγηση μη-επανδρωμένων εναέριων οχημάτων (UAV)
No full textΤο θέμα της πτυχιακής "Σχεδιασμός διαδρομής εσωτερικού χώρου και πλοήγηση μη επανδρωμένων εναέριων οχημάτων (UAV)" εστιάζει στις τεχνολογικές προκλήσεις και λύσεις για την κίνηση UAV σε κλειστούς χώρους. Τα UAV, όπως τα quadrotors, έχουν γνωρίσει μεγάλη ανάπτυξη τα τελευταία χρόνια λόγω της ευελιξίας τους και των πολλαπλών εφαρμογών τους. Στους εσωτερικούς χώρους, όπου το GPS δεν λειτουργεί, απαιτούνται εξελιγμένα συστήματα πλοήγησης και σχεδιασμού διαδρομών, με στόχο την ασφαλή και αποτελεσματική κίνηση των UAV. Η εργασία επικεντρώνεται στην ανάπτυξη αλγορίθμων και τεχνικών που θα επιτρέπουν τη σωστή πλοήγηση και την αποφυγή εμποδίων σε περιβάλλοντα χωρίς εξωτερική καθοδήγηση.Complete
