https://repository.cyi.ac.cy/handle/123456789/844 2026-05-03T11:52:04Z 2026-05-03T11:52:04Z Faka, Marina Orabi, Rahaf Tsagka, Anastasia Papageorgiou, Andreani Vassallo, Valentina Hermon, Sorin Bakirtzis, Nikolas https://repository.cyi.ac.cy/handle/CyI/2631 2026-04-07T09:36:48Z 2025-01-01T00:00:00Z

Title: Hypothetical Reconstruction for the Conservation, Preservation and Valorisation of Cultural Heritage: the Kampanopetra Basilica in Salamis, Cyprus Authors: Faka, Marina; Orabi, Rahaf; Tsagka, Anastasia; Papageorgiou, Andreani; Vassallo, Valentina; Hermon, Sorin; Bakirtzis, Nikolas Editors: Campana, S; Ferdani, D; Graf, H; Guidi, G; Hegarty, Z; Pescarin, S; Remondino, F Abstract: This article describes a digital documentation and visualisation project pursued by the Andreas Pittas Art Characterization (APAC) Laboratories of the Science & Technology in Archaeology and Culture Research Center (STARC) in the framework of the work of the Technical Committee for Cultural Heritage (TCCH), funded by the EU and implemented by United Nations Development Programme (UNDP) in Cyprus. The project’s aim was to create a hypothetical 3D (virtual reconstruction and maquette) of the Kampanopetra basilica in ancient Salamis, one of the largest Early Christian churches in Cyprus. The basilica complex is an archaeological site excavated more than 50 years ago and is in need of continuous conservation and special protection. The 3D outcome is useful to map the present state of preservation, for its future conservation and cultural valorisation. The workflow included 3D on-site documentation with image and range-based techniques combined with topographic measurements. The 3D hypothetical reconstruction model included 3 main parts: the documentation process, the authoring process and the integration of the model within the collaborative platform. The 3D reconstruction benefitted from the plans and drawings included in the archaeological report, combined with the utilisation of the 3D documentation of the site along with comparative material - namely examples of contemporary basilica structures in Cyprus and the broader Eastern Mediterranean basin. The produced Reconstruction Models are hosted in two different Web Viewers, the 3D HOP and ATON. The research team pursues key questions, research problems and innovative approaches in archaeology and cultural heritage through the application of advanced science and technology and integrated expertise in humanities, digital heritage and visualisation. The hypothetical reconstruction provides a general visualisation which can be used to inform the general public but also to provide the basis for its systematic and archaeologically detailed representation in the future.

2025-01-01T00:00:00Z Kanawade, Vijay Jokinen, Tuija Pikridas, Michael Sciare, Jean https://repository.cyi.ac.cy/handle/CyI/2604 2026-01-20T07:58:37Z 2025-12-19T00:00:00Z

Title: Application of deep learning algorithms in aerosol science Authors: Kanawade, Vijay; Jokinen, Tuija; Pikridas, Michael; Sciare, Jean Abstract: Atmospheric aerosols influence the Earth’s radiative balance through scattering/absorption and by modifying cloud microphysics, air quality and human health. However, aerosol forcing remains the largest uncertainty in our ability to accurately estimate the effective radiative forcing. This uncertainty primarily stems from the spatial variability of precursor emissions and the complexity of the physicochemical mechanisms driving particle formation and growth. New particle formation (NPF) is the largest source of aerosol numbers. NPF involves nucleation and condensation of low-volatility vapors, such as sulfuric acid or highly oxidized organic compounds. Traditionally, identifying NPF events relies on the manual visualization of aerosol number size distributions, which is both time-consuming and subjective. To overcome this challenge, we applied a deep learning-based object detection algorithm, You Only Look Once (YOLO) version 8, to automatically identify and classify NPF events from contour plots of aerosol number size distributions. The algorithm was trained and tested using data from 20 globally distributed sites across Asia, Europe, Africa, and North America. The YOLO model effectively detected the characteristic banana-shaped patterns of NPF events and estimated event start times and particle growth rates in different size ranges. Across all sites, its accuracy ranged between 0.74 and 0.97 at lower confidence levels, demonstrating strong robustness and adaptability to diverse atmospheric conditions. This study shows that YOLO can serve as a fast, accurate, and scalable tool for automated NPF detection, enabling more efficient global monitoring of aerosol formation. The approach strengthens our ability to quantify aerosols, enhance climate modeling, and deepen our understanding of the role of fine particles in Earth’s atmospheric system.

2025-12-19T00:00:00Z Marenco, Franco Kezoudi, Maria Papetta, Alkistis Sciare, Jean https://repository.cyi.ac.cy/handle/CyI/2562 2025-12-16T09:29:24Z 2024-01-01T00:00:00Z

Title: Cal/val of European spaceborne lidars leveraging the ACTRIS National Facility in Cyprus Authors: Marenco, Franco; Kezoudi, Maria; Papetta, Alkistis; Sciare, Jean

2024-01-01T00:00:00Z Karpasitis, Andreas Hadjinicolaou, Panos Zittis, Georgios https://repository.cyi.ac.cy/handle/CyI/2556 2025-12-04T10:28:10Z 2025-11-26T00:00:00Z

Title: Precipitation projections and variability in the Eastern Mediterranean, Middle East and North Africa region, based on the CMIP6 ensemble Authors: Karpasitis, Andreas; Hadjinicolaou, Panos; Zittis, Georgios Abstract: The Eastern Mediterranean, Middle East and North Africa are regions characterized by a hot and arid climate and limited water availability. This environmental hotspot is projected to experience significant impacts from climate change, particularly through shifts in precipitation patterns driven by global warming. However, future rainfall projections remain highly uncertain, and changes in extremes and variability have not be thoroughly studied. In this study, we analyze a multi-model ensemble of CMIP6 projections to assess future changes in both total and extreme precipitation relative to a recent-past reference period. To capture a range of potential outcomes, we consider two socioeconomic pathways: SSP2-4.5 and SSP5-8.5. Our results indicate a projected decline in total precipitation over the coastal regions of North Africa, while the models concurrently suggest an intensification of extreme precipitation events in these areas. Additionally, an increase in interannual rainfall variability is projected along the Mediterranean coast of North Africa. On the contrary, in the southern parts of the Arabian Peninsula, an increase in precipitation is projected, with a decrease in the intensity of the precipitation extremes. These results underscore the importance of improved quantification of rainfall variability and its implications for water resources, agriculture, and ecosystem sustainability in this climate-sensitive region.

2025-11-26T00:00:00Z