Multidisciplinary shallow underwater geophysical prospecting at Delos island

Geophysical imaging methods have been applied to reconstruct the cultural dynamics in the two different submerged sites in Delos island. The geophysical results provided useful information for understanding the complexity of the submerged archaeological sites

Enhancing risk assessment and monitoring for cultural heritage sites through data cubes: a multidimensional approach

The Eastern Mediterranean, Middle East, and North Africa (EMMENA) regions are rich in Cultural Heritage (CH) sites that have been subject to various threats, including conflicts, natural disasters, and urban development. Effective risk assessment and monitoring are essential to preserve and protect these assets. Towards that direction novel technologies and their integration can be valuable for a holistic framework of managing diverse datasets and providing a robust safeguarding of CH assets. A data cube is a multidimensional representation of data that allows for efficient and flexible analysis, designed to support online analytical processing (OLAP) and data mining. Data cubes can be regarded as a three-dimensional structure, with each cell representing a unique combination of values from the different dimensions. By creating a data cube that includes several satellite and geospatial data sources, organizations can gain a more holistic understanding of the risks and opportunities associated with CH sites as well as to identify patterns and trends that might not be apparent in individual data sets. Within this context, it becomes apparent that data cubes allow for a multidimensional view of the risk landscape and can be used to create data-driven predictive models forecasting risks and opportunities for CH assets, - in order for them to be preserved and protected for future generations. The risk assessment and monitoring framework used in this study can be easily transferred, in order to monitor CH sites in any sensitive region and can be adapted to include data from other sources and monitor different types of threats, including climate change related, environmental, and social risks

Underwater geophysical prospection in ancient Olous, Crete

We employ electrical resistivity tomography and magnetic gradiometry methods to the ultra-shallow submerged and littoral archaeological site of Olous. This allows reconstruction of the built environment that nowadays lie below the sea bottom, thus completing the respective archaeological evidence

Creating a strong rain danger map for Cyprus

In this paper we present a method for creating a strong rain danger map for Cyprus which is based only on terrain information without the need of meteorological and surface type data. The map is a combination of two methods developed at DLR over the past years for prediction of flood dangers from local strong rain events and for dangers from flash floods originating from strong rain events in upstream regions. Beside the explanation of the methods the results are presented and cross-checked with a flood event from 2003 of the Pedaios River south of Nicosia

Creating a strong rain danger map for Cyprus

In this paper we present a method for creating a strong rain danger map for Cyprus which is based only on terrain information without the need of meteorological and surface type data. The map is a combination of two methods developed at DLR over the past years for prediction of flood dangers from local strong rain events and for dangers from flash floods originating from strong rain events in upstream regions. Beside the explanation of the methods the results are presented and cross-checked with a flood event from 2003 of the Pedaios River south of Nicosia

Predictors of colorectal cancer screening awareness among people working in a hospital environment

Abstract Background Compliance rates for colorectal cancer (CRC) screening are much lower than those desired. Appropriate information on CRC risks and screening methods is supposed to stimulate motivation for screening. We aimed to identify parameters associated with the decision for CRC screening and colonoscopy in a population expected to have high awareness of disease prevention

Dynamic risk assessment and certification in the power grid : a collaborative approach

Publisher Copyright: © 2022 IEEE.The digitisation of the typical electrical grid introduces valuable services, such as pervasive control, remote monitoring and self-healing. However, despite the benefits, cybersecurity and privacy issues can result in devastating effects or even fatal accidents, given the interdependence between the energy sector and other critical infrastructures. Large-scale cyber attacks, such as Indostroyer and DragonFly have already demonstrated the weaknesses of the current electrical grid with disastrous consequences. Based on the aforementioned remarks, both academia and industry have already designed various cybersecurity standards, such as IEC 62351. However, dynamic risk assessment and certification remain crucial aspects, given the sensitive nature of the electrical grid. On the one hand, dynamic risk assessment intends to re-compute the risk value of the affected assets and their relationships in a dynamic manner based on the relevant security events and alarms. On the other hand, based on the certification process, new approach for the dynamic management of the security need to be defined in order to provide adaptive reaction to new threats. This paper presents a combined approach, showing how both aspects can be applied in a collaborative manner in the smart electrical grid.∗This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101021936. †A. Liatifis, P. Radoglou-Grammatikis and P. Sarigiannidis are with the Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani 50100, Greece - E-Mail: {aliatifis, pradoglou, psarigiannidis}@uowm.gr ‡P. Rufaza Alcazar and A. Skarmeta are with the Department of Information and Communications Engineering, University of Murcia, Murcia 30100, Spain -E-Mail: {perdro.ruzafaa, askarmeta}@um.es § D. Papamartzivanos, S. Menesidou, and T. Krousarlis are with UBITECH Limited, 26 Nikou & Despinas Pattchi, Limassol 3071, Cyprus - E-mail: {dpapamartz, smenesidou, tkrousarlis}@ubitech.com ¶M. Alberto and I. Angulo are with TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Cientifico Y Tecnologico De Bizkaia, Astondo Bidea, Edificio 700, Derio Bizkaia 48160, Spain - E-mail: {Alberto.Molinuevo, inaki.angulo}@tecnalia.com ∥A. Sarigiannidis is with the Sidroco Holdings Ltd, Nicosia, Cyprus - E-Mail: [email protected] ∗∗T. Lagkas is with the Department of Computer Science, International Hellenic University, Kavala Campus, 65404, Greece - E-Mail: [email protected] ††V. Argyriou is with the Department of Networks and Digital Media, Kingston University London, Penrhyn Road, Kingston upon Thames, Surrey KT1 2EE, UK - E-Mail: [email protected] reviewe

Enhancing risk assessment and monitoring for cultural heritage sites through data cubes: a multidimensional approach

The Eastern Mediterranean, Middle East, and North Africa (EMMENA) regions are rich in Cultural Heritage (CH) sites that have been subject to various threats, including conflicts, natural disasters, and urban development. Effective risk assessment and monitoring are essential to preserve and protect these assets. Towards that direction novel technologies and their integration can be valuable for a holistic framework of managing diverse datasets and providing a robust safeguarding of CH assets. A data cube is a multidimensional representation of data that allows for efficient and flexible analysis, designed to support online analytical processing (OLAP) and data mining. Data cubes can be regarded as a three-dimensional structure, with each cell representing a unique combination of values from the different dimensions. By creating a data cube that includes several satellite and geospatial data sources, organizations can gain a more holistic understanding of the risks and opportunities associated with CH sites as well as to identify patterns and trends that might not be apparent in individual data sets. Within this context, it becomes apparent that data cubes allow for a multidimensional view of the risk landscape and can be used to create data-driven predictive models forecasting risks and opportunities for CH assets, in order for them to be preserved and protected for future generations. The risk assessment and monitoring framework used in this study can be easily transferred, in order to monitor CH sites in any sensitive region and can be adapted to include data from other sources and monitor different types of threats, including climate change related, environmental, and social risks

Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum be available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets with centimeter resolution. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery as an alternative to costly hyperspectral sensors for drones. Combining drone-based RGB and multispectral imagery into a single cube dataset provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require the input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types with a maximum depth of six meters. The use of suitable end-member spectra, which are representative of the seafloor types of the study area, are important parameters in model tuning. The results of this study are promising, showing good correlation (R2 > 0.75 and Lin’s coefficient > 0.80) and less than half a meter average error when they are compared with sonar depth measurements. Consequently, the integration of imagery from various drone-based sensors (visible range) assists in producing detailed bathymetry maps for small-scale shallow areas based on optical modelling