702 research outputs found
UCNS3D v1.0.0 TEST problems
No full text<p>The dataset represent a collection of DATA sets for UCNS3D v1.0.0</p>
Environmental ethics: values in and duties to the natural world (summarized with commentary by Panagiotis Perros)
No full textSummarized with commentary in Greek by Panagiotis Perros.Environmental ethics stands on a frontier, as radically theoretical as it is applied. Alone, it asks whether there can be nonhuman objects of duty. Animals, plants, endangered species, ecosystems, and even Earth are progressively unfamiliar as objects of duty, and puzzles arise both for theory and practice. Answers to such questions are as urgent as any humans face, and intimately related to the four principal issues on the world agenda: peace, population, development, and environment
Selective Memory and the Legacy of Archaeological Figures in Contemporary Athens: The Case of Heinrich Schliemann and Panagiotis Stamatakis
No full textThe legacy of antiquity has loomed large over the Greek capital since thefoundation of the modern Greek state. Archaeologists have served as the main catalysts in the country’s endeavour to connect antiquity and modernity. Thus, the legacy of deceased archaeologists is tangible in many parts of Athens and a reminder of the significance of archaeology as a discipline in modern Greece. This article examines how the memory ofHeinrich Schliemann and Panagiotis Stamatakis has been appropriated (or misappropriated) in the Greek capital. They worked together to bring to light treasures from Mycenae (1876) but shared a contemptuous relationship for the remainder of their lives. We aim to understandhow society and the state treated not only the mortal remains of these two individuals but also their legacies. Hence, the abundance or absence of material evidence in Athens related to the maintenance of their memory will reveal how the archaeologists themselves worked to preserve or erase their posthumous legacy and how this has been appropriated
R-CAUSTIC: Rippling CAUSTICs underwater Image dataset
No full text<p><strong>Description</strong></p><p>Rippling caustics seem to be the main factor degrading the underwater RGB image quality and affecting the image- based 3D reconstruction process in very shallow waters. These effects are adversely affecting image matching algorithms by throwing off most of them, leading to less accurate matches and causing issues in the Simultaneous Localization and Mapping (SLAM) based navigation of the Remotely Operated Vehicles (ROV) and Autonomous Underwater Vehicles (AUV) on shallow waters. Also, they are the main cause for dissimilarities in the generated textures and orthoimages. In order to fill the gap in the literature regading underwater rippling caustics imagery with real ground truth and reference images, the first real-world underwater caustics benchmark dataset which contains 1465 underwater images is presented. Together with the RGB imagery, the corresponding generated ground truth images are delivered for facilitating the training and testing of machine learning and deep learning methods for image classification. R-CAUSTIC dataset also provides the necessary data to evaluate, at least to some extent, the performance of 3D reconstruction approaches. Data were acquired using a GoPro Hero 4 Black action camera with image dimensions of 4000 x 3000 pixels, focal length of 2.77mm and pixel size of 1.55μm and a tripod. Action cameras are widely used for underwater image acquisition. The dataset was captured in near-shore underwater sites at depths varying from 0.5 to 2m. No artificial light sources were used. Due to the wind, the turbulent surface of the water created dynamic rippling caustics on the seabed. In total 1465 RGB images were collected, separated in 7 different datasets; five of them containing stereo images, one of them tri-stereo images and one consists of multi-stereo imagery acquired in 7 different camera poses.</p><p> </p><p><strong>Publication</strong></p><p>The paper is availbale in Open Access here: https://ieeexplore.ieee.org/document/10172291</p><p><strong>If you use this dataset please cite it as R-CAUSTIC</strong> [Reference].<br>[Reference]: <strong>P. Agrafiotis, K. Karantzalos and A. Georgopoulos, "Seafloor-Invariant Caustics Removal From Underwater Imagery," in </strong><i><strong>IEEE Journal of Oceanic Engineering</strong></i><strong>, vol. 48, no. 4, pp. 1300-1321, Oct. 2023, doi: 10.1109/JOE.2023.3277168.</strong></p><p>BibTeX:</p><p>@ARTICLE{10172291,
author={Agrafiotis, Panagiotis and Karantzalos, Konstantinos and Georgopoulos, Andreas},
journal={IEEE Journal of Oceanic Engineering},
title={Seafloor-Invariant Caustics Removal From Underwater Imagery},
year={2023},
volume={48},
number={4},
pages={1300-1321},
doi={10.1109/JOE.2023.3277168}}</p><p> </p>
R-CAUSTIC: Rippling CAUSTICs underwater Image dataset
No full text<p> </p>
<h3><strong>Version 2 available! Please make sure to download the latest version of the dataset! <br></strong></h3>
<p> </p>
<p><strong>Description</strong></p>
<p>Rippling caustics seem to be the main factor degrading the underwater RGB image quality and affecting the image- based 3D reconstruction process in very shallow waters. These effects are adversely affecting image matching algorithms by throwing off most of them, leading to less accurate matches and causing issues in the Simultaneous Localization and Mapping (SLAM) based navigation of the Remotely Operated Vehicles (ROV) and Autonomous Underwater Vehicles (AUV) on shallow waters. Also, they are the main cause for dissimilarities in the generated textures and orthoimages. In order to fill the gap in the literature regading underwater rippling caustics imagery with real ground truth and reference images, the first real-world underwater caustics benchmark dataset which contains 1465 underwater images is presented. Together with the RGB imagery, the corresponding generated ground truth images are delivered for facilitating the training and testing of machine learning and deep learning methods for image classification. R-CAUSTIC dataset also provides the necessary data to evaluate, at least to some extent, the performance of 3D reconstruction approaches. Data were acquired using a GoPro Hero 4 Black action camera with image dimensions of 4000 x 3000 pixels, focal length of 2.77mm and pixel size of 1.55μm and a tripod. Action cameras are widely used for underwater image acquisition. The dataset was captured in near-shore underwater sites at depths varying from 0.5 to 2m. No artificial light sources were used. Due to the wind, the turbulent surface of the water created dynamic rippling caustics on the seabed. In total 1465 RGB images were collected, separated in 7 different datasets; five of them containing stereo images, one of them tri-stereo images and one consists of multi-stereo imagery acquired in 7 different camera poses.</p>
<p> </p>
<p><strong>Publication</strong></p>
<p>The paper is availbale in Open Access here: https://ieeexplore.ieee.org/document/10172291</p>
<p><strong>If you use this dataset please cite it as R-CAUSTIC</strong> [Reference].<br>[Reference]: <strong>P. Agrafiotis, K. Karantzalos and A. Georgopoulos, "Seafloor-Invariant Caustics Removal From Underwater Imagery," in </strong><em><strong>IEEE Journal of Oceanic Engineering</strong></em><strong>, vol. 48, no. 4, pp. 1300-1321, Oct. 2023, doi: 10.1109/JOE.2023.3277168.</strong></p>
<p>BibTeX:</p>
<p>@ARTICLE{10172291, author={Agrafiotis, Panagiotis and Karantzalos, Konstantinos and Georgopoulos, Andreas}, journal={IEEE Journal of Oceanic Engineering}, title={Seafloor-Invariant Caustics Removal From Underwater Imagery}, year={2023}, volume={48}, number={4}, pages={1300-1321}, doi={10.1109/JOE.2023.3277168}}</p>
<p> </p>
Does genetic diversity on corporate boards lead to improved environmental performance?
No full textElsevier
Journal of International Financial Markets, Institutions and Money
Volume 84, April 2023, 101756
Journal of International Financial Markets, Institutions and Money
Does genetic diversity on corporate boards lead to improved environmental performance?
Author links open overlay panelRenatas Kizys a, Emmanuel C. Mamatzakis b, Panagiotis Tzouvanas c
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https://doi.org/10.1016/j.intfin.2023.101756
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Under a Creative Commons license
open access
Highlights
•
We examine the effect of boards’ genetic diversity (GENETICD) on corporate ESG performance.
•
ESG performance and disclosures are higher in more genetically diverse firms.
•
The positive GENETICD effect on ESG performance is driven by the environmental pillar.
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Corporate carbon performance significantly improves with increases in GENETICD.
We study the effects of boards’ genetic diversity on corporate environmental performance. Using a multidimensional information set for 3690 US firms during the period from 2005 to 2019, and three different measures of genetic diversity, we find that, pursuant to the diversity theory, which posits that diversity improves the quality of management decisions and business ethics, genetic diversity leads to improved environmental performance. We also find that genetic diversity improves carbon and governance performance, and ESG disclosure. Particularly, a one percentage point increase in boards’ genetic diversity will increase the carbon performance, measured by the inverse of the carbon emissions to total assets ratio, and environmental performance by 3.54% and 5.57%, respectively. Our results remain robust to different model specifications, while also controlling for endogeneity. In terms of policy implications, results suggest that the key to tackling climate challenges is to promote boards’ genetic diversity
Dataset in support of the Southampton doctoral thesis 'The boatbuilding tradition of the Aegean during the Late Neolithic – Early Bronze Age periods. Typological classification, digital reconstruction and seakeeping assessment'
No full textDataset in support of the Southampton doctoral thesis 'The boatbuilding tradition of the Aegean during the Late Neolithic – Early Bronze Age periods. Typological classification, digital reconstruction and seakeeping assessment' Appendix D - Resistance data and Appendix C - Stability data.
This dataset is focused on two appendices:
Appendix D - Resistance data. D.1 Resistance data produced by the author via MAXSURF Resistance for this thesis.
Appendix C - Stability data
C1. Stability data – STIX and ISO criteria, produced by the author via MAXSURF Stability software for his thesis
This research was funded by Southampton Marine and Maritime Institute (SMMI), Vice-Chancellor's Scholarship, Greek Archaeological Committee UK (GACUK)
</span
Aerodynamic analysis of large wind farms using two-scale coupled modelling approaches.
No full textThe effects of turbine aerodynamics and response characteristics of the atmospheric boundary layer on the overall wind farm efficiency are investigated in this research. Various wind farm modelling strategies, which include a theoretical and several CFD models, are presented. This study consists of three main parts: (i) improve and validate an existing theoretical wind farm model, (ii) infinitely large wind farm modelling with actuator-disc and fully-resolved turbine models, and (iii) finite-size wind farm modelling with a numerical weather prediction model.
In the first part, an extended theoretical model based on a two-scale coupled momentum balance method is proposed to estimate aerodynamic effects of wind turbine towers on the performance of large wind farms. The modified theoretical model predicts that the optimal turbine spacing should increase with the value of normalised support-structure drag, as well as additional parameters describing the response characteristics of the atmospheric boundary layer to the total farm drag. The Detached-Eddy simulations of a periodic array of fully staggered actuator discs (AD) show a reasonably good agreement (within 10% in the prediction of power) with the modified theoretical model.
In the second part, a fully resolved (FR) NREL 5MW turbine model is employed in two URANS simulations (with and without the turbine tower) of a fully developed wind farm boundary layer. The FR-URANS results show stronger tower effects than both AD-RANS and theoretical model predictions, which is a strong indication of the necessity of considering turbine support structure within large wind farm models. The possibility of performing DDES is also investigated with the same FR turbine model and periodic domain setup. The results show complex turbulent flow characteristics within a large wind farm, where typical hairpin and hub vortices have been clearly captured. In addition, the computational cost of DDES has been found to be similar to URANS (for a given number of rotations), which is a positive sign for conducting DDES in
future studies.
In the third part, a numerical weather prediction model is used as a realistic farm-scale flow model to investigate how the streamwise pressure gradient, Coriolis force and acceleration/deceleration terms in the farm-scale momentum balance equation tend to change in time. The results suggest that the streamwise pressure gradient may be enhanced substantially by the resistance caused by the wind farm, whereas its influence on the other two terms appears to be relatively minor. These results suggest the importance of modelling the farm-induced pressure gradient accurately for various weather conditions in future studies of large wind farmsEngD in Renewable Energy Marine Structures (REMS
gCube 4.3.0 - G_CQLParser v. 1.2.1
No full text<p>
The gCube System - Process Optimisation - Planner Service<br>
--------------------------------------------------<br>
<br>
G_CQLParser<br>
<br>
This software is part of the gCube Framework (https://www.gcube-system.org/): an<br>
open-source software toolkit used for building and operating Hybrid Data<br>
Infrastructures enabling the dynamic deployment of Virtual Research Environments<br>
by favouring the realisation of reuse oriented policies.<br>
<br>
The projects leading to this software have received funding from a series of <br>
European Union programmes including: <br>
* the Sixth Framework Programme for Research and Technological Development - <br>
DILIGENT (grant no. 004260); <br>
* the Seventh Framework Programme for research, technological development and <br>
demonstration - D4Science (grant no. 212488), D4Science-II (grant no. <br>
239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine <br>
(grant no. 283644); <br>
* the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680), <br>
EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant <br>
no. 654119), SoBigData (grant no. 654024);<br>
<br>
<br>
Version<br>
--------------------------------------------------<br>
<br>
1.2.1-4.3.0-126705 (2017-03-19)<br>
<br>
Please see the file named "changelog.xml" in this directory for the release notes.<br>
<br>
<br>
Authors<br>
-------<br>
<br>
* Panagiotis Liakos (p.liakos-AT-di.uoa.gr),<br>
National Kapodistrian University of Athens, Department Informatics.<br>
<br>
<br>
<br>
Maintainers<br>
--------------------------------------------------<br>
<br>
* Nikolaos Laskaris (laskarisn-AT-di.uoa.gr), National and Kapodistrian University of Athens<br>
<br>
<br>
Download information<br>
--------------------------------------------------<br>
<br>
Source code is available from SVN: http://svn.research-infrastructures.eu/public/d4science/gcube/branches/search/gCQLParser/1.2/gCQLParser<br>
<br>
Binaries can be downloaded from the gCube website: https://www.gcube-system.org/<br>
<br>
<br>
Installation<br>
--------------------------------------------------<br>
<br>
Installation documentation is available on-line in the gCube Wiki: <br>
https://wiki.gcube-system.org/gcube/index.php<br>
<br>
<br>
Documentation <br>
-------------<br>
Documentation is available on-line from the Projects Documentation Wiki:<br>
<br>
https://wiki.gcube-system.org/gcube/index.php/ASL<br>
<br>
<br>
Support<br>
--------------------------------------------------<br>
<br>
Bugs and support requests can be reported in the gCube issue tracking tool:<br>
https://support.d4science.org/projects/gcube/<br>
<br>
<br>
Licensing<br>
--------------------------------------------------<br>
<br>
This software is licensed under the terms you may find in the file named "LICENSE" in this directory.<br>
</p>
gCube 4.0.0 - G_CQLParser v. 1.2.1
No full text<p>
The gCube System - Process Optimisation - Planner Service<br>
--------------------------------------------------<br>
<br>
G_CQLParser<br>
<br>
This software is part of the gCube Framework (https://www.gcube-system.org/): an<br>
open-source software toolkit used for building and operating Hybrid Data<br>
Infrastructures enabling the dynamic deployment of Virtual Research Environments<br>
by favouring the realisation of reuse oriented policies.<br>
<br>
The projects leading to this software have received funding from a series of <br>
European Union programmes including: <br>
* the Sixth Framework Programme for Research and Technological Development - <br>
DILIGENT (grant no. 004260); <br>
* the Seventh Framework Programme for research, technological development and <br>
demonstration - D4Science (grant no. 212488), D4Science-II (grant no. <br>
239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine <br>
(grant no. 283644); <br>
* the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680), <br>
EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant <br>
no. 654119), SoBigData (grant no. 654024);<br>
<br>
<br>
Version<br>
--------------------------------------------------<br>
<br>
1.2.1-4.0.0-126705 (2016-11-27)<br>
<br>
Please see the file named "changelog.xml" in this directory for the release notes.<br>
<br>
<br>
Authors<br>
-------<br>
<br>
* Panagiotis Liakos (p.liakos-AT-di.uoa.gr),<br>
National Kapodistrian University of Athens, Department Informatics.<br>
<br>
<br>
<br>
Maintainers<br>
--------------------------------------------------<br>
<br>
* Nikolaos Laskaris (laskarisn-AT-di.uoa.gr), National and Kapodistrian University of Athens<br>
<br>
<br>
Download information<br>
--------------------------------------------------<br>
<br>
Source code is available from SVN: http://svn.research-infrastructures.eu/public/d4science/gcube/branches/search/gCQLParser/1.2/gCQLParser<br>
<br>
Binaries can be downloaded from the gCube website: https://www.gcube-system.org/<br>
<br>
<br>
Installation<br>
--------------------------------------------------<br>
<br>
Installation documentation is available on-line in the gCube Wiki: <br>
https://wiki.gcube-system.org/gcube/index.php<br>
<br>
<br>
Documentation <br>
-------------<br>
Documentation is available on-line from the Projects Documentation Wiki:<br>
<br>
https://wiki.gcube-system.org/gcube/index.php/ASL<br>
<br>
<br>
Support<br>
--------------------------------------------------<br>
<br>
Bugs and support requests can be reported in the gCube issue tracking tool:<br>
https://support.d4science.org/projects/gcube/<br>
<br>
<br>
Licensing<br>
--------------------------------------------------<br>
<br>
This software is licensed under the terms you may find in the file named "LICENSE" in this directory.<br>
</p>
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