635 research outputs found
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
Parallel Evolutionary Algorithms: A Review
No full textDuring recent years the area of Evolutionary Algorithms (EAs) in general and the field of Parallel Evolutionary Algorithms (PEA) in particular has matured up to a point, where the application to a complex real-world problem in some applied science is now potentially feasible and to the benefit of both fields. The availability of faster and cheaper parallel computers makes it possible to apply EAs to large populations and very complex populations. This paper presents a review of current implementation techniques for EAs on parallel hardware. 1. Introduction Evolutionary Algorithms (EAs) are stochastic search and optimization techniques which were inspired by the analogy of evolution and population genetics. They have been demonstrated to be effective and robust in searching very large, varied, spaces in a wide range of applications [14]. During recent years the area of Evolutionary Algorithms in general and the field of Parallel Evolutionary Algorithms (PEAs) in particular has matured u..
An evolutionary algorithm for a real vehicle routing problem
No full textThe NP-hard Vehicle Routing Problem (VRP) is central in the optimisation of distribution networks. Its main objective is to determine a set of vehicle trips of minimum total cost. The ideal schedule will efficiently exploit the company's recourses, service all customers and satisfy the given (mainly daily) constraints. There have been many attempts to solve this problem with conventional techniques but applied to small-scale simplified problems. This is due to the complexity of the problem and the large volume of data to be processed. Evolutionary Algorithms are search and optimization techniques that are capable of confronting that kind of problems and reach a good feasible solution in a reasonable period of time. In this paper we develop an Evolutionary Algorithm in order to solve the VRP of a specific transportation company in Volos, Greece with different vehicle capacities. The algorithm has been tested with different configurations and constraints, and proved to be effective in reaching a satisfying solution for the company's needs
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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Highlights
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We examine the effect of boards’ genetic diversity (GENETICD) on corporate ESG performance.
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ESG performance and disclosures are higher in more genetically diverse firms.
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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)
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Co-operating populations - Improving the performance of Parallel Genetic Algorithms with Co-operating populations with different evolution behaviours
No full textA framework for unifying Simple and Parallel Genetic Algorithm implementations as Co-operating Populations is presented. Using this framework, a method called Co-operating Populations with Different Evolution Behaviours (CoPDEB), for generalizing and improving the performance of Parallel Genetic Algorithms (PGAs) is also presented. The main idea of CoPDEB is to maintain a number of populations exhibiting different evolution behaviours. CoPDEB was tested on three problems (the optimization of a real function, the TSP problem and the problem of training a Recurrent Artificial Neural Network), and appears to significantly increase the problem-solving capabilities over PGAs with the same evolution behaviour on each population. This paper also studies the effect of the migration rate (Epoch) and the population size on the performance of both PGAs and CoPDEB. Keywords: Parallel genetic algorithms, co-operating populations, different evolution behaviour, epoch. 1. Introduction GAs are stoc..
Ideas in trenches: Power and polemics in Panagiotis Kondylis
No full textThe author of the article attempts to examine the positions of Panagiotis Kondylis on the intellectual history and ideas’ polemical nature that is the basic feature for understanding the configuration and development of an idea-theory in history. In order to achieve a full understanding of the specific concept of the intellectual history, first of all we have to analyze the Greek thinker's positions on the power and the way in which the search for power as a basic and irrevocable anthropological condition leads to a polemic condition within the social field. This polemic condition is also evident in the field of ideas, as ideas can be seen as the attempt to form worldviews by the respective subject or group of subjects that have the purpose of self-preservation and expanding their power. Therefore, in this article the emphasis is placed on two different areas of Kondylis' thought, the philosophy of man and then on the description of the ideas’ formulation. 
On Modelling Evolutionary Algorithm Implementations through Co-operating Populations
No full textδημοσιεύσεις μελών--ΣΤΕΦ--Τμήμα Μηχανικών Πληροφορικής, 2011In this paper we present a framework for modelling Simple and Parallel Evolutionary Algorithm implementations as Co-operating Populations. Using this framework, a method called Co-operating Populations with Different Evolution Behaviours (CoPDEB), for generalizing and improving the performance of Parallel Evolutionary Algorithms (PEAs) is also presented. The main idea of CoPDEB is to maintain a number of populations exhibiting different evolution behaviours. CoPDEB was tested on three problems (the optimization of a real function, the TSP problem and the problem of training a Recurrent Artificial Neural Network), and appears to significantly increase the problemsolving capabilities over PEAs with the same evolution behaviour on each population. This paper also studies the effect of the migration rate (Epoch) and the population size on the performance of both PEAs and CoPDEB
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