Μελέτη ανθρωπογενούς παρέμβασης σε περιοχή "Natura" με χρήση φωτογρ/τριας, τηλεσκόπησης & Γεωγρ. Συστ. Πληροφοριών-Δέλτα Αχελώου

103 σ.Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) "Περιβάλλον και Ανάπτυξη"Η συγκεκριμένη εργασία έχει ως αντικείμενο την μελέτη ανθρωπογενούς παρέμβασης στην υγροτοπική περιοχή που σχηματίζεται στην ευρύτερη περιοχή του Δέλτα του Αχελώου. Στόχος της είναι να τονίσει την συμβολή των αναπτυξιακών εργαλείων της Τηλεπισκόπησης και των Γ.Σ.Π. σε μία τέτοια έρευνα και μελέτη.The subject of this thesis is to study the humain intervention within the wetland area formed in the greater region of Acheloos bank. It also aims to highlight the contribution of Remote Sensing and G.I.S. developmental tools in such study.Θεοφανία Ι. Παπανικολάου-Παναγέ

A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks

Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner, while preserving data privacy. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. Towards mitigating the carbon footprint of FL, the current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization, by orchestrating the computational and communication resources of the involved devices, while guaranteeing a certain FL model performance target. A penalty function is introduced in the offline phase of the GA that penalizes the strategies that violate the constraints of the environment, ensuring a safe GA process. Evaluation results show the effectiveness of the proposed scheme compared to two state-of-the-art baseline solutions, achieving a decrease of up to 83% in the total energy consumption.Comment: 6 pages, 6 figures, Accepted in IEEE PIMRC 2023 Conference, Latest revision with small corrections (typos etc.

Machine Learning-driven EEG Analysis towards brain-controlled vehicle

Με τη ραγδαία ανάπτυξη της τεχνολογίας, ο ανθρώπινος εγκέφαλος και οι υπολογιστές μπορούν να συνεργαστούν με τη βοήθεια βιοηλεκτρονικών συσκευών που χρησιμοποιούν βιο-σήματα, τα οποία ανιχνεύονται από μια συγκεκριμένη κατηγορία αισθητήρων που ονομάζονται βιο-αισθητήρες. Ένας νέος τομέας έρευνας που σχετίζεται με τη μελέτη των βιο-σημάτων έχει επικεντρωθεί ιδιαίτερα στην τεχνολογία ελεγχόμενη από το μυαλό. Πιο συγκεκριμένα, ο άμεσος έλεγχος ενός οχήματος με χρήση εγκεφαλικών κυμάτων μπορεί να βοηθήσει τα άτομα με αναπηρίες να ανακτήσουν τις οδηγικές τους ικανότητες, καθώς και να προσφέρει μια νέα επιλογή για υγιή άτομα να χειριστούν ένα όχημα. Η παρούσα πτυχιακή εργασία περιγράφει ένα όχημα ελεγχόμενο με το μυαλό (BCV) που χρησιμοποιεί την τεχνολογία Brain Computer Interface (BCI) για να ερμηνεύσει δεδομένα Ηλεκτροεγκεφαλογραφίας (EEG), να χειριστεί μια συσκευή και να αξιολογήσει τα εγκεφαλικά κύματα, προκειμένου να παραμείνει όσο το δυνατόν πιο κοντά στην ανθρώπινη φύση. Το σύστημα, το οποίο βασίζεται σε τεχνικές Μηχανικής Μάθησης, περιλαμβάνει τα ακόλουθα χαρακτηριστικά: (α) Επεξεργασία δεδομένων EEG για την ανάπτυξη διαφόρων μεθόδων εξαγωγής χαρακτηριστικών (β) χρήση κατάλληλων μηχανισμών μείωσης των διαστάσεων των δεδομένων, οι οποίοι στοχεύουν στην εύρεση συσχετισμών στα δεδομένα με σκοπό την απομάκρυνση μη κρίσιμων πληροφορίων, (γ) εφαρμογή μεθόδων ταξινόμησης που είναι σε θέση να προβλέψουν τις επιθυμητές ετικέτες που σχετίζονται με την κίνηση (αριστερό χέρι, δεξί χέρι, και τα δύο πόδια, γλώσσα), (δ) αντιστοίχηση των προβλεπόμενων σχετικά με την οδήγηση ετικετών σε πραγματικές κινήσεις (στροφή αριστερά, στροφή δεξιά, αύξηση ταχύτητας, μείωση ταχύτητας) και (ε) ενσωμάτωση των καλύτερων μοντέλων, με τη χρήση της μεθόδου ψηφοφορίας, σε ένα τελικό σύστημα BCV.Due to the rapid development of technology, the Human Brain and Computers are interfered with by Bio-Electronic devices employing bio-signals, which are detected by a particular class of sensors called bio-sensors. A new emerging research, the study of bio-signals has focused particularly on mind-controlled technology. More specifically, directly controlling a vehicle using brain waves might assist people with impairments regain their driving abilities as well as offer a fresh option for healthy people to operate a vehicle. The current thesis describes a Brain Controlled Vehicle (BCV) that uses Brain Computer Interface (BCI) technology to interpret Electroencephalography (EEG) data, operate a device, and evaluate brain waves, in order to stay as close as possible to the human nature. The system, which is based on Machine Learning techniques, comprises the following features: (a) Processing of EEG data in order to perform various feature extraction methods; (b) make use of a proper dimensionality reduction method that will find correlations in the data and discard non-critical information; (c) implement classification methods that are able to predict the desired motion related labels (left hand, right hand, both feet, tongue); (d) map the predicted motion related labels into real motions (turn left, turn right, accelerate, slow down) and (e) integrate the best models, with the use of a voting method, into a final BCV system

A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks

Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and academia. Federated Learning (FL) has emerged as a key privacy preserving decentralized AI technique. Despite efforts currently being made in FL, its environmental impact is still an open problem. Targeting the minimization of the overall energy consumption of an FL process, we propose the orchestration of computational and communication resources of the involved devices to minimize the total energy required, while guaranteeing a certain performance of the model. To this end, we propose a Soft Actor Critic Deep Reinforcement Learning (DRL) solution, where a penalty function is introduced during training, penalizing the strategies that violate the constraints of the environment, and ensuring a safe RL process. A device level synchronization method, along with a computationally cost effective FL environment are proposed, with the goal of further reducing the energy consumption and communication overhead. Evaluation results show the effectiveness of the proposed scheme compared to four state-of-the-art baseline solutions in both static and dynamic environments, achieving a decrease of up to 94% in the total energy consumption.Comment: 27 Pages Single Column, 6 Figures, Submitted for possible publication in the IEEE Transactions on Green Communications and Networking (TGCN). arXiv admin note: text overlap with arXiv:2306.1423

Impact of agricultural management on soil aggregates and associated organic carbon fractions: analysis of long-term experiments in Europe

Inversion tillage is a commonly applied soil cultivation practice in Europe, which often has been blamed for deteriorating topsoil stability and organic carbon (OC) content. In this study, the potential to reverse these negative effects in the topsoil by alternative agricultural management practices are evaluated in seven long-term experiments (running from 8 to 54 years the moment of sampling) in five European countries (Belgium, Czech Republic, Hungary, Italy and UK). Topsoil samples (0–15 cm) were collected and analysed to evaluate the effects of conservation tillage (reduced and no tillage) and increased organic inputs of different origin (farmyard manure, compost, crop residues) combined with inversion tillage on topsoil stability, soil aggregates and, within these, OC distribution using wet sieving after slaking. Effects from the treatments on the two main components of organic matter, i.e. particulate (POM) and mineral associated (MAOM), were also evaluated using dispersion and size fractionation. Reduced and no-tillage practices, as well as the additions of manure or compost, increased the aggregates mean weight diameter (MWD) (up to 49 % at the Belgian study site) and topsoil OC (up to 51 % at the Belgian study site), as well as the OC corresponding to the different aggregate size fractions. The incorporation of crop residues had a positive impact on the MWD but a less profound effect both on total OC and on OC associated with the different aggregates. A negative relationship between the mass and the OC content of the microaggregates (53–250 µm) was identified in all experiments. There was no effect on the mass of the macroaggregates and the occluded microaggregates (mM) within these macroaggregates, while the corresponding OC contents increased with less tillage and more organic inputs. Inversion tillage led to less POM within the mM, whereas the different organic inputs did not affect it. In all experiments where the total POM increased, the total soil organic carbon (SOC) was also affected positively. We concluded that the negative effects of inversion tillage on topsoil can be mitigated by reducing the tillage intensity or adding organic materials, optimally combined with non-inversion tillage methods.</p

Opportunities for Mitigating Soil Compaction in Europe-Case Studies from the SoilCare Project Using Soil-Improving Cropping Systems

Soil compaction (SC) is a major threat for agriculture in Europe that affects many ecosystem functions, such as water and air circulation in soils, root growth, and crop production. Our objective was to present the results from five short-term (25 cm) compaction using subsoiling tillage treatments to depths of 35 cm (Sweden) and 60 cm (Romania). The other SSs addressed both topsoil and subsoil SC (>25 cm, Norway and United Kingdom; >30 cm, Italy) using deep-rooted bio-drilling crops and different tillage types or a combination of both. Each SS evaluated the effectiveness of the SICSs by measuring the soil physical properties, and we calculated SC indices. The SICSs showed promising results-for example, alfalfa in Norway showed good potential for alleviating SC (the subsoil density decreased from 1.69 to 1.45 g cm(-1)) and subsoiling at the Swedish SS improved root penetration into the subsoil by about 10 cm-but the effects of SICSs on yields were generally small. These case studies also reflected difficulties in implementing SICSs, some of which are under development, and we discuss methodological issues for measuring their effectiveness. There is a need for refining these SICSs and for evaluating their longer-term effect under a wider range of pedoclimatic conditions

Mycobacterium abscessus Complex Identification with Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry

We determined that the Vitek MS Plus matrix-assisted laser desorption ionization–time of flight mass spectrometry using research-use-only (RUO) v.4.12 and in vitro -diagnostic (IVD) v.3.0 databases accurately identified 41 Mycobacterium abscessus subsp. abscessus and 13 M. abscessus subsp. massiliense isolates identified by whole-genome sequencing to the species but not the subspecies level, from Middlebrook 7H11 and Burkholderia cepacia selective agars. Peak analysis revealed three peaks potentially able to differentiate between subspecies

Soil-Improving Cropping Systems for Sustainable and Profitable Farming in Europe

Soils form the basis for agricultural production and other ecosystem services, and soil management should aim at improving their quality and resilience. Within the SoilCare project, the concept of soil-improving cropping systems (SICS) was developed as a holistic approach to facilitate the adoption of soil management that is sustainable and profitable. SICS selected with stakeholders were monitored and evaluated for environmental, sociocultural, and economic effects to determine profitability and sustainability. Monitoring results were upscaled to European level using modelling and Europe-wide data, and a mapping tool was developed to assist in selection of appropriate SICS across Europe. Furthermore, biophysical, sociocultural, economic, and policy reasons for (non)adoption were studied. Results at the plot/farm scale showed a small positive impact of SICS on environment and soil, no effect on sustainability, and small negative impacts on economic and sociocultural dimensions. Modelling showed that different SICS had different impacts across Europe-indicating the importance of understanding local dynamics in Europe-wide assessments. Work on adoption of SICS confirmed the role economic considerations play in the uptake of SICS, but also highlighted social factors such as trust. The project's results underlined the need for policies that support and enable a transition to more sustainable agricultural practices in a coherent way