SHORELINE EFFECTS OF 10-28 RUNWAY OF “MACEDONIA" AIRPORT EXTENSION INTO THE SEA LABORATORY STUDY

Προκειμένου να διερευνηθούν οι επιπτώσεις στις παρακείμενες ακτές από την επέκταση στη θάλασσα του διαδρόμου προσαπογειώσεων "10-28", του κρατικού διεθνή αερολιμένα Θεσσαλονίκης "Μακεδονία", ανατέθηκε από το Υ.ΠΕ.ΧΩ.Δ.Ε./Ε.Υ.Δ.Ε. Αεροδρομίων Βόρειας Ελλάδας, στο Εργαστήριο Λιμενικών Έργων του Εθνικού Μετσόβιου Πολυτεχνείου η έρευνα σε φυσικό προσομοίωμα. Κατά την πειραματική μελέτη της κυματικής διαταραχής στην περιοχή του αερολιμένα, παρατηρήθηκε μικρή αύξηση της κυματικής διαταραχής λόγω ανάκλασης των προσπιπτόντων κυματισμών στο μέτωπο του προβλεπόμενου έργου και προσωρινές ζώνες στασιμότητας ροής στις γωνίες του, ενώ δεν παρατηρήθηκε συστηματική και αξιοσημείωτη αλλοίωση της ακτογραμμής από την παρουσία του έργου. Οι κατασκευαστικές εργασίες (λιμενικά έργα) έχουν ξεκινήσει από το τέλος του 2006. Τα αποτελέσματα των πειραματικών μετρήσεων στο φυσικό προσομοίωμα επιβεβαιώνονται με την απόκριση του έργου στη φύση δεδομένου ότι δεν έχουν σημειωθεί μέχρι σήμερα αλλοιώσεις και διάβρωση της ακτογραμμής.Thessaloniki International Airport “Macedonia” had decided the extension of the existing Runway “10-28” by about 1 km west into the sea. In order to assure that no catastrophic erosion of the adjacent beaches will occur due to such a big construction into the sea, the Ministry of Environment, Physical Planning and Public Works assigned Laboratory of Harbour Works of National Technical University of Athens the experimental investigation of its coastal impacts. Wave perturbation, wave overtopping and beach response to the construction of the Runway were tested. The construction of the Runway in the sea has already started. The experimental results were confirmed by field observation

Detection of Fake Generated Scientific Abstracts

The widespread adoption of Large Language Models and publicly available ChatGPT has marked a significant turning point in the integration of Artificial Intelligence into people's everyday lives. The academic community has taken notice of these technological advancements and has expressed concerns regarding the difficulty of discriminating between what is real and what is artificially generated. Thus, researchers have been working on developing effective systems to identify machine-generated text. In this study, we utilize the GPT-3 model to generate scientific paper abstracts through Artificial Intelligence and explore various text representation methods when combined with Machine Learning models with the aim of identifying machine-written text. We analyze the models' performance and address several research questions that rise during the analysis of the results. By conducting this research, we shed light on the capabilities and limitations of Artificial Intelligence generated text

PREDICTION OF WAVE TRANSMISSION COEFFICIENT THROUGH FLUSSING CULVERTSUSING NEURAL NETWORKS AND EXPERIMENTAL MEASUREMENTS

Στην παρούσα εργασία, υπολογίζεται ο συντελεστής κυματικής μετάδοσης Kt, διά μέσου ενός αγωγού ανανέωσης, τοποθετημένου στο κέντρο ενός κυματοθραύστη και με το μέσο του να βρίσκεται στη στάθμη ηρεμίας ύδατος, με τη χρήση Τεχνητών Νευρωνικών Δικτύων (ΤΝΔ). Χρησιμοποιώντας πειραματικές μετρήσεις του ύψους του κυματισμού ανάντη και κατάντη του κυματοθραύστη για διάφορες κυματικές συνθήκες και για μεταβαλλόμενες γεωμετρικές διαστάσεις του αγωγού ανανέωσης, τα ΤΝΔ εκπαιδεύονται προκειμένου να υπολογίζουν το συντελεστή κυματικής μετάδοσης, όταν τα πειράματα δε θα είναι διαθέσιμα. Με κύριο στόχο την αποδοτικότητα τους, τα προτεινόμενα ΤΝΔ επιλέγονται με κριτήριο το μέσο τετραγωνικό σφάλμα και το συντελεστή συσχέτισης. Κατά το στάδιο αξιολόγησης – επαλήθευσης προκύπτει ότι τα ΤΝΔ, εφόσον ενισχυθούν με καινούρια πειραματικά δεδομένα, είναι δυνατόν να χρησιμοποιηθούν με σχετική αξιοπιστία κατά τον αρχικό σχεδιασμό των αγωγών ανανέωσης στην κατασκευή λιμένων.In the present paper, an application of composite modeling is presented, for the estimation of wave transmission through flushing culverts. Specifically, Artificial Neural Network (ANN) are training with experimental measurements for the prediction of the transmission coefficient, when these will not be available in the future. Investigating the structure, the most appropriate ANN is choosing based on the mean squared error and the coefficient of variation. Despite the limited available experimental data, showed satisfactory behavior and may be used as an alternative tool for an estimation of the transmission coefficient through flushing culverts in harbour design in combination with proposed empirical equations or numerical models

Frontal sinuses and human evolution

The frontal sinuses are cavities inside the frontal bone located at the junction between the face and the cranial vault and close to the brain. Despite a long history of study, understanding of their origin and variation through evolution is limited. This work compares most hominin species? holotypes and other key individuals with extant hominids. It provides a unique and valuable perspective of the variation in sinuses position, shape, and dimensions based on a simple and reproducible methodology. We also observed a covariation between the size and shape of the sinuses and the underlying frontal lobes in hominin species from at least the appearance of Homo erectus. Our results additionally undermine hypotheses stating that hominin frontal sinuses were directly affected by biomechanical constraints resulting from either chewing or adaptation to climate. Last, we demonstrate their substantial potential for discussions of the evolutionary relationships between hominin species. Variation in frontal sinus shape and dimensions has high potential for phylogenetic discussion when studying human evolution

Shape investigation and probabilistic representation of coastal storms. Applications to Mykonos and Barcelona

The main objective of the present work is the probabilistic representation of coastal storms’ parameters through non-parametric probability distributions that can give satisfactory estimates in the whole range of the variables’ values. Their proper probabilistic representation can provide crucial information for many applications including the probabilistic design of marine and coastal structures. Different non-parametric univariate distributions in combination with different copula types or the conditional model are applied, to check the closeness of their fit to the available bivariate coastal storm data (e.g., the storms’ maximum wave height and the associated peak wave period). Moreover, except for the classical kernel distribution, a new, recently developed Box-Cox transformed kernel method is also examined and applied. The latter has been already evaluated so far to sea states data but not to storm data. Therefore, the adopted methodology is implemented to real coastal storms in two different locations, derived from wave recordings from Mykonos (Greece) and Barcelona (Spain). Furthermore, some of the significant statistical properties of the observed coastal storms, such as the calm period, the duration, and the shape (triangular or trapezoid) are also detected. From the present analysis, the derived methodology for representing accurately coastal storms is clarified, giving thus a useful tool to engineers and researchers to consider extreme events probability of occurrence, which is essential for the design of coastal structures and consequently for the protection of coastal communities

Gas transfer under breaking waves: experiments and an improved vorticity-based model

In the present paper a modified vorticity-based model for gas transfer under breaking waves in the absence of significant wind forcing is presented. A theoretically valid and practically applicable mathematical expression is suggested for the assessment of the oxygen transfer coefficient in the area of wave-breaking. The proposed model is based on the theory of surface renewal that expresses the oxygen transfer coefficient as a function of both the wave vorticity and the Reynolds wave number for breaking waves. Experimental data were collected in wave flumes of various scales: a) small-scale experiments were carried out using both a sloping beach and a rubble-mound breakwater in the wave flume of the Laboratory of Harbor Works, NTUA, Greece; b) large-scale experiments were carried out with a sloping beach in the wind-wave flume of Delft Hydraulics, the Netherlands, and with a three-layer rubble mound breakwater in the Schneideberg Wave Flume of the Franzius Institute, University of Hannover, Germany. The experimental data acquired from both the small- and large-scale experiments were in good agreement with the proposed model. Although the apparent transfer coefficients from the large-scale experiments were lower than those determined from the small-scale experiments, the actual oxygen transfer coefficients, as calculated using a discretized form of the transport equation, are in the same order of magnitude for both the small- and large-scale experiments. The validity of the proposed model is compared to experimental results from other researchers. Although the results are encouraging, additional research is needed, to incorporate the influence of bubble mediated gas exchange, before these results are used for an environmental friendly design of harbor works, or for projects involving waste disposal at sea

Prediction of wave transmission coefficient using neural networks and experimental measurements

In the present paper, an application of composite modeling is presented, for the estimation of wave transmission through flushing culverts. Specifically, experimental measurements are used and an Artificial Neural Network (ANN) is structured for the prediction of the transmission coefficient. Measurements were obtained from physical model tests that have been conducted in 2D and 3D experimental facilities, at the Laboratory of Harbour Works, at National Technical University of Athens. The derived ANN, despite the limited available experimental data, showed satisfactory behaviour and may be used as an alternative tool for a first estimation of the transmission coefficient through flushing culverts in harbour design in combination with proposed empirical equations or numerical models. © 2015 Taylor & Francis Group, London