This study analyzes how spatial organization impacts science communication at the St. Petersburg Science Festival in Florida. Through map analysis, qualitative interviews, and a close reading of evaluation reports, the author determines that sponsorship, logistics, exhibitor ambience, and map usability and design are the factors most affecting the spatial performance of science. To mitigate their effects, technical communicators can identify these factors and provide the necessary revisions when considering how science is communicated to the public.

This thesis examines an automated method to detect surface deformations on automotive panels for the purpose of quality control along a manufacturing assembly line. Automation in the automotive manufacturing industry is becoming more prominent, but quality control is still largely performed by human workers. Quality control is important in the context of automotive body panels as deformations can occur along the assembly line such as inadequate handling of parts or tools around a vehicle during assembly, rack storage, and shipping from subcontractors. These defects are currently identified and marked, before panels are either rectified or discarded. This work attempts to develop an automated system to detect deformations to alleviate the dependence on human workers in quality control and improve performance by increasing speed and accuracy. Some techniques make use of an ideal CAD model behaving as a master work, and panels scanned on the assembly line are compared to this model to determine the location of deformations. This thesis presents a solution for detecting deformations of various scales without a master work. It also focuses on automated analysis requiring minimal intuitive operator-set parameters and provides the ability to classify the deformations as dings, which are deformations that protrude from the surface, or dents, which are depressions into the surface. A complete automated deformation detection system is proposed, comprised of a feature extraction module, segmentation module, and classification module, which outputs the locations of deformations when provided with the 3D mesh of an automotive panel. Two feature extraction techniques are proposed. The first is a general feature extraction technique for 3D meshes using octrees for multi-resolution analysis and evaluates the amount of surface variation to locate deformations. The second is specifically designed for the purpose of deformation detection, and analyzes multi-resolution cross-sections of a 3D mesh to locate deformations based on their estimated size. The performance of the proposed automated deformation detection system, and all of its sub-modules, is tested on a set of meshes which represent differing characteristics of deformations in surface panels, including deformations of different scales. Noisy, low resolution meshes are captured from a 3D acquisition, while artificial meshes are generated to simulate ideal acquisition conditions. The proposed system shows accurate results in both ideal situations as well as non-ideal situations under the condition of noise and complex surface curvature by extracting only the deformations of interest and accurately classifying them as dings or dents.

This thesis examines an automated method to detect surface deformations on automotive panels for the purpose of quality control along a manufacturing assembly line. Automation in the automotive manufacturing industry is becoming more prominent, but quality control is still largely performed by human workers. Quality control is important in the context of automotive body panels as deformations can occur along the assembly line such as inadequate handling of parts or tools around a vehicle during assembly, rack storage, and shipping from subcontractors. These defects are currently identified and marked, before panels are either rectified or discarded. This work attempts to develop an automated system to detect deformations to alleviate the dependence on human workers in quality control and improve performance by increasing speed and accuracy. Some techniques make use of an ideal CAD model behaving as a master work, and panels scanned on the assembly line are compared to this model to determine the location of deformations. This thesis presents a solution for detecting deformations of various scales without a master work. It also focuses on automated analysis requiring minimal intuitive operator-set parameters and provides the ability to classify the deformations as dings, which are deformations that protrude from the surface, or dents, which are depressions into the surface. A complete automated deformation detection system is proposed, comprised of a feature extraction module, segmentation module, and classification module, which outputs the locations of deformations when provided with the 3D mesh of an automotive panel. Two feature extraction techniques are proposed. The first is a general feature extraction technique for 3D meshes using octrees for multi-resolution analysis and evaluates the amount of surface variation to locate deformations. The second is specifically designed for the purpose of deformation detection, and analyzes multi-resolution cross-sections of a 3D mesh to locate deformations based on their estimated size. The performance of the proposed automated deformation detection system, and all of its sub-modules, is tested on a set of meshes which represent differing characteristics of deformations in surface panels, including deformations of different scales. Noisy, low resolution meshes are captured from a 3D acquisition, while artificial meshes are generated to simulate ideal acquisition conditions. The proposed system shows accurate results in both ideal situations as well as non-ideal situations under the condition of noise and complex surface curvature by extracting only the deformations of interest and accurately classifying them as dings or dents.

This thesis examines an automated method to detect surface deformations on automotive panels for the purpose of quality control along a manufacturing assembly line. Automation in the automotive manufacturing industry is becoming more prominent, but quality control is still largely performed by human workers. Quality control is important in the context of automotive body panels as deformations can occur along the assembly line such as inadequate handling of parts or tools around a vehicle during assembly, rack storage, and shipping from subcontractors. These defects are currently identified and marked, before panels are either rectified or discarded. This work attempts to develop an automated system to detect deformations to alleviate the dependence on human workers in quality control and improve performance by increasing speed and accuracy. Some techniques make use of an ideal CAD model behaving as a master work, and panels scanned on the assembly line are compared to this model to determine the location of deformations. This thesis presents a solution for detecting deformations of various scales without a master work. It also focuses on automated analysis requiring minimal intuitive operator-set parameters and provides the ability to classify the deformations as dings, which are deformations that protrude from the surface, or dents, which are depressions into the surface. A complete automated deformation detection system is proposed, comprised of a feature extraction module, segmentation module, and classification module, which outputs the locations of deformations when provided with the 3D mesh of an automotive panel. Two feature extraction techniques are proposed. The first is a general feature extraction technique for 3D meshes using octrees for multi-resolution analysis and evaluates the amount of surface variation to locate deformations. The second is specifically designed for the purpose of deformation detection, and analyzes multi-resolution cross-sections of a 3D mesh to locate deformations based on their estimated size. The performance of the proposed automated deformation detection system, and all of its sub-modules, is tested on a set of meshes which represent differing characteristics of deformations in surface panels, including deformations of different scales. Noisy, low resolution meshes are captured from a 3D acquisition, while artificial meshes are generated to simulate ideal acquisition conditions. The proposed system shows accurate results in both ideal situations as well as non-ideal situations under the condition of noise and complex surface curvature by extracting only the deformations of interest and accurately classifying them as dings or dents.

This thesis examines an automated method to detect surface deformations on automotive panels for the purpose of quality control along a manufacturing assembly line. Automation in the automotive manufacturing industry is becoming more prominent, but quality control is still largely performed by human workers. Quality control is important in the context of automotive body panels as deformations can occur along the assembly line such as inadequate handling of parts or tools around a vehicle during assembly, rack storage, and shipping from subcontractors. These defects are currently identified and marked, before panels are either rectified or discarded. This work attempts to develop an automated system to detect deformations to alleviate the dependence on human workers in quality control and improve performance by increasing speed and accuracy. Some techniques make use of an ideal CAD model behaving as a master work, and panels scanned on the assembly line are compared to this model to determine the location of deformations. This thesis presents a solution for detecting deformations of various scales without a master work. It also focuses on automated analysis requiring minimal intuitive operator-set parameters and provides the ability to classify the deformations as dings, which are deformations that protrude from the surface, or dents, which are depressions into the surface. A complete automated deformation detection system is proposed, comprised of a feature extraction module, segmentation module, and classification module, which outputs the locations of deformations when provided with the 3D mesh of an automotive panel. Two feature extraction techniques are proposed. The first is a general feature extraction technique for 3D meshes using octrees for multi-resolution analysis and evaluates the amount of surface variation to locate deformations. The second is specifically designed for the purpose of deformation detection, and analyzes multi-resolution cross-sections of a 3D mesh to locate deformations based on their estimated size. The performance of the proposed automated deformation detection system, and all of its sub-modules, is tested on a set of meshes which represent differing characteristics of deformations in surface panels, including deformations of different scales. Noisy, low resolution meshes are captured from a 3D acquisition, while artificial meshes are generated to simulate ideal acquisition conditions. The proposed system shows accurate results in both ideal situations as well as non-ideal situations under the condition of noise and complex surface curvature by extracting only the deformations of interest and accurately classifying them as dings or dents.

Nowadays, information is increasingly presented in the form of various graphic materials. One of the key means of information visualisation is a map. Maps are complex representations and it is necessary to comprehend several cartographic concepts and to acquire skills and strategies for their efficient use. For this reason, it is important to give maps sufficient attention in the course of education. For the education to be of high quality, it is fundamental to understand the process of the map use and factors affecting this process and its successfulness. Therefore, the general purpose of the dissertation thesis is to develop this understanding. Specifically, the thesis has four main aims. One of them is to identify a map skill level of Czech students while using thematic maps since the popularity of thematic maps is increasing together with the number of cartographic insufficiencies they contain. These insufficiencies can inter alia cause a formation of misconceptions both about the maps and phenomena and regions they display. The second main aim is to identify and describe strategies that students choose to solve tasks which require the use of a thematic map. Additionally, the sub-aim is to understand the influence of chosen factors on the level of map skills as well as on choice and efficiency...

The current research work aims at the development of Biomedical Neuroengineering tools (Biotechnologies) for the in-depth functional study, rapid diagnosis, continuous monitoring and well-timed management of acute and chronic brain disorders, of individuals that are subjected to or suffer from any kind of systemic hypoxaemia or more localized brain hypoxia; as well as the functional assessment and continuous control of adaptability during the training of “altinauts” and generally of individuals that practice activities and function within environments of increased visual-cognitive-motor response demands (a type of brain “stress test”). For this purpose, we subject the entire visuocognitive system, from the elementary sensory to the most complex cognitive level, to an experimental test of categorical discrimination of complex visuocognitive stimuli, following ultra-rapid visual stimulation that leads to a motor response upon categorization of targets (images of animals elicit productive responses) and to its suppression upon categorization of nontargets (images of nonanimals elicit inhibitory responses). The oscillatory electro-physiological responses that are concurrently recorded at the occipital-temporal-parietal brain areas are analyzed in the time-domain (<20Hz) and in the joint time-frequency domain broadband (1-60Hz) with the Continuous Wavelet Transform that optimizes the multiresolution analysis of the high frequency (≥20Hz) γ-band oscillatory activity. This visuocognitive categorization test takes place in normoxaemic as well as hypoxaemic conditions (monitored reduction in the blood oxygen saturation from ≥97% to around 80% under conditions of hypobaric hypoxia within a hypobaric chamber), in order to assess electrophysiological markers that can detect and capture in the most sensitive and dynamic way even so transient, short-living and rather mild changes in brain function. The statistical parametric analysis of the time-frequency maps and the generalized, statistically safer, method of analysis of variance have established as the most sensitive and reliable the following markers: the major deflections of the evoked potentials, the phase-coherence factor of the oscillations across single-trials and the elicited energy of the evoked/phase-locked and the induced/total oscillatory activity. These electrophysiological markers in conjunction with psychometric tests allow for the investigation of the stages/levels of the decline as well as of the compensatory reserves in the visual-perceptive and cognitive-mental brain functions in order to determine the functional sensitivity thresholds of different brain functions to hypoxia. They open up the way for the functional characterization, the diagnosis and monitoring of brain insults or other acute and chronic pathological brain conditions. / Η παρούσα ερευνητική εργασία στοχεύει στην ανάπτυξη εργαλείων Βιοϊατρικής Νευρομηχανικής (Βιοτεχνολογίες) για την σε βάθος λειτουργική μελέτη, ταχεία διάγνωση, συνεχή παρακολούθηση και έγκαιρη αντιμετώπιση οξέων και χρόνιων εγκεφαλικών διαταραχών, ατόμων που υπόκεινται σε ή πάσχουν από οιαδήποτε μορφή συστηματικής υποξαιμίας ή πιο εντοπισμένης εγκεφαλικής υποξίας, καθώς και για την λειτουργική αξιολόγηση και το συνεχή έλεγχο της προσαρμοστικότητας κατά την εξάσκηση των «υψιβατών», και γενικότερα ατόμων που ασκούν δραστηριότητες και λειτουργούν μέσα σε περιβάλλοντα αυξημένων οπτικο-γνωστικο-κινητικών απαιτήσεων (ένα είδος «στρες τεστ» για τον εγκέφαλο). Για το σκοπό αυτό υποβάλλουμε ολόκληρο το οπτικογνωστικό σύστημα, από το στοιχειώδες αισθητηριακό έως το πιο πολύπλοκο νοητικό επίπεδο, σε μια πειραματική δοκιμασία κατηγορικής διάκρισης σύνθετων οπτικογνωστικών ερεθισμάτων, μετά από υπερταχεία οπτική διέγερση που οδηγεί στην έκλυση κινητικής απάντησης κατά την κατηγοριοποίηση στόχων (εικόνες «ζώων» εκλύουν παραγωγικές αποκρίσεις) και στην καταστολή της κατά την κατηγοριοποίηση μη-στόχων (εικόνες «μη-ζώων» εκλύουν ανασταλτικές αποκρίσεις). Οι ταλαντωτικές ηλεκτροφυσιολογικές αποκρίσεις που συγχρόνως καταγράφονται στις ινιακές-κροταφικές-βρεγματικές περιοχές του εγκεφάλου αναλύονται στο πεδίο του χρόνου (<20Hz) και στο συζευγμένο χρονοφασματικό πεδίο ευρυζωνικά (1-60Hz) με το συνεχή μετασχηματισμό του κυματίου που βελτιστοποιεί την πολυφασματική ανάλυση της υψίσυχνης (≥20Hz) γ-ταλαντωτικής δραστηριότητας. Αυτή η δοκιμασία οπτικογνωστικής κατηγοριοποίησης λαμβάνει χώρα τόσο σε νορμοξαιμικές όσο και υποξαιμικές συνθήκες (ελεγχόμενη μείωση στον κορεσμό του αίματος σε οξυγόνο από ≥97% γύρω στο 80% για 15 λεπτά κάτω από συνθήκες υποβαρικής υποξίας μέσα σε υποβαρικό θάλαμο), προκειμένου να ελέγξουμε ηλεκτροφυσιολογικούς δείκτες που μπορούν να ανιχνεύσουν και να συλλάβουν με τον πιο ευαίσθητο και δυναμικό τρόπο ακόμη και τόσο βραχύβιες και σχετικά ήπιες μεταβολές της εγκεφαλικής λειτουργίας. Η στατιστική παραμετρική ανάλυση των χρονοφασματικών χαρτών και η γενικευμένη, στατιστικά πιο ασφαλής, μέθοδος ανάλυσης των διακυμάνσεων ανέδειξαν ως πλέον ευαίσθητους και αξιόπιστους τους ακόλουθους δείκτες: τις κύριες αιχμές των προκλητών δυναμικών, τον παράγοντα φασικής συνάφειας των ταλαντώσεων μεταξύ των μοναδιαίων καταγραφών και την εκλυόμενη ενέργεια των προκλητών/φασικά-κλειδωμένων και επαγόμενων/ολικών ταλαντώσεων. Οι ηλεκτροφυσιολογικοί αυτοί δείκτες σε συνδυασμό με ψυχομετρικές δοκιμασίες επιτρέπουν τη διερεύνηση των σταδίων/επιπέδων κάμψης καθώς και των αποθεμάτων αντιρρόπησης των οπτικο-αντιληπτικών και γνωστικών-νοητικών λειτουργιών του εγκεφάλου για τον καθορισμό των λειτουργικών ουδών ευαισθησίας διάφορων εγκεφαλικών λειτουργιών στην υποξία. Ανοίγουν μάλιστα το δρόμο. για το λειτουργικό χαρακτηρισμό, τη διάγνωση και την παρακολούθηση εγκεφαλικών προσβολών ή άλλων οξέων και χρόνιων παθολογικών καταστάσεων του εγκεφάλου.