Testing Flood Estimation Methods On Ancient Closed Conduits

Beside a large number of ancient tunnels in long-distance water conveyance systems toancient cities in Turkiye, five peculiar closed conduits, through which almost the entire discharge of water courses were flowing, are investigated. These are the Cevlik (Seleucia Pieria) tunnel inHatay province; vaulted structures covering the river bed in Bergama (Pergamon), in Sultanhisar(Nysa), in Acarlar near Ephesus (all four are leading examples of largest closed conduits from Roman times in the world); and the Bezirgan tunnel east of Kalkan, being an interesting example of emissary conduits draining the floods of closed basins. The hydraulic capacities of these conduits are determined; their corresponding flood return periods are estimated by four synthetic flood hydrograph methods. However, it was not possible to deduce any generalized conclusion based on the comparison of these results

Learning and Using Context on a Humanoid Robot Using Latent Dirichlet Allocation

2014 Joint IEEE International Conferences on Development and Learning and Epigenetic Robotics (ICDL-Epirob), Genoa, Italy, 13-16 October 2014In this work, we model context in terms of a set of concepts grounded in a robot's sensorimotor interactions with the environment. For this end, we treat context as a latent variable in Latent Dirichlet Allocation, which is widely used in computational linguistics for modeling topics in texts. The flexibility of our approach allows many-to-many relationships between objects and contexts, as well as between scenes and contexts. We use a concept web representation of the perceptions of the robot as a basis for context analysis. The detected contexts of the scene can be used for several cognitive problems. Our results demonstrate that the robot can use learned contexts to improve object recognition and planning.Scientific and Technological Research Council of Turkey (TUBiTAK

Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation

In this article, we formalize and model context in terms of a set of concepts grounded in the sensorimotor interactions of a robot. The concepts are modeled as a web using Markov Random Field, inspired from the concept web hypothesis for representing concepts in humans. On this concept web, we treat context as a latent variable of Latent Dirichlet Allocation (LDA), which is a widely-used method in computational linguistics for modeling topics in texts. We extend the standard LDA method in order to make it incremental so that (i) it does not re-learn everything from scratch given new interactions (i.e., it is online) and (ii) it can discover and add a new context into its model when necessary. We demonstrate on the iCub platform that, partly owing to modeling context on top of the concept web, our approach is adaptive, online and robust: It is adaptive and online since it can learn and discover a new context from new interactions. It is robust since it is not affected by irrelevant stimuli and it can discover contexts after a few interactions only. Moreover, we show how to use the context learned in such a model for two important tasks: object recognition and planning.Scientific and Technological Research Council of TurkeyMarie Curie International Outgoing Fellowship titled “Towards Better Robot Manipulation: Improvement through Interaction

A Probabilistic Concept Web on a Humanoid Robot

It is now widely accepted that concepts and conceptualization are key elements towards achieving cognition on a humanoid robot. An important problem on this path is the grounded representation of individual concepts and the relationships between them. In this article, we propose a probabilistic method based on Markov Random Fields to model a concept web on a humanoid robot where individual concepts and the relations between them are captured. In this web, each individual concept is represented using a prototype-based conceptualization method that we proposed in our earlier work. Relations between concepts are linked to the cooccurrences of concepts in interactions. By conveying input from perception, action, and language, the concept web forms rich, structured, grounded information about objects, their affordances, words, etc. We demonstrate that, given an interaction, a word, or the perceptual information from an object, the corresponding concepts in the web are activated, much the same way as they are in humans. Moreover, we show that the robot can use these activations in its concept web for several tasks to disambiguate its understanding of the scene

A Probabilistic Concept Web on a Humanoid Robot

It is now widely accepted that concepts and conceptualization are key elements towards achieving cognition on a humanoid robot. An important problem on this path is the grounded representation of individual concepts and the relationships between them. In this article, we propose a probabilistic method based on Markov Random Fields to model a concept web on a humanoid robot where individual concepts and the relations between them are captured. In this web, each individual concept is represented using a prototype-based conceptualization method that we proposed in our earlier work. Relations between concepts are linked to the cooccurrences of concepts in interactions. By conveying input from perception, action, and language, the concept web forms rich, structured, grounded information about objects, their affordances, words, etc. We demonstrate that, given an interaction, a word, or the perceptual information from an object, the corresponding concepts in the web are activated, much the same way as they are in humans. Moreover, we show that the robot can use these activations in its concept web for several tasks to disambiguate its understanding of the scene

Co-learning nouns and adjectives

In cognitive robotics community, categories belonging to adjectives and nouns have been learned separately and independently. In this article, we propose a prototype-based framework that conceptualize adjectives and nouns as separate categories that are, however, linked to and interact with each other. We demonstrate how this co-learned concepts might be useful for a cognitive robot, especially using a game called "What object is it?" that involves finding an object based on a set of adjectives

Learning and Using Context on a Humanoid Robot Using Latent Dirichlet Allocation

In this work, we model context in terms of a set of concepts grounded in a robot's sensorimotor interactions with the environment. For this end, we treat context as a latent variable in Latent Dirichlet Allocation, which is widely used in computational linguistics for modeling topics in texts. The flexibility of our approach allows many-to-many relationships between objects and contexts, as well as between scenes and contexts. We use a concept web representation of the perceptions of the robot as a basis for context analysis. The detected contexts of the scene can be used for several cognitive problems. Our results demonstrate that the robot can use learned contexts to improve object recognition and planning

Odontoid Screw Fixation Repair of Type II Odontoid Fractures

Type II odontoid fractures are most commen types of upper cervical spinal injuries. It should be treated surgically because of the risk of sudden respiratory arrest and neurological deficits related to posterior slippage of odontoid body. Transoral odontoid resection and upper cervical stabilisation via posterior approach have been accepted as surgical treatment modalities for many years. Odontoid screwing method has been started as a a new surgical method with easy performance, low complication rate and cost-effect recently. Furthermore it has the advantage of non - limiting the rotational movement of the head. We operated on ten cases with type II odontoid fracture, four of them were female and six were male. Mean age was 61.8. No peroperative and postoperative complications were detected, and all of the cases were mobilised and discharged on the next day following surgery. Odontoid screwing method can be accepted as first choice for surgical treatment of type II odontoid fractures with compact transvers ligament

Efficacy, Immunogenicity, and Safety of the Two-Dose Schedules of TURKOVAC versus CoronaVac in Healthy Subjects: A Randomized, Observer-Blinded, Non-Inferiority Phase III Trial

We present the interim results of the efficacy, immunogenicity, and safety of the two-dose schedules of TURKOVAC versus CoronaVac. This was a randomized, observer-blinded, non-inferiority trial (NCT04942405). Volunteers were 18-55 years old and randomized at a 1:1 ratio to receive either TURKOVAC or CoronaVac at Day 0 and Day 28, both of which are 3 mu g/0.5 mL of inactivated severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) adsorbed to aluminum hydroxide. The primary efficacy outcome was the prevention of polymerase chain reaction (PCR)confirmed symptomatic coronavirus disease 2019 (COVID-19) at least 14 days after the second dose in the modified per-protocol (mPP) group. Safety analyses were performed in the modified intention-to-treat (mITT) group. Between 22 June 2021 and 7 January 2022, 1290 participants were randomized. The mITT group consisted of 915 participants, and the mPP group consisted of 732 participants. During a median follow-up of 90 (IQR 86-90) days, the relative risk reduction with TURKOVAC compared to CoronaVac was 41.03% (95% CI 12.95-60.06) for preventing PCR-confirmed symptomatic COVID-19. The incidences of adverse events (AEs) overall were 58.8% in TURKOVAC and 49.7% in CoronaVac arms (p = 0.006), with no fatalities or grade four AEs. TURKOVAC was non-inferior to CoronaVac in terms of efficacy and demonstrated a good safety and tolerability profile