Hexagonal Tilings: Tutte Uniqueness

We develop the necessary machinery in order to prove that hexagonal tilings are uniquely determined by their Tutte polynomial, showing as an example how to apply this technique to the toroidal hexagonal tiling.Comment: 12 figure

Development of a manufacturability analysis system for reinforced plastics components.

This thesis describes the research and development of a systematic and consistent methodology to perform manufacturability analysis of Reinforced Plastic Parts (RPP). The proposed methodology evaluates the part model in the early stages of the product development process considering the capabilities and constraints of available manufacturing processes, materials and tooling required in standard RPP production. Critical Manufacturing Part Features (CMPF) are identified and the relationship between the model's geometrical information, the expert's geometric reasoning, and the knowledge about the involved manufacturing processes are clarified and set together in an efficient feature-rule-based manufacturability analysis system. The prototype system named 'FEBAMAPP', combines solid modelling (SM), automatic feature recognition (AFR), object oriented programming (OOP), and a rule-based system (RBS) in order to assess the manufacturability of the proposed design. The novelty of this research is based in the use of a Face Vector (FVector) concept to transform geometrical and topological information of the solid model into a suitable input data to be used in the Neural Network Feature Recognition System. Further novelty arises from the fact that this is the first attempt to use neural networks in the recognition of 3-D features in hollow parts including the presence of fillets along the edges of the part. The manufacturability evaluation can be performed considering different combinations of materials along with different manufacturing processes giving the designer the opportunity of selecting an appropriate combination for any specific application. Promising results have been obtained during the test of the system, where 100 % recognition of trained features with 90% confidence has been achieved. Also, good results have been obtained in the recognition of non-trained features such as the Cross-Slot feature, which is recognised as a Slot feature. After automatic feature recognition, Manufacturability Analysis is focused on internal and external characteristics of the model's features, where potential manufacturing difficulties are identified and feedback in terms of design suggestions is then used to advise the design process and improve the overall manufacturability of the part. This manufacturability evaluation in terms of internal and external characteristics of the features has proved to be efficient in detecting detailed design errors that can be costly in further manufacturing stages in the product development process

Combination Strategies for Semantic Role Labeling

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful -they all outperform the current best results reported in the CoNLL-2005 evaluation exercise- but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback

Hexagonal Tilings and Locally C6 Graphs

We give a complete classification of hexagonal tilings and locally C6 graphs, by showing that each of them has a natural embedding in the torus or in the Klein bottle. We also show that locally grid graphs are minors of hexagonal tilings (and by duality of locally C6 graphs) by contraction of a perfect matching and deletion of the resulting parallel edges, in a form suitable for the study of their Tutte uniqueness.Comment: 14 figure

Nesting Success of Kemp’s Ridley Sea Turtles, Lepidochelys kempi, at Rancho Nuevo, Tamaulipas, Mexico, 1982–2004

The Kemp’s ridley sea turtle, Lepidochelys kempi, was on the edge of extinction owing to a combination of intense egg harvesting and incidental capture in commercial fishing trawls. Results from a cooperative conservation strategy initiated in 1978 between Mexico and the United States to protect and restore the Kemp’s ridley turtle at the main nesting beach at Rancho Nuevo, Tamaulipas, Mexico are assessed. This strategy appears to be working as there are signs that the species is starting to make a recovery. Recovery indicators include: 1) increased numbers of nesting turtles, 2) increased numbers of 100+ turtle nesting aggregations (arribadas), 3) an expanding nesting season now extending from March to August, and 4) significant nighttime nesting since 2003. The population low point at Rancho Nuevo was in 1985 (706 nests) and the population began to significantly increase in 1997 (1,514 nests), growing to over 4,000 nests in 2004. The size and numbers of arribadas have increased each year since 1983 but have yet to exceed the 1,000+ mark; most arribadas are still 200–800+ turtles

Design of Autonomous Medical Response Agent (AMRA) Aggregate Information Dashboard (AID)

Future astronauts in deep space missions will rely on tools and technologies empowering them to self-diagnose and self-treat medical conditions. Given communications delays and limited bandwidth in future long-duration exploration missions (LDEMs), medical decision support technologies must empower the crew to manage routine medical activities, acute medical incidents, as well as emergency medical scenarios independently from ground support.The Autonomous Medical Response Agent (AMRA) is envisioned as a digital tool enabling crew to issue medical complaints and interact with a medical decision support algorithm which develops a differential diagnosis and recommends a treatment protocol for the condition. AMRA will draw from individual crew medical history in addition to crew symptoms to more efficiently identify high-risk medical conditions. A new symptom could be indicative of a chronic condition or a normal adaptation to long-duration spaceflight, but could just as easily be indicative of an adverse vehicle condition affecting the entire crew.While real-time communication with a flight surgeon may not possible, the crew will nonetheless require a means to communicate and document both routine and emergency medical incidents to ground support. Conversely, flight surgeons and medical specialists on the ground will need to understand information such as crew vitals or responses to medical check-ups and examinations within the larger context of crew schedule, mission activities, and vehicle performance. A user interface which establishes communication protocols between an individual crew member and AMRA, as well as ground support to the crew is a significant area of research demanding input and consideration.The design of AMRA AID is intended to: a) represent routine medical activities as well as new (unplanned) medical incidents within the larger context of crew schedule and mission activities, and b) increase confidence between ground support and crew members over the course of LDEMs. Maintaining situation awareness of unplanned medical incidents between ground and crew will be a critical element within LDEMs. Two medical incidents headache and difficulty breathing are being explored within a user interface prototype which captures communications protocols between crew members and mission control, human health monitoring, vehicle or environmental monitoring, as well as crew schedule and mission activities holistically