Attitude of Brazilian veterinarians in the recognition and treatment of pain in horses and cattle

Objective: The objective of this study was to assess the use of analgesics, describe the attitudes of Brazilian veterinarians towards pain relief in horses and cattle and evaluate the differences due to gender, year of graduation and type of practice. Study design: Prospective survey. Methods: Questionnaires were sent to 1000 large animal veterinarians by mail, internet and delivered in person during national meetings. The survey investigated the attitudes of Brazilian veterinarians to the recognition and treatment of pain in large animals and consisted of sections asking about demographic data, use of analgesic drugs, attitudes to pain relief and to the assessment of pain. Descriptive statistics were used to analyze frequencies. Simple post hoc comparisons were performed using the chi-square test. Results: Eight hundred questionnaires were collected, but 87 were discarded because they were incomplete or blank. The opioid of choice for use in large animals was butorphanol (43.4%) followed by tramadol (39%). Flunixin (83.2%) and ketoprofen (67.6%) were the most frequently used NSAIDs by Brazilian veterinarians. Respondents indicated that horses received preoperative analgesics for laparotomy more frequently (72.9%) than cattle (58.5%). The most frequently administered preoperative drugs for laparotomy in horses were flunixin (38.4%) and xylazine (23.6%), whereas the preoperative drugs for the same surgical procedure in cattle were xylazine (31.8%) and the local administration of lidocaine (48%). Fracture repair was considered the most painful surgical procedure for both species. Most veterinarians (84.1%) believed that their knowledge in this area was not adequate. Conclusions and clinical relevance: Although these Brazilian veterinarians thought that their knowledge on recognition and treatment of pain was not adequate, the use of analgesic in large animals was similar in Brazil to that reported in other countries. © 2013 Association of Veterinary Anaesthetists and the American College of Veterinary Anesthesia and Analgesia.

Neural networks models for wear patterns recognition of single-point dresser

Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.

Improving face recognition with multispectral fusion and support vector machines

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science

Pattern recognition issues on anisotropic smoothed particle hydrodynamics

This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.

Recognition of two morpho-types in eastern South American brackens (Pteridium-Dennstaedtiaceae-Polypodiopsida)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Deep learning architecture for gesture recognition

Pós-graduação em Engenharia Mecânica - FEG

Scanning Electron Microscopy of Male Terminalia and Its Application to Species Recognition and Phylogenetic Reconstruction in the Drosophila saltans Group

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Heterorhabditis amazonensis RSC5 (Rhabditida: Heterorhabditidae) movement and host recognition

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A brief look at the least-squares approach as a classifier applied to restricted-vocabulary speech recognition

In this letter, a speech recognition algorithm based on the least-squares method is presented. Particularly, the intention is to exemplify how such a traditional numerical technique can be applied to solve a signal processing problem that is usually treated by using more elaborated formulations.

The Cuticular Hydrocarbons Profiles in the Colonial Recognition of the Neotropical Eusocial Wasp, Mischocyttarus cassununga (Hymenoptera: Vespidae)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Frontal sinus recognition for human identification

Many methods based on biometrics such as fingerprint, face, iris, and retina have been proposed for person identification. However, for deceased individuals, such biometric measurements are not available. In such cases, parts of the human skeleton can be used for identification, such as dental records, thorax, vertebrae, shoulder, and frontal sinus. It has been established in prior investigations that the radiographic pattern of frontal sinus is highly variable and unique for every individual. This has stimulated the proposition of measurements of the frontal sinus pattern, obtained from x-ray films, for skeletal identification. This paper presents a frontal sinus recognition method for human identification based on Image Foresting Transform and shape context. Experimental results (ERR = 5,82%) have shown the effectiveness of the proposed method.