Abstract
Os produtores da agricultura familiar são considerados uma classe específica no sistema social, visto que reúnem características das duas outras classes sociais, assim, ao mesmo tempo em que atuam como donos dos meios de produção também são trabalhadores. Nesse sentido o sistema de criação deve evitar erros de manejo sanitário para possibilitar uma maximização do lucro para os produtores. Dentre os erros mais comuns estão os que possibilitam as doenças parasitárias constituindo um dos principais entraves quando o manejo é inadequado. Portanto, o objetivo deste trabalho foi identificar a influência das doenças parasitárias na produção de subsistência do pequeno produtor da agricultura familiar. Foram visitadas 30 propriedades rurais escolhidas aleatoriamente na região Oeste Potiguar da zona rural do município de Mossoró, onde foi realizado um diagnóstico parasitário de endoparasitos (contagem de ovos por grama e larvas) e ectoparasitos. De acordo com o local e diagnóstico parasitários foram identificados ovos de Strongyloidea em 100% (30/30) das propriedades, Moniezia 23,3% (7/30) e oocistos de Eimeria 73,3% (22/30). As larvas identificadas por propriedade foram Haemonchus spp 90% (27/30), Trichostrongylus spp 86,6% (26/30), Oesophagostomum spp 20% (6/30) e Trichuris spp 3,3% (1/30). Quanto aos ectoparasitos foram identificados por locais de coleta em Damalinia caprae 46,6% (14/30) e Rhipicephalus microplus 10% (3/30). Concluindo que os produtores pesquisados apresentam criação de rebanhos caprinos com erros de manejo sanitário, ocasionando a presença de ecto e endoparasitos que podem influenciar nos baixos lucros associado a elevados gastos.

Abstract

Abstract
Children represent a specific group of road traffic accident (RTA) victims. Performing a personal injury assessment (PIA) on a child presents a significant challenge, especially when assessing permanent disabilities and needs. However, medico-legal recommendations for PIA in such cases are lacking. The main objective of this study was to analyse the differences between children and a young- and middle-aged adult population of RTA victims to contribute to the development of relevant guidelines. Secondary objectives were to identify and characterize specifics of children's posttraumatic damages regarding: (i) temporary and permanent outcomes; and (ii) medico-legal damage parameters in the Portuguese context. We performed a retrospective study of RTA victims by comparing two groups (n = 114 each) matched for acute injury severity (SD = 0.01): G1 (children) and G2 (young- and middle-aged adults). Logistic regression was used to estimate the odds ratios. G1 presented a greater chance of evolving without or with less severe body, functional and situational outcomes (three-dimensional assessment methodology), and with lower permanent functional disability values than G2. Our findings suggest that childhood trauma generally has a better prognosis than trauma in young- and middle-aged adults. This study generated evidence on the subject and highlighted the most significant difficulties encountered by medico-legal experts when performing PIA in children.

Abstract

Abstract
Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

Abstract
AOB-1 is an Adaptive Optics (AO) facility currently designed to feed the Gemini infrared Multi Object Spectrograph (GIRMOS) on the GEMINI North 8m class telescope located in Hawaii. This AO system will be made of two AO modes. A laser tomography AO (LTAO) mode using 4 LGS (laser guide stars) and [1-3] NGS (natural guide stars) for high performance over a narrow field of view (a few arcsec). The LTAO reconstruction will benefit from the most recent developments in the field, such as the super-resolution concept for the multi-LGS tomographic system, the calibration and optimization of the system on the sky, etc. The system will also operate in Ground Layer Adaptive Optics (GLAO) mode providing a robust solution for homogeneous partial AO correction over a wide 2’ FOV. This last mode will also be used as a first step of a MOAO (Multi-object adaptive optics) mode integrated in the GIRMOS instrument. Both GLAO and LTAO modes are optimized to provide the best possible sky coverage, up to 60% at the North Galactic Pole. Finally, the project has been designed from day one as a fast-track, cost effective project, aiming to provide a first scientific light on the telescope by 2027 at the latest, with a good balance of innovative and creative concepts combined with standard and well controlled components and solutions. In this paper, we will present the innovative Phase A concepts, design and performance analysis of the two AO modes (LTAO and GLAO) of the AOB-1 project. © 2023 7th Adaptive Optics for Extremely Large Telescopes Conference, AO4ELT7 2023. All rights reserved.