Foram determinados os principais parâmetros morfodinâmicos da praia de Imbé, através da execução de nove perfis praiais entre 18/05/89 e 21/04/90. Os dados foram coletados visualmente e com apoio de instrumental oceanográfico, e em cada perfil foram definidas as características do tamanho (Mz) e volume de sedimentos, declividade do perfil praial (m), velocidade e direção dos ventos e das correntes litorâneas, altura da onda (Hb) e profundidade (db) na linha de rebentação, bem como o período (T) e ângulo de incidência da onda (α), “surf scaling parameter” (€) e o coeficiente de rebentação (βb). A análise destes parâmetros define o estágio morfodinâmico do perfil praial, em Imbé, como intermediário a dissipativo e com baixa variabilidade temporal. Estas condições são confirmadas pelo tipo de rebentação deslizante e mergulhante. Os resultados do estudo sobre as correntes litorâneas sugerem que é possível a predição de suas velocidades.
Waves data and a simple mathematical model was used to determine the longshore sediments transport at the Rio Grande do Sul coast. The model was developed by the application of the Energy Flow Method (U.S. Army, 1984), to estimate quantitatively the littoral drift potential for different segments of the coast. The coast was divided in segments or straight lines, which has the same orientation. The coefficient of proportionality between the wave energy and the sediment transport was obtained using beach profile surveys and sediment grain size. The largest drift taxes were of the order of -2.900.000 m3/year and -2.600.000 m3/year for NE, along of the Hermenegildo beach and Between Cassino and Solidão Beach, respectively. reasonable agreement exists between coastal erosion observations in previous studies and the result obtained by this model, which suggests that a predictive capability has been established.
Lagoa dos Patos in southern Brazil is part of the largest lagoonal system in South America. The Holocene lagoonal sediments of the Lagoa dos Patos, mostly muds, have an average thickness of about 6 m as determined by 297 km of 7.0 kHz echograms. Holocene muddy sedimentation developed over a probable Upper Pleistocene coastal plain, whose surface has a subbottom reflector that could not be penetrated by the energy of a 7.0 kHz seismic wave. The characteristics of this reflecting surface suggest indurated Pleistocene muds and/or sediments that are coarser than the overlying muddy deposits of Holocene lagoon. Based on stratigraphic correlation and the local sea level curve, we estimate that Holocene sedimentation started about 8.0 ka ago. This yields an average deposition rate of 0.75 mm/yr. A broadly comparable average rate of 0.52 ± mm/yr was obtained for cored intervals between 14C samples from the upper part of these muddy Holocene lagoon deposits. These long-term sedimentation rates are much slower than rates based on two determinations of 210Pb for surface muds deposits in the last 150 years, which yielded values of 3.5 and 8.3 mm/yr. Quite possibly the high short-term rates may be the result of more rapid lagoonal sedimentation related to deforestation of the watershed of the lagoon and other impact types related with human activities during the 150 years of European colonization in the Rio Grande do Sul state. Also, the aim of this study is to identify present and possible future environmental problems related with high lagoonal sedimentation rates such as the water quality, port dredge and the presence of mud deposits on the oceanic beach.
Background: Eggs have acquired a greater importance as an inexpensive and high-quality protein. The Brazilian egg industry has been characterized by a constant production expansion in the last decade, increasing the number of housed animals and facilitating the spread of many diseases. In order to reduce the sanitary and financial risks, decisions regarding the production and the health status of the flock must be made based on objective criteria. The use of Artificial Neural Networks (ANN) is a valuable tool to reduce the subjectivity of the analysis. In this context, the aim of this study was at validating the ANNs as viable tool to be employed in the prediction and management of commercial egg production flocks.Materials, Methods & Results: Data from 42 flocks of commercial layer hens from a poultry company were selected. The data refer to the period between 2010 and 2018 and it represents a total of 600,000 layers. Six parameters were selected as “output” data (number of dead birds per week, feed consumption, number of eggs, weekly weight, weekly egg production and flock uniformity) and a total of 13 parameters were selected as “input” data (flock age, flock identification, total hens in the flock, weekly weight, flock uniformity, lineage, weekly mortality, absolute number of dead birds, eggs/hen, weekly egg production, feed consumption, flock location, creation phase). ANNs were elaborated by software programs NeuroShell Predictor and NeuroShell Classifier. The programs identified input variables for the assembly of the networks seeking the prediction of the variables called outgoing that are subsequently validated. This validation goes through the comparison between the predictions and the real data present in the database that was the basis for the work. Validation of each ANN is expressed by the specific statistical parameters multiple determination (R2) and Mean Squared Error (MSE). For instance, R2 above 0.70 expresses a good validation. ANN developed for the output variable “number of dead birds per week” presented R2= 0.9533 and MSE= 256.88. For “feed consumption”, the results were R2= 0.7382 and MSE= 274.56. For “number of eggs (eggs/hen)”, the results were R2= 0.9901 and MSE= 172.26. For “weekly weight”, R2= 0.9712 and MSE= 11154.41. For “weekly egg production”, R2= 0.8015 and MSE= 72.60. For “flock uniformity”, R2= -2.9955 and MSE= 431.82.Discussion: From the six ANN designed in this study, in five it was possible to validate the predictions by comparing predictions with the real data. In one output parameter (“flock uniformity”), it was not possible to have adequate validation due to insufficient data in our database. For “number of dead birds per week”, “feed consumption”, “weekly weight” and “uniformity”, the most important variable was “flock age” (27.5%, 52.5%, 55.2% and 37.9%, respectively). For “number of eggs (eggs/hen)”, “uniformity” (52.1%) was the most relevant variable for prediction. For “weekly egg production”, “flock age” and “number of eggs (eggs/hen)” were the most important zootechnical parameters, both with a relative contribution of 38.2%. The results showed that even with the use of a robust tool such as ANNs, it is necessary to have well-noted and clear information that expresses the reality of the flocks. In any case, the results presented allow us to state that ANNs are capable for the management of data generated in a commercial egg production facility. The process of evaluation of these data would be improved if ANNs were routinely used by the professionals linked to this activity.
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