Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

The introduction of navigation in liver surgery in Brazil

The authors thoroughly report the development, the technical aspects and the performance of the first navigated liver resections, by laparotomy and laparoscopy, in Brazil, done at the National Cancer Institute, Ministry of Health, using a surgical navigator.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Follicle and corpus luteum size and vascularity as predictors of fertility at the time of artificial insemination and embryo transfer in beef cattle

Abstract:Two ultrasound based fertility prediction methods were tested prior to embryo transfer (ET) and artificial insemination (AI) in cattle. Female bovines were submitted to estrous synchronization prior to ET and AI. Animals were scanned immediately before ET and AI procedure to target follicle and corpus luteum (CL) size and vascularity. In addition, inseminated animals were also scanned eleven days after insemination to target CL size and vascularity. All data was compared with fertility by using gestational diagnosis 35 days after ovulation. Prior to ET, CL vascularity showed a positive correlation with fertility, and no pregnancy occurred in animals with less than 40% of CL vascularity. Prior to AI and also eleven days after AI, no relationship with fertility was seen in all parameters analyzed (follicle and CL size and vascularity), and contrary, cows with CL vascularity greater than 70% exhibit lower fertility. In inseminated animals, follicle size and vascularity was positive related with CL size and vascularity, as shown by the presence of greater CL size and vascularity originated from follicle with also greater size and vascularity. This is the first time that ultrasound based fertility prediction methods were tested prior to ET and AI and showed an application in ET, but not in AI programs. Further studies are needed including hormone profile evaluation to improve conclusion.

Plant height reduction in populations of triticale (X triticosecale Wittmack) by induced mutations and artificial crosses

Induced mutations by gamma radiation (0, 5, 10, 20 and 40 kR doses) and reciprocal crosses were tested as mechanisms of enhancing genetic variability for plant height in two triticale cultivars, BR4 and EMBRAPA18. The reciprocal crosses and all doses of radiation showed similar increase in genetic amplitude for this trait, being suitable for increasing variability in breeding programs. Genotypes showed different responses as the gamma ray doses were increased, expressing shorter plant height. The decision of using induced mutations or artificial crosses depends on the resources available and the selection method to be used

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.