The aim of this study was to compare effect of combinations of intravenous isotonic sodium bicarbonate (NaHCO3), acetate Ringer, lactate Ringer and small-volume hypertonic sodium chloride (NaCI) solutions along with oral electrolyte solutions (OES) on the treatment of neonatal calf diarrhea with moderate dehydration and metabolic acidosis. Thirty-two calves with diarrhea were used in the study. Calves were randomly assigned to receive acetate Ringer solution (n=8), lactate Ringer solution (n=8), isotonic NaHCO3 (n=8) and 7.2% saline solutions (n=8), and two liters of OES were administrated to all calves orally at the end of intravenous administration. Blood samples for blood gas and biochemical analyses were collected at 0 hours and at 0.5, 1, 2, 4, 6 and 24 hours intervals. All the calves had mild to moderate metabolic acidosis on admission. Increased plasma volume and sodium concentration, but decreased serum total protein were observed within 0.5 hours following administration of hypertonic 7.2% NaCI + OES, compared to other 3 groups. In conclusion, administration of intravenous hypertonic 7.2% NaCI solution in small volume along with OES provided fast and effective improvement of dehydration and acid-base abnormalities within short time in treatment of calf diarrhea with moderate dehydration and metabolic acidosis.
In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.