Predicting Mean and Bankfull Discharge from Channel Cross-Sectional Area by Expert and Regression Methods

This study employed four methods—non-linear regression, fuzzy logic (FL), artificial neural networks (ANNs), and genetic algorithm (GA)-based nonlinear equation—for predicting mean discharge and bank-full discharge from cross-sectional area. The data compiled from the literature were separated into two groups—training (calibration) and testing (verification). Using training data sets, the methods were calibrated to obtain optimal values of the coefficients of the non-linear regression method; optimal number of fuzzy subsets, their base widths and fuzzy rules for the fuzzy method; and the optimal number of neurons in the hidden layer, the learning rate and momentum factor values for the ANN model. The GA-based method employed 100 chromosomes in the initial gene pool, 80% cross over rate and 4% mutation rate in determining the optimal values of the coefficients of the constructed nonlinear equation. The calibrated methods were then applied to the test data sets. The test results showed that the non-linear regression, ANN and GA-based methods were comparable in predicting the mean discharge while the fuzzy method produced high errors and low accuracy. The GA-based method had the highest accuracy of 75%. In terms of predicting bankfull discharge, all methods produced satisfactory results, although the fuzzy method had the lowest accuracy of 33%. The results of sensitivity analysis, which is limited to the GA-based and nonlinear regression methods, showed that the GA-based method calibrated with low bankfull discharge values can be successfully applied to predict high bankfull discharge values. This has important implications for predicting bankfull rates at ungauged sites. On the other hand, the sensitivity analysis results also showed that both the non-linear regression and GA-based methods have poor extrapolation capability for predicting mean discharge data.     

Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flames

The formation of soot in premixed flames of methane, ethane, propane, and butane was studied at three different equivalence ratios. Soot particle sizes, number densities, and volume fractions were determined using classical light scattering measurement techniques. The experimental data revealed that the soot properties were sensitive to the fuel type and combustion parameter equivalence ratio. Increase in equivalence ratio increased the amount of soot formed for each fuel. In addition, methane flames showed larger particle diameters at higher distances above the burner surface and propane, ethane, and butane flames came after the methane flames, respectively. Three-layer, feed-forward type artificial neural networks having seven input neurons, one output neuron, and five hidden neurons for soot particle diameter predictions and seven hidden neurons for volume fraction predictions were used to model the soot properties. The network could not be trained and tested with sufficient accuracy to predict the number density due to a large data range and greater uncertainty in determination of this parameter. The number of complete data set used in the model was 156. There was a good agreement between the experimental and predicted values, and neural networks performed better when predicting output parameters (i.e. soot particle diameters and volume fractions) within the limits of the training data.     

Forecasting Ambient Air SO2 Concentrations Using Artificial Neural Networks

An Artificial Neural Networks (ANNs) model is constructed to forecast SO₂ concentrations in Izmir air. The model uses meteorological variables (wind speed and temperature) and measured particulate matter concentrations as input variables. The correlation coefficient between observed and forecasted concentrations is 0.94 for the network that uses all three variables as input parameters. The root mean square error value of the model is 3.60 μg/mt³. Considering the limited number of available input variables, model performances show that ANNs are a promising method of modeling to forecast ambient air SO₂ concentrations in Izmir.     

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg–Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.     

Analysis of clonal diversity in mouse immunoglobulin heavy chain genes selected for size of the antigen combining site

Two consecutive phase II clinical studies were designed to evaluate the efficacy and safety of bolus and continuous infusion (CI) mitoxantrone (MTZ) in 39 patients with newly diagnosed acute lymphocytic leukemia (ALL). MTZ was used as part of the classical ALL induction regimen. Twenty patients were treated with bolus MTZ (10 mg/m2 for 3 days) combined with vincristine and prednisone. The same regimen was given to a second set of 19 patients, except that MTZ was administered as a 24-hr CI. Both groups received bimonthly intensifications with vincristine and prednisone for 3 years, along with oral maintenance therapy. Patients in the CI-MTZ study arm received additional MTZ on the first day of intensification cycles. Seventeen patients (85%) in the bolus arm and 15 patients (79%) in the CI arm achieved complete remission (CR). Median disease-free survivals (DFS) in the bolus and CI groups were 11 and 15 months after median follow-ups of 16 (3.5-96) and 13 (2.3-32) months, respectively. At 2.5 years, DFS rates were 29.4% and 34.4% in the bolus and CI groups (p > 0.05). There were no significant differences between two groups in rates of early death, degree of organ toxicity, or duration of neutropenia and thrombocytopenia. Significant cardiac toxicity was not observed in either group. Bolus or CI administration of MTZ was equally effective and was well tolerated. Neither the mode of administration nor increasing the dose intensity of MTZ by incorporating intensification cycles reduced relapse rates. Development of new antileukemia agents and novel treatment approaches are still needed to improve the high relapse rates in adult ALL once a complete response is achieved.     

Vickers Microhardness Studies on B<Subscript>4</Subscript>C Reinforced/Unreinforced Foamable Aluminium Composites

In this work we have investigated the effects of production processes on the structural and mechanical properties of B₄C reinforced/unreinforced foamable aluminium composite materials. All samples were produced with the powder metallurgy method. The production method included compression, extrusion and rolling processes. The Vickers microhardness test was applied to determine the mechanical properties of the samples. Vickers microhardness, elastic modulus and yield strength values of the samples were separately calculated and compared with each other. The experimental microhardness results were analysed using Meyer’s law, the proportional sample resistance model, the elastic–plastic deformation model and the Hays Kendall (HK) approach. The results determined that the HK approach was the most suitable model among the other applied microhardness models.     

Simple diagnostic approach for determining of faulted PV modules in string based PV arrays

This paper proposes a simple diagnostic method to determine the number of open and short circuited PV modules in a string of a PV system by taking into account the economical factor, such as minimum number of sensors. The diagnostic algorithm has as inputs the irradiance level, the PV modules temperature, the number of PV modules present in the string analyzed and its output power. So, just temperature and irradiance sensors, as well as a power meter by string are needed in the monitoring system forming part of the fault diagnostic system. The proposed fault detection method has been successfully validated experimentally.