Effect of Shot Peening Operation on the Microstructure and Wear Behavior of AZ31 Magnesium Alloy

Protection of Metals and Physical Chemistry of Surfaces - Shot peening is a treatment used to increase surface hardness and wear resistance. In this study, the effect of shot peening on the...     

A neural approach for estimation of per capita electricity consumption due to age and income

Electricity consumption is influenced by number of adults and children and their relationship at household level. Household income also plays a critical role on expenditure on electricity. Accordingly, this article presents a joint probability model of electricity demand based on occupants’ age grades and household income levels. A bottom-up strategy is developed using a micro level database of 70 Australian households. A neural regression-generalization technique is devised to estimate electricity demand using back-propagation and cognitive mapping. The aggregated result is then validated against 2012 Australian national census. Accordingly, the model is improved based on a top-down review. The results show per capita electricity demand by adult and child at 0.408 kW (69 kWh/week) and 0.226 kW (38 kWh/week), respectively. The equivalent dollar values are $13.6/week and $7.6/week in 2012. At macro level, the model reveals per capita demand by all individuals at 0.324 kW (54.35 kWh/week) equivalent to dollar value of $10.87/week, across Australia. The results also show higher percentage of per capita demand for adults in high and medium income classes, and the otherwise for low income class. Ratio of child’s demand over adult’s demand is highest among the low income households, and lowset among the middle income households, while best balance between adult and child per capita demand belongs to the high income.

     

Modeling and Optimization of SE and SI of Copper Flotation via Hybrid GA–ANN

In the present paper, a new attitude has been proposed for optimization of the separation efficiency (SE) and the Gaudin’s selectivity index (SI) in a flotation process by Hybrid artificial neural network (ANN) and genetic algorithm (GA). The chemical reagent’s dosage (collector, frother and fuel oil), pH, solid percentage, feed rate, Cu, Mo, and Fe grades in the flotation feed were selected as input variables and the SE-Cu and SI-Mo and SI-Fe were selected as output ones. Multilayer NN with back propagation (BP) algorithm was trained by the standard Bayesian regulation algorithm in which the validation data set did not required to be apart from its training. This algorithm with four-layer was used to relate output and input variables. Employment of Hybrid GA–ANN method resulted in significant improvement on GA fitnesses, as SE-Cu = 88, SI-Mo = 4.47 and SI of Fe = 12.85 were achieved. The input parameters corresponding to the fitnesses were as follows: pH = 12.25; the grade of Cu = 0.55%, Mo = 0.04% and Fe = 5.53%; the collector, frother and fuel–oil concentrations being 16.55, 15.54 and 2.71 (g/ton), respectively; the solid percentage was 25.84% and feed rate was 38,380 ton/day. The best fitness of GA was obtained after 10 generations by MSE value of 2.23.     

Design-Operation of Multi-Hydropower Reservoirs: HBMO Approach

To illustrate and test the applicability and performance of the innovative honey-bee mating optimization (HBMO) algorithm in highly non-convex hydropower system design and operation, two problems are considered: single reservoir and multi-reservoir. Both hydropower problems are formulated to minimize the total present net cost of the system, while achieving the maximum possible ratio for generated power to installed capacity. The single hydropower reservoir problem is approached by the developed algorithm in 10 different runs. The first feasible solution was generated initially and later improved significantly and solutions converged to a near optimal solution very rapidly. In the application of the proposed algorithm to a five-reservoir hydropower system with 48 periods and a total of 230 decision variables, in early mating flights, the first feasible solution was identified and the results converged to a near optimal solution in later mating flights. In the case of the multi-reservoir problem, an efficient gradient-based nonlinear-programming solver (LINGO 8.0) failed to find a feasible solution and for the single hydropower reservoir design problem it performed much worse than the proposed algorithm.     

A comparison of Zayandehrood River water values for agriculture and the environment

River water management is challenging not only since they are open systems with changing physical structures, but also because the water values are mostly unknown over varied sectors. If policymakers grasp water values, water management will be more efficient. This research intends to examine the values of water in agriculture, which receives the most substantial portion of water resources, with the values of water in the environment in Isfahan located in the Zayandehrood River basin of Iran. The consequences of contingent valuation and production function methods revealed that per cubic metre value of water is 13 times higher in the environment than agriculture. The government should reconsider the higher value of the environment despite it is a non‐market value. The contingent valuation model additionally proved that women exhibited 21% more willingness to pay than men in order to protect the environment; however, they are paid less by 36%.     

Document Image Coding for Processing and Retrieval

Document images belong to a unique class of images where the information is embedded in the language represented by a series of symbols on the page rather than in the visual objects themselves. Since these symbols tend to appear repeatedly, a domain-specific image coding strategy can be designed to facilitate enhanced compression and retrieval. In this paper we describe a coding methodology that not only exploits component-level redundancy to reduce code length but also supports efficient data access. The approach identifies and organizes symbol patterns which appear repeatedly. Similar components are represented by a single prototype stored in a library and the location of each component instance is coded along with the residual between it and its prototype. A representation is built which provides a natural information index allowing access to individual components. Compression results are competitive and compressed-domain access is superior to competing methods. Applications to network-related problems have been considered, and show promising results.     

Investigation of basic molecular gas structural effects on hydrodynamics and thermal behaviors of rarefied shear driven micro/nano flow using DSMC

In the present work, rarefied gas flow between two parallel moving plates maintained at the same uniform temperature is simulated using the direct simulation Monte Carlo (DSMC) method. Heat transfer and shear stress behavior in the micro/nano-Couette flow is studied and the effects of the important molecular structural parameters such as molecular diameter, mass, degrees of freedom and viscosity–temperature index on the macroscopic behavior of gases are investigated. Velocity, temperature, heat flux and shear stress in the domain are studied in details. Finally, a discussion on the role of the molecular structural parameters in the decrease or increase of amounts of hydrodynamics and thermal properties of the gas is presented.     

Principles and Applications of Light Backscattering Imaging in Quality Evaluation of Agro-food Products: a Review

In recent years, due to the increasing consciousness of quality in the food and health sector, much progress has been made in developing non-invasive techniques for the evaluation or inspection of internal qualitative parameters of fruits, vegetables, and processed foodstuffs considering, e.g., moisture content, soluble solid content, acidity, and mechanical properties. This paper reviews the theoretical and technical principles as well as the recent achievements and applications of light backscattering imaging for nondestructive evaluation of food and agricultural produce. The results suggest the potential use of this emerging technique in the food industry. Further attempts are pointed out to improve its performance through utilizing advanced image processing coupled with artificial intelligence techniques.     

Green synthesis and characterization of RGO/Cu nanocomposites as photocatalytic degradation of organic pollutants in waste-water

Recently, nanocomposite photocatalysts based on semiconductors have attracted much attention due to their suitable bandgap. Combination of tow of several semiconductors can slow down the electron-hole recombination. In this regard, we have depicted an eco-friendly and green fabrication technique to synthesize RGO/Cu nanocomposite by the reduction of graphene oxide and Cu²⁺ ion utilizing spearmint extract as a reductant and capping agent. The sample was identified by FTIR, XRD, FESEM, EDS, HRTEM, and CV. The results of photocatalytic performance revealed that RGO/Cu is an efficient catalyst for degrading organic pollutants. This compound can eliminate Rhodamine B (RhB) and Methylene blue (MB) 91.0% and 72.0%, respectively.     

Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the production yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 greenhouses in the region during four periods of plant cultivation in 2009–2010. The total input energy and energy ratio for basil production were 14,308,998 MJ ha^?1 and 0.02, respectively. The developed ANN was a multilayer perceptron (MLP) with seven neurons in the input layer, one, two and three hidden layer(s) of various numbers of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chemical fertilizers, farm yard manure, chemicals, electricity and transportation. Results showed, the ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coefficient of determination (R²) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, respectively. Sensitivity analysis revealed that chemical fertilizers are the most significant parameter in the basil production.