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.     

Thermal Analysis of Non-linear Convective–Radiative Hyperbolic Lumped Systems with Simultaneous Variation of Temperature-Dependent Specific Heat and Surface Emissivity by MsDTM and BPES

With the advent of temperatures near absolute zero, it is often claimed that at very low temperatures the effect of thermal wave propagation must be included by the hyperbolic heat conduction equation (HHCE). In this paper the non-linear convective–radiative HHCE is investigated. Opposite to common numerical analyses, analytical expressions are obtained for the temperature variations by the multi-step differential transformation method. Some conclusions about alteration of the specific heat of the material, temperature steeping, and Vernotte number have been formulated.     

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...     

An Anti-Gray Hole Attack Scheme in Mobile Ad Hoc Network

International Journal of Wireless Information Networks - Dynamic variation of network topology in mobile ad hoc networks (MANET) forces network nodes to work together and rely on each other for...     

Failure Analysis and Repair of a Catastrophically Damaged Gas Turbine Compressor Disk Using SEM Technique and CFD Analysis

During a major overhaul of an 85?MW gas turbine unit in Iran-Rey power plant, 39 cracks were detected with different lengths and locations on the compressor disk of stage 11. All of the cracks initiated from the dovetail regions. Preliminary visual inspections and further micro-fractography using the scanning electron microscope demonstrated that the fretting fatigue phenomenon was the main cause of failure. Four repair methods were suggested to restart the unit. The first one was to remove all of the cracks from the disk by machining, or the so-called blending. The second, third, and fourth ways were to remove the entire rotor blades of stage 11, to remove the entire rotor and stator blades of the stage 11 simultaneously, and to remove those rotor blades of stage 11 corresponding to the damaged dovetails, respectively. Although the first way of solution was initially carried out on the damaged disk, the first author offered that restarting the unit with the blended disk is not reliable enough because of the presence of a large number of repair points on the disk. Using the numerical investigations based on the computational fluid dynamics, it was found that only the second suggestion (i.e., removing the entire rotor blades of the stage 11) might be applicable. Ultimately, the entire stage 11 rotor blades were removed from the blended disk, and the gas turbine unit was successfully restarted without encountering abnormal operation. Although the performed process resulted in approximately 20% output power loss compared with the unit's power before the blades' removal, the unit was quickly restored to be ready to restart, and the electric power could be generated during the period of peak consumption.     

Target tracking of autonomous robotic vehicles using range‐only measurements: a switched logic‐based control strategy

This paper considers the pursuing or target tracking problem where an autonomous robotic vehicle is required to move towards a maneuvering target using range‐only measurements. We propose a switched logic‐based control strategy to solve the pursuing problem that can be described as comprising a continuous cycle of two distinct phases: (1) the determination of the bearing, and (2) the steering control of the vehicle to follow the direction computed in the previous step while the range is decreasing. We provide guaranteed conditions under which the switched closed‐loop system achieves convergence of the relative distance error to a small neighborhood around zero. Simulation results are presented and discussed.Copyright © 2011 John Wiley & Sons, Ltd.     

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.

     

Optimal design of stepped spillways using the HBMO algorithm

Studies on stepped spillways as flood energy dissipators have been conducted to understand the hydraulics on the stepped face of roller-compacted concrete dams as well as overlays of embankment dams. Significant energy losses occur along the stepped chute so that the energy dissipation structure becomes smaller and more economic. In addition, considering the design discharge, downstream face slope, height of spillway, different combinations of spillway width and number of spillway steps may lead to different head losses. In each feasible combination, the remaining head after the steps should be dissipated by downstream energy dissipators. Design and construction of spillways and energy disspators are very cost-consuming and build up a major part of the dam's construction expenses. Thus, the cost of a financially viable stepped spillway project that consists of the steps’ cost and downstream dissipator's cost should be minimised. In this paper, the honey-bee mating optimisation (HBMO) algorithm is used to determine the best combination of design variables so as to minimise the total cost of both spillway chute and stilling basin. Results are compared with those previously obtained by genetic algorithm (GA) and show the promising potential of the HBMO algorithm in this field of application.     

Ultrathin buffer layer at organic/organic interface for managing the recombination profile in organic light‐emitting diodes: Metal versus dielectric buffer

We report on the utilization of an ultrathin buffer layer at the organic/organic (O/O) interface to enhance device efficiency in organic light‐emitting diodes. Two different kinds of buffer layers are examined: metal and dielectric. It is shown that employment of an ultrathin Ag layer with a thickness of 1–2 nm enhances the device performance, while a MgF₂ dielectric buffer cannot affect the device properties considerably. In particular, the turn‐on voltage of the device with an appropriate buffer layer is reduced about 3 V, its current efficiency increases by a factor of more than three, and the power efficiency increases by a factor of more than five in comparison to the control device when a Ag buffer layer is introduced at the O/O interface. By employment of the buffer layer at the interface, an accumulation of current carriers appears within the device that redistribute the recombination profile toward the interior part of the emissive layer. Also, morphological examinations reveal that distinguishable phase segregation occurs in the blend of the hole‐transport layer. In particular, the polymer component remains at the surface and facilitates the hole transport into the successive layers. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43894.