Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling

Water Resources Management - The water evaluation and planning (WEAP) approach and the invasive weed optimization algorithm (IWOA) are herein employed to determine the optimal operating policies in...     

Effect of thermal treatment on ZnO substrate for epitaxial growth

ZnO is a highly efficient photon emitter, and has optical and piezoelectric properties that are attractive for a variety of applications in sensors and potentially optoelectronic devices such as emitters. Due to its identical stacking order and close lattice match to GaN, it is also being developed as a substrate material for GaN epitaxy. However, the surface finish of the ZnO is such that much of the damage induced by sawing and follow up mechanical polishing remains. We developed a thermal treatment method to eliminate surface damage on the 0 face of ZnO (0 0 0 1) to prepare it for epitaxial growth. Atomic force microscopy images of ZnO (0 0 0 1) annealed at 1050 °C for 3 h etc. show that residual scratches from mechanical polishing are removed and atomically flat, terrace-like surfaces are attained. In addition, low-temperature photoluminescence and high-resolution X-ray diffraction measurements have been employed to investigate the effect of annealing on ZnO substrates.     

Optimal scheduling of distributed energy resources in a distribution system based on imperialist competitive algorithm considering reliability worth

A model for energy trading in microgrids (MGs) is proposed in this paper. Imperialist competitive algorithm is used as a powerful method to determine the optimal schedule of all generation units in MG. The optimal scheduling is determined over a planning horizon considering all the constraints of the MG elements and the load demands. Scheduling of distributed generations (DGs) in the MG affects the risk of blackout in the power system. Therefore, a new objective function is presented to investigate the effect of DGs scheduling on the risk of partial or total blackout. DGs generate the great portion of energy in a MG. Generated power by some of DGs is strongly dependent on the weather and ambient conditions. Therefore, the power generation forecast is the major concern for constructing the model for MG energy trading. In this paper, artificial neural network (ANN) is used to predict hourly power outputs of DGs in the MG. Based on the ANN-based forecast module, the imperialist competitive algorithm is developed to determine the MG scheduling, by which the sources can be managed and an optimal operation can be achieved.     

Stochastic models for transfer point location problem

The transfer point location problem has been introduced recently and for the case of minimax objective and planar topology, has only been studied for situations in which demand points are not weighted and have known coordinates. In this paper, we consider the case in which demand points are weighted and their coordinates have bivariate uniform distribution. Also, the problem is developed from a conceptual view and different distance measures are used to make models more applicable in real world situations. The problem is to find the best location for the transfer point such that the maximum expected weighted distance to all demand points through the transfer point is minimized. Depending on assumptions for uniform distributions, two models are considered, convexity conditions are discussed, properties of the optimal solution are obtained and methods to solve the problems are proposed. Finally, numerical examples are given.     

Recent Advances in CNT-based FET Transistor Biosensors to Detect Biomarkers of Clinical Significance

Silicon - The development of advanced materials has attracted interest for promising novel applications in biosensing and realm of nanophotonics. This interest in nanoelectronics, and...     

Application of Bayesian Decision Networks for Groundwater Resources Management Under the Conditions of High Uncertainty and Data Scarcity

This paper presents management of groundwater resource using a Bayesian Decision Network (BDN). The Kordkooy region in North East of Iran has been selected as study area. The region has been sub-divided into three zones based on transmissivity (T) and electrical conductivity (EC) values. The BDN parameters: prior probabilities and Conditional Probability Tables - CPTs) have been identified for each of the three zones. Three groups of management scenarios have been developed based on the two decision variables including “Crop pattern” and “Domestic water demand” across the three zones of the study area: 1) status quo management for all three zones represent current conditions; 2) the effect of change in cropping pattern on management endpoints and 3) the effect of future increased domestic water demand on management endpoints. The outcomes arising from implementing each scenario have been predicted by use of the constructed BDN for each of the zones. Results reveal that probability of drawdown in groundwater levels of southern areas is relatively high compared with other zones. Groundwater withdrawal from northern and northwestern areas of the study area should be limited due to the groundwater quality problems associated with shallow groundwater of these two zones. The ability of the Bayesian Decision Network to take into account key uncertainties in natural resources and perform meaningful analysis in cases where there is not a vast amount of information and observed data available – and opportunities for enabling inputs for the analysis based partly on expert elicitation,emphasizes key advantages of this approach for groundwater management and addressing the groundwater related problems in a data-scarce area.     

A goal programming-TOPSIS approach to multiple response optimization using the concepts of non-dominated solutions and prediction intervals

Multiple response problems include three stages: data gathering, modeling and optimization. Most approaches to multiple response optimization ignore the effects of the modeling stage; the model is taken as given and subjected to multi-objective optimization. Moreover, these approaches use subjective methods for the trade off between responses to obtain one or more solutions. In contradistinction, in this paper we use the Prediction Intervals (PIs) from the model building stage to trade off between responses in an objective manner. Our new method combines concepts from the goal programming approach with normalization based on negative and positive ideal solutions as well as the use of prediction intervals for obtaining a set of non-dominated and efficient solutions. Then, the non-dominated solutions (alternatives) are ranked by the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) approach. Since some suggested settings of the input variables may not be possible in practice or may lead to unstable operating conditions, this ranking can be extremely helpful to Decision Makers (DMs). The consideration of statistical results together with the selection of the preferred solution among the efficient solutions by Multiple Attribute Decision Making (MADM) distinguishes our approach from others in the literature. We also show, through a numerical example, how the solutions of other methods can be obtained by modifying the relevant approach according to the DM’s requirements.     

Carbon nanotubes: properties,synthesis, purification,and medical applications

Current discoveries of different forms of carbon nanostructures have motivated research on their applications in various fields. They hold promise for applications in medicine, gene, and drug delivery areas. Many different production methods for carbon nanotubes (CNTs) have been introduced; functionalization, filling, doping, and chemical modification have been achieved, and characterization, separation, and manipulation of individual CNTs are now possible. Parameters such as structure, surface area, surface charge, size distribution, surface chemistry, and agglomeration state as well as purity of the samples have considerable impact on the reactivity of carbon nanotubes. Otherwise, the strength and flexibility of carbon nanotubes make them of potential use in controlling other nanoscale structures, which suggests they will have a significant role in nanotechnology engineering.     

Influence of applied time and power of ultrasonic pretreatment on convective drying of potato slices

Food Science and Biotechnology - Ultrasound is a novel technology that can be applied as a pretreatment for the convective drying in order to reduce its undeniable shortcomings. The objectives of...