Thermomechanical Characterization of Mg-9Al-1Zn Alloy Using Power Dissipation Maps

A processing map is developed on the basis of the Dynamic Material Model for Mg-9Al-1Zn. The model considers the work piece as a dissipator of power and power loss variation with temperature and strain rate constitutes the power dissipation map. To this end, the thermomechanical (i.e., hot compression) characteristics of a Mg-9Al-1Zn alloy was studied in the temperature range of 250-425 °C and strain rates of 0.001-1 s^?1. The strain rate sensitivity (m), power dissipation efficiency (η), and instability parameter (ξ) are computed based on the experimental hot compression data. The deformation mechanisms of different regions in the maps are analyzed and corresponding microstructures are investigated. The processing map of Mg-9Al-1Zn alloy exhibits five workability domains. Dynamic recrystallization (DRX) was observed in three of the domains, while in the two other domains grain boundary sliding, twining, and precipitation are the dominant mechanisms. The optimum hot working conditions of Mg-9Al-1Zn alloy are located in the two domains where DRX takes place. They correspond to 375 °C/0.001 s^?1 and 380 °C/1 s^?1 with peak efficiency of 42 and 36%, respectively.     

A novel application of stand-alone photovoltaic system in agriculture: solar-powered Microner sprayer

In this paper, a stand-alone photovoltaic (SAPV) power supply system for Microner sprayer is identified and proposed. The designed system was composed of three main parts: sprayer, solar power supply and control system. Initially, the control board and data acquisition system were designed and simulated by the Proteus software and then implemented using an AVR microcontroller and tested via LabVIEW in the laboratory. Next, a prototype system was fabricated for evaluation purposes. A PV panel size of 88.5?cm², positioned horizontally above operator's head that generates 26.4?Wh/day was used as a solar energy source. A small 2?Ah (12?V) battery is installed in the system as a stabiliser. This sprayer can work seven to nine hours daily. It is calculated that the average loss of collected energy due to non-application of maximum power point tracker was approximately 25%.     

Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components

Water Resources Management - Precise estimation of groundwater level (GWL) might be of great importance for attaining sustainable development goals and integrated water resources management....     

A charge sharing–based switching scheme for SAR ADCs

This paper presents an energy-efficient switching scheme for successive approximation register (SAR) analogue-to-digital converter (ADC). The proposed scheme employs charge recycling method to keep the capacitor arrays free of transitional energy between bit generations except reset phase. In comparison with the conventional switching scheme, the proposed one achieves 100% transitional energy saving without considering reset phase. In addition, configuration of a 10-bit SAR ADC shows that the proposed switching scheme reduces the capacitor area by 25% compared with the conventional switching scheme.     

No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms

This article studies a no-wait two-stage flexible flow shop scheduling problem with setup times aiming to minimize the total completion time. The problem is solved using an adaptive imperialist competitive algorithm (AICA) and genetic algorithm (GA). To test the performance of the proposed AICA and GA, the algorithms are compared with ant colony optimisation, known as an effective algorithm in the literature. The performance of the algorithms are evaluated by solving both small and large-scale problems. Their performance is evaluated in terms of relative percentage deviation. Finally the results of the study are discussed and conclusions and potential areas for further study are highlighted.     

Weighted label propagation based on Local Edge Betweenness

The Journal of Supercomputing - In complex networks, especially social networks, networks could be divided into disjoint partitions. However, nodes could be partitioned such that the number of...     

A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty

Organizations have recently become interested in applying new approaches to reduce fuel consumptions, aiming at decreasing green house gases emission due to their harmful effects on environment and human health; however, the large difference between practical and theoretical experiments grows the concern about significant changes in the transportation environment, including fuel consumptions, carbon dioxide (CO₂) emissions cost and vehicles velocity, that it encourages researchers to design a near-reality and robust pollution routing problem. This paper addresses a new time window pickup-delivery pollution routing problem (TWPDPRP) to deal with uncertain input data for the first time in the literature. For this purpose, a new mixed integer linear programming (MILP) approach is presented under uncertainty by taking green house emissions into consideration. The objective of the model is to minimize not only the travel distance and number of available vehicles along with the capacity and aggregated route duration restrictions but also the amount of fuel consumptions and green house emissions along with their total costs. Moreover, a robust counterpart of the MILP is introduced by applying the recent robust optimization theory. Computational results for several test problems indicate the capability and suitability of the presented MILP model in saving costs and reducing green house gases concurrently for the TWPDPRP problem. Finally, both deterministic and robust mathematical programming are compared and contrasted by a number of nominal and realizations under these test problems to judge the robustness of the solution achieved by the presented robust optimization model.     

From 2D to 3D geodesic-based garment matching

Multimedia Tools and Applications - A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of...     

An adaptive neuro‐fuzzy system for stock portfolio analysis

We propose an adaptive neuro‐fuzzy inference system (ANFIS) for stock portfolio return prediction. Previous work has shown that portfolio optimization can be improved by using predicted stock earnings rather than historical earnings. We show that predicted portfolio returns can be improved by using ANFIS and taking as input a variety of technical and fundamental attributes about various indices of the stock market. To generate membership functions, we use a robust noise rejection‐clustering algorithm. The neuro‐fuzzy model is tested on portfolios constituted from the Tehran Stock Exchange. In our experiments, the proposed method performs better in predicting the portfolio return than the classical Markowitz portfolio optimization method, a multiple regression, a neural network, and the Sugeno–Yasukawa method. © 2010 Wiley Periodicals, Inc.