A new localization method in internet of things by improving beetle antenna search algorithm

Wireless Networks - Localization is regarded as one of the important challenges in the internet of things and Wireless Sensor Networks. The failure to localize sensors properly causes data loss and...     

Determining the Main Factors in Declining the Urmia Lake Level by Using System Dynamics Modeling

Urmia Lake in Iran is the second largest saline lake in the world. This ecosystem is the home for different species. Due to various socio-economical and ecological criteria, Urmia Lake has important role in the Northwestern part of the country but it has faced many problems in recent years. Because of droughts, overuse of surface water resources and dam constructions, water level has decreased in such a way that one quarter of the lake has changed to saline area in the last 10 years. The purpose of this research is to determine the main factors which reduce the lake’s water level. To this end, a simulation model, based on system dynamics method, is developed for the Urmia Lake basin to estimate the lake’s level. After successful verification of the model, results show that (among the proposed factors) changes in inflows due to the climate change and overuse of surface water resources is the main factor for 65% of the effect, constructing four dams is responsible for 25% of the problem, and less precipitation on lake has 10% effect on decreasing the lake’s level in the recent years. In the future, the model also can be used by managers as a decision support system to find the effects of building new dams or other infrastructures.     

Predicting potential deadlocks in multithreaded programs

In a multithreaded program, competition of threads for shared resources raises the deadlock possibility, which narrows the system liveness. Because such errors appear in specific schedules of concurrent executions of threads, runtime verification of threads behavior is a significant concern. In this study, we extended our previous approach for prediction of runtime behavior of threads may lead to an impasse. Such a prediction is of importance because of the nondeterministic manner of competing threads. The prediction process tries to forecast future behavior of threads based on their observed behavior. To this end, we map observed behavior of threads into time‐series data sets and use statistical and artificial intelligence methods for forecasting subsequent members of the sets as future behavior of the threads. The deadlock prediction is carried out based on probing the allocation graph obtained from actual and predicted allocation of resources to threads. In our approach, we use an artificial neural network (ANN) because ANNs enjoy the applicable performance and flexibility in predicting complex behavior. Using three case studies, we contrasted results of the current and our previous approaches to demonstrate results. Copyright © 2015 John Wiley & Sons, Ltd.     

Determination of partition coefficient of migrants in food simulants by the PRV method

Partition coefficients of six migrants between four simulated foods (water, 10% ethanol, 3% acetic acid, 95% ethanol) and air were determined by the phase ratio variation (PRV) method using headspace analyses by gas chromatography. The migrants were ethyl acetate, methyl ethyl ketone, propyl alcohol, butyraldehyde, acetaldehyde and acetonitrile. The results showed that migrant absorption by the four food simulants was highly dependent on the physicochemical properties of migrants and foods, such as polarity, solubility and hydrogen-bonding.     

Wetting Behavior and Reactivity of Molten Silicon with h-BN Substrate at Ultrahigh Temperatures up to 1750 °C

For a successful implementation of newly proposed silicon-based latent heat thermal energy storage systems, proper ceramic materials that could withstand a contact heating with molten silicon at temperatures much higher than its melting point need to be developed. In this regard, a non-wetting behavior and low reactivity are the main criteria determining the applicability of ceramic as a potential crucible material for long-term ultrahigh temperature contact with molten silicon. In this work, the wetting of hexagonal boron nitride (h-BN) by molten silicon was examined for the first time at temperatures up to 1750 °C. For this purpose, the sessile drop technique combined with contact heating procedure under static argon was used. The reactivity in Si/h-BN system under proposed conditions was evaluated by SEM/EDS examinations of the solidified couple. It was demonstrated that increase in temperature improves wetting, and consequently, non-wetting-to-wetting transition takes place at around 1650 °C. The contact angle of 90° ± 5° is maintained at temperatures up to 1750 °C. The results of structural characterization supported by a thermodynamic modeling indicate that the wetting behavior of the Si/h-BN couple during heating to and cooling from ultrahigh temperature of 1750 °C is mainly controlled by the substrate dissolution/reprecipitation mechanism.     

An efficient and stable method to cluster software modules using ant colony optimization algorithm

The Journal of Supercomputing - Software evolution is a natural phenomenon due to the changing requirements. Understanding the program structure is a significant and complicated factor in...     

A platform for electrochemical sensing of biomolecules based on Europia/reduced graphene oxide nanocomposite

This study aimed at preparing and evaluating the europium oxide–reduced graphene oxide (rGO) composites. Inorganic nanoparticles anchored onto rGO sheets through a facile sonochemical method. The resultant products were characterized by FT-IR, XRD, SEM. Their activity in biomolecules’ analysis were examined by cyclic voltammetry. The rectified electrodes revealed an incredibly electroactive manner. The obtained progress provided excellent materials for scrutiny of biomolecules. The linear relationship was used in the region of 100–1500 µM ascorbic acid (AA), 50–600 µM dopamine (DA), and 10–700 µM uric acid (UA), between current intensities and concentrations. The detection restrictions (LOD) (S/N?=?3) decreased to 8 µM, 1.1 µM and 0.085 µM for AA, DA and UA respectively by differential pulse voltammetry (DPV).