Performance evaluation of sequential spectrum handoff in cognitive radio networks: a single‐user case

Powerful spectrum handover schemes enable cognitive radios to exploit transmission opportunities in primary users’ channels appropriately. In this paper, modeling and performance evaluation of sequential spectrum sensing strategy are addressed. To this end, the average number of handovers required for finding a transmission opportunity is evaluated assuming that a prior knowledge of the presence and absence probabilities of the primary users is available. Moreover, the average throughput maximization of a secondary user by optimizing its spectrum sensing time is formulated, and a set of illustrative numerical results is then presented to validate the analytical analysis.Copyright © 2013 John Wiley & Sons, Ltd.     

An overview of dynamic anomaly detection in social networks via control charts

Detection of anomalies in social networks has always been a topic of interest in the realm of social network analysis. However, in recent years, researchers from industrial process monitoring area have applied their methods for monitoring social networks. In this review paper, we discuss social network monitoring approaches that apply control charts to detect anomalies in social networks. We mention the characteristics of the approaches and will shed some light on different topics that can be considered for further investigation by interested researchers.     

CREEPING FLOW OF VISCOELASTIC FLUIDS THROUGH TAPERED SLIT DIES: AN ANALYTICAL SOLUTION

In the present study, it will be shown that at vanishingly small Reynolds numbers the extensional behavior of a fluid has a strong effect on its kinematics when flowing through a tapered slit die. To show this, the Cauchy equations of motion were simplified using the creeping flow approximation. Assuming the flow to be steady, laminar, two-dimensional, and incompressible, the governing PDEs were reduced to a set of coupled ODEs using a series solution in terms of the powers of 1/r. Two different constitutive equations, both incorporating an extensional parameter, were used in the analysis: (i) the Giesekus model and (ii) the Simplified Phan-Thien-Tanner (SPTT). An analytical solution was found for each fluid model clearly depicting the strong influence of the extensional parameter on the flow becoming non-radial within the channel.     

Peridynamic assessment of mechanical and thermal characteristics of a defected PMMA/HA composite beam

Polymer-based composites are designed to improve mechanical and thermal characteristics. This study utilized a peridynamic methodology to simulate polymethyl methacrylate/hydroxyapatite composite beams. The simulation involved the crack growth within the computational domain, and an analysis was conducted to evaluate the mechanical and thermal properties of the defected system. The outcomes derived from the peridynamic analysis revealed that an augmentation in the hydroxyapatite ratio within the samples resulted in a decrease in their mechanical and thermal efficiencies. To elucidate further, at an impact velocity of 2 mm/s, the flexural modulus increased to 3.69 GPa, the flexural strength decreased to 132.34 MPa, and the thermal conductivity converged to 0.148 W/m·K, when the hydroxyapatite ratio was at 15%. In the course of the conducted investigations, it became evident that the impact velocity significantly influences the evolutionary behavior of particles within the samples. In particular, with an increase in the impact velocity up to 5 mm/s, the thermal conductivity decreased to 0.139 W/m·K. The results of this study indicate that by modifying the hydroxyapatite ratio and impact velocity, it is possible to control the mechanical and thermal properties of the polymethyl methacrylate/hydroxyapatite composite beams. This optimization allows for their suitability in various engineering applications.     

Effect of a magnetic field on natural convection in an inclined half-annulus enclosure filled with Cu–water nanofluid using CVFEM

In this paper, the effect of a magnetic field on natural convection in a half-annulus enclosure with one wall under constant heat flux using control volume based finite element method. The fluid in the enclosure is a water-based nanofluid containing Cu nanoparticles. The effective thermal conductivity and viscosity of nanofluid are calculated using the Maxwell–Garnetts (MG) and Brinkman models, respectively. Numerical simulations were performed for different governing parameters namely the Hartmann number, Rayleigh number and inclination angle of enclosure. The results indicate that Hartmann number and the inclination angle of the enclosure can be control parameters at different Rayleigh number. In presence of magnetic field velocity field retarded and hence convection and Nusselt number decreases.     

Application of neural networks to predict the transient performance of a Run-Around Membrane Energy Exchanger for yearly non-stop operation

Application of soft computing methods (i.e. neural networks and genetic algorithms) for modeling and controlling the dynamic and transient behavior of systems has been increasing during the last decade. In this study, a neural network (NN) model is developed to predict the transient heat and moisture transfer performances (i.e., the sensible and latent effectivenesses) of a novel HVAC energy exchanger, called the Run-Around Membrane Energy Exchanger (RAMEE), which is able to transfer both heat and moisture between exhaust and supply air streams. The training data set for the NN model covers a wide range of outdoor conditions and system parameters and is produced using a Transient Numerical Model (TNM) that has been experimentally validated for some transient applications. Two separate NNs (one for sensible and one for latent energy transfer) each with 12 inputs and one output, are selected to represent the RAMEE. The ability of NN models to predict the performance of a given RAMEE design in different climates is numerically validated. The mean absolute difference (MAD) between the results of TNM and NN models for different locations are 0.5 °C for the sensible model and 0.2 g_v/kg_a for the latent model, which indicates satisfactory agreement for energy exchange calculations. These NN models are very fast and easy to use therefore, they might be used for design purposes or estimating the annual energy savings in different buildings with continuous operation and a RAMEE in their HVAC system.     

Nitrogen and potassium requirements of tomato plants for the optimization of fruit quality and antioxidative capacity during storage

While nitrogen (N) and potassium (K) fertilizers are commonly used in tomato fields for increasing plant growth and productivity, concepts regarding the interactive effects of these elements on tomato fruit quality during storage are ambiguous. The interactive effects of potassium (0, 250 and 500 mg kg soil^??1) and nitrogen (0, 150 and 300 mg kg soil^??1) fertilizers were tested on tomato fruit size at harvest, and their quality was evaluated after 30 days of storage at 4 °C. Fruit mass, firmness, taste-related parameters, inorganic nutrients, bioactive compounds, and antioxidative capacity of the fruits were investigated after the storage period. Application of 250–500 mg K in combination with 150 mg N per kg soil improved fruit mass at harvest and reduced loss of mass during storage. By increasing the soil’s K supply, the fruits exhibited significant increases in their amount of potassium, TSS and TA contents, antioxidative capacity, concentrations of lycopene, phenolics and ascorbic acid. Nonetheless, increasing the soil’s K concentration reduced Ca concentration and firmness of the fruits. By increasing the N concentration in the soil, a trend of decline was observed in the fruits’ TSS, soluble phenolics, lycopene and ascorbic acid contents. However, higher amounts of N and TA contents were detected in fruits treated with 300 mg N kg soil^??1. N treatments did not affect the antioxidative capacity of the fruits. Accordingly, application of 250 mg K?+?150 mg N kg soil^??1 was suggested as the optimum treatment that could yield tomato fruits with high quality and that would also have improved storage capability.     

Numerical analysis of rock fracturing by gas pressure using the extended finite element method

High energy gas fracturing is a simple approach of applying high pressure gas to stimulate wells by generating several radial cracks without creating any other damages to the wells.In this paper,a numerical algorithm is proposed to quantitatively simulate propagation of these fractures around a pressurized hole as a quasi-static phenomenon.The gas flow through the cracks is assumed as a one-dimensional transient flow,governed by equations of conservation of mass and momentum.The fractured medium is modeled with the extended finite element method,and the stress intensity factor is calculated by the simple,though sufficiently accurate,displacement extrapolation method.To evaluate the proposed algorithm,two field tests are simulated and the unknown parameters are determined through calibration.Sensitivity analyses are performed on the main effective parameters.Considering that the level of uncertainty is very high in these types of engineering problems,the results show a good agreement with the experimental data.They are also consistent with the theory that the final crack length is mainly determined by the gas pressure rather than the initial crack length produced by the stress waves.     

Unrestricted deep metric learning using neural networks interaction

Pattern Analysis and Applications - In many machine learning applications and algorithms, the algorithm performance and accuracy are highly dependent on the metric used to measure the distance...     

A fuzzy group data envelopment analysis model for high-technology project selection: A case study at NASA

The assessment and selection of high-technology projects is a difficult decision making process at the National Aeronautic and Space Administration (NASA). This difficulty is due to the multiple and often conflicting objectives in addition to the inherent technical complexities and valuation uncertainties involved in the assessment process. As such, a systematic and transparent decision making process is needed to guide the assessment process, shape the decision outcomes and enable confident choices to be made. Various methods have been proposed to assess and select high-technology projects. However, applying these methods has become increasingly difficult in the space industry because there are many emerging risks implying that decisions are subject to significant uncertainty. The source of uncertainty can be vagueness or ambiguity. While vague data are uncertain because they lack detail or precision, ambiguous data are uncertain because they are subject to multiple interpretations. We propose a data envelopment analysis (DEA) model with ambiguity and vagueness. The vagueness of the objective functions is modeled by means of multi-objective fuzzy linear programming. The ambiguity of the input and output data is modeled with fuzzy sets and a new α-cut based method. The proposed models are linear, independent of α-cut variables, and capable of maximizing the satisfaction level of the fuzzy objectives and efficiency scores, simultaneously. Moreover, these models are capable of generating a common set of multipliers for all projects in a single run. A case study involving high-technology project selection at NASA is used to demonstrate the applicability of the proposed models and the efficacy of the procedures and algorithms.