Decision making in dynamic stochastic Cournot games

In this paper, the Cournot competition is modeled as a stochastic dynamic game. In the proposed model, a stochastic market price function and stochastic dynamic decision functions of the rivals are considered. Since the optimal decision of a player needs the estimation of the unknown parameters of the market and rivals’ decisions, a combined estimation-optimization algorithm for decision making is proposed. The history of the rivals’ output quantities (supplies) and the market clearing price (MCP) are the only available information to the players. The convergence of the algorithm (for both estimation and decision making processes) is discussed. In addition, the stability conditions of the equilibrium points are analyzed using the converse Lyapunov theorem. Through the case studies, which are performed based on the California Independent System Operator (CA-ISO) historical public data, the theoretical results and the applicability of the proposed method are verified. Moreover, a comparative study among the agents using the proposed method, naïve expectation and adaptive expectation in the market is performed to show the effectiveness and applicability of the proposed method.     

Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems

Optimization algorithms are important tools for the solution of combinatorial management problems. Nowadays, many of those problems are addressed by using evolutionary algorithms (EAs) that move toward a near-optimal solution by repetitive simulations. Sometimes, such extensive simulations are not possible or are costly and time-consuming. Thus, in this study a method based on artificial neural networks (ANN) is proposed to reduce the number of simulations required in EAs. Specifically, an ANN simulator is used to reduce the number of simulations by the main simulator. The ANN is trained and updated only for required areas in the decision space. Performance of the proposed method is examined by integrating it with the non-dominated sorting genetic algorithm (NSGAII) in multi-objective problems. In terms of density and optimality of the Pareto front, the hybrid NSGAII-ANN is able to extract the Pareto front with much less simulation time compared to the sole use of the NSGAII algorithm. The proposed NSGAII-ANN methodology was examined using three standard test problems (FON, KUR, and ZDT1) and one real-world problem. The latter addresses the operation of a reservoir with two objectives (meeting demand and flood control). Thus, based on this study, use of the NSGAII-ANN integrative algorithm in problems with time-consuming simulators reduces the required time for optimization up to 50 times. Results of the real-world problem, despite lower computational-time requirements, show a performance similar to that achieved in the aforementioned test problems.     

Uncertainty based distributed generation expansion planning in electricity markets

Decision making under uncertainty is a difficult task in most areas. The distributed generation expansion planning is certainly one of the problems where it is difficult to find an optimal solution for, due to the existence of various uncertain parameters that affect the optimal solution. This paper presents a distributed generation planning strategy based on the multi-objective decision making (MODM) approach under uncertainties. The proposed strategy achieves a Pareto set of locations, sizes, and typical types, for new DG installations in a case-study power network (as the result of a multi-objective optimization). The technical constraints of DG units are also considered in the MODM process. The effects of the uncertain parameters on choosing the best plan(s) are considered using robustness, flexibility, and risk exposure attributes. This aim is performed based on a composite index which is weighted sum of the three mentioned attributes. This paper considers four categories of uncertain parameters including the peak loading factor, electricity market price, and investment and operating costs for the DG long-term planning.     

Experimental investigation of the governing parameters in the electrospinning of poly(3‐hydroxybutyrate) scaffolds: Structural characteristics of the pores

We sought to determine the impact of electrospinning parameters on a trustworthy criterion that could evidently improve the maximum applicability of fibrous scaffolds for tissue regeneration. We used an image analysis technique to elucidate the web permeability index (WPI) by modeling the formation of electrospun scaffolds. Poly(3‐hydroxybutyrate) (P3HB) scaffolds were fabricated according to predetermined conditions of levels in a Taguchi orthogonal design. The material parameters were the polymer concentration, conductivity, and volatility of the solution. The processing parameters were the applied voltage and nozzle‐to‐collector distance. With a law to monitor the WPI values when the polymer concentration or the applied voltage was increased, the pore interconnectivity was decreased. The quality of the jet instability altered the pore numbers, areas, and other structural characteristics, all of which determined the scaffold porosity and aperture interconnectivity. An initial drastic increase was observed in the WPI values because of the chain entanglement phenomenon above a 6 wt % P3HB content. Although the solution mixture significantly (p < 0.05) changed the scaffold architectural characteristics as a function of the solution viscosity and surface tension, it had a minor impact on the WPI values. The solution mixture gained the third place of significance, and the distance was approved as the least important factor. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

Microglial Cell Activation Increases Saturated and Decreases Monounsaturated Fatty Acid Content,but Both Lipid Species are Proinflammatory

Neuroinflammation is a component of age-related neurodegenerative diseases and cognitive decline. Saturated (SFA) and monounsaturated (MUFA) fatty acids are bioactive molecules that may play different extrinsic and intrinsic roles in neuroinflammation, serving as exogenous ligands for cellular receptors, or endogenous components of cell structural, energetic and signaling pathways. We determined the fatty acyl profile of BV2 microglial cells before and after acute activation with lipopolysaccharide (LPS). We also investigated the effect of SFA and MUFA pretreatment on the production of an invasive, neurotoxic phenotype in BV2 cells. Acute activation of BV2 microglia resulted in an increase in the relative content of SFA (12:0, 16:0, 18:0, 20:0, 22:0, and 24:0 increased significantly), and a relative decrease in the content of MUFA (16:1n7, 18:1n7, 18:1n9, 20:1n9, 24:1n9 decreased significantly). In agreement, the major stearoyl-CoA desaturase (SCD) isoform in BV2 cells, SCD2, was significantly down-regulated by LPS. We next treated cells with SFA (16:0 or 18:0) or MUFA (16:1n7 or 18:1n9), and found that levels of secreted IL6 were increased, as was secreted MMP9-mediated proteolytic activity. To test the functional significance, we treated SH-SY5Y neuronal cells with conditioned medium from BV2 cells pretreated with fatty acids, and found a small but significant induction of cell death. Our findings suggest differential intrinsic roles for SFA and MUFA in activated microglial cells, but similar extrinsic roles for these fatty acid species in inducing activation. Expansion of SFA is important during microglial cell activation, but either supplemental SFA or MUFA may contribute to chronic low-grade neuroinflammation.     

Comparison of pH-dependent sonodisruption of re-assembled casein micelles by 35 and 130 kHz ultrasounds

Sonodisruption behavior of re-assembled casein micelles was compared at two ultrasound frequencies (35 and 130 kHz) by turbidity measurement and laser-diffraction based particle size analysis. Sonochemical ultrasound (130 kHz) was more effective than power ultrasound (35 kHz) in micelle disruption. This was attributed to the higher strain rates generated upon implosion of cavities, as well as the liberation of more free radicals to the surrounding medium. The higher the pH of solution, the more effective was the ultrasonic disruption due to a looser expanded assembly of particles at higher pH values. Sonochemical ultrasound decreased the consistency coefficient of casein solutions and increased their flow index except at a pH value of 6.35, while power ultrasound did not affect the flow behavior of solutions across the whole pH range.     

Multilevel resistive switching and synaptic plasticity of nanoparticulated cobaltite oxide memristive device

Multilevel resistive switching(RS)is a key property to embrace the full potential of memristive devices for non-volatile memory and neuromorphic computing applications.In this study,we employed nanopar-ticulated cobaltite oxide(Co3O4)as a model material to demonstrate the multilevel RS and synaptic learning capabilities because of its multiple and stable redox state properties.The Pt/Co3O4/Pt memris-tive device exhibited tunable RS properties with respect to different voltages and compliance currents(CC)without the electroforming process.That is,the device showed voltage-dependent RS at a higher CC whereas CC-dependent RS was observed at lower CC.The device showed four different resistance states during endurance and retention measurements and non-volatile memory results indicated that the CC-based measurement had less variation.Besides,we investigated the basic and complex synap-tic plasticity properties using the analog current-voltage characteristics of the Pt/Co3O4/Pt device.In particular,we mimicked the potentiation-depression and four-spike time-dependent plasticity(STDP)rules such as asymmetric Hebbian,asymmetric anti-Hebbian,symmetric Hebbian,and symmetric anti-Hebbian learning rules.The results of the present work indicate that the cobaltite oxide is an excellent nanomaterial for both multilevel RS and neuromorphic computing applications.     

Performance analysis of multi-antenna relay networks with imperfect channel estimation

In this paper, we present the performance of multi-antenna selective combining decode-and-forward (SC-DF) relay networks over independent but non-identical Nakagami-m fading channels with imperfect channel estimation. The outage probability, moment generating function (MGF) and symbol error probability (SEP) will be derived in closed-form using the SNR statistical characteristics. To make the analysis trackable, we have derived the MGF and SEP for integer values of fading severity, m. Also, to make the relations more simple, we develop high signal to noise ratio (SNR) analysis for the performance metrics of our system. Subsequently, we propose optimal and adaptive power allocation algorithms along with the equal power allocation method. Finally, for comparison with analytical formulas, we perform some Monte-Carlo simulations.     

An exhaustive criterion for estimating quality of images in electrical impedance tomography with application to clinical imaging

This study proposes a versatile criterion for estimating quality of images in electrical impedance tomography. The point spread function (PSF) is calculated throughout the domain based on the scattering of energy as responses to a small anomaly spirally moved from the centre to the boundary. The proposed PSF is a measure of weighted spatial variance (WSV) of the conductivity over the whole domain. For each element, the weighting factor is a normalized multiplication of the area of that element by its square intensity. The WSV collectively incorporates all image attributes, i.e., spatial resolution, artifact, amplitude response, positioning error and shape deformation. The location of artifacts, which significantly influences reconstructed images in reality, is taken into account as well. The results illustrate that the proposed measure is more tolerant than existing criteria in evaluating performance of EIT systems in both theory and practice.     

Compiling Vector Pascal to the XeonPhi

Intel's XeonPhi is a highly parallel x86 architecture chip made by Intel. It has a number of novel features which make it a particularly challenging target for the compiler writer. This paper describes the techniques used to port the Glasgow Vector Pascal Compiler to this architecture and assess its performance by comparisons of the XeonPhi with 3 other machines running the same algorithms. Copyright © 2015 John Wiley & Sons, Ltd.