The effect of acetic acid on the CO2 corrosion of grade X70 steel

The effect of acetic acid (HAc) on the CO₂ corrosion of grade X70 steel was investigated using X-ray diffraction (XRD), scanning electron microscopy (SEM), polarization tests and electrochemical impedance spectroscopy (EIS). In the absence of acetic acid, a fairly dense layer of iron carbonate (FeCO₃/siderite) was formed. At 500 ppm HAc, FeCO₃ layer became more porous. In addition, anodic/cathodic polarization curves were activated with the more pronounced effect on the cathodic side. By adding 1000 ppm HAc, similar polarization behavior was obtained and FeCO₃ layer became yet more porous than previous conditions. At 2000 ppm HAc, FeCO₃ layer disappeared completely, while polarization behavior changed and the limiting diffusive current density was observed in the cathodic side. There were two major increases in the corrosion rate at 500 and 2000 ppm HAc. The EIS results reflected similar behavior for the specimens exposed to the solutions with 0–1000 ppm HAc. Under these conditions, a charge transfer controlled behavior due to the FeCO₃ layer was observed which was accelerated by increasing HAc concentration. At 2000 ppm HAc, the corrosion behavior changed considerably and the formation/adsorption of corrosion product followed by the dissolution process was observed.     

基于非对称信道状态信息的协作通信：一种契约理论建模方法

In relay-assisted cooperative com-munication, relay nodes help forwarding the information of a source node in case of link failure between the source and a destination. Although user cooperation improves the over-all efficiency of the network, it requires incen-tive to stimulate potential relay nodes to assist the source by forwarding its data to the desti-nation. Moreover, the potential relays are bet-ter informed than the source about their chan-nel conditions to destination, which results in asymmetric information between the source and the relays. In this paper, we study the problem of lack of forwarding incentive in cooperative communication when channel state information of relays is private infor-mation and not known by the source. To tackle this problem, we apply the principle of contract theory to a cooperative wireless system. Source first designs incentive compatible and individually rational contract, consisting of a set of power-credit pairs. Then it broadcasts contract items to nearby nodes. Once the source node receives reply messages from the volunteer relays, it chooses one or more relays based on its re-quirements and communication starts. Simulation results show how credit assignment works in order to stimulate relays to cooperate and prevents relays from cheating behavior.     

Simultaneous Determination of Nitrite and Nitrate in Milk Samples by Ion Chromatography Method and Estimation of Dietary Intake

The presence of nitrate and nitrite in foods may be considered hazardous after ingestion in the gastrointestinal tract due to their reaction with naturally occurred secondary amines to form potentially carcinogenic nitrosamines. Due to this fact, a new method was developed in this study for the simultaneous determination of nitrite and nitrate in milk samples using by ion chromatography. Proposed mobile phase composed of sodium hydrogen carbonate and sodium carbonate (1.0 and 3.2 mmol/L) with a flow rate of 0.7 ml/min. The average recoveries for nitrate and nitrite were higher than 86 and 88%, respectively. The limit of detection for nitrate and nitrite were 0.24 and 0.09 mg/L, respectively. The results of 102 real milk samples showed nitrate was found in all of the samples (100%) with a mean of 34 ± 11 mg/L, while nitrite was found in none of the samples. The mean intake of nitrate in all age groups was lower than World Health Organization guideline. The present assessment concludes that the maximum contaminant level was equal to 82.8 mg/L nitrate. This method was fast, sensitive and accurate and is capable of being an alternative method in food control laboratories for investigation of nitrite and nitrate content. This is the first study of the determination and survey of nitrite and nitrate and exposure assessment of the Iranian population to nitrite and nitrate level in milk, which was widely used in infants and adolescents as one of the basic food components.     

A new adaptive fuzzy inference system for electricity consumption forecasting with hike in prices

Large increase or hike in energy prices has proven to impact electricity consumption in a way which cannot be drawn from historical data, especially when price elasticity of demand is not significant. This paper proposes an integrated adaptive fuzzy inference system (FIS) to estimate and forecast long-term electricity consumption when prices experience large increase. To this end, first a novel procedure for construction and adaptation of Takagi–Sugeno fuzzy inference system (TS-FIS) is suggested. Logarithmic linear regressions are estimated with historical data and used to construct an initial first-order TS-FIS. Then, in the adaptation phase, expert knowledge is used to define new fuzzy rules which form a new secondary FIS for electricity forecasting. To show the applicability and usefulness of the proposed model, it is applied for forecasting of annual electricity consumption in Iran where removing energy subsidies has resulted in a hike in electricity prices. Gross domestic product (GDP), population and electricity price are three inputs for the initial TS-FIS. A questionnaire survey was conducted to collect the expert estimation on possible change in electricity per capita, change in electricity intensity and the ratio of GDP elasticity to population elasticity when price hikes. Based on the information collected, a fuzzy rule base is formed and used to construct the secondary FIS which is used for electricity forecasting until 2016. Furthermore, the performance of the proposed model of this paper is compared with three other models namely ANFIS, ANN and one-stage regression in terms of their mean absolute percentage error. The comparison shows a superior performance for the proposed FIS model.     

A multiplier-less digital design of a bio-inspired stimulator to suppress synchronized regime in a large-scale,sparsely connected neural network

Excessive synchronous firing of neurons is the sign of several neurological disorders such as Parkinson and epilepsy. In addition, growing evidence suggests that astrocytes have significant roles in neural synchronization. Drawing on these concepts and based on the latest studies, a bio-inspired stimulator which essentially is a dynamical model of the astrocyte biophysical model is proposed. The performance of the proposed bio-inspired stimulator is investigated on a large-scale, sparsely connected neural network which models a local cortical population. Next, a multiplier-less digital circuit for the bio-inspired stimulator is designed, and finally, the complete digital circuit of the closed-loop system is implemented in hardware on the ZedBoard development kit. Considering software simulations and hardware FPGA implementation, the proposed bio-inspired stimulator is able to prevent the hyper-synchronous neural firing in a network of excitatory and inhibitory neurons. Based on the obtained results, it is demonstrated that the proposed stimulator has a demand-controlled characteristic and can be a good candidate as a new deep brain stimulation (DBS) technique to effectively suppress the hyper-synchronous neural oscillations.

     

ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the transformation start temperature of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 138 different experimental data were gathered from the literature. The data used in the ANN model are arranged in a format of fourteen input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is transformation start temperature. In this model, the training and testing results in the ANN have shown strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels.     

Lattice Boltzmann simulation of double diffusive natural convection in a square cavity with a hot square obstacle

Double diffusion convection in a cavity with a hot square obstacle inside is simulated using the lattice Boltzmann method. The results are presented for the Rayleigh numbers 104,105 and 106, the Lewis ...     

Physiopathology of pulmonary emphysema. Analysis of structural changes in the pulmonary architecture and therapeutic implications

In the pulmonary emphysema a serious respiratory inadequacy can result from the impossibility of the thoracic cage and of the diaphragm to expand beyond a maximum limit, before that a significnat part of pulmonary parenchyma has been destroyed by the pathogenic process. The resection of part of the lung, selectively in the areas where the emphysematous injuries are more pronounced, brings again the ventilation of the residual pulmonary parenchyma toward more physiological expansion values of the thoracic cage and diaphragm, thus decreasing the respiratory work, improving the ventilation mechanics and the bronchial obstruction. The time duration of the improvement achieved with the lung volume reduction is still to be demonstrated. A clinical and therapeutic analysis of this pathology is carried out.     

Knowledge-empowered agent information system for privacy payoff in eCommerce

Today, many online companies are gathering information and assembling sophisticated databases that know a great deal of information about many people, generally without the knowledge of those people. Such endeavor has resulted in the unprecedented attrition of individual??s right to informational self-determination. On the one hand, Consumers are powerless to prevent the unauthorized dissemination of their personal information, and on the other, they are excluded from its profitable commercial exchange. This paper focuses on developing knowledge-empowered agent information system for privacy payoff as a means of rewarding consumers for sharing their personal information with online businesses. The design of this system is driven by the following argument: if consumers?? personal information is a valuable asset, should they not be entitled to benefit from their asset as well? The proposed information system is a multi-agent system where several agents employ various knowledge and requirements for personal information valuation and interaction capabilities that most users cannot do on their own. The agents in the information system bear the responsibility of working on behalf of consumers to categorize their personal data objects, report to consumers on online businesses?? trust and reputation, determine the value of their compensation using risk-based financial models, and finally negotiate for a payoff value in return for the dissemination of users?? information. The details of the system as well as a proof-of-concept implementation using JADE (Java Agent Development Environment) are presented here.