Flame synthesis of graphene films in open environments

Few-layer graphene is grown on copper and nickel substrates at high rates using a novel flame synthesis method in open-atmosphere environments. Transmittance and resistance properties of the transferred films are similar to those grown by other methods, but the concentration of oxygen, as assessed by X-ray photoelectron spectroscopy, is actually less than that for graphene grown by chemical vapor deposition under near vacuum conditions. The method involves utilizing a multi-element inverse-diffusion-flame burner, where post-flame species and temperatures are radially-uniform upon deposition at a substrate. Advantages of the specific flame synthesis method are scalability for large-area surface coverage, increased growth rates, high purity and yield, continuous processing, and reduced costs due to efficient use of fuel as both heat source and reagent. Additionally, by adjusting local growth conditions, other carbon nanostructures (i.e. nanotubes) are readily synthesized.     

A 6-GHz capacitively-coupled Colpitts CMOS quadrature voltage controlled oscillator

In this work a new low-noise low-power Colpitts quadrature voltage controlled oscillator (QVCO) made by coupling two identical current-switching differential Colpitts voltage controlled oscillators (VCO) is proposed; coupling of the VCOs is done using some capacitors in an “in-phase anti-phase” scheme. In this coupling configuration first harmonics (as well as higher harmonics) from each VCO are injected to the other VCO, as opposed to coupling schemes in which only even harmonics are injected. An analysis of the linearized circuit which confirms 90° phase difference between output signals of the proposed circuit is presented. Since no extra noise sources or power consumption are introduced to the core VCOs, the proposed QVCO achieves low phase noise performance and low power consumption. The proposed circuit is designed and simulated in a commercial 0.18 μm CMOS technology. The simulated phase noise of the proposed QVCO at 3 MHz offset frequency is ?138.3 dBc/Hz, at 6 GHz. The circuit dissipates 8.16 mW from a 1.8 V supply and its frequency can be tuned from 5.6 to 6.3 GHz.     

A High Efficiency Fully Integrated OOK Transmitter for WBAN

A 2.4 GHz high efficiency radio frequency(RF) transmitter for wireless body area network(WBAN) in medical applications is presented in this paper. The transmitter architecture with high energy efficiency is proposed to achieve a high data rate with low power consumption. In conventional transmitters,the oscillator and power amplifier are turned off when the transmitter sends 0. The required time for turning oscillator ON/OFF is longer than the other blocks of the transmitter. In the proposed transmitter, the low power oscillator is on all the time while the power amplifier and modulator are turned off when "0" data is sent. The transmitter consumes 3.2 mW at 0.5 dBm output by 285 Mbps data rate and the energy consumption per transmitted bit with 0.5 dBm output power is 10pJ/(bitmW). The proposed transmitter was designed in0.18 μm CMOS technology.     

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 Novel Cultural Crowd Model Toward Cognitive Artificial Intelligence

Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements; time, capacity, speed, and culture. Cross-cultural variations are increasing the complexity level because each mass and event have different characteristics and challenges. However, no prior study has employed the six Hofstede Cultural Dimensions (HCD) for predicting crowd behaviors. This study aims to develop the Cultural Crowd-Artificial Neural Network (CC-ANN) learning model that considers crowd’s HCD to predict their physical (distance and speed) and social (collectivity and cohesion) characteristics. The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated regulation plan’s time and capacity. We designed the experiments as four groups to analyze the proposed model’s outcomes and extract the interrelations between the HCD of crowd’s grouped individuals and their physical and social characteristics. Furthermore, the extracted interrelations were verified with the dataset’s statistical correlation analysis with a P-value < 0.05. Results demonstrate that the predicted crowd’s characteristics were positively/negatively affected by their considered cultural features. Similarly, analyzing outcomes identified the most influential HCD for predicting crowd behavior. The results also show that the CC-ANN model improves the prediction and learning performance for the crowd behavior because the achieved accepted level of accuracy does not exceed 10 to 20 epochs in most cases. Moreover, the performance improved by 90%, 93% respectively in some cases. Finally, all prediction best cases were related to one or more cultural features with a low error of 0.048, 0.117, 0.010, and 0.014 mean squared error, indicating a novel cultural learning model.     

Proposed soil classification based on the experiences of soft-ground tunneling in Iran

Bulletin of Engineering Geology and the Environment - An essential step in successful mechanized and conventional tunneling operations is a thorough characterization of ground conditions and...     

Application of modified complementary reservoir approach in analysis of water distribution networks under pressure-deficient conditions

Hydraulic simulation models which simulate water distribution systems in different operating conditions are essential tools to evaluate network reliability. Simulation models found in most commercial software are not effectively capable of analyzing demand nodes under critical conditions such as fire-fighting demand or network pipe breakage. In the current study, a combination of hydraulic model and complementary reservoir solution (CRS) method is used for solving network's problems in critical conditions for both series-looped networks and a part of water network in Ilam city (Iran). Obtained results show that CRS provide more than actual need on the demand node for some reservoir total pressure heads. Thus, two modified versions of CRS method are proposed to deal with failures of CRS in such cases. Obtained results demonstrate improved efficiency in the combined model for analyzing networks in abnormal conditions.     

Role of catalytic protein and stabilising agents in the transformation of Ag ions to nanoparticles by Pseudomonas aeruginosa

Biological routes of synthesising metal nanoparticles (NPs) using microbes have been gaining much attention due to their low toxicity and eco‐friendly nature. Pseudomonas aeruginosa JP2 isolated from metal contaminated soil was evaluated towards extracellular synthesis of silver NPs (AgNPs). Cell‐free extract (24 h) of the bacterial isolate was reacted with AgNO₃ for 24 h in order to fabricate AgNPs. Preliminary observations were recorded in terms of colour change of the reaction mixture from yellow to greyish black. UV‐visible spectroscopy of the reaction mixture has shown a progressive increase in optical densities that correspond to peaks near 430 nm, depicting reduction of ionic silver (Ag⁺) to atomic silver (Ag⁰) thereby synthesising NPs. X‐ray diffraction spectra exhibited the 2θ values to be 38.4577° confirming the crystalline and spherical nature of NPs [9.6 − 26.7 (Ave. = 17.2 nm)]. Transmission electron microscopy finally confirmed the size of the particles varying from 5 to 60 nm. Moreover, rhamnolipids and proteins were identified as stabilising molecules for the AgNPs through Fourier transform‐infrared spectroscopy. Characterisation of bacterial crude and purified protein fractions confirmed the involvement of nitrate reductase (molecular weight 66 kDa and specific activity = 3.8 U/mg) in the Synthesis of AgNPs.Inspec keywords: microorganisms, silver, nanoparticles, enzymes, molecular biophysics, ultraviolet spectra, visible spectra, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectra, catalysis, biochemistry, nanobiotechnologyOther keywords: catalytic protein, stabilising agents, Pseudomonas aeruginosa, metal nanoparticles, UV–visible spectroscopy, optical densities, ionic silver, atomic silver, X‐ray diffraction spectra, transmission electron microscopy, nitrate reductase, rhamnolipids, Fourier transform‐infrared spectroscopy, Ag     

Single-machine earliness–tardiness scheduling with two competing agents and idle time

Two-agent scheduling has gained a lot of research attention recently. Two competing agents who have their own objective functions have to perform their respective set of jobs on one or more shared machines. This study considers a two-agent single-machine earliness and tardiness scheduling problem where jobs have distinct due dates and unforced idleness in between any two consecutive jobs is allowed. The objective is to minimize the total earliness and tardiness of jobs from one agent given that the maximum earliness–tardiness of jobs from the other agent cannot exceed an upper bound. In other words, each job from the second agent has a hard due window, whereas each job from the first agent will incur a penalty if completed either before or after its due date. Two mathematical models of the problem are presented, and several necessary optimality conditions are derived. By exploiting the established dominance properties, heuristic algorithms are developed for the problem. Finally, computational experiments are conducted to assess the models and heuristic procedures.     

Sustainable product‐package design in a food supply chain: A multi‐criteria life cycle approach

This paper presents a multi‐criteria decision‐making approach for the selection of a sustainable product‐package design, accounting for the different actors within a food supply chain. The study extends the focus of sustainable packaging design to the collective of all supply chain actors. Decision criteria are identified via a literature review, and current product‐package alternatives are collected via interviews. With the inputs of these criteria and the alternative designs, a multi‐criteria decision‐making problem is formulated and solved using Best Worst Method (BWM). BWM finds the weights of the criteria. Using these weights, the ranking of the alternatives is found. The implementation of the analysis took place for three selected products of the Kraft Heinz Company. Data on the preferences of the supply chain members of these selected products were collected, and the optimal package designs were selected. It is shown through sensitivity analysis that modifying the weights that decision makers assign to the preferences of the supply chain members and the importance of the dimensions of sustainability have an effect on the selection of the optimal design.