Stability enhancement of garlic essential oil using new opopanax gum/gelatin nanofibres

Opopanax gum/gelatin nanofibre was produced by the electrospinning method, and the efficiency of this new technique for encapsulation of garlic essential oil was investigated. Fourier-transform infrared spectroscopy results showed the physical and weak interactions between opopanax gum and gelatin, as well as the improvement of their thermal stability in the nanofibres structure. Incorporating 10% garlic essential oil based on biopolymer weight in nanofibres was considered the best percentage. The results confirmed the presence of garlic essential oil in the nanofibres and the improvement of its thermal stability by entrapment to the nanofibre structure. The data of analysis of the stability of garlic essential oil in the forms of free and encapsulated conditions showed that its stability increased at 25 °C from 20 days to more than 60 days. It indicated the efficiency of opopanax gum/gelatin nanofibres as an applicable coating material for stability enhancement of essential oils.     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.     

Development of artificial neural networks for real time, in situ ellipsometry data reduction

Development of real time in situ monitoring and control of thin film depositions using ellipsometry requires both data acquisition and processing to be rapid. Present speeds of measurement and computation of basic parameters, Ψ and Δ, are sufficient for data acquisition which is essentially real time. However, computation of film parameters, such as thickness and optical properties, generally cannot keep up with the incoming data and must be performed in a batch mode after the deposition.

This work describes the development of enhanced, high speed data reduction algorithms using artificial neural networks (ANN). The networks are trained using computed data and subsequently give values of film parameters in the millisecond time regime. The ANN outputs are used as initial estimates in a variably damped least squares algorithm for accuracy improvement. The combination of these two algorithms provides very accurate solutions in 75 ms per point on a DEC VAX 8800 multiprocessor system running at a combined 12 Mips. This speed is suitable for real time film monitoring and control for growth rates up to 10 nm per second. Results for fixed angle of incidence, single wavelength, in situ data for Ni deposited on BK7 substrates are presented.     

Advances in the Application of Magnetic Nanoparticles for Sensing

Magnetic nanoparticles (MNPs) are of high significance in sensing as they provide viable solutions to the enduring challenges related to lower detection limits and nonspecific effects. The rapid expansion in the applications of MNPs creates a need to overview the current state of the field of MNPs for sensing applications. In this review, the trends and concepts in the literature are critically appraised in terms of the opportunities and limitations of MNPs used for the most advanced sensing applications. The latest progress in MNP sensor technologies is overviewed with a focus on MNP structures and properties, as well as the strategies of incorporating these MNPs into devices. By looking at recent synthetic advancements, and the key challenges that face nanoparticle‐based sensors, this review aims to outline how to design, synthesize, and use MNPs to make the most effective and sensitive sensors.     

Study the effect of distribution of density of states on the depletion width of organic Schottky contacts

Organic semiconductor to metal Schottky contacts have been widely used in electronic devices and to investigate the properties of organic semiconductors. In designing and characterizing these devices the full depletion approximation is used. The analytical and numerical simulations presented in this paper suggest that this approximation is not generally valid. Simulations of a Schottky contact between regioregular-poly 3 hexylthiophene (rr-P3HT) and aluminum show that this approximation becomes worse as molecular order decreases, with the potential profile increasingly deviating from the expected quadratic function of position. Also the depletion width decreasing well below that predicted using the approximation. In this work the slope of the band tail is used as a measure of disorder.     

Calculation of multicomponent vapor–liquid critical point using the Dividing Rectangles global optimization algorithm

In this paper, the Dividing Rectangles (D.R.) global optimization technique is used to predict the critical temperature and pressure for multicomponent systems. The D.R. optimization algorithm is a fast and reliable optimization method without any need for adjustable parameters, initial guesses or objective function derivation. For calculating the critical point the tangent plane distance (TPD) in terms of the Helmholtz energy is selected. The Peng Robinson (PR) equation of state is applied for determining thermodynamic relations. The procedure of the critical point calculation by means of global optimization is described stepwise in order to clarify the concept and method of programing. The D.R.-based computer program is tested for some multicomponent systems and the results are compared with the Simulated Annealing (SA) global optimization method and with experimental data. The results indicated that the number of the objective function calls (the main parameter that increases the speed and efficiency of an optimization algorithm) is reduced to one twentieth with almost the same accuracy.     

Laparoscopy for the impalpable testes. Initial experience of one center

Endoscopic treatment of acute pancreatitis has been proposed in different conditions: acute biliary pancreatitis, pancreatitis related to pancreas divisum or in case of pseudocysts. The efficacy of such endoscopic procedure is discussed.     

A kinetic study on the reduction of manganese III in presence of citric acid

The rate of reduction of manganese III stabilised by citric acid under variable conditions of Mn(III) concentration, ionic strength, pH and temperature in buffered media was found to obey a second order rate equation, first order with respect to Mn(III) autocatalysed by Mn(II). The reduction was found to proceed to an equilibrium value. The observed rate constants were found to follow a linear relationship with hydrogen ion concentration. Interpretation of results is based on the assumption that there are two reducible species in equilibrium; the aquocomplex and its hydrolysed form. A reaction mechanism is proposed to account for the experimental results. The overall energy (ΔE) and entropy (ΔS) of activation are compared with those for the reduction of Mn(III) in presence of other organic substrates.     

Applying MLP-ANN as a novel and accurate method to estimate gas density

In the gas industries, to increase the degree of accuracy of calculation and estimation in different processes, the importance of accurate prediction of gas properties is highlighted. The gas density, as one of the key properties in gas engineering, has a major effect in calculations. So, in the present paper, multi-layer perceptron artificial neural network (MLP-ANN) was used to predict the gas density based on molecular weight, critical pressure and critical temperature of gas, pressure, and temperature. To this end, a total number of 1240 reliable data of gas density were gathered from literature for the training and testing phases. The MLP-ANN outputs were compared with the actual data in different manners, such as statistical and graphical analyses. The coefficient of determination (R²), average absolute relative deviation (AARD), and root mean squared error (RMSE) for overall process were calculated as 1, 0.0088444, and 0.0259, respectively. The determined parameters and graphical analysis showed that the MLP-ANN has great potential and high degree of accuracy in gas density estimation.