Impact of non‐solvent on regeneration of cellulose dissolved in 1‐(carboxymethyl)pyridinium chloride ionic liquid

Cellulose dissolved in ionic liquid (1‐(carboxymethyl)pyridinium chloride)/water (60/40 w/w) mixture is regenerated in various non‐solvents, namely water, ethanol, methanol and acetone, to gain more insight into the contribution of non‐solvent medium to the morphology of regenerated cellulose. To this end, the initial and regenerated celluloses were characterized with respect to crystallinity, thermal stability, chemical structure and surface morphology using wide‐angle X‐ray diffraction, thermogravimetric analysis, Fourier transform infrared spectroscopy and scanning electron microscopy. According to the results, regardless of non‐solvent type, all regenerated samples have the same chemical structure and lower crystallinity in comparison to the initial cellulose, making them a promising candidate for efficient biofuel production based on enzymatic hydrolysis of cellulose. The reduction in crystallinity of regenerated samples is explained based on the potential of the non‐solvent to break the hydrogen bonds between cellulose chains and ionic liquid molecules as well as the affinity of water and non‐solvent which can be evaluated based on Hansen solubility parameter. The latter also determines the phase‐separation mechanism during the regeneration process, which in turn affects surface morphology of the regenerated cellulose. The pivotal effect of regenerated cellulose crystallinity on its thermal stability is also demonstrated. Regenerated cellulose with lower crystallinity is more susceptible to molecular rearrangement during heating and hence exhibits enhanced thermal stability. © 2019 Society of Chemical Industry     

Quasi-static and dynamic crushing behaviors of aluminum and steel tubes with a cutout

Tubular members are commonly used as an energy absorber in engineering structures and many such members have a cutout. In this study, the crushing behaviors of tubes with a cutout are characterized and the effects of cutout on the energy absorption capabilities of these tubes are quantified. Systematic parametric studies were carried out to study the effect of material properties, including yield and ultimate strength of material, strain rate effect, location of cutout, tube length and impact speed on the crushing behaviors and energy absorption capacity of aluminum and steel tubes. First, a numerical model was constructed with a commercial explicit finite element code. It will be first proven that the numerical simulation can produce sufficiently accurate results in an economic manner. Subsequently, the crushing behavior of aluminum and steel tubes with a cutout was experimentally characterized and their energy absorption capacity was evaluated in terms of mean crushing force, peak crushing force and specific energy absorption (SEA). Tubes of various lengths with a cutout located at different locations, subject to both quasi-static and dynamic impact loadings were considered. For steel tubes, the numerical simulation investigated the influence of the strain rate effect and variation in strain hardening ratio of the material. Empirical equations describing the mean and peak crushing forces of aluminum and steel tubes with a cutout were developed using linear and nonlinear regression methods applied to the results obtained from the numerical and experimental studies.     

THEORETICAL STUDY OF NONUNIFORM DROPLETS CONCENTRATION DISTRIBUTION ON VENTURI SCRUBBER PERFORMANCE

A new three-dimensional dispersion model has been developed and was used to study particulate removal operations in a venturi type scrubber. The model takes into account the effect of nonuniform droplets concentration distribution on the particulate removal efficiency of the scrubber of Brink and Contant (1958). Experimental data was used to test the results of the mathematical model. The results from the model are in good agreement with the experimental data. After validating the model, it was used to predict the effect of parameters such as liquid to gas flow rate ratio, gas throat velocity, and angle of the divergent section and nozzle diameter on the extent of nonuniformity of drops. Removal efficiency will increase with increasing uniformity of droplets concentration distribution. Thus, any factor that increases removal efficiency increases uniformity and vice versa. The factors affecting uniformity are: droplet diameter, liquid jet penetration length and size of scrubber.     

Bayesian estimation based on vague lifetime data

In Classical Bayesian approach, estimation of lifetime data usually is dealing with precise information. However, in real world, some informations about an underlying system might be imprecise and represented in the form of vague quantities. In these situations, we need to generalize classical methods to vague environment for studying and analyzing the systems of interest. In this paper, we propose the Bayesian estimation of failure rate and mean time to failure based on vague set theory in the case of complete and censored data sets. To employ the Bayesian approach, model parameters are assumed to be vague random variables with vague prior distributions. This approach will be used to induce the vague Bayes estimate of failure rate and mean time to failure by introducing and applying a theorem called “Resolution Identity” for vague sets. In order to evaluate the membership degrees of vague Bayesian estimate for these quantities, a computational procedure is investigated. In the proposed method, the original problem is transformed into a nonlinear programming problem which is then divided into eight subproblems to simplifying computations.     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.     

Attribute weighted Naive Bayes classifier using a local optimization

The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.     

Privacy-preserving biometric verification with outsourced correlation filter computation

Multimedia Tools and Applications - In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage,...     

Solving singular control problems using uniform trigonometrization method

This study investigates the application of a recently developed construct, the uniform trigonometrization method (UTM), to the singular control problems in chemical engineering. The UTM involves minimal modifications to the original problem, thereby generating near-singular control solutions that can be used for conceptual design and serve as an alternate to direct techniques like nested and simultaneous approaches. Eight classical singular control problems with known analytical solutions and three complex singular control problems from chemical engineering domain are solved in this study. The results obtained using the UTM for these problems are found to match well with the literature and are of higher resolution as compared to the results obtained using a direct pseudospectral-based solver. The ability of the UTM to handle complex chemical engineering problems with both singular controls and state path constraints has also been demonstrated in this study.     

Towards quantum reversible ternary coded decimal adder

Quantum ternary logic is a promising emerging technology for the future quantum computing. Ternary reversible logic circuit design has potential advantages over the binary ones like its logarithmic reduction in the number of qudits. In reversible logic all computations are done in an invertible fashion. In this paper, we propose a new quantum reversible ternary half adder with quantum cost of only 7 and a new quantum ternary full adder with a quantum cost of only 14. We termed it QTFA. Then we propose 3-qutrit parallel adders. Two different structures are suggested: with and without input carry. Next, we propose quantum ternary coded decimal (TCD) detector circuits. Two different approaches are investigated: based on invalid numbers and based on valid numbers. Finally, we propose the quantum realization of TCD adder circuits. Also here, two approaches are discussed. Overall, the proposed reversible ternary full adder is the best between its counterparts comparing the figures of merits. The proposed 3-qutrit parallel adders are compared with the existing designs and the improvements are reported. On the other hand, this paper suggested the quantum reversible TCD adder designs for the first time. All the proposed designs are realized using macro-level ternary Toffoli gates which are built on the top of the ion-trap realizable ternary 1-qutrit gates and 2-qutrit Muthukrishnan–Stroud gates.