Optimal oxygen concentration strategy through an isothermal oxidative coupling of methane plug flow reactor to obtain a high yield of C2 hydrocarbons

An optimal oxygen concentration trajectory in an isothermal OCM plug flow reactor for maximizing C₂ production was determined by the algorithm of piecewise linear continuous optimal control by iterative dynamic programming (PLCOCIDP). The best performance of the reactor was obtained at 1,085 K with a yield of 53.9%; while, at its maximum value, it only reached 12.7% in case of having no control on the oxygen concentration along the reactor. Also, the effects of different parameters such as reactor temperature, contact time, and dilution ratio (N₂/CH₄) on the yield of C₂ hydrocarbons and corresponding optimal profile of oxygen concentration were studied. The results showed an improvement of C₂ production at higher contact times or lower dilution ratios. Furthermore, in the process of oxidative coupling of methane, controlling oxygen concentration along the reactor was more important than controlling the reactor temperature. In addition, oxygen feeding strategy had almost no effect on the optimum temperature of the reactor. Finally, using the optimal oxygen strategy along the reactor has more effect on ethylene selectivity compared to ethane.     

Microfluidic-Assisted CTC Isolation and In Situ Monitoring Using Smart Magnetic Microgels

Capturing rare disease-associated biomarkers from body fluids can offer an early-stage diagnosis of different cancers. Circulating tumor cells (CTCs) are one of the major cancer biomarkers that provide insightful information about the cancer metastasis prognosis and disease progression. The most common clinical solutions for quantifying CTCs rely on the immunomagnetic separation of cells in whole blood. Microfluidic systems that perform magnetic particle separation have reported promising outcomes in this context, however, most of them suffer from limited efficiency due to the low magnetic force generated which is insufficient to trap cells in a defined position within microchannels. In this work, a novel method for making soft micromagnet patterns with optimized geometry and magnetic material is introduced. This technology is integrated into a bilayer microfluidic chip to localize an external magnetic field, consequently enhancing the capture efficiency (CE) of cancer cells labeled with the magnetic nano/hybrid microgels that are developed in the previous work. A combined numerical-experimental strategy is implemented to design the microfluidic device and optimize the capturing efficiency and to maximize the throughput. The proposed design enables high CE and purity of target cells and real-time time on-chip monitoring of their behavior. The strategy introduced in this paper offers a simple and low-cost yet robust opportunity for early-stage diagnosis and monitoring of cancer-associated biomarkers.     

An adaptive heuristic to the bounded-diameter minimum spanning tree problem

Given a graph G and a bound d?≥?2, the bounded-diameter minimum spanning tree problem seeks a spanning tree on G of minimum weight subject to the constraint that its diameter does not exceed d. This problem is NP-hard; several heuristics have been proposed to find near-optimal solutions to it in reasonable times. A decentralized learning automata-based algorithm creates spanning trees that honor the diameter constraint. The algorithm rewards a tree if it has the smallest weight found so far and penalizes it otherwise. As the algorithm proceeds, the choice probability of the tree converges to one; and the algorithm halts when this probability exceeds a predefined value. Experiments confirm the superiority of the algorithm over other heuristics in terms of both speed and solution quality.     

Automatic Detection of Words Associations in Texts Based on Joint Distribution of Words Occurrences

In this article, we propose a novel approach for measuring word association based on the joint occurrences distribution in a text. Our approach relies on computing a sum of distances between neighboring occurrences of a given word pair and comparing it with a vector of randomly generated occurrences. The idea behind this assumption is that if the distribution of co‐occurrences is close to random or if they tend to appear together less frequently than by chance, such words are not semantically related. We devise a distance function S that evaluates the words association rate. Using S, we build a concept tree, which provides a visual and comprehensive representation of keywords association in a text. In order to illustrate the effectiveness of our algorithm, we apply it to three different texts, showing the consistency and significance of the obtained results with respect to the semantics of documents. Finally, we compare the results obtained by applying our proposed algorithm with the ones achieved by both human experts and the co‐occurrence correlation method. We show that our method is consistent with the experts' evaluation and outperforms with respect to the co‐occurrence correlation method.     

A learning approach to the bandwidth multicolouring problem

In this article, a generalisation of the vertex colouring problem known as bandwidth multicolouring problem (BMCP), in which a set of colours is assigned to each vertex such that the difference between the colours, assigned to each vertex and its neighbours, is by no means less than a predefined threshold, is considered. It is shown that the proposed method can be applied to solve the bandwidth colouring problem (BCP) as well. BMCP is known to be NP-hard in graph theory, and so a large number of approximation solutions, as well as exact algorithms, have been proposed to solve it. In this article, two learning automata-based approximation algorithms are proposed for estimating a near-optimal solution to the BMCP. We show, for the first proposed algorithm, that by choosing a proper learning rate, the algorithm finds the optimal solution with a probability close enough to unity. Moreover, we compute the worst-case time complexity of the first algorithm for finding a 1/(1–?) optimal solution to the given problem. The main advantage of this method is that a trade-off between the running time of algorithm and the colour set size (colouring optimality) can be made, by a proper choice of the learning rate also. Finally, it is shown that the running time of the proposed algorithm is independent of the graph size, and so it is a scalable algorithm for large graphs. The second proposed algorithm is compared with some well-known colouring algorithms and the results show the efficiency of the proposed algorithm in terms of the colour set size and running time of algorithm.     

Multi-focus image fusion for visual sensor networks in DCT domain

The objective of image fusion is to combine relevant information from multiple images into a single image. The discrete cosine transform (DCT) based methods of image fusion are more efficient and time-saving in real-time systems using DCT based standards of still image or video. Existing DCT based methods are suffering from some undesirable side effects like blurring or blocking artifacts which reduce the quality of the output image. Furthermore, some of these methods are rather complex and this contradicts the concept of the simplicity of DCT based algorithms. In this paper, an efficient approach for fusion of multi-focus images based on variance calculated in DCT domain is presented. Due to simplicity of our proposed method, it can be easily used in real-time applications. The experimental results verify the efficiency improvement of our method both in output quality and complexity reduction in comparison with several recent proposed techniques.     

Analytical investigation and numerical verification of Casimir effect on electrostatic nano-cantilevers

In this paper, the two-point boundary value problem (BVP) of the nano-cantilever deflection subjected to Casimir and electrostatic forces is investigated using analytical and numerical methods to obtain the instability point of the nano-beam. In the analytical treatment of the BVP, the nonlinear differential equation of the model is transformed into the integral form by using the Green’s function of the cantilever beam. Then, closed-form solutions are obtained by assuming an appropriate shape function for the beam deflection to evaluate the integrals. The pull-in parameters of the beam are computed under the combined effects of electrostatic and Casimir forces. Electrostatic microactuators and freestanding nanoactuators are considered as special cases of our study. The detachment length and the minimum initial gap of freestanding nanocantilevers, which are the basic design parameters for NEMS switches, are determined. The results of the analytical study are verified by numerical solution of the BVP. The centerline of the beam under the effect of electrostatic and Casimir forces at small deflections and at the point of instability is obtained numerically to test the validity of the shape function assumed for the beam deflection in the analytical investigation. Finally, the large deformation theory is applied in numerical simulations to study the effect of the finite kinematics on the pull-in parameters of nano-canilevers.     

Statistical, DCT and vector quantisation-based video codec

The authors present a novel hybrid statistical, DCT and vector quantisation-based video-coding technique. In intra mode of operation, an input frame is divided into a number of non-overlapping pixel blocks. A discrete cosine transform then converts the coefficients in each block into the frequency domain. Coefficients with the same frequency index at different blocks are put together generating a number of matrices, where each matrix contains the coefficients of a particular frequency index. The matrix, which contains the DC coefficients, is losslessly coded. Matrices containing high frequency coefficients are coded using a novel statistical encoder. In inter mode of operation, overlapped block motion estimation / compensation is employed to exploit temporal redundancy between successive frames and generates a displaced frame difference (DFD) for each inter-frame. A wavelet transform then decomposes the DFD-frame into its frequency subbands. Coefficients in the detail subbands are vector quantised while coefficients in the baseband are losslessly coded. To evaluate the performance of the codec, the proposed codec and the adaptive subband vector quantisation (ASVQ) video codec, which has been shown to outperform H.263 at all bitrates, were applied to a number of test sequences. Results indicate that the proposed codec outperforms the ASVQ video codec subjectively and objectively at all bitrates.     

The effect of concentration on gas sensor model based on graphene nanoribbon

Graphene nanoribbon (GNR), a superior material with two-dimensional structure and monolayer honeycomb of carbon, is noteworthy and important in all fields’ mainly electronic, chemistry, biology, physics and nanotechnology. Recently, observing about sensors demonstrates that for better accuracy, faster response time and enlarged sensitivity, it needs to be improved. Nowadays, carbon-based equipments as an exclusive substance are remarkable in the sensing technology. High conductivity as unique properties caused that graphene can be used in biological applications. Gas sensor based on graphene can be supposed to have great sensitivity for gas molecules detection. In this study, graphene-based carbon dioxide sensor analytically is modeled. In addition, new methods of gas sensor model based on the gradient of GNR conductance are provided. Also, a field effect transistor-based structure as a modeling platform is suggested. Ultimately, optimum model is evaluated by comparison study between analytical model and experimental performance.