Steady and unsteady flow analysis in microdiffusers and micropumps: a critical review

In recent research, there has been a growing interest in the analysis of flow through microdiffusers and micropumps in order to characterize and optimize the performance of these devices. In this review, the recent advances in the numerical and experimental analysis of the steady and pulsating flows through microdiffusers and valveless micropumps are surveyed. The differences between the performance of microdiffusers and micropumps in steady and unsteady flow regimes are described. Qualitative and quantitative discussions of the effects of different design parameters on the performance of microdiffusers and valveless micropumps in both steady and unsteady flow regimes along with the contradictory results reported in the literature in this regard are provided. In addition, a summary of the latest micropump technologies along with the advantages and disadvantages of each mechanism with the emphasis on the innovative and less-reviewed micropumps are presented. Two important types of fixed microvalves, as part of valveless micropumps are described in details. Experimental flow visualization of steady and pulsating flows through microdiffusers and micropumps as a useful tool for better understanding the underlying micro-fluid dynamics is discussed. The present review reveals that there are many possible areas of research in the field of steady and unsteady flows through microdiffusers and micropumps in order to understand the effects of all important design parameters on the performance of these devices.     

Fabrication of biopolymer based nanocomposite wound dressing: evaluation of wound healing properties and wound microbial load

The aim of this study is to introduce natural‐based polymers, chitosan and starch, to design a remedial nanocomposite, comprising of cerium oxide nanoparticles and silver nanoparticles, to investigate their effects in accelerating wound healing and in wound microbial load. Cerium oxide nanoparticles synthesized in starch solution added to the colloidal dispersion of synthesized silver nanoparticles in chitosan to make a three‐component nanomaterial. Mice were anaesthetized and two parallel full‐thickness round wounds were excised under aseptic conditions with the help of sterile dermal biopsy punch. Furthermore, effects of silver‐chitosan and silver‐cerium‐chitosan nanocomposite had evaluated on rate of wound closure and collagen density and on microbial load of wound in full‐thickness model. Results showed that both silver chitosan and silver‐cerium‐chitosan had significant impact on acceleration of wound closure and collagen content and on reduction of wound microbial load in comparison with control group, which was, received no treatments. However, the silver‐cerium‐chitosan nanocomposite is more potent than silver‐chitosan group and control group in wound closure. The wound healing effects of silver‐cerium‐chitosan nanocomposite are due to unique features of its three components and this nanocomposite promises impressive remedies for clinical application.Inspec keywords: wounds, nanocomposites, nanomedicine, nanoparticles, proteins, cerium, silver, polymers, colloids, patient treatmentOther keywords: biopolymer‐based nanocomposite wound dressing, wound healing properties, wound microbial load, natural‐based polymers, chitosan, remedial nanocomposite, cerium oxide nanoparticles, nanoceria, silver nanoparticles, starch solution, three‐component nanomaterial, synthesised silver nanoparticles, ketamine intraperitoneal injection, silver‐cerium‐chitosan nanocomposite, wound closure, collagen density, wound healing effects, wound care, aseptic conditions, sterile dermal biopsy punch, Ag‐Ce     

Bioinspired Nacre‐Like Ceramic with Nickel Inclusions Fabricated by Electroless Plating and Spark Plasma Sintering

Hybrid composites of layered brittle‐ductile constituents assembled in a brick‐and‐mortar architecture are promising for applications requiring high strength and toughness. Mostly, polymer mortars have been considered as the ductile layer in brick‐and‐mortar composites. However, low stiffness of polymers does not efficiently transfer the shear between hard ceramic bricks. Theoretical models point to metals as a more efficient mortar layer. However, infiltration of metals into ceramic scaffold is non‐trivial, given the low wetting between metals and ceramics. The authors report on an alternative approach to fabricate brick‐and‐mortar ceramic‐metal composites by using electroless plating of nickel (Ni) on alumina micro‐platelets, in which Ni‐coated micro‐platelets are subsequently aligned by a magnetic field, taking advantage of ferromagnetic properties of Ni. The assembled Ni‐coated ceramic scaffold is then sintered using spark plasma sintering (SPS) to locally create Ni mortar layers between ceramic platelets, as well as to sinter the ceramic micro‐platelets. The authors report on materials and mechanical properties of the fabricated composite. The results show that this approach is promising toward development of bioinspired ceramic‐metal composites.

     

Controlled gates for multi-level quantum computation

Multi-level (ML) quantum logic can potentially reduce the number of inputs/outputs or quantum cells in a quantum circuit which is a limitation in current quantum technology. In this paper we propose theorems about ML-quantum and reversible logic circuits. New efficient implementations for some basic controlled ML-quantum logic gates, such as three-qudit controlled NOT, Cycle, and Self Shift gates are proposed. We also propose lemmas about r-level quantum arrays and the number of required gates for an arbitrary n-qudit ML gate. An equivalent definition of quantum cost (QC) of binary quantum gates for ML-quantum gates is introduced and QC of controlled quantum gates is calculated.     

Simulation model for natural gas transmission pipeline network system

This paper focuses on developing a simulation model for the analysis of transmission pipeline network system (TPNS) with detailed characteristics of compressor stations. Compressor station is the key element in the TPNS since it provides energy to keep the gas moving. The simulation model is used to create a system that simulates TPNS with different configurations to get pressure and flow parameters. The mathematical formulations for the TPNS simulation were derived from the principles of flow of fluid through pipe, mass balance and compressor characteristics. In order to determine the unknown pressure and flow parameters, a visual C++ code was developed based on Newton–Raphson solution technique. Using the parameters obtained, the model evaluates the energy consumption for various configurations in order to guide for the selection of optimal TPNS. Results from the evaluations of the model with the existing TPNS and comparison with the existing approaches showed that the developed simulation model enabled to determine the operational parameters with less than 10 iterations. Hence, the simulation model could assist in decisions regarding the design and operations of the TPNS.     

Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems

The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. An innovative data-driven FDD methodology has been presented in this paper on the basis of a distributed configuration of three adaptive neuro-fuzzy inference system (ANFIS) classifiers for an industrial 440 MW power plant steam turbine with once-through Benson type boiler. Each ANFIS classifier has been developed for a dedicated category of four steam turbine faults. A preliminary set of conceptual and experimental studies has been conducted to realize such fault categorization scheme. A proper selection of four measured variables has been configured to feed each ANFIS classifier with the most influential diagnostic information. This consequently leads to a simple distributed FDD system, facilitating the training and testing phases and yet prevents operational deficiency due to possible cross-correlated measured data effects. A diverse set of test scenarios has been carried out to illustrate the successful diagnostic performances of the proposed FDD system against 12 major faults under challenging noise corrupted measurements and data deformation corresponding to a specific fault time history pattern.     

Online learning of task-driven object-based visual attention control

We propose a biologically-motivated computational model for learning task-driven and object-based visual attention control in interactive environments. In this model, top-down attention is learned interactively and is used to search for a desired object in the scene through biasing the bottom-up attention in order to form a need-based and object-driven state representation of the environment. Our model consists of three layers. First, in the early visual processing layer, most salient location of a scene is derived using the biased saliency-based bottom-up model of visual attention. Then a cognitive component in the higher visual processing layer performs an application specific operation like object recognition at the focus of attention. From this information, a state is derived in the decision making and learning layer. Top-down attention is learned by the U-TREE algorithm which successively grows an object-based binary tree. Internal nodes in this tree check the existence of a specific object in the scene by biasing the early vision and the object recognition parts. Its leaves point to states in the action value table. Motor actions are associated with the leaves. After performing a motor action, the agent receives a reinforcement signal from the critic. This signal is alternately used for modifying the tree or updating the action selection policy. The proposed model is evaluated on visual navigation tasks, where obtained results lend support to the applicability and usefulness of the developed method for robotics.     

Attention control with reinforcement learning for face recognition under partial occlusion

In this paper a new method for handling occlusion in face recognition is presented. In this method the faces are partitioned into blocks and a sequential recognition structure is developed. Then, a spatial attention control strategy over the blocks is learned using reinforcement learning. The outcome of this learning is a sorted list of blocks according to their average importance in the face recognition task. In the recall mode, the sorted blocks are employed sequentially until a confident decision is made. Obtained results of various experiments on the AR face database demonstrate the superior performance of proposed method as compared with that of the holistic approach in the recognition of occluded faces.     

Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition

This paper presents a novel learning approach for Face Recognition by introducing Optimal Local Basis. Optimal local bases are a set of basis derived by reinforcement learning to represent the face space locally. The reinforcement signal is designed to be correlated to the recognition accuracy. The optimal local bases are derived then by finding the most discriminant features for different parts of the face space, which represents either different individuals or different expressions, orientations, poses, illuminations, and other variants of the same individual. Therefore, unlike most of the existing approaches that solve the recognition problem by using a single basis for all individuals, our proposed method benefits from local information by incorporating different bases for its decision. We also introduce a novel classification scheme that uses reinforcement signal to build a similarity measure in a non-metric space. Experiments on AR, PIE, ORL and YALE databases indicate that the proposed method facilitates robust face recognition under pose, illumination and expression variations. The performance of our method is compared with that of Eigenface, Fisherface, Subclass Discriminant Analysis, and Random Subspace LDA methods as well.     

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.