Numerical simulation of planar shear flow passing a rotating cylinder at low Reynolds numbers

Two-dimensional shear flow over a rotating circular cylinder is investigated using lattice Boltzmann method. Simulations are performed at a fixed blockage ratio (B = 0.1) while the Reynolds number, nondimensional shear rate (K) and absolute rotational speed range as 80 ≤ Re ≤ 180, 0 ≤ K ≤ 0.2 and −2 ≤ β ≤ 2, respectively. To verify the simulation, the results are compared to previous experimental and numerical data. Quantitative information about the flow variables such as drag and lift coefficients, pressure coefficient and vorticity distributions on the cylinders is highlighted. It is found that, generally, with the increment in |β|, the absolute value of time average lift coefficient increases and time average drag coefficient decreases, and beyond a certain magnitude of β, the vortex shedding vanishes. It is also revealed that the drag coefficient decreases as the Reynolds number increases while the effect of the Reynolds number on lift is almost negligible. At the end, correlations for drag and lift coefficients ([`(C<sub>D</sub> )] ,[`(C_L )]){(\overline {C_D } ,\overline {C_L })} are extracted from the numerical data.     

Terahertz graphene optics

The magnitude of the optical sheet conductance of single-layer graphene is universal, and equal to e ²/4? (where 2??? = h (the Planck constant)). As the optical frequency decreases, the conductivity decreases. However, at some frequency in the THz range, the conductivity increases again, eventually reaching the DC value, where the magnitude of the DC sheet conductance generally displays a sample- and doping-dependent value between ??e ²/h and 100 e ²/h. Thus, the THz range is predicted to be a non-trivial region of the spectrum for electron transport in graphene, and may have interesting technological applications. In this paper, we present the first frequency domain measurements of the absolute value of multilayer graphene (MLG) and single-layer graphene (SLG) sheet conductivity and transparency from DC to 1 THz, and establish a firm foundation for future THz applications of graphene.      

Query optimization mechanisms in the cloud environments: A systematic study

The goal of a query optimizer is to provide an optimal Query Execution Plan (QEP) by comparing alternative query plans. In a distributed database system over cloud environment, the relations required by a query plan may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent plan alternatives to find an optimal QEP. Although it is not computationally reasonable to explore exhaustively all possible plans in such large search space. Although query optimization mechanisms are important in the cloud environments, to the best of our knowledge, there exists no complete and systematic review on investigating these issues. Therefore, in this paper, four categories to study these mechanisms are considered which are search‐based, machine learning‐based, schema‐based, and security‐based mechanisms. Also, this paper represents the advantages and disadvantages of the selected query optimization techniques and investigates the metrics of their techniques. Finally, the important challenges of these techniques are reviewed to develop more efficient query optimization techniques in the future.     

A Semi-Procedural Convolutional Material Prior

Lightweight material capture methods require a material prior, defining the subspace of plausible textures within the large space of unconstrained texel grids. Previous work has either used deep neural networks (trained on large synthetic material datasets) or procedural node graphs (constructed by expert artists) as such priors. In this paper, we propose a semi-procedural differentiable material prior that represents materials as a set of (typically procedural) grayscale noises and patterns that are processed by a sequence of lightweight learnable convolutional filter operations. We demonstrate that the restricted structure of this architecture acts as an inductive bias on the space of material appearances, allowing us to optimize the weights of the convolutions per-material, with no need for pre-training on a large dataset. Combined with a differentiable rendering step and a perceptual loss, we enable single-image tileable material capture comparable with state of the art. Our approach does not target the pixel-perfect recovery of the material, but rather uses noises and patterns as input to match the target appearance. To achieve this, it does not require complex procedural graphs, and has a much lower complexity, computational cost and storage cost. We also enable control over the results, through changing the provided patterns and using guide maps to push the material properties towards a user-driven objective.     

Test-Time Optimization for Video Depth Estimation Using Pseudo Reference Depth

In this paper, we propose a learning-based test-time optimization approach for reconstructing geometrically consistent depth maps from a monocular video. Specifically, we optimize an existing single image depth estimation network on the test example at hand. We do so by introducing pseudo reference depth maps which are computed based on the observation that the optical flow displacement for an image pair should be consistent with the displacement obtained by depth-reprojection. Additionally, we discard inaccurate pseudo reference depth maps using a simple median strategy and propose a way to compute a confidence map for the reference depth. We use our pseudo reference depth and the confidence map to formulate a loss function for performing the test-time optimization in an efficient and effective manner. We compare our approach against the state-of-the-art methods on various scenes both visually and numerically. Our approach is on average 2.5× faster than the state of the art and produces depth maps with higher quality.     

A sample-level DCNN for music auto-tagging

Multimedia Tools and Applications - Deep convolutional neural networks (DCNNs) has been widely used in music auto-tagging which is a multi-label classification task that predicts tags of audio...     

Nanostructured silver fibers: Facile synthesis based on natural cellulose and application to graphite composite electrode for oxygen reduction

The development of cheaper electrocatalysts for fuel cells is an important research area. This work proposes a new, simpler and low-cost approach to develop nanostructured silver electrocatalysts by using natural cellulose as a template. Silver was deposited by reduction of Ag complexes on the surface of cellulose fibers, followed by heat removal of the template to create self-standing nanostructured silver fibers (NSSFs). X-Ray diffraction (XRD) showed fcc silver phase and X-Ray photoelectron spectroscopy (XPS) demonstrated that the surface was partially oxidized. The morphology of the fibers consisted of 50 nm nanoparticles as the building blocks, and they possessed a specific surface area of about 25 m²/g, which is sufficiently high for electrocatalytic applications. The NSSFs were incorporated in a graphite composite electrode. The resulting modified electrode displayed a good electrocatalytic activity for the reduction of dissolved oxygen in basic media. In an O₂-saturated 0.1 M KOH solution, the overpotential to initiate the oxygen reduction reaction reduced and the limiting current increased by increasing the relative amount of silver fibers from 0 to 5 wt%.     

Modeling and solving a multi-objective supplier quota allocation problem considering transaction costs

Suppliers’ evaluation is a subject, which has attracted the attention of many researchers. The performance of potential suppliers is evaluated against multiple criteria rather than considering a single factor such as cost or quality. One of the major objectives of suppliers’ evaluation is to determine the optimal quota assigned to each supplier while needing to replenish an order. This problem has been studied by many researchers as a multi-objective problem. The usual objectives are minimizing the purchasing cost, rejected units, and late delivered units. However, in a few researches maximizing the evaluation scores of the selected suppliers is considered as fourth objective. In this paper, we present a model with five objectives including minimizing the transaction costs of purchasing from suppliers as well as the four addressed objectives. We convert the model to a single objective one using the well-known weighting method, solve it utilizing two meta-heuristic algorithms, and analyze the efficiency of the heuristics. The reason why we utilize the meta-heuristic algorithms is that the problem is proved to be an NP-hard one.     

Fabrication and characterization of polyvinyl chloride mixed matrix membranes containing high aspect ratio anatase titania and hydrous manganese oxide nanoparticle for efficient removal of heavy metal ions: Competitive removal study

In this study, novel polyvinyl chloride (PVC) ultrafiltration mixed matrix membranes (MMMs) containing high aspect ratio anatase titania (ANT) and hydrous manganese oxide (HMO) nanoparticles were synthesized in order to decontaminate cadmium and copper metal ions. ANT and HMO nanoparticles with various loadings in a range of 5-15 were used in the casting solution of MMMs. The characterization of fabricated MMMs was carried out with respect to the structural morphology, hydrophilicity, and composition by scanning electron microscopy (SEM), water contact angle, and Fourier transform infrared spectroscopy (FTIR), respectively. With an increase in the nanoparticle loadings, the change in other membrane characteristics such as mean pore size, pure water flux, and porosity were also evaluated. The results revealed that the mean pore size and water flux increased at a higher loading of ANT and HMO nanoparticles, while the contact angle and porosity of the membranes showed reverse trends. Moreover, the higher flux of pure water was obtained for PVC-ANT MMMs compared to PVC-HMO MMMs because of the larger mean pore size, higher porosity, and hydrophilicity of PVC-ANT MMMs. In this study, the decontamination of cadmium and copper metal ions in single and binary systems of heavy metals was investigated and the effect of Mn²⁺ (as an interfering ion) was also studied. The evaluation of the metal ion removal data demonstrated that the affinity sequence of both the PVC-ANT-15 and PVC-HMO-15 MMMs for heavy metal removal was Cu²⁺ > Cd²⁺ > Mn²⁺ in the single and binary systems. It was found through ultrafiltration (UF) experiments that PVC-ANT MMM was the most efficient membrane in the elimination of heavy metals due to the superior ANT adsorption capacity. However, the overall findings disclosed that both ANT and HMO nanoparticles are suitable candidates for the preparation of MMMs used in cadmium and copper decontamination from aqueous solutions.     

Divided-wall distillation column design using molecular tracking

Divided-wall column (DWC) is an intensified separation process and so far developing a simple procedure for designing these units has been challenging. In this work, the concept of molecular tracking has been integrated with conventional methods to build a simple and easy-to-use methodology for designing DWCs for multicomponent separations. Application of the proposed approach is highlighted through several three- and four-component mixtures. The configuration obtained using molecular tracking gives a design with lower energy demands for the column reboiler, compared to other design methodology, which directly impacts the OPEX of the system.