Preparation and characterization of poly(vinyl alcohol)/gum tragacanth/cellulose nanocomposite film

The effects of gum tragacanth obtained from two species of Astragalus Gossypinus (GT-G) and A. Parrowianus (GT-P) at two levels of 10% and 30% combined with cellulose nanofibers (CNF; 5%) on the physico-mechanical and structural properties of polyvinyl alcohol (PVA) nanocomposite film were investigated in this study. The water solubility and water vapor permeability of the films decreased with increasing the content of both gums, especially in the film containing 30% GT-P. The highest values of the tensile strength (39.3 MPa) and elongation at break (445%) belonged to the treatment containing 10% GT-P (90/10P/0). The FTIR and DSC analyses confirmed good interactions between GT and PVA in the 90/10P/0 treatment. SEM images indicated the dense structure of this film as the optimum treatment. Although the presence of CNF in the films containing GT-G improved some properties, especially the Young modulus, it impaired all the functional properties of nanocomposite GT-P film.     

DCT statistics-based digital dropout detection in degraded archived media

With the rapid development of visual digital media, the demand for better quality of service has increased the pressure on broadcasters to automate their error detection and restoration activities for preserving their archives. Digital dropout is one of the defects that affect archived visual materials and tends to occur in block by block basis (size of 8 × 8). It is well established that human visual system (HVS) is highly adapted to the statistics of its visual natural environment. Consequently, in this paper, we have formulated digital dropout detection as a classification problem which predicts block label based on statistical features. These statistical features are indicative of perceptual quality relevant to human visual perception, and allow pristine images to be distinguished from distorted ones. Here, the idea is to extract discriminant block statistical features based on discrete cosine transform (DCT) coefficients and determine an optimal neighborhood sampling strategy to enhance the discrimination ability of block representation. Since this spatial frame based approach is free from any motion computation dependency, it works perfectly in the presence of fast moving objects. Experiments are performed on video archives to evaluate the efficacy of the proposed technique.     

Adiabatic quantum optimization with qudits

Most realistic solid state devices considered as qubits are not true two-state systems. If the energy separation of the upper energy levels from the lowest two levels is not large, then these upper states may affect the evolution of the ground state over time and therefore cannot be neglected. In this work, we study the effect of energy levels beyond the lowest two energy levels on adiabatic quantum optimization in a device with a double-well potential as the basic logical element. We show that the extra levels can be modeled by adding additional ancilla qubits coupled to the original logical qubits, and that the presence of upper levels has no effect on the final ground state. We also study the influence of upper energy levels on the minimum gap for a set of 8-qubit spin glass instances.     

Face Editing Using Part-Based Optimization of the Latent Space

We propose an approach for interactive 3D face editing based on deep generative models. Most of the current face modeling methods rely on linear methods and cannot express complex and non-linear deformations. In contrast to 3D morphable face models based on Principal Component Analysis (PCA), we introduce a novel architecture based on variational autoencoders. Our architecture has multiple encoders (one for each part of the face, such as the nose and mouth) which feed a single decoder. As a result, each sub-vector of the latent vector represents one part. We train our model with a novel loss function that further disentangles the space based on different parts of the face. The output of the network is a whole 3D face. Hence, unlike part-based PCA methods, our model learns to merge the parts intrinsically and does not require an additional merging process. To achieve interactive face modeling, we optimize for the latent variables given vertex positional constraints provided by a user. To avoid unwanted global changes elsewhere on the face, we only optimize the subset of the latent vector that corresponds to the part of the face being modified. Our editing optimization converges in less than a second. Our results show that the proposed approach supports a broader range of editing constraints and generates more realistic 3D faces.     

A new hybrid control technique for operation of DC microgrid under islanded operating mode

This study proposes a novel combined primary and secondary control approach for direct current microgrids, specifically in islanded mode. In primary control, this approach establishes an appropriate load power sharing between the distributed energy resources based on their rated power. Simultaneously, it considers the load voltage deviation and provides satisfactory voltage regulation in the secondary control loop. The proposed primary control is based on an efficient droop mechanism that only deploys the local variable measurements, so as to overcome the side effects caused by communication delays. In the case of secondary control, two different methods are devised. In the first, low bandwidth communication links are used to establish the minimum required data transfer between the converters. The effect of communication delay is further explored. The second method excludes any communication link and only uses local variables. Accordingly, a self-sufficient control loop is devised without any communication requirement. The proposed control notions are investigated in MATLAB/Simulink platform to highlight system performance. The results demonstrate that both proposed approaches can effectively compensate for the voltage deviation due to the primary control task. Detailed comparisons of the two methods are also provided.     

Dynamic response of Kevlar®29/epoxy laminates under projectile impact-experimental investigation

This authors of this article investigated the dynamic response of woven Kevlar®29/epoxy laminates subjected to the impact loading. The cylindrical aluminum foam projectile and steel projectile were used to exert the impulse on the laminates. Deformation/failure modes, deflections, strain histories, and failure mechanisms were obtained and discussed. The results showed that with the high toughness of Kevlar fiber, the deformation modes of the laminates exhibited some characteristics similar to the metal panel, such as large global deformation. The failure mechanisms like matrix failure, fiber splitting, and fibrillation were observed. These micron failures led to the macroscopic delamination and fracture of the laminates.     

Electrochemical Behavior of Single CuO Nanoparticles: Implications for the Assessment of their Environmental Fate

The electrochemical behavior of copper oxide nanoparticles is investigated at both the single particle and at the ensemble level in neutral aqueous solutions through the electrode‐particle collision method and cyclic voltammetry, respectively. The influence of Cl^? and NO₃^? anions on the electrochemical processes occurring at the nanoparticles is further evaluated. The electroactivity of CuO nanoparticles is found to differ between the two types of experiments. At the single‐particle scale, the reduction of the CuO nanoparticles proceeds to a higher extent in the presence of chloride ion than of nitrate ion containing solutions. However, at the multiparticle scale the CuO reduction proceeds to the same extent regardless of the type of anions present in solution. The implications for assessing realistically the environmental fate and therefore the toxicity of metal‐based nanoparticles in general, and copper‐based nanoparticles in particular, are discussed.     

The Modeling of Magnetorheological Elastomers: A State-of-the-Art Review

The magnetorheological elastomers (MREs) are novel multifunctional materials wherein their viscoelastic properties can be varied instantly under an application of applied magnetic field. Due to their field-dependent stiffness and damping properties, MREs are widely used in the development and design of MRE-based adaptive vibration isolators and absorbers and also biomedical engineering. Moreover, MREs due to their inherent magnetostriction effect have enormous potential for the development of soft actuators. The dynamic behavior of MREs is affected by various material parameters (e.g., matrix and particle types, particle concentration, additives) as well as mechanical and magnetic loading parameters (e.g., frequency, amplitude, temperature, magnetic flux density). Understanding and predicting the effect of materials and loading parameters on the response behavior of MREs are of paramount importance for the design of MRE-based adaptive structures and systems. This review paper mainly aims to provide a comprehensive study of material constitutive models to predict the nonlinear magnetomechanical behavior of MREs. Particular emphasis is paid to physics-based models including continuum- and microstructure-based models. Moreover, phenomenological models describing the dynamic magnetoviscoelastic behavior of MREs as well as the effect of temperature on the magnetomechanical behavior of such materials are properly addressed.