Design and energy absorption enhancement of vehicle hull under high dynamic loads

V-shape hulls are widely used in peacekeeping efforts such as demining vehicles in order to deflect the blast energy and reduce the effects of mine blast. Blast resistant design and energy absorption enhancement of V-shape plates were carried out using finite element analysis package ABAQUS. Various geometries of V-shape plates with and without interlayer of materials like Al-foams and honeycomb were employed to analyze their effects on the deformation of the plate and applied stresses and strains. The results obtained show that application of metallic foams leads to better response of the plate and consequently results in more energy dissipation, less dame to vehicle and enhances crew survivability.     

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.     

A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem‐solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one‐to‐one personalized and adaptive teaching. In this paper, a novel Flowchart‐based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem‐solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi‐agent system using an automatic text‐to‐flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem‐solving skills. In the end, in order to investigate the efficacy of FITS in problem‐solving ability acquisition, a quasi‐experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem‐solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem‐solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.     

MC-MLAS: Multi-channel Minimum Latency Aggregation Scheduling in Wireless Sensor Networks

Data aggregation is a key, yet time-consuming functionality in wireless sensor networks (WSNs). Multi-channel design is a promising technique to alleviate interference as a primary reason for long latency of TDMA aggregation scheduling. Indeed, it provides more potential of parallel transmissions over different frequency channels, thus minimizing time latency. In this paper, we focus on designing a multi-channel minimum latency aggregation scheduling protocol, named MC-MLAS, using a new joint approach for tree construction, channel assignment, and transmission scheduling. To our best knowledge, this is the first work in the literature which combines orthogonal channels and partially overlapping channels to consider the total latency involved in data aggregation. Extensive simulations verify the superiority of MC-MLAS in WSNs.     

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.     

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.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.