Uncertainty propagation in dynamics of composite plates: A semi-analytical non-sampling-based approach

In this study, the influences of spatially varying stochastic properties on free vibration analysis of composite plates were investigated via development of a new approach named the deterministic-stochastic Galerkin-based semi-analytical method. The material properties including tensile modulus, shear modulus, and density of the plate were assumed to be spatially varying and uncertain. Gaussian fields with first-order Markov kernels were utilized to define the aforementioned material properties. The stochastic fields were decomposed via application of the Karhunen-Loeve theorem. A first-order shear deformation theory was assumed, following which the displacement field was defined using admissible trigonometric modes to derive the potential and kinetic energies. The stochastic equations of motion of the plate were obtained using the variational principle. The deterministic-stochastic Galerkin-based method was utilized to find the probability space of natural frequencies, and the corresponding mode shapes of the plate were determined using a polynomial chaos approach. The proposed method significantly reduced the size of the mathematical models of the structure, which is very useful for enhancing the computational efficiency of stochastic simulations. The methodology was verified using a stochastic finite element method and the available results in literature. The sensitivity of natural frequencies and corresponding mode shapes due to the uncertainty of material properties was investigated, and the results indicated that the higher-order modes are more sensitive to uncertainty propagation in spatially varying properties.     

A new data aggregation approach for WSNs based on open pits mining

Wireless Networks - Wireless sensor networks consist of a large number of sensor nodes with limited energy, which is widely used in various Internet of things scenarios in recent years. Regarding...     

High-flux PVDF/PVP nanocomposite ultrafiltration membrane incorporated with graphene oxide nanoribbones with improved antifouling properties

A novel polyvinylidene fluoride (PVDF) nanocomposite membrane containing graphene oxide nanoribbones (GONRs) as a new nanofiller and polyvinylpyrrolidone (PVP) as pore former agent was prepared via phase inversion method. GONRs were prepared by oxidative unzipping of multi-walled carbon nanotubes (MWCNTs) via chemical approach. Chemical vapor deposition method was used to synthesis MWCNTs. The effects of adding GONRs and PVP into the casting solution on morphology, hydrophilicity and pure water flux (PWF) of the prepared nanocomposite membranes were explored. Antifouling experiments were also performed. It was found that compared to the neat PVDF membrane, PWF of the PVDF/PVP, PVDF/(0.5GONRs) and PVDF/(0.5GONRs)/PVP membranes were improved 80%, 44.9%, and 241.6%, respectively. The obtained results showed that GONRs and PVP exhibit synergistic effects in controlling the membrane properties. This work shows that GONRs can be suitable as nanofiller for preparation of high performance PVDF ultrafiltration membranes with improved antifouling properties.     

Crystal structure refinement and the magnetic and electro-optical properties of Er3+–Mn2+-substituted Y-type barium hexaferrites

The influence of Er³⁺–Mn²⁺ substitution on the properties of Y-type hexaferrites (chemical composition: Ba_2–xEr_xZn_0.6Co_0.6Cu_0.8Fe_12?yMn_yO₂₂ (x = 0.0, 0.3, and 0.5 and y = 0.0, 0.4, and 0.6)), which were synthesized by the sol-gel autocombustion method, was investigated. The X-ray diffraction spectra were analyzed by the Rietveld refinement method, and hexaferrite was observed to possess a single-phase crystalline structure, whereas the Fourier-transform infrared spectra clarified the formation of the iron oxide base material. The morphology of the grains revealed that they were hexagonal and without agglomeration. The band gap of the samples decreased as the Er³⁺–Mn²⁺ concentration increased. Dielectric and impedance spectroscopies of the prepared samples indicated the role of polarization in the variation in the dielectric and impedance parameters. Particularly, the occurrence of space-charge polarization increased the dielectric constant at lower frequencies. Further, the Cole–Cole plot revealed a semicircle in the lower frequency region, thereby indicating that the grain boundary contributed the most to the dielectric constants. Modulus spectroscopy revealed that the charge mobility increased as the concentration of Er³⁺–Mn²⁺ increased. Additionally, the magnetic analysis indicated that Mn²⁺ preferably replaces Fe³⁺ at the octahedral site, thereby reducing the magnetization of the prepared samples through a reduced superexchange interaction. Furthermore, increasing the coercivity values thermally stabilized the sample, and this is vital for perpendicular magnetic recording.     

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CP-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified ‘as a proof of concept’. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five ‘serological/protein biomarker’ criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.

     

Evaluating orthotropic continuum analysis of stress wave propagation through a jointed rock mass

Bulletin of Engineering Geology and the Environment - Continuum modeling and discontinuum modeling are two approaches that are used to study the problem of stress wave propagation in jointed rock...     

Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing

Desert sands in Iran, which usually contain small amounts of silt and sulfate, do not have significant strength, and thus, are not suitable for foundations or road construction. This paper applies the results of 90 Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) tests on sulfate silty sand stabilized with different lime and microsilica percentages as the two main stabilizers. Based on the obtained databank from the tests, Back Propagation Artificial Neural Network (BP-ANN) and Evolutionary Polynomial Regression (EPR) models are developed to predict the UCS and CBR values. Assessing the different architectures (one- and two-hidden layer neural networks) and functions (polynomial, exponential and hyperbolic tangent functions for the EPR models), a BP-ANN model with 5-5-8-1 layers and an EPR model with a hyperbolic tangent function showing high accuracy are introduced as the best models for predicting the UCS. Through a sensitivity analysis, the most and the least influential parameters on the UCS are presented and the results are further discussed using scanning electron microscopy (SEM). The presented EPR models can be useful for practitioners when selecting the optimized percentage of stabilizers or for controlling purposes in the QC/QA phases of deep soil mixing projects. In this regard, the application of the proposed models to the design of deep soil mixing is presented and elaborated using an example. In this example, the optimum and the best practical amounts of stabilizers are obtained through the graphical optimization of the models. In addition, by applying the developed relationships to a new case, the comprehensiveness of the developed relationships is further declared and it is shown that the proposed relationships are practical and can be efficiently used in the preliminary design stage.     

A Novel Parallel Cellular Automata Algorithm for Multi-Objective Reservoir Operation Optimization

Water Resources Management - In this paper, a novel Parallel Cellular Automata (PCA) approach is presented for multi-objective reservoir operation optimization. The problem considers the...