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  • Isogeometric Collocation: A Mixed Displacement-Pressure Method for Nearly Incompressible Elasticity
  • Abstract We investigate primal and mixed u−p isogeometric collocation methods for application to nearly-incompressible isotropic elasticity. The primal method employs Navier’s equations in terms of the displacement unknowns, and the mixed method employs both displacement and pressure unknowns. As benchmarks for what might be considered acceptable accuracy, we employ constant-pressure Abaqus finite elements that are widely used in engineering applications. As a basis of comparisons, we present results for compressible elasticity. All the methods were completely satisfactory for the compressible case. However, results for low-degree primal methods exhibited displacement locking and in general deteriorated in the nearly-incompressible case. The results for…
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  • Effective Elastic Properties of 3-Phase Particle Reinforced Composites with Randomly Dispersed Elastic Spherical Particles of Different Sizes
  • Abstract Higher-order multiscale structures are proposed to predict the effective elastic properties of 3-phase particle reinforced composites by considering the probabilistic spherical particles spatial distribution, the particle interactions, and utilizing homogenization with ensemble volume average approach. The matrix material, spherical particles with radius a1, and spherical particles with radius a2, are denoted as the 0th phase, the 1st phase, and the 2nd phase, respectively. Particularly, the two inhomogeneity phases are different particle sizes and the same elastic material properties. Improved higher-order (in ratio of spherical particle sizes to the distance between the centers of spherical particles) bounds on effective elastic properties…
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  • Reduced Order Machine Learning Finite Element Methods: Concept, Implementation, and Future Applications
  • Abstract This paper presents the concept of reduced order machine learning finite element (FE) method. In particular, we propose an example of such method, the proper generalized decomposition (PGD) reduced hierarchical deeplearning neural networks (HiDeNN), called HiDeNN-PGD. We described first the HiDeNN interface seamlessly with the current commercial and open source FE codes. The proposed reduced order method can reduce significantly the degrees of freedom for machine learning and physics based modeling and is able to deal with high dimensional problems. This method is found more accurate than conventional finite element methods with a small portion of degrees of freedom. Different…
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  • Salinity Stress in Wheat: Effects, Mechanisms and Management Strategies
  • Abstract Salinity stress is a major threat to global food production and its intensity is continuously increasing because of anthropogenic activities. Wheat is a staple food and a source of carbohydrates and calories for the majority of people across the globe. However, wheat productivity is adversely affected by salt stress, which is associated with a reduction in germination, growth, altered reproductive behavior and enzymatic activity, disrupted photosynthesis, hormonal imbalance, oxidative stress, and yield reductions. Thus, a better understanding of wheat (plant) behavior to salinity stress has essential implications to devise counter and alleviation measures to cope with salt stress. Different approaches…
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  • Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil
  • Abstract One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square…
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  • Decision-Making Problems under the Environment of m-Polar Diophantine Neutrosophic N-Soft Set
  • Abstract Fuzzy models are present everywhere from natural to artificial structures, embodying the dynamic processes in physical, biological, and social systems. As real-life problems are often uncertain on account of inconsistent and indeterminate information, it seems very demanding for an expert to solve those problems using a fuzzy model. In this regard, we develop a hybrid new model m-polar Diophantine neutrosophic N-soft set which is based on neutrosophic set and soft set. Additionally, we define several different sorts of compliments on the proposed set. A proposed set is a generalized form of fuzzy, soft, Pythagorean fuzzy, Pythagorean fuzzy soft, and Pythagorean…
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  • Fluid and Osmotic Pressure Balance and Volume Stabilization in Cells
  • Abstract A fundamental problem for cells with their fragile membranes is the control of their volume. The primordial solution to this problem is the active transport of ions across the cell membrane to modulate the intracellular osmotic pressure. In this work, a theoretical model of the cellular pump-leak mechanism is proposed within the general framework of linear nonequilibrium thermodynamics. The model is expressed with phenomenological equations that describe passive and active ionic transport across cell membranes, supplemented by an equation for the membrane potential that accounts for the electrogenicity of the ionic pumps. For active ionic transport, the model predicts that…
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  • A Survey on Machine Learning in COVID-19 Diagnosis
  • Abstract Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What’s more, the advantages and limitations of different diagnosis methods are compared. Although the…
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  • A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems
  • Abstract The sparrow search algorithm (SSA) is a newly proposed meta-heuristic optimization algorithm based on the sparrow foraging principle. Similar to other meta-heuristic algorithms, SSA has problems such as slow convergence speed and difficulty in jumping out of the local optimum. In order to overcome these shortcomings, a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy (CLSSA) is proposed in this paper. Firstly, in order to balance the exploration and exploitation ability of the algorithm, chaotic mapping is introduced to adjust the main parameters of SSA. Secondly, in order to improve the diversity of the population…
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  •   Views:999       Downloads:394        Download PDF
  • A New Method to Evaluate Linear Programming Problem in Bipolar Single-Valued Neutrosophic Environment
  • Abstract A bipolar single-valued neutrosophic set can deal with the hesitation relevant to the information of any decision making problem in real life scenarios, where bipolar fuzzy sets may fail to handle those hesitation problems. In this study, we first develop a new method for solving linear programming problems based on bipolar singlevalued neutrosophic sets. Further, we apply the score function to transform bipolar single-valued neutrosophic problems into crisp linear programming problems. Moreover, we apply the proposed technique to solve fully bipolar single-valued neutrosophic linear programming problems with non-negative triangular bipolar single-valued neutrosophic numbers (TBSvNNs) and non-negative trapezoidal bipolar single-valued neutrosophic…
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  •   Views:729       Downloads:212        Download PDF
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