Computer Methods In Engineering Research Articles

Side chain inset of neurogenerative amino acids to metalloproteins: a therapeutic signature for huntingtin protein in Huntington's disease.

Huntington's disease is a dominant autosomal inherited neurodegenerative disease that results in progressive impairment, characterized by dementia, chorea, and behavioral and cognitive decline. The objective of this study was to investigate the potential activity of metalloproteins against the huntingtin protein using various insertion-based engineering computational methods. Metalloproteins, metal protein complexes involved in important biochemical and physiological processes, were explored as potential drug candidates for Huntington's disease. A total of 18 metalloproteins were selected as drug candidates and studied to assess their potential inhibitory effects on the huntingtin protein. The screening process was based on the lowest binding energy. The metalloprotein with the lowest docking score was chosen for side chain insertion of neurogenerative amino acids. The engineered metalloprotein was then evaluated based on physiochemical properties, allergenicity, toxicity, and surface accessibility. Cloning and expression analysis was performed to further investigate its potential as a therapeutic agent. The metalloprotein chosen for side chain insertion, cytochrome C oxidase, showed promising results. It was computed as a probable non-allergen and exhibited no toxic domains, indicating its non-toxic nature. Additionally, it demonstrated a strong binding affinity with the huntingtin protein, with a binding energy of -1,253.3 Kcal/mol. Metal-based proteins, when engineered with additional neurogenerative amino acids, hold potential as drug candidates for treating neurodegenerative diseases such as Huntington's disease. The successful development of these engineered metalloproteins could offer therapeutic advantages. Further testing, both in vitro and in vivo, is necessary to evaluate their efficacy and validate their potential activity as novel drugs for the treatment of neurodegenerative diseases.

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

A computational framework for agglomeration in thermochemically reacting granular flows

A computational framework is developed which couples a series of models, each describing vastly different physical events, in order to characterize particle growth (agglomeration) in thermochemically reacting granular flows. The modelling is purposely simplified to expose the dominant mechanisms which control agglomeration. The overall system is comprised of relatively simple coupled submodels describing impact, heat production, bonding and fragmentation, each of which can be replaced by more elaborate descriptions, if and when they are available. Inverse problems, solved with a genetic algorithm, are then constructed to ascertain system parameters which maximize agglomeration likelihood within a range of admissible data.

THE K-VERSION OF FINITE ELEMENT METHOD FOR NONLINEAR OPERATORS IN BVP

In the companion papers [1,2], authors introduced the concepts of k-version of finite element method and k, hk, pk, hkp-processes of the finite element method for boundary value problems described by self-adjoint and non-self adjoint operators using Ĥk,p(Ω) spaces with specific details including numerical studies for weak forms and least square processes. It was demonstrated that a variationally consistent (VC) weak form is possible when the differential operator is self-adjoint, however, in case of non-self-adjoint operators the weak forms are variationally inconsistent (VIC) which lead to degenerate computational processes that can produce spurious oscillations in the computed solutions. In this paper we demonstrate that when the boundary value problems are described by non-linear differential operators, Galerkin processes and weak forms can never be variationally consistent and hence result in degenerate computational processes and suffer from same problems as in the case of non-self-adjoint operators ...

THE k-VERSION OF FINITE ELEMENT METHOD FOR SELF-ADJOINT OPERATORS IN BVP

In this paper a new mathematical and computational framework for boundary value problems described by self-adjoint differential operators is presented. In this framework, numerically computed solutions, when converged, possess the same degree of global smoothness in terms of differentiability up to any desired order as the theoretical solutions. This is accomplished using spaces Ĥk,p that contain basis functions of degree p and order k - 1 (or the order of the space k). It is shown that the order of space k is an intrinsically important independent parameter in all finite element computational processes in addition to the discretization characteristic length h and the degree of basis functions p when the theoretical solutions are analytic. Thus, in all finite element computations, all quantities of interest (e.g., quadratic functional, error or residual functional, norms and seminorms, error norms, etc.) are dependent on h, p as well as k. Therefore, for fixed h and p, convergence of the finite element process can also be investigated by changing k, hence k-convergence and thus the k-version of finite element method. With h, p, and k as three independent parameters influencing all finite element processes, we now have k, hk, pk, and hpk versions of finite element methods. The issue of minimally conforming finite element spaces is reexamined and it is demonstrated that the definition of currently believed minimally conforming space which permit weak convergence of the highest-order derivatives of the dependent variables appearing in the bilinear form is not justifiable mathematically or from physics view point. A new criterion is proposed for establishing the minimally conforming spaces which is more in agreement with the physics and mathematics of the BVP. Significant features and merits of the proposed mathematical and computational framework are presented, discussed, illustrated, and substantiated mathematically as well as numerically with the Galerkin and least-squares finite element formulations for self-adjoint boundary-value problems.

MESH-FREE METHOD REVISITED: TWO NEW APPROACHES FOR THE TREATMENT OF ESSENTIAL BOUNDARY CONDITIONS

The enforcement of essential boundary conditions in mesh-free methods is a difficult task due to the lack of Kronecker delta properties of mesh-free shape functions. In this paper, two approaches are introduced to impose essential boundary conditions. The first approach is a node interpolation method (NIM). In this approach, the shape functions that are associated with essential boundary conditions are constructed using node interpolation. These shape functions are then combined with mesh-free shape functions. Because the shape functions from the NIM are true interpolants, the essential boundary conditions can be imposed with the same ease as in the finite element method. The second approach is the direct imposition method (DIM). This approach rearranges the discretized system equations, and directly provides the known values of the essential boundary conditions in the nodal variable vector. Thus, the true solution of the discretized equations can be achieved with the satisfaction of essential boundary conditions.

Preface: Advances in Nuclear Engineering Computational Methods

"Preface: Advances in Nuclear Engineering Computational Methods." Nuclear Science and Engineering, 92(1), p. 3

Digital Computer Methods in Engineering

Digital Computer Methods in Engineering

Matrix Computer Methods in Engineering

Matrix Computer Methods in Engineering