Efficient synthesis of magnetic nanoparticle-Musa acuminata peel composite for the adsorption of anionic dye

The impregnation of magnetite (Mt) nanoparticle (NPs) onto Musa acuminata peel (MApe), to form a novel magnetic combo (MApe-Mt) for the adsorption of anionic bromophenol blue (BPB) was studied. The SEM, EDX, BET, XRD, FTIR and TGA were used to characterize the adsorbents. The FTIR showed that the OH and CO groups were the major sites for BPB uptake onto the adsorbent materials. The average Mt crystalline size on MApe-Mt was 21.13 nm. SEM analysis revealed that Mt NPs were agglomerated on the surface of the MApe biosorbent, with an average Mt diameter of 25.97 nm. After Mt impregnation, a decrease in BET surface area (14.89 to 3.80 m²/g) and an increase in pore diameter (2.25–3.11 nm), pore volume (0.0052–0.01418 cm³/g) and pH point of zero charge (6.4–7.2) was obtained. The presence of Pb(II) ions in solution significantly decreased the uptake of BPB onto both MApe (66.1–43.8%) and MApe-Mt (80.3–59.1%), compared to other competing ions (Zn(II), Cd(II), Ni(II)) in the solution. Isotherm modeling showed that the Freundlich model best fitted the adsorption data (R² > 0.994 and SSE < 0.0013). In addition, maximum monolayer uptake was enhanced from 6.04 to 8.12 mg/g after Mt impregnation. Kinetics were well described by the pseudo-first order and liquid film diffusion models. Thermodynamics revealed a physical, endothermic adsorption of BPB onto the adsorbents, with ΔH^o values of 15.87–16.49 kJ/mol, corroborated by high desorption (over 90%) of BPB from the loaded materials. The viability of the prepared adsorbents was also revealed in its reusability for BPB uptake.     

本征值有限元近似的一个抽象误差估计式

设T：LZ（fi）MLZ（fi）是自共轭全连续算子，SgCLZ（fi）是分片。次有限元空间，几：LZ、St是有限秩自共轭算子，IITh-Tllo、0（h、0）．考虑本征值问题：及其近似用（·，·）和DD·D【。·分别表示h（m中内积和范数·ID·卜F表示w认｝（m中范数，简记D卜队。为D卜卜·因为T自共轭全连续，所以它有可数无穷个本征值h，人，...人，...．其相应的本征函数（2丹构成完全标准直交系，所以VZELZ（m设几的本征值为A。l，汕。，...，汕n，相应的本征函数为如山，卜则。二1·不失一般性，可EitL。设tik一大干二＞，E是到Ah对应的本…     

Analysis and modeling of radiometric error caused by imaging blur in optical remote sensing systems

Imaging blur changes the digital output values of imaging systems. It leads to radiometric errors when the system is used for measurement. In this paper, we focus on the radiometric error due to imaging blur in remote sensing imaging systems. First, in accordance with the radiometric response calibration of imaging systems, we provide a theoretical analysis on the evaluation standard of radiometric errors caused by imaging blur. Then, we build a radiometric error model for imaging blur based on the natural stochastic fractal characteristics of remote sensing images. Finally, we verify the model by simulations and physical defocus experiments. The simulation results show that the modeling estimation result approaches to the simulation computation. The maximum difference of relative MSE (Mean Squared Error) between simulation computation and modeling estimation can achieve 1.6%. The physical experimental results show that the maximum difference of relative MSE between experimental results and modeling estimation is only 1.29% under experimental conditions. Simulations and experiments demonstrate that the proposed model is correct, which can be used to estimate the radiometric error caused by imaging blur in remote sensing images. This research is of great importance for radiometric measurement system evaluation and application.     

The a posteriori error estimates of Chebyshev–Petrov–Galerkin methods for second-order equations

In this paper, the a posteriori error estimates of Chebyshev–Petrov–Galerkin approximations are investigated. For simplicity, we choose the Poisson equation with Dirichlet boundary conditions to discuss the a posteriori error estimators, and deduce their efficient and reliable properties. Some numerical experiments are performed to verify the theoretical analysis for the a posteriori error estimators.     

FULLY SYMBOLIC GENERATION OF COMPLEX MULTIBODY MODELS*

Abstract

In this paper, a quasistatic finite element model of a spur gear pair is developed. A node-to-target contact formulation is given, where calculations of initial gaps are based upon the actual geometry of the gear flanks rather than upon a contact node and a facet or a line segment. By using a special contact search algorithm, profile modifications and mounting errors are easily incorporated in the analysis. The problem, which also includes friction, is solved by using a nonsmooth Newton method. The static transmission error can be calculated with accuracy with a relatively small number of nodes along the gear flanks. Several examples are given in order to demonstrate the model.     

Analysis of the anisotropy of group velocity error due to spatial finite difference schemes from the solution of the 2D linear Euler equations

Numerical differencing schemes are subject to dispersive and dissipative errors, which in one dimension, are functions of a wavenumber. When these schemes are applied in two or three dimensions, the errors become functions of both wavenumber and the direction of the wave. For the Euler equations, the direction of flow and flow velocity are also important. Spectral analysis was used to predict the error in magnitude and direction of the group velocity of vorticity–entropy and acoustic waves in the solution of the linearised Euler equations in a two‐dimensional Cartesian space. The anisotropy in these errors, for three schemes, were studied as a function of the wavenumber, wave direction, mean flow direction and mean flow Mach number. Numerical experiments were run to provide confirmation of the developed theory. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal error estimates of a decoupled scheme based on two-grid finite element for mixed Stokes–Darcy model

Although the numerical results suggest the optimal convergence order of the two-grid finite element decoupled scheme for mixed Stokes–Darcy model with Beavers–Joseph–Saffman interface condition in literatures, the numerical analysis only gets the optimal error order for porous media flow and a non-optimal error order that is half order lower than the optimal one in fluid flow. The purpose of this paper is to fill in the gap between the numerical results and the theoretical analysis.     

Intensity‐based estimation of extreme loss event probability and value at risk

We develop a methodology for the estimation of extreme loss event probability and the value at risk, which takes into account both the magnitudes and the intensity of the extreme losses. Specifically, the extreme loss magnitudes are modeled with a generalized Pareto distribution, whereas their intensity is captured by an autoregressive conditional duration model, a type of self‐exciting point process. This allows for an explicit interaction between the magnitude of the past losses and the intensity of future extreme losses. The intensity is further used in the estimation of extreme loss event probability. The method is illustrated and backtested on 10 assets and compared with the established and baseline methods. The results show that our method outperforms the baseline methods, competes with an established method, and provides additional insight and interpretation into the prediction of extreme loss event probability. Copyright © 2012 John Wiley & Sons, Ltd.     

A note on a priori error estimates for augmented mixed methods

In this note we describe a strategy that improves the a priori error bounds for augmented mixed methods under appropriate hypotheses. This means that we can derive a priori error estimates for each one of the involved unknowns. Usually, the standard a priori error estimate is for the total error. Finally, a numerical example is included, that illustrates the theoretical results proven in this paper.     

Approximating exponential and logarithmic functions using polynomial interpolation

This article takes a closer look at the problem of approximating the exponential and logarithmic functions using polynomials. Either as an alternative to or a precursor to Taylor polynomial approximations at the precalculus level, interpolating polynomials are considered. A measure of error is given and the behaviour of the error function is analysed. The results of interpolating polynomials are compared with those of Taylor polynomials.