Random Dispersion Research Articles

Experiment and mesoscopic modelling on the dynamic compressive behaviors of a new carbon fiber-reinforced cement-based composite

This paper presents an experimental and numerical study on the compressive behaviors of a new carbon fiber-reinforced cement-based composite (CFRCC) that is composed of magnesium oxysulfate cement, carbon fiber and some chemical admixtures. Parametric studies on the effects of carbon fiber content and strain rate on the compressive behaviors of CFRCC, are conducted using a series of quasi-static and dynamic compressive tests. The research item includes the failure morphology, stress-strain curve, dynamic increase factor (DIF), etc. Besides, a three-dimensional (3D) mesoscale modelling approach is proposed to simulate and analyze the mechanical responses and the failure mechanisms of CFRCC subjected to dynamic compressive loading, where the random dispersion of chopped fibers are realistically considered. The results indicate that the cracking and failure modes of CFRCC are significantly affected by the bridging effect of carbon fibers randomly dispersed in cement matrix. With the increase of fiber content, the dynamic peak stress and peak strain of CFRCC at similar strain rates show an increasing and decreasing trend, respectively. An evident strain-rate hardening effect could be found on the initial elastic modulus and the peak stress of CFRCC as well. While the relationship between the DIF of CFRCC and the logarithm of strain rate could be described using a quadratic function. Moreover, the mesoscale modelling approach has been validated by a good agreement between the numerical and tested results, demonstrating the feasibility of the developed mesoscale model in simulating and investigating the dynamic behaviors of CFRCC. All in all, the CFRCC has proven to be of great significance in resisting to impact loads, and more insights should be provided into its dynamic properties.

A Preliminary Study of Liver Fat Quantification Using Reported Ultrasound Speed of Sound and Attenuation Parameters.

The quantification of liver fat as a diagnostic assessment of steatosis remains an important priority for non-invasive imaging systems. We derive a framework in which the unknown fat volume percentage can be estimated from a pair of ultrasound measurements. The precise estimation of ultrasound speed of sound and attenuation within the liver is found to be sufficient for estimating fat volume assuming a classic model of the properties of a composite elastic material. In this model, steatosis is represented as a random dispersion of spherical fat vacuoles with acoustic properties similar to those of edible oils. Using values of speed of sound and attenuation from the literature in which normal and steatotic livers were studied near 3.5 MHz, we describe agreement of the new estimation method with independent measures of fat. This framework holds the potential for translation to clinical scanners with which the two ultrasound measurements can be made and used for improved quantitative assessment of steatosis.

Interpretable Optimization Training Strategy-Based DCNN and Its Application on CT Image Recognition

The theoretical basis of the discrete random sample batch classification is not clear and the sample division is not scientific during the process of Deep Convolutional Neural Network (DCNN) model training. Aiming at the problems above, starting from the DCNN detection recognition mechanism, the theory of random discrete samples is given and proved, and a scientific quantitative batch of sample input method is proposed. Combined with image preprocessing, based on the strategy of random dispersion of samples, and scientifically quantified sample input batches, the DCNN model is trained with limited label samples, and then the CT image recognition of pulmonary nodules is carried out. Experimental results based on the LIDC-ID-RI public dataset show that the sensitivity, specificity, and accuracy of the proposed method have reached 96.40%, 95.60%, and 96.00%, respectively. Compared with the multiscale convolutional neural network method and the multiscale multimode image fusion method, the recognition accuracy of the proposed method is improved by 1.6 and 3.49 percentage points, respectively.

Storage Conditions and Adsorption Thermodynamic Properties for Purple Corn.

Adsorption isotherms provide insight into the thermodynamic properties governed by food storage conditions. Adsorption isotherms of purple corn of the Canteño variety were evaluated at 18, 25, and 30 °C, for the equilibrium relative humidity (ERH) range between 0.065 and 0.95. The equilibrium moisture (Xe) was determined by the continuous weight-change method. Seven mathematical models of isotherms were modeled, using the coefficient of determination R2, mean absolute error (MAE), and estimated standard error (ESE) as the convergence criterion. Thermodynamic parameters such as isosteric heat (qst), Gibbs Free Energy (ΔG), differential entropy (ΔS), activation energy (Ea), and compliance with the isokinetic law were evaluated. It was observed that the adsorption isotherms presented cross-linking around 75% ERH and 17% Xe, suggesting adequate storage conditions at these values. The GAB and Halsey models reported better fit (R2 > 97%, MAE < 10%, ESE < 0.014 and random residual dispersion). The reduction of Xe from 17 to 7%, increases qst, from 7.7022 to 0.0165 kJ/g, while ΔG decreases considerably with the increase in Xe, presenting non-spontaneous endergonic behavior, and linear relationship with ΔS, evidencing compliance with the isokinetic theory, governed by qst. Ea showed that more energy is required to remove water molecules from the upper layers bound to the monolayer, evaluated using CGAB. The models predicted the storage conditions, and the thermodynamic parameters show the structural stability of the purple corn grains of the Canteño variety during storage.

Calculation model and analysis of fragment position with light field imaging mechanism under considering engineering uncertainty error factors

The spatial positions of multiple fragments formed by projectile explosion have random uncertainty dispersion state, so it is difficult to recognize each fragment and measure its spatial position when multiple fragments reach a certain area at the same time. In order to solve the problem, we propose a test method for measuring the spatial positions of multiple fragments based on dual light field intersection imaging mechanism, study refocusing principle of micro-lens array and four-dimensional light field imaging information, and establish the analytical model of spatial positions of multiple fragments. We set up the uncertainty error evaluation function of two light field cameras intersection test method from the perspective of engineering design, and give the uncertainty analysis method under engineering model. The influence of various factors on the uncertainty of test results under the engineering model is analyzed by calculation. Combined with the actual test data of the wooden target, the results show that the fragment spatial position test method based on dual light field intersection imaging mechanism is feasible and scientific.

Random-Effect Models for Degradation Analysis Based on Nonlinear Tweedie Exponential-Dispersion Processes

The degradation data of highly reliable products are usually analyzed by stochastic process models, such as Wiener process, gamma process and inverse Gaussian process models. If such a specific degradation model is wrongly assumed, then poor analysis results of reliability assessment would be obtained. Therefore, a class of exponential-dispersion processes, named Tweedie exponential-dispersion process (TEDP), is proposed to describe the products&#x2019; degradation paths. The TEDP model which comprises the aforementioned stochastic processes as its special cases, is more flexible and applicable for degradation modeling. Considering the nonlinear characteristics of degradation paths and the unit-to-unit variability among the product units, random-effect models are established based on the nonlinear TEDP models with random drift and dispersion parameters. To improve the mathematical tractability of these models, the variational inference, expectation maximization algorithm and differential evolution algorithm are used to estimate the unknown model parameters. Furthermore, two nonlinear TEDP models with accelerated factors and random effects are developed for accelerated degradation analysis. Finally, a simulation study and three real applications are presented to show the effectiveness and superiority of the proposed models and methods.

Interface engineering of organic-inorganic heterojunctions with enhanced charge transfer

Heterostructure materials are increasingly utilized in solar energy conversion to pursue high efficiency and long-term stability. The charge transfer across interfaces gives rise to major energy loss arising from non-ideal interfacial effects, i.e., high interfacial energy barrier and low interfacial contacting area. Herein, we demonstrate a facile interface engineering strategy to eliminate non-ideal interfacial effects. A heterojunction of CN@C-P25 is constructed via polyphenol-assisted assembly of titania (P25) and carbon nitride (CN). The uniform dispersion of P25 on CN enlarges the interfacial contacting area of 3.2-fold compared with random dispersion, while the transformation of polyphenols into conjugated carbon facilitates the interfacial charge transfer by switching a 0.4 eV Schottky contact to a 0.1 eV Ohmic contact between CN and P25. A 2.5-fold enhancement of charge transfer flux is obtained with an initial reaction rate of 5185 μmol h−1 g−1 for photocatalytic nicotinamide regeneration.

Simulating Lagrangian Subgrid-Scale Dispersion on Neutral Surfaces in the Ocean.

To capture the effects of mesoscale turbulent eddies, coarse‐resolution Eulerian ocean models resort to tracer diffusion parameterizations. Likewise, the effect of eddy dispersion needs to be parameterized when computing Lagrangian pathways using coarse flow fields. Dispersion in Lagrangian simulations is traditionally parameterized by random walks, equivalent to diffusion in Eulerian models. Beyond random walks, there is a hierarchy of stochastic parameterizations, where stochastic perturbations are added to Lagrangian particle velocities, accelerations, or hyper‐accelerations. These parameterizations are referred to as the first, second and third order “Markov models” (Markov‐N), respectively. Most previous studies investigate these parameterizations in two‐dimensional setups, often restricted to the ocean surface. On the other hand, the few studies that investigated Lagrangian dispersion parameterizations in three dimensions, where dispersion is largely restricted to neutrally buoyant surfaces, have focused only on random walk (Markov‐0) dispersion. Here, we present a three‐dimensional isoneutral formulation of the Markov‐1 model. We also implement an anisotropic, shear‐dependent formulation of random walk dispersion, originally formulated as a Eulerian diffusion parameterization. Random walk dispersion and Markov‐1 are compared using an idealized setup as well as more realistic coarse and coarsened (50 km) ocean model output. While random walk dispersion and Markov‐1 produce similar particle distributions over time when using our ocean model output, Markov‐1 yields Lagrangian trajectories that better resemble trajectories from eddy‐resolving simulations. Markov‐1 also yields a smaller spurious dianeutral flux.

Approximation parameters of mining area curvatures induced by an operation of the 338/2 seam of hard coal

Article presents a method of determination of an approximation parameters of average graphs of mining terrain curvatures caused by an exploitation of the 338/2 hard coal seam in the Upper Silesian Coal Basin in Poland. Approximation by the use of the higher orders polynomials has been done. An order of polynomial and a number of its segments belong to the parameters of approximation. Both values on the basis of minimization of a variability coefficient of random dispersion of observed curvatures have been determined. Parameters of approximation for the average graphs of curvatures measured after the end of an operation of one, two, three and four longwalls in the 338/2 seam have been calculated.

Evaluation of Flame Propagation Speed in a System of Random Discrete Sources for Spherical Particles in a Three-Dimensional Space

Realistic estimation of combustion phenomenon can be achieved by considering a random distribution for the particles instead of uniform distribution. Hence, in this study, theoretical investigation of aluminum particles combustion is studied three dimensionally with the consideration of random dispersion for discrete sources. Moreover, flame propagation speed is predicted through a proposed thermal model in a reaction medium. In addition, it is inferred that the propagation speed of the flame is a function of concentration and diameter of aluminum dust; while the minimum ignition energy is a function of particle diameter and equivalence ratio. A suitable prediction of the model is concluded by comparing the obtained results with the experimental and analytical data.

Stochastic modulational instability in the nonlinear Schrödinger equation with colored random dispersion

We study modulational instability (MI) in optical fibers with random group-velocity dispersion (GVD). We consider Gaussian and dichotomous colored stochastic processes. We resort to different analytical methods (namely, the cumulant expansion and the functional approach) and assess their reliability in estimating the MI gain of stochastic origin. If the power spectral density (PSD) of the GVD fluctuations is centered at null wave number, we obtain low-frequency MI sidelobes which converge to those given by a white-noise perturbation when the correlation length tends to 0. If instead the stochastic processes are modulated in space, one or more MI sidelobe pairs corresponding to the well-known parametric resonance (PR) condition can be found. A transition from small and broad sidelobes to peaks nearly indistinguishable from PR-MI is predicted, in the limit of large perturbation amplitudes and correlation lengths of the random process. We find that the cumulant expansion provides good analytical estimates for small PSD values and small correlation lengths, when the MI gain is very small. The functional approach is rigorous only for the dichotomous processes, but allows us to model a wider range of parameters and to predict the existence of MI sidelobes comparable to those observed in homogeneous fibers of anomalous GVD.

Novel evidence from Taxus fuana forests for niche-neutral process assembling community

Understanding the mechanisms underlying community assembly is helpful for conservation and restoration of communities, particularly those that contain rare and endangered species like Taxus fuana , which are endemic to the Western Himalayas. The niche (limiting similarity) vs. neutral (randomness) assembly of the T . fuana forest community in Gyirong County, Tibet, China, was investigated. The net relatedness index ( NRI ) was calculated using a phylogenetic tree. The phylogenetic characteristics of the community and its relationships with environment were analyzed. The value of the mean NRI at the community level was less than −1.96, indicating that the phylogenetic structure was overdispersed; whereas majority of the NRI s at the tree, shrub, and herb layers were within −1.96 to 1.96, indicating random dispersion. Environmental factors accounted for 44.38%, 46.52%, 24.04%, and 14.07% of the variation at the community level, tree, shrub, and herb layer, respectively. The phylogenetic structure at the community level and tree layer were significantly influenced by both topographic and soil factors, while shrub and herb layers tended to be affected by a single environmental factor. Community assembly of the T. fuana forest was simultaneously affected by niche and neutral processes, and their variations were closely related to the environment. Neutral process dominated community assembly in the shrub and herb layers. However, the interaction of limiting similarity and randomness played a dominant role at the community level and tree layer; and contributed to maintenance of biodiversity stability. The synergy of multiple environmental factors had a more obvious influence on community assembly than individual environmental factors, especially at the community level. These findings would help to understand the conservation of rare and endangered tree species, such as T. fuana , in the native community; and highlight the importance of random and non-random processes in assembly and biodiversity maintenance of alpine plant communities.

Impact of green human resource management practices on employee engagement from the perspective of banking employees

The scope of green human resource management (GHRM) has gotten intense concern from researchers over the last decade. However, little work has been accomplished in taking GHRM practices to attain distinct goals of the workplace, such as employee engagement (EE). Using random sampling and dispersion of the questionnaire among HR managers (n = 432) and by utilising SEM modelling, this study endorsed that employee engagement is anticipated to be improved by employing GHRM into practices within three ways, green appraisal practices (GAP), green employee involvement practices (GEIP) and green training practices (GTP). The profound investigation demonstrated the positive nexus of each GHRM practice (i.e., GAP, GEIP and GTP) on employee engagement drawing on resource-based theory. The present findings assure a significant contribution to the literature on GHRM and employee engagement along with several implications both practical and managerial. This study promotes GHRMs' notion and uplifts firms seeking employee engagement toward a particular workplace. Some future potential are reported for worldly scholars based on current deficiencies of the study.

Количественная оценка неопределенности случайного рассеивания среднего зазора и натяга в сопряжениях

Actual dimensions of suitable parts made according to the same drawings can fluctuate between the given limit dimensions, and the clearances and interferences in the conjugations — depending on the random scattering of the actual dimensions with the deviation of the shape of the real surface or profile. The error of the shape of the real surface affects the probabilistic estimate of the results of sorting parts, leads to uncertainty in the size of the maximum of the material, a random scattering of the average clearance and interference in the mating and, as a consequence — the uncertainty to use all that come in for assembly of parts. The main result of the study is related with ensuring the receipt for the assembly of an equal number of parts, to the conclusion possessing novelty of the analytical dependences of finding a quantitative estimate of the uncertainty of the random scattering of the average clearance and interference in the mating, the random scattering of the average size relative to the upper and lower acceptance boundaries. Using the assembly method proposed with the initial reduction of real clearances and interferences in the mating by means of narrowing the tolerances of the actual dimensions to the tolerance of the shape of real surfaces in diametric terms within the random dispersion of the average size relative to the upper and lower acceptance boundaries allows to ensure the receipt on the assembly of an equal number of parts is achieved, including in the tested batches of parts with a breakdown of the tolerances of the actual dimensions into an equal number of dimensional groups.

Prediktivna kontrola adaptivnog modela za sistem magnetnog vešanja pod disperzijama početne pozicije i poremećajima napona

A nonlinear magnetic suspension system consisting of a mechanical motion and electromagnetic circuit is considered in this paper. An online algorithm that is used to stabilize the suspended mass targeting the desired ball position, ball velocity and coil current is presented. A steady-state condition, in which the ball position, the ball velocity, and the coil current were assumed constant, was used as a reference trajectory for the closed-loop simulations. An adaptive model predictive control method was employed to control the coil voltage while the discrete plant model and operating conditions were changed at each time step. The effectiveness of the proposed control law was validated in the presence of disturbances in initial ball position, steady-state ball position, suspended mass, and voltage using the Monte-Carlo simulation method. The sinusoidal, step, and impulse voltage disturbances were applied consequently to the system while imposing random dispersions of the initial ball position. Numerical results with five hundred trials illustrated that the designed algorithm can stabilize the system and track the desired reference without exceeding the state and input constraints despite the wide range of dispersions in initial ball position, steady-state ball position, suspended mass, and input voltage disturbances.

Improved thermal conductivity of ceramic-epoxy composites by constructing vertically aligned nanoflower-like AlN network

Ceramic-polymer composites with good thermal conductivity, low dielectric constant and low dielectric loss have significant applicability in microelectronics and wireless communication systems. However, traditional thermal conductivity ceramic-polymer composites – realized simply through the random dispersion of spherical or near-spherical ceramic powder fillers – cannot have both high thermal conductivity and good electrical insulation, which greatly hinders their practical application. In this study, we first used metallic Al powder corroded by ultrasonic cavitation as the aluminium source, and then prepared spherical aluminium nitride (AlN) powders with many nano petals grown on the surface using the melamine-assisted nitriding method; subsequently, the ice-template method was utilized to construct a three-dimensional (3D) AlN framework with vertical columnar holes, and finally, nanoflower-like AlN-epoxy (EP) composites were prepared by vacuum infiltration. The unique nano petal structures on the surface of AlN powder and the welding between AlN nano-petals during vacuum sintering increased the contact area between nanoflower-like AlN powders and lowered their contact thermal resistance. Moreover, the construction of vertical AlN channels was conducive to the formtion of thermally conductive pathways in AlN-epoxy composites. As a result, we obtained ceramic-polymer composites with improved thermal conductivity, among which the composites with 20 vol% nanoflower-like AlN powder had the highest thermal conductivity – 2.26 W/m·K – compared to a pure matrix, which is equivalent to an enhancement of 830%.

Rational design of Ti3C2Cl2 MXenes nanodots-interspersed MXene@NiAl-layered double hydroxides for enhanced pseudocapacitor storage

Although electrodes based on two dimensional hybrids with interstratification-assemble have been widely studied for supercapacitors, the performance enhancement still remains challenge mainly due to the random dispersion of surface passivated two dimensional nanosheets. Herein, a new covalent surface functionalization of MXene-based Ti3C2Cl2 nanodots-interspersed MXene@NiAl-layered double hydroxides (QD-Ti3C2Cl2@NiAl-LDHs) hybrid electrode with superior pseudocapacitor storage performance has been elaborately designed by electrostatic-assembled. As a result, the QD-Ti3C2Cl2@NiAl-LDHs electrode exhibits a super specific capacitance of 2010.8F g−1 at 1.0 A g−1 and high energy density of 100.5 Wh kg−1 at a power density of 299.8 W kg−1. In addition, 94.1% capacitance retention is achieved after cycling for 10,000 cycles at 1.0 A g−1, outperforming previously reported of two dimensional hybrids electrode for supercapacitor. Furthermore, density functional theory (DFT) calculations show that the superior pseudocapacitor storage performance of the QD-Ti3C2Cl2@NiAl-LDHs may be attributed to the creation of numerous electrochemical active sites and the enhancement of electrical conductivity by the QD-Ti3C2Cl2 MXene. This work provides new strategy for developing excellent pseudocapacitor supercapacitor based on two dimensional hybrid electrode.

Local-scale environmental filtering shape plant taxonomic and phylogenetic diversity in an isolated Amazonian tepui (Tepequém table mountain)

Understanding how environmental drivers induce changes in plant composition and diversity across evolutionary time can provide important insights into community assembly mechanisms. We evaluated how taxonomic and phylogenetic diversity and structure of plant communities change along a local-scale edaphic and elevational gradient in the Tepequém table mountain, Brazilian Amazon. We selected three phytophysiognomies along the altitudinal gradient: Open Rupestrian Grassland, Shrubby Rupestrian Grassland, and Forest. We compared community composition and taxonomic and phylogenetic diversity between phytophysiognomies, and analysed effects of altitude and soil properties on species richness and phylogenetic metrics using linear mixed-effects models. The highest species richness and phylogenetic diversity were found at a lower elevation for Forest. All standardised phylogenetic metrics were significantly lower in Shrubby Rupestrian Grassland. This phytophysiognomy showed phylogenetic clustering. Forest showed a cluster pattern when only terminal nodes are considered and random dispersion to deep phylogenetic nodes. Open Rupestrian Grassland also showed a random phylogenetic structure. The linear mixed-effects models showed that species richness and different phylogenetic structure metrics were explained by altitude and soil properties. Our study revealed that key plant diversity dimensions (i.e. taxonomic and phylogenetic) are shaped by a local-scale edaphic and elevational gradient on an isolated tepui of Brazilian Amazonian.

A Moran eigenvector spatial filtering specification of entropy measures

AbstractRegional science investigations of geographical disparities in socio‐economic development sometimes utilize entropy, which measures a phenomenon's distributional uniformity across geographical space. Entropy also is widely utilized to measure random phenomenon dispersion, and often used to identify the most probable allocation of a phenomenon in space. Its common formulation is with empirical frequencies, following Shannon. Batty introduces spatial entropy assuming equal probability over space. His specification considers probabilities as fundamentally being spatially independent, which does not hold in most empirical geographical analyses. Hence, an entropy measure can be further modified by controlling extra variation caused by spatial autocorrelation. This paper proposes a Moran eigenvector spatial filtering (MESF) entropy specification that accounts for spatial autocorrelation when modelling georeferenced data. Using eigenvectors from a transformed spatial weights matrix, MESF identifies and isolates spatially autocorrelated components within a georeferenced variable. Coupling it with a non‐normal distribution, such as a binomial or beta probability model, which researchers often employ to describe empirical probabilities, expands its utility. The proposed method is examined with an application to regional income inequality in Poland during 2005–2012. This application demonstrates that accounting for spatial autocorrelation further enhances an entropy measure, showing that the MESF specification provides a flexible method for controlling spatial autocorrelation in an entropy formulation.

Random Stiffness Tensor of Particulate Composites with Hyper-Elastic Matrix and Imperfect Interface.

The main aim of this study is determination of the basic probabilistic characteristics of the effective stiffness for inelastic particulate composites with spherical reinforcement and an uncertain Gaussian volume fraction of the interphase defects. This is determined using a homogenization method with a cubic single-particle representative volume element (RVE) of such a composite and the finite element method solution. A reinforcing particle is spherical, located centrally in the RVE, surrounded by the thin interphase of constant thickness, and remains in an elastic reversible regime opposite to the matrix, which is hyper-elastic. The interphase defects are represented as semi-spherical voids, which are placed on the outer surface of this particle. The interphase is modeled as hyper-elastic and isotropic, whose effective stiffness is calculated by the spatial averaging of hyper-elastic parameters of the matrix and of the defects. A constitutive relation of the matrix is recovered experimentally by its uniaxial stretch. The 3D homogenization problem solution is based upon a numerical determination of strain energy density in the given RVE under specific uniaxial and biaxial stretches as well as under shear deformations. The analytical relation of the effective composite stiffness to the input uncertain parameter is recovered via the response function method, using a polynomial basis and an optimized order. Probabilistic calculations are completed using three concurrent approaches, namely the iterative stochastic finite element method (SFEM), Monte Carlo simulation and by the semi-analytical method. Previous papers consider the composite fully elastic, which limits the applicability of the resulting effective stiffness tensor computed therein. The current study voids this assumption and defines the composite as fully hyper-elastic, thus extending applicability of this tensor to strains up to 0.25. The most important research finding is that (1) the effective stiffness tensor is sensitive to random interface defects in its hyper-elastic range, (2) its resulting randomness is not close to Gaussian, (3) the semi-analytical method is not perfectly suited to stochastic calculations in this region of strains, as opposed to the linear elastic region, and (4) that the increase in random dispersion of defects volume fraction has a much higher effect on the stochastic characteristics of this stiffness tensor than fluctuation of the strain.