Modeling Of Growth Processes Research Articles

Guest editorial: Special issue on Extreme learning machine and applications (II)

It is our great pleasure to present this special issue on Extreme learning machine (ELM) and applications (II) to all NCA readers and neural computing society. Our initial schedule was to publish the special issue on ELM and applications with about 15 articles and to report the recent development of ELM theory and applications as well as the challenges in designing, analysing and implementing the algorithms and systems. However, after rigorously reviewing all of received papers on the basis of innovativeness and relevance for all NCA readers, we realized that many of them were high quality and finally selected 30 top papers. After getting the permission from Editor-inChief, Professor John MacIntyre, we are very happy to present these selected top papers in two special issues. The following is the brief introduction of 15 articles in the special issue: Extreme Learning Machine and Applications (II). In ‘‘Fault detection and diagnosis method for batch process based on ELM-based fault feature phase identification’’, the authors propose a fault detection and diagnosis algorithm for batch processes with ELM methodology. In this work, ELM is first utilized to identify the feature phases for each fault, and the whole batch is divided into a few ‘‘short stages’’. The multiway Fisher discriminant analysis (MFDA) models are then built for these divided ‘‘short stages’’ to perform fault detection and diagnosis for a batch process. The simulation results have shown the excellent performance of the algorithm for the fault detection in a hydrostatic testing process. In ‘‘Multiplekernel-learning-based extreme learning machine for classification design’’, the authors develop two multiple kernel classifiers. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called equivalent kernels. Experimental results have shown that, for a large number of data sets, the proposed classifiers are fast, accurate and easily trained. In ‘‘Variational Bayesian extreme learning machine’’, the authors present a Bayesian probabilistic model based on ELM to avoid the ill-posed problem in input–hidden node matrix. Both the regression experiments and classification experiments have clearly shown the excellent performance compared with a few existing ELMbased schemes. In ‘‘A recurrent neural network for modelling crack growth of aluminium alloy’’, the authors develop a new recurrent neural model for crack growth process of aluminium alloy. It has been shown that a recurrent neural network with the feedback loops at the output layer is constructed to model the dynamic relationship between the crack growth and cyclic stress excitations of aluminium alloy. The extreme learning machine is then used to uniformly randomly assign the input weights in a proper range and globally optimize both the output weights and feedback parameters, to ensure that the dynamics of crack growth under variable amplitude loading can be accurately modelled. The simulations with experimental data have shown excellent results. In ‘‘Extend semi-supervised ELM and a frame work’’, the authors analyse the semi-supervised ELM (SELM) indepth and propose an extended SELM algorithm that can efficiently solve the classification problems with a small number of labelled samples. The simulation results have shown excellent classification performance compared with both ELM and SELM algorithms. In ‘‘Feature adaptive online sequential extreme learning machine for lifelong & Zhihong Man zman@swin.edu.au

Spatial development of transport structures in apple (Malus × domestica Borkh.) fruit.

The void network and vascular system are important pathways for the transport of gases, water and solutes in apple fruit (Malus × domestica Borkh). Here we used X-ray micro-tomography at various spatial resolutions to investigate the growth of these transport structures in 3D during fruit development of “Jonagold” apple. The size of the void space and porosity in the cortex tissue increased considerably. In the core tissue, the porosity was consistently lower, and seemed to decrease toward the end of the maturation period. The voids in the core were more narrow and fragmented than the voids in the cortex. Both the void network in the core and in the cortex changed significantly in terms of void morphology. An automated segmentation protocol underestimated the total vasculature length by 9–12% in comparison to manually processed images. Vascular networks increased in length from a total of 5 m at 9 weeks after full bloom, to more than 20 m corresponding to 5 cm of vascular tissue per cubic centimeter of apple tissue. A high degree of branching in both the void network and vascular system and a complex three-dimensional pattern was observed across the whole fruit. The 3D visualizations of the transport structures may be useful for numerical modeling of organ growth and transport processes in fruit.

Electron Microscopy Study of Grain Boundary Behavior in Pb-Free Solder Materials for Mitigation of Whiskers

The changes of microstructure in pure-Sn after deformation were investigated by electron microscopy. Two types of specimens were prepared: Sample-1; pure-Sn/Fe-42Ni, Sample-2; single crystalline pure-Sn. The growth of curling whiskers on Sample-1 was observed in-situ in a scanning electron microscope (SEM). A thin foil specimen of the curling whisker was made with a focused ion beam (FIB) mill. Transmission electron microscope (TEM) analysis confirmed that the curling whisker was of a single crystal regardless of its external shape. New models for growth process of a bent single-crystalline whisker were proposed. The models are composed of epitaxial growth and recrystallization. Electron back scatter diffraction (EBSD) analysis was performed for Sample-2. The results of EBSD strongly suggested that recrystallization proceeds even at room temperature. These experimental results are very important to understand the behavior of dynamical deformation and recrystallization of Sn metal, and useful in consideration the mitigation of whiskers.

Generating Stochastic Ellipsoidal Forest and Wildland Fire Scar Scenarios for Strategic Forest Management Planning under Uncertainty

Explicit consideration of the possibility of wildfires enhances strategic planning for landscape management; however, incorporating the stochastic processes that govern fire occurrence and spread in optimization models that are used to inform landscape management presents new challenges. In this article, we describe a method of generating spatially explicit fire scenarios in the form of elliptical fire scars with associated probabilities. Computational experiments demonstrate that good scenarios can be achieved using currently available computer technologies. We also describe how more complex fire ignition and growth process models can be used to generate more realistic fire pattern scenarios and provide insight into the quality and effort required for various approximations. We conclude by discussing how our methodology could be used to perform some of the tasks for which fire managers use traditional burn probability models.

Prediction of Nonradical Au(0)-Containing Precursors in Nanoparticle Growth Processes

This density functional theory (DFT) investigation examines the formation of nonradical Au(0) species from the reduction of Au(I) species. The Au(I) complexes of interest are AuCl2(-), AuBr2(-), AuI2(-), AuClPH3, and AuCl(H)SCH3(-), which are precursors for gold nanoparticle and cluster formation. Reaction of two of the Au(I) species with a hydride results in ejection of two of the ligands and formation of Au2 with two ligands still attached. AuX2(-) (where X = Cl, Br, or I) reactions eject two halides and form Au2X2(2-). AuClL(-) (where L = PH3, HSCH3, or SCH3(-)) reactions can eject either chloride, HCl, PH3, HSCH3, or SCH3(-) and form Au(0)L2(q-) or Au(0)ClL(q-) (q = 0, 1, 2). The Au2Cl2(2-) complex can further react with AuCl2(-), which forms Au3Cl3(2-) and a chloride anion. The new Au3Cl3(2-) species can then react with AuCl2(-) or Au2Cl2(2-) or with another Au3Cl3(2-). Larger clusters can be formed from these precursors. In this work, reactions in both methanol and benzene solvents are considered as models for one-phase and two-phase gold nanoparticle growth processes. Overall, this investigation shows how Au(0)-containing species can be formed without assuming the formation of Au(0) atoms (radical species).

Ramified Challenges: Monitoring and Modeling of Hairy Root Growth in Bioprocesses--A Review.

The review presents a comprehensive overview on available solutions for the monitoring and modeling of various aspects of hairy root growth processes. Several online and offline measurement principles get explained exemplary and are being compared. It was found that no direct online measurement principle for hairy root biomass in submerged and solid-state culturing environment is available. However, certain indirect methods involving one or more measurement values have been developed for biomonitoring of hairy roots especially in bioreactors. In the field of modeling of hairy root growth processes, four independent architectures (continuous models, metabolic flux analysis, agent-based models, and artificial neural networks) are described and compared including literature references. The discussion is structured into microscopic model approaches, addressing only certain aspects of growth, and macroscopic model approaches, describing the hairy root network as a whole. An agent-based macroscopic model based on phenomenological data acquired with systematic imaging of hairy roots on culture dishes together with a 3D visualization of simulation results is presented in detail.

Growth process modeling of semiconductor nanowires for scale-up of nanomanufacturing: A review

To keep up with the increasing need for nanomanufacturing (NM), research on the scale-up of NM has become an emerging field of study. This review article first discusses the author understanding on research classification of scale-up NM research, which entails scale-up process research and scale-up methodology research. The scale-up methodology research includes establishing modeling, simulation, and control methodologies that enable and support economic production at commercial scale. Since NM process modeling provides the basis for process monitoring and control, guided inspection and sensing strategy, and more-efficient experimental design strategy for robust synthesis of nanomaterials, semiconductor nanowire growth is used as an example to review different process modeling strategies for scalable NM. The modeling strategies from the existing literature on nanowire growth studies are summarized into four categories: (i) physical modeling; (ii) statistical modeling; (iii) physical-statistical modeling; and (iv) cross-domain modeling and validation. In addition to illustrating modeling approaches in the literature, suitable domains of applications for each modeling strategy are discussed. Two potential areas worthy of efforts in future research are highlighted.

Growth Process Modeling of III–V Nanowire Synthesis via Selective Area Metal–Organic Chemical Vapor Deposition

Growth Process Modeling of III–V Nanowire Synthesis via Selective Area Metal–Organic Chemical Vapor Deposition

Growth behavior of lanthanum conversion coating on hot-dip galvanized steel

La conversion coatings in various growth stages were obtained by immersing hot-dip galvanized (HDG) sheets in the lanthanum nitrate solution for different times from 10s to 240min. A growth process model of the La conversion coating on hot-dip galvanized steel was suggested. It is shown that the conversion coating grows more quickly by the zinc grain boundaries, where the cracks occur first and develop gradually on the whole surface of the conversion coating. When the immersion time is increased (more than 60min), some rod-like precipitations containing more Zn and less La are deposited on the conversion coating. Potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) measurement results reveal that, with increasing immersion time, the protective property of the La conversion coatings is gradually increased in the early growth stage and then decreased when the cracks develop.

A recurrent neural network for modeling crack growth of aluminium alloy

A new recurrent neural model for crack growth process of aluminium alloy is developed in this work. It is shown that a recurrent neural network with the feedback loops at the output layer is constructed to model the dynamic relationship between the crack growth and cyclic stress excitations of aluminium alloy. The output feedback loops in the neural model play the role of capturing the fine changes of crack growth dynamics. The Extreme Learning Machine is then used to uniformly randomly assign the input weights in a proper range and globally optimize both the output weights and feedback parameters, to ensure that the dynamics of crack growth under variable-amplitude loading can be accurately modeled. The simulation results with the averaged experimental data of the 2024-T351 aluminium alloy show that the excellent modeling and prediction performance of the recurrent neural model can be achieved for fatigue crack growth of aluminium alloys.

Agent‐Based Modeling of Growth Processes

AbstractGrowth processes abound in nature, and are frequently the target of modeling exercises in the sciences. In this article we illustrate an agent‐based approach to modeling, in the case of a single example from the social sciences: bullying.

Abnormal grain growth in nonequilibrium systems: effects of point defect patterning.

We present a mean-field model for abnormal grain growth processes described by competition of both deterministic and stochastic mechanisms with the grain mobility depending on the grain size. The derived approach is applied to study delayed dynamics of grain growth in system of point defects subjected to irradiation according to the swelling rate theory for irradiated materials. We have shown that with the irradiation dose increase, the corresponding grain growth dynamics is slowed down and growth processes with power-law time asymptotics are realized. We discuss behavior of grain size distributions with competing regular and fluctuation mechanisms.

Graphene growth process modeling: a physical–statistical approach

As a zero-band semiconductor, graphene is an attractive material for a wide variety of applications such as optoelectronics. Among various techniques developed for graphene synthesis, chemical vapor deposition on copper foils shows high potential for producing few-layer and large-area graphene. Since fabrication of high-quality graphene sheets requires the understanding of growth mechanisms, and methods of characterization and control of grain size of graphene flakes, analytical modeling of graphene growth process is therefore essential for controlled fabrication. The graphene growth process starts with randomly nucleated islands that gradually develop into complex shapes, grow in size, and eventually connect together to cover the copper foil. To model this complex process, we develop a physical–statistical approach under the assumption of self-similarity during graphene growth. The growth kinetics is uncovered by separating island shapes from area growth rate. We propose to characterize the area growth velocity using a confined exponential model, which not only has clear physical explanation, but also fits the real data well. For the shape modeling, we develop a parametric shape model which can be well explained by the angular-dependent growth rate. This work can provide useful information for the control and optimization of graphene growth process on Cu foil.

Comparison of Thermodynamic Databases for the Modeling of SiC Growth by PVT

The numerical modeling of the SiC bulk growth process by physical vapor transport has been established as the essential tool for the process development, especially for understanding and predicting the favorable growth conditions. An accurate computation of mass transfers is strongly dependent on the equilibrium partial pressure calculations. In this paper, we compare the relative impact of the different thermodynamic databases available on the full PVT process modeling. We found that whatever the database used, the trends regarding growth rate calculation, crystal shape, Si/C ratio are correctly described and none of the database would bring about unacceptable errors from the process development point of view even if some discrepancies in the absolute values could be obtained.

The Fiber Walk: A Model of Tip-Driven Growth with Lateral Expansion

Tip-driven growth processes underlie the development of many plants. To date, tip-driven growth processes have been modeled as an elongating path or series of segments, without taking into account lateral expansion during elongation. Instead, models of growth often introduce an explicit thickness by expanding the area around the completed elongated path. Modeling expansion in this way can lead to contradictions in the physical plausibility of the resulting surface and to uncertainty about how the object reached certain regions of space. Here, we introduce fiber walks as a self-avoiding random walk model for tip-driven growth processes that includes lateral expansion. In 2D, the fiber walk takes place on a square lattice and the space occupied by the fiber is modeled as a lateral contraction of the lattice. This contraction influences the possible subsequent steps of the fiber walk. The boundary of the area consumed by the contraction is derived as the dual of the lattice faces adjacent to the fiber. We show that fiber walks generate fibers that have well-defined curvatures, and thus enable the identification of the process underlying the occupancy of physical space. Hence, fiber walks provide a base from which to model both the extension and expansion of physical biological objects with finite thickness.

A brief review of mathematical models of thin film growth and surfaces. A possible route to avoid defects in stents.

The morphology of thin films has been extensively studied in the last years. The properties of a thin film are closely related to its microstructure, especially to its morphology and surface roughness. Optical reflectivity, conductivity, and porosity are characteristics that depend on the film structure. The knowledge of atomistic details of the thin film growth process is useful for the development of new techniques and the control of thin films and new materials. Models of growth process are very powerful tools that can help researchers to predict and control physical, chemical, and mechanical properties. In this work we briefly summarize the theoretical models that have been used in the studies of thin films growth. By describing the deposition process of atoms/molecules on the surface of the substrate, one can study the evolution of the bulk and the surface roughness of a thin film. If an experimental growth process is appropriately described by a theoretical model (or even a combination of one or more different models), it can also provide indications to control the surface roughness and porosity of the film. Controlling the growth process one can obtain materials with a set of desired properties, namely tribological, porosity, and electrical ones. These characteristics are necessary for example, for hosting a solid lubricant on the surface of the material. We believe that the models presented in this work can be very useful in understanding the mechanisms of control and adherence of electrodeposited films which are commonly used in medical applications such as stent devices. We also believe that the models can be helpful to the understanding surface problems related to the superficial defects in stents.

Aging and Religious Participation in Late Life

The aim of the study is to evaluate the relationship between two dimensions of religiosity and the process of aging. Secondary analysis of longitudinal data from the Florida Retirement Study was used to assess the trajectories of religious development over time. We analyzed data from six interview waves with 1,000 older adults aged 72 or over. A baseline model of growth processes only indicated significant variation and mean decline in religious attendance, but no significant variation nor mean change in religious beliefs over time. A final model including a set of 17 covariates was estimated, and the model fit statistics indicated very good fit for this latent growth curve model. The decline in mean religious attendance across time did not accompany a mean increase in religious beliefs as expected. There were numerous individual differences in the trajectory of decline for religious attendance, as well as in the initial levels of attendance and religious beliefs.

Aerosol Impacts on the Microphysical Growth Processes of Orographic Snowfall

AbstractThe Regional Atmospheric Modeling System was used to simulate four winter snowfall events over the Park Range of Colorado. For each event, three hygroscopic aerosol sensitivity simulations were performed with initial aerosol profiles representing clean, moderately polluted, and highly polluted scenarios. Previous work demonstrates that the addition of aerosols can produce a snowfall spillover effect, during events in which riming growth of snow is prevalent in the presence of supercooled liquid water, that is due to a modified orographic cloud containing more numerous but smaller cloud droplets. This study focuses on the detailed microphysical processes that lead to snow growth in each event and how these processes are modulated by the addition of hygroscopic aerosols. A conceptual model of hydrometeor growth processes is presented, along a vertical orographic transect, that reveals zones of vapor deposition of ice and liquid, riming growth, evaporation, sublimation, and regions in which the Wegener–Bergeron–Findeisen (WBF) snow growth process is active. While the aerosol-induced spillover effect is largely determined by the degree of reduction in ice particle riming, an enhancement in the WBF snow growth process under more polluted conditions largely offsets the loss of rime growth, thus leading to a minimal net change in the regional precipitation.

A Discrete-Time Convergence Model for Proliferation-able Stem Cell and its Estimation using Kalman Filter

The main challenge of stem cell biology is to characterize proliferation and differentiation processes; since, very little is known regarding the molecular stimuli responsible for their regulatory mechanisms. On the other hand, comprehensive molecular analysis is yet too complex to perform intuitively. Therefore, computational models are essential for the optimization of clinical understanding in order to enhance the therapeutic process. In this article, a discrete-time convergence model for stem cell growth process based on clinical observations and engineering predictions has been proposed. Typically, stem cell populations are in quiescent; but in response to molecular stimuli they become activated and proliferate, and undergo divisional cycle before experiencing the terminal differentiation or disappearance process. The objective of this paper is to present a computational analysis of stem cell proliferation process, and standardize a model which converges with the experimental hypothesis. More importantly, it has also been shown that, inherent homogeneous and heterogeneous properties of stem cell populations are also the necessary conditions for this convergent theory. In addition, Kalman filter has been used for estimating the unknowns as well as effciency of the proposed model. Simulation results based on synthetic data are presented to illustrate the performance of the proposed technique.

黄土丘陵半干旱区柠条林株高生长过程新模型

Height growth of Caragana korshinskii in semi-arid loess hilly region was not always monotonous increasing with time.Because growth power was less than growth resistance in late growth stage,plant height weakly reduced with time.In this article,two growth model were established.Adding a growth resistance factor to the classical Logistic model,the first one was improved growth model,which made growth rate negative in late growth stage.The second one was multi-year growth model,which was based on the growth observation data of ripe Caragana korshinskii with a high density of 87 brush per 100 square.The result showed that the improved model had higher goodness of fit and correlation coefficient than that of Logistic model using modeling data to fit and test data to verify.Model established in this article was different with classic plant growth model.Because resistance factor was introduced into differential equation,growth curve,where extreme coordinate exist,was not strict monotonous.Because multi year height growth curves were synthesized into one coordinate system,the ratio of position parametera and intrinsic growth rate b in new model became more and more large with growth.Growth vertex appeared in middle ten days of August,which was consistent with the actual growth process of height.The difference of growth vertex and plant height in late growth stage,which was called growth loss,was calculated.Because Caragana korshinskii shrub was low,the process that dry topped phenomenon of Caragana korshinskii could influence on plant height in winter could not be ignored.This process caused the height at initial growth point in second year lower than that at last growth point in first year.In consideration of dry topped phenomenon,termination point of the winter drying phenomenon in multi-year growth model from 2002 to 2003 located in February,which was consistent with the rhythm of plant growth.This research provided evidence for describing growth process of shrub in semi-arid region.