Segregation Model Research Articles

Grain boundary segregation models for high-entropy alloys: Theoretical formulation and application to elucidate high-entropy grain boundaries

Grain boundary (GB) segregation models are derived for multi-principal element alloys (MPEAs) and high-entropy alloys (HEAs). Differing from classical models where one component is taken as a solvent and others are considered solutes, these models are referenced to the bulk composition to enable improved treatments of MPEAs and HEAs with no principal components. An ideal solution model is first formulated and solved to obtain analytical expressions that predict GB segregation and GB energy in MPEAs and HEAs. A regular solution model is further derived. The GB composition calculated using the simple analytical expression derived in this study and data from the Materials Project agrees well with a prior atomistic simulation for NbMoTaW. The simplicity of the derived analytical expressions makes them useful for not only conveniently predicting GB segregation trends in HEAs but also analyzing nascent interfacial phenomena in compositionally complex GBs. As an application example, the models are used to further derive a set of equations to elucidate an emergent concept of high-entropy grain boundaries.

Effect of discretization choices when modeling the thermo-chemical history of the accreting core

Different discretizations methods applied to models of core/mantle segregations are tested (single stage, multistage accretion, results of N-body simulations) in order to test the sensitivity of the thermo-chemical coupling to the type of discretization used. We found that while single stage and large discretization of segregation steps yield very different core temperature, multistage models of accretion and core mantle segregation, at least for the model presented in this paper, tend to yield similar results, whether it is for the light element concentrations or the final temperature of the core. As long as a magma ocean existence throughout the entire process of core/mantle segregation is posited, and that only the impactor's masses of metal and silicate are re-equilibrated at each step, the degree of discretization of continuous model does not matter as long as it encompasses more than 10 steps of calculations.

A phase-field model of concurrent Suzuki segregation and partial dislocation glide

Co–Al–W-base superalloys with the classic γ−γ′ microstructure offer exciting high temperature property profiles. High temperature deformation in such alloys involves shearing of the γ′ phase by a a/3〈112〉 superpartial to create a superlattice intrinsic stacking fault (SISF) while the trailing a/6〈112〉 partial is pinned at the γ−γ′ interface. Experiments indicate that Suzuki segregation to such faults lowers their energy, which in turn lowers the resistance to partial dislocation glide. However, quantifying the extent of solute segregation and its impact on the dynamics of partial dislocation glide from experiments is challenging. In order to fulfill the purpose computationally, in this paper, we present a phase-field model of the phenomenon of concurrent solute segregation and partial dislocation glide. Our simulations reveal that under creep conditions in a representative pseudobinary alloy belonging to the Co–Al–W system, there is a marked increase in the partial dislocation velocity due to solute segregation. The rise in dislocation velocity due to segregation is found to be more significant for lower resolved shear stresses on the dislocation. Higher solute interdiffusivity also leads to enhanced partial glide velocities. Our model can be extended to multicomponent systems, which can, in turn, be used to accelerate the design of novel Co-base superalloys by predicting the kinetics of the displacive-diffusive shearing of the γ′ phase for prospective alloy systems.

Multilevel-thermoplastic waste segregation using Archimedes Henry gas solubility trained deep quantum network

The management of plastic waste is complex process worldwide. The manual process to sort garbage is termed to be complex and expensive task and hence the scientists studied automatic sorting technique which can elevate the efficacy of recycling task. Moreover, the waste segregation models are adapted to several groups of materials to sort the waste. The usage of deep models is considered to be effective in classifying the thermoplastic waste segregation. The images are attained and delivered to pre-processing with median filter followed by segmentation. PSI-Net considering Archimedes Henry Gas Solubility Optimization (AHGSO) is adapted to segment pre-processed images. Thereafter, texture features along with statistical features are obtained for undergoing first level classification. The first level categorization is executed using Deep Quantum Neural Network (DQNN) wherein the model is trained using AHGSO and it classifies into three classes, namely “no plastic,”“Resin,” and “plastic.” In addition, the second level classification is done in case of plastic or resin using Deep Maxout Network (DMN)-AHGSO wherein DMN undergoes training by means of AHGSO. The DMN-AHGSO gained premium accuracy of 92.9%, F-measure of 92.6%, and precision of 92.9%.

Elucidation of Spatial Positioning of Ribosomes around Chromosome in Escherichia coli Cytoplasm via a Data-Informed Polymer-Based Model.

The spatial arrangement of ribosomes and chromosome in Escherichia coli's cytoplasm challenges conventional wisdom. Contrary to the notion of ribosomes acting as inert crowders to the chromosome in the cytoplasm, here we propose a nuanced view by integrating a wide array of experimental data sets into a polymer-based computer model. A set of data-informed computer simulations determines that a delicate balance of attractive and repulsive interactions between ribosomes and the chromosome is required in order to reproduce experimentally obtained linear densities and brings forth the view that ribosomes are not mere inert crowders in the cytoplasm. The model finds that the ribosomes represent themselves as a poor solvent for the chromosome with a 50 nm mesh size, consistent with previous experimental analysis. Our multidimensional analysis of ribosome distribution, both free (30S and 50S) and bound (70S polysome), uncovers a relatively less pronounced segregation pattern than previously thought. Notably, we identify a ribosome-rich central region within the innermost core of the nucleoid. Moreover, our exploration of the chromosome mesh size and the conformation of bound ribosomes suggests that these ribosomes maintain elongated shapes, enabling them to navigate through the chromosome mesh and access the central core. This dynamic localization challenges the static segregation model and underscores the pivotal role of ribosome-chromosome interactions in cellular media.

Modeling Maillard reaction kinetics in spray dryers using both lumped‐ and distributed‐parameter modeling approaches

AbstractThis study aimed to develop a simulation model for the kinetics of Maillard reactions during the spray‐drying process of model systems. Two different modeling approaches were used: lumped‐parameter and distributed‐parameter models. The reaction kinetic model was developed by fitting experiment results from iso‐thermal heating experiments with different model systems at various temperatures and moisture contents with Arrhenius‐type equations. The developed reaction kinetic models were coupled with drying kinetics and segregation models adopted from previous studies. The prediction results from the models developed were then compared with those from spray‐drying experiments, and both modeling approaches showed a reasonable agreement with the experimental data. The distributed parameters modeling approach showed a 17.7% improvement in sum of square errors compared to the traditional lumped‐distributed modeling approach.Practical ApplicationsTwo different mathematical models with different approaches were developed and have shown reasonable performance in predicting the kinetics of Maillard reactions in spray drying. The models developed in this study can be used to improve the spray‐drying process of thermally sensitive products where Maillard reaction or other types of thermal degradation may occur.

Rheological Properties and Segregation of Fresh UHPC with Fibers Affected by Initial Temperature of Concrete Mix

Within the framework of the project GAČR 21-24070S Model of fibre segregation in dependence on rheological properties of fresh HPC, the rheological properties and fibre segregation in fresh concrete mix depending on temperature changes have been verified. The rheological properties and fibre segregation have been verified under three temperature conditions (very low temperatures around 5 °C, normal temperature conditions around 20 °C and high temperatures above 30 °C). Comparison of the properties of UHPC with fibres at different temperature conditions has been performed on fresh mixtures mainly by spill tests. Subsequently, hardened test bodies, i.e. 40/40/160 beams, were verified in tensile bending and compression in all cases. The actual fibre segregation within the individual samples has also been verified by microscopic analysis. Different temperature conditions have been simulated by heating or cooling the input raw materials. The results have been compared by degree of changes in rheological properties and mechanical parameters.

Green Symphony: A Brief Review of Waste Segregation Techniques

Proper waste segregation poses challenges like insufficient awareness, inadequate infrastructure, and limited resources. In this study, we've scrutinized five waste segregation methods employing IoT, Arduino, Deep Learning, Machine Learning, and Artificial Intelligence. The IoT-based system utilizes sensors, and cameras to identify diverse waste types, transmit data to the IoT module, and activate sorting mechanisms like robotic arms [1]. This leads to remote monitoring, and automated waste sorting, fostering efficient recycling. An advanced garbage monitoring system with sensors and an Arduino Uno detects waste types, monitors garbage levels, and issues alerts when bins are full, contributing to waste management efficiency [2]. DL is integrated into the system, categorizing waste images using CNNs, and employing transfer nearest neighbor algorithm predicts waste management alerts based on three sensors, automating notifications to authorities with a 93.3% accuracy rate [3]. AI-driven waste segregation models, particularly for non-biodegradable plastics, reduce waste management costs, achieving over 80% accuracy in sorting plastics. The culmination is the (WSD) model [5], incorporating IoT, Arduino, ML, DL, and AI. Utilizing sensors and imaging devices, this model generates real-time data for efficient monitoring and sorting, featuring a K-nearest neighbor algorithm with 93.3% accuracy and DL precision in material classification. The WSD model provides an automated and eco-friendly solution, reducing manual labor and improving recycling practices for effective waste management. Advanced technology-based techniques like the WSD model offer solutions, emphasizing the importance of choosing technologies aligned with available resources and infrastructure for optimal impact.

Equilibria in schelling games: computational hardness and robustness

In the simplest game-theoretic formulation of Schelling’s model of segregation on graphs, agents of two different types each select their own vertex in a given graph so as to maximize the fraction of agents of their type in their occupied neighborhood. Two ways of modeling agent movement here are either to allow two agents to swap their vertices or to allow an agent to jump to a free vertex. The contributions of this paper are twofold. First, we prove that deciding the existence of a swap-equilibrium and a jump-equilibrium in this simplest model of Schelling games is NP-hard, thereby answering questions left open by Agarwal et al. [AAAI ’20] and Elkind et al. [IJCAI ’19]. Second, we introduce two measures for the robustness of equilibria in Schelling games in terms of the minimum number of edges or the minimum number of vertices that need to be deleted to make an equilibrium unstable. We prove tight lower and upper bounds on the edge- and vertex-robustness of swap-equilibria in Schelling games on different graph classes.

Multimodal classroom interaction analysis using video-based methods of the pedagogical tactic of (un)grouping

ABSTRACT Grouping of people and/or things in school can involve challenging pedagogical problems and is a recurrent issue in research literature. Grouping of pupils sometimes aids learning, but detailed video-based analysis of how teachers (and pupils) group or ungroup (termed ‘(un)grouping’) in classrooms is rare. This multimodal classroom interaction analysis study builds on previous work by exploring how the Pedagogy Analysis Framework can help untangle complicated classroom interactions involving (un)grouping and identifies sixteen types of (un)grouping. The sample size is one class of thirty pupils (10-year-olds), their class teacher, and teaching assistant. Four research methods were used (lesson video analysis, teacher verbal protocols, pupil group verbal protocols, and individual teacher interviews). Six hours of data were video recorded (managed using NVivo). Data were analysed by two educational researchers, the class teacher, and two groups of pupils (three girls and three boys). The methodology is Straussian Grounded Theory. Data were recorded in 2019. We present how often participants (un)grouped during a lesson. We propose and use a grounded theory for (un)grouping which we call the ‘Exclusion, Segregation, Integration, and Inclusion (ESII) model’. Additionally, we discuss how misinformation and disinformation can complicate analysis of (un)grouping and examine different perspectives on (un)grouping.

Homophily, selection, and choice in segregation models

Schelling's 1971 work on the dynamics of segregation showed that even a small degree of homophily, the desire to live among like neighbors, can lead to a starkly segregated population. One of the driving factors for this result is that the notion of homophily used is based on group identities that are exogenous and immutable. In contrast, we consider a homophily that arises from the desire to be with neighbors who are behaviorally similar, not necessarily those who have the same group identity. The distinction matters because behaviors are neither exogenous nor immutable but choices that can change as individuals adapt to their neighborhoods. We show that in such an environment, integration rather than segregation is the typical outcome. However, the tendency toward adaptation and integration can be impeded when economic frictions in the form of income inequality and housing cost are present.

ABMSCORE: a heuristic algorithm for forming strategic coalitions in agent-based simulation

ABSTRACT Integrating human behavior into agent-based models has been challenging due to its diversity. An example is strategic coalition formation, which occurs when an individual decides to collaborate with others because it strategically benefits them, thereby increasing the expected utility of the situation. An algorithm called ABMSCORE was developed to help model strategic coalition formation in agent-based models. The ABMSCORE algorithm employs hedonic games from cooperative game theory and has been applied to various situations, including refugee egress and smallholder farming cooperatives. This paper discusses ABMSCORE, including its mechanism, requirements, limitations, and application. To demonstrate the potential of ABMSCORE, a new application example is given, which is based on a complex version of the Thomas Schelling’s segregation model. The intent of the paper is to provide the potential user with enough information so that they can apply ABMSCORE to their simulation products.

Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features

Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still problematic since other genomic factors cannot be fully integrated for combined analyses. This research paper presents a novel gene identification and segregation model with five key processes (a) pre-processing, (b) treatment of class imbalances, (c) feature extraction, (d) feature selection, and (e) gene classification. To increase the data quality, the gathered initial information is first pre-processed utilizing data cleaning and data normalization. This turns the raw data into something that is both useful and effective. In actuality, the sample is skewed against drivers because passenger mutation markers appear in proportionally less instances than drivers do. To address the Class Imbalance Problem, improved K-Means + SMOTE are applied to the preprocessed data. The most crucial characteristics, including those at the gene and mutation levels, are then extracted from the balanced dataset. To lessen the computational load in terms of time, the best features from the retrieved features are selected using Forensic interpretation tailored hunger food search optimization (FIHFSO). The ideal features are used to train the deep learning classifier that conducts the separation procedure. In this research, an Improved Recurrent Neural Network (I-RNN) is used to make a final decision about genes. At 90% of learning percentage, the accuracy of the proposed method achieves 0.98% of 0.83, 0.81, 0.65, 0.80, 0.92 and 0.63% which is compared to the other methods like HGS, FBIO, AOA, AO, GOA and PRO respectively.

Residential Micro-Segregation and Social Capital in Lima, Peru

This article addresses the bidirectional relationship between residential micro-segregation, in the form of built barriers to mobility, and social capital. I engage with two bodies of the literature. On the one hand, I critique a widespread top-down model of residential segregation. This model suggests that higher-status groups drive segregation through direct (e.g., secluded neighbourhoods) and indirect (e.g., by funnelling housing demand) measures. On the other hand, I provide evidence of the bounding effects of segregation on social capital. While some scholars suggest residential homogeneity favours social capital, others argue that benefits occur within privileged neighbourhoods. The effects of segregation on social capital are less clear at lower scales and in highly unequal Global South cities. My argument is twofold. First, I uncover the dynamics of segregation below the neighbourhood scale. I use the notion of horizontal micro-segregation to identify the social and spatial conditions associated with a higher concentration of street-level segregating infrastructure. My methodological approach draws on data for all residential blocks in Lima, Peru (N = 99,685). I find that suburban-inspired urban design is positively associated with micro-segregating infrastructure, upon controlling for other factors such as socioeconomic status, density, and urbanization age of each block. Second, I provide evidence of the bounding effects of segregation on social capital. Using ten waves of the Lima Cómo Vamos survey (2010–2019), I show that micro-segregating infrastructure is associated with higher trust in neighbours and lower civic engagement. These findings indicate that exposure to segregation affects social capital within and across secluded neighbourhoods throughout the socioeconomic spectrum.

On the surface segregation of Sn in cold-rolled Fe under continuous annealing conditions

The surface segregation of Sn in cold-rolled Fe-0.03Sn and Fe-0.01Sn (at.%) model alloys was investigated for annealing parameters characteristic of continuous galvanizing lines (CGLs). The most significant increase in surface coverage occurred during linear heating between 500 and 675 °C, where no significant change in segregation was observed with isothermal holding for 60 – 480 s at peak annealing temperatures of 675 – 825 °C. While the bulk diffusion of Sn in Fe determined the segregation rate during extended isothermal holding up to 10800 s, it could not account for the rapid increase in coverage during heating. It was determined that Sn segregation was accelerated during linear heating by rapid diffusion along dislocation pipes in the cold-rolled starting microstructure. Integrating the decrease in diffusivity due to recrystallization into the McLean model for interfacial segregation resulted in an experimentally verified description of segregation kinetics during linear heating. It was also able to predict the experimentally observed increase in segregation when the linear heating rate between 500 °C and 675 °C was decreased from 5 to 1 °C/s. No significant difference in surface segregation was observed between the 0.01 and 0.03 at.% Sn addition for isothermal holding of 480 s or less, which can be explained by the saturation of easy adsorption sites. A bond-breaking model was used to illustrate their origin from imperfect coordination within the surface layer. As significant surface segregation can be achieved within the CGL processing window, Sn microalloying appears as a promising strategy to improve galvanized coating quality.

Particle-size segregation patterns in a partially filled triangular rotating drum

In this paper a fully coupled particle-size segregation model for granular flows (Barker et al., J. Fluid Mech., vol. 909, 2021, p. A22) is used to simulate the development of the patterns in a triangular rotating drum. The results are compared with the experimental patterns formed with bidisperse and tridisperse granular mixtures, and with varying compositions and fill heights. In all cases the agreement between the simulations and experiments is remarkably good. The experimental patterns are generated in a narrow gap between transparent front and back sidewalls. These prevent three-dimensional motion, but also impose friction on the flow, making it thinner and faster than it would otherwise be. This promotes segregation, as it simultaneously increases the shear rate and reduces the local pressure. To obtain the correct flow dynamics and segregation, width-averaged sidewall friction is incorporated into the two-dimensional simulations, which are performed in OpenFOAM $^{\circledR}$ . The free-surface avalanche forms a boundary layer within which all the segregation occurs. Material in the lower reach of the avalanche is continuously deposited into an underlying solid body of grains, which rotates with the drum, and is eventually re-entrained into the avalanche along its upper reach. The changing geometry of the granular region (as the drum rotates) implies that the avalanche is constantly adjusting its length, position and depth. This generates a complex quasi-periodic flow, which when combined with particle-size segregation generates amazing patterns in the solid rotating granular body after only two drum rotations.

DEM Modelling of Segregation in Granular Materials: A Review

DEM Modelling of Segregation in Granular Materials: A Review

CFD modelling of the powder segregation in multi-material laser directed energy deposition

One of the prevailing challenges in multi-material laser Directed Energy Deposition is the segregation of the constituents during the delivery of the feedstock into the melt pool. This phenomenon is of particular concern when metal-ceramic mixtures are involved, as the differences in the inertial properties of the powders are higher and segregation is aggravated. This issue affects the reliability of the process; indeed, powder segregation can influence the composition of the melt pool and, thus, that of the final part or coating. In this work, Computational Fluid Dynamics modelling is adopted to study this phenomenon and to explore strategies to overcome it. Firstly, a base multi-material CFD model is built. Secondly, a simplified algorithm is proposed to reduce the computational cost of simulating multiple scenarios. This model is first validated numerically against the base model. Then the simplified multi-material CFD model is experimentally validated. Thirdly, the developed tool is implemented to elaborate two strategies to minimise powder segregation in a Co-WC powder mixture. The first one is quite straightforward and little development is necessary to incorporate it to the industry. Conversely, the second one, though promising, needs further development and its practicality needs to be evaluated before being ready for industrial implementation.

Effects of Cerium Content on the Nonmetallic Inclusions and Impact Toughness of Transformation‐Induced Plasticity Steel

The effects of Ce treatment on the inclusion evolution and impact toughness of as‐cast transformation‐induced plasticity (TRIP) steel are systematically investigated by scanning electron microscope (SEM) and impact toughness testing. The results reveal that in Fe‐2Mn‐1.5Al‐1Si‐0.2C steel without Ce, the primary inclusions consist of AlN, MnS, and Al2O3. After Ce addition, the inclusions evolve into Ce‐Al‐O, Ce2O2S, CeS, and Ce2O3 inclusions. The evolution of inclusions exhibits an initial reduction in both quantity and size with increasing Ce content, followed by a subsequent increase. In cases of excessive Ce content, there is an observable rise in the overall inclusion count, marked by the emergence of CeAs, CeP, and CeC2 inclusions. The segregation model suggests that P‐containing inclusions predominantly precipitate in the late solidification process, while As‐ and C‐containing inclusions form during the solid phase. When the Ce content reaches 0.0075 wt%, the minimum values of the number density and average diameter are 96.7 mm−2 and 1.6 μm, respectively. Furthermore, the influence of the size, morphology, quantity, and type of inclusions on the impact toughness are analyzed. The highest recorded impact energy is 6.8 J with a Ce content of 0.0075 wt%.

Data-driven Agent-based Modeling: Experimenting with the Schelling’s Model

Agent-based modeling and simulation is a practical computational technique for studying complex systems of autonomous agents in various disciplines. Agent-based models facilitate the study of emergent phenomena by simulating heterogeneous agents and their flexible behaviors and interactions. However, developing an agent-based model of a complex system is often time-consuming and vulnerable to the modeler’s biases. Addressing this challenge requires a paradigm shift from knowledge-driven modeling to data-driven modeling. In this research, we initiate and experiment with automating the process of composing agent-based models by developing data-driven model extraction. To achieve this objective, we conduct experiments employing different variations of Schelling’s segregation model, a well-known agent-based model, each featuring different parameter sets and complexity levels.