Computer Modeling Of Processes Research Articles

Automatic activity-travel sequence generator using language, grammar, and machine theory

ABSTRACT Activity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data.

Controlling microstructure and B2 ordering kinetics in Fe–Al system through additive manufacturing

Multi-material fabrication between steel and aluminum is challenging because of the formation of several intermetallic phases. Embrittling B2 ordered intermetallics form in the steel rich side and are stable till about 60% of Al dilution in steel. In this work by using multi-length scale characterization coupled with integrated computational process and thermokinetic modeling, we show that the ordered B2 intermetallics in the steel rich side of the Fe–Al system forms via a nucleation and growth mechanism. The extent of B2 ordered intermetallics can be controlled by modifying the directed energy deposition-additive manufacturing (DED-AM) process parameters. Our findings lay the foundation for enabling fabrication of crack-free functionally graded compositions between the two alloys.

Evolution of Physical, Thermal, and Mechanical Properties of Poly(methyl Methacrylate)-Based Elium Thermoplastic Polymer During Polymerization.

Elium-based thermoplastic composites are a key material for future use in the marine, wind energy, and automotive industries because of their recyclability and ease of manufacture. To optimize the processing of the Elium composites to yield optimal structural properties, computational process modeling can be used to relate processing parameters to residual stresses and material durability. The key ingredient for reliable and accurate process modeling is the evolution of physical, thermal, and mechanical properties during polymerization. The objective of this study is to use molecular dynamics to predict the mass density, bulk modulus, shear modulus, Young's modulus, Poisson's ratio, glass transition temperature, and coefficient of thermal expansion as a function of the extent of reaction of the polymer. The predicted properties compare favorably to the experimentally measured values in the fully polymerized state. This data set of properties provides needed input data for process modeling of Elium-based composites for process parameter optimization and improved durability and performance.

Chemical engineering perspective of pyrolysis reactor and condenser system for biomass valorisation

The manuscript investigates the design, operating, and economic parameters of components in a biomass pyrolysis system. It covers two key components, the pyrolysis reactor and condenser, with plans to discuss other components in future publications. The study relies on experimental data from a fixed-bed reactor, bio-oil retention experiments using various vials, and computational modeling of the pyrolysis process using a lumped model. The experimental data were thoroughly analysed, considering different types of pyrolysis reactors and heating methods. Dimensionless numbers were employed to predict the impact of changing feed characteristics during pyrolysis. The second part of the study focuses on the operation of conventional condensers and how Bio-oil deposition on their surfaces affects efficiency and cost. Experimental estimation of Bio-oil film thickness on stainless steel and glass surfaces is presented, along with its effects. Finally, a hybrid condenser, combining stainless steel with a glass lining, is proposed to enhance efficiency and reduce costs based on operational requirements.

A physically-informed machine learning model for freeform bending

AbstractThis work aims at a fast computational process model of the free-form bending process. It proposes a novel physically-informed machine learning model, which is trained with experimental data of bending constant radii and utilizes additional physical bending knowledge by integrating Timoshenko’s beam theory. The model is able to predict the resulting plastic deformation of the tube after exiting the die by computing an elastic representation of the tube’s deformation with beam theory at each time step. This elastic representation serves as input for a regression model similar to a partially connected neural network. This physically-informed machine learning model generalizes the constant training radii to complex bend geometries consisting of transitional sections and true spline geometries. It is compared to a benchmark finite element simulation and has an improved prediction quality for complex kinematics while reducing the computation time by four orders of magnitude.

Advancing Organizational Science With Computational Process Theories

Organizational scholars commonly refer to organizations as complex systems unfolding as a function of work processes. Consequently, the direct study of work processes necessitates our attention. However, organizational scholars tend not to study work processes directly. Instead, organizational scholars commonly develop theories about relationships among psychological construct phenomena that indirectly reference people’s affective, behavioral, cognitive, and/or social processes as underlying explanations. Specifically, construct-oriented theories summarize processes in operation across actors, time, and contexts, and thus, provide limited insights into how focal phenomena manifest directly as a function of process operations. Construct theories remain one-step removed from articulating sequences of actions and two-steps removed from describing generative mechanisms responsible for observed actions. By “missing the action,” construct theories offer incomplete explanatory accounts and imprecise interventions. We assert that researchers in organizational science can make progress towards addressing these concerns by directing greater attention to developing computational process theories. We begin by presenting a framework for differentiating theories based on their focus (constructs versus processes) and modality (narrative versus computational). We use the framework to contrast narrative construct theories to computational process theories. We then describe key design principles for developing computational process theories and explain those principles using a leadership example. We use simulated data, from the computational process model we develop, to explicitly demonstrate the differences between construct and process thinking. We then discuss how computational process theories advance theory development. We conclude with a discussion of the long-term benefits of computational process theories for organizational science.

Optimal Molecular Dynamics System Size for Increased Precision and Efficiency for Epoxy Materials.

Molecular dynamics (MD) simulation is an important tool for predicting thermo-mechanical properties of polymer resins at the nanometer length scale, which is particularly important for efficient computationally driven design of advanced composite materials and structures. Because of the statistical nature of modeling amorphous materials on the nanometer length scale, multiple MD models (replicates) are typically built and simulated for statistical sampling of predicted properties. Larger replicates generally provide higher precision in the predictions but result in higher simulation times. Unfortunately, there is insufficient information in the literature to establish guidelines between MD model size and the resulting precision in predicted thermo-mechanical properties. The objective of this study was to determine the optimal MD model size of epoxy resin to balance efficiency and precision. The results show that an MD model size of 15,000 atoms provides for the fastest simulations without sacrificing precision in the prediction of mass density, elastic properties, strength, and thermal properties of epoxy. The results of this study are important for efficient computational process modeling and integrated computational materials engineering (ICME) for the design of next-generation composite materials for demanding applications.

Generation of a Virtual Cell using a Phase Field Approach to Model Amoeboid Crawling.

The process of chemotaxis of living cells is complex. Cells follow gradients of an external signal because the interior of the cells gets polarized. The description of the exterior and the interior of the cell together with its motion for the convenient realization of the computational modeling of the whole process is a complex technical problem. Here, we employ a phase field model to characterize the interior of the cell, permitting the integration of stochastic partial differential equations, responsible for the polarization in the interior of the cell, and simultaneously, the calculation of the shape deformations of the cell, including its locomotion. We detail the mathematical description of the process and the procedure to calculate numerically the phase field with a simple reaction-diffusion equation for a single concentration.

Three-dimensional transient heat transfer analysis of micro-plasma arc welding process using volumetric heat source models

Abstract The micro plasma arc welding process is associated with different physical phenomena simultaneously. This results in complexities to comprehend the actual mechanism involved during the process. Therefore, a robust numerical model that can compute the weld pool shape, temperature distribution, and thermal history needs to be addressed. Unlike, other arc welding processes, the micro plasma arc welding process utilizes thin sheets of thickness between 0.5 and 2 mm. However, joining thin sheets using a high-density arc welding process quickens the welding defects such as burn-through, thermal stresses, and welding-induced distortions. The incorporation of a surface heat source model for computational modeling of the high energy density welding process impedes heat transfer analysis. In that respect, researchers have developed numerous volumetric heat source models to examine the welding process holistically. Although, selecting volumetric heat source models for miniature welding is a significant task. The present work emphasis developing a rigorous yet efficient model to evaluate weld pool shape, temperature distribution, and thermal history of plasma arc welded Ti6Al4V sheets. The computational modeling is performed using a commercially available COMSOL Multiphysics 5.4 package with a finite element approach. Two different prominent thermal models, namely, Parabolic Gaussian and Conical power energy distribution models are used. A comparative analysis is carried out to determine the most suitable heat source model for evaluating temperature distribution, peak temperature, and thermal history. The analysis is done by juxtaposing the simulated half-cross-section weld macrographs with the published experimental results from independent literature. The numerical results showed that the proximity of top bead width magnitude was obtained using the Parabolic Gaussian heat source model for low heat input magnitude of 47.52 and high heat input magnitude of 65.47 J·mm−1, respectively. In terms of percentage error, the maximum top bead width percentage error for the Parabolic heat source model is 13.26%. However, the maximum top bead width percentage error for the Conical heat source model is 18.36%. Likewise, the maximum bottom bead width percentage error for the Parabolic heat source model and the Conical heat source model is 12.3 and 25.8%, respectively. Overall, it was observed that the Parabolic heat source model produces the least deviating outcomes when compared with the Conical distribution. It was assessed that the Parabolic Gaussian heat source model can be a viable heat source model for numerically evaluating micro-plasma arc welded Ti6Al4V alloy of thin sheets.

Modeling of sputter-based atomic layer deposition with altered parameters

Computational modeling of the sputtering process forming atomic layers of some technologically significant materials was performed based on sputter rate relation. Bismuth atomic layer deposition at different polar angles and ion fluence were shown. The atomic layers getting deposited were studied as a function of incident Argon ion energy at different pressure and a curve analogous to the Bragg curve was obtained. The sputtering parameters were varied to get different atomic layers of TiOx films and the variation of coverage with sticking coefficient was shown. The rate of change of partial sputtering yield with coverage was depicted in the sputter-based deposition of TiN films. The deposition pressure and time were varied to get films of different thicknesses. All the simulated results were correlated with experimental findings with features like variation of reactive gas pressure, electron aided momentum transfer in Bi, multilayer anatase and rutile formation in TiO2 and effect of process parameters on TiN.

Computational Modeling and Simulation of the Microwave Hybrid Heating Process: A State of the Art Review

Computational Modeling and Simulation of the Microwave Hybrid Heating Process: A State of the Art Review

FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes.

Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyse and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit.

Amortized Bayesian Model Comparison With Evidential Deep Learning.

Comparing competing mathematical models of complex processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for guiding decisions. However, many interesting models are intractable with standard Bayesian methods, as they lack a closed-form likelihood function or the likelihood is computationally too expensive to evaluate. In this work, we propose a novel method for performing Bayesian model comparison using specialized deep learning architectures. Our method is purely simulation-based and circumvents the step of explicitly fitting all alternative models under consideration to each observed dataset. Moreover, it requires no hand-crafted summary statistics of the data and is designed to amortize the cost of simulation over multiple models, datasets, and dataset sizes. This makes the method especially effective in scenarios where model fit needs to be assessed for a large number of datasets, so that case-based inference is practically infeasible. Finally, we propose a novel way to measure epistemic uncertainty in model comparison problems. We demonstrate the utility of our method on toy examples and simulated data from nontrivial models from cognitive science and single-cell neuroscience. We show that our method achieves excellent results in terms of accuracy, calibration, and efficiency across the examples considered in this work. We argue that our framework can enhance and enrich model-based analysis and inference in many fields dealing with computational models of natural processes. We further argue that the proposed measure of epistemic uncertainty provides a unique proxy to quantify absolute evidence even in a framework which assumes that the true data-generating model is within a finite set of candidate models.

Abstract 6538: Development and refinement of functional gene expression signatures as a computational tool for comprehensive characterization of transcriptomic data

Abstract Functional gene expression signatures (FGES) are widely used as biomarkers in cancer diagnostics as descriptive and predictive models for treatment selection. Since FGES scores are based solely on gene expression profiles, they can be applied to data from bulk and single-cell RNA-seq or gene expression microarrays. However, FGES development and implementation has several challenges including technical limitations of calculating FGES scores based on ranked gene expressions such as background noise and gene cross-correlations and difficulty in assessing the biological relevance of FGES. Here, we developed an FGES validation pipeline that addressed these limitations by examining publicly available and internally-developed FGES. The pipeline was developed by applying a modified single-sample gene set enrichment analysis (ssGSEA) to a manually curated database of more than 50,000 RNA-Seq samples, including sorted cells, cell lines, and tumor and normal tissue biopsies. We defined technical and biological criteria to validate signature quality: 1) single gene expression noise distribution minimum is greater than zero; 2) cross-correlation of genes comprising a signature is positive; 3) specificity, whereby a FGES differentiates a group of interest and has a minimal overlap with other unrelated signatures and molecular pathways; 4) generalizability, requiring FGES score to differentiate a group of interest across datasets of different origin, cell type, or diagnosis. We tested these criteria on FGES that describe cellular processes such as epithelial-mesenchymal transition (EMT) and senescence as well as FGES that can delineate cell types such as macrophages. We were able to better distinguish 12 different cell types using the corresponding cell type-specific FGES with significantly higher ssGSEA scores (p-adj ≤ 0.00004 in 99.9% cases) than 911 analogous MSigDB gene signatures. Our EMT FGES, tuned specifically for melanoma, exhibited higher precision than the MSigDB Hallmark EMT gene set (p < 0.001) for predicting histological pigment scores. The senescence FGES also accurately distinguished senescent from non-senescent cells in datasets of differing cell origin and data acquisition methods, demonstrating signature generalizability. Finally, using macrophage-describing FGES, we showed that specificity of a publicly available gene set can be significantly improved (p-adj = 0.00001) by tuning it according to the aforementioned criteria. Taken together, our proposed workflow enables better assessment of the technical quality and biological adequacy of FGES for their further use in transcriptomic analyses and computational models of complex biological processes and tumorigenesis. Citation Format: Elena Ocheredko, Nadezhda Lukashevich, Maria Savchenko, Sofia Kust, Syune Ambaryan, Dmitry Tabakov, Maria Sorokina, Linda Balabanian, Kushal Suryamohan, Anastasiya Zotova, Nathan Fowler, Aleksander Bagaev. Development and refinement of functional gene expression signatures as a computational tool for comprehensive characterization of transcriptomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6538.

Age-related reduction of amplitude modulation frequency selectivity.

The perception of amplitude modulations (AMs) has been characterized by a frequency-selective process in the temporal envelope domain and simulated in computational auditory processing and perception models using a modulation filterbank. Such AM frequency-selective processing has been argued to be critical for the perception of complex sounds, including speech. This study aimed at investigating the effects of age on behavioral AM frequency selectivity in young (n = 11, 22-29 years) versus older (n = 10, 57-77 years) listeners with normal hearing, using a simultaneous AM masking paradigm with a sinusoidal carrier (2.8 kHz), target modulation frequencies of 4, 16, 64, and 128 Hz, and narrowband-noise modulation maskers. A reduction of AM frequency selectivity by a factor of up to 2 was found in the older listeners. While the observed AM selectivity co-varied with the unmasked AM detection sensitivity, the age-related broadening of the masked threshold patterns remained stable even when AM sensitivity was similar across groups for an extended stimulus duration. The results from the present study might provide a valuable basis for further investigations exploring the effects of age and reduced AM frequency selectivity on complex sound perception as well as the interaction of age and hearing impairment on AM processing and perception.

Troubleshooting Bayesian cognitive models.

Using Bayesian methods to apply computational models of cognitive processes, or Bayesian cognitive modeling, is an important new trend in psychological research. The rise of Bayesian cognitive modeling has been accelerated by the introduction of software that efficiently automates the Markov chain Monte Carlo sampling used for Bayesian model fitting-including the popular Stan and PyMC packages, which automate the dynamic Hamiltonian Monte Carlo and No-U-Turn Sampler (HMC/NUTS) algorithms that we spotlight here. Unfortunately, Bayesian cognitive models can struggle to pass the growing number of diagnostic checks required of Bayesian models. If any failures are left undetected, inferences about cognition based on the model's output may be biased or incorrect. As such, Bayesian cognitive models almost always require troubleshooting before being used for inference. Here, we present a deep treatment of the diagnostic checks and procedures that are critical for effective troubleshooting, but are often left underspecified by tutorial papers. After a conceptual introduction to Bayesian cognitive modeling and HMC/NUTS sampling, we outline the diagnostic metrics, procedures, and plots necessary to detect problems in model output with an emphasis on how these requirements have recently been changed and extended. Throughout, we explain how uncovering the exact nature of the problem is often the key to identifying solutions. We also demonstrate the troubleshooting process for an example hierarchical Bayesian model of reinforcement learning, including supplementary code. With this comprehensive guide to techniques for detecting, identifying, and overcoming problems in fitting Bayesian cognitive models, psychologists across subfields can more confidently build and use Bayesian cognitive models in their research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Problem of Modeling the Geometry of Twisted Fuel Rods with X-Type Cross Section in the Performance of Thermohydraulic Calculations

For creating computational models of processes affecting the safety of nuclear facilities, in accordance with the federal rules and regulations in the field of atomic energy use, the selected modeling methods, as well as the accepted approximations and assumptions shall be justified. The paper considers the issues of taking into account the specifics of modeling the geometry of twisted fuel rods with x-type cross section using nodalization software, as well as possible approaches to describing the geometry of such fuel rods during thermohydraulic calculations. The paper defines a preferred method for specifying geometry, which allows to correctly take into account the maximum value of the heat flux from the surface of the twisted fuel rods with x-type cross section when calculating temperatures in the core and the possibility of a heat exchange crisis on the surface of such fuel rods in case of violations of normal operation.

FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes

FitMultiCell: Simulating and parameterizing computational models of multi-scale and multi-cellular processes

Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data.

Functional connectivity (FC) and neural excitability may interact to affect symptoms of autism spectrum disorder (ASD). We tested this hypothesis with neural network simulations, and applied it with functional magnetic resonance imaging (fMRI). A hierarchical recurrent neural network embodying predictive processing theory was subjected to a facial emotion recognition task. Neural network simulations examined the effects of FC and neural excitability on changes in neural representations by developmental learning, and eventually on ASD-like performance. Next, by mapping each neural network condition to subject subgroups on the basis of fMRI parameters, the association between ASD-like performance in the simulation and ASD diagnosis in the corresponding subject subgroup was examined. In the neural network simulation, the more homogeneous the neural excitability of the lower-level network, the more ASD-like the performance (reduced generalization and emotion recognition capability). In addition, in homogeneous networks, the higher the FC, the more ASD-like performance, while in heterogeneous networks, the higher the FC, the less ASD-like performance, demonstrating that FC and neural excitability interact. As an underlying mechanism, neural excitability determines the generalization capability of top-down prediction, and FC determines whether the model's information processing will be top-down prediction-dependent or bottom-up sensory-input dependent. In fMRI datasets, ASD was actually more prevalent in subject subgroups corresponding to the network condition showing ASD-like performance. The current study suggests an interaction between FC and neural excitability, and presents a novel framework for computational modeling and biological application of a developmental learning process underlying cognitive alterations in ASD.

UAV Cluster Behavior Modeling Based on Spatial-Temporal Hybrid Petri Net

Currently, we are facing an increasing trend in the use of unmanned aerial vehicles (UAV) in various activities, both civilian and military. The application of UAVs in the battlefield has received extensive attention amid a global new military revolution. A UAV cluster is a large and complex real-time feedback system that integrates a communication and sensor network, control system, calculation, and physical process. The heterogeneous UAVs conduct complex behaviors, which requires a comprehensive description and analysis of UAV cluster modeling. The integrated modeling of the heterogeneous UAV cluster is of great significance and value to test and verify the new combat mode. In this paper, we present a novel representation framework based on the Petri nets. We used a spatial-temporal hybrid Petri net to illustrate the discrete state and continuous process of a heterogeneous UAV cluster system, and effectively achieved the fusion of a physical and computational process and interaction event modeling in the cluster system. Finally, the formal verification of UAV cluster attack mission modeling was carried out by UPPAAL, suggesting the proposed modeling method is feasible and effective.