Development Of Computational Models Research Articles

Experimental Biomechanics of Neonatal Brachial Plexus Avulsion Injuries Using a Piglet Model.

A brachial plexus avulsion occurs when the nerve root separates from the spinal cord during birthing trauma, such as shoulder dystocia or a difficult vaginal delivery. A complete paralysis of the affected levels occurs post-brachial plexus avulsion. Despite being reported in 10-20% of brachial plexus birthing injuries, it remains poorly diagnosed during the acute stages of injury, leading to poor intervention approaches. The poor diagnosis of brachial plexus avulsion injury can be attributed to the currently unavailable biomechanics of brachial plexus avulsion. While the biomechanical properties of neonatal brachial plexus are available, the forces required to avulse a neonatal brachial plexus remain unknown. This study aims to provide detailed biomechanics of the required forces and corresponding strains for neonatal brachial plexus avulsion. Biomechanical tensile testing was performed on an isolated, clinically relevant piglet spinal cord and brachial plexus complex, and the required avulsion forces and strains were measured. The reported failure forces and corresponding strains were 3.9 ± 1.6 N at a 27.9 ± 6.5% strain, respectively. The obtained data are required to understand the avulsion injury biomechanics and provide the necessary experimental data for computational model development that serves as an ideal surrogate for understanding complicated birthing injuries in newborns.

Computational Model of the Effective Thermal Conductivity of a Bundle of Round Steel Bars.

During the heat treatment of round steel bars, a heated charge in the form of a cylindrically formed bundle is placed in a furnace. This type of charge is a porous granular medium in which a complex heat flow occurs during heating. The following heat transfer mechanisms occur simultaneously in this medium: conduction in bars, conduction within the gas, thermal radiation between the surfaces of the bars, and contact conduction across the joints between the adjacent bars. This complex heat transfer can be quantified in terms of effective thermal conductivity. This article presents the original model of the effective thermal conductivity of a bundle of round steel bars. This model is based on the thermo-electric analogy. Each heat transfer mechanism is assigned an appropriate thermal resistance. As a result of the calculations, the impact of the following parameters on the intensity of heat transfer in the bundle was examined: temperature, the thermal conductivity of the bars, the thermal conductivity of the gas, diameter, and emissivity of the bar, and bundle porosity. Calculations were performed for a temperature range of 25-800 °C, covering a wide spectrum of variables, including bar diameters, bundle porosity, and type of gas. The knowledge obtained thanks to the calculations performed will facilitate the optimization of heat treatment processes for the considered charge. The greatest scientific value of the presented research is the demonstration that, thanks to the developed computational model, it is possible to analyze a very complex heat transfer phenomenon using relatively simple mathematical relationships.

THE POTENTIAL OF USING GENERATIVE ARTIFICIAL INTELLIGENCE TO SIMULATE THE RESILIENCE OF REGIONAL SOCIO-ECONOMIC SYSTEMS

Minimization of human involvement in the construction of simulation models is an urgent task, the solution of which is in demand in the practice of modeling the life cycle of complex dynamic systems. One of the ways to solve it is to adapt existing solutions in the field of generative artificial intelligence to the specifics of developing computer models of complex systems. The article discusses the main areas of automation of simulation modeling using modern artificial intelligence technologies. The authors propose concepts for using large-scale language models to generate scenarios for simulation modeling of the resilience of regional socio-economic systems. Theoretical and practical issues of implementing the proposed solutions in modern simulation environments are considered. A description of a generalized algorithm for assessing the resilience of regional systems "Business-Community-Government" is given, built on the basis of integrating simulation modeling and generative artificial intelligence.

A Validation Framework for Bulk Distribution Logistics Simulation Models

Background: Simulation of business processes allows decision-makers to explore the implications and trade-offs of alternative approaches, policies and configurations. Trust in the simulation as a stand-in proxy of the real system depends on the validation of the computer model as well as on that of the data used to run it and judge its behaviour. Though validation frameworks exist, they provide little guidance for validation in the context of data-poor endeavours, such as those where observations as sourced from historical records were acquired for purposes other than the simulation itself. As simulation of complex business systems as logistic distribution networks can only rely on this type of data, there is a need to address this void and provide guidance for practitioners and fostering the conversation among academics. Objective: This paper presents a high-level development and validation framework applicable to simulation in data-poor environments for modelling the process of bulk distribution of commodities. Method: Traditionally accepted approaches were synthesised so as to develop an into a flexible three-stage modelling and validation approach to guide the process and improve the transparency of adapting available data sources for the simulation itself. The framework suggests the development of parallel paths for the development of computer and data models which, in the last stage, are merged into a phenomenological model resulting from the combination of both. The framework was applied to a case study involving the distribution of bulk commodities over a country-wide network to show its feasibility. Results: The method was flexible, inclusive of other frameworks, and suggested considerations to be made during the acquisition and preparation of data to be used for the modelling and exploration of uncharted scenarios. Conclusions: This work provides an integrative, transparent, and straightforward method for validating exploratory-type simulation models for endeavours in which observations cannot be acquired through direct experimentation on the target system.

Prediction of miRNA-disease associations based on PCA and cascade forest

BackgroundAs a key non-coding RNA molecule, miRNA profoundly affects gene expression regulation and connects to the pathological processes of several kinds of human diseases. However, conventional experimental methods for validating miRNA-disease associations are laborious. Consequently, the development of efficient and reliable computational prediction models is crucial for the identification and validation of these associations.ResultsIn this research, we developed the PCACFMDA method to predict the potential associations between miRNAs and diseases. To construct a multidimensional feature matrix, we consider the fusion similarities of miRNA and disease and miRNA-disease pairs. We then use principal component analysis(PCA) to reduce data complexity and extract low-dimensional features. Subsequently, a tuned cascade forest is used to mine the features and output prediction scores deeply. The results of the 5-fold cross-validation using the HMDD v2.0 database indicate that the PCACFMDA algorithm achieved an AUC of 98.56%. Additionally, we perform case studies on breast, esophageal and lung neoplasms. The findings revealed that the top 50 miRNAs most strongly linked to each disease have been validated.ConclusionsBased on PCA and optimized cascade forests, we propose the PCACFMDA model for predicting undiscovered miRNA-disease associations. The experimental results demonstrate superior prediction performance and commendable stability. Consequently, the PCACFMDA is a potent instrument for in-depth exploration of miRNA-disease associations.

Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition.

Explaining how neuronal activity gives rise to cognition arguably remains the most significant challenge in cognitive neuroscience. We introduce neuro-cognitive multilevel causal modeling (NC-MCM), a framework that bridges the explanatory gap between neuronal activity and cognition by construing cognitive states as (behaviorally and dynamically) causally consistent abstractions of neuronal states. Multilevel causal modeling allows us to interchangeably reason about the neuronal- and cognitive causes of behavior while maintaining a physicalist (in contrast to a strong dualist) position. We introduce an algorithm for learning cognitive-level causal models from neuronal activation patterns and demonstrate its ability to learn cognitive states of the nematode C. elegans from calcium imaging data. We show that the cognitive-level model of the NC-MCM framework provides a concise representation of the neuronal manifold of C. elegans and its relation to behavior as a graph, which, in contrast to other neuronal manifold learning algorithms, supports causal reasoning. We conclude the article by arguing that the ability of the NC-MCM framework to learn causally interpretable abstractions of neuronal dynamics and their relation to behavior in a purely data-driven fashion is essential for understanding biological systems whose complexity prohibits the development of hand-crafted computational models.

A Graph-Based Machine-Learning Approach Combined with Optical Measurements to Understand Beating Dynamics of Cardiomyocytes.

The development of computational models for the prediction of cardiac cellular dynamics remains a challenge due to the lack of first-principled mathematical models. We develop a novel machine-learning approach hybridizing physics simulation and graph networks to deliver robust predictions of cardiomyocyte dynamics. Embedded with inductive physical priors, the proposed constraint-based interaction neural projection (CINP) algorithm can uncover hidden physical constraints from sparse image data on a small set of beating cardiac cells and provide robust predictions for heterogenous large-scale cell sets. We also implement an in vitro culture and imaging platform for cellular motion and calcium transient analysis to validate the model. We showcase our model's efficacy by predicting complex organoid cellular behaviors in both in silico and in vitro settings.

Computational analysis of hydrogen bubble formation and dynamics in electrolytic systems using COMSOL

This study explores the numerical modeling of hydrogen bubble dynamics in electrolytic processes, utilizing COMSOL Multiphysics software. The focus is on the development of precise computational models to simulate the processes of bubble formation, growth, and movement in water electrolysis systems, which are crucial for optimizing hydrogen production. Using 2D axisymmetric modeling, the research applies several interface-capturing techniques, including phase field, level set, and moving mesh methods, to accurately capture the behavior of hydrogen bubbles in various operational conditions. By analyzing these dynamics, the study aims to improve the understanding of bubble-related phenomena in electrolysis, such as formation patterns, bubble size, and the terminal velocities of rising hydrogen bubbles. Additionally, the effects of density differences between hydrogen and water are examined to assess their impact on the overall efficiency of electrolysis. The results indicate that the moving mesh method offers the best performance in accurately modeling bubble dynamics, providing insights that can contribute to the optimization of electrolysis processes for efficient hydrogen production.

Neuro-Fuzzy: Multivariable Identification of a Pumping System with Variable Demand

Water supply systems (WSS) comprise a set of equipment, works and services aimed at supplying water, covering domestic, industrial and public consumption. WSS face challenges arising from hourly variation in demand, influenced by society's consumption patterns. These variations cause fluctuations in system pressures and energy inefficiencies. As a way of analyzing this aspect, intelligent techniques were used to identify an automated WSS with variable demand for the development of computational models. Two multivariable and non-linear models were developed based on Artificial Neural Networks (ANN) and Neuro-Fuzzy system (NF). The objective is to enable simulations of operation scenarios, analysis, design and implementation of new intelligent control algorithms. To collect the data used in training and validation, experiments were carried out throughout the system's operating region. For cross validation, tests were carried out in operating regions different from those used for training. The models were evaluated using performance criteria, such as: Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Final Prediction Error (FPE) and Adjustment percentage. The results were obtained using an experimental bench and showed adjustments greater than 99%. The cross-validation results evaluated with performance indicators show the superiority of intelligent models in comparison to parametric and mathematical models in the multivariable and non-linear identification of water pumping systems for simulation and dynamics prediction purposes.

Advanced tumor growth modeling: A numerical study integrating phase plane analysis with finite volume method

This study investigates tumor density dynamics through computational modeling, focusing on key parameters: proliferation rate m, diffusion coefficient τ, and additional factors c and κ. Systematic exploration reveals nuanced interactions shaping tumor growth, regression, and dispersal. Higher m values accelerate proliferation, while increased τ facilitates dispersal. Parameters c and κ further refine our understanding of tumor behavior. These insights inform the development of computational models for oncological research, with implications for targeted therapeutic interventions. The aim can be realized by utilizing the propose Finite Volume Method to develop a representation of cancer models and improve the efficiency of their simulations. Furthermore, the numerical results will be validated by comparing them with the Tanh-Coth method formulation to assess the model's accuracy and efficiency. The findings suggest that accurate modeling of tumor dynamics can aid in predicting tumor progression and response to treatments, ultimately contributing to personalized medicine and more effective therapeutic strategies. Phase plane analysis shows the schemes are conditionally stable and accurate to the fourth order in time. The method demonstrates exceptional agreement and error-free findings compared to 3D plots, underscoring its potential utility in clinical and research settings.

Toward a Quantum-Inspired Framework for Modelling Legal Rules

There is a growing demand for the development of computational models of legal rulesets, yet scholars acknowledge an incompatibility between the deterministic nature of current legal coding processes and the indeterminate nature of the law. Previously, legal scholars have identified conceptual links between the indeterminacy of quantum mechanics and the law, but very few have attempted to formally model the law using a framework inspired by the formalism of quantum information. I highlight similarities regarding indeterminacy as it is understood by legal and scientific scholars, motivating the construction of a quantum-inspired framework to model ambiguous legal rules. I outline how multi-qubit systems can be used to represent ambiguous legal rules formally, wherein each state of the system represents competing orthogonal interpretations of the law. I show that the amplitude corresponding to an interpretation state can represent its likelihood of validity and secondary interpretive characteristics, such as jurisprudential alignment. Using measures of classical and quantum information theory, these states can be analysed to yield information relating to forms of ambiguity, intra- and intertextuality, and inconsistencies present in a legal ruleset and interpretations. Finally, I illustrate the potential for generative artificial intelligence to assist in producing these quantum-inspired models of legal rules.

Antihypertensive, Anti-Inflammatory, and Antiangiogenic In Silico Activity of Lactoferrin-Derived Peptides of Equine Milk Hydrolysate

Background: Equine milk, including its whey proteins, is a source of nutrients and functional components in the human diet, and is especially beneficial for people with weakened immune systems, newborns, and athletes. Objectives Whey proteins in equine milk constitute approximately 20% of the total protein content and include various fractions such as albumin, globulin, and lactoferrin. Lactoferrin is one of the most extensively studied whey proteins in equine milk. Methods: HPLC-Mass analysis, enzymatic hydrolysis, modeling of 3D structure and biological activity in silico. Results:It has antioxidant, anti-inflammatory, and immunomodulatory properties, making it a promising candidate for influencing the various aspects of cardiovascular disease pathogenesis. The products of Lactoferrin hydrolysis by trypsin were confirmed using HPLC. The half-lives of the hydrolysate in the bloodstream and in an intestine-like environment were predicted in silico. Various biological activities (antihypertensive, anti-inflammatory, and antiangiogenic) were also estimated in silico and compared with the corresponding activities of lactoferrin hydrolysate amino acid sequences from camel and dromedary milk. Conclusions:The three-dimensional modeling of lactoferrin hydrolysate peptides was performed to support the development of computational models or simulations, as well as to investigate their potential antimicrobial, anti-inflammatory, or immune-modulating functions in clinical or nutritional applications.

Developing and Applying Computational Models to Uncover Mechanistic Insights into Complex Biological Processes Across Molecular, Cellular, and Systemic Levels

The complexity of biological processes spans molecular, cellular, and systemic levels, requiring advanced computational models to unravel the intricate mechanisms underlying these phenomena. This research explores the development and application of computational models to gain mechanistic insights into diverse biological systems. By integrating multi-scale data from genomics, proteomics, and cellular imaging, this study leverages machine learning algorithms, dynamical systems modeling, and network analysis to simulate and analyze biological interactions. Key areas of focus include understanding signaling pathways, cellular differentiation, and systemic physiological responses. The research also highlights the role of computational tools in bridging experimental data with theoretical predictions, providing a robust framework for hypothesis generation and testing. Challenges such as data heterogeneity, scalability, and model interpretability are addressed, emphasizing the need for interdisciplinary approaches. This study aims to advance the field of computational biology by offering novel insights into complex biological systems and fostering applications in personalized medicine, drug development, and synthetic biology.

Computational Modeling of Proton Exchange Membrane Electrolyzer

Hydrogen holds great promise to help decarbonize our energy sector. Proton exchange membrane electrolyzers (PEMELs) are undergoing rapid development and deployment for the generation of green hydrogen based on renewable sources of electricity. The inherent capacity of PEMELs to readily respond to fluctuations in electricity input is an added advantage for the generation of hydrogen. PEMEL optimization with regard to materials, component design, and operating conditions is critical for high performance and efficiency. The development of a robust computational model for PEMELs can facilitate not only an improved understanding of the relevant physical and electrochemical phenomena but also help to develop and test effective strategies for performance optimization. The current research is aimed at developing a 3D parallel flowfield channel PEMEL model in COMSOL Multiphysics. To date, results have been obtained for a single-phase model with the baseline operating temperature and relative humidity (RH) of 60°C and 90%, respectively, under the following assumptions: (1) the feed gas (water vapor) is treated as an ideal gas, (2) the flow in the channel is laminar and incompressible, (3) membrane, catalyst layer (CL), and porous transport layer (PTL) microstructures are assumed to be homogeneous and isotropic, and (4) all physical and electrochemical processes are considered to be at steady state. The simulated polarization curve was validated against in-house experimental data for Nafion 115 at 60°C. We then investigated the effect of operating temperature and membrane thickness on current density, overpotentials due to cell resistance, and polarization curves. It was observed that the PEMEL performance improved significantly with temperature, owing primarily to the higher protonic conductivity of the membrane and improved catalyst kinetics. Polarization curves were also obtained for Nafion 212 (50.4 µm) and 117 (178 µm) at 80°C and the thinnest membrane, i.e. Nafion 212, performed better than its thicker counterparts due to reduced ohmic losses. We are currently developing a two-phase flow PEMEL model wherein liquid water is supplied to the anode inlet, and oxygen gas mixed with liquid water emerges from the anode exit. At high current density, oxygen bubbles can obstruct the reactant flow to the anode catalyst layer resulting in suboptimal PEMEL performance. Results will be presented for the dynamics of two-phase flow in a PEMEL system under various operating conditions from which we may draw insights to optimize PEMEL performance.

Efficiency of Energy Exchange Strategies in Model Bacteriabot Populations

Micro/nanorobotics is becoming part of the future of medicine. One of the most efficient approaches to the construction of small medical robots is to base them on unicellular organisms. This approach inherently allows for obtaining complex capabilities, such as motility or environmental resistance. Single-celled organisms usually live in groups and are known to interact in many ways (matter, energy, and information), paving the way for potentially beneficial emergent effects. One such naturally expected effect is an increase in the sustainability of a population as a result of a more even redistribution of energy within the population. Our in silico experiments show that under harsh conditions, such as resource scarcity and a rapidly changing environment, altruistic energy exchange (supplying energy to weaker agents) can indeed markedly increase the sustainability of model bacteriabot groups, potentially increasing the efficiency of treatment. Although our work is limited exclusively to the development and use of a phenomenological computer model, we consider our results to be an important argument in favor of practical efforts aimed at implementing altruistic energy exchange strategies in real swarms of single-cell medical robots.

Test-retest reliability of behavioral and computational measures of advice taking under volatility.

The development of computational models for studying mental disorders is on the rise. However, their psychometric properties remain understudied, posing a risk of undermining their use in empirical research and clinical translation. Here we investigated test-retest reliability (with a 2-week interval) of a computational assay probing advice-taking under volatility with a Hierarchical Gaussian Filter (HGF) model. In a sample of 39 healthy participants, we found the computational measures to have largely poor reliability (intra-class correlation coefficient or ICC < 0.5), on par with the behavioral measures of task performance. Further analysis revealed that reliability was substantially impacted by intrinsic measurement noise (indicated by parameter recovery analysis) and to a smaller extent by practice effects. However, a large portion of within-subject variance remained unexplained and may be attributable to state-like fluctuations. Despite the poor test-retest reliability, we found the assay to have face validity at the group level. Overall, our work highlights that the different sources of variance affecting test-retest reliability need to be studied in greater detail. A better understanding of these sources would facilitate the design of more psychometrically sound assays, which would improve the quality of future research and increase the probability of clinical translation.

The history of hydrological studies on the Mekong floodplains – from colonial experiments to computational models

ABSTRACT This paper investigates the interwoven history of hydrological studies and water infrastructure development on the Mekong floodplains. On the basis of an extensive literature review, archival work, and key informant interviews, we unravel the making of an expert-led understanding of the Mekong floodplains – from the detailed reports written by colonial engineers during the French protectorate in the late 19th century to the development of the first computational models, developed in the 1960s on punch-cards and in Fortran. We show how these studies not only reflect the objectives of successive powers and their relationships to local communities but also shaped the landscape and hydrology of the Mekong floodplains and continue to do so through a variety of water infrastructure development projects. This serves as a call to conceive of hydrological knowledge as contingent rather than as a neutral depiction of physical processes that exists independently from the conditions of its production.

Diffuse Optical Spectroscopy: Technology and Applications: introduction to the feature issue.

Welcome to the 2024 Feature Issue on Diffuse Optical Spectroscopy: Technology and Applications in Biomedical Optics Express! This feature issue provides an exemplary sample of established and emerging DOS technologies as well as their biomedical applications through 27 contributed research papers and 1 invited review article. DOS researchers are inherently multidisciplinary, advancing topics spanning the basic theory of light-tissue interactions, computational modeling, technique and system development and preclinical and clinical applications. You will find this full range of topics represented in this feature issue.

Artificial intelligence and omics in malignant gliomas.

Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemotherapy and radiotherapy, the prognosis remains unfavorable. The heterogeneity of GBM and the presence of the blood-brain barrier further complicate the therapeutic process. It is crucial to adopt a multifaceted approach in GBM research to understand its biology and advance toward effective treatments. In particular, omics research, which primarily includes genomics, transcriptomics, proteomics, and epigenomics, helps us understand how GBM develops, finds biomarkers, and discovers new therapeutic targets. The availability of large-scale multiomics data requires the development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large omics databases. Although the application of AI tools in GBM-omics is currently in its early stages, a thorough exploration of AI utilization to uncover different aspects of GBM (subtype classification, prognosis, and survival) would have a significant impact on both researchers and clinicians. Here, we aim to review and provide database resources of different AI-based techniques that have been used to study GBM pathogenesis using multiomics data over the past decade. We summarize different types of GBM-related omics resources that can be used to develop AI models. Furthermore, we explore various AI tools that have been developed using either individual or integrated multiomics data, highlighting their applications and limitations in the context of advancing GBM research and treatment.

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.