Development Of Computational Models Research Articles

Development of a Framework for Modeling Protection Mechanisms Provided By Multi-Layered Organic Coatings Applied to Aerospace Al Alloys Via Finite Element Analysis

Aluminum alloys, such as AA2024 and AA7050, are widely used in aerospace applications due to their high strength-to-weight ratio. However, because of the heterogeneous microstructure of these alloys, they are susceptible to microgalvanic corrosion and pitting in chloride-containing environments. The corrosion susceptibility is further increased when these alloys are in contact with more noble alloys, such as in locations near fasteners, where the galvanic interaction leads to accelerated corrosion attack of the less noble Al alloy. To protect these structures against corrosion attack, organic coatings are commonly used. Typical coating systems are usually composed of three layers: a conversion coating, a primer, and a topcoat. When the topcoat is breached, the primer can provide active corrosion protection by the release of inhibitors embedded in the primer. Depending on the type of inhibitor, it can provide chemical protection by the formation of a passive film on the surface of the exposed metallic area and/or provide cathodic protection by the preferential dissolution of sacrificial anodes.The efficacy of the corrosion protection provided by the multilayered coating system to Al alloys in aircraft components exposed to atmospheric corrosion is affected by a large number of variables. The dynamic environment in which the component is exposed to (e.g., relative humidity, temperature, salt loading density), the geometry of the component, the chemistry of the primer, and the type of alloy are examples of important parameters that impact the corrosion and protection mechanisms. To unravel the impact of each one of these variables and/or the synergistic effects between them, a large volume of experimental work is needed, making the prediction of coating failure, corrosion damage, and, ultimately, component lifetime, remarkably challenging. In this context, the development of computational models that can simulate corrosion and corrosion protection mechanisms is attractive, as they can be used as tool to effectively assess the large parameter space and increase efficiency of experimental work.This work focuses on developing a framework for modeling the protection mechanisms offered by chromate-based and Mg-rich primers via finite element analysis. Chemical and electrochemical protection mechanisms were modeled taking into consideration leaching of inhibitor from the primer, mass transport and the impact of the inhibitors in the electrochemistry of the Al alloys. The impact of environmental, geometric, and coating parameters on the effectiveness of the protection mechanisms was assessed. Ultimately, the framework developed will provide a better understanding of protection mechanisms of the organic coating systems and serve as a framework to study new coating systems.

Parametric study on the residual compressive strength of RC columns subjected to different standard fire durations and load ratios

PurposeSince columns are critical structural elements, they shall withstand hazards without any considerable damage. In the case of a fire, although concrete has low thermal conductivity compared to other construction materials, its properties are changed at elevated temperatures. Most critically, the residual compressive strengths of reinforced concrete columns are significantly reduced after fire exposure. Validation of the worthiness of rehabilitating concrete structures after fire exposure is highly dependent on accurately determining the residual strengths of fire-damaged essential structural elements such as columns.Design/methodology/approachIn this study, eight reinforced-concrete columns (200 × 200 × 1,500 mm) that were experimentally examined in a prior related study have been numerically modelled using ABAQUS software to investigate their residual compressive strengths after exposure to different durations of standard fire (i.e. one and two hours) while subjected to different applied load ratios (i.e. 20 and 40% of the compressive resistance of the column). Outcomes of the numerical simulations were verified against the prior study's experimental results.FindingsIn a subsequent phase, the results of a parametric study that has been completed as part of the current study to investigate the effects of the applied load ratios show that the application of axial load up to 80% of the compressive resistance of the column did not considerably influence the residual compressive strength of the shorter columns (i.e. 1,500 and 2,000-mm high). However, increasing the height of the column to 2,500 or 3,000 mm considerably reduced the residual compressive strength when the load ratio applied on the columns exceeded 60 and 40%, respectively. Also, when the different columns were simulated under two-hour standard fire exposure, the dominant failure was buckling rather than concrete crushing which was the typical failure mode in most columns.Originality/valueThe outcomes of the numerical study presented in this paper reflect the residual compressive strength of RC columns subjected to various applied load ratios and standard fire durations. Also, the parametric study conducted as part of this research on the effects of higher load ratios and greater column heights on the residual compressive strength of the fire-damaged columns is practical and efficient. The developed computer models can be beneficial to assist engineers in assessing the validity of rehabilitating concrete structures after being exposed to fire.

A fibrin enhanced thrombosis model for medical devices operating at low shear regimes or large surface areas.

Over the past decade, much of the development of computational models of device-related thrombosis has focused on platelet activity. While those models have been successful in predicting thrombus formation in medical devices operating at high shear rates (> 5000 s-1), they cannot be directly applied to low-shear devices, such as blood oxygenators and catheters, where emerging information suggest that fibrin formation is the predominant mechanism of clotting and platelet activity plays a secondary role. In the current work, we augment an existing platelet-based model of thrombosis with a partial model of the coagulation cascade that includes contact activation of factor XII and fibrin production. To calibrate the model, we simulate a backward-facing-step flow channel that has been extensively characterized in-vitro. Next, we perform blood perfusion experiments through a microfluidic chamber mimicking a hollow fiber membrane oxygenator and validate the model against these observations. The simulation results closely match the time evolution of the thrombus height and length in the backward-facing-step experiment. Application of the model to the microfluidic hollow fiber bundle chamber capture both gross features such as the increasing clotting trend towards the outlet of the chamber, as well as finer local features such as the structure of fibrin around individual hollow fibers. Our results are in line with recent findings that suggest fibrin production, through contact activation of factor XII, drives the thrombus formation in medical devices operating at low shear rates with large surface area to volume ratios.

Development of Computational Model of Motorcycle and Rider During Collision

The goal of this project is to develop the computational model of motorcycle and rider for deformable body. Also, to identify the response of rider and motorcycle on collision. Computationalmodel is the one method that can replace the actual experiment on crash test. From the simulation can save cost by actual impact crash test. This project begins with design the model of actual motorcycle. Because the model of the project using Honda Wave 100R is to complex and the computer not powerful enough to generate mesh the simplified model is use for the project. Then, the material of this project using ANSI 304 stainless steel. The simulation of the experiment run by the ANSYS software to calculatemathematical model result after impact. Finally, the result of deformation was recorded to compare the result of deformation on actual crash test. The Comparison result of deformation actual and simulation are quietly similar

Towards a New Paradigm for Brain-inspired Computer Vision

Brain-inspired computer vision aims to learn from biological systems to develop advanced image processing techniques. However, its progress so far is not impressing. We recognize that a main obstacle comes from that the current paradigm for brain-inspired computer vision has not captured the fundamental nature of biological vision, i.e., the biological vision is targeted for processing spatio-temporal patterns. Recently, a new paradigm for developing brain-inspired computer vision is emerging, which emphasizes on the spatio-temporal nature of visual signals and the brain-inspired models for processing this type of data. In this paper, we review some recent primary works towards this new paradigm, including the development of spike cameras which acquire spiking signals directly from visual scenes, and the development of computational models learned from neural systems that are specialized to process spatio-temporal patterns, including models for object detection, tracking, and recognition. We also discuss about the future directions to improve the paradigm.

Modeling the relationship between air temperature and grapefruit quality traits.

Consumers of grapefruit require consistent fruit quality with a good physical appearance and taste. The air temperature during the growing season affects both the external (external color index (ECI)) and internal (titratable acidity (TA) and total soluble solids ratio (TSS/TA)) fruit quality of grapefruit. The objective of this study was to develop computer models that encompass the relationship between preharvest air temperature and fruit quality to predict fruit quality of grapefruit at harvest. There was a logarithmic relationship between the number of days with a daily minimum air temperature ≤13 °C and ECI, with a greater number of days resulting in higher ECI. In addition, there was a second-order polynomial relationship between the number of hours ≥21 °C and both TA and TSS/TA, with a greater number of hours resulting in lower TA and higher TSS/TA. Model performance for predicting the ECI, TA, and TSS/TA during 2004-05 and 2005-06 growing seasons was good, with Nash and Sutcliffe coefficient of efficiency (NSE) values for each season of 0.835 and 0.917 respectively for ECI, 0.896 and 0.965 respectively for TA and 0.898 and 0.966 respectively for TSS/TA. Applying the model to statistical survey data covering 13 growing seasons demonstrated that the TSS/TA model was robust. Statistical models were developed that predicted the development of grapefruit ECI, TA, and TSS/TA. The TSS/TA model was confirmed after application to long-term statistical survey data covering 13 growing seasons. © 2022 Society of Chemical Industry.

Towards an agent-based model using a hybrid conceptual modelling approach: A case study of relationship conflict within large enterprise system implementations

ABSTRACT An often-overlooked activity within the design and development of computational models for socio-technical systems, is the development of a comprehensive conceptual model defining the scope of the model with respect to actors, technical resources, environment and abstraction level. With specific reference to modelling large IT and IS implementations, this incurs the added challenges of dealing with qualitative information relating to project scope and implementation processes, along with quantitative and qualitative information regarding the social network of the project resources and emergent behaviours that result from interactions between them. Our approach involves a Multi-Paradigm Hybrid Study to develop a Cross-Disciplinary Hybrid Model of relationship conflict within an enterprise system implementation. We identify that Soft Systems Methodology, Social Network Analysis, Unified Modelling Language are complementary approaches from Operational Research, Social Sciences and Software Engineering, that provide a powerful combination of techniques to develop hybrid conceptual models of complex socio-technical systems.

Computational modelling in health and disease: highlights of the 6th annual SysMod meeting

Abstract Summary The Community of Special Interest (COSI) in Computational Modelling of Biological Systems (SysMod) brings together interdisciplinary scientists interested in combining data-driven computational modelling, multi-scale mechanistic frameworks, large-scale -omics data and bioinformatics. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, a meeting for computer scientists, biologists, mathematicians, engineers and computational and systems biologists. The 2021 SysMod meeting was conducted virtually due to the ongoing COVID-19 pandemic (coronavirus disease 2019). During the 2-day meeting, the development of computational tools, approaches and predictive models was discussed, along with their application to biological systems, emphasizing disease mechanisms. This report summarizes the meeting. Availability and implementation All resources and further information are freely accessible at https://sysmod.info.

Theories of Context Effects in Multialternative, Multiattribute Choice

Over the past several decades, researchers in psychology, neuroscience, marketing, and economics have been keen to understand context effects in multialternative, multiattribute decision making. These effects occur when choices among existing alternatives are altered by the addition of a new alternative to the choice set. The effects violate classic decision theories and have led to the development of computational and mathematical models that explain how underlying cognitive and neural mechanisms give rise to the effects. This article reviews dynamic models of these effects, comparing mechanisms across models. Most models of context effects incorporate an attention mechanism, which suggests that attention plays an important role in multialternative, multiattribute decision making. I conclude by discussing recent empirical studies of attention and context effects and hypothesize that changes in attention could be responsible for recently observed reversals in context effects.

Molecular structure design and interface behavior of ionic liquids on metal surfaces: A theoretical study

The electrodeposition of Mo or Ni, a traditional and mature technique, is of vital importance in industrial applications owing to the extraordinary mechanical properties of these elements when used as assisted coatings. The exploration of green electrodeposition technology has attracted much attention, especially with ionic liquids (ILs) emerging as promising electrolytes or additives owing to their particular physical and electrochemical properties. Many experimental studies have been conducted in this field; however, theoretical calculation, which offers an effective and low-cost approach to designing and choosing appropriate ILs, has rarely been applied. With the rapid development of computational chemistry, calculation, modeling, and simulation of chemical systems have been an important technique to understand and predict behaviors at the molecular level. Combining computational chemistry with experimental study has emerged as a highly efficient methodology for accelerating the development of traditional experimental studies. In this work, the chemical properties of pyridine-base ILs are investigated by density functional theory for the prediction of their potential adsorption performance. Molecular dynamic (MD) simulations of the interfacial behavior between the designed ILs and Ni and Mo surfaces were carried out to evaluate the dynamic processes. In this way, potential ILs as high performance electrolytes and additives for electrodeposition can be chosen on a theoretical basis. The study can be regarded as providing theoretical guidance for choosing appropriate electrolytes or additives for electrodeposition as well as for understanding the interfacial behavior between ILs and metal surfaces.

In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods.

Liver microsomal stability is an important property considered for the screening of drug candidates in the early stage of drug development. Determination of hepatic metabolic stability can be performed by an in vitro assay, but it requires quite a few resources and time. In recent years, machine learning methods have made much progress. Therefore, development of computational models to predict liver microsomal stability is highly desirable in the drug discovery process. In this study, the in silico classification models for the prediction of the metabolic stability of compounds in rat and human liver microsomes were constructed by the conventional machine learning and deep learning methods. The performance of the models was evaluated using the test and external sets. For the rat liver microsomes (RLM) stability, the best model yielded the AUC values of 0.84 and 0.71 on the test and external validation sets, respectively. For the human liver microsome (HLM) stability, the best model exhibited the AUC values of 0.86 and 0.77 on the test and external validation sets, respectively. In addition, several important substructure fragments were detected using information gain and frequency substructure analysis methods. The applicability domain of the models was defined using the Euclidean distance-based method. We anticipate that our results would be helpful for the prediction of liver microsomal stability of compounds in the early stage of drug discovery.

Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning.

Background: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. Aim: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. Design: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0–5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model’s accuracy, specificity, and sensitivity. Results: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). Conclusion: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling.

Numerical simulations on thermomechanical performance of 3D printed chopped carbon fiber‐reinforced polyamide‐6 composites: Effect of infill design

Abstract3D printing (3DP) of polymer composite products and solutions mainly relies on experimental techniques for research & development and product/process/system understanding. Several studies experimentally investigated the effect of infill patterns and densities on the mechanical performance of 3D printed polymer composites. However, due to the unlimited design flexibility of 3DP processes and polymer composite recipes, it is vital to explore numerical simulation tools to speed up research and development time and reduce costs. In this study, we present the development of computational modeling for 3D printed polymer composites using a numerical simulation tool (Digimat‐AM®) to predict the fused filament fabrication process‐induced deflections, residual stresses, and warpage in 3D printed specimens. Digimat‐AM® provides a platform to simulate the fabrication of 3D printed parts, which can assist the designers, engineers, and researchers to predict the manufacturing and resulting product issues beforehand. This study aims to understand the effect of different infill patterns and densities on deflections, residual stresses, warpage, and mechanical properties on 3D printed samples. A significant impact of infill pattern and density is observed on deflections, residual stresses, and warpages from numerical simulation results. In addition, the mechanical testing simulations were performed on the specimens with 3DP process‐induced defects obtained from the process simulation results. Finally, the numerical simulation results for mechanical testing were validated and compared with physical testing on 3D printed specimens. The results found a satisfactory agreement where differences remain with an acceptable range of 0.22%–7.27%.

Personalized Metabolic Avatar: A Data Driven Model of Metabolism for Weight Variation Forecasting and Diet Plan Evaluation.

Development of predictive computational models of metabolism through mechanistic models is complex and resource demanding, and their personalization remains challenging. Data-driven models of human metabolism would constitute a reliable, fast, and continuously updating model for predictive analytics. Wearable devices, such as smart bands and impedance balances, allow the real time and remote monitoring of physiological parameters, providing for a flux of data carrying information on user metabolism. Here, we developed a data-driven model of end-user metabolism, the Personalized Metabolic Avatar (PMA), to estimate its personalized reactions to diets. PMA consists of a gated recurrent unit (GRU) deep learning model trained to forecast personalized weight variations according to macronutrient composition and daily energy balance. The model can perform simulations and evaluation of diet plans, allowing the definition of tailored goals for achieving ideal weight. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim to achieve long-lasting results in pursuing a healthy lifestyle.

Computational cardiac physiology for new modelers: Origins, foundations, and future.

Mathematical models of the cardiovascular system have come a long way since they were first introduced in the early 19th century. Driven by a rapid development of experimental techniques, numerical methods, and computer hardware, detailed models that describe physical scales from the molecular level up to organs and organ systems have been derived and used for physiological research. Mathematical and computational models can be seen as condensed and quantitative formulations of extensive physiological knowledge and are used for formulating and testing hypotheses, interpreting and directing experimental research, and have contributed substantially to our understanding of cardiovascular physiology. However, in spite of the strengths of mathematics to precisely describe complex relationships and the obvious need for the mathematical and computational models to be informed by experimental data, there still exist considerable barriers between experimental and computational physiological research. In this review, we present a historical overview of the development of mathematical and computational models in cardiovascular physiology, including the current state of the art. We further argue why a tighter integration is needed between experimental and computational scientists in physiology, and point out important obstacles and challenges that must be overcome in order to fully realize the synergy of experimental and computational physiological research.

Computer Model-Driven Design in Cardiovascular Regenerative Medicine

Continuing advances in genomics, molecular and cellular mechanobiology and immunobiology, including transcriptomics and proteomics, and biomechanics increasingly reveal the complexity underlying native tissue and organ structure and function. Identifying methods to repair, regenerate, or replace vital tissues and organs remains one of the greatest challenges of modern biomedical engineering, one that deserves our very best effort. Notwithstanding the continuing need for improving standard methods of investigation, including cell, organoid, and tissue culture, biomaterials development and fabrication, animal models, and clinical research, it is increasingly evident that modern computational methods should play increasingly greater roles in advancing the basic science, bioengineering, and clinical application of regenerative medicine. This brief review focuses on the development and application of computational models of tissue and organ mechanobiology and mechanics for purposes of designing tissue engineered constructs and understanding their development in vitro and in situ. Although the basic approaches are general, for illustrative purposes we describe two recent examples from cardiovascular medicine—tissue engineered heart valves (TEHVs) and tissue engineered vascular grafts (TEVGs)—to highlight current methods of approach as well as continuing needs.

Development of a hybrid computational intelligent model for daily global solar radiation prediction

Providing an accurate and reliable solar radiation prediction is highly significant for optimal design and management of thermal and solar photovoltaic systems. It is massively essential for producing renewable and clean energy production. In the current research, an exploration of newly developed computation intelligent models based on the hybridization of Covariance Matrix Adaptive Evolution Strategies (CMAES) with Extreme Gradient Boosting (XGB) and Multi-Adaptive Regression Splines (MARS) models for building robust predictive models for daily scale solar radiation. The proposed hybrid models are examined at four meteorological stations (i.e., Boromo, Dori, Gaoua, and Po) located in the Burkina Faso region, Sub-Sahara Africa. The prediction matrix is constructed using several related climate parameters. Based on the statistical and graphical evaluation, the proposed models demonstrated an outstanding predictability performance. The computational experiments showed that the proposed framework consistently enhanced prediction accuracy by 17.2%, 4.9%, 39.8%, and 44.5%, at Boromo, Dori, Gaoua, and Po stations using the Root Mean Squared Error over the testing phase against the existing methods proposed in the literature. The resulting method has the potential to be used as a surrogate tool for solar radiation prediction on the stations. The findings of this study contribute to a growing body of literature towards advancing the study of machine learning methods for predicting renewable energy production.

Iron status of blood donors.

This review examines recent research on the prevalence and importance of iron deficiency in blood donors, and on efforts to mitigate it. Premenopausal females, teenagers, and high-frequency donors are at the highest risk for donation-induced iron deficiency, in both high-resource and low-resource settings. The physiology relating iron stores to hemoglobin levels and low hemoglobin deferral is well elucidated in blood donor populations, yet the clinical effects attributable to iron loss in the absence of anemia are challenging to identify. Expanded adoption of ferritin testing is improving donor management but may cause decreases in the blood supply from temporary donor loss. The potential for personalized donor management is emerging with development of computational models that predict individual interdonation intervals that aim to optimize blood collected from each donor while minimizing low hemoglobin deferrals. Measures to reduce iron deficiency are available that can be deployed on a standardized or, increasingly, personalized basis. Blood centers, regulators, and donors should continue to evaluate different tactics for addressing this problem, to obtain a balanced approach that is optimal for maintaining adequate collections while safeguarding donor health.

Assessing the Accuracy of Different Remapping Methods in Adaptive Mesh Refinement

Additive manufacturing of metals has attracted much attention over the last years, promoting the development of several computational models for numerical simulation of the laser powder bed fusion (LPBF) process. Nevertheless, the finite element analysis of the LPBF process requires a large computational time. Thus, adaptive mesh refinement strategies are commonly adopted to reduce computational cost, which require some remapping procedure to transfer the state variables from the old mesh to the new one. The present study analyses two different remapping algorithms, namely the Inverse Isoparametric Mapping (IIM) and the Dual Kriging (DK) method. The IIM method uses the shape functions of the finite elements, while the DK method provides an explicit parametric interpolation. The case study adopted covers both coarsening and refinement procedures, using a mathematical function to define the mapped state variable. The accuracy of the remapping methods was lower in the refinement in comparison with the coarsening procedure. The error in the approximation is lower using the DK method in comparison with the IIM method. However, the IIM method does not suffer from error propagation in successive stages of either refinement/derefinement or coarsening/decoarsening.

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE).

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development.