Computational Model Research Articles

Multiscale Computational Modeling of 3D Printed Continuous Fiber-Reinforced Composites

The printing parameters used during the printing procedure have a significant effect on the mechanical characteristics of 3D printed continuous fiber reinforced composites (3DP-CFRPCs). However, conducting experimental assessments of the material characteristics of 3DP-CFRPCs may require more effort and incur more costs. Computational material modeling may be used as a viable alternative to investigate the behavior of 3DP-CFRPCs under various printing conditions. The current work used material modeling approaches to examine the impact of different printing settings on the elastic characteristics of 3DP-CFRPCs. The inherent flexibility of beads is primarily established by homogenizing the pores within the matrix via the use of the Mori-Tanaka process. Subsequently, the elastic modulus is calculated by using finite element modeling on Representative Volume Element (RVE), which takes into account the microstructure and other printing attributes. An inconsistency was seen in the variation of projected elastic properties across models distinguished by various microstructures, with a more pronounced differentiation observed between intricate and simpler microstructures. Computational modeling has enhanced our understanding of the elastic properties of 3DP-CFRPCs under various printing conditions. Moreover, it has been shown that alterations in printing parameters have diverse impacts on the pliable characteristics of 3DP-CFRPCs. The impact of layer thickness on the mechanical characteristics of 3DP-CFRPCs was determined to be more substantial compared to the effect of printing temperature. The application of offset layup printing techniques enhanced the elastic properties of 3DP-CFRPCs, with the degree of improvement varying based on the orientation. As the level of porosity increased, the influence of pores situated between beads on the overall stiffness of 3DP-CFRPCs gradually diminished, while the impact of matrix pores on the overall stiffness of 3DP-CFRPCs gradually intensified.

Intelligent automation computational modelling for contextual consulting services using Industry 4.0

<p>The methodology towards operational management and service delivery associated with consulting firms, irrespective of any business domain, are less revisited in perspective from the automation-based process management. Adoption of Industry 4.0 has been attempted by various researchers from the business process management; however, there are less evidence of any computational model towards it. Apart from this, existing models are accompanied by various loopholes which makes its further challenging to analyze it on practical environment. Hence, the proposed study introduces a novel computational and analytical framework which is capable of performing the predictive modelling in order to meet the contextual service development and delivery demands in distributed environment. The novelty of this model is its inclusion of contextual data aggregation, contextual constraint analysis, predictive maintenence, and self-adjusting machine which are core attributes of Industry 4.0 automation standards. The study outcome shows that proposed system offers 20% cost reduction and 89% of minimized service delivery time in contrast to existing related work. Same has been also observed in benchmarked outcomes with state-of-art models.</p>

Recent approaches in computational modelling for controlling pathogen threats.

In this review, we assess the status of computational modelling of pathogens. We focus on three disparate but interlinked research areas that produce models with very different spatial and temporal scope. First, we examine antimicrobial resistance (AMR). Many mechanisms of AMR are not well understood. As a result, it is hard to measure the current incidence of AMR, predict the future incidence, and design strategies to preserve existing antibiotic effectiveness. Next, we look at how to choose the finite number of bacterial strains that can be included in a vaccine. To do this, we need to understand what happens to vaccine and non-vaccine strains after vaccination programmes. Finally, we look at within-host modelling of antibody dynamics. The SARS-CoV-2 pandemic produced huge amounts of antibody data, prompting improvements in this area of modelling. We finish by discussing the challenges that persist in understanding these complex biological systems.

Feature selection technique on convolutional neural network – multilabel classification task

Automated text-based recommendation, an artificial intelligence development, finds application in document analysis like job resumes. The classification of job resumes poses challenges due to the ambiguity in categorizing multiple potential jobs in a single application file, termed multi-label classification, deep learning, particularly convolutional neural networks (CNN), offers flexibility in enhancing feature representations. Despite its robust learning capabilities, the black-box design of deep learning lacks interpretability and demands a substantial number of parameters, requiring significant computational resources. The primary challenge in multilabel learning is the ambiguity of labels not fully explained by traditional equivalence relations. To address this, the research employs feature selection techniques, specifically the Chi-square method. The goal is to reduce features in deep learning models while considering label relevance in multi-label text classification, easing computational workload while preserving model performance. Experimental tests, both with and without the Chi-square feature selection technique on the dataset, underscore its substantial impact on the classification model's ability. The conclusion emphasizes the influence of the Chi-square feature selection technique on performance and computational time. In summary, the research underscores the importance of balancing computational efficiency and model interpretability, especially in complex multi-label classification tasks like job applications.

A Low Computational Burden Model Predictive Control for Dynamic Wireless Charging

A Low Computational Burden Model Predictive Control for Dynamic Wireless Charging

Novel computational intelligence-based model for effective traffic management in intelligent transportation system

<p><span lang="EN-US">The evolution of intelligent transportation system (ITS) is essentially meant for upgrading the driving experience with more safety and accessibility of various analytical information from its extensive network. However, a significant gap is observed that doesn't cater up the complete demands of ITS. It has been also noted that computational intelligence (CI) based approach is slowly gaining pace in solving the transport related problems in ITS as compared to its other counterpart existing methodologies like artificial intelligence. The proposed manuscript introduces a novel computational framework towards assisting in relaying routing and navigational services using CI-based approach. A design of novel navigational controller unit is presented for global ITS scenario towards yielding an optimal decision of routing. The CI-based approach is implemented by integrating fuzzy process with evolutionary searching, learning and probability theory in most simplified form. The study also introduces a novel concept of relaying decision as feedback from navigational controller unit to specific vehicular node discretely unlike existing traffic controller system with an agenda to offer faster and effective clearance of queued vehicular nodes from target area. The study outcome shows higher consistency in its relay with better performance from existing study model in ITS.</span></p>

Prediction of Human Microbe-Drug Association based on Layer Attention Graph Convolutional Network

: Human microbes are closely associated with a variety of complex diseases and have emerged as drug targets. Identification of microbe-related drugs is becoming a key issue in drug development and precision medicine. It can also provide guidance for solving the increasingly serious problem of drug resistance enhancement in viruses. Methods: In this paper, we have proposed a novel model of layer attention graph convolutional network for microbe-drug association prediction. First, multiple biological data have been integrated into a heterogeneous network. Then, the heterogeneous network has been incorporated into a graph convolutional network to determine the embedded microbe and drug. Finally, the microbe-drug association scores have been obtained by decoding the embedding of microbe and drug based on the layer attention mechanism. Results: To evaluate the performance of our proposed model, leave-one-out crossvalidation (LOOCV) and 5-fold cross-validation have been implemented on the two datasets of aBiofilm and MDAD. As a result, based on the aBiofilm dataset, our proposed model has attained areas under the curve (AUC) of 0.9178 and 0.9022 on global LOOCV and local LOOCV, respectively. Based on aBiofilm dataset, the proposed model has attained an AUC value of 0.9018 and 0.8902 on global LOOCV and local LOOCV, respectively. In addition, the average AUC and standard deviation of the proposed model for 5- fold cross-validation on the aBiofilm and MDAD datasets were 0.9141±6.8556e-04 and 0.8982±7.5868e-04, respectively. Also, two kinds of case studies have been further conducted to evaluate the proposed models. Conclusion: Traditional methods for microbe-drug association prediction are timeconsuming and laborious. Therefore, the computational model proposed was used to predict new microbe-drug associations. Several evaluation results have shown the proposed model to achieve satisfactory results and that it can play a role in drug development and precision medicine.

Unlocking the Potential of Drug Delivery Systems: A Comprehensive Review of Formulation Strategies and Technologies in the Field of Pharmaceutics

: The creation of innovative drug delivery systems to enhance therapeutic effectiveness, safety, and patient compliance has resulted in considerable developments in pharmaceutics in recent years. The most recent formulation techniques and technologies are reviewed in this article to improve medication distribution and accomplish specific therapeutic goals. : This article thoroughly summarizes the most recent formulation techniques and technologies used to enhance medication delivery and provide specific therapeutic effects. It discusses the variety of medication delivery methods, including nanoparticles, liposomes, micelles, and dendrimers, and explores the application of nanotechnology and biotechnology in drug delivery. Additionally, the paper emphasizes the significance of targeted drug delivery systems and their capacity to cross biological barriers including the blood-brain barrier and tumor microenvironment. : The review also addresses the challenges faced in developing and commercializing drug delivery systems and suggests potential solutions to overcome them. Furthermore, the article emphasizes the role of computational modeling and simulation in designing and optimizing drug delivery systems. : Overall, this review paper offers insightful information for pharmaceutics researchers, scientists, and practitioners that will help in the creation of novel drug delivery systems that improve patient outcomes and quality of life.

Characterization of hyperactive mutations in the renal potassium channel ROMK uncovers unique effects on channel biogenesis and ion conductance.

Hypertension affects one billion people worldwide and is the most common risk factor for cardiovascular disease, yet a comprehensive picture of its underlying genetic factors is incomplete. Amongst regulators of blood pressure is the renal outer medullary potassium (ROMK) channel. While select ROMK mutants are prone to premature degradation and lead to disease, heterozygous carriers of some of these same alleles are protected from hypertension. Therefore, we hypothesized that gain-of-function (GoF) ROMK variants which increase potassium flux may predispose people to hypertension. To begin to test this hypothesis, we employed genetic screens and a candidate-based approach to identify six GoF variants in yeast. Subsequent functional assays in higher cells revealed two variant classes. The first group exhibited greater stability in the endoplasmic reticulum, enhanced channel assembly, and/or increased protein at the cell surface. The second group of variants resided in the PIP2-binding pocket, and computational modeling coupled with patch-clamp studies demonstrated lower free energy for channel opening and slowed current rundown, consistent with an acquired PIP2-activated state. Together, these findings advance our understanding of ROMK structure-function, suggest the existence of hyperactive ROMK alleles in humans, and establish a system to facilitate the development of ROMK-targeted antihypertensives.

Three-dimensional fiber patterning in the annulus fibrosus can be derived from vertebral endplate topography.

The annulus fibrosus (AF) of the Intervertebral disc (IVD) is composed of concentric lamellae of helically wound collagen fibers. Understanding the spatial variation of collagen fiber orientations in these lamellae, and the resulting material anisotropy, is crucial to predicting the mechanical behavior of the complete IVD. This study builds on a prior model predicated on path-independent displacement of fiber endpoints during vertebral body growth to predict a complete, three-dimensional annulus fibrosus fiber network from a small number of subject-independent input parameters and vertebral endplate topographies obtained from clinical imaging. To evaluate the model, it was first fit to mid-plane fiber orientations obtained using polarized light microscopy in a population of bovine caudal discs for which computed tomography images vertebral endplates were also available. Additionally, the model was used to predict the trajectories based on human lumbar disc geometries and results were compared to previously reported data. Finally, the model was employed to investigate potential disc-related variations in fiber angle distributions. The model was able to accurately predict experimentally measured fiber distributions in both bovine and human discs using only endplate topography and three input parameters. Critically, the model recapitulated previously observed asymmetry between the inclinations of right- and left-handed fibers in the posterolateral aspect of the human AF. Level to level variation of disc height and aspect ratio in the human lumbar spine was predicted to affect absolute values of fiber inclination, but not this asymmetry. Taken together these results suggest that patient-specific distributions of AF fiber orientation may be readily incorporated into computational models of the spine using only disc geometry and a small number of subject-independent parameters.

A brief review on computer simulations of chalcopyrite surfaces: structure and reactivity.

Chalcopyrite, the world's primary copper ore mineral, is abundant in Latin America. Copper extraction offers significant economic and social benefits due to its strategic importance across various industries. However, the hydrometallurgical route, considered more environmentally friendly for processing low-grade chalcopyrite ores, remains challenging, as does its concentration by froth flotation. This limited understanding stems from the poorly understood structure and reactivity of chalcopyrite surfaces. This study reviews recent contributions using density functional theory (DFT) calculations with periodic boundary conditions and slab models to elucidate chalcopyrite surface properties. Our analysis reveals that reconstructed surfaces preferentially expose S atoms at the topmost layer. Furthermore, some studies report the formation of disulfide groups (S22-) on pristine sulfur-terminated surfaces, accompanied by the reduction of Fe3+ to Fe2+, likely due to surface oxidation. Additionally, Fe sites are consistently identified as favourable adsorption locations for both oxygen (O2) and water (H2O) molecules. Finally, the potential of computer modelling for investigating collector-chalcopyrite surface interactions in the context of selective froth flotation is discussed, highlighting the need for further research in this area.

SIMPLIFIED COMPUTATIONAL TOOL FOR PREDICTING FORMATION DAMAGE IN OIL WELLS

The present study underscores the significance of regular well test analysis to ensure the accuracy and consistency of data pertaining to the permeability and skin of exploratory wells. This study advances the development of a novel computer model, ASPIRE, which predicts formation damage in oil wells by analysing well-test data. In this context, identifying blocked pore throats in the formation close to the wellbore by paraffinic deposits (scales) and its influence on permeability necessitates routine well-test analysis. The software employs a modified linear regression model developed in this study to perfectly fit the well-test data's infinitely acting linear flow (IARF) region to a straight line. The cases "CASE 1" and "CASE 2" were considered for constant rate transient drawdown well test analyses. The findings indicate that Case 1, with a high skin factor, requires remedial action, such as stimulation, to increase the formation's permeability close to the wellbore for enhanced productivity. In contrast, Case 2 has higher permeability and lower skin factor than Case 1, so stimulation is not required. In addition, R-squared analyses were conducted to validate the results obtained from the developed tool. The analyses reveal an R-square value of 1 for all cases, indicating a 100% accuracy. Therefore, the study concludes that the developed tool is instrumental in accurately predicting formation damage in oil wells.

The potency of <i>Elaeocarpus grandiflorus </i>Leaf Extract as Anti-obesity: <i>in vivo </i>and <i>in silico</i> Study

The research aims to determine the effect of ethanol extract of Elaeocarpus grandiflorus leaves extract as anti-obesity by in vivo and silico computational models. The pre-and -post-tests were carried out on 20 female Wistar rats, divided into 4 groups. The control group (K) received no treatment and the experimental groups were treated with E. grandiflorus extract of 200 mg/kg BW (P1), 400 mg/kg BW (P2), and 800 mg/kg BW (P3) for 14 days. The body weight, abdominal circumference, and abdominal fat mass were measured on Day 1 and Day 15. The results showed differences in body weight (p=0.02), abdominal circumference (p=0.01), and abdominal fat mass (p=0.00). In silico exploration, bioactive compounds of rutin, orientin, luteolin, vitexin, iso orientin, isovitexin, kaempferol, and quercetin were identified, and targeted ELAVL1, IGF1R, CREB1, AKT1, and PIK3R1 of the AMPK signaling pathway that involved in the anti-obesity mechanism. The high binding affinity values was rutin-EVAL1 (-9.3), orientin-ELAVL1 (-8.3), and quercetin- IGFR1 (-8.2). It can be concluded that the ethanol extract of E. grandiflorus leaves has the potential to be developed as an anti-obesity agent.

Structural Characterization of DNA Binding Domain of Essential Mammalian Protein TTF 1.

Transcription Termination Factor 1 (TTF1) is a multifunctional mammalian protein with vital roles in various cellular processes, including Pol I-mediated transcription initiation and termination, pre-rRNA processing, chromatin remodelling, DNA damage repair, and polar replication fork arrest. It comprises of two distinct functional regions; the N-terminal regulatory region (1-445 aa), and the C-terminal catalytic region (445-859 aa). The Myb domain located at the C-terminal region is a conserved DNA binding domain spanning from 550 to 732 aa (183 residues). Despite its critical role in various cellular processes, the physical structure of TTF1 remains unsolved. Attempts to purify the functional TTF1 protein have been unsuccessful till date. Therefore, we focused on characterizing the Myb domain of this essential protein. We started with predicting a 3-D model of the Myb domain using homology modelling, and ab-initio method. We then determined its stability through MD simulation in an explicit solvent. The model predicted is highly stable, which stabilizes at 200ns. To experimentally validate the computational model, we cloned and expressed the codon optimized Myb domain into a bacterial expression vector and purified the protein to homogeneity. Further, characterization of the protein shows that, Myb domain is predominantly helical (65%) and is alone sufficient to bind the Sal Box DNA. This is the first-ever study to report a complete in-silico model of the Myb domain, which is physically characterized. The above study will pave the way towards solving the atomic structure of this essential mammalian protein.

Quantum Mechanical Cluster Models for Calculations on Enzymatic Reaction Mechanisms: Set-Up and Accuracy.

Enzymes turnover substrates into products with amazing efficiency and selectivity and as such have great potential for use in biotechnology and pharmaceutical applications. However, details of their catalytic cycles and the origins surrounding the regio- and chemoselectivity of enzymatic reaction processes remain unknown, which makes the engineering of enzymes and their use in biotechnology challenging. Computational modelling can assist experimental work in the field and establish the factors that influence the reaction rates and the product distributions. A popular approach in modelling is the use of quantum mechanical cluster models of enzymes that take the first- and second coordination sphere of the enzyme active site into consideration. These QM cluster models are widely applied but often the results are dependent on model choice and selection. Herein, we show that QM cluster models can produce highly accurate results that reproduce experimental product distributions and free energies of activation, regarded that large cluster models with >300 atoms are used. In this tutorial review, we give general guidelines on the set-up and applications of the QM cluster method and discuss its accuracy and reproducibility. Finally, several representative QM cluster model examples on metal-containing enzymes are presented, which highlight the strength of the approach.

Neural pathways associated with reduced rigidity during pallidal deep brain stimulation for Parkinson's disease.

Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) can markedly reduce muscle rigidity in people with Parkinson's disease (PD); however, the mechanisms mediating this effect are poorly understood. Computational modeling of DBS provides a method to estimate the relative contributions of neural pathway activations to changes in outcomes. In this study, we generated patient-specific biophysical models of GPi DBS (derived from individual 7T MRI) - including pallidal efferent, putamenal efferent and internal capsule pathways - to investigate how activation of neural pathways contributed to changes in forearm rigidity in PD. Ten individuals (17 arms) were tested off medication under four conditions: off stimulation, on clinically optimized stimulation, and on stimulation specifically targeting the dorsal GPi or ventral GPi. Quantitative measures of forearm rigidity, with and without a contralateral activation maneuver, were obtained using a robotic manipulandum. Clinically optimized GPi DBS settings significantly reduced forearm rigidity (p < 0.001), which aligned with GPi efferent fiber activation. The model demonstrated that GPi efferent axons could be activated at any location along the GPi dorsal-ventral axis. These results provide evidence that rigidity reduction produced by GPi DBS is mediated by preferential activation of GPi efferents to the thalamus, likely leading to a reduction in excitability of the muscle stretch reflex via overdriving pallidofugal output.

Pulsed MPI Relaxometry of Brownian and Néel Field-Dependent Relaxation in Superparamagnetic Magnetite Nanoparticles Confirm Theory and Simulations.

Superparamagnetic iron oxide nanoparticles (SPIOs) are used as tracers in Magnetic Particle Imaging (MPI). It is crucial to understand the magnetic properties of SPIOs for optimizing MPI imaging contrast, resolution, and sensitivity. Brownian and Néel relaxation theory developed in the early 1950s posits that relaxation times can vary with particle size, shell thickness, medium viscosity, and the applied field strength. Magnetic relaxation can soon provide a unique imaging capability, the ability to distinguish bound from unbound MPI tracers in vivo. Yet experimental validation of these theories has not been completed. In this paper, a novel method of pulsed magnetic field relaxometry is used to directly probe the relaxation behavior of superparamagnetic magnetite nanoparticles over a spectrum of magnetic field amplitudes, providing the first experimental validation of theoretical relaxation models. It is also shown that closed-form approximations generated in the early 1970s accurately match both data and numerical Fokker Planck computational models, which are computationally burdensome. This means researchers can trust these approximations for future modeling. All the findings can be translated to sinusoidal excitations used in conventional MPI scanningtrajectories.

Identification of Active Molecules against Thrombocytopenia through Machine Learning.

Thrombocytopenia, which is associated with thrombopoietin (TPO) deficiency, presents very limited treatment options and can lead to life-threatening complications. Discovering new therapeutic agents against thrombocytopenia has proven to be a challenging task using traditional screening approaches. Fortunately, machine learning (ML) techniques offer a rapid avenue for exploring chemical space, thereby increasing the likelihood of uncovering new drug candidates. In this study, we focused on computational modeling for drug-induced megakaryocyte differentiation and platelet production using ML methods, aiming to gain insights into the structural characteristics of hematopoietic activity. We developed 112 different classifiers by combining eight ML algorithms with 14 molecule features. The top-performing model achieved good results on both 5-fold cross-validation (with an accuracy of 81.6% and MCC value of 0.589) and external validation (with an accuracy of 83.1% and MCC value of 0.642). Additionally, by leveraging the Shapley additive explanations method, the best model provided quantitative assessments of molecular properties and structures that significantly contributed to the predictions. Furthermore, we employed an ensemble strategy to integrate predictions from multiple models and performed in silico predictions for new molecules with potential activity against thrombocytopenia, sourced from traditional Chinese medicine and the Drug Repurposing Hub. The findings of this study could offer valuable insights into the structural characteristics and computational prediction of thrombopoiesis inducers.

Influence of extended working hours and physical recovery on absenteeism in the footwear industry from a system dynamics model

Objectives. Work-related musculoskeletal disorders (WMSDs) are recurrent in the footwear industry, resulting in absenteeism. This study aimed to quantitatively analyze the influence of overtime work and physical recovery time on the occurrence of WMSD-related absenteeism using a system dynamics model. As ergonomic methods have limitations in quantitatively simulating the behavior of these relationships, the integration of computational modeling techniques has emerged as a methodological alternative to bridge this gap. Methods. An ergonomic work analysis (EWA) was developed in a production cell of a large company. A model of causal relationships (causal loop diagram) and a simulation model (flow and stock diagram) were then developed, where three scenarios for overtime and physical recovery time were analyzed. Results. Working an additional hour resulted in a 42% increase in physical overload, leading to 7.62 leave requests per year and 78.7 days of employee absenteeism. Increasing the physical recovery time by 15 min reduced the overload to 36.5%, resulting in 6.8 leave requests per year and 71.1 days of employee absenteeism. Conclusions. Properly managing excess workload and providing adequate physical recovery for professionals is necessary to mitigate the productivity impacts of absenteeism in the footwear industry.

An Optimization Method for Stiffness and Damping Mistuning Identification From Blade Tip Timing Data

Abstract System identification of dynamic properties is of large interest in the turbomachinery industry to create more accurate computational models and more effective designs. Previous identification techniques were able to accurately capture the blade variability, known as mistuning, in the stiffness as well as the average damping of all the blades. Mistuning is vital to accurately identify and study because the symmetry of the system is broken and can lead to vibration localization and high amplitudes. In this work, a new method is proposed to not only capture the stiffness mistuning values, but also the blade-to-blade variability in damping. These damping mistuning values have been shown to have a significant effect on the dynamics of bladed disk systems. Incorporation of the damping mistuning into the computational model can be done utilizing an augmented component mode mistuning method with either structural or proportional damping. The mistuning values for this new identification method were compared to a well-established direct method and a previously studied optimization method. Blade responses were then found using a harmonic analysis and the newly identified mistuning values. These blade responses were then compared to experimental tip timing data from full scale rotating experiments. These comparisons show that the new model is able to better reproduce experimental data using computational models that incorporate both stiffness and damping mistuning values.