Computational Modeling Analysis Research Articles

Computational Solution and Analysis of Fuzzy Nonlinear HIV Infection Model via New Multistage Fuzzy Variational Iteration Method

Computational Solution and Analysis of Fuzzy Nonlinear HIV Infection Model via New Multistage Fuzzy Variational Iteration Method

Computational modeling and theoretical analysis of polyvinylidene fluoride microarrays for hybrid piezo‐pyroelectric energy harvesting

AbstractPoly(vinylidene fluoride) (PVDF) is a promising piezoelectric and pyroelectric material for energy harvesting applications. This paper investigates the electrical outputs of PVDF microarrays using both simulation studies and theoretical calculations. The finite element method in COMSOL Multiphysics® is utilized to model PVDF microarrays and simulate their responses under mechanical loading and temperature changes. Theoretical calculations are also performed to estimate the piezoelectric and pyroelectric voltage outputs using established mathematical formulas. A series of PVDF microarrays are modeled in two dimensions with defined geometry, materials, boundary conditions, and parametric sweeps of wind speed and temperature. The simulation results for electrical outputs show good agreement with the theoretical calculations, demonstrating the validity of both approaches. The maximum voltage outputs are on the order of 10−3 V for the range of wind speeds from 6.5 to 11 m/s and temperatures from 319.3 to 336.3 K. In addition, the mechanical and thermal sensitivities are quantified. This work provides a framework for design and optimization of PVDF‐based energy harvesters. The combined methodology enables characterization of the relationships between operational conditions, PVDF microarray properties, and electrical outputs.

Computational and stability analysis of Ebola virus epidemic model with piecewise hybrid fractional operator.

In this manuscript, we developed a nonlinear fractional order Ebola virus with a novel piecewise hybrid technique to observe the dynamical transmission having eight compartments. The existence and uniqueness of a solution of piecewise derivative is treated for a system with Arzel'a-Ascoli and Schauder conditions. We investigate the effects of classical and modified fractional calculus operators, specifically the classical Caputo piecewise operator, on the behavior of the model. A model shows that a completely continuous operator is uniformly continuous, and bounded according to the equilibrium points. The reproductive number R0 is derived for the biological feasibility of the model with sensitivity analysis with different parameters impact on the model. Sensitivity analysis is an essential tool for comprehending how various model parameters affect the spread of illness. Through a methodical manipulation of important parameters and an assessment of their impact on Ro, we are able to learn more about the resiliency and susceptibility of the model. Local stability is established with next Matignon method and global stability is conducted with the Lyapunov function for a feasible solution of the proposed model. In the end, a numerical solution is derived with Newton's polynomial technique for a piecewise Caputo operator through simulations of the compartments at various fractional orders by using real data. Our findings highlight the importance of fractional operators in enhancing the accuracy of the model in capturing the intricate dynamics of the disease. This research contributes to a deeper understanding of Ebola virus dynamics and provides valuable insights for improving disease modeling and public health strategies.

ОСОБЛИВОСТІ ПРОЄКТУВАННЯ МЕХАНІЗАЦІЇ КРИЛА ЛІТАКІВ ТРАНСПОРТНОЇ КАТЕГОРІЇ

The paper analyses the methods of designing wing mechanisation for transport category aircraft. The wing layout with the placement of wing mechanisation means on the leading and trailing edges of the wing is analysed. Specific requirements for wing mechanisation units are considered. The analysis takes into account the requirements of aircraft operation, which provide easy access to inspect the mechanisation units and their attachment mechanisms. The need for constant monitoring of the synchronous operation of all mechanisation sections and ensuring their smooth movement is emphasised. The design of any type of mechanisation means involves the simultaneous solution of a number of development tasks: kinematic scheme, installation and control system, support structure and the device itself. In most cases, the drive mechanisms are also developed. The design of a new aircraft is a multi-stage iterative process that includes external design, development of a technical proposal, preliminary design, detailed design, production of prototypes, ground and flight tests, which determine the actual characteristics of the aircraft and the degree of its compliance with the technical specifications. Consider the advantages created by wing mechanisation equipment. The mechanisation of the AN-140 is considered as a representative of aircraft whose wings have trailing edge mechanisation but no leading edge mechanisation. The mechanisation of the AN-74 is also considered as a representative of aircraft that use two- or three-slotted retractable flaps, winglets or nose shields of large curvature over the entire wing span. The design technologies in integrated computer information systems are characterised, which allow to build a single process of computer modelling and engineering analysis and to conduct multivariate studies to select optimal design solutions in a sufficiently short time. A block diagram of the process of detailed design of flaps is proposed.

Strong Protection by 4-Hydroxyestrone against Erastin-Induced Ferroptotic Cell Death in Estrogen Receptor-Negative Human Breast Cancer Cells: Evidence for Protein Disulfide Isomerase as a Mechanistic Target for Protection.

Ferroptosis is a recently identified form of regulated cell death, characterized by excessive iron-dependent lipid peroxidation. Recent studies have demonstrated that protein disulfide isomerase (PDI) is an important mediator of chemically induced ferroptosis and also a new target for protection against ferroptosis-associated cell death. In the present study, we identified that 4-hydroxyestrone (4-OH-E1), a metabolic derivative of endogenous estrogen, is a potent small-molecule inhibitor of PDI, and can strongly protect against chemically induced ferroptotic cell death in the estrogen receptor-negative MDA-MB-231 human breast cancer cells. Pull-down and CETSA assays demonstrated that 4-OH-E1 can directly bind to PDI both in vitro and in intact cells. Computational modeling analysis revealed that 4-OH-E1 forms two hydrogen bonds with PDI His256, which is essential for its binding interaction and thus inhibition of PDI's catalytic activity. Additionally, PDI knockdown attenuates the protective effect of 4-OH-E1 as well as cystamine (a known PDI inhibitor) against chemically induced ferroptosis in human breast cancer cells. Importantly, inhibition of PDI by 4-OH-E1 and cystamine or PDI knockdown by siRNAs each markedly reduces iNOS activity and NO accumulation, which has recently been demonstrated to play an important role in erastin-induced ferroptosis. In conclusion, this study demonstrates that 4-OH-E1 is a novel inhibitor of PDI and can strongly inhibit ferroptosis in human breast cancer cells in an estrogen receptor-independent manner. The mechanistic understanding gained from the present study may also aid in understanding the estrogen receptor-independent cytoprotective actions of endogenous estrogen metabolites in many noncancer cell types.

Neural correlates of model-based behavior ininternet gaming disorder and alcohol usedisorder.

An imbalance between model-based and model-free decision-making systems is a commonfeature in addictive disorders. However, little is known about whether similar decision-making deficits appear in internet gaming disorder (IGD). This study compared neurocognitive featuresassociated with model-based and model-free systems in IGD and alcohol use disorder (AUD). Participants diagnosed with IGD (n = 22) and AUD (n = 22), and healthy controls (n = 30) performed the two-stage task inside the functional magnetic resonance imaging (fMRI) scanner. We used computational modeling and hierarchical Bayesian analysis to provide a mechanistic account of their choice behavior. Then, we performed a model-based fMRI analysis and functional connectivity analysis to identify neural correlates of the decision-making processes in each group. The computational modeling results showed similar levels of model-based behavior in the IGD and AUD groups. However, we observed distinct neural correlates of the model-based reward prediction error (RPE) between the two groups. The IGD group exhibited insula-specific activation associated with model-based RPE, while the AUD group showed prefrontal activation, particularly in the orbitofrontal cortex and superior frontal gyrus. Furthermore, individuals with IGD demonstrated hyper-connectivity between the insula and brain regions in the salience network in the context of model-based RPE. The findings suggest potential differences in the neurobiological mechanisms underlying model-based behavior in IGD and AUD, albeit shared cognitive features observed in computational modeling analysis. As the first neuroimaging study to compare IGD and AUD in terms of the model-based system, this study provides novel insights into distinct decision-making processes in IGD.

Abstract 3556: A pan-cancer ex vivo drug screen database for next-generation pharmacogenomics and functional precision oncology

Abstract Large-scale drug screens across thousands of drugs and biological models enable pan-cancer analyses that can reveal novel insights into therapeutic response and resistance. Existing databases, such as the Cancer Dependency Map only screen established 2D cell lines, which are known to poorly translate to patients. Ex vivo models such as patient-derived cells, spheroids, and organoids, have consistently demonstrated superior translatability from drug screening responses as they better replicate the original tumor. However, the cost, complexity, and difficulty of developing and screening ex vivo models has thus far prevented the establishment of any large-scale database comparable to existing cell line databases. We present the pan-preclinical (PPC) project, a large pan-cancer database of ex vivo drug responses comprising 2.1M raw dose-response measurements with curve-level summary statistics and metadata. The PPC database is assembled from over 30 previously published ex vivo studies and contains 2,880 ex vivo models spanning 106 cancer subtypes tested against subsets of 3,023 drugs. Compounds are harmonized across studies and paired with detailed chemoinformatics derived from PubChem. Each ex vivo sample is matched to an underlying patient and paired, where available, with genomics, transcriptomics, and clinical information such as sex, treatment history, and overall survival. Two thirds of the underlying studies did not originally make the underlying drug screening data available at publication time, making the PPC database a novel resource beyond data integration. Results: To investigate the power of the PPC database, we developed a preliminary computational modeling and statistical analysis pipeline. A hierarchical Bayesian factor model was developed to estimate dose-response curves, remove batch effects across studies, and impute missing drugs. Low-dimensional projections of ex vivo drug responses show biologically meaningful clustering. Statistical analyses of both observed and imputed drug responses recapitulate standard of care drugs as significantly effective across several cancer types. Overall, we envision the PPC project laying the foundation for the next generation of large-scale pharmacogenomic analyses and aiding in the coming era of functional precision oncology. Citation Format: Anneliese Markus, Karl Pichotta, Jeffrey Quinn, Jessica White, Christopher Tosh, Jinrui Liu, Erin Coyne, Wesley Tansey. A pan-cancer ex vivo drug screen database for next-generation pharmacogenomics and functional precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3556.

Computational Modelling and Biomechanical Analysis of Age-Related Craniocerebral Injuries: Insights into Bridging Veins

The aim of this study is to explain the higher incidence of subdural haematomas in elderly people compared to young adult. This research addresses the phenomenon by developing two distinct numerical models of the human head, simulating individuals people aged 77 and 28, respectively. These models are methodically constructed based on medical imaging data acquired through collaboration with hospitals and subsequently verified through empirical experimentation. Studies have shown that the main factor that influences the vulnerability to bridging vein rupture in older adults is the degenerative processes of nervous tissue. The most visible structural damage was observed in the outflow cuff segment. This phenomenon can be primarily attributed to specific geometric parameters associated with this anatomical region. The presented research emphasises the importance of computational models in understanding the pathomechanics of brain structures. As a result of the analyses, it was proven that the neurodegenerative processes of the brain that occur with age are crucial in understanding the higher incidence of subdural haematomas in elderly people.

Computational analysis and chaos control of the fractional order syphilis disease model through modeling

In this paper, we present a dynamic model of syphilis. The goal of this work is to provide a mathematical model for syphilis disease that contains information about the suspect, infected, recovered, and exposed. Boundedness, positivity, and unique solutions at equilibrium points are used in the qualitative and quantitative study of a proposed model using the power law kernel. The Routh-Hurwitz stability criteria are used to address the model's local and global stability. Chaos control will use the regulate for linear responses approach to bring the system to stabilize according to its points of equilibrium. Using Lyapunov function tests, the global stability of free and endemic equilibrium points is confirmed. The Lipschitz condition and fixed point theory are utilized to satisfy the requirements for the existence and uniqueness of the exact solution. The generalized form of the power law kernel is solved using a two-step Lagrange polynomial method to investigate the impact of the fractional operator using numerical simulations that illustrate the effects of various parameters on the illness.

Pulmonary vascular reactivity in growth restricted fetuses using computational modelling and machine learning analysis of fetal Doppler waveforms

The aim of this study was to investigate the pulmonary vasculature in baseline conditions and after maternal hyperoxygenation in growth restricted fetuses (FGR). A prospective cohort study of singleton pregnancies including 97 FGR and 111 normally grown fetuses was carried out. Ultrasound Doppler of the pulmonary vessels was obtained at 24–37 weeks of gestation and data were acquired before and after oxygen administration. After, Machine Learning (ML) and a computational model were used on the Doppler waveforms to classify individuals and estimate pulmonary vascular resistance (PVR). Our results showed lower mean velocity time integral (VTI) in the main pulmonary and intrapulmonary arteries in baseline conditions in FGR individuals. Delta changes of the main pulmonary artery VTI and intrapulmonary artery pulsatility index before and after hyperoxygenation were significantly greater in FGR when compared with controls. Also, ML identified two clusters: A (including 66% controls and 34% FGR) with similar Doppler traces over time and B (including 33% controls and 67% FGR) with changes after hyperoxygenation. The computational model estimated the ratio of PVR before and after maternal hyperoxygenation which was closer to 1 in cluster A (cluster A 0.98 ± 0.33 vs cluster B 0.78 ± 0.28, p = 0.0156). Doppler ultrasound allows the detection of significant changes in pulmonary vasculature in most FGR at baseline, and distinct responses to hyperoxygenation. Future studies are warranted to assess its potential applicability in the clinical management of FGR.

Hybrid Analytical Model for AC Copper Loss Computation of Hairpin Winding

Hybrid Analytical Model for AC Copper Loss Computation of Hairpin Winding

Beyond peaks and troughs: Multiplexed performance monitoring signals in the EEG.

With the discovery of event-related potentials elicited by errors more than 30 years ago, a new avenue of research on performance monitoring, cognitive control, and decision making emerged. Since then, the field has developed and expanded fulminantly. After a brief overview on the EEG correlates of performance monitoring, this article reviews recent advancements based on single-trial analyses using independent component analysis, multiple regression, and multivariate pattern classification. Given the close interconnection between performance monitoring and reinforcement learning, computational modeling and model-based EEG analyses have made a particularly strong impact. The reviewed findings demonstrate that error- and feedback-related EEG dynamics represent variables reflecting how performance-monitoring signals are weighted and transformed into an adaptation signal that guides future decisions and actions. The model-based single-trial analysis approach goes far beyond conventional peak-and-trough analyses of event-related potentials and enables testing mechanistic theories of performance monitoring, cognitive control, and decision making.

Elastic Buckling Behavior of Functionally Graded Material Thin Skew Plates with Circular Openings

This study investigates the elastic buckling behavior of Functionally Graded Material (FGM) thin skew plates featuring a circular opening. FGMs, known for their unique property gradients, have gained prominence in structural engineering due to their mechanical performance and durability. Including a circular opening introduces a critical geometric consideration, influencing the structural stability and load-carrying capacity of FGM plates. The study examines the effects of the skew angle, plate’s aspect ratio, opening position, and size on the critical buckling load, normalized buckling load, and various buckling failure modes through computer modeling and finite element analysis. The results offer valuable insights into the interplay between material heterogeneity, geometric configuration, and structural stability. For instance, the critical buckling load increases by 29%, 82%, and 194% with an increment in skew angle from 0° to 30°, 45°, and 60°, respectively. Moreover, as the opening shifts from the plate’s edge closer to the center, the critical buckling load decreases by 26%. The critical buckling load is also dependent on the power index, as an increase in the power index from 0.2 to 5 reduced the buckling load by 1698 kN. This research contributes to the advancement of our understanding of FGM thin plates’ behavior under skew loading conditions, with implications for the design and optimization of innovative structures. The findings presented provide a foundation for further exploration of advanced composite materials and their applications in structural engineering.

Dynamical and computational analysis of fractional order mathematical model for oscillatory chemical reaction in closed vessels

One of the most fascinating chemical reactions is an oscillating one. The reactant and the autocatalyst are the two chemical species that are considered in this system. Firstly, we convert the oscillatory chemical reaction model into a fractional order oscillatory chemical reaction model for derivatives of arbitrary order provided in the sense of Caputo. The recommended methodology is centered on the shifted Jacobi collocation technique (JCT) and the shifted Jacobi operational matrix. In this work, we offer computational simulations of fractional order oscillatory chemical reaction models by using collocation technique and Newton polynomial interpolation (NPI) technique. The primary benefit of the collocation method is to study a general estimation for temporal and spatial discretizations. The numerical strategy also simplifies fractional differentiation equations (FDEs) by simplifying them into a simple issue that can be resolved by finding solutions to a few algebraic equations. We also present a comparison between the collocation and NPI techniques through the figures. The mathematical outcomes and data demonstrate that the offered strategy is an efficient procedure with outstanding reliability for resolving differential equations of arbitrary order. Some theorems related to the analysis of the collocation technique are also presented and explained here.

Probing corrosion protective mechanism of an amide derivative additive on anode for enhanced alkaline Al-air battery performance

Screening green corrosion inhibitors to prevent Al anode corrosion is essential for the development of high-performance Al-air batteries. This paper explores the potential of amide derivative, thiobenzamide (TBA), as the corrosion inhibitor for Al anode, and provides a universal and preferred option for improving Al-air batteries. An array of techniques were employed to probe the inhibition effect of TBA for Al-air battery in 4 M NaOH electrolyte. It is encouraging that TBA as a mixed-type corrosion inhibitor reached the inhibition efficiency of 67.8 % at an optimum addition of 0.07 M, and the specific capacity of Al-air battery dramatically increased from 461.9 mAh·g−1 (for the uninhibited system) to 1532.6 mAh·g−1 (for the TBA added system). The results of X-ray photoelectron spectroscopy (XPS) combined with ab initio molecular dynamics (AIMD) confirm that TBA can be adsorbed on the surface of Al to form a protective film, thus preventing the hydrogen evolution self-corrosion of Al anode. Herein the adsorption behavior occurred mainly through the Al–S–Al and N–Al bonding interaction between S/N atoms in TBA and Al atoms on Al surface based on the investigation of coordination numbers. The computational modeling analysis has revealed an electrolyte regulation way to improve the performance of Al-air batteries in alkaline medium.

Advancements and Challenges in Solid-State Battery Technology: An In-Depth Review of Solid Electrolytes and Anode Innovations

The primary goal of this review is to provide a comprehensive overview of the state-of-the-art in solid-state batteries (SSBs), with a focus on recent advancements in solid electrolytes and anodes. The paper begins with a background on the evolution from liquid electrolyte lithium-ion batteries to advanced SSBs, highlighting their enhanced safety and energy density. It addresses the increasing demand for efficient, safe energy storage in applications like electric vehicles and portable electronics. A major part of the paper analyzes solid electrolytes, key to SSB technology. It classifies solid electrolytes as polymer-based, oxide-based, and sulfide-based, discussing their distinct properties and application suitability. The review also covers advancements in anode materials for SSBs, exploring materials like lithium metal, silicon, and intermetallic compounds, focusing on their capacity, durability, and compatibility with solid electrolytes. It addresses challenges in integrating these anode materials, like the interface stability and lithium dendrite growth. This review includes a discussion on the latest analytical techniques, experimental studies, and computational models to understand and improve the anode–solid electrolyte interface. These are crucial for tackling interfacial resistance and ensuring SSBs’ long-term stability and efficiency. Concluding, the paper suggests future research and development directions, highlighting SSBs’ potential in revolutionizing energy storage technologies. This review serves as a vital resource for academics, researchers, and industry professionals in advanced battery technology development. It offers a detailed overview of materials and technologies shaping SSBs’ future, providing insights into current challenges and potential solutions in this rapidly evolving field.

Stratified sample tiling

The paper introduces a practical method for the construction of large-scale point sets for analysis of computer models. The constructed experimental design is useful for (probabilistic) integration, construction of approximation or a screening. The essence of the presented approach is the stratification of the design domain into an orthogonal grid of substrata and a subsequent tiling with tiles of points. If optimized, such tiles experience a major reduction of the number of degrees of freedom in the optimization process. That way, optimal or near-optimal point patterns can be feasibly identified and are further utilized for construction of larger point sets, thanks to the idea of self-similarity and structured space stratification The space-filling properties of the resulting point sets may be further enhanced by various “scrambling” strategies, which may remove the undesired sample collapsibility achieved via regular tiling. The performance of the constructed point sets is compared to Quasi Monte Carlo (QMC), Randomized Quasi Monte Carlo (RQMC) sequences, which are still today considered by engineers and even scientists as choices for variance reduction of numerical integration Further, the mentioned sampling strategies are compared in the terms of robustness when integrating a multivariate function with a localized feature. It is concluded that the proposed sampling approach reaches a superior performance in numerical integration and identification of function extremes as compared to sampling methods used by practicing researchers and engineers. Additionally, the reader is supplied with the open-access, ready-to-use implementation of the presented algorithm named SampleTiler.

Double dissociation of dopamine and subthalamic nucleus stimulation on effortful cost/benefit decision making

Deep brain stimulation (DBS) and dopaminergic therapy (DA) are common interventions for Parkinson's disease (PD). Both treatments typically improve patient outcomes, and both can have adverse side effects on decision making (e.g., impulsivity).1,2 Nevertheless, they are thought to act via different mechanisms within basal ganglia circuits.3 Here, we developed and formally evaluated their dissociable predictions within a single cost/benefit effort-based decision-making task. In the same patients, we manipulated DA medication status and subthalamic nucleus (STN) DBS status within and across sessions. Using a series of descriptive and computational modeling analyses of participant choices and their dynamics, we confirm a double dissociation: DA medication asymmetrically altered participants' sensitivities to benefits vs. effort costs of alternative choices (boosting the sensitivity to benefits while simultaneously lowering sensitivity to costs); whereas STN DBS lowered the decision threshold of such choices. To our knowledge, this is the first study to show, using a common modeling framework, a dissociation of DA and DBS within the same participants. As such, this work offers a comprehensive account for how different mechanisms impact decision making, and how impulsive behavior (present in DA-treated patients with PD and DBS patients) may emerge from separate physiological mechanisms.

Computational analysis of fractional Michaelis-Menten enzymatic reaction model

<abstract><p>In this study for examining the fractional Michaelis-Menten enzymatic reaction (FMMER) model, we suggested a computational method by using an operational matrix of Jacobi polynomials (JPs) as its foundation. We obtain an operational matrix for the arbitrary order derivative in the Caputo sense. The fractional differential equations (FDEs) are then reduced to a set of algebraic equations by using attained operational matrix and the collocation method. The approach which utilized in this study is quicker and more effective compared to other schemes. We also compared the suggested method with the Vieta-Lukas collocation technique (VLCM) and we obtain excellent results. A comparison between numerical outcomes is shown by figures and tables. Error analysis of the recommended methods is also presented.</p></abstract>

Сравнительный анализ теоретических моделей определения теплопроводности наножидкости

This article is devoted to the study of thermal conductivity of nanofluid. A nanofluid is a liquid in which nanometer-sized solid particles are dispersed. These particles are called nanoparticles. Nanofluids have new promising thermophysical properties compared to conventional heat transfer fluids. Thermal conductivity is one of the main thermophysical properties of a liquid. Thermal conductivity is of great importance in processes where heat transfer and fluid flow occur. The article presents well-known theoretical models for determining the thermal conductivity of nanofluid. A brief description of these models is given. Some experimental work on determining the thermal conductivity of various nanofluids is considered. A computational study of the effect of aluminum oxide (Al2O3) and silicon dioxide (SiO2) nanoparticles on the change in thermal conductivity of a nanofluid has been performed. A comparative analysis of known computational models and experimental data is carried out. The accuracy of the calculated models is determined by determining the thermal conductivity of the nanofluid.