Variation Of Model Parameters Research Articles

In Silico Elucidation of Key Drivers of Staphyloccocus aureus–Staphyloccocus epidermidis–Induced Skin Damage in Atopic Dermatitis Lesions

Staphylococcus aureus (SA) colonises and can damage skin in atopic dermatitis (AD) lesions, despite being commonly found with Staphylococcus epidermidis (SE), a commensal that can inhibit SA’s virulence and kill SA. Here, we developed an in silico model, termed a “virtual skin site”, describing the dynamic interplay between SA, SE, and the skin barrier in AD lesions to investigate the mechanisms driving skin damage by SA and SE. We generated 106 virtual skin sites by varying model parameters to represent different skin physiologies and bacterial properties. In silico analysis revealed that virtual skin sites with no skin damage in the model were characterised by parameters representing stronger SA and SE growth attenuation compared to those with skin damage. This inspired a treatment strategy combining SA-killing with an enhanced SA-SE growth attenuation, which in silico simulations found recovers many more damaged virtual skin sites to a non-damaged state, compared to SA-killing alone. This study demonstrates that in silico modelling can help elucidate key factors driving skin damage caused by SA-SE colonisation in AD lesions and help propose strategies to control it, which we envision will contribute to the design of promising treatments for clinical studies.

Investigation on damage creep constitutive model of rock under the coupled effect of freeze–thaw cycles and loading

AbstractThe investigation on damage creep properties of rock under freeze–thaw conditions are essential for assessing the long‐term stability of rock mass engineering in cold regions. This research analyzed the damage characteristics of rock under the coupled effect of freeze–thaw cycles and loading; the damage variable under the coupled effect of freeze–thaw cycles and loading was proposed. A damage creep constitutive model was developed, and the determination method for the model parameters was proposed. The rationality of the model was calibrated using test data, and the calculation results of the proposed model were compared with classical Nishihara model. Additionally, the research analyzed the variation of model parameters with the number of freeze–thaw cycles and discussed the damage creep mechanisms of rock under the coupled effect of freeze–thaw cycles and loading.

Extraction of single diode PV cell/module model parameters using a hybrid BMO approach with Lambert's W function

The accurate estimation of photovoltaic (PV) system parameters is crucial for effective simulation. Evaluation, modelling, and control of solar energy systems. The performance of photovoltaic installations is significantly influenced by the variability of model parameters and the difficulties in accessing them. As a result, the continuous objective is to consistently strive to search for and identify these parameters. This study proposes a hybrid algorithm that combines Bird Mating Optimiser algorithm with Lambert W-function (LBMO) with Wang's analytical method called (WLBMO) to optimise the parameters of the single diode model (SDM). The algorithm utilises a fast and precise iterative method for extracting I0 and Iph , and a Bird Mating Optimiser with Lambert W-function for extracting α, Rsh and Rs . The effectiveness of the proposed method is evaluated on the RTC solar cell and three commercial photovoltaic models. The specific results demonstrate the superior performance of our proposed WLBMO algorithm compared to known alternatives. For the R.T.C. France solar cell's single diode model, WLBMO achieves the smallest RMSE of 9.8630E-04, and similarly for the PWP201 model, yielding a low RMSE of 2.3845E-03. Comparative analysis, including Wang and LBMO, further validates our approach. Additionally, WLBMO excels among other methods for the STM6-120/36 commercial PV module, with a lowest RMSE of 1.7599E-03 and an exceptional minimum power error of 3.2270E-05. Similarly, for the KC200GT model, our method produces the smallest RMSE of 4.7569E-03. These consistent findings affirm the accuracy and efficacy of our WLBMO algorithm for PV model parameter estimation, emphasising its capacity to achieve significant error rate reductions of 92.856%, 1.147%, 49.732%, and 89.221% for the R.T.C France solar cell, Photowatt-PWP201, STM6-40/36, and KC200GT modules, respectively, and highlighting its pivotal role in optimising solutions.

Characterizing the electric field- and rate-dependent hysteresis of piezoelectric ceramics shear motion with the Bouc-Wen model

Piezoelectric technology is widely used in the field of precision drives. However, when piezoelectric actuators are employed at high voltages and high frequencies, their hysteresis can greatly affect the accuracy of such systems. The Bouc-Wen model has typically been used by researchers to characterise the hysteresis of piezoelectric materials. Besides, to extract the parameters of this model from experimental data, particle swarm optimization (PSO) is often employed. However, the majority of such studies only considers hysteresis as a function of the electric field strength and not as a function of actuation frequency. In addition, most of such existing reports have focused on longitudinal type piezo actuators. In this context, the research presented here complements this body of knowledge by demonstrating the application of the PSO algorithm for determining the parameters of the Bouc-Wen model when hysteresis depends not only on the electric field but also on the rate of the driving signal and when applied on a shear-type piezoelectric actuator. The obtained experimental data showed that with the increase of electric field strength, both the piezoelectric coefficient and hysteresis value increased significantly, and that with the increase of frequency, the piezoelectric coefficient decreased, while the hysteresis value changed slightly. The variations of the Bouc-Wen model parameters with the electric field and frequency were identified and then used to predict the shear motion trajectory of the piezoelectric stack. It was found that in the identified range of electric field and frequency, the predicted error rate of the hysteresis loop amplitude was less than 16.5% with a minimum value of 0.1%, and the error rate of the hysteresis loop width was less than 14.5% with a minimum value of 2.7%. Based on the identified electric field- and rate-dependent Bouc-Wen model, a novel finite element (FE) model was also proposed to analyse the effect of electrical and mechanical coupling on the piezoelectric movement.

A gap analysis on modelling of sea lice infection pressure from salmonid farms. I. A structured knowledge review

Sustainability of aquaculture, an important component of the blue economy, relies in part on ensuring assessment of environmental impact and interactions relating to sea lice dispersing from open pen salmon and trout farms. We review research underpinning the key stages in the sea lice infection process to support modelling of lice on wild salmon in relation to those on farms. The review is split into 5 stages: larval production; larval transport and survival; exposure and infestation of new hosts; development and survival of the attached stages; and impact on host populations. This modular structure allows the existing published data to be reviewed and assessed to identify data gaps in modelling sea lice impacts in a systematic way. Model parameterisation and parameter variation is discussed for each stage, providing an overview of knowledge strength and gaps. We conclude that a combination of literature review, empirical data collection and modelling studies are required on an iterative basis to ensure best practice is applied for sustainable aquaculture. The knowledge gained can then be optimised and applied at regional scales, with the most suitable modelling frameworks applied for the system, given regional limitations.

Voltage measurement-based recursive adaptive method for internal short circuit fault diagnosis in lithium-ion battery packs

Internal short circuit (ISC) has been identified as a major cause of thermal runaway in lithium-ion (Li-ion) battery systems, making the investigation of ISC fault diagnosis a focal research topic in electric vehicles and battery energy storage systems. Recently, several studies have applied multivariate analysis techniques to analyze abnormal behavior in cell voltage measurements and achieved an accurate diagnosis of ISC faults. However, model parameter variations under different operating conditions and state of charge (SOC), which results in unacceptable false alarm rates, is yet to be fully considered. To effectively address this problem, this study introduces a recursive approach on the basis of principal component analysis (PCA) to update the monitoring model and adapt to parameter changes. Moreover, an adaptive threshold with fusion factors is devised to reduce fault false alarm rates. In particular, this study employs sliding window techniques to amplify weak fault features to tackle the challenge of early-stage detection difficulty for ISC faults and provides an optimal window width determination algorithm based on fault-free data. Experimental results on a real Li-ion battery pack test platform under various operating conditions and SOC levels demonstrate that the proposed method maintains extremely high fault detection rates for ISC faults of different intensities. Furthermore, it is established that the adaptive updating of the model in the absence of faults greatly reduces false alarm rates.

Analytical and numerical computation of self-oscillating gels driven by the Belousov-Zhabotinsky reaction

Self-oscillating gel is a class of deformable polymers driven by Belousov-Zhabotinsky (BZ) reactions, which can form periodic deformations without any external stimuli, and are widely used in the research of micro actuators, AI sensors, drug release carriers or biomimetic materials. However, quantitative study on formation of the self-oscillating gel is limited especially from the perspective of energy conservation. This work adopts frequency domain analysis to the chemo-mechanical model, and the basic frequency is obtained to evaluate the maintenance energy of the deformable gel. For accurate computation, boundary value problem with unknown period is formulated; then, continuation algorithm based on technique of perturbation is performed to obtain the periodic trajectories with varying model parameters. The results could be implemented to design self-oscillating gels with prescribed periodicity.

A study on fractional order financial model by using Caputo–Fabrizio derivative

This paper aims to provide an understanding of the dynamics and behavior of the financial model in the context of fractional differential equations. The model explores the interactions and connections among four key financial variables: interest rates, investment demand, price index, and savings amounts. We establish the existence of unique solution for the fractional financial model and stability of the model using iterative approach by employing Banach’s fixed point theory. A numerical method is carried out to perform the numerical simulation of the Caputo–Fabrizio financial model. The work capitalizes on the insights gained by scrutinizing the repercussions stemming from variations in model parameters on the dynamics of the solutions obtained through our numerical scheme. Our findings highlight that the interplay among diverse financial components can lead to chaotic behavior in specific scenarios.

Investigating clot-flow interactions by integrating intravital imaging with in silico modeling for analysis of flow, transport, and hemodynamic forces

As a blood clot forms, grows, deforms, and embolizes following a vascular injury, local clot-flow interactions lead to a highly dynamic flow environment. The local flow influences transport of biochemical species relevant for clotting, and determines the forces on the clot that in turn lead to clot deformation and embolization. Despite this central role, quantitative characterization of this dynamic clot-flow interaction and flow environment in the clot neighborhood remains a major challenge. Here, we propose an approach that integrates dynamic intravital imaging with computer geometric modeling and computational flow and transport modeling to develop a unified in silico framework to quantify the dynamic clot-flow interactions. We outline the development of the methodology referred to as Intravital Integrated In Silico Modeling or IVISim, and then demonstrate the method on a sample set of simulations comprising clot formation following laser injury in two mouse cremaster arteriole injury model data: one wild-type mouse case, and one diYF knockout mouse case. Simulation predictions are verified against experimental observations of transport of caged fluorescent Albumin (cAlb) in both models. Through these simulations, we illustrate how the IVISim methodology can provide insights into hemostatic processes, the role of flow and clot-flow interactions, and enable further investigations comparing and contrasting different biological model scenarios and parameter variations.

Comparative Investigation of DSP-Based Speed Control of PMSM Using Proportional Integral and Takagi-Sugeno Fuzzy Logic Controller

This paper presents the simulation and experimental results of speed control of a three-phase Permanent Magnet Synchronous Motor (PMSM). The control techniques employed in this work include a Proportional Integral (PI) controller and a Takagi-Sugeno Fuzzy Logic Controller (TS-FLC). While theory and simulation help in predicting the PI controller settings for real drive implementation, further adjustment of the PI controller coefficients is still needed for optimal performance. However, the complexity of PI controller tuning can be overcome by employing a fuzzy logic controller, which is less affected by variations in model parameters and load torque. Two variants of the proposed TS-FLC are implemented: a simple algorithm with 9 rules and a standard algorithm with 49 rules. The drive system utilizes Field Oriented Control (FOC). The control program is developed using the C/C++ programming language and executed on the Texas Instruments TMS320LF28335 Digital Signal Processor (DSP), known for its intelligent motor control capabilities. The drive system evaluation was performed through simulations and experimentation using the MCK28335 professional development kit provided by Technosoft Company. The simulation and experimental results of the proposed controllers are compared under various conditions, including speed reversal and load torque variations.

Hybrid Flatness-Based Control of Dual Star Induction Machine Drive System for More Electrical Aircraft

Abstract This paper develops a precise method control system for tracking control of a power drive system based on a multi-phase machine under motor parameter and load torque variations. By adding a simple feedforward term based on the flatness theory, a conventional flux oriented control (FOC) can be enforced to have a perfect tracking performance under model parameter and load torque variations. Hence, a hybrid flatness-based control (HFBC) technique is applied to the control of a dual star induction machine (DSIM) and compared to a classical vector control strategy regarding tracking behaviour, robustness, and perturbations rejection. Finally, the simulation and experimental results are provided to verify the effectiveness of the proposed HFBC under uncertainties such as motor parameter and load torque variations. Furthermore, an enhancement of the drive system’s control performances is demonstrated by the improvement of the technique of separation of the objectives of tracking and disturbance rejection. The simulation and experimental results are presented, demonstrating the superiority of the HFBC.

A Monte Carlo approach to understanding the impacts of initial-condition uncertainty, model uncertainty, and simulation variability on the predictability of chaotic systems: Perspectives from the one-dimensional logistic map.

The predictability of the logistic map is investigated for the joint impact of initial-condition (IC) and model uncertainty (bias + random variability) as well as simulation variability. To this end, Monte Carlo simulations are carried out where IC bias is varied in a wide range of 10-15-10-3, and, similarly, model bias is introduced in comparable range. It is found that while the predictability limit of the logistic map can be continuously extended by reducing IC bias, the introduction of the model bias imposes an upper limit to the predictability limit beyond which further reductions in IC bias do not lead to an extension in the predictability limit, effectively restricting the feasible joint space spanned by the IC-model biases. It is further observed that imposing a lower limit to the allowed variability among ensemble solutions (so as to prevent the ensemble variability from collapse) results in a similar constraint in the joint IC-model-bias space; but this correspondence breaks down when the imposed variability limit is too high (∼x>0.7 for the logistic map). Finally, although increasing the IC random variability in an ensemble is found to consistently extend the allowed predictability limit of the logistic map, the same is not observed for model parameter random variability. In contrast, while low levels of model parameter variability have no impact on the allowed predictability limit, there appears to be a threshold at which an abrupt transition occurs toward a distinctly lower predictability limit.

Cause–effect relationship between model parameters and damping performance of hydraulic shock absorbers

Despite long-term research and development of modern shock absorbers, the effect of variations of several crucial material and model parameters still remains dubious. The goal of this work is therefore a study of the changes of shock absorber dynamics with respect to typical parameter ranges in a realistic model. We study the impact of shim properties, as well as geometric features such as discharge coefficients and bleed orifice cross section. We derive cause–effect relationships by nonlinear parameter fitting of the differential equations of the model and show digressive and progressive quadratic damping curves for shim number and thickness, sharp exponential curves for discharge coefficients, and leakage width, as well as a linear decrease of damping properties with bleed orifice area. Temperature increase affecting material properties, such as density and viscosity of the mineral oil, is found to have a mostly linear relationship with damping and pressure losses. Our results are not only significant for the general understanding of shock absorber dynamics, but also serve as a guidance for the development of specific models by following the proposed methodology.

Enhanced model tree for quantifying output variances due to random data sampling: Productivity prediction applications

The data used for productivity modeling represents a sample of a company's performance taken at a particular time, which leads to varying model parameters and results in a variance of the predicted productivity. This research enhances the capability of the nonlinear machine learning method called Model Tree by integrating error propagation theory to determine the variance for the predicted productivity based on a relatively large dataset available for productivity analysis. The enhanced model tree is formalized and applied in modeling structural steel fabrication productivity and piping spool fabrication productivity. The enhanced model tree is preferred over artificial neural networks due to the model's capability to explain the productivity model concerning the variance of its predicted point value and the underlying reasoning logic. The enhanced model tree will potentially find its applications in construction beyond predicting labor productivity and make a further impact on implementing explainable artificial intelligence (XAI) in wide-ranging engineering domains.

Comparison of tensile strain capacity models of oil and gas pipelines

In the field of strain-based pipeline design, the widely used pipeline tensile strain capacity models mainly include CSA Z662, PRCI-CRES, and EXXON MOBIL. In this paper, the methods for calculating the ultimate tensile strain, toughness, and other key parameters of various models are emphatically introduced, and their applicable ranges are compared. Ninety-six sets of full-scale test and curved wide plate test data are collected in this paper. The prediction accuracy of various tensile strain capacity models under different steel grades is compared and verified by the test results. The CSA Z662 model has few parameters to consider, the TSC prediction value is relatively low, and EXXON MOBIL has more nonconservative situations when the strain is less than 4%. The PRCI-CRES model has the highest prediction accuracy.

Sensitivity study of the critical angle in elastic orthorhombic media

AbstractThe critical angle plays a crucial role in data processing for refraction seismology. In the context of three‐dimensional data, the critical angle exhibits azimuthal dependence, particularly in the presence of an anisotropic model. In this paper, we propose a method to determine the critical angle (phase angle) and analyse the sensitivity of the critical angle to the model parameters and the available azimuthal range for both transversely isotropic medium with a vertical symmetry axis and orthorhombic models. For more complex orthorhombic models, the critical angle can be computed while considering changes in azimuth. In a numerical example, we apply sensitivity analysis to examine the existence of the critical angle and determine its corresponding values to variations in model parameters and the azimuthal range. Additionally, we conduct computations of the critical angle for the simplified acoustic and elliptical orthorhombic models. This analysis can be extended to encompass all model parameters for different wave modes (pure and converted waves), and they provide generalized predictions for the available range of data in seismic data processing applications.

Effect of adaptation gain and reference model in MIT and Lyapunov rule–based model reference adaptive control for first- and second-order systems

An adaptive control law encompasses a regulating control rule compensating for system dynamics variations by adjusting the controller characteristics to maintain the overall system performance. Recently, some techniques have been developed based on fundamental aspects of the adaptability of living organisms. The adaptive control method is a technique that measures the dynamic characteristics of the plant automatically and continuously to make a comparison with its required output. It utilizes the difference between these two to commute adaptable system parameters to maintain optimal performance regardless of the system variations. The behavior of the adaptation rule is significantly affected by the adaptation gain value. Here, it has also been investigated that the adaptation gain range is wide for the systems with the lower order. The appropriate range of adaptation gain decreases as the order of the system increases. In the present work, the model reference adaptive control (MRAC) for first- and second-order systems has been designed and investigated using a wide range of values for the adaptation gain and variations in the reference model parameters. The MIT (Massachusetts Institute of Technology) and Lyapunov rules are applied for the analyses of systems. On the MATLAB/Simulink platform, all the adaptation process comparisons, variations, and investigations have been carried out by altering the adaptation gain and the reference model parameters. The obtained results present encouraging outcomes.

Copula-based modeling and simulation of 3D systems of curved fibers by isolating intrinsic fiber properties and external effects

In this paper we lay the foundation for data-driven 3D analysis of virtual fiber systems with respect to their microstructure and functionality. In particular, we develop a stochastic 3D model for systems of curved fibers similar to nonwovens, which is fitted to tomographic image data. By systematic variations of model parameters, efficient computer-based scenario analyses can be performed to get a deeper insight how effective properties of this type of functional materials depend on their 3D microstructure. In a first step, we consider single fibers as polygonal tracks which can be modeled by a third-order Markov chain. For constructing the transition function of the Markov chain, we formalize the intuitive notions of intrinsic fiber properties and external effects and build a copula-based transition function such that both aspects can be varied independently. Using this single-fiber model, in a second step we derive a model for the entire fiber system observed in a bounded sampling window and fit it to two different 3D datasets of nonwovens measured by CT imaging. Considering various geometric descriptors of the 3D microstructure related to effective properties of the pore space, we evaluate the goodness of model fit by comparing geometric descriptors of the 3D morphology of model realizations with those of tomographic image data.

Estimating a Time-Varying Distribution-Led Regime

This paper estimates the distribution-led regime of the US economy for the period 1947–2019. We use a time varying parameter model, which allows for continuous changes in the regime over time. To the best of our knowledge this is the first paper that has attempted to do this. This innovation is important, because there is no reason to expect that the regime of the US economy (or any economy for that matter) will remain constant over time. On the contrary, there are significant reasons that point to changes in the regime. We find that the US economy became more profit-led in the first postwar decades until the 1970s and has become less profit-led since. In the last fifteen years of our sample the effect of changes in distribution on economic activity is statistically insignificant.

Obstetric operating room staffing and operating efficiency using queueing theory

IntroductionStrategies to achieve efficiency in non-operating room locations have been described, but emergencies and competing priorities in a birth unit can make setting optimal staffing and operation benchmarks challenging. This study used Queuing Theory Analysis (QTA) to identify optimal birth center operating room (OR) and staffing resources using real-world data.MethodsData from a Level 4 Maternity Center (9,626 births/year, cesarean delivery (CD) rate 32%) were abstracted for all labor and delivery operating room activity from July 2019—June 2020. QTA has two variables: Mean Arrival Rate, λ and Mean Service Rate µ. QTA formulas computed probabilities: P0 = 1-(λ/ µ) and Pn = P0 (λ/µ)n where n = number of patients. P0…n is the probability there are zero patients in the queue at a given time. Multiphase multichannel analysis was used to gain insights on optimal staff and space utilization assuming a priori safety parameters (i.e., 30 min decision to incision in unscheduled CD; ≤ 5 min for emergent CD; no greater than 8 h for nil per os time). To achieve these safety targets, a < 0.5% probability that a patient would need to wait was assumed.ResultsThere were 4,017 total activities in the operating room and 3,092 CD in the study period. Arrival rate λ was 0.45 (patients per hour) at peak hours 07:00–19:00 while λ was 0.34 over all 24 h. The service rate per OR team (µ) was 0.87 (patients per hour) regardless of peak or overall hours. The number of server teams (s) dedicated to OR activity was varied between two and five. Over 24 h, the probability of no patients in the system was P0 = 0.61, while the probability of 1 patient in the system was P1 = 0.23, and the probability of 2 or more patients in the system was P≥2 = 0.05 (P3 = 0.006). However, between peak hours 07:00–19:00, λ was 0.45, µ was 0.87, s was 3, P0 was 0.48; P1 was 0.25; and P≥2 was 0.07 (P3 = 0.01, P4 = 0.002, P5 = 0.0003).ConclusionQTA is a useful tool to inform birth center OR efficiency while upholding assumed safety standards and factoring peaks and troughs of daily activity. Our findings suggest QTA is feasible to guide staffing for maternity centers of all volumes through varying model parameters. QTA can inform individual hospital-level decisions in setting staffing and space requirements to achieve safe and efficient maternity perioperative care.