Variations In Input Parameters Research Articles

A model for scheduling the resource deployment in a multi-stage ramp-up production system

Rapid and continuous changes in customer product requirements affect demand, requiring the supply chain to be more responsive. A new product ramp-up is integral to responsiveness, where it is paramount to implement it successfully and manage it effectively. A smooth ramp-up process minimises problems and delays, leading to lower costs and higher profitability. This study develops and analyses a model that describes a multi-stage ramp-up production system to identify the most cost-effective policy for controlling multiple ramp-ups. We propose a search-based optimisation approach to solve the problem. Through numerical analyses, we develop a decision framework to classify the patterns of resource deployment and planning, aiming to make the ramp-up process efficient and responsive to increasing demand. Additionally, we conduct sensitivity analyses to examine how variations in input parameters affect system behaviour. Our findings offer managerial implications and insights based on the numerical results.

Accommodating version of a schematic eye for emmetropia and myopia.

To develop an accommodating, wide-angle, schematic eye for emmetropia and myopia in which spectacle refraction and accommodation level are input parameters. The schematic eye is based on an earlier unaccommodated refraction-dependent eye for myopia developed by Atchison in 2006. This has a parabolic gradient index lens and parameters derived from biometric and optical measurements on young adults. Several parameters are linearly dependent upon spectacle refraction (anterior radius of curvature of the cornea, axial length and vertex radii of curvature and conic asphericities of a biconic retina). The new accommodated schematic eye incorporates accommodation-dependent changes in several lens-related parameters. These changes are based on literature values for anterior chamber depth, lens thickness, vitreous chamber depth, lens surface radii of curvature and lens front surface asphericity. A parabolic variation of refractive index with relative distance from the lens centre is retained, with the same edge and centre refractive indices as the earlier model, but the distribution has been manipulated to maintain focus near the retina for the emmetropic case at 0 and 4 D accommodation. The asphericity of the lens back surface is changed so that spherical aberration and peripheral refraction approximately match typical literature trends. The model is used to compare spherical aberration and peripheral refraction in eyes with up to 4 D of myopia and 4 D of accommodation. The levels of spherical aberration in the unaccommodated schematic eyes are similar to literature values for young adults, but the changes in spherical aberration with accommodation are approximately two-thirds of that found in an experimental study. As intended, peripheral refractions in the accommodated schematic eyes are similar to those of their unaccommodated counterparts. The wide-angle model extends the range of schematic eyes to include both refraction and accommodation as variable input parameters. It may be useful in predicting aspects of retinal image quality.

Natural language processing‐based deep transfer learning model across diverse tabular datasets for bond strength prediction of composite bars in concrete

AbstractAs conventional machine learning models often struggle with scarcity and structural variation of training data, this paper proposes a novel regression transfer learning framework called transferable tabular regressor (TransTabRegressor) to address this challenge. The TransTabRegressor integrates natural language processing (NLP) for feature encoding, transformer for enhanced feature representation, and deep learning (DL) for robust modeling, facilitating effective transfer learning across tabular datasets using reducing input parameters. By leveraging the NLP data processor, the framework embeds both parameter names and values, enabling it to recognize and adapt to different expressions of similar parameters. For instance, the bond strength of fiber‐reinforced polymer (FRP) bars embedded in ultra‐high‐performance concrete (UHPC) is critical for ensuring the integrity of FRP‐UHPC structures. While pullout tests are widely adopted for their simplicity to generate substantial data, beam tests provide a closer approximation to actual stress conditions but are more complex thus resulting in limited data size. As a verification, the framework is applied to predict the bond strength of FRP bars embedded in UHPC using limited beam test data. A pre‐trained model is first established using 479 pieces of pullout test data. Subsequently, two transfer learning models are developed by fine‐tuning on 115 pieces of beam test data, where 66 correspond to concrete splitting failure and 49 correspond to pullout failure. For comparative analysis, XGBoost and neural network models are directly trained on the beam test data. Evaluation results demonstrate that the transfer learning models achieve significantly improved prediction accuracy and generalization capability. This study significantly highlights the effectiveness of the proposed TransTabRegressor in handling data scarcity and variability in input parameters across various engineering applications.

Experimental evaluation and optimization of the heat pipes integrated thermoelectric generator using response surface methodology

In this study, integration of heat pipes with thermoelectric generators is proposed for the recovery of waste heat. Heat energy is transferred from a heat source to thermoelectric elements via heat pipes, enabling energy conversion. To dissipate excess heat from the system after conversion, additional heat pipes are positioned on the cold sides of thermoelectric elements. Cooling of the condenser sections of these heat pipes is facilitated using air flow provided by a fan. A novel approach in this study involves the innovative integration of heat pipes with thermoelectric generators. This integration provides a new pathway for optimizing waste heat recovery systems by combining advanced thermal management techniques with thermoelectric technology. Experiments conducted at different power inputs (12–42 W) and air velocities (0.4, 0.8, 1.2 m/s) determine the performance of the thermoelectric generator. Furthermore, a power usage efficiency analysis was conducted to discuss the applicability of the proposed method in electronics. At low air velocities, values below the ideal value of 1 were achieved. Experimental data were analyzed using response surface methodology to assess the impact of varying input parameters on the power output and efficiency of the thermoelectric generator. Optimization studies highlighted the importance of effectively cooling the cold side of thermoelectric elements. The highest power output and efficiency are achieved under conditions of 42 W power input, 1.2 m/s air velocity, and use of 4 heat sinks, yielding approximately 0.8 W and 2 %, respectively. Furthermore, the response surface methodology analysis indicated that the most significant effect on system outputs was the power input. Moreover, the results of this study lay the groundwork for future strategic advancements in thermoelectric generators, enhancing their role in sustainable energy solutions.

Insights on CDI parametric controls and dependencies using gloabal sensitivity analysis

Capacitive deionization (CDI) emerges as a promising desalination technology due to its high energy efficiency and potential for energy recovery. Its performance improvement and application extension highly require understanding how variations in multiple input parameters, as well as their interactions, lead to variations in output, which is not straightforward in the complex system of CDI. In this work we use the extended Fourier amplitude sensitivity test (eFAST) method to conduct a global sensitivity analysis (GSA) in order to examine the effects of several parameters, including material properties, operating conditions, and ionic properties, on the energy consumption and salt removal efficiency of CDIs. The research reveals that high capacitance and efficient mass transfer are crucial for CDI advancement, with a trade-off between desalination rate and energy efficiency. The interplay between input parameters is revealed by factorial design and GSA, which show that no single parameter alone determines salt adsorption capacity but that feedwater salt concentration and current density are important when considering both individual and coupled effects. The study suggests that the specific application and feedwater characteristics should be taken into consideration when deciding between the CC and CV modes of desalination. While CC mode excels in water production and energy efficiency for high salinity feedwater pretreatment, CV mode is better at treating low salinity waterr. The comprehensive parametric analysis provides valuable insights into optimizing CDI design and operating conditions to balance desalination capacity and energy efficiency.

Performance Evaluation of Different Clustering Techniques and Parameters of Hybrid PSO- and GA-ANFIS on Optimization and Prediction of Biomethane Yield of Alkali-Pretreated Groundnut Shells

AbstractThe study focuses on optimizing biomethane yield in the anaerobic digestion of alkali-pretreated groundnut shells, involving varied input parameters. Biomethane optimization will improve the economy of the technology, which will assist in managing the environmental challenges of fossil fuel combustion. Traditional methods prove challenging, inaccurate, and uneconomical, necessitating efficient optimization models. This research hybridizes particle swarm optimization (PSO) and genetic algorithms (GA) with adaptive neuro-fuzzy inference system (ANFIS) models, assessing input parameters’ influence on biomethane yield through renowned performance metrics. Comparing the best model in the hybrid analysis, encompassing pretreatments A-E, the PSO-ANFIS (RMSE = 1.1719, MADE = 0.6525, MAE = 0.9314, Theil’s U = 0.1844, and SD = 0.7737) outperformed the GA-ANFIS (RMSE = 1.9338, MADE = 0.9318, MAE = 1.6557, Theil’s U = 0.2734, SD = 1.0598), using the same cluster radius of 0.50. Furthermore, compared to the GA-ANFIS model, the PSO-ANFIS model demonstrated significant improvements across various metrics: RMSE by 39.40%, MADE by 29.97%, MAE by 43.75%, Theil’s U by 32.56%, and SD by 27.00%. Results indicate that the PSO-ANFIS model outperforms the GA-ANFIS model, emphasizing the importance of suitable clustering algorithms and precise parameter adjustment for optimal performance in predicting biomethane yield from pretreated lignocellulose feedstocks. Graphical Abstract

Uncertainty in the embodied energy assessment of buildings: a case of EWS type low-cost house in India

Studies on embodied energy of buildings often present results as a single-point estimate without considering the variability in input parameters and the uncertainty it poses on results. Uncertainty analysis is useful to improve the reliability of results. This study examines uncertainty in the embodied energy of a single-storey EWS type residential building due to variability in the embodied energy intensity of materials and material wastage factors. Data quality indicator (DQI) based semi-qualitative method is used for modelling the variability in input parameters. Monte Carlo Simulation is used for uncertainty propagation. The building embodied energy is found to vary from 4 to 9 GJ/m2 based on variation in input data. The mean building embodied energy is found to be 6.3 GJ/m2. The embodied energy intensity of materials is found to cause more variation in building embodied energy in comparison with mass of materials. 'Brick’ is identified as the significant key parameter based on the relative contribution to uncertainty and embodied energy at the building level. Cement and rebar are identified as key parameters. The DQI based methodology presented in this study is useful for Life Cycle Assessment practitioners to evaluate uncertainty in the embodied energy assessment of residential buildings.

Wear Mechanism of an AlCrN-Coated Solid Carbide Endmill Cutter and Machined Surface Quality under Eco-Friendly Settings during Open Slot Milling of Tempered JIS SKD11 Steel

In the die and mold industry, tempered JIS SKD11 steel is selected to manufacture cold-forming dies that require an optimum balance of toughness, strength, and wear resistance. Therefore, the machinability of tempered JIS SKD11 in the milling machining process is challenging. The use of eco-friendly machining settings is intended to diminish tool wear and enhance the quality of the machined surface as well as the accuracy of the machined components. Adapting to the aforementioned factors for cold-forming dies is a pivotal issue. In this study, the machinability of tempered JIS SKD11 steel was analyzed under dry, MQL, cryogenic cooling with liquid nitrogen (LN2), and liquid carbon dioxide (LCO2) machining settings during open slot milling operations with varying input parameters, including cutting speeds and cutting feeds. An in-depth evaluation of output responses, including tool wear, surface roughness, cutting temperature in the cutting zone, and microhardness of the machined surface, was also conducted. The findings unveiled that the flank wear of the cutters and surface roughness of the machined surfaces obtained minimum values of 0.22 mm and 0.197 µm, respectively, during open slot milling operations at a cutting speed of 100 m/min and a cutting feed of 204 mm/min under cryogenic cooling with liquid carbon dioxide (LCO2). The findings from this study suggest that employing cryogenic cooling with LCO2 could serve as a viable substitute for dry, MQL, and cryogenic cooling with LN2 methods to enhance the machinability of hardened JIS SKD11 steel.

Forecasting PM10 Concentrations Using Artificial Neural Network in Imphal City

In this study, a forecasting system is developed for predicting PM10 levels in Imphal City over the next three days (+1, +2, and +3 days) using artificial neural networks (ANN). The experimental findings indicate that the ANN model can achieve reasonably accurate predictions of air pollutant levels. Moreover, optimizations in model performance are explored through variations in input parameters and experimental setups. Initially, predictions for each of the +1, +2, and +3 days are made independently using the same training dataset. Subsequently, cumulative predictions for +2 and +3 days are generated using previously predicted values from preceding days, yielding improved prediction accuracy. Additionally, the study identifies the optimal size of the training dataset, determining that using data spanning 3 to 15 past days yields the minimum error rates in predicting pollutant concentrations. Finally, the investigation includes the consideration of days-of-week as an input parameter, which enhances forecast accuracy noticeably.

Sensitivity analysis of fibrous thick-walled tubes with mechano-sensitive remodeling fibers in homeostasis

In this article, we apply the sensitivity analysis method to capture the influence of various parameters on the inflation pressure, axial force, and the deformation for an inflated and axially stretched cylinder. The material consists of an isotropic ground substance material reinforced with fibers that undergo a continuous and mechano-sensitive remodeling process. The input parameters of the mechanical system are assumed to be distributed according to the uniform probability distribution function. In the sensitivity analysis, we apply the Sobol method to determine how the variations of input parameters affect the inflation as well as the axial force in the cylinder. Special attention is given to the fiber remodeling process associated with a homeostatic balance between the constant fiber creation process and the strain-stabilized fiber dissolution. The results may help to understand the importance of the effect of material parameter changes, for example, due to remodeling processes in the context of diseases or recovering processes, on the overall tissue behavior.

Projection-based reduced order modeling of multi-species mixing and combustion

High-fidelity simulations of mixing and combustion processes are computationally demanding and time-consuming, hindering their wide application in industrial design and optimization. This study proposes projection-based reduced order models (ROMs) to predict spatial distributions of physical fields for multi-species mixing and combustion problems in a fast and accurate manner. The developed ROMs explore the suitability of various regression methods, including kriging, multivariate polynomial regression (MPR), k-nearest neighbors (KNN), deep neural network (DNN), and support vector regression (SVR), for the functional mapping between input parameters and reduced model coefficients of mixing and combustion problems. The ROMs are systematically examined using two distinct configurations: steam-diluted hydrogen-enriched oxy-combustion from a triple-coaxial nozzle and fuel-flexible combustion in a practical gas-turbine combustor. The projected low-dimensional manifolds are capable of capturing important combustion physics, and the response surfaces of reduced model coefficients present pronounced nonlinear characteristics of the flowfields with varying input parameters. The ROMs with kriging present a superior performance of establishing the input–output mapping to predict almost all physical fields, such as temperature, velocity magnitude, and combustion products for both test problems. The accuracy of DNN is less encouraging owing to the stringent requirement on the size of training database. KNN performs well in the region near the design points but its effectiveness diminishes when the test points are distant from the sampling points, whereas SVR and MPR exhibit large prediction errors. For the spatial prediction at unseen design points, the ROMs achieve a prediction time of up to eight orders of magnitude faster than conventional numerical simulations, rendering an efficient tool for the fast prediction of mixing and combustion fields and potentially an alternative of a full-order numerical solver.

Investigating Laser Ablation Process Parameters for the Fabrication of Customized Microneedle Arrays for Therapeutic Applications.

Microneedles are an innovation in the field of medicine that have the potential to revolutionize drug delivery, diagnostics, and cosmetic treatments. This innovation provides a minimally invasive means to deliver drugs, vaccines, and other therapeutic substances into the skin. This research investigates the design and manufacture of customized microneedle arrays using laser ablation. Laser ablation was performed using an ytterbium laser on a polymethyl methacrylate (PMMA) substrate to create a mold for casting polydimethylsiloxane (PDMS) microneedles. An experimental design was conducted to evaluate the effect of process parameters including laser pulse power, pulse width, pulse repetition, interval between pulses, and laser profile on the desired geometry of the microneedles. The analysis of variance (ANOVA) model showed that lasing interval, laser power, and pulse width had the highest influence on the output metrics (diameter and height) of the microneedle. The microneedle dimensions showed an increase with higher pulse width and vice versa with an increase in pulse interval. A response surface model indicated that the laser pulse width and interval (independent variables) significantly affect the response diameter and height (dependent variable). A predictive model was generated to predict the microneedle topology and aspect ratio varying from 0.8 to 1.5 based on the variation in critical input process parameters. This research lays the foundation for the design and fabrication of customized microneedles based on variations in specific input parameters for therapeutic applications in dermal sensors, drug delivery, and vaccine delivery.

Free vibration analysis of nanocomposite plate in contact with fluid using a novel quasi-3D shear deformation theory and artificial neural network

This article presents an analytical approach leveraging a novel quasi-three-dimensional shear deformation theory to analyze the free vibration response of functionally graded graphene platelet-reinforced composite (FG-GPLRC) plates in a liquid medium. The study meticulously determines material properties using the Halpin-Tsai micromechanical model, offering a thorough characterization of these advanced nanocomposite structures. Differential motion equations, incorporating transverse shear and normal stresses, are derived via Hamilton’s principle. The natural frequencies of the nanocomposite plates are computed using Galerkin’s method and validated against published results, affirming their accuracy and reliability. The key innovation of this research is introducing a new trigonometric shape function, which exhibits superior precision compared to previously proposed shape functions and existing theories. Furthermore, an artificial neural network (ANN) model is developed to train the computed results, enabling precise prediction of the natural frequencies without further computational runs. The Bayesian Regularization algorithm, implemented in Matlab, is utilized for this purpose. Across different GPLs patterns and varying input parameters, the error percentage between the ANN model predictions and the results from the Galerkin’s method is consistently low, often below 0.1%. The ANN model demonstrates robust generalization by effectively predicting outcomes with input data beyond its training range. Additionally, it provides immediate computational results, eliminating the delays of traditional methods. The article also examines the effects of various factors, including material characteristics, geometric parameters, and the fluid medium, on the free vibration behavior of FG-GPLRC plates.

Enhanced prediction of thermomechanical systems using machine learning, PCA, and finite element simulation

This research paper presents a comprehensive methodology for analyzing wet clutches, focusing on their intricate thermomechanical behavior. The study combines advanced encoding techniques, such as Principal Component Analysis (PCA), with metamodeling, to efficiently predict pressure and temperature distributions on friction surfaces. By parametrically varying input parameters and utilizing Finite Element Method (FEM) simulations, we generate a dataset comprising 200 simulations, divided into training and testing sets. Our findings indicate that PCA encoding effectively reduces data dimensionality while preserving essential information. Notably, the study reveals that only a few PCA components are required for accurate encoding: two components for temperature distribution and pressure, and three components for heat flux density. We compare various metamodeling techniques, including Linear Regression, Decision Trees, Random Forest, Support Vector Regression, Gaussian Processes, and Neural Networks. The results underscore the varying performance of these techniques, with Random Forest excelling in mechanical metamodeling and Neural Networks demonstrating superiority in thermal metamodeling.

The Use of Bilinear Cohesive Zone Model to Study the Fracture of Mode I in Adhesive Joints

Adhesively bonded joints can be numericallysimulated using the Cohesive Zone Model (CZM) concepts.CZM are widely used for the strength prediction of adhesivejoints. The critical strain energy release rate and criticalinterface strength are the parameters which must be knownwhen cohesive elements in ABAQUS software are used. Theformulation of the cohesive finite elements is based on theCZM approach with the bilinear traction-separation law. Inthis work, the parameters of two industrial adhesivesHuntsman Araldite 2015 and resin LY3505/XB3405 forbonding of epoxy composites are identified. DoubleCantilever Beam (DCB) test data was used for theidentification. Finally, cohesive parameters are identifiedcomparing numerically simulated load-displacement curveswith experimental data retrieved from literature.Parametric study is performed to evaluate the variation ofinput parameters like initial stiffness, element size, peakstress and energy release rate ‘G’. From the numericalevaluation, it was noted that CZM simulation relies largelyon element size and peak cohesive strength.

Economic order quantity for substitutable growing items

ABSTRACT In the realm of inventory management, the role of substitution in managing growing items is crucial. While several studies have examined growing and substitutable items separately, none have addressed replenishment policies for inventory systems with substitutable growing items. This paper introduces a multi-item inventory management problem for growing items with potential demand substitution. Two cases are defined and mathematically modeled. Due to the nonlinearity of the constrained models, a grid search heuristic algorithm is proposed as the solution methodology. The algorithm’s performance is compared with genetic and simulated annealing algorithms, two state-of-the-art metaheuristics. The models and solution approach are evaluated through numerous numerical examples, demonstrating the grid search heuristic algorithm’s superiority in quality and computational time. Sensitivity analyses are conducted to assess the impact of variations in input parameters on the objective function. Managerial insights are derived from the results, and the paper concludes with directions for future research on both the problem and solution methodology.

Determination of delamination-related material properties and sensitivities in a ply drop composite using a semi-numerical approach

The aims of this paper are to investigate damage initiation and propagation in the vicinity of a ply drop focusing on delamination, to extract delamination-related material properties without conducting classical single and mixed mode fracture tests, and to analyze the sensitivities of delamination evolution with respect to these material properties. Quasi-static tensile tests were conducted on the coupon level for a UD composite with a ply drop. A combined numerical/experimental approach was followed to identify the material properties associated with delamination evolution. For that purpose, a finite element model was created using the cohesive zone model. The delamination-related properties were characterized by fitting the numerical model to the experiments by calibrating the cohesive zone parameters such that the global coupon response and the delamination extent for different load levels were in good agreement. Based on the identified parameter set, a sensitivity analysis was carried out to raise awareness of the influence of material properties on damage evolution. Damage around the ply drop was governed by mode II delamination. Several sensitivities were found to be non-linear with respect to a variation of input parameters. The methodology can be used in future work focusing on different ply drop configurations, and the results so far can serve as a database for benchmark simulations of delamination evolution in the vicinity of ply drops.

Influence of parameter variation intervals on pyrolysis sensitivity analysis for charring and non-charring materials

This study applies the Morris method for a sensitivity analysis to evaluate the input parameters’ influence on the heat release rate in a pyrolysis model, focusing on two materials, poly(methyl methacrylate) (non-charring) and poly(vinyl chloride) (charring), examined under a cone calorimeter. A key aspect of our exploration was the role of input parameter variation intervals on the sensitivity outcomes. We analyzed three interval-setting methods: 1. A standard ±10% deviation from the nominal value, commonly used in the literature. 2. A range determined by the experimental uncertainties for individual parameters. 3. A span from minimum to maximum values found in existing literature for each parameter. Our intensive literature review supported the framing of intervals for the latter two methods. Our findings underscore the critical role of the selected variation interval. Specifically, while a uniform ±10% variation identified activation energies as the primary influencers—consistent with prior literature—the introduction of experimental uncertainties shifted this prominence toward heats of reaction. Thus, the selected interval can drastically reshape the perceived importance of certain parameters. This original work challenges the traditionally employed variation ranges in sensitivity studies, emphasizing the need for a nuanced approach.

Modeling and stability analysis of the transmission dynamics of Monkeypox with control intervention

Monkeypox is a deadly disease from the Orthopox family. The paper explores a nonlinear trend in the dynamics of Monkeypox propagation. In this model, we include vaccination, treatment, and level of awareness as the main parameters of the propagation of Monkeypox. We also include the quarantined infected individuals with severe complications in the Monkeypox dynamics. We show the boundedness and positivity of the model and also demonstrate the stability of the model using the Banach fixed point theory and the Picard successive approximation method to ensure that it is meaningful mathematically and epidemiologically. We obtain the unique solution of the model under appropriate conditions. This work finds three equilibrium points: Monkeypox free, Rodents free, and endemic. We show the local stability of monkeypox free equilibria and rodent-free endemic equilibria through reproduction number. Furthermore, Dulac’s function technique demonstrates the global stability of endemic equilibrium. We show the transformation from endemic equilibria to Monkeypox-free equilibria via rodent-free. We also obtain the condition of transcritical bifurcation. We analyze the system’s dynamic behavior to develop efficient infection control strategies. We find that if we increase awareness, vaccination, treatment, and quarantine rates, we effectively control the transmission. The model is connected with a continuous model using an ordinary differential equation. We examine the complex dynamics of Monkeypox infection under diverse system input factors through numerical simulation of the proposed model with variable input parameters in reducing Monkeypox. We present the theory of Monkeypox disease control in humankind as an application in real-world problems. The work can be helpful in the vector-borne disease control system.

A Credibility Framework for Extreme Value-at-Risk

Value-at-risk estimates derived from extreme value data by fitting fat-tailed distributions can be so large that their validity is open to question. In this paper, an objective criterion, and a framework from which it was developed, are presented in order to decide whether or not a fitted distribution is inappropriate for the purpose of value-at-risk calculation. That criterion is based on established extreme value theory (principally the Pickands-Balkema-deHaan Theorem), which is used to calculate a sequence of reference value-at-risk estimates using Generalised Pareto distributions. Those estimates are used to develop a closed-form formula for calculating a theoretical ’maximum’ value-at-risk. The method is validated by generating 100 random data sets and testing them against the framework for varying input parameter values. Approximately 75% of those cases passed the validation test.