Values Of Input Parameters Research Articles

Analysis and evaluation of the effectiveness of program codes for particle swarm and ant colony methods for lower semi-continuous functions

In this paper, a comparative analysis of two stochastic methods of global optimization is carried out: the Particle Swarm Optimization method and the Ant Colony Optimization method. The analysis and evaluation of efficiency were carried out using parameters such as the number of calls to the objective function, the rate of convergence, resources, computational efficiency, sensitivity to parameters, and resistance to initial conditions. The article presents the program codes of these methods in the Python environment, designed to calculate the global extremum of semi-continuous functions from below. Well- known test functions were used to construct (by gluing) semi-continuous functions at different values of input parameters.

A fractional derivative model of the dynamic of dengue transmission based on seasonal factors in Thailand

Climate variability affects the changes in controlling diseases transferred by insects. An increase in the population, the growth of communities, and a lack of public health infrastructure bring about the return of diseases of which insects are carriers, one of the illness issues. Therefore, the disease control is significant to help reduce the burden on the government and strengthen the country's public health structure. This research proposes a novel approach to modeling dengue fever dynamics, we employ a fractional derivative model with the Atangana–Baleanu–Caputo derivative, which offers a more accurate representation of real-world disease dynamics compared to traditional integer-order models. Basic qualifications are proposed. Equilibrium points and basic reproduction numbers are analyzed. The next-generation matrix method is used to identify the transmission. Besides, parameter sensitivity analysis is performed to learn about factors affecting input parameter values' effects on the basic reproduction number. It was found that the most common parameter affecting the transmission was the biting rate of mosquitoes was 1. In addition, the existence and uniqueness of the solution are examined using the Banach fixed point theorem. The Toufik–Atangana method is used for the numerical examination of a fractional version of the proposed model. We compared different values of fractional-order α=0.965, 0.975, 0.985, 0.995 and 1 it was found that when the order of derivatives decreases, the transmission shall decrease accordingly. This research provides valuable insights for developing effective control strategies to reduce the burden of dengue fever and strengthen public health systems.

Automated process control in electric arc furnaces in the aspect of digital twin technology

An approach to managing the main modes of smelting steel in heavy-duty electric arc furnaces (EAF) using digital twin technology was defined and formulated. It was noted, that the existing power regulators do not have the function of balancing the effective power of phases and, accordingly, electric arcs because they are focused on working with a certain average value of the signal. It is proposed to use the analysis of dynamic characteristics based on instantaneous values of input parameters instead of operating ones, as it’s usually implemented in most devices. This allows us to obtain more accurate data on the arc state and reduce the amount of time and computing power required to obtain a result and form recommendations. Based on the data obtained as a result of long - term observations of the heavy-duty EAF-135 operation, the relationship of the constant component of the arc voltage (CCAV) with the metal oxidation is shown. An example of its use as a criterion for controlling the melting oxidative stage is given. This reduces the consumption of electrochemical sensors for each melting in the case of serial metal production. Based on the recorded data, it is possible to timely determine the unevenness of the arc power release between the furnace electrodes and issue recommendations on gas burners operation regulating to equalize the rate of scrap melting at electrodes with less power release. The authors propose the idea of using digital twins based on models of the active power distribution across the melting bath zones and dependence of metal oxidation on oxygen blowing for monitoring and controlling the electric mode and the oxygen blast mode at the oxidative stage of the melting process. Simplified schemes of these twins are given.

Estimation of surface quality for turning operations using machine learning approach

The present article examines the effects of machining parameters on machined surfaces to determine optimum turning parameters for AISI-316 under dry machining environment. L27-OA with different levels of Cutting Speed (CS), Feed Rate (FR) and Depth-of-Cut (DOC) is used for experimentation. Surface Roughness (Ra) and Material Removal Rate (MRR) are considered as the response parameters. Among three Machine Learning (ML) models viz. Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Gradient Boosting Regression (GBR), GBR yielded the best results, with significantly higher R2 scores and lower RMSE values. An integration of GBR-PSO algorithms is used to determine 50 sets of, Pareto solutions and desirability analysis rendered the most suitable input parameter values as 111.66 m/min for CS, 0.15 mm/rev for FR and 1.23 mm DOC. At validation stage, Ra and MRR predicted by ML have a nominal difference of 15.19% and 0.115% compared to measured values.

Utilizing Taguchi method and in situ X-ray powder diffraction monitoring to determine the influence of mechanical activation conditions on the physico-chemical properties and Al leachability of K-feldspar

K-feldspar represents an important natural resource of potassium and aluminum. Within the framework of this study, the K-feldspar was mechanically activated in a planetary ball mill under different conditions planned according to 43 Taguchi orthogonal array experimental design. As outputs, specific surface area (SBET), median particle size (d50), amorphization degree of mineral phases , and Al recovery were used. It was found that the initial d50 value of 293 μm could be reduced to 6.7 μm and the SBET value of 4.7 m2/g could be increased up to 32.5 m2/g upon milling. Both microcline KAl3SiO8, and albite NaAl3SiO8 could be almost completely amorphized, whereas quartz SiO2 still maintained some crystallinity even under the most intensive conditions. Increasing SBET and decreasing the d50 values did not lead to a significant improvement in Al leach recovery, whereas a clear relationship between the amorphization of microcline and the recovered aluminum was found. Analysis of Variance (ANOVA) showed that increasing ball-to-powder ratio is the most beneficial for the improvement in Al recovery. In situ powder X-ray diffraction monitoring performed in an oscillation ball mill under synchrotron irradiation has shown very rapid amorphization of microcline phase at the beginning. However, amorphization of microcline was only partial after two hours of the treatment in this mill, apart from almost complete process in the planetary ball mill. In the end, regressions for the calculation of Al recovery by knowing the values of input parameters were calculated. In general, by just using mechanical activation without the subsequent roasting process that is commonly used to boost metal recoveries, it was possible to quantitatively recover aluminum from K-feldspar.

Pareto Optimization and Tuning of a Laser Wakefield Accelerator.

Optimization of accelerator performance parameters is limited by numerous trade-offs, and finding the appropriate balance between optimization goals for an unknown system is challenging to achieve. Here, we show that multiobjective Bayesian optimization can map the solution space of a laser wakefield accelerator (LWFA) in a very sample-efficient way. We observe that there exists a wide range of Pareto-optimal solutions that trade beam energy versus charge at similar laser-to-beam efficiency. Moreover, many applications such as light sources require particle beams at certain target energies. We demonstrate accurate energy tuning of the LWFA from 150 to 400MeV via the simultaneous adjustment of eight parameters. To further advance this use case, we propose an inverse model that allows a user to specify desired beam parameters. Trained on the forward Gaussian process model, the inverse model generates input parameter value ranges within which the desired setting is likely to be reached. The method reveals different strategies for accelerator tuning and is expected to drastically facilitate the operation of LWFAs in the near future.

Exploration of diverse solutions for the calibration of imperfect climate models

Abstract. The calibration of Earth system model parameters is subject to data, time, and computational constraints. The high dimensionality of this calibration problem, combined with errors arising from model structural assumptions, makes it impossible to find model versions fully consistent with historical observations. Therefore, the potential for multiple plausible configurations presenting different trade-offs between skills in various variables and spatial regions remains usually untested. In this study, we lay out a formalism for making different assumptions about how ensemble variability in a perturbed physics ensemble relates to model error, proposing an empirical but practical solution for finding diverse near-optimal solutions. A meta-model is used to predict the outputs of a climate model reduced through principal component analysis. Then, a subset of input parameter values yielding results similar to a reference simulation is identified. We argue that the effective degrees of freedom in the model performance response to parameter input (the “parametric component”) are, in fact, relatively small, illustrating why manual calibration is often able to find near-optimal solutions. The results explore the potential for comparably performing parameter configurations that have different trade-offs in model errors. These model candidates can inform model development and could potentially lead to significantly different future climate evolution.

Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions

Abstract The cementitious composite’s resistance to the introduction of harmful ions is the primary criterion that is used to evaluate its durability. The efficacy of glass and eggshell powder in cement mortar exposed to 5% sulfuric acid solutions was investigated in this study using artificial intelligence (AI)-aided approaches. Prediction models based on AI were built using experimental datasets with multi-expression programming (MEP) and gene expression programming (GEP) to forecast the percentage decrease in compressive strength (CS) after acid exposure. Furthermore, SHapley Additive exPlanations (SHAP) analysis was used to examine the significance of prospective constituents. The results of the experiments substantiated these models. High coefficient of determination (R 2) values (MEP: 0.950 and GEP: 0.913) indicated statistical significance, meaning that test results and anticipated outcomes were consistent with each other and with the MEP and GEP models, respectively. According to SHAP analysis, the amount of eggshell and glass powder (GP) had the most significant link with CS loss after acid deterioration, showing a positive and negative correlation, respectively. In order to optimize efficiency and cost-effectiveness, the created models possess the capability to theoretically assess the decline in CS of GP-modified mortar across various input parameter values.

ANN Prediction of Laser Power, Cutting Speed, and Number of Cut Annual Rings and Their Influence on Selected Cutting Characteristics of Spruce Wood for CO2 Laser Processing.

In this work, we focus on the prediction of the influence of CO2 laser parameters on the kerf properties of cut spruce wood. Laser kerf cutting is mainly characterized by the width of kerf and the width of the heat-affected zone, which depend on the laser power, cutting speed, and structure of the cut wood, represented by the number of cut annual rings. According to the measurement results and ANN prediction results, for lower values of the laser power (P) and cutting speed (v), the effect of annual rings (ARs) is non-negligible. The results of the sensitivity analysis show that the effect of v increases at higher energy density (E) values. With P in the range between 100 and 500 W, v values between 3 and 50 mm·s-1, and AR numbers between 3 and 11, the combination of P = 200 W and v = 50 mm·s-1, regardless of the AR value, leads to the best cut quality for spruce wood. In this paper, the main goal is to show how changes in the input parameters affect the characteristics of the cutting kerf and heat-affected zones for all possible input parameter values.

Promoting low carbon construction using alkali-activated materials: A modeling study for strength prediction and feature interaction

Abstract In place of Portland cement concrete, alkali-activated materials (AAMs) are becoming more popular because of their widespread use and low environmental effects. Unfortunately, reliable property predictions have been impeded by the restrictions of conventional materials science methods and the large compositional variability of AAMs. A support vector machine (SVM), a bagging regressor (BR), and a random forest regressor (RFR) were among the machine learning models developed in this study to assess the compressive strength (CS) of AAMs in an effort to gain an answer to this topic. Improving predictions in this crucial area was the goal of this study, which used a large dataset with 381 points and eight input factors. Also, the relevance of contributing components was assessed using a shapley additive explanations (SHAP) approach. In terms of predicting AAMs CS, RFR outperformed BR and SVM. Compared to the RFR model’s 0.96 R 2, the SVM and BR models’ R 2-values were 0.89 and 0.93, respectively. In addition, the RFR model’s greater accuracy was indicated by an average absolute error value of 4.08 MPa compared to the SVM’s 6.80 MPa and the BR’s 5.83 MPa, which provided further proof of their validity. According to the outcomes of the SHAP research, the two factors that contributed the most beneficially to the strength were aggregate volumetric ratio and reactivity. The factors that contributed the most negatively were specific surface area, silicate modulus, and sodium hydroxide concentration. Using the produced models to find the CS of AAMs for various input parameter values can help cut down on costly and time-consuming laboratory testing. In order to find the best amounts of raw materials for AAMs, academics and industries could find this SHAP study useful.

Phenomenological Nondimensional Parameter Decomposition to Enhance the Use of Simulation Modeling in Fire Probabilistic Risk Assessment of Nuclear Power Plants

Simulation modeling is crucial in support of probabilistic risk assessment (PRA) for nuclear power plants (NPPs). There is a challenge, however, associated with simulation modeling that relates to the time and resources required for collecting data to determine the values of the input parameters. To alleviate this challenge, this article develops a formalized methodology to generate surrogate values of input parameters grounded on the decomposition of phenomenological nondimensional parameters (PNPs) while avoiding detailed data collection. While the fundamental principles of the proposed methodology can be applicable to various hazards, the developments in this article focus on fire PRA as an example application area for which resource intensiveness is recognized as a practical challenge. This article also develops a computational platform to automate the PNP decomposition and seamlessly integrates it with state-of-practice fire scenario analysis. The applicability of the computational platform is demonstrated through a multi-compartment fire case study at an NPP. The computational platform, with its embedded PNP decomposition methodology, can substantially reduce the effort required for input data collection and extraction, thereby facilitating the efficient use of simulation modeling in PRA and enhancing the fire scenario screening analysis.

How Many Monte Carlo Samples are Needed for Probabilistic Cost-Effectiveness Analyses?

ObjectivesProbabilistic sensitivity analysis (PSA) is conducted to account for the uncertainty in cost and effect of decision options under consideration. PSA involves obtaining a large sample of input parameter values (N) to estimate the expected cost and effect of each alternative in the presence of parameter uncertainty. When the analysis involves using stochastic models (eg, individual-level models), the model is further replicated P times for each sampled parameter set. We study how N and P should be determined. MethodsWe show that PSA could be structured such that P can be an arbitrary number (say, P=1). To determine N, we derive a formula based on Chebyshev’s inequality such that the error in estimating the incremental cost-effectiveness ratio (ICER) of alternatives (or equivalently, the willingness-to-pay value at which the optimal decision option changes) is within a desired level of accuracy. We described 2 methods to confirm, visually and quantitatively, that the N informed by this method results in ICER estimates within the specified level of accuracy. ResultsWhen N is arbitrarily selected, the estimated ICERs could be substantially different from the true ICER (even as P increases), which could lead to misleading conclusions. Using a simple resource allocation model, we demonstrate that the proposed approach can minimize the potential for this error. ConclusionsThe number of parameter samples in probabilistic cost-effectiveness analyses should not be arbitrarily selected. We describe 3 methods to ensure that enough parameter samples are used in probabilistic cost-effectiveness analyses.

Two improving approaches for faulty interaction localization using logistic regression analysis

Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial testing. An accurate and fast FIL provides helpful information to fix defects causing the test failure. One type of conventional FIL approach, which analyzes test results of whole test cases and estimates the suspiciousness of each combination, has two main concerns; (1) the accuracy is not enough, (2) the huge time cost is sometimes needed. In this paper, we propose two novel approaches to improve those concerns. FROGa attempts to estimate suspiciousness more accurately using logistic regression analysis. FROGb attempts to estimate failure-inducing combinations at high speed by estimating the subsets of them using logistic regression analysis and exploring just their supersets. Through evaluation experiments using a large number of artificial test results based on several real software systems, we observed that FROGa has very high accuracy, and FROGb can drastically reduce time cost for targets that have been difficult to complete by the conventional method.

Experimental assessment and optimization of the performance of a biodiesel engine using response surface methodology

BackgroundBiodiesel is a renewable and ecofriendly fuel for internal combustion engines. However, fuel standards need to be adapted for efficiency and commercial use. This paper deals with a novel process of its production using a purification step that counters the high costs of production and experimental analysis using multiresponse optimization.MethodsSoybean oil was chosen as a biodiesel of 5%, 10%, and 15% blend with common diesel fuel and is experimentally tested in a variable compression ratio compression ignition engine. The biodiesel is blended with common diesel fuel to run the engine without any modification in its setup, which also solves most of the operational problems. The functional relationship between the input parameters and the performance characteristics of the engine is evaluated by statistical response surface methodology using the Box–Behnken design model, which generates a design of experiment resulting in an optimum experimental run that reduces the overall cost of the experimental investigation. Uncertainty analysis is done to minimize the gap between the results considering the errors of each piece of equipment. Validation of the results is also carried out.ResultsThe analysis of variance is used to measure the acceptability of the model and the competency of the model to predict output performance. The optimum value of input parameters which are obtained are 4.5 kg for the load, the compression ratio of 18, and B05 for the fuel blend, which results in maximum performance of brake power of 3 kW, minimum fuel consumption and emissions of CO and NOx, which are 0.39 kg/kWh, 0.01%, and 50 ppm.ConclusionsCost analysis reveals that biodiesel produced from the novel process of transesterification is reasonable as compared with the conventional process. It is also environmentally more sustainable, which cannot be ignored. This technique can be used in future research for cost-effective production fields such as combustion parameters and biofuels produced from waste, which need to be explored.

Artificial intelligence model for predicting the load-bearing capacity of eccentrically compressed short concrete filled steel tubular columns

The purpose of this work is to develop the artificial neural network (ANN) model to determine the load-bearing capacity of concrete filled steel tubular (CFST) columns of circular cross-section in a wide range of input parameters. Short columns are considered for which deflections do not lead to a significant increase in the eccentricity of the axial force. The input parameters of the artificial neural network are the outer diameter of the pipe, the wall thickness, the yield strength of steel, the compressive strength of concrete, and the relative eccentricity of the axial force. The artificial neural network is trained on the synthetic data. For training, the dataset of 179,025 numerical experiments with different values of input parameters was generated. Numerical experiments were carried out using the finite element method in a simplified formulation, which makes it possible to reduce the three-dimensional problem of determining the stress-strain state of a CFST column to a two-dimensional problem. The results of testing the developed model on the data from full-scale experiments are presented.

Interplay of natural and anthropogenic inputs on the groundwater contamination of Beenaganj-Chachura block.

The present research work investigates the impact of natural and anthropogenic inputs on the chemistry and quality of the groundwater in the Beenaganj-Chachura block of Madhya Pradesh, India. A total of 50 groundwater samples were examined for nitrates, fluoride, chlorides, total dissolved solids, calcium, magnesium, pH, total hardness, and conductivity, and their impact on entropy-weighted water quality index and pollution index of groundwater (PIG) was investigated via the response surface methodology (RSM) using the central composite design. According to analytical findings, Ca, Mg, Cl-, SO42-, and NO3- exceed the desired limit and permitted limit set by the Bureau of Indian Standards (BIS) and the World Health Organization (WHO). According to PIG findings, 76, 16, and 8% of groundwater samples, respectively, fell into the insignificant, low, and moderate pollution categories. The regression coefficients of the quadratic RSM models for the experimental data provided excellent results. Thus, RSM provides an excellent means to obtain the optimized values of input parameters to minimize the PIG values.

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.

Estimating the Economic Impact of Levalbuterol's Potential Transition From the National Reimbursement Drug List for the Treatment of Pediatric Asthma in China: A Budget Impact Analysis.

Background Levalbuterol is a short-actingβ2-agonist (SABA) indicated for treating or preventing asthma exacerbation. It was included in the 2020 Chinese National Reimbursement Drug List (NRDL). This study estimates the economic impact of levalbuterol's status change within and withdrawal from the NRDL in treating pediatric asthma from a publicly funded medical insurance perspective. Methodology A prevalence-based budget impact model was developed. The analysis compared a world witha levalbuterol scenario to a world withoutlevalbuterol. Epidemiological data were obtained from the existing literature. Cost data were estimated based on the drug dosage in clinical trials, real-world settings, and expert opinions. Scenario analysis considered the same length ofstay (LOS) in the two groups. One-way sensitivity analyses were carried out to show the impact of varying individual parameters. Results In the base-case analysis, compared to the world withoutscenario, the preservation of levalbuterol resulted in cost savings of ¥82.8 million in China over three years. In the scenario analysis, savings decreased to ¥76.1 million over three years. Sensitivity analysis showed that, for the most part, the results were robust to changes in input parameter values. Conclusions Using levalbuterol may lead to substantial cost savings for Chinese society.

Statistical Issues on the Neutrino Mass Hierarchy with Δχ2

The neutrino mass hierarchy determination (ν MHD) is one of the main goals of the major current and future neutrino experiments. The statistical analysis usually proceeds from a standard method, a single-dimensional estimator 1D−Δχ2 that shows some drawbacks and concerns, together with a debatable strategy. The drawbacks and considerations of the standard method will be explained through the following three main issues. The first issue corresponds to the limited power of the standard method. The Δχ2 estimator provides us with different results when different simulation procedures were used. Regarding the second issue, when χminNH2 and χminIH2 are drawn in a 2D map, their strong positive correlation manifests χ2 as a bidimensional variable, instead of a single-dimensional estimator. The overlapping between the χ2 distributions of the two hypotheses leads to an experiment sensitivity reduction. The third issue corresponds to the robustness of the standard method. When the JUNO sensitivity is obtained using different procedures, either with Δχ2 as one-dimensional or χ2 as two-dimensional estimator, the experimental sensitivity varies with the different values of the atmospheric mass, the input parameter. We computed the oscillation of Δχ2¯ with the input parameter values, Δm2input. The MH significance using the standard method, Δχ2, strongly depends on the values of the parameter Δm2input. Consequently, the experiment sensitivity depends on the precision of the atmospheric mass. This evaluation of the standard method confirms the drawbacks.

Calibration of models predicting the load-bearing capacity of bonded anchors using a genetic algorithm

The paper deals with the topic of resistance of bonded headless post-installed anchors to uncracked concrete, in particular with the calibration of selected models predicting their ultimate tensile load. In particular, the optimization of two models for predicting the tensile capacity of an anchor is investigated in this paper. First model is based on the determination of the ultimate capacity based on separated failure modes (failure of the extraction of concrete cone and the bond failure). The second optimised model, proposed in this paper, summarizes the effect of both of these parameters into a single exponential function. A model combining the effect of concrete strength and adhesive strength has the potential to better capture the true nature of failure in which both materials are involved. In order to compare these models, an extensive database of experimental results was compiled (including own experiments and also results from various authors). The calibration consisted in finding the most appropriate values of the individual input parameters of the models to fit the experimental results as closely as possible. The models for predicting the tensile capacity of anchors are multiparametric. Therefore, a method using elements of genetic algorithms was used for optimization, suitable for this purpose. Several possible statistical evaluation criteria were used for the evaluation of the fit of the models. The optimization of the models showed that the proposed model combining the effect of concrete strength and bond strength can be optimized to better fit the experimental results.