Varying Parameter Values Research Articles

Ant Colony Optimization for Resistor Color Code Detection

In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other local solutions. The first stage of testing focuses on exploring variations of parameter values. Applying the best parameters resulted in 85% accuracy and 43 seconds for 20 resistor images. This method is expected to prevent local solutions without wasteful computation of the matrix.

Study on the Use of Artificially Generated Objects in the Process of Training MLP Neural Networks Based on Dispersed Data.

This study concerns dispersed data stored in independent local tables with different sets of attributes. The paper proposes a new method for training a single neural network-a multilayer perceptron based on dispersed data. The idea is to train local models that have identical structures based on local tables; however, due to different sets of conditional attributes present in local tables, it is necessary to generate some artificial objects to train local models. The paper presents a study on the use of varying parameter values in the proposed method of creating artificial objects to train local models. The paper presents an exhaustive comparison in terms of the number of artificial objects generated based on a single original object, the degree of data dispersion, data balancing, and different network structures-the number of neurons in the hidden layer. It was found that for data sets with a large number of objects, a smaller number of artificial objects is optimal. For smaller data sets, a greater number of artificial objects (three or four) produces better results. For large data sets, data balancing and the degree of dispersion have no significant impact on quality of classification. Rather, a greater number of neurons in the hidden layer produces better results (ranging from three to five times the number of neurons in the input layer).

On nonlinear optical solitons of fractional Biswas-Arshed Model with beta derivative

The Biswas-Arshed (BA) model is an essential wave propagation model in nonlinear optics and optical fibers. In this manuscript, the enhanced G′/G-expansion method is taken into consideration. The BA model is used with this technique to locate visual soliton solutions. The solutions to the hyperbolic and trigonometric functions are determined using this technique. Each solution includes a large number of model and method parameters. The wave solutions are defined by the waves' character, which is determined by the three-dimensional (3D) and two-dimensional (2D) wave patterns with varying parameter values. The structure of the soliton shifts depending on the specific values of the parameters. These shifts are reflected in the 2D pictures that demonstrate the various soliton configurations. Finally, the obtained soliton solutions can be applied to high-speed data transmission to describe propagation pulses and to transmit light between two ends in optical fibers and communication systems.

Safe Operating Limits of Vehicle Dynamics Under Parameter Uncertainty Using Koopman Spectrum

Abstract Ground vehicles operate under different driving conditions, which require the analysis of varying parameter values. It is essential to ensure the vehicle’s safe operation under all these conditions of the parameter variation. In this paper, we investigate the safe operating limits of a ground vehicle by performing the reachability analysis for varying parameters using the Koopman spectrum approach. The reachable set is computed using the Koopman principal eigenfunctions obtained from a convex optimization formulation for different values of the parameter. We consider the two degrees-of-freedom nonlinear quarter-car model to simulate the vehicle’s dynamics. Based on the obtained reachable sets, we provide the mean and variance computation framework with parametric uncertainty. The results show that the reachable set for each value provides valuable information regarding the safe operating limits of the vehicle and can assist in developing safe driving strategies.

Assessment of 2D-based tests for the qualification of simulation software for dXCT

The interest in using computer simulations of dimensional x-ray computed tomography (dXCT) for various metrological purposes—such as measurement planning, performance prediction, performance optimisation and, finally, measurement uncertainty estimation—is increasing along with the ever-growing demand for more reliable measurements with dXCT. However, before a piece of simulation software can be used for tasks related to coordinate metrology, it has to be ensured that it is able to simulate physical laws, characteristic effects and basic CT system functionalities correctly and with sufficient accuracy. In short, the software must be qualified for dimensional metrology tasks. As one part of such a qualification process, a method is presented here for determining conformity intervals of 2D tests (projection-based tests) based on 3D tests (testing based on dimensional evaluations in a reconstructed volume) for the assessment of dXCT simulation software. The method consists of varying relevant parameter values in order to verify their influence on 3D measurement results. The results of the 3D tests with varied parameter values are then transferred to the quantities tested in the 2D tests and used as the basis for determining conformity intervals. Two approaches are applied for determining whether or not a variation of a parameter value is significant: (a) statistical and (b) heuristic. Two examples are presented, each based on simulated images, which show the application of the two different approaches for determining conformity intervals for the results of the 2D tests.

Capital controls as a bargaining device: The case of Iceland

AbstractIceland imposed strict capital controls when its banks failed in October 2008. Legacy problems and a large stock of carry‐trade funds locked in by the controls hindered lifting them, but they also helped in resolving the banks. We formulate and calibrate a bargaining model describing the strategic interaction between Iceland and international investors. Outcomes indicate a judicious bargaining approach by Icelandic authorities which was ultimately successful and led to resolution by settlement. The results are robust to reasonable variations in parameter values and are consistent with outcomes in investment arbitration trials and anecdotal evidence from Icelandic case law.

Influence of Soil Heterogeneity on Water Flow and Solute Transport Characterized by Dye Tracer Experiments

Soil heterogeneity is a key factor affecting horizontal water flow and solute transport in unsaturated soil, in which particle distribution, pore structure, and pore connectivity in the soil all present fractal characteristics. The initial water content of soil has significant influence in the resulting heterogeneity of the soil structure, which can affect the water flow and solute transport. In this study, through a series of horizontal transport laboratory simulation experiments in soil columns, the initial water content dependency of transport was elucidated using the fractal Richards’ equation (FRE) model. We first observed that water flows faster in soil columns with higher initial water content, but the law is opposite in the initial process of water flow. By analyzing the variation of parameter values in the FRE model, we speculated that the abnormal phenomenon of early diffusion is caused by the heterogeneity of the soil structure. To elucidate the mechanisms of the water content’s dependency of the solute transport process and to identify the circumstance of altering of flow regime, a series of dye tracer experiments were conducted. Results show that in soil columns with low water content, the inflow of external fluid leads to the agglomeration of soil particles. Combined with matric suction, the heterogeneity of the soil structure is enhanced, which affects the formation of water flow and solute transport paths. It even leads to preferential flow in unsaturated soil columns with low water content.

A New Exponentiated Beta Burr Type X Distribution: Model, Theory, and Applications

In recent years, many attempts have been carried out to develop the Burr type X distribution, which is widely used in fitting lifetime data. These extended Burr type X distributions can model the hazard function in decreasing, increasing and bathtub shapes, except for unimodal. Hence, this paper aims to introduce a new continuous distribution, namely exponentiated beta Burr type X distribution, which provides greater flexibility in order to overcome the deficiency of the existing extended Burr type X distributions. We first present its density and cumulative function expressions. It is then followed by the mathematical properties of this new distribution, which include its limit behaviour, quantile function, moment, moment generating function, and order statistics. We use maximum likelihood approach to estimate the parameters and their performance is assessed via a simulation study with varying parameter values and sample sizes. Lastly, we use two real data sets to illustrate the performance and flexibility of the proposed distribution. The results show that the proposed distribution gives better fits in modelling lifetime data compared to its sub-models and some extended Burr type X distributions. Besides, it is very competitive and can be used as an alternative model to some nonnested models. In summary, the proposed distribution is very flexible and able to model various shaped hazard functions, including the increasing, decreasing, bathtub, and unimodal.

Probabilistic simulation of biogas production from anaerobic co-digestion using Anaerobic Digestion Model No. 1: A case study on agricultural residue

Anaerobic co-digestion is a well-established approach to overcome drawbacks associated with the digestion of individual substrates, but the identification of suitable substrate combinations requires extensive experimental work. We develop a framework for the probabilistic estimation of methane production potential from mixed substrates using Anaerobic Digestion Model No. 1 and validate the proposed framework using agricultural residues as a case study. The validity of anaerobic digestion simulations is often questioned due to the inherent difficulty in parameterizing such complex models. Here, an extensive literature survey was combined with Monte Carlo analysis and a Gaussian Mixture Model approach to account for parameter variability, leading to a probabilistic estimate of steady-state biogas production. Predicted values of biogas yield and methane content were validated using literature data for mono-substrates before extending the simulations to new substrate combinations. Results indicated that co-digestion increases mean methane content by up to 8 % compared to mono-digestion of individual substrates. However, the predicted probability distributions are multimodal: traditional point estimates for methane production rates may be sensitive to small variations in parameter values, yielding misleading results. The results emphasize the necessity of a probabilistic approach in anaerobic digestion modeling for informed decision making, especially in subsequent techno-economic feasibility assessments.

Some novel operational laws and spherical fuzzy Choquet‐Frank operators and their application to MCDM

AbstractSome decision‐making problems are required to be analysed using insufficient and vague information under an extended space of fuzziness, considering flexibility in the decision‐making process and the interdependency and interaction between the criteria. To tackle these challenges, a spherical fuzzy set (SFS), which is an extension of the ordinary picture fuzzy set (PFS), can be used to fulfil the requirement of an extended space of fuzziness by quantifying uncertainty. Similarly, the Frank operations can provide flexibility to the decision‐making process due to the involvement of a parameter, whereas the Choquet integral considers all possible interactive characteristics between different criteria. Therefore, this paper integrates these three important concepts to make more sound and realistic decisions by extending the existing arithmetic operation for spherical fuzzy numbers (SFNs) using Frank t‐norm and t‐conorm. In addition, the paper introduces some novel Choquet‐Frank aggregation operators, such as spherical fuzzy Choquet‐Frank averaging (SFCFA) and spherical fuzzy Choquet‐Frank geometric (SFCFG). To demonstrate the effectiveness of the proposed operators, a multi‐criteria decision making (MCDM) problem related to the selection of an enterprise resource planning (ERP) system is analysed for the available five options , and . Sensitivity analysis is performed to investigate the impact of taking variation in parameter values () on aggregated values. Results confirmed that the best alternatives for SFCFA and SFCFG operators are and , respectively, while is the worst alternative for both these operators. To show the effectiveness of the proposed operators, a comparative analysis has been performed to compare the results obtained from other existing aggregation operators. Results concluded that the findings resemble most of the existing operators which confirms the feasibility of the proposed operators.

Snooker Statistics and Zipf’s Law

Zipf’s law is well known in linguistics: the frequency of a word is inversely proportional to its rank. This is a special case of a more general power law, a common phenomenon in many kinds of real-world statistical data. Here, it is shown that snooker statistics also follow such a mathematical pattern, but with varying parameter values. Two types of rankings (prize money earned and centuries scored), and three different time frames (all-time, decade, and year) are considered. The results indicate that the power law parameter values depend on the type of ranking used, as well as the time frame considered. Furthermore, in some cases, the resulting parameter values vary significantly over time, for which a plausible explanation is provided. Finally, it is shown how individual rankings can be described somewhat more accurately using a log-normal distribution, but that the overall conclusions derived from the power law analysis remain valid.

Empirical examination of financial and economic viability of Moringa oleifera production in the Bono East Region, Ghana

ABSTRACT This paper aims to assess the financial and economic viability of Moringa oleifera production as well as processing yield using data collected from 75 moringa farmers and a processing firm in the Bono East Region of Ghana. Descriptive statistics, as well as the discounted and non-discounted measures of project worth, were employed as analytical tools. A three-scenario based sensitivity analysis followed this to measure the robustness of the viability indicators to varying parameter values. The results revealed that an investment in Moringa oleifera production has a payback period of 1 year, 10 months, NPV of GH₵ 4,857.46(US$ 833), a BCR of 1.65 and an IRR of 63%, indicating Moringa oleifera production in the Bono East Region is viable. Considering the viability of this plantation and the potential it portrays for sustainable development, income generation, and job creation, we recommend that governments, Non-Governmental Organizations, and donor agencies support, invest, and encourage the cultivation, consumption, and activities of value addition to Moringa oleifera in their programs. This will lead to nutritional security, youth empowerment and liberation of villages and rural communities from intense poverty. It is also recommended that the government includes moringa plantation in its planting for food and jobs programs.

Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model

Marine biogeochemical (BGC) models are highly uncertain in their parameterization. The value of the BGC parameters are poorly known and lead to large uncertainties in the model outputs. This study focuses on the uncertainty quantification of model fields and parameters within a one-dimensional (1-D) ocean BGC model applying ensemble data assimilation. We applied an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) into a 1-D vertical configuration of the BGC model Regulated Ecosystem Model 2 (REcoM2) at two BGC time-series stations: the Bermuda Atlantic Time-series Study (BATS) and the Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED). We assimilated 5-day satellite chlorophyll-a (chl-a) concentration and monthly in situ net primary production (NPP) data for 3 years to jointly estimate 10 preselected key BGC parameters and the model state. The estimated set of parameters resulted in improvements in the model prediction up to 66% for the surface chl-a and 56% for NPP. Results show that assimilating satellite chl-a concentration data alone degraded the prediction of NPP. Simultaneous assimilation of the satellite chl-a data and in situ NPP data improved both surface chl-a and NPP simulations. We found that correlations between parameters preclude estimating parameters independently. Co-dependencies between parameters also indicate that there is not a unique set of optimal parameters. Incorporation of proper uncertainty estimation in BGC predictions, therefore, requires ensemble simulations with varying parameter values.

Automatic diagnosis of newly emerged heart failure from serial electrocardiography by repeated structuring & learning procedure

Heart failure (HF) diagnosis, typically visually performed by serial electrocardiography, may be supported by machine-learning approaches. Repeated structuring & learning procedure (RS&LP) is a constructive algorithm able to automatically create artificial neural networks (ANN); it relies on three parameters, namely maximal number of hidden layers (MNL), initializations (MNI) and confirmations (MNC), arbitrarily set by the user. The aim of this study is to evaluate RS&LP robustness to varying values of parameters and to identify an optimized combination of parameter values for HF diagnosis. To this aim, the Leiden University Medical Center HF database was used. The database is constituted by 129 serial ECG pairs acquired in patients who experienced myocardial infarction; 48 patients developed HF at follow-up (cases), while 81 remained clinically stable (controls). Overall, 15 ANNs were created by considering 13 serial ECG features as inputs (extracted from each serial ECG pair), 2 classes as outputs (cases/controls), and varying values of MNL (1, 2, 3, 4 and 10), MNI (50, 250, 500, 1000 and 1500) and MNC (2, 5, 10, 20 and 50). The area under the curve (AUC) of the receiver operating characteristic did not significantly vary with varying parameter values (P ≥ 0.09). The optimized combination of parameter values, identified as the one showing the highest AUC, was obtained for MNL = 3, MNI = 500 and MNC = 50 (AUC = 86 %; ANN structure: 3 hidden layers of 14, 14 and 13 neurons, respectively). Thus, RS&LP is robust, and the optimized ANN represents a potentially useful clinical tool for a reliable automatic HF diagnosis.

Sensitivity of Modeled Microphysics to Stochastically Perturbed Parameters

AbstractThis study examines the characteristics of several model parameter perturbation methodologies for ensemble simulations of cloud microphysical processes in convection. A simplified 1D model is used to focus the results on cloud microphysics without the complication of feedbacks to the dynamics and environment. Several parameter perturbation methods are tested, including non‐stochastic and stochastic with various distributions and parameter covariance. We find that an ensemble comprised of different time‐invariant parameters (non‐stochastic) exhibits little bias, but small spread. In addition, its behavior does not respect the time evolution of convection through its various phases. Stochastic parameter (SP) methods in which no inter‐parameter covariance is applied produce greater spread, but significant bias. The bias is particularly large for lognormal parameter perturbation distributions. The ensemble spread is retained and the bias reduced when time‐varying parameter covariance is applied. In this case, the SP scheme is able to adapt to the time and state‐dependent covariance structures and produce ensemble characteristics that are consistent with the specific microphysical processes operating at any given time. The results suggest that SP schemes would benefit from inclusion of parameter covariances, and specifically those that vary with the state of the system. It also suggests that a Normal or LogNormal SP scheme with no covariance may significantly impact the ensemble bias. Finally, the results indicate that high temporal and spatial resolution observations may be needed to characterize the variability in parameter values and covariance.

MITA interleaver for OFDM-IDMA and SCFDMA-IDMA techniques using QPSK modulation over PLC

The paper aims to examine the performance of multiplicative interleaving with tree algorithm (MITA) interleaver for grouped IDMA systems i.e., SCFDMA-IDMA and OFDM-IDMA, using QPSK modulation over powerline channel. The prime objective is to evaluate the MITA algorithm for its complexity and throughput over multiple communication systems so that it fulfills the requirements of 5G technology. Higher throughput and low complexity are achieved by the structure of MITA interleaver, in which more users are allotted interleavers per clock cycle as compared to existing interleavers primarily, random, tree, and FLRITI. The analysis is carried out in MATLAB environment for varying parameter values such as data length and user count and is plotted in terms of bit error rate (BER). Next, the effect of Convolutional coding is observed on grouped IDMA systems and lastly comparison in terms complexity is carried out. The simulation results over grouped IDMA systems show that MITA interleaver has better BER performance than FLRITI for large user count and the response further improves with greater number of users. The comparison of interleavers in terms of complexity reveals that MITA has lower complexity than FLRITI. Thus, MITA interleaver can be preferred over FLRITI and is a better option to be implemented in 5G technologies.

Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic

Among the set of parameters for which data are collected for decision-making based on artificial intelligence methods, often only some of the parameters are significant. This article compares methods for determining the significant parameters based on the theory of mathematical statistics, and fuzzy and boolean logic. The testing was conducted on several test data sets with a different number of parameters and different variability of parameter values. It was shown that for data sets with a small number of parameters (<5), the most accurate result was given for a method based on the theory of mathematical statistics and boolean logic. For a data set with a large number of parameters—the most suitable is the method of fuzzy logic.

Non-topological, topological and rogue wave Soliton solutions for Sharma Tasso Olver equation

This paper proposes three types of envelope solitons for the Sharma-Tasso-Olver equation with power-law nonlinearity. The ansatz approach is used to investigate the presence of these solitons identified during the solution derivation. As a result, we acquired the bright, singular, and dark solitons, which have a wide range of applications in applied science and engineering. Furthermore, the physical structures of the obtained solitons are investigated using two-dimensional, three-dimensional, and three-dimensional revolution plots with varying parameter values.

Agent-based modelling of the global phonographic market

Based on empirical properties of the global phonographic market, we propose a simple epidemiological model of innovation spread on a network whose structure reflects the structure of the real-world market. The global phonographic market consists of the national markets of various size and mutual interconnections. In the model, we assume that the national markets are the subnetworks that have a scale-free structure achieved by the preferential attachment construction with a node hierarchy and binary edges, while the global market is a complete, directed, weighted network of these subnetworks treated as nodes with the edges representing the cultural and geographical proximity of the national markets. “Viruses” with a defined strength or aggressiveness occur independently at one of the nodes of a selected subnetwork and correspond to a piece of music that has been released by an artist somewhere in the world. We study the virus dynamics set by varying parameter values and observe a variety of phenomena including local and global pandemics and the existence of an epidemic threshold in the subnetworks. Apart from studying the model, we also present some results on empirical data from the European and the U.K. phonographic markets, which are then utilized in the model construction.

Model Matematika SEIPRS Penyebaran Penyakit Pneumonia dengan Pengaruh Vaksinasi dan Pengobatan

Pneumonia is an infection or acute inflammation located in the lung tissue and is caused by several microorganisms, such as bacteria, viruses, parasites, fungi and even exposure to chemicals or physical damage. In this article, we discuss the SEIPRS mathematical model on the spread of pneumonia. The SEIPRS mathematical model is formed from five interacting populations, namely the Susceptible population is healthy individuals but susceptible to pneumonia which is denoted by S, the Exposed population is latent individuals or exposed to pneumonia which is denoted by E, the Infected population is individuals infected with pneumonia which is denoted by I, and the treatment population is infected individuals who are given treatment denoted by P, and the recovered population is the recovered population denoted by R. In this article, the search for equilibrium points in the SEIPRS mathematical model and stability analysis is carried out. The analysis in this model produces two equilibrium points, namely the equilibrium point without disease at the condition R0<1, the endemic equilibrium point R0>1, and the basic reproduction number (R0) as the threshold value for the spread of disease. In this study, simulations were carried out with variations in parameter values ​​to see population dynamics. Population results show that increasing rates of vaccination and treatment can reduce the rate of spread of pneumonia.