Selection Of Parameter Values Research Articles

A Novel Hybrid Intelligent Approach to Assess Blasting-Induced Overbreak Incorporating Geological Conditions in Different Tunnel Sections

Overbreak induced by tunnel blasting is a harmful phenomenon. Accurate assessment of overbreak can effectively reduce investment and ensure operational safety. In this study, a hybrid intelligent model for assessing blasting-induced overbreak is proposed which can accurately predict overbreak and effectively evaluate the importance of feature parameters. To ensure accurate prediction of overbreak, hyperparameters of four machine learning algorithms are optimized using a whale optimization algorithm. Their performance is compared based on three regression metrics: R2, RMSE, and VAF. Given the limitations of traditional feature importance analysis methods, the Shapley Additive Explanation method is used in conjunction with the random forest algorithm. After accurately predicting overbreak caused by different sections of the tunnel, the impact of each input parameter on overbreak is analyzed, and recommendations for design values of certain significant parameters are provided. The research indicates that the proposed method can accurately predict overbreak caused by actual engineering blasts and provide insights into the selection of design parameter values.

The Implementation of Bayesian Optimization for Automatic Parameter Selection in Convolutional Neural Network for Lung Nodule Classification

Lung cancer's high mortality rate makes early detection crucial. Machine learning techniques, especially convolutional neural networks (CNN), play a very important role in lung nodule detection. Traditional machine learning approaches often require manual feature extraction, while CNNs, as a specialized type of neural network, automatically learn features directly from the data. However, tuning CNN hyperparameters, such as network structure and training parameters, is computationally intensive. Bayesian Optimization offers a solution by efficiently selecting parameter values. This study develops a CNN classification model with hyperparameter tuning using Bayesian Optimization, achieving a 97.2% accuracy. Comparatively, Particle Swarm Optimization and Genetic Algorithm methods each resulted in 96.4% accuracy. The research concludes that Bayesian Optimization is an effective approach for CNN hyperparameter tuning in lung nodule classification.

Simulation of peak properties in thermoluminescence dosimeters with the potential stimulation of all electron traps

In thermoluminescence (TL) models, the competition between energy levels for electron trapping is a direct consequence of conduction band mediation in electron transport. From a theoretical perspective, these competitive effects are considered responsible for many TL phenomena. Furthermore, while they are easily detectable in simulation studies, there are no clear and reliable criteria for their experimental verification. In the present work, it is suggested that in certain materials with narrow band gap energies, existing electron traps can be accessed by thermal stimulation up to 500 °C. It is possible to predict properties through simulation that can then be verified experimentally. The simulation is applied to a complex TL glow curve consisting of four electron trapping levels and one luminescence center. With the appropriate selection of simulation parameter values, the competition is limited solely to electron transport, rather than to significant differences in parameter values. The simulation indicated that the last peak serves as the primary competitor. Furthermore, the shape of the TL glow curve depends on the strength of the competition. When the competitor is strong, the lower temperature peaks behave as first-order peaks, and the shape of the glow curve remains stable as a function of dose. The last peak, which acts as the final competitor, exhibits behavior similar to that of the peak derived from the one trap, one recombination (OTOR) model. This OTOR-like behavior provides an excellent experimental test for the simulation results and, consequently, for the model used. The validity of the simulation was assessed by comparing the input values of kinetic parameters with the output values obtained from computerized glow curve deconvolution (CGCD) analysis for each TL glow curve.

Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627

Abstract. New (a)biotic conditions resulting from climate change are expected to change disturbance dynamics, such as windthrow, forest fires, droughts, and insect outbreaks, and their interactions. These unprecedented natural disturbance dynamics might alter the capability of forest ecosystems to buffer atmospheric CO2 increases, potentially leading forests to transform from sinks into sources of CO2. This study aims to enhance the ORCHIDEE land surface model to study the impacts of climate change on the dynamics of the bark beetle, Ips typographus, and subsequent effects on forest functioning. The Ips typographus outbreak model is inspired by previous work from Temperli et al. (2013) for the LandClim landscape model. The new implementation of this model in ORCHIDEE r8627 accounts for key differences between ORCHIDEE and LandClim: (1) the coarser spatial resolution of ORCHIDEE; (2) the higher temporal resolution of ORCHIDEE; and (3) the pre-existing process representation of windthrow, drought, and forest structure in ORCHIDEE. Simulation experiments demonstrated the capability of ORCHIDEE to simulate a variety of post-disturbance forest dynamics observed in empirical studies. Through an array of simulation experiments across various climatic conditions and windthrow intensities, the model was tested for its sensitivity to climate, initial disturbance, and selected parameter values. The results of these tests indicated that with a single set of parameters, ORCHIDEE outputs spanned the range of observed dynamics. Additional tests highlighted the substantial impact of incorporating Ips typographus outbreaks on carbon dynamics. Notably, the study revealed that modeling abrupt mortality events as opposed to a continuous mortality framework provides new insights into the short-term carbon sequestration potential of forests under disturbance regimes by showing that the continuous mortality framework tends to overestimate the carbon sink capacity of forests in the 20- to 50-year range in ecosystems under high disturbance pressure compared to scenarios with abrupt mortality events. This model enhancement underscores the critical need to include disturbance dynamics in land surface models to refine predictions of forest carbon dynamics in a changing climate.

Investigation of optical soliton solutions for the cubic-quartic derivative nonlinear Schrödinger equation using advanced integration techniques

This paper aims to investigate optical soliton solutions in the context of the cubic-quartic derivative nonlinear Schrödinger equation with differential group delay, incorporating perturbation terms for the first time. Motivated by the need to better understand soliton dynamics in advanced optical communication systems, we employ three integration techniques: the direct algebraic approach, Kudryashov’s method with an addendum, and the unified Riccati equation expansion method. Our study reveals that, by appropriately selecting parameter values, the resulting solutions include Jacobi elliptic functions that describe straddle solitons, bright, dark, and singular solitons. We also identify the conditions under which these soliton pulses can exist. Furthermore, we provide numerical simulations to illustrate these solutions under specific parameter settings, highlighting their potential applications in optical fiber systems.

On the Feasibility and Benefits of Extensive Evaluation

Benchmark and system parameters often have a significant impact on performance evaluation, which raises a long-lasting question about which settings we should use. This paper studies the feasibility and benefits of extensive evaluation. A full extensive evaluation, which tests all possible settings, is usually too expensive. This work investigates whether it is possible to sample a subset of the settings and, upon them, generate observations that match those from a full extensive evaluation. Towards this goal, we have explored the incremental sampling approach, which starts by measuring a small subset of random settings, builds a prediction model on these samples using the popular ANOVA approach, adds more samples if the model is not accurate enough, and terminates otherwise. To summarize our findings: 1) Enhancing a research prototype to support extensive evaluation mostly involves changing hard-coded configurations, which does not take much effort. 2) Some systems are highly predictable, which means that they can achieve accurate predictions with a low sampling rate, but some systems are less predictable. 3) We have not found a method that can consistently outperform random sampling + ANOVA. Based on these findings, we provide recommendations to improve artifact predictability and strategies for selecting parameter values during evaluation.

The statistical building blocks of animal movement simulations.

Animal movement plays a key role in many ecological processes and has a direct influence on an individual's fitness at several scales of analysis (i.e., next-step, subdiel, day-by-day, seasonal). This highlights the need to dissect movement behavior at different spatio-temporal scales and develop hierarchical movement tools for generating realistic tracks to supplement existing single-temporal-scale simulators. In reality, animal movement paths are a concatenation of fundamental movement elements (FuMEs: e.g., a step or wing flap), but these are not generally extractable from a relocation time-series track (e.g., sequential GPS fixes) from which step-length (SL, aka velocity) and turning-angle (TA) time series can be extracted. For short, fixed-length segments of track, we generate their SL and TA statistics (e.g., means, standard deviations, correlations) to obtain segment-specific vectors that can be cluster into different types. We use the centroids of these clusters to obtain a set of statistical movement elements (StaMEs; e.g.,directed fast movement versus random slow movement elements) that we use as a basis for analyzing and simulating movement tracks. Our novel concept is that sequences of StaMEs provide a basis for constructing and fitting step-selection kernels at the scale of fixed-length canonical activity modes: short fixed-length sequences of interpretable activity such as dithering, ambling, directed walking, or running. Beyond this, variable length pure or characteristic mixtures of CAMs can be interpreted as behavioral activity modes (BAMs), such as gathering resources (a sequence of dithering and walking StaMEs) or beelining (a sequence of fast directed-walk StaMEs interspersed with vigilance and navigation stops). Here we formulate a multi-modal, step-selection kernel simulation framework, and construct a 2-mode movement simulator (Numerus ANIMOVER_1), using Numerus RAMP technology. These RAMPs run as stand alone applications: they require no coding but only the input of selected parameter values. They can also be used in R programming environments as virtual R packages. We illustrate our methods for extracting StaMEs from both ANIMOVER_1 simulated data and empirical data from two barn owls (Tyto alba) in the Harod Valley, Israel. Overall, our new bottom-up approach to path segmentation allows us to both dissect real movement tracks and generate realistic synthetic ones, thereby providing a general tool for testing hypothesis in movement ecology and simulating animal movement in diverse contexts such as evaluating an individual's response to landscape changes, release of an individual into a novel environment, or identifying when individuals are sick or unusually stressed.

Existence of static wormhole solutions in [formula omitted] gravity

This study explores wormhole solutions by analyzing energy conditions within the framework of f(R,A) modified theory of gravity, which is derived from the inverse of the Ricci tensor. For this purpose, we consider a linear model, defined as f(R,A)=R+αA, where R denotes the Ricci scalar, α is the coupling parameter, and A represents the anticurvature scalar. We further introduce a specific form of shape function to examine the characteristics of energy conditions for the distribution of anisotropic matter. Moreover, we examine the behavior of shape function and energy conditions for both isotropic and barotropic matter content as well. Through the selection of suitable parameter values, it has been shown that energy conditions are breached in some specified regions, indicating the existence of exotic matter. The thorough analysis confirms the presence of wormhole geometries in f(R,A) gravity.

Optimizing the performance of a new current source type grid-forming modular multilevel converter using genetic algorithms

Modular Multilevel Converters (MMC) have found extensive application in connecting Renewable Energy Sources to the power grid. Recently, MMCs operated in the Grid Forming (GFM) mode have garnered significant interest due to their superior performance in weak AC networks. The Virtual Synchronous Generator (VSG) is a GFM technology designed to emulate the behavior of a real synchronous generator (SG). There is flexibility in selecting the VSG’s parameters as they are not constrained by the physical attributes of an actual SG. This paper proposes employing the Genetic Algorithm (GA) to select these parameters, underscoring its simplicity and effectiveness in meeting diverse constraints and objectives. Small Signal (SS) models are constructed at potential operational points, with GA leveraging the SS model’s eigenvalues to select parameter values that enhance dynamic performance, ensuring resilience across a wide range of operating conditions. The effectiveness of these designed parameters is validated through Electromagnetic Transients (EMT) simulations.

Effect of Cremophor RH40, Hydroxypropyl Methylcellulose, and Mixing Speed on Physicochemical Properties of Films Containing Nanostructured Lipid Carriers Loaded with Furosemide Using the Box-Behnken Design.

This study aimed to examine the characteristics of H-K4M hydroxypropyl methylcellulose (HPMC) films containing nanostructured lipid carriers (NLCs) loaded with furosemide. A hot homogenization technique and an ultrasonic probe were used to prepare and reduce the size of the NLCs. Films were made using the casting technique. This study used a Box-Behnken design to evaluate the influence of three key independent variables, specifically H-K4M concentration (X1), surfactant Cremophor RH40 concentration (X2), and mixing speed (X3), on the physicochemical properties of furosemide-loaded NLCs and films. The furosemide-loaded NLCs had a particle size ranging from 54.67 to 99.13 nm, and a polydispersity index (PDI) ranging from 0.246 to 0.670. All formulations exhibited a negative zeta potential, ranging from -7.05 to -5.61 mV. The prepared films had thicknesses and weights ranging from 0.1240 to 0.2034 mm and 0.0283 to 0.0450 g, respectively. The drug content was over 85%. Film surface wettability was assessed based on the contact angle, ranging from 32.27 to 68.94°. Film tensile strength varied from 1.38 to 7.77 MPa, and their elongation at break varied from 124.19 to 170.72%. The ATR-FTIR analysis confirmed the complete incorporation of the drug in the film matrix. Therefore, the appropriate selection of values for key parameters in the synthesis of HPMC films containing drug-loaded NLCs is important in the effective development of films for medical applications.

A study on the dose evaluation of critical groups for unrestricted reuse of the site after the decommissioning of Kori Unit 1

In the calculation of DCGLs using RESRAD code, the selection of input parameter values is paramount, which is basically possible when specific measures for selecting reuse scenarios and probabilistic evaluation methods are established. When referring to the impact of the age group considered in the environmental impact assessment among the existing operations of the plant, it may also need to be applied in the decommissioning project, and there will be considerations along with site reuse scenarios and probabilistic evaluations. Therefore, the main purpose of this study is to distinguish the types of exposure affected by residual radioactivity contamination during the process of deriving DCGL to be used in the final site release stage of NPPs and to evaluate them by reasonably assigning age group parameter values. In addition, the case of entering a single value for parameter values by age group and the case of entering probabilistic distributions were compared and analyzed.

MULTI-OBJECTIVE GENETIC ALGORITHM FOR THE ASSEMBLY LINE WORKER ASSIGNMENT AND BALANCING PROBLEM: A CASE STUDY IN THE AUTOMOTIVE SUPPLY INDUSTRY

The primary challenge in assembly line design is the need for more appropriately allocating tasks and workers to workstations. This study addresses the problem of line balancing and worker assignments, considering the performance disparities among workers during the line balancing process. In the relevant literature, this problem is known as the Assembly Line Worker Assignment and Balancing (ALWAB) problem. This research examines a multi-objective ALWAB Type-2 problem, simultaneously evaluating cycle time and squared load assignment objectives. The study is conducted based on a real-life scenario in a sub-industry automotive industry that manufactures cable equipment. To solve this problem, a multi-objective genetic algorithm approach is proposed. Recognising that the selection of parameter values will influence the algorithm’s performance, parameter calibration has been performed. A full factorial experimental design and the irace method have been utilised for this purpose. The results are compared with those using parameter values utilised for similar problems in the literature. Furthermore, a sensitivity analysis has been carried out to examine the impact of various relative weight values of the objectives on the result. The results indicate that the experimental design generally yields superior results compared to other methods.

Quantitative Analysis of the Complex Time Evolution of a Camphor Boat

The motion of a camphor boat on the water’s surface is a long-studied example of the direct transformation of chemical energy into a mechanical one. Recent experimental papers have reported a complex character of boat motion depending on the location of the camphor source. If the source is close to the stern, the boat moves at a constant speed. When it is shifted towards the boat center, oscillations of speed are observed. When the source is close to the boat center, pulses of speed followed by oscillations appear. Here, we focus on numerical simulations of camphor boat motion. We discuss approximations that allow us to reduce the numerical complexity of the problem and formulate a model in which the equation for boat velocity is coupled with a one-dimensional reaction–diffusion equation for camphor surface concentration. We scanned the phase space of model parameters and found the values that give qualitative agreement with the experiments. The model predicts all types of boat motion (continuous, oscillating, and pulsating) observed in experiments. Moreover, the model with selected parameter values shows that for specific locations of the camphor source, a spike in speed is followed by transient oscillations, which are an inherent part of speed relaxation.

Influence of the number of attribute binarization thresholds on the quality of the generated set of rules for the method of logical analysis of data

For efficient operation of machine learning methods, it is necessary to set their parameters up properly. Both the computational complexity and the accuracy of the method for the problem being solved can depend on the selected parameter values. The paper discusses the method of logical analysis of data, which is used to solve classification problems and consists of a number of steps. At each step, it is necessary to set the method parameters up to suit the problem being solved. When setting parameters, one should be guided by a compromise between the accuracy of the method and the computational complexity. With comparable values for classification accuracy of several variants of the method implementation, preference will always be given to the simplest of them in terms of computational complexity. Since the method works only with binary characteristics, at the first stage it is necessary to binarize quantitative characteristics. The binarization procedure is associated with the choice of the “number of binarization thresholds” parameter. This paper proposes an experimental approach to determine the best value of the specified method parameter for the problem being solved.

Tuning or Control Parameter Values in Evolutionary Art

The article is devoted to the selection of parameter values of the evolutionary algorithm for creating new images. Based on simple geometric figures, an impression of them is created using the classical approach of the evolutionary algorithm, and these obtained results are subjected to genotypic (a measure of non-overlap with the edge matrix) and phenotypic evaluation: using for this purpose measures of aesthetics formed on the basis of Benford’s law and fractal dimension. The aim of the paper is to find optimal values of the algorithm’s parameters so that the aesthetics evaluation measures are satisfactory. The paper also shows the detected relationships by analyzing Pearson correlation measures.

An Optimal Eighth-Order One-Parameter Single-Root Finder: Chaotic Dynamics and Stability Analysis

In this paper, we focus on a class of optimal eighth-order iterative methods, initially proposed by Sharma et al., whose second step can choose any fourth-order iterative method. By selecting the first two steps as an optimal fourth-order iterative method, we derive an optimal eighth-order one-parameter iterative method, which can solve nonlinear systems. Employing fractal theory, we investigate the dynamic behavior of rational operators associated with the iterative method through the Scaling theorem and Möbius transformation. Subsequently, we conduct a comprehensive study of the chaotic dynamics and stability of the iterative method. Our analysis involves the examination of strange fixed points and their stability, critical points, and the parameter spaces generated on the complex plane with critical points as initial points. We utilize these findings to intuitively select parameter values from the figures. Furthermore, we generate dynamical planes for the selected parameter values and ultimately determine the range of unstable parameter values, thus obtaining the range of stable parameter values. The bifurcation diagram shows the influence of parameter selection on the iteration sequence. In addition, by drawing attractive basins, it can be seen that this iterative method is superior to the same-order iterative method in terms of convergence speed and average iterations. Finally, the matrix sign function, nonlinear equation and nonlinear system are solved by this iterative method, which shows the applicability of this iterative method.

Freight car model with Y25 bogies stability analysis

Y25 (25Tn) bogies have been used in freight cars for over 50 years. It is a proven design with a positive opinion in many European countries. In addition to the generally positive opinion about the operational properties of the Y25 bogies, there are sometimes publications whose authors notice certain imperfections related mainly to the so-called running instability, resulting in derailment under certain motion conditions. This paper presents results simulation of motion tests of a freight car with Y25 bogies. A freight car model was created using the VI-Rail software. The research focus on determining the critical velocity and examining nature of the model solutions in the overcritical velocity range. Straight track sa well as curved track motion is analysed. Empty and full wagon properties are considerd. Impact of changes the suspension system selected parameters values on vehicle model solutions stability as well as the vehicl impact on track was analysed.

Mathematical model of anaerobic propionate degradation by incorporating the r/K selection theory

Although anaerobic digestion mathematical model (ADMM) has been widely applied for system optimization and mechanism understanding, the selection of key kinetic and stoichiometric parameter values for ADMM is still a challenge. An ADMM incorporating the r/K selection theory was proposed to examine the enrichment of r- and K-strategists in completely mixed reactors (CMRs) and sequencing batch reactors (SBRs). With the influent propionate concentration (PC) of 0.75 g COD/L, K-strategists predominated at the sludge retention time (SRT) higher than 13 d for propionate oxidizing bacteria (POB) and 9 d for acetoclastic methanogens (AM) in CMRs, while r-strategists dominated at the SRT less than 20 d for POB and 5 d for AM in SBRs. Under the SRT of 20 d, in CMRs, both K-strategists of POB and AM increased with increasing PCs, while r-strategists of POB only appeared when the PC exceeded 6 g COD/L and r-strategists of AM could not be enriched under all PC conditions; in SBRs, K-strategists of POB and AM showed the superiority when the PC was lower than 0.5 and 1.5 g COD/L, respectively. Only K-strategists of hydrogenotrophic methanogens (HM) were enriched in all simulated scenarios. Based on sensitivity analysis, SRT and the first order decay rate had a high influence on the enrichment of HM, AM, and POB. Overall, the selection of r-/K-strategists of POB and AM could be achieved by regulating SRT, operational mode, and PC.

Adaptive Methods or Variational Inequalities with Relatively Smooth and Reletively Strongly Monotone Operators

The article is devoted to some adaptive methods for variational inequalities with relatively smooth and relatively strongly monotone operators. Based on the recently proposed proximal version of the extragradient method for this class of problems, we study in detail the method with adaptively selected parameter values. An estimate for the rate of convergence of this method is proved. The result is generalized to a class of variational inequalities with relatively strongly monotone δ-generalized smooth variational inequality operators. For the problem of ridge regression and variational inequality associated with box-simplex games, numerical experiments were performed demonstrating the effectiveness of the proposed method of adaptive selection of parameters during the running of the algorithm.

Boosting VLSI Design Flow Parameter Tuning with Random Embedding and Multi-objective Trust-region Bayesian Optimization

Modern very large-scale integration (VLSI) design requires the implementation of integrated circuits using electronic design automation (EDA) tools. Due to the complexity of EDA algorithms, there are numerous tool parameters that have imperative impacts on the chip design quality. Manual selection of parameter values is excessively laborious and constrained by experts’ experience. Due to the high complexity and lack of parallelization, most existing parameter tuning methods cannot make sufficient exploration in a large search space. In this article, we boost the efficiency and performance of parameter tuning with random embedding and multi-objective trust-region Bayesian optimization. Random embedding can effectively cut down the number of variables in the search process and thus reduce the runtime of Bayesian optimization. Multi-objective trust-region Bayesian optimization allows the algorithm to explore diverse solutions with excellent parallelism. Due to the ability to do more exploration in limited runtime, the proposed framework can achieve better performance than existing methods in our experiments.