Parametrized Research Articles

The influences of vertical load on lateral dynamic responses of end-bearing friction pile

This paper revisits the influences of vertical load on the lateral dynamic responses of a single pile with rigorous analytical methods. The vertical load of pile is supported by the combination of end-bearing and side friction that develops along the pile length. The equilibrium equations of surrounding soil are analyzed by making use of the continuum-based solution, whereas the pile is modeled by the conventional Euler-Bernoulli beam theory. According to the assumption of perfect contact between the pile and the soil, the axial force function is deduced, which yields the analytical forms of vertical load and mechanical parameters of the pile-soil system. Using the formulation of lateral soil resistance and taking into account the influences of vertical load, the dynamic responses along the end-bearing friction pile are subsequently achieved via the Adomian decomposition method. The results obtained in this scheme were compared with analytical techniques available in the previous literature, which are applicable to the dynamic responses in the absence of vertical load. The results of numerical analysis indicate that the influences of vertical load on both the lateral displacement and the bending moment of the pile could be significant and, in general, enable them to undergo remarkable redistribution.

Controlling chaotic dynamics of a delayed Hassell–Varley type predator–prey model with non-linear harvesting efforts in prey by using imprecise biological parameters

In this article, we have studied the dynamical behaviors of a delayed predator–prey model with non-linear harvesting effort using imprecise parameters. A method has been proposed for dealing with these imprecise parameters using the parametric form of the interval numbers. The proposed predator–prey model represents the Hassell–Varley predation and Michaelis–Menten prey harvest. We have studied the feasibility of various equilibrium points and their stability. The study reveals that the delay model exhibits stable limit cycle fluctuations due to variation in the delay parameter. Numerical simulations are performed to illustrate all analytical results.

Scanning for dark matter subhaloes in Hubble Space Telescope imaging of 54 strong lenses

ABSTRACT The cold dark matter (DM) model predicts that every galaxy contains thousands of DM subhaloes; almost all other DM models include a physical process that smooths away the subhaloes. The subhaloes are invisible, but could be detected via strong gravitational lensing, if they lie on the line of sight to a multiply imaged background source, and perturb its apparent shape. We present a predominantly automated strong lens analysis framework, and scan for DM subhaloes in Hubble Space Telescope imaging of 54 strong lenses. We identify five DM subhalo candidates, including two especially compelling candidates (one previously known in SLACS0946 + 1006) where a subhalo is favoured after all of our tests for systematics. We find that the detectability of subhaloes depends upon the assumed parametric form for the lens galaxy’s mass distribution, especially its degree of azimuthal freedom. Using separate components for DM and stellar mass reveals two DM subhalo candidates and removes four false positives compared to the single power-law mass model that is common in the literature. We identify 45 lenses without substructures, the number of which is key to statistical tests able to rule out models of, for example, warm or self-interacting DM. Our full analysis results are available at https://github.com/Jammy2211/autolens_subhalo.

Parameterizing Debt Maturity

AbstractWe examine ways to describe the maturity structure of public debts using few parameters. We compile a novel data set of all promised future payments for U.S. and UK government debt from every month since 1869, and more recently for Peru, Poland, Egypt, and Nigeria. We show there is a unique parametric form which does not arbitrarily restrict debt issuance. We use this model to parsimoniously describe the evolution of public debt maturities and to characterize the relationship between maturity and the term structure of interest rates in the United States since 1940.

Probing accelerating cosmos via reconstructed Hubble parameter and its influence on f(T) gravity models

This article introduces a new way of studying the accelerating cosmos without relying on specific models. It proposes a new parametric form of the Hubble parameter (HP) and uses a Markov Chain Monte Carlo (MCMC) approach to determine the model parameters. The analysis incorporates a comprehensive dataset consisting of 34 cosmic chronometers (CC) data points, 40 non-correlated baryonic acoustic oscillations (BAO) points, and 1701 updated Pantheon+ supernovae type Ia (SNeIa) data points. The study investigates various aspects of the model's behavior, such as the transition from deceleration to acceleration, and the evolution of jerk and snap parameters. Constraints are applied to the model parameters as well as the Hubble constant (H0). Additionally, the parameter values obtained are used to study f(T) gravity models and compare them with observational data. This research provides valuable insights into the nature of the accelerating Universe and highlights the importance of using a parametrized approach in cosmological studies.

Water Demand Modeling using Machine Learning Method in Bandung City, Indonesia

This research was conducted at Bandung City with the aim of building a model using machine learning methods so that it can estimated clean water demands in Bandung City, as well as knowing the external factors that are considered to affect the model. Machine learning is a part of Artificial Intelligence (AI) discipline. The modeling is carried out using independent variables in the form of climate parameters which are rainfall, rainy days, and humidity, as well as the dependent variable in the form of drinking water needs which are represented by raw water. Data collection is done through secondary data. The model was built by using the TPOT module, and produces the AdaBoost.R2 algorithm as the most optimal model, by using the model algorithm, the best sub-model is produced with the most influential external factors, namely rainy days and humidity which has an MAE of 326,077.70 and a MAPE of 4.75%. This model is compared with the ARIMA model which has an MAE of 330,672.088 and an MAPE of 5.07%.

Solution of generalized fractional Jaulent–Miodek model with uncertain initial conditions

This paper analyses a coupled system of generalized coupled system of fractional Jaulent–Miodek equations, including uncertain initial conditions with fuzzy extension. In this regard, an extension of the homotopy with a generalized integral algorithm is adopted for a class of time-fractional fuzzy Jaulent–Miodek models by mixing the fuzzy q-homotopy analysis algorithm with a generalized integral transform and Caputo fractional derivative. The triangular fuzzy numbers (TFNs)are expressed in double parametric form using κ-cut and r-cut and utilized to explain the uncertainties arising in the initial conditions of highly nonlinear differential equations with generalized Hukuhara differentiability (gH-differentiability). The TFNs are controlled by the κ-cut and r-cut, and the variability of uncertainty is examined using a “triangular membership function” (TMF). The results are analyzed by finding the solutions for different spatial coordinate values of time with κ-cut and r-cut for both lower and upper bounds and validated through numerical and graphical representations in crisp cases. Finally, it can be seen that the uncertain probability density function rapidly decreases at the left and right edges when the fractional order is increased, and it is observed that the obtained solutions are more accurate than the existing results through the Hermite wavelet method in the literature.

Fuzzy-based adaptive event-triggered control for nonlinear cyber-physical systems against deception attacks via a single parameter learning method

This paper investigates the adaptive fuzzy control issue of cyber-physical systems (CPSs) against unknown deception attacks and external disturbance. Based on the backstepping design framework, the uncertainty term composed of the approximation error term generated by the fuzzy logic system, the external disturbance and the deception attack signals are regarded as a whole in the recursive design process. Then, by combining the single parameter learning method with the adaptive fuzzy technique, the uncertain terms containing approximation error, disturbance and injection attacks can be transformed into the form of linear parameterization with only one unknown scalar parameter, which greatly reduce the calculation process. In addition, the dynamic event-triggered adaptive control technology can be integrated into the control design to reduce the amount of data transmission. Theoretical analysis shows that all the signals in the closed-loop system are bounded under the proposed control scheme, and the Zeno behavior is excluded. Finally, the two-stage chemical reactor and mass-spring-damper systems are employed to demonstrate the validity of the proposed adaptive fuzzy control solution.

Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network

Microstructural inhomogeneity in additively manufactured (AM) components leads to uncertainty in their fatigue performance. While purely data-driven methods can only provide deterministic outcomes and lack physical interpretability. Furthermore, considering the dispersion of fatigue life, a probabilistic neural network framework integrating physical information, namely a physics-informed probabilistic neural network (PIPNN), is proposed for predicting the fatigue life of AM parts. The framework describes the dispersion of fatigue life in the parametric form of probability statistics. It incorporates physical laws and models to constrain neurons and loss function, enabling the network to learn deeper physical laws that align with the fatigue process, thus enhancing the interpretability and prediction reliability of the model. Fatigue experiments were performed on Hastelloy X superalloy specimens fabricated using laser powder bed fusion, serving as the basis for validating and comparing the PIPNN model with a probabilistic neural network. The results indicate that PIPNN adeptly captures the heteroskedasticity of fatigue life and exhibits superior prediction accuracy and more reliable prediction performance in fatigue-life prediction. PIPNN offers a physically consistent method for fatigue-life prediction considering probabilistic statistics.

Inverse scattering transform for the coupled modified complex short pulse equation: Riemann–Hilbert approach and soliton solutions

In this paper, we develop a Riemann–Hilbert (RH) approach to the inverse scattering transform for the coupled modified complex short pulse (cmcSP) equation, which appeared as a reduction of the four-component system of short pulse type introduced by Popowicz in 2017. This approach allows us to present the general soliton solutions of the cmcSP equation in parametric form. Compared with the early results for the scalar modified complex short pulse equation, the cmcSP equation possesses richer soliton solutions, which include smooth solitons, cuspons, breathers and their various interactions.

Развитие методики оценки финансовых рисков как фактор обеспечения экономической безопасности предприятий

Due to the increasing role of financial risks of various nature and orientation, the paper formulates an approach to defining this definition. Financial risks are decomposed in terms of various classification features. The key financial risks in relation to the companies that are representatives of the oil and gas sector as a pivotal sector for the Russian economy have been specified. The analysis of modern approaches to the assessment of fi-nancial risks allows to conclude the priority of using probit- and logit-regression scoring models for these aims due to the high level of accuracy and simplicity of interpretation of their results. Therefore, as part of the re-search, a scoring model has been developed, which provides the possibility of carrying out a comprehensive assessment of financial risk. Based on the analysis of corporate reporting of the leading domestic oil and gas companies, the proposed model was tested and calibrated, its final form and weight coefficients of parameters were determined. The results obtained from the analysis of financial risks make it possible to draw a conclu-sion about the fairness of the model and its high reliability.

Solving time-independent Schrödinger equation variationally using random numbers

Finding wavefunctions for even the simplest of interacting particle systems consisting of two particles is extremely difficult. It is therefore highly desirable that an accurate and easily implementable method be available to instructors and students of quantum-mechanics for obtaining wavefunctions for these particles. The usual approach taken to do this is to use parametrized functional form for the wavefunction in conjunction with the variational method to find approximate wavefunction and energy for the ground-state of such systems. In this paper, we employ random numbers to obtain ground-state wavefunctions and energies of two interacting particles in different one-dimensional potentials. The idea behind using random numbers is to search freely for functions that lead to lower and lower energy, converging eventually to its lowest value. The method presented is easily applicable numerically using a simple algorithm, and the wavefunctions obtained are highly accurate. Thus, the method presented makes study of two interacting particles accessible to instructors and students alike in a transparent manner.

Insights into Hildebrand Solubility Parameters - Contributions from Cohesive Energies or Electrophilicity Densities?

We introduce certain concepts and expressions from conceptual density functional theory (DFT) to study the properties of the Hildebrand solubility parameter. The original form of the Hildebrand solubility parameter is used to qualitatively estimate solubilities for various apolar and aprotic substances and solvents and is based on the square root of the cohesive energy density. Our results show that a revised expression allows the replacement of cohesive energy densities by electrophilicity densities, which are numerically accessible by simple DFT calculations. As an extension, the reformulated expression provides a deeper interpretation of the main contributions and, in particular, emphasizes the importance of charge transfer mechanisms. All calculated values of the Hildebrand parameters for a large number of common solvents are compared with experimental values and show good agreement for non- or moderately polar aprotic solvents in agreement with the original formulation of the Hildebrand solubility parameters. The observed deviations for more polar and protic solvents define robust limits from the original formulation which remain valid. Likewise, we show that the use of machine learning methods leads to only slightly better predictability.

A generalized closed-form model of cutting energy for arbitrary-helix cylindrical milling tools and its applications

The knowledge of energy consumption of different machine tool production processes leading to products is necessary for energy labeling of machined parts in the increasingly sustainability-aware world thus the need for better machining energy modeling techniques. The milling process dynamics is complicated thus numerical and averaging techniques are hitherto usually applied in the cutting energy modeling thus limiting decision-making. This work proposes a generalized force-based closed-form model for the milling process cutting energy. To the best of the author’s knowledge, the model is the first closed-form cutting energy model for milling which not only applies to the conventional cylindrical milling tools with constant helix angle but also to cylindrical milling tools with any helix angle variation. The demonstrated applications of the proposed model include modeling of milling machine electrical energy consumption, modeling/optimization of milling project energy/efficiency and helix angle optimization for passive reduction of cutting energy. The proposed model is checked with experimentally-verified results in literature. For example, the model agrees with numerically computed cutting energy in literature by absolute error of 0.0320%–0.4025% and modeling of milling machine electrical energy consumption using the proposed model recorded the goodness-of-fit indices of 0.9980 [Formula: see text]-value and −0.1271 mean percentage error compared to a published experimental data. A parametric plot and an optimization based on genetic algorithm showed that increase of helix angle increases cutting energy due to increased influence of edge forces, and the effect is more pronounced at higher helix angles. Various potential applications of the presented model are highlighted in the concluding section.

Phase transition of Bianchi-type I cosmological model in [formula omitted] gravity

In the context of the f(T) theory of gravity, where T represents as a torsion scalar, we explore the Locally Rotational Symmetric Bianchi type-I dark energy model. An effective cosmological model is used, and its isotropization is examined, producing a conclusion that accurately depicts the evolution of our Universe. We looked at dark energy (DE) and dark matter (DM) in both interacting and non-interacting forms. The exact solution of the field equations was achieved by using special form of the time-varying deceleration parameter in terms of the Hubble parameter. The dark matter energy density (Ω(dm)) decreases with redshift while dark energy density (Ω(de)) grow with redshift. The physical and geometrical behaviour of the models are examined in both forms. A combination of dark matter and dark energy can eliminate the coincidence issue in the ΛCDM model. Using the most recent observations, we also discussed the physical characteristics of the generated models about the early Universe and late-time acceleration. It has been noted that this model has an initial singularity, initial decelerating growth and late-time transitions to an accelerating expansion phase.

Improving Chest Monitoring through Magnetic Resonance Angiogram Image Contrast Enhancement.

Magnetic resonance angiography is a medical procedure used to offer an image of the blood vessels and organs of the body. Given the worldwide spread of cardiovascular diseases, more and more resources are invested in treating them. One of the most modern treatments involves the acquisition of images of the heart. Sometimes the contrast of these images is not satisfactory. Injecting invasive enhancement substances to obtain a better view of the cardiac route is not advisable. However, software algorithms can solve the problem. This study proposes and tests a local adaptive contrast-adjustment algorithm using the dual-tree complex wavelet transform. The method has been tested with medical data from a public database to allow comparisons to other methods. The selected algorithm further improved the contrast of images. The performances are given for evaluation, both visually (to help doctors make accurate diagnoses) and in parametric form (to show engineers which parts of the algorithm might need improvement). Compared to other contrast enhancement methods, the proposed wavelet algorithm shows good results and greater stability. Thus, we aim to avoid future pointless complications due to unnecessary contrast substances.

Constrained [formula omitted]CDM dark energy models in higher derivative [formula omitted]-gravity theory

In the present paper, we have investigated dark energy scenarios in higher derivative f(R,Lm) gravity theory in a flat spacetime universe by constraining cosmographic series of Hubble function with Hubble constant H(z) datasets. We have considered an arbitrary function f(R,Lm)=γR2+λLmn, where R is the Ricci-scalar, Lm is matter Lagrangian, γ, λ and n are non-vanishing parameters. We have obtained contour plots of model parameters for the best fit values of (H0,q0,j0,s0,γ,λ,n,Ωm0) with 1−σ and 2−σ errors with H(z) datasets, and using these best fit model parameters, we have tried to explore the effective dark energy properties of the universe by defining the terms ρeff, peff and ωeff. We have found the values of effective equation of state parameter ωeff≈−0.96 at present and tends to cosmological constant value at late-time. We have found that the dark energy density term ρΛ∼Λ and it behaves just like time-dependent cosmological constant Λ-term. We have found a constrained ΛCDM transit phase expanding and currently accelerating dark energy model. Also, we have estimated the present age of the universe as t0=14.28 Gyrs.

Developing a model of the radiating surface of a flame over a flammable liquid spill in the presence of wind

The object of the study is a spill fire. The subject of the study is the geometric characteristics of the flame, in particular, the length and angle of inclination. The model of the radiating surface of a flame over a burning liquid spill of an arbitrary shape is constructed. The essence of the approach is that the length of the flame at a given point is equal to the length of the flame at the point of the circular spill located at the same distance from the boundary of the spill. It allows generalizing the known empirical dependences for the case of spills of arbitrary shape. The flame length is a power-law function of the distance to the spill boundary and the mass loss rate per unit area. To take into account the effect of wind on the shape of the flame, the empirical dependence of the length and angle of inclination of the flame on the wind speed is used. It is assumed that the wind deforms the flame in such a way that all points of the flame surface deviate by the same angle from the vertical. Wind inclines the flame from the vertical axis more significantly for the smaller size of the spill and smaller mass loss rate per unit area. This is due to the formation of more powerful upward currents over the combustion center when its size and intensity of liquid combustion increase. A model of the radiating surface of the flame was constructed in a parametric form. The results obtained from the model are in good agreement with the experimental ones. The relative error for the angle of deviation of the flame by the wind from the vertical axis does not exceed 9%. In practice, this opens up opportunities for calculating the thermal impact on nearby technological objects, as well as determining safe zones for the location of personnel and equipment involved in fire suppression. The model can be used to specify the thermal effect of fire on steel and concrete structures.

Near-time optimal feedrate planning for the NURBS curve considering interpolation error constraints

Compared with general time-optimal robot feedrate planning, trajectory machining tasks require consideration of feedrate command constraints and the complexity of parameter spline curves. The interpolation error generated during the interpolation stage affects the command accuracy and contour accuracy of the trajectory. To ensure that the robot end-effector feedrate maintains consistency with the feedrate command and to improve the contour accuracy of the trajectory, this study proposes a two-stage method: near-time optimal feedrate planning for the NURBS curve considering interpolation error constraints. In the first stage, the end-effector velocity constraint under joint velocity constraint is calculated, and the feedrate command is corrected based on that constraint. Then, the corrected feedrate command is transformed into parameter form, and feedrate planning is carried out utilizing the direct transcription method. In the second stage, interpolation is carried out after feedrate planning, and interpolation error is calculated to detect any over-limit positions. If any over-limit positions are detected, the first over-limit position is recorded, and feedrate planning with reduced feedrate limit is carried out at that position. This process is repeated until the interpolation error constraint is satisfied. A series of simulations and experiments were conducted in this study. The simulation results show that the proposed method can consider feedrate command constraints and limit interpolation errors within specified constraints. The experimental results show that the proposed method can reduce contour errors, particularly at low feedrate command.

Synthetic complex Weyl superconductors, chiral Josephson effect and synthetic half-vortices

We show that the most generic form of spin-singlet superconducting order parameter for chiral fermions in systems with broken time reversal symmetry and inversion symmetry is of the Delta _s+igamma ^5Delta _5 where Delta _s is the usual order parameter and Delta _5 is the pseudo-scalar order parameter. After factoring out the U(1) phase e^{iphi }, this form of superconductivity admits yet additional complex structure in the plane of (Delta _s,Delta _5). The polar angle chi in this plane, which we call the chiral angle, can be controlled by the external flux bias. We present a synthetic setup based on stacking of topological insulators (TIs) and superconductors (SCs). Alternatively flux biasing the superconductors with a fluxes pm Phi leads to Delta _5=Delta _0 sin (chi ), where Delta _0 is the superconducting order parameter of the SC layers, and the chiral angle chi ={2pi }Phi /Phi _0 is directly given by the flux Phi in units of the flux quantum Phi _0=h/(2e). This can be used as a building block to construct a two-dimensional Josephson array. In this setup chi will be a background field defining a pseudoscalar Delta _5 that can be tuned to desired configuration. While in a uniform background field Delta _5 the dynamics of phi is given by standard XY model and its associated vortices, a staggered background pm Delta _5 (or equivalently chi and chi +pi in alternating lattice sites) creates a new set of minima for the phi field that will support half-vortex excitations. An isolated single engineered “half-vortex” in the chi field in an otherwise uniform background will bind a phi -half-vortex. This is similar to the way a p-wave superconducting vortex core binds a Majorana fermion.