Extra Parameter Research Articles

Smoothing the [formula omitted] tension with a phantom dynamical dark energy model

The discrepancy between Planck data and direct measurements of the current expansion rate H0 and the matter fluctuation amplitude S8 has become one of the most intriguing puzzles in cosmology nowadays. The H0 tension has reached 5σ in the context of standard cosmology i.e ΛCDM. Therefore, explanations to this issue are mandatory to unveil its secrets. Despite its success, ΛCDM is unable to give a satisfying explanation to the tension problem. Unless some systematic errors are hidden in the observable measurements, physics beyond the standard model of cosmology must be advocated. In this perspective, we study a phantom dynamical dark energy model as an alternative to ΛCDM in order to explain the aforementioned issues. This phantom model is characterized by one extra parameter, Ωpdde, compared to ΛCDM. We obtain a strong positive correlation between H0 and Ωpdde, for all data combinations. Using Planck18 measurements together with BAO and Pantheon, we find that the H0 and the S8 tensions are 3.4σ and 2.75σ, respectively. By introducing a prior on the absolute magnitude, MB, of the SN Ia, the H0 tension decreases to 2.49σ with H0=69.7−0.86+0.83 km s−1 Mpc−1 and the S8 tension reaches the value 2.51σ with S8=0.82650.012+0.011.

Parameter-Free Ultralocal Model-Based Deadbeat Predictive Current Control for PMVMs Using Finite-Time Gradient Method

Traditional ultra-local model based predictive current control (UL-PCC) method has strong robustness on current control, however, the UL-PCC still rely on inductance to design the controller gain which really deteriorate its robustness on parameter. To solve it, a parameter free ultra-local model based deadbeat predictive current control (PF-DPCC) method using finite-time gradient method (FGM) is proposed in this paper for permanent magnet vernier motor (PMVM) drives. The UL-PCC is firstly modified considering rotor speed without any extra parameter. Then, the impact of initial controller gain on robustness is analyzed and an extreme low duty-cycle current signal which has negligible impact on current is injected to estimate the controller gain of PF-DPCC adaptively on the basis of the deadbeat concept and the FGM. Hence, all motor parameters are not required in advance and the initial controller gain can be set as 1 directly. Then, the robustness can be effectively improved and the dependence on initial value of controller gain can be eliminated. Finally, the effectiveness and the correctness of the proposed PF-DPCC are experimentally verified on a 400 W PMVM drive platform.

Simulation of alfalfa yield with AquaCrop

The AquaCrop simulation model, originally designed for annual crops, is expanded here to simulate alfalfa, a perennial forage crop. A new routine was added to the model to mimic the assimilate partitioning between above and below-ground plant parts to account for the utilization of reserves in Spring and for their storage in the Fall. The simulation of the transfer of assimilates requires only three extra crop parameters which makes the model also easy to calibrate. To evaluate the model, yield data collected in Louvain-La-Neuve (Belgium), Isparta (Turkey), and Ottawa (Canada) for different alfalfa cultivars, various years and field and irrigation management strategies were used. To assess the accuracy and robustness of the simple assimilate remobilization process, simulations were run for the three different environments with a common set of crop parameters which were parameterized. The dispersion between the observed and simulated cumulative dry above-ground biomass during the growing cycle was small (r² = 0.97; nRMSE = 11%; Nash-Sutcliffe model EF = 0.97), and a systematic over- or underestimation by the model was not observed (Willmott’s index of agreement, d = 0.99). When evaluating the goodness of fit of the 81 individual harvest events, the results were still very satisfactory although the nRMSE doubled. The simulations indicated that the AquaCrop model adapted to perennial crops and with a novel storage-remobilization routine, could predict well alfalfa yields in various climates and environments, with and without water and fertility stress, and for three different alfalfa cultivars.

Improved Triple-Phase-Shift Modulation for Bidirectional CLLC Converters

CLLC resonant converters are an attractive option for battery charging applications due to their bidirectional power flow and high efficiency. Pulse frequency modulation (PFM) is the simple method to drive CLLC converters. However, PFM cannot regulate the output voltage under the light-load condition properly, which leads to high switching and core losses. Phase shift modulation techniques like dual-phase shift (DPS) modulation can overcome this challenge. However, more improvements are demanded due to high circulating currents in phase shift modulations. In this paper, an improved modulation technique based on triple-phase shift (TPS) has been applied in CLLC converters to improve the light-load efficiency. TPS modulation like any other phase-shift modulation, keeps the switching frequency of CLLC converters fixed at the resonant frequency under the light-load condition and regulates the output voltage by its three phase shift parameters. The improved TPS modulation has lower circulating current at the same average output current compared to DPS modulation. Therefore, higher efficiency, smaller peak, and RMS current values can be achieved under the light-load condition. In addition, the proposed TPS modulation has a better flexibility in regulating the output voltage under the light-load condition due to an extra phase shift parameter compared to DPS modulation. The proposed TPS modulation has been analyzed thoroughly in this paper and a proper selection of phase shift values has been explained to improve the efficiency of the converter. Finally, advantages of the improved TPS modulation have been validated by simulation and a 1 kW experimental setup.

A Note on Harris Extended Generalized Exponential Distribution

We introduce a four-parameter extension of the exponential distribution, which has the Exponentiated exponential, Marshall-Olkin exponential, and exponential distribution as sub-models. The proposed distribution has two important properties: it involves more parameters than the baseline model to obtain more flexibility and the extra parameters have a clear interpretation and representation. The proposed model is more flexible than any of its sub-models. Its probability density function can be monotone increasing, decreasing, or unimodal and its associated hazard rate may be increasing, decreasing, unimodal or bathtub-shaped. Statistical expression is obtained for certain structural statistical properties such as ordinary and incomplete moments, moment generating function, order statistics, quantile function, reliability function, and Renyi entropy. The maximum likelihood estimation method is used to obtain the estimates of the model parameters. An application of the new model to two lifetime data demonstrates the flexibility of the model.

Hybrid model development for parameter estimation and process optimization of hydrophobic interaction chromatography

Hydrophobic Interaction Chromatography (HIC) is often employed as a polishing step to remove aggregates for the purification of therapeutic proteins in the biopharmaceutical industry. To accelerate the process development and save the costs of performing time- and resource-intensive experiments, advanced model-based process design and optimization are necessary. Due to the unclear adsorption mechanism of the salt-dependent interaction between the protein and resin, the development of an accurate mechanistic model to describe the complex HIC behavior is challenging. In this work, an isotherm derived from Wang et al. is modified by adding three extra parameters together with an equilibrium dispersive model to represent the HIC process. To reduce the development effort of isotherm equations and extract missing information from the available data, a hybrid model is constructed by combining a simple and well-known multi-component Langmuir isotherm (MCL) with a neural network (NN). It is observed that the structure of the hybrid model is of critical importance to the accuracy of the developed model. During parameter estimation, a regularization strategy is incorporated to prevent overfitting. Furthermore, the impact of NN structures and regularization rates are comprehensively investigated. One of the interesting findings was that a simple NN with one hidden layer with two nodes and sigmoid as the activation function, significantly outperforms the mechanistic model, with a 62% improvement in accuracy in calibration and 31.4% in validation. To ensure the generalizability of the developed hybrid model, an in-silico dataset is generated using the mechanistic model to test the extrapolation capability of the hybrid model. Process optimization is also carried out to find the optimal operating conditions under product quality constraints using the developed hybrid model.

Semantic segmentation of pavement cracks based on an improved U-Net

Cracks are one of the types of pavement defects that can affect the safety and quality of roads, so identifying such defects is an important part of road maintenance. In this paper, based on the U-Net coding-decoding structure, an ECA channel attention module is added to the coding stage, thus improving feature extraction without introducing too many extra parameters and computational effort. The insertion of the FCNhead decoding dock in the decoding stage can improve the performance of the model on semantic segmentation tasks while maintaining its efficiency and interpretability, thus better meeting the needs of practical applications. Experimental results on the CFD dataset and the crack500 dataset show that the algorithm improves the accuracy of crack detection and has better robustness.

Monocular Depth Estimation Using Res-UNet with an Attention Model

Depth maps are single image metrics that carry the information of a scene in three-dimensional axes. Accurate depth maps can recreate the 3D structure of a scene, which helps in understanding the full geometry of the objects within the scene. Depth maps can be generated from a single image or multiple images. Single-image depth mapping is also known as monocular depth mapping. Depth maps are ill-posed problems that are complex and require extensive calibration. Therefore, recent methods use deep learning to develop depth maps. We propose a new method in monocular depth estimation to develop a high-quality depth map. Our approach is based on a convolutional neural network in which we used Res-UNet with a spatial attention model to develop depth maps. The addition of an attention mechanism increases the capability of feature extraction and enhances the boundaries features. It does not add any extra parameters to the network. With our proposed model, we demonstrate that a simple CNN model aided with an attention mechanism can create high-quality depth maps with a small iteration and training time. Our model performs very well compared to the existing state-of-the-art methods on the benchmark NYU-depth v2 dataset. Our model is flexible and can be applied to any depth mapping or multi-segmentation tasks.

A soil-brine retention model for wetting processes considering the hysteresis effects

Wetting hydrological processes are relevant to a considerable number of natural hazards, including landslides, dam breaks, surface runoff after floods, etc. The notable difference between wetting and drying soil–water retention curves (SWRC) has been attributed to the hysteresis phenomenon. Although recent studies confirm enhanced impurity levels of permeating fluid, most modeling approaches simply consider water retention along the drying path and neglect the impurity of pore water. Therefore, the main objective of this paper is to introduce a new water retention model along the wetting path by taking the osmotic potential into account, reflecting the influence of dissolved salts. The new model, referred to as the soil-brine retention curve (SBRC) was developed based on the 1980 van Genuchten model (VG). The results reveal the robustness of the model in predicting the experimental wetting retention data for different soil types exposed to both saline and distilled water. Statistical analysis confirms the capability of the new model with an average R2 of 0.97 and RMSE of 0.047. The wetting branch can be estimated based on an existing drying curve, assuming predefined values of entrapped air and air expulsion value. However, a more reliable prediction can be made if two extra parameters of entrapped air and air expulsion are calibrated for the pure water wetting curve. Furthermore, for the case of saline water, the wetting SBRC can be estimated using one more parameter than those used for pure water.

Assessment of the vulnerability of hybrid coastal aquifers: application of multi-attribute decision-making and optimization models

ABSTRACT This study introduced an innovative hybrid framework using statistical-based, multi-attribute decision-making (MADM), and multi-objective optimization methods to assess the vulnerability of the Oman's Al-Khoud coastal aquifer without temporal variations.. Firstly, an extra parameter, bedrock topography (BT), was added to a commonly used index model, GALDIT and the parameter of aquifer type was removed from the model. Also, the random forest (RF) method was used to define the relative importance of parameters. Then, both frequency ratio (FR) and stepwise weight assessment ratio analysis (SWARA) methods were applied to modify the GALDIT rates. The GALDIT weights were optimized using the non-dominated sorting genetic algorithm-II (NSGA-II). Finally, the coastal aquifer vulnerability index (CAVI) model was obtained based on the hybrid FR-SWARA and NSGA-II models. The CAVI vulnerability map indicated high vulnerability in the Northern aquifer areas. Furthermore, the Spearman correlation coefficient between the CAVI and total dissolved solids (TDS) obtained 0.78.

An approximate solution for variable‐order fractional optimal control problem via Müntz‐Legendre wavelets with an application in epidemiology

A class of variable‐order fractional optimal control problems (VO‐FOCPs) is solved by applying Müntz‐Legendre wavelets. Different from classical wavelets (such as Legendre and Chebyshev), the Müntz‐Legendre wavelets have an extra parameter representing the fractional order; therefore, they provide more reliable results for certain fractional calculus problems. Using the regularized beta function, the Riemann‐Liouville fractional integral operator (RLFIO) of these wavelets is precisely determined. We then transform the given VO‐FOCPs into parameter optimization problems that can be easily solved. The convergence analysis and error estimation of the proposed method are provided. Four examples are solved to illustrate the high accuracy of the approach. The method is also applied to a cancer‐obesity interaction model to analyze the interactions between the tumor, immune, normal, and fat cells when the chemotherapeutic drugs are injected into a body.

Effects of an electric charge on Casimir wormholes: changing the throat size

In this paper we continue the investigation of the connection between Casimir energy and the traversability of a wormhole. In addition to the negative energy density obtained by a Casimir device, we include the effect of an electromagnetic field generated by an electric charge. This combination defines an electrovacuum source which has an extra parameter related to the size of the throat. Even if the electromagnetic energy density is positive, the null energy condition is still violated. The main reason is that the electromagnetic field satisfies the property rho =-p_{r}. As a consequence, the traversable wormhole throat can be changed as a function of the electric charge. This means that the throat is no longer Planckian and the traversability is slightly less in principle but slightly greater in practice.

Multivariate Contaminated Normal Censored Regression Model: Properties and Maximum Likelihood Inference

The Multivariate Contaminated Normal (MCN) distribution which contains two extra parameters with respect to parameters of the multivariate normal distribution, one for controlling the proportion of mild outliers and the other for specifying the degree of contamination, has been widely applied in robust statistics in the case of elliptically heavy-tailed empirical distributions. This article extends the MCN model to data with possibly censored values due to limits of quantification, referred to as the MCN with censoring (MCN-C) model, and further establishes the censored multivariate linear regression model where the random errors have the MCN distribution, named as the MCN censored regression (MCN-CR) model. Two computationally feasible Expectation Conditional Maximization (ECM) algorithms are developed for maximum likelihood estimation of MCN-C and MCN-CR models. An information-based method is used to approximate the standard errors of location parameters and regression coefficients. The capability and effectiveness of the MCN-C and MCN-CR models are illustrated via two real-data examples. A simulation study is conducted to investigate the superiority of the proposed models in terms of fit, accuracy of parameter estimation and censored data recovery as compared with classical approaches. Supplementary materials for this article are available online.

Relaxation of imbalance in a disordered XX model with on-site dephasing

The relaxation of observables to their non-equilibrium steady states in a disordered XX chain subjected to dephasing at every site has been intensely studied in recent years. We comprehensively analyze the relaxation of staggered magnetization, i.e., imbalance, in such a system, starting from the N\'eel initial state. We analytically predict emergence of several timescales in the system and extract results which match with large-system numerics without any extra fitting parameter until a universal timescale. An often reported stretched exponential decay is just one of the regimes which holds in a finite window of time and is therefore in fact not a true stretched exponential decay. Subsequently, the asymptotic decay of imbalance is governed by a power law irrespective of the disorder. We show that this emerges from the continuum limit of the low magnitude eigenspectrum of the Liouvillian. However, for finite systems, due to discreteness of the spectrum, the final phase of relaxation is governed by the relevant smallest Liouvillian gap.

Semantic-Aligned Cross-Modal Visual Grounding Network with Transformers

Multi-modal deep learning methods have achieved great improvements in visual grounding; their objective is to localize text-specified objects in images. Most of the existing methods can localize and classify objects with significant appearance differences but suffer from the misclassification problem for extremely similar objects, due to inadequate exploration of multi-modal features. To address this problem, we propose a novel semantic-aligned cross-modal visual grounding network with transformers (SAC-VGNet). SAC-VGNet integrates visual and textual features with semantic alignment to highlight important feature cues for capturing tiny differences between similar objects. Technically, SAC-VGNet incorporates a multi-modal fusion module to effectively fuse visual and textual descriptions. It also introduces contrastive learning to align linguistic and visual features on the text-to-pixel level, enabling the capture of subtle differences between objects. The overall architecture is end-to-end without the need for extra parameter settings. To evaluate our approach, we manually annotate text descriptions for images in two fine-grained visual grounding datasets. The experimental results demonstrate that SAC-VGNet significantly improves performance in fine-grained visual grounding.

Robust Discriminant Subspace Clustering With Adaptive Local Structure Embedding.

Unsupervised dimension reduction and clustering are frequently used as two separate steps to conduct clustering tasks in subspace. However, the two-step clustering methods may not necessarily reflect the cluster structure in the subspace. In addition, the existing subspace clustering methods do not consider the relationship between the low-dimensional representation and local structure in the input space. To address the above issues, we propose a robust discriminant subspace (RDS) clustering model with adaptive local structure embedding. Specifically, unlike the existing methods which incorporate dimension reduction and clustering via regularizer, thereby introducing extra parameters, RDS first integrates them into a unified matrix factorization (MF) model through theoretical proof. Furthermore, a similarity graph is constructed to learn the local structure. A constraint is imposed on the graph to guarantee that it has the same connected components with low-dimensional representation. In this spirit, the similarity graph serves as a tradeoff that adaptively balances the learning process between the low-dimensional space and the original space. Finally, RDS adopts the l2,1 -norm to measure the residual error, which enhances the robustness to noise. Using the property of the l2,1 -norm, RDS can be optimized efficiently without introducing more penalty terms. Experimental results on real-world benchmark datasets show that RDS can provide more interpretable clustering results and also outperform other state-of-the-art alternatives.

Robust and non-robust bound states in the continuum in rotationally symmetric periodic waveguides.

A fiber grating and a one-dimensional (1D) periodic array of spheres are examples of rotationally symmetric periodic (RSP) waveguides. It is well known that bound states in the continuum (BICs) may exist in lossless dielectric RSP waveguides. Any guided mode in an RSP waveguide is characterized by an azimuthal index m, the frequency ω, and Bloch wavenumber β. A BIC is a guided mode, but for the same m, ω and β, cylindrical waves can propagate to or from infinity in the surrounding homogeneous medium. In this paper, we investigate the robustness of nondegenerate BICs in lossless dielectric RSP waveguides. The question is whether a BIC in an RSP waveguide with a reflection symmetry along its axis z, can continue its existence when the waveguide is perturbed by small but arbitrary structural perturbations that preserve the periodicity and the reflection symmetry in z. It is shown that for m = 0 and m ≠ 0, generic BICs with only a single propagating diffraction order are robust and non-robust, respectively, and a non-robust BIC with m ≠ 0 can continue to exist if the perturbation contains one tunable parameter. The theory is established by proving the existence of a BIC in the perturbed structure mathematically, where the perturbation is small but arbitrary, and contains an extra tunable parameter for the case of m ≠ 0. The theory is validated by numerical examples for propagating BICs with m ≠ 0 and β ≠ 0 in fiber gratings and 1D arrays of circular disks.

Analytical marginalization over photometric redshift uncertainties in cosmic shear analyses

ABSTRACT As the statistical power of imaging surveys grows, it is crucial to account for all systematic uncertainties. This is normally done by constructing a model of these uncertainties and then marginalizing over the additional model parameters. The resulting high dimensionality of the total parameter spaces makes inferring the cosmological parameters significantly more costly using traditional Monte Carlo sampling methods. A particularly relevant example is the redshift distribution, p($z$ ), of the source samples, which may require tens of parameters to describe fully. However, relatively tight priors can be usually placed on these parameters through calibration of the associated systematics. In this paper, we show, quantitatively, that a linearization of the theoretical prediction with respect to these calibrated systematic parameters allows us to analytically marginalize over these extra parameters, leading to a factor of ∼30 reduction in the time needed for parameter inference, while accurately recovering the same posterior distributions for the cosmological parameters that would be obtained through a full numerical marginalization over 160 p($z$ ) parameters. We demonstrate that this is feasible not only with current data and current achievable calibration priors but also for future Stage-IV data sets.

Constraining the number of spacetime dimensions from GWTC-3 binary black hole mergers

In modified gravity models that allow for additional noncompact spacetime dimensions, energy from gravitational waves can leak into these extra spacetime dimensions, leading to a reduction in the amplitude of the observed gravitational waves, and thus are a source of potential systematics in the inferred luminosity distances to gravitational wave sources. Since binary black hole (BBH) mergers are standard sirens, we use the pair-instability supernova mass gap and its predicted features to determine a mass scale in order to break the mass-redshift degeneracy and thus infer the redshift of the source. We simultaneously fit the BBH population and the extra spacetime dimensions parameters from gravitational leakage models using BBH observations from the recently released GWTC-3 catalog. We set constraints on the number of spacetime dimensions and find that $D=3.9{3}_{\ensuremath{-}0.05}^{+0.08}$ at 68% C.L. for models that are independent of a screening scale, finding that the GWTC-3 constraint is as competitive as that set from GW170817 and its electromagnetic counterpart. For models where gravity leaks below a certain screening scale ${R}_{c}$, we find $D=4.4{9}_{\ensuremath{-}0.87}^{+1.63}$ and ${\mathrm{log}}_{10}\text{ }{R}_{c}/\mathrm{Mpc}=4.7{5}_{\ensuremath{-}0.99}^{+0.86}$ with a transition steepness ${\mathrm{log}}_{10}\text{ }n=0.8{5}_{\ensuremath{-}0.86}^{+0.76}$ for the leakage, which for the first time are constrained jointly with the BBH population at cosmological distances. These constraints are consistent with General Relativity (GR), where gravitational waves propagate in $D=3+1$ spacetime dimensions. Using the BBH population to probe modifications to standard cosmological models provides an independent test of GR that does not rely on any electromagnetic information but purely on gravitational wave observations.

A length scale insensitive phase field model based on geometric function for brittle materials

The commonly used phase field model has a broad application in static and dynamic scenarios with brittle materials. Following the assumption of irreversibility of fracture, monotonically increasing phase and tensile strain energy are applied in the commonly used phase field model. By doing so, a clear stiffness reduction, however, is observed in stress–strain curve before reaching fracture strength, which conflicts with the linear elastic behavior of brittle materials. The limitation has also been mentioned in publications when length scale is the only parameter to adjust fracture strength. To solve these problems, a generalized quadratic geometric function is proposed to build a length scale insensitive phase field model. Governing equations and one-dimensional analytical solutions (homogeneous and nonhomogeneous solutions) of this length scale insensitive phase field model are presented. By adjusting the extra parameter in the generalized quadratic geometric function and zeroing negative phase, different phase profile (ranged from exponential profile to linear profile) and fracture strength can be obtained, which could be more suitable to cover different types of fractures. Meanwhile, a narrower crack band and linear elastic behavior can be guaranteed. LS-DYNA user-defined element and user-defined material subroutines are used to implement the proposed length scale insensitive phase field model. A tensile test successfully verifies the properties of a narrower crack band and linear elastic behavior. Three representative numerical examples show the feasibility of this model to simulate fracture propagation, including a Mode Ⅰ failure of wedge splitting test, a mixed-mode failure of l-shaped panel test and a mixed-mode failure of notched beam test. From the crack band and force–displacement curve, the proposed model can handle fracture propagation better for brittle materials.