Homogeneous Domains Research Articles

Statistics and sensitivity of axion wind detection with the homogeneous precession domain of superfluid helium-3

The homogeneous precession domain (HPD) of superfluid He3 has recently been identified as a detection medium which might provide sensitivity to the axion-nucleon coupling gaNN competitive with, or surpassing, existing experimental proposals. In this work, we make a detailed study of the statistical and dynamical properties of the HPD system in order to make realistic projections for a full-fledged experimental program. We include the effects of clock error and measurement error in a concrete readout scheme using superconducting qubits and quantum metrology. This work also provides a more general framework to describe the statistics associated with the axion gradient coupling through the treatment of a transient resonance with a nonstationary background in a time-series analysis. Incorporating an optimal data-taking and analysis strategy, we project a sensitivity approaching gaNN∼10−12 GeV−1 across a decade in axion mass. Published by the American Physical Society 2024

Coupled Electrochemical-Thermal Runaway Model of Lithium-Ion Cells Operating Under High Ambient Temperatures

The risk of thermal runaway (TR) in high energy density Lithium-ion batteries (LIBs), which may initiate at around 90 °C, is a critical safety concern, particularly in regions where summer temperatures can reach nearly 50 °C. While multiple exothermic reactions that cause TR and modeled using Arrhenius equations lead to good predictions in controlled oven tests, their use in practical applications is questionable as these do not consider internal electrochemical processes that cause temperature rise and trigger exothermic reactions. Further, limited literature focuses on coupling electrochemical thermal models with exothermic reactions. This study demonstrates a method to couple the electrochemical and thermal runaway models for a commercial cylindrical Lithium-ion cell. The proposed model averages pseudo-2D electrochemical heat and couples it to a two-dimensional, axisymmetric heat transfer model of 18650-type Lithium-ion cell. The jellyroll structure is approximated as a homogeneous and anisotropic domain for electrochemical and exothermic heating. Simulations are performed through several, uninterrupted charge-discharge cycles at different ambient temperatures and C-rates. We show that while cycling rate is critical in instigating and accelerating TR, parameters like ambient temperature, particle radii and initial electrolyte concentration also play a role in determining the core temperature and its rate of growth in the cell.

Use of the fuzzy algorithm in samples with imprecise descriptions to define typological domains.

The identification of homogeneous spatial domains is an important step in mineral resource modeling. Inspired by the growing integration of machine learning into geostatistical modeling, this study addresses the fuzzy algorithm for automated definition of typological classes, thus improving the reliability of information. The biggest challenge in building the 3D model is optimizing the time spent grouping the domains since 53% of the samples in the database have inaccurate lithological descriptions. A combination of statistical analyzes of grades and geological characteristics was carried out to set up the fuzzy inference system based on expert knowledge. The results were verified by comparing the clustering obtained with other machine learning techniques. The algorithm proved to be effective in maintaining a better separation of typologies. Other benefits achieved using computational intelligence were the quantification of uncertainty in lithological descriptions; replicability and standardization of concepts for defining groups and automation of steps, allowing agile updating of the model when new data is inserted

Robot base position and spacecraft cabin angle optimization via homogeneous stiffness domain index with nonlinear stiffness characteristics

The use of mobile robots for machining large components has received considerable research interest for the application of industrial robots in the machinery manufacturing sector. However, the low structural stiffness of industrial robots can result in poor machining quality under the action of cutting forces. Therefore, this paper proposes a simultaneous optimization method the mobile robot base position and cabin angle using homogeneous stiffness domain (HSD) index for large spacecraft cabins. First, a nonlinear joint stiffness model that considers the gravity compensator mechanism is established to describe the stiffness characteristics of heavy-duty robots more accurately. Subsequently, a HSD index is proposed to evaluate the overall stiffness values and stiffness fluctuation for all robot postures in the machining program. An optimization model is then established based on the HSD under the constraints of machining accessibility, joint angle limitation and singularity. The optimal base position and cabin angle are determined simultaneously using the sparrow search algorithm. Finally, simulation and milling experiments are used to demonstrate that the optimization method proposed in this paper can effectively improve the machining quality.

Noise-based correction for electrical impedance tomography

Objective. Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC works with in vivo data and (5) to test whether NBC is stable across model and perturbation geometries. Approach. EIT was performed in silico in a 2D homogeneous circular domain and an anatomically realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels in each case. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D as well as a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the error in the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested for in vivo EIT data of lung ventilation in a human thorax and cortical activity in a rat brain. Main results. On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally and in silico. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy reconstructions. For in vivo data, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation. Significance. In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.

HeteLFX: Heterogeneous recommendation with latent feature extraction

This study proposes a heterogeneous recommendation model that does not rely on data sharing. Previous studies have predominantly focused on nested homogeneous domains that share data. However, this approach encounters issues as it could lead to diminished recommendation performance when there is a scarcity of redundant data within these domains. To overcome these challenges, we propose the HeteLFX model, which extracts and bridges the latent features (LF) of each domain. This model resolves the problems by leveraging the metainformation of domain items to generate an LF. LF is extracted for each domain, and bridges are established based on the relevance of the latent knowledge, thereby enabling heterogeneous recommendations. The efficacy of the HeteLFX model was assessed by comparing it with four other heterogeneous recommendation systems, which are variants of X-Map and NX-Map. The results revealed that the HeteLFX model improved performance by reducing the mean absolute error (MAE) by approximately 0.3, thereby underscoring the superiority of the model. Additionally, HeteLFX reduced the MAE by up to approximately 0.45, depending on the relevance of the data within the domain.

Bulk resistance and contact impedance: Particular solution for annulus, homogeneous domain and dimensional analysis of the complete electrode model

Viewing the complexity of multiphase flows, in which multi-dimensional and time-changing fluid structures, known as flow patterns, must be captured, electrical impedance tomography (EIT) stands out as an alternative for fluid flow visualization. Using an EIT system, this study presents an enhanced approach for measuring film thickness in two-phase flows. Incorporating bulk resistance and contact impedance contributions, the proposed ohmic model significantly improves accuracy, particularly in low-conductivity scenarios, yielding a robust, accurate, and rapid technique for obtaining images from an EIT-based low-cost acquisition system. Tomographic experiments validate the model’s efficacy using cylindrical sensors and diverse conductivity mediums, including two distinct phantoms. The dimensional analysis of the model yields five dimensionless numbers, revealing interrelations between electrical parameters and sensor construction. A correlation based on a dimensionless number for the contact impedance is obtained. Additionally, the experiments demonstrate a strong interplay with the polarization phenomenon.

A REMARK ON THE N-INVARIANT GEOMETRY OF BOUNDED HOMOGENEOUS DOMAINS

Abstract Let $\mathbf {D}$ be a bounded homogeneous domain in ${\mathbb {C}}^n$ . In this note, we give a characterization of the Stein domains in $\mathbf {D}$ which are invariant under a maximal unipotent subgroup N of $Aut(\mathbf {D})$ . We also exhibit an N-invariant potential of the Bergman metric of $\mathbf {D}$ , expressed in a Lie theoretical fashion. These results extend the ones previously obtained by the authors in the symmetric case.

Rigidity properties of holomorphic isometries into homogeneous Kähler manifolds

We prove two rigidity results on holomorphic isometries into homogeneous Kähler manifolds. The first shows that a Kähler-Ricci soliton induced by the homogeneous metric of the Kähler product of a special generalized flag manifold (i.e. a flag of classical type or integral type) with a bounded homogeneous domain is trivial, i.e. Kähler-Einstein. In the second one we prove that: (i) a flat space is not relative to the Kähler product of a special generalized flag manifold with a homogeneous bounded domain, (ii) a special generalized flag manifold is not relative to the Kähler product of a flat space with a homogeneous bounded domain and (iii) a homogeneous bounded domain is not relative to the Kähler product of a flat space with a special generalized flag manifold. Our theorems strongly extend the results of Cheng and Hao [Ann. Global Anal. Geom. 60 (2021), pp. 167–180], Cheng, Di Scala, and Yuan [Internat. J. Math. 28 (2017), p. 1750027], Loi and Mossa [Proc. Amer. Math. Soc. 149 (2021), pp. 4931–4941], Loi and Mossa [Proc. Amer. Math. Soc. 151 (2023), pp. 3975–3984] and Umehara [Tokyo J. Math. 10 (1987), pp. 203–214].

Modeling gas flow in low-permeability formations: An efficient combination of mixed finite elements and high order time integration schemes

Numerical simulation of gas flow in low permeability formations is a challenging task due to the high nonlinearity induced by (i) the compressibility of the gas, (ii) the Klinkenberg slippage effect and (iii) the Langmuir adsorption of the gas on the pore surface. Because of these nonlinearities, modeling gas flow in low permeability formations requires a great deal of computational effort. In this work, we develop an efficient numerical model using advanced spatial and temporal discretization methods for a simultaneous solution of the coupled equations of gas flow, cubic Peng-Robinson equation of state, slippage effect and Langmuir adsorption. The spatial discretization is performed with the lumped hybrid formulation of the mixed finite element method which is well adapted for fluid flow in heterogeneous porous media. The time integration is performed with high-order methods via the method of lines (MOL) which allows large time steps and efficient solution of the nonlinear system of equations. Numerical experiments, performed for gas extraction in a heterogeneous domain, point out the high efficiency of the new model since it can be until 10 times more efficient than the classical first-order time discretization method. Results of global sensitivity analysis show that the intrinsic gas-phase permeability and the Klinkenberg factor are the most influential parameters controlling the pumping rate in the case of gas extraction in a homogeneous domain.

Thermal cycle stability and microstructure of Pb(Mg1/3Nb2/3)O3-PbTiO3 single crystals

The Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT) ferroelectric single crystals have been commercially available as important components in medical ultrasound transducers due to their excellent piezoelectric and electromechanical coupling performance. The variation in piezoelectric and dielectric properties of PMN-PT single crystals with ambient temperature is an important application indicator. In this work, the PMN-PT single crystals after direct current poling (DCP) and alternating current poling (ACP) were subjected to the cyclic thermal treatment process. The thermal cycling stability and microstructural changes in PMN-PT single crystals were investigated. The ACP single crystals exhibit a higher dielectric constant ε33T/ε0 (6500–7600) and piezoelectric coefficient d33 (2100–2500 pC N−1) compared to the DCP single crystals (ε33T/ε0 of 4100–5000, d33 of 1200–1300 pC N−1). Under thermal cycling at 60 °C, the DCP and ACP single crystals exhibit good thermal cycling stability after 150 cycles. Microstructural observations show that the domain structure of the DCP single crystals exhibits “staggered domain walls, inhomogeneous domain size, variety of domain structure,” while the relatively homogeneous stripe-like domains were observed in the ACP single crystals. After thermal cycling, new fine striped domains appear in both the DCP and ACP single crystals due to the instability of rotated polarization, but the piezoelectric and dielectric properties are not greatly affected. This work provides an intensive understanding of the effects of thermal cycling on the domain structure, which is useful for applications.

Numerical simulation of gradient catalyst layer design in proton exchange membrane water electrolyzer with enhanced performance

The catalyst loading, ionomer content and agglomerate radius in the catalyst layer (CL) of proton exchange membrane water electrolyzer (PEMWE) plays critical role on its electrochemical performance. Conventionally, the CL in PEMWE is modeled with uniform composition and homogeneous domain. In this study, a two-dimensional PEMWE model considering linear gradient distribution of catalyst, ionomer and agglomerate radius in CL was developed and their effect on the electrolyzer performance were systematically investigated. The results show that, when the total catalyst, ionomer loading and the average agglomerate radius in CL are kept constant, the optimum PEMWE performance was achieved with low catalyst loading, high ionomer content, and small agglomerates radius distribution at the CL-PEM interface. Compared with the uniform CL, this optimized gradient CL design results into a 7.01% improvement in the PEMWE current density. Meanwhile, under the gradient CL design, increased maximum temperature and oxygen concentration with lower liquid water saturation is found inside PEMWE, which might affect the temperature distribution uniformity within the PEMWE. The results of this work provide insightful guidance for the CL optimization design of PEMWE.

Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations

This paper describes an innovative method that recursively applies the machine learning Random Forest to an assumed homogeneous aerographic domain around measurement sites to predict concentrations and emissions of ammonia, an atmospheric pollutant that causes acidification and eutrophication of soil and water and contributes to secondary PM2.5. The methodology was implemented to understand the effects of weather and emission changes on atmospheric ammonia concentrations. The model was trained and tested by hourly measurements of ammonia concentrations and atmospheric turbulence parameters, starting from a constant emission scenario. The initial values of emissions were calculated based on a bottom-up emission inventory detailed at the municipal level and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations for each iteration, the emissions were modified, the model’s training and testing were repeated, and the model converged to a very high performance in predicting ammonia concentrations and establishing hourly time-varying emission profiles. The ammonia concentration predictions were extremely accurate and reliable compared to the measured values. The relationship between NH3 concentrations and the calculated emissions rates is compatible with physical atmospheric turbulence parameters. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow the identification of the effect of atmospheric turbulence on pollutant accumulation. The proposed methodology is suitable for validating and confirming emission time series and defining highly accurate emission profiles for the improvement of the performances of chemical and transport models (CTMs) in combination with in situ measurements and/or optical depth from satellite observation.

Effect of phase distribution and defect passivation on amplified spontaneous emission of quasi-2D Dion–Jacobson perovskite

Quasi-2D Dion–Jacobson (DJ) halide perovskites with large exciton binding energy, self-assembled quantum wells, and high quantum yield attract growing attention in light-emitting diodes and solar devices. DJ-layered perovskites have the eliminated van der Waals gap and show improved photophysical features. However, there are a variety of defects and complex phase distributions produced during the solution procedure and the fast crystal development. In this paper, we reveal the effect of phase distribution and defect passivation on amplified spontaneous emission of quasi-2D perovskite (PDMA)(MA)2Pb3Br10 thin films through solvent engineering and additive methods, by establishing the correlation between the precursor compositions and the photophysical performance of the layered DJ perovskites. The energy transfer rate and the photoluminescence quantum yield increase due to the spatially homogeneous domain distribution and the reduced defect density after the addition of KBr in the DMSO:DMF. More importantly, we realize green amplified spontaneous emission and single-mode vertical-cavity surface-emitting lasing with low thresholds of 8.8 and 10.5 μJ cm−2, respectively. This work provides a guideline to achieve low-threshold multicolor lasers based on DJ perovskites.

Analytical modeling of 2D groundwater flow in a semi-infinite heterogeneous domain with variable lateral sources

<abstract> <p>In nature, aquifers are usually composed of distinct kinds of media, i.e., heterogeneous domains rather than homogeneous domains. Groundwater level and flow changes in such domains are more complicated than those in homogeneous domains; thus, building a mathematical model for addressing groundwater flow in heterogeneous aquifers is the present research goal. In conventional research on similar topics, many one-dimensional (1D) analytical models have been presented, but it is challenging to simulate real-world scenarios. This study develops a two-dimensional (2D) analytical model for modeling groundwater flow in a conceptual sloping heterogeneous domain imposed by variable recharge. This model can consider distinct slope angles, medium heterogeneity, and any type of lateral recharge for a semi-infinite domain. The results indicate that groundwater level and flow discharge are greatly affected by the abovementioned factors. The recharge intensity significantly affects the peak of the groundwater level. For example, when the recharge rate increases by 30%, the peak water level increases by 50% as the groundwater flows from the sandy loam zone to the loam zone. The loops delineating the relationship between discharge and groundwater level for different bottom slopes cannot become close for heterogeneous aquifers. The presented 2D analytical model can simulate and better predict results of groundwater changes than previous 1D analytical models. Further, this model can simultaneously consider the effect of varying recharge over time and space on groundwater level change.</p> </abstract>

Multitree Genetic Programming With Feature-Based Transfer Learning for Symbolic Regression on Incomplete Data.

Data incompleteness is a serious challenge in real-world machine-learning tasks. Nevertheless, it has not received enough attention in symbolic regression (SR). Data missingness exacerbates data shortage, especially in domains with limited available data, which in turn limits the learning ability of SR algorithms. Transfer learning (TL), which aims to transfer knowledge across tasks, is a potential solution to solve this issue by making amends for the lack of knowledge. However, this approach has not been adequately investigated in SR. To fill this gap, a multitree genetic programming-based TL method is proposed in this work to transfer knowledge from complete source domains (SDs) to incomplete related target domains (TDs). The proposed method transforms the features from a complete SD to an incomplete TD. However, having many features complicates the transformation process. To mitigate this problem, we integrate a feature selection mechanism to eliminate unnecessary transformations. The method is examined on real-world and synthetic SR tasks with missing values to consider different learning scenarios. The obtained results not only show the effectiveness of the proposed method but also show its training efficiency compared with the existing TL methods. Compared to state-of-the-art methods, the proposed method reduced an average of more than 2.58% and 4% regression error on heterogeneous and homogeneous domains, respectively.

Meta-learning Based Domain Generalization Framework for Fault Diagnosis With Gradient Aligning and Semantic Matching

Intelligent fault diagnosis models have demonstrated a superior performance in industrial prognostics health management scenarios. However, these models may struggle to generalize in complicated industrial environments, when encountering new working conditions and handling low-resource and heterogeneous data. To cope with the aforementioned issues, we focus on constructing a universal training framework with domain generalization technique that will encourage fault diagnosis model to generalize well in unseen working conditions. Firstly, a model-agnostic meta-learning based training framework called Meta-GENE is proposed for homogeneous and heterogeneous domain generalization. Secondly, a gradient aligning algorithm is introduced in meta-learning framework to learn domain-invariant strategy for robust prediction in unseen working conditions. Thirdly, a semantic matching technique is proposed for utilizing heterogeneous data to alleviate low-resource problem. Our method has yielded excellent performance on the PHM09 fault diagnosis dataset and achieved superior results on a set of generalization tasks across various working conditions.

Homogeneous bulk heterojunction domains with continuous charge pathway via hydrophobic membrane filtration for stable photo and dark current operations

Hydrophobic membrane filtration addresses solubility issues related to solvent and molecular weight. Organic optoelectronics with filtration exhibit stable operation under varying light intensities, reducing leakage current and improving charge flow.

Delineating homogeneous domains of fractured rocks using topological manifolds and deep learning

Determining homogeneous domains statistically is helpful for engineering geological modeling and rock mass stability evaluation. In this text, a technique that can integrate lithology, geotechnical and structural information is proposed to delineate homogeneous domains. This technique is then applied to a high and steep slope along a road. First, geological and geotechnical domains were described based on lithology, faults, and shear zones. Next, topological manifolds were used to eliminate the incompatibility between orientations and other parameters (i.e. trace length and roughness) so that the data concerning various properties of each discontinuity can be matched and characterized in the same Euclidean space. Thus, the influence of implicit combined effect in between parameter sequences on the homogeneous domains could be considered. Deep learning technique was employed to quantify abstract features of the characterization images of discontinuity properties, and to assess the similarity of rock mass structures. The results show that the technique can effectively distinguish structural variations and outperform conventional methods. It can handle multisource engineering geological information and multiple discontinuity parameters. This technique can also minimize the interference of human factors and delineate homogeneous domains based on orientations or multi-parameter with arbitrary distributions to satisfy different engineering requirements.

Nanodiamond reinforced self-healing and transparent poly(urethane–urea) protective coating for scratch resistance

ABSTRACT With increasing demand for scratch-resistant flexible electronics, the development of transparent coatings with good scratch resistance and self-healing properties has emerged as a key research topic. In this study, a high-strength self-healing poly(urethane – urea) (PUU)-based nanocomposite coating was prepared by introducing functionalized nanodiamond (ND) into a PUU matrix via solution blending. The PUU matrix had hard-segment repeating units and was constructed using isophorone diamine and isophorone isocyanate. The ND particles were modified using a silane coupling agent, 3-aminopropyltriethoxysilane, to obtain well-dispersed KH-ND nanoparticles. KH-ND promoted microphase separation in the PU matrix, inducing the formation of dense and homogeneous hard domains that dissipated stress, prevented further crack development, and improved the mechanical properties and scratch resistance of the coating. In addition, the coating exhibited excellent self-healing properties. Fourier-transform infrared spectroscopy, scanning electron microscopy, and atomic force microscopy were used to characterize the self-healing and hardening mechanisms of the coating. The environmentally friendly PUU/KH-ND coating is easy to prepare and has broad application prospects in transparent and anti-scratch coatings for flexible electronics, automobiles, and home appliances.