Heterogeneous Domains Research Articles

Cooperative roles of twinning, transformation and strain partitioning on the excellent cryogenic strength-toughness synergy of dual-heterogeneous metastable high entropy alloys

In a metastable Fe50Mn30Co10Cr10 high entropy alloy (HEA), a dual-heterogeneous structure, encompassing both grain size and dislocation heterogeneity, has been constructed to obtain excellent cryogenic mechanical performance by varying the annealing temperature (630 and 740 °C) following severe deformation. The HS-630 sample, consisting of recovered coarse-grains (CG), recrystallized fine-grains (FGs) and un-recrystallized ultrafine-grains (UFGs), possesses higher yield strength (YS) of ∼965 MPa, ultimate tensile strength (UTS) of ∼1680 MPa and uniform elongation (UEL) of ∼45 % when compared to the fully-recrystallized HS-740 sample at liquid nitrogen temperature (LNT). The difference in YS is primarily caused by the grain boundary and dislocation strengthening of UFGs, whereas the higher ductility of the HS-630 sample is associated with the hierarchical activation of transformation- and twinning-induced plasticity (TRIP and TWIP) effects, and compatible strain partitioning between heterogeneous domains. The dual-heterogeneity results in different critical stresses of ε-phase nucleation in heterogeneous domains, and promotes sustainable TRIP effect in CGs and the emergence of TWIP effect in UFGs with strain progressing. Moreover, the strain of the ductile transformed ε-phase is mainly accommodated by non-basal slip, which contributes to the strain hardening in the later deformation stage. Furthermore, the higher cryogenic impact toughness of the HS-630 sample can be attributed to the synergistic effects of the fracture mode, the transformation characteristics, and the heterogeneous strain partitioning.

From threat to opportunity: Hydrologic uncertainty regionalization across large domains

Study regionThe study was carried out in Northern Canada (90,000 km2) and Southern Canada (18,000 km2). These basins represent case studies with a larger application target of high latitude regions. Study focusAs model domains become large, ungauged basins are inevitably encountered, and basin heterogeneity increases. This study aims to develop the computationally frugal Model Agnostic Uncertainty Transfer (MAUT) method to regionalize an uncertainty analysis from a set of donor basins to a receiver. The goal is an evaluation of the MAUT method for regionalizing over sufficiently heterogeneous domains to be applicable to high latitude data sparse regions. New hydrological insights for the regionWe propose the Model Agnostic Uncertainty Transfer (MAUT) method, a surrogate method to perform uncertainty analysis in large-domain modelling with a focus on high-latitude regions where data scarcity is especially severe. With the MAUT method, antecedent precipitation and simulated flow are related. Deviation from this relationship is assumed to represent modelling uncertainty and is quantified by quantile regression lines. These lines act as transfer functions requiring only antecedent precipitation to generate uncertainty bounds. The MAUT method requires uncertainty donors and a target receiver model. Key results suggest the method is relatively insensitive to precipitation dataset differences. Simulation length was the most sensitive input, with 15–20 years being ideal for reasonable results. Overall, the MAUT method is viable for high latitude domains.

Privacy-preserving Cross-domain Recommendation with Federated Graph Learning

As people inevitably interact with items across multiple domains or various platforms, cross-domain recommendation (CDR) has gained increasing attention. However, the rising privacy concerns limit the practical applications of existing CDR models, since they assume that full or partial data are accessible among different domains. Recent studies on privacy-aware CDR models neglect the heterogeneity from multiple-domain data and fail to achieve consistent improvements in cross-domain recommendation; thus, it remains a challenging task to conduct effective CDR in a privacy-preserving way. In this article, we propose a novel, as far as we know, federated graph learning approach for Privacy-Preserving Cross-Domain Recommendation (PPCDR) to capture users’ preferences based on distributed multi-domain data and improve recommendation performance for all domains without privacy leakage. The main idea of PPCDR is to model both global preference among multiple domains and local preference at a specific domain for a given user, which characterizes the user’s shared and domain-specific tastes toward the items for interaction. Specifically, in the private update process of PPCDR, we design a graph transfer module for each domain to fuse global and local user preferences and update them based on local domain data. In the federated update process, through applying the local differential privacy technique for privacy-preserving, we collaboratively learn global user preferences based on multi-domain data and adapt these global preferences to heterogeneous domain data via personalized aggregation. In this way, PPCDR can effectively approximate the multi-domain training process that directly shares local interaction data in a privacy-preserving way. Extensive experiments on three CDR datasets demonstrate that PPCDR consistently outperforms competitive single- and cross-domain baselines and effectively protects domain privacy.

Partial label learning with heterogeneous domain adaptation

Partial label learning (PLL) seeks a classification model with partially labeled (PL) instances. Each PL instance is attached with a candidate label set, with only one being ground-truth. A fundamental assumption of the previous PLL works is that there are sufficient PL instances in the learning process. Nevertheless, this assumption may not always be valid. In real-world scenarios, there may be only a few PL instances available for training. To this end, we introduce a new PLL method with heterogeneous domain adaptation (PLL-HDA). PLL-HDA leverages the knowledge from the source domain to induce a PLL classifier of the target domain, which contains only a few PL instances for training. Firstly, PLL-HDA introduces a common subspace to measure the instances from the source and target domains, where the instances from these domains are described by heterogeneous features of distinct dimensions. Secondly, we augment the features of the common subspace by merging the original features from both domains. Thirdly, a large-margin-based PLL learning system is established on the augmented features. It leads to a simplified dual form, which can be directly resolved by off-the-shelf support vector machine solvers. Lastly, a heuristic framework is proposed to resolve the PLL-HDA classification model. In this framework, the tasks of learning the classifier on the augmented features and identifying the ground-truth labels of target domain are conducted alternately. Extensive experiments on both the controlled PLL and real-world PLL datasets illustrate the superiority of PLL-HDA over the existing PLL methods. Our code is available at: https://github.com/Sux86.

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.

A Co-Training Framework for Heterogeneous Heuristic Domain Adaptation.

The purpose of this article is to address unsupervised domain adaptation (UDA) where a labeled source domain and an unlabeled target domain are given. Recent advanced UDA methods attempt to remove domain-specific properties by separating domain-specific information from domain-invariant representations, which heavily rely on the designed neural network structures. Meanwhile, they do not consider class discriminate representations when learning domain-invariant representations. To this end, this article proposes a co-training framework for heterogeneous heuristic domain adaptation (CO-HHDA) to address the above issues. First, a heterogeneous heuristic network is introduced to model domain-specific characters. It allows structures of heuristic network to be different between domains to avoid underfitting or overfitting. Specially, we initialize a small structure that is shared between domains and increase a subnetwork for the domain which preserves rich specific information. Second, we propose a co-training scheme to train two classifiers, a source classifier and a target classifier, to enhance class discriminate representations. The two classifiers are designed based on domain-invariant representations, where the source classifier learns from the labeled source data, and the target classifier is trained from the generated target pseudolabeled data. The two classifiers teach each other in the training process with high-quality pseudolabeled data. Meanwhile, an adaptive threshold is presented to select reliable pseudolabels in each classifier. Empirical results on three commonly used benchmark datasets demonstrate that the proposed CO-HHDA outperforms the state-of-the-art domain adaptation methods.

Semi-supervised heterogeneous domain adaptation for few-sample credit risk classification

Credit risk classification is a crucial area in machine learning-enhanced financial decision support systems, and numerous studies have achieved significant progress. However, data in the modern financial landscape is inherently complex, characterized by a lack of labeled data, cross-domain heterogeneity, and class imbalance. These three major problems hinder the achievement of credit risk classification. Therefore, we propose a semi-supervised heterogeneous domain adaptation method and an imbalanced data augmentation method to overcome these challenges with a single neural network-based model. Experimental results obtained from four representative benchmark datasets confirm the superior performance of the proposed method.

Biocompatibility and radiosensitivity of a fiber optical-based dosimeter: biological applications

This study introduces a cutting-edge fiber-optic dosimetry (FOD) sensor designed for measuring radiation in biological settings. The accuracy and precision of dosimeters for small animals, particularly prolonged exposure to nonuniform radiation fields, are always challenging. A state-of-the-art in-vivo dosimeter utilizing glass-encapsulated Thermoluminescence cylindrical detector (TLD) was introduced. The FODs are implanted into the rat during a prolonged irradiation scenario involving 137Cs where the rat has the freedom to move within a heterogeneous radiation domain. The implantation surgery was verified with X-ray computed tomography (CT) in addition to biochemical and pathological tests to assess the biocompatibility of FOD in vivo. A versatile FOD is designed for industrial and medical fields, which demand accurate and resilient radiation dosimeters. The dose measurements are associated with precise two-dimensional (2D) radiation distribution imaging. Three cylindrical FODs and three standards TLD_100 for each rat were tested. The measurements of peak irradiation before and after exposure reveal greater stability and superior sensitivity when compared to standard thermo-luminescence detectors in an in-vivo animal test. To the best of our knowledge, FOD testing on live animals is presented for the first time in this paper. Regarding the safety and biocompatibility of FOD, no morphological signs with any kind of inflammation or sensitivity toward the FOD material have been remarked. Moreover, with the current FOD, there is no oedema between the epidermal, dermal, and subdermal sections at the site of implantation. The results also show the stable levels of white blood cells (lymphocytes, granulocytes, MID) as blood inflammatory markers before surgery and at the time of extraction of the implanted dosimeters, thus confirming the biocompatibility for each optical fiber cylinder dosimeter. As a result, the new dosimeters have excellent biocompatibility in living tissues and have 100% accurate reusability intensity of the delivered radiation doses compared to TLD_100 which demonstrated a 45% reduction in its intensity accuracy.

Heterogeneous domain adaptation by class centroid matching and local discriminative structure preservation

Heterogeneous domain adaptation by class centroid matching and local discriminative structure preservation

Enhanced strength-ductility synergy in a gradient pseudo-precipitates heterostructured Al-2.5%Mg alloy: Design, fabrication, and deformation mechanism

Heterostructures of alloyed composites, comprising heterogeneous domains with dramatically different constitutive properties, hold remarkable potential to expand the realm of material design systems and resolve the trade-off between strength and ductility. This study introduces an innovative materials design method for synthesizing gradient pseudo-precipitates heterostructure (GPHS) in non-heat-treatable Al-2.5%Mg alloys. Utilizing cost-effective mild steel as both the diffusion source and protective layer, this heterostructure is achieved through pin-less friction stir-assisted cyclic localized deformation process. Exogenous Fe atoms diffuse across the interface by friction stir-induced heat conduction, forming Fe-Al second-phase particles in the Al alloy matrix. A rapid inter-diffusion mechanism is activated in conjunction with dense dislocation walls, grain boundaries, and sub-structures, resulting in the formation of pseudo-precipitates. These pseudo-precipitates are ultimately dispersed in a gradient distribution throughout the entire thickness of the Al alloy matrix induced by localized incremental deformation. The GPHSed Al-2.5%Mg alloy exhibits an enhanced synergy of strength and ductility, with a uniform elongation increase from 11 % to 21.2 %, while maintaining the strength. Multiple strengthening and hardening mechanisms, such as solid solution strengthening, dislocation hardening, and second phase strengthening, work synergistically to promote mechanical performance. Notably, the hetero-deformation between hard pseudo-precipitates and soft Al alloy matrix induces additional strain hardening, leading to high ductility. This work provides a fresh perspective on the design and fabrication of high-performance alloys with advanced heterostructures, especially for non-heat-treatable alloys.

Coupled multiphysical model for investigation of influence factors in the application of microbially induced calcite precipitation

The study presents a comprehensive coupled thermo-bio-chemo-hydraulic (T-BCH) modeling framework for stabilizing soils using microbially induced calcite precipitation (MICP). The numerical model considers relevant multiphysics involved in MICP, such as bacterial ureolytic activities, biochemical reactions, multiphase and multicomponent transport, and alteration of the porosity and permeability. The model incorporates multiphysical coupling effects through well-established constitutive relations that connect parameters and variables from different physical fields. It was implemented in the open-source finite element code OpenGeoSys (OGS), and a semi-staggered solution strategy was designed to solve the couplings, allowing for flexible model settings. Therefore, the developed model can be easily adapted to simulate MICP applications in different scenarios. The numerical model was employed to analyze the effect of various factors, including temperature, injection strategies, and application scales. Besides, a T-BCH modeling study was conducted on the laboratory-scale domain to analyze the effects of temperature on urease activity and precipitated calcium carbonate. To understand the scale dependency of MICP treatment, a large-scale heterogeneous domain was subjected to variable biochemical injection strategies. The simulations conducted at the field-scale guided the selection of an injection strategy to achieve the desired type and amount of precipitation. Additionally, the study emphasized the potential of numerical models as reliable tools for optimizing future developments in field-scale MICP treatment. The present study demonstrates the potential of this numerical framework for designing and optimizing the MICP applications in laboratory-, prototype-, and field-scale scenarios.

Structure and age of ancient walls from the city of Nan, northern Thailand

The structure and age of remains of two ancient walls located in the city of Nan, a former part of the Lanna Kingdom in modern northern Thailand, were investigated. The remains differ in appearance with a well-preserved brick wall present at Mahawong Road (MHW), while an earthwork with a brick reinforcement core is found at Phaya Wat Temple (PWT). We employed an electrical resistivity tomography (ERT) survey and optically stimulated luminescence dating (OSL) to determine the buried structure of the earthwork at the PWT site and the production age of bricks from both locations. The ERT results reveal lateral heterogeneous resistivity domains cross-cutting the wall, which presumably relates to the construction of a dam. According to OSL dating, the PWT wall was likely built during the time of the war with Burma that ultimately resulted in a foreign occupation of Nan in the sixteenth century. This structure played a role in both flood mitigation and military defence of the city. The structure at MHW represents a city wall that was built at the end of the nineteenth century after Nan was relocated to its present position and rather reflects representative and social needs.

Robust multiple subspaces transfer for heterogeneous domain adaptation

Heterogeneous domain adaptation (HDA) aims to execute knowledge transfer from a source domain to a heterogeneous target domain. Previous works typically inject knowledge from the source and target domain into a common subspace. However, this may lead to the ineffectiveness of knowledge transfer due to the existence of heterogeneity. To overcome this drawback, in this paper, we propose a robust multiple subspaces transfer method for heterogeneous domain adaptation. Specifically, knowledge of two domains is projected into a union of multiple subspaces via a self-expressive model, in which joint distribution alignment and dynamic Laplacian regularization on self-repressive coefficients are included in the loss for characterizing transferability. Moreover, we provide a comprehensive analysis of stability, complexity, generalization, and convergence guarantee for the proposed method. Experiments on benchmark vision and Language datasets verify effectiveness of the proposed approach for heterogeneous domain adaptation.

Optimal heterogeneous domain adaptation for text classification in transfer learning

The prime challenge of unsupervised symmetric heterogeneous cross-domain adaptation is to train the source domain and apply the trained knowledge to the target domain. Most of the existing algorithms for unsupervised transfer learning create the subspace of the source domain features and target domain features for training purposes. It is an extensive computational process as most of the techniques require labeled source data. Many techniques also suffer from the loss of originality of features in both domains. This paper aims to consider the feature vectors of both the source and target domain for training the data based on the similarity of exemplar (feature) vectors of different instances, known as Instance Similarity Feature (ISF). The use of vectorization method for the similarity of features is proposed in this paper. The exemplar vectors are chosen randomly for the target datasets. Hence, to acquire relevant factual data in the knowledge base for training in our research, we worked to increase the domain separation error between source and target instances. To avoid the instability caused due to poor exemplar vector selection, the K-means clustering approach is followed after feature similarity, known as K-means Instance Similarity Feature (KISF). Many existing transfer learning techniques are based on the original feature set, which can cause degeneracy, hence affecting Accuracy. In order to vanquish the limitations of existing approaches, we have introduced novel optimal models with KISF and Ant Lion Optimizer (KISFA), KISF with Particle Swarm Optimization (KISFP) and KISF with Biogeography Based Optimization (KISFB). High-dimensionality can impact efficacy of the model, hence, feature selection with nature-based optimizer namely: Ant Lion Optimizer, Particle Swarm Optimization and Biogeography-Based Optimization are applied. We measure the performance of the proposed models by using Support Vector Machine, Logistic Regression, Random Forest, Naive Baye’s, K-Nearest Neighbor and Decision Tree as classifiers, and Accuracy and F1-score as fitness functions. Extensive experiments are performed on four datasets with 50 iterations. The proposed model is compared with eleven other techniques and our technique outperforms all other techniques in average Accuracy. The validation is performed on the dataset using 10-fold cross-validation. The statistical test was performed using ANOVA, proving that our technique is significantly better than other techniques.

Hexagonal single crystals of perylene with patterned heterogeneous domains

Hexagonal single crystals of perylene were grown on a substrate by solution evaporation. Fluorescence microscopy images showed patterned heterogeneous domains with different fluorescence colors. Polarized ultra-low-frequency Raman microscopy revealed that the crystal structure is homogeneous throughout the entire crystal despite the domains observed in the fluorescence images. We concluded that hexagonal crystals were formed by the further growth of the rhombic β-crystal, which is one of the two polymorphs of the perylene crystal. This mechanism explains the formation of domains with different crystal qualities, resulting in different fluorescence colors.

Crack nucleation in heterogeneous bars: h- and p-FEM of a phase field model

Failure initiation and subsequent crack trajectory in heterogeneous materials, such as functionally graded materials and bones, are yet insufficiently addressed. The AT1 phase field model (PFM) is investigated herein in a 1D setting, imposing challenges and opportunities when discretized by h- and p-finite element (FE) methods. We derive explicit PFM solutions to a heterogeneous bar in tension considering heterogeneous E(x) and GIc(x)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G_{Ic}(x)$$\\end{document}, necessary for verification of the FE approximations. GIc(x)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G_{Ic}(x)$$\\end{document} corrections accounting for the element size at the damage zone in h-FEMs are suggested to account for the peak stress underestimation. p-FEMs do not require any such corrections. We also derive and validate penalty coefficient for heterogeneous domains to enforce damage positivity and irreversibility via penalization. Numerical examples are provided, demonstrating that p-FEMs exhibit faster convergence rates comparing to classical h-FEMs. The new insights are encouraging towards p-FEM discretization in a 3D setting to allow an accurate prediction of failure initiation in human bones.

On the Doubly Non-local Hele-Shaw–Cahn–Hilliard System: Derivation and 2D Well-Posedness

Starting from a classic non-local (in space) Cahn–Hilliard–Stokes model for two-phase flow in a thin heterogeneous fluid domain, we rigorously derive by mathematical homogenization a new effective mixture model consisting of a coupling of a non-local (in time) Hele-Shaw equation with a non-local (in space) Cahn–Hilliard equation. We then analyse the resulting model and prove its well-posedness. A key to the analysis is the new concept of sigma-convergence in thin heterogeneous domains allowing to pass to the homogenization limit with respect to the heterogeneities and the domain thickness simultaneously.

Microbial mediated carbon and nitrogen cycling in the spatially heterogeneous vadose zone: A modeling study

AbstractSpatially distributed properties of the subsurface result in varying water saturation and preferential flow paths, which lead to heterogeneous solute transport patterns and heterogeneous microbial environments. This, in turn, influences the distribution of nutrients and energy gradients, microbial biomass, and activity thereof. By their very nature, current field sampling techniques do not resolve subsampling scale heterogeneities in microbial biomass and activity, resulting in inaccurate estimates of microbially mediated carbon and nitrogen turnover in the heterogeneous subsurface. Thus, in this study, we undertook a numerical modeling approach to study the impact of spatial heterogeneity on microbially mediated carbon and nitrogen turnover in the vadose zone. We adapted an established biogeochemical process network that captures a variety of respiration pathways, carbon decomposition strategies, and microbial life processes to simulate microbially mediated carbon and nitrogen turnover in variably saturated spatially heterogeneous settings, using an established numerical tool (OGS#BRNS). The fractionation of microbial communities into active and inactive states, as well as immobile and mobile states followed could be linked to the bulk average saturation. Lastly, we identified three reactive systems, distinguished by the rate ratio of aerobic respiration and transfer of oxygen from the air to the water phase, to evaluate the impact of spatial heterogeneity on carbon and nitrogen removal in subsurface heterogeneous domains. Specifically, when this ratio is approximately 1, there is no impact on carbon removal, while when this ratio is very high, then carbon removal decreases as the domain tends to be oxygen limited.

Discovering causally invariant features for out-of-distribution generalization

Out-of-distribution (OOD) generalization aims to generalize a model trained on source domains to unseen target domains. Recently, causality-based generalization methods have focused on learning invariant causal relationships around the label variable, as causal mechanisms are robust across different domains. However, these methods would yield an inaccurate causal variable set due to the lack of heterogeneous domain data or a prior causal structure, which severely weakens their generalization capacity. To address this problem, we propose a Causally Invariant Features Discovery (CIFD) framework, which combines causal structure discovery and causal effect estimation for selecting a high-quality causal variable set and realizing better OOD generalization. Specifically, CIFD first identifies all potential causal variables by learning a double-layer-based local causal structure around the label variable. Secondly, CIFD uses a double-layer causal effect estimator for estimating the causality of potential causal variables and obtaining true causal variables. The comprehensive experiments on both regression and classification tasks clearly demonstrate the superiority of our framework over the state-of-art methods.

From neglect to spotlight: the underappreciated role of B cells in cutaneous inflammatory diseases.

The skin, covering our entire body as its largest organ, manifests enormous complexities and a profound interplay of systemic and local responses. In this heterogeneous domain, B cells were considered strangers. Yet, recent studies have highlighted their existence in the skin and their distinct role in modulating cutaneous immunity across various immune contexts. Accumulating evidence is progressively shedding light on the significance of B cells in maintaining skin health and in skin disorders. Herein, we integrate current insights on the systemic and local contributions of B cells in three prevalent inflammatory skin conditions: Pemphigus Vulgaris (PV), Systemic Lupus Erythematosus (SLE), and Atopic Dermatitis (AD), underscoring the previously underappreciated importance of B cells within skin immunity. Moreover, we address the potential adverse effects of current treatments used for skin diseases, emphasizing their unintentional consequences on B cells. These comprehensive approaches may pave the way for innovative therapeutic strategies that effectively address the intricate nature of skin disorders.