Heterogeneous Model Research Articles

A Comprehensive Review of Modeling of Solid Oxide Fuel Cells: From Large Systems to Fine Electrodes.

Commercialization of solid oxide fuel cell (SOFC) systems requires improved SOFC performance and durability, which is highly dependent on the coupling of the SOFC stack with other auxiliary components, SOFC stack configuration, and electrode microstructure. Optimization of SOFC systems at the system/stack/cell/electrode scale via experimentation is expensive and challenging, whereas numerical modeling can be fast and cost-effective. Although many excellent reviews on SOFCs have been published, the previous articles lack practical problem-oriented literature classification and do not cover new emerging models, such as artificial intelligence (AI) assisted models, heterogeneous models, and so on. These models are important for accelerating the solution of large-scale multiphysics models and describing mesoscopic electrode behaviors. In this review, a top-down approach is adopted that can truly guide SOFC system/stack/cell/electrode design to meet targeted applications. Another distinct feature of this review is the inclusion of the latest developments in SOFC modeling. This review offers a thorough summary and in-depth analysis of an extensive collection of research on SOFC simulations, classifying the models into distinct categories based on their varying scales, and serves as a valuable tool to assist researchers in selecting the most suitable models for diverse research objects.

A Heterogeneous Attractor Model for Neural Dynamical mechanism of Movement Preparation

A Heterogeneous Attractor Model for Neural Dynamical mechanism of Movement Preparation

EHG: efficient heterogeneous graph transformer for multiclass node classification

Graph neural networks empowered by the Transformer’s self-attention mechanism have arisen as a preferred solution for many graph classification and prediction tasks. Despite their efficacy, these networks are often hampered by their quadratic computational complexity and large model size, which pose significant challenges during graph training and inference. In this study, we present an innovative approach to heterogeneous graph transformation that adeptly navigates these limitations by capturing the rich diversity and semantic depth of graphs with various node and edge types. Our method, which streamlines the key–value interaction to a straightforward linear layer operation, maintains the same level of ranking accuracy while significantly reducing computational overhead and accelerating model training. We introduce the “EHG” model, a testament to our approach’s efficacy, showcasing remarkable performance in multiclass node classification on heterogeneous graphs. Our model’s evaluation on the DBLP, ACM, OGBN-MAG, and OAG datasets reveals its superiority over existing heterogeneous graph models under identical hyperparameter configurations. Notably, our model achieves a reduction of approximately 25% in parameter count and nearly 20% savings in training time compared to the leading heterogeneous graph-transformer models.

Inferring interphase chromosomal structure from multiplexed fluorescence in situ hybridization data: A unified picture from human and mouse cells.

We analyze multiplexed fluorescence in situ hybridization (m-FISH) data for human and mouse cell lines. The m-FISH technique uses fluorescently-labeled single-stranded probes which hybridize to specific chromosomal regions, thereby allowing the measurement of the spatial positions of up to ∼100 tagged sites for several thousands of interphase chromosomes. Our analysis focuses on a wide range of different cell lines and two distinct organisms and provides a unified picture of chromatin structure for scales ranging from 5kb (kilobases) up to 2 Mb (megabases), thus covering a genomic region of almost three orders of magnitude. Confirming recent analysis [Remini et al., Phys. Rev. E 109, 024408 (2024)], we show that there are two characteristic arrangements of chromatin referred to as phase α (crumpled globule) and phase β (looped domain) and discuss the physical properties of these phases. We show that a simple heterogeneous random walk model captures the main behavior observed in experiments and brings considerable insights into chromosomal structure.

A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography.

Magnetic resonance electrical properties tomography can extract the electrical properties of in-vivo tissue. To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are prone to various artifacts such as noise amplification and boundary artifact. Deep learning-based approaches are robust to these artifacts but need extensive training datasets and suffer from generalization to unseen data. To address these issues, we introduce a joint three-plane physics-constrained deep learning framework for polynomial fitting MR-EPT by merging physics-based weighted polynomial fitting with deep learning. Within this framework, deep learning is used to discern the optimal polynomial fitting weights for a physics based polynomial fitting reconstruction on the complex B1+ data. For the prediction of optimal fitting coefficients, three neural networks were separately trained on simulated heterogeneous brain models to predict optimal polynomial weighting parameters in three orthogonal planes. Then, the network weights were jointly optimized to estimate the polynomial weights in each plane for a combined conductivity reconstruction. Based on this physics-constrained deep learning approach, we achieved an improvement of conductivity estimation accuracy in comparison to a single plane estimation and a reduction of computational load. The results demonstrate that the proposed method based on 3D data exhibits superior performance in comparison to conventional polynomial fitting methods in terms of capturing anatomical detail and homogeneity. Crucially, in-vivo application of the proposed method showed that the method generalizes well to in-vivo data, without introducing significant errors or artifacts. This generalization makes the presented method a promising candidate for use in clinical applications.

Spectral principle for frequency synchronization in repulsive laser networks and beyond.

Network synchronization of lasers is critical for achieving high-power outputs and enabling effective optical computing. However, the role of network topology in frequency synchronization of optical oscillators and lasers remains not well understood. Here, we report our significant progress toward solving this critical problem for networks of heterogeneous laser model oscillators with repulsive coupling. We discover a general approximate principle for predicting the onset of frequency synchronization from the spectral knowledge of a complex matrix representing a combination of the signless Laplacian induced by repulsive coupling and a matrix associated with intrinsic frequency detuning. We show that the gap between the two smallest eigenvalues of the complex matrix generally controls the coupling threshold for frequency synchronization. In stark contrast with attractive networks, we demonstrate that local rings and all-to-all networks prevent frequency synchronization, whereas full bipartite networks have optimal synchronization properties. Beyond laser models, we show that, with a few exceptions, the spectral principle can be applied to repulsive Kuramoto networks. Our results provide guidelines for optimal designs of scalable optical oscillator networks capable of achieving reliable frequency synchronization.

Lithium-ion battery heterogeneous electrochemical-thermal-mechanical multiphysics coupling model and characterization of microscopic properties

In the design and optimization process of lithium-ion battery electrodes, microscopic performance characterization is extremely crucial. The current multiphysics field coupling models for lithium-ion batteries predominantly use homogeneous descriptions of electrode particles and pores, which restricts the characterization of microscale electrode properties. There are fewer heterogeneous multiphysics field coupling models capable of simultaneously integrating electrochemical, thermal, and mechanical fields. Addressing this issue, this study introduces a lithium-ion battery heterogeneous electrochemical-thermal-mechanical (HETM) multiphysical field coupled model that considers the diameter and spatial distribution of active particles. This model employs the Monte Carlo method to reconstruct two-dimensional electrodes and establishes the corresponding electrochemical-thermal-mechanical multiphysical field coupled theoretical and finite element simulation models. The HETM model is validated through discharge and charge curves at various C-rates and material, demonstrating higher accuracy and the ability to provide more precise data for the design and optimization of lithium-ion batteries. The model considers the bidirectional coupling between electrochemical-thermal and electrochemical-mechanical processes, as well as the unidirectional coupling between thermal and mechanical interactions, allowing for a more comprehensive analysis of both the microscopic and macroscopic characteristics of the battery.

How to build and solve continuous-time heterogeneous agents models in asset pricing? The martingale approach and the finite difference method

How to build and solve continuous-time heterogeneous agents models in asset pricing? The martingale approach and the finite difference method

Heterogeneous graph representation learning via mutual information estimation for fraud detection

In the fraud detection, fraudsters frequently engage with numerous benign users to disguise their activities. Consequently, the fraud graph exhibits not only homogeneous connections between the fraudsters and the same labelled nodes, but also heterogeneous connections, where fraudsters interact with the legitimate nodes. Heterogeneous graph representation learning aims at extracting the structural and semantic information and embed it into the low-dimensional node representation. Recently, maximizing the mutual information between the local node embedding and the summary representation has achieved the promising results on node classification tasks. However, existing deep graph infomax methods still have the following limitations. Firstly, attribute information of nodes in the graph is not fully utilized for capturing the semantic relationships between nodes. Secondly, the local and global supervision signal are not simultaneously exploited for the node embedding learning. Thirdly, the multiplex heterogeneous relations among nodes are ignored. To address these issues, a heterogeneous graph representation learning model by mutual information estimation (MIE-HetGRL) is proposed in this paper to identify the fraudsters in the fraud review graph. Concretely, a high-order mutual information estimation is proposed to integrate the local and global mutual information as the supervision signal. Then we devise a semantic attention fusion module to aggregate the relation-aware node embeddings into a compact node representation. Finally, a joint contrastive learning is designed for facilitating the training and optimization of model. The experimental results show that our proposed model significantly outperforms state-of-the-art baselines for fraud detection.

Integrating AI and climate change scenarios for multi-risk assessment in the coastal municipalities of the Veneto region.

Global climate is experiencing exceptional warming, leading to a rise in extreme events worldwide. Coastal regions are particularly vulnerable to climate change (CC), due to dense populations, interconnected economies, and fragile ecosystems. These areas face escalating risks as CC intensifies the severity and frequency of extreme weather phenomena, like heavy precipitation, sea-level rise (SLR), storm surges. Integrated approaches are crucial to assess the combined impacts of atmospheric and marine hazards at the land-sea interface. Machine Learning (ML) offer innovative solutions to analyse multi-risk events, leveraging large and heterogeneous datasets and modelling complex, non-linear interactions. This study introduces a two-tier ML approach to estimate risks associated with extreme weather events for the Veneto coastal municipalities under current and future scenarios. The model, tested and validated with present-day data, showed satisfactory performance (error margin ∼20%). The model was applied to mid-term (until 2045) and long-term (until 2100) periods under different CC scenarios, represented by various Representative Concentration Pathways (RCP). Mid-term analysis reveals an increasing risk trend, driven by SLR under RCP8.5, underscoring the significance of considering non-linear interactions between multiple marine and atmospheric hazards. Long-term analysis highlights how future risks depend mainly on precipitation and SLR across the analysed CC scenarios (RCP2.6/4.5/8.5). Results indicate a gradual increase in the expected annual risk trend, with RCP8.5 scenario showing the most severe outcomes. By 2100, the risks under RCP8.5 are projected to be ten times higher than those observed during the historical period, highlighting the importance of developing effective strategies to address these challenges.

INTER-INDIVIDUAL RESPONSE DIFFERENCES ON CARDIORESPIRATORY FITNESS IN HEART FAILURE

https://youtu.be/wu8t-3F-vJc BACKGROUND Previous studies have shown an association between low cardiorespiratory fitness (CRF) and mortality in adults with heart failure with preserved ejection fraction (HFpEF). While exercise-based cardiac rehabilitation (EBCR) has been reported to increase CRF in adults with HFpEF, true inter-individual response differences (IIRD) to EBCR among those with HFpEF is not known nor has it ever been assessed. The purpose of this study was to address this gap. METHODS Using data from the recent American Heart Association and American College of Cardiology statement on supervised exercise training for chronic HFpEF, eight randomized controlled trials representing 503 adults (260 exercise, 243 control) were included and pooled for a standard deviation of individual response (SDIR) meta-analysis on changes in CRF, assessed as VO2peak in mL.kg-1min-1. The inverse heterogeneity (IVhet) model was used to pool results. RESULTS The pooled 95% confidence interval (CI) for the SDIR included zero (mean, 0.5, 95% CI, -1.9 to 2.0 mL.kg-1min-1). With each study deleted from the model once, results continued to not reach statistical significance. The 95% prediction interval for the SDIR, indicative of what result one might expect is they conducted their own randomized controlled trial, was -3.3 to 3.3 mL.kg-1min-1. The probability of a clinically meaningful difference of at least 1.0 mL.kg-1.min-1 in VO2peak was 50.6% (only possibly clinically important). CONCLUSION There is a lack of exercise-associated IIRD on VO2peak in adults with HFpEF once random and within-subject variation are properly accounted for. Thus, a search for potential moderators and mediators, including genetic interactions, on VO2peak in adults with HFpEF cannot be supported at this time.

Heterogeneous labour, city size and transportation in agglomeration equilibrium

ABSTRACT This paper analyses the theoretical mechanism of skill heterogeneity across city size and transportation under the ‘new’ new economic geography (NNEG) framework. We make a heterogeneous selection model from the NNEG framework and a heterogeneous assumption for skilled workers with continuous density function ∫ s _ s ¯ ⁡ f ( s ) ds , in which both iceberg-type transport costs and the value of spatial interactions between workers from across-cities are taken into account in a spatial economy. Our framework shows that the presence of transportation and city size creates a mechanism which always promotes spatial sorting for heterogeneous workers according to skills’ spatial distribution. Agglomeration equilibrium in large cities is the unique equilibrium and it is stable when the cost of trade and the value of spatial interactions between workers exist across-cities. It is obvious that tougher competition in larger cities requires more high-skilled workers, thus becoming more attractive to skilled job seekers. Highlights Introducing the quasi-linear quadratic utility function into the heterogeneous labour analytical framework to combine the relationship between city size and price elasticity. Considering both the iceberg-type transport costs and the value of spatial interactions between workers in our heterogeneous equilibrium model. Providing endogenous cutoff marginal cost determined by the spatial distribution of heterogeneous labour and transportation among local and surrounding areas. Offering a new theoretical framework from the assumption of probability density function for heterogeneous labour.

Accuracy of two-compartment modelling of gas exchange with ventilation-perfusion mismatch in inhalational anesthesia.

Multi-compartment computer models of heterogeneity in alveolar ventilation-perfusion ratios (VA/Q scatter) across the lung explain the significant alveolar-arterial (A-a) partial pressure gradients and associated alveolar dead-space fractions (VDA/VA) seen in anesthetized patients for both carbon dioxide and for anesthetic gases of different blood solubilities. However, the accuracy of a simpler two-compartment model of VA/Q scatter to do this has not been tested or compared to calculations from the traditional Riley model with "ideal", unventilated (shunt) and unperfused (deadspace) compartments. Measurements of gas partial pressures in inspired and expired gas and arterial and mixed venous blood from 29 patients undergoing inhalational general anesthesia for cardiac surgery was used to compare the accuracy of two simple models of VA/Q scatter and lung gas exchange in predicting measured alveolar and arterial partial pressure differences, and associated alveolar dead-space calculations for the modern anesthetic gases isoflurane, sevoflurane and desflurane. These models were the Riley model, and a two-compartment model with reciprocal proportions of allocation of VA and Q, with and without additional true-shunt. A multi-compartment "log-normal" model was also tested. Mean (95% confidence interval) of the measured alveolar dead-space fraction for the three anesthetic gases G combined (VDA/VAG) was 0.557 (0.523, 0.592). Mean VDA/VAG from the two-compartment model incorporating an additional true-shunt lung compartment (0.539 (0.498, 0.580) was similar to the measured value (p = 0.347), and was 0.501 (0.457, 0.546) without a true-shunt compartment. The log-normal model outputs were 0.491 (0.453, 0.529)). The Riley model outputs for VDA/VAG severely underestimated this (0.327 (0.294, 0.361)). Satisfactory prediction of the A-a partial pressure gradients and alveolar dead-space for the modern volatile anesthetic gases measured in vivo requires a model with more than one gas-exchanging lung compartments, which the traditional Riley model lacks. A simple "reciprocal" two-compartment model achieves this.

Spatial Heterogeneity Modeling of the Neighborhood Effects and Socio-economic Factors on Burglary Crimes in Nigeria

Spatial Heterogeneity Modeling of the Neighborhood Effects and Socio-economic Factors on Burglary Crimes in Nigeria

Behavior changes influence mpox transmission in the United States, 2022–2023: Insights from homogeneous and heterogeneous models

Abstract In 2022, an unprecedented mpox epidemic rapidly swept the globe, primarily transmitted through sexual contact among men who have sex with men (MSM). However, our understanding of how changes in human behavior influence this outbreak remains incomplete. In this study, we introduce a novel two-layer network model to investigate the impact of human behavior on mpox transmission within the United States (US) during 2022-2023, leveraging surveillance data. We theoretically explore mpox transmission under behavioral changes using homogeneous and heterogeneous mean-field approximations. While the heterogeneous model captures differences in individual behavior, its variations do not significantly affect the overall spread, validating the feasibility of using only homogeneous models to study behavioral changes. Utilizing infection data, we exhibit the influence of behavior changes across varying transmission levels of mpox, emphasize the significant role of sexual behavior among MSM, and recommend enhancing surveillance of non-sexual cases to enable timely control of spread. Utilizing vaccination data, we demonstrate the critical impact of behavior changes on the transmission capacity of monkeypox virus (MPXV), contrasting the limited effectiveness of vaccine campaigns. This study highlights the importance of human behavior in controlling the spread of future outbreaks, offering valuable insights for the strategic development of public health interventions aimed at mitigating such occurrences.

Characterization of Rock Pore Geometry and Mineralization Process via a Random Walk-Based Clogging Scheme

Among the technologies for carbon neutral, CO2 geological sequestration is one of the most promising. The mechanisms of CO2 behavior within pore space are complex and influenced by multiple factors, with the geometric structure of porous formations being particularly critical to the technology’s efficiency. Among several important and challenging problems in geological sequestration, this work addresses the issue of selecting lithologies based on their geometrical structure. This study proposes a mathematical approach to characterize the geometric structure of porous rock and fluid flow using a random walk (RW) method. Our approach simulates the time evolution of particle flow through highly disordered and heterogeneous digital rock models under a pressure gradient imposed between inlet and outlet surfaces. Through RW simulations, a probabilistic model for mineralization via a clogging (pore-filling) model is introduced, to examine the accumulation of particles within porous structures over time: single-phase clogging and multiple-phase clogging. In single-phase clogging, the porosity decrease can be described as a monotonically non-increasing function of the deposition probability. However, this is no longer true in the multiple-phase strategy because large deposition probability blocks the capillaries near the inlet surface, preventing the fluid from easily invading easily the outlet. In this study, numerical studies conducted on four types of natural rocks—Bentheimer, Doddington, Estaillades, and Ketton—revealed that Ketton exhibits the highest permeability. Our results suggest that Bentheimer, Doddington, and Ketton formations are suitable candidates for CO2 sequestration, while Estaillades is less favorable from a geometric standpoint. The methods presented in this work contribute to effectively identifying natural rocks with geometric structures advantageous for CO2 storage.

TRIM28-dependent developmental heterogeneity determines cancer susceptibility through distinct epigenetic states.

Mutations in cancer risk genes increase susceptibility, but not all carriers develop cancer. Indeed, while DNA mutations are necessary drivers of cancer, only a small subset of mutated cells go on to cause the disease. To date, the mechanisms underlying individual cancer susceptibility remain unclear. Here, we took advantage of a unique mouse model of intrinsic developmental heterogeneity (Trim28+/D9) to investigate whether early-life epigenetic variation influences cancer susceptibility later in life. We found that heterozygosity of Trim28 is sufficient to generate two distinct early-life epigenetic states associated with differing cancer susceptibility. These developmentally primed states exhibit differential methylation patterns at typically silenced heterochromatin, detectable as early as 10 days of age. The differentially methylated loci are enriched for genes with known oncogenic potential, frequently mutated in human cancers and correlated with poor prognosis. This study provides genetic evidence that intrinsic developmental heterogeneity can prime individual, lifelong cancer susceptibility.

Analysis of a diffusive brucellosis model with partial immunity and stage structure in heterogeneous environment

In this paper, in order to study comprehensive effect of stage-structure, incomplete immunity and spatial diffusion on the transmission dynamics of sheep brucellosis, we formulate a reaction-diffusion brucellosis model with partial immunity and stage structure in heterogeneous environment. Firstly, the well-posedness of the system is investigated, including the existence of global solution and its ultimate boundedness, and then the basic reproduction number R0 is defined using the next generation operator. Further, the threshold criteria on the global dynamics of the model are established in terms of R0 in two special cases. That is, if R0<1, the disease-free steady state is globally asymptotically stable, while if R0>1 1$\\end{document}]]>, the model is uniformly persistent and there at least exists a endemic steady state. Furthermore, for the homogeneous space and heterogeneous diffusion model, by constructing suitable Lyapunov functions, we obtain the global asymptotic stability for the disease-free steady-state when R0≤1 and the global asymptotic stability endemic steady states when R0>11$\\end{document}]]>. Finally, two simulation examples are given to verify our theoretical results.

Learnable Anchor Embedding for Asymmetric Face Recognition

Face verification and identification traditionally follow a symmetric matching approach, where the same model (e.g., ResNet-50 vs. ResNet-50) generates embeddings for both gallery and query images, ensuring compatibility. However, real-world scenarios often demand asymmetric matching, especially when query devices have limited computational resources or employ heterogeneous models (e.g., ResNet-50 vs. SwinTransformer). This asymmetry can degrade face recognition performance due to incompatibility between embeddings from different models. To tackle this asymmetric face recognition problem, we introduce the Learnable Anchor Embedding (LAE) model, which features two key innovations: the Shared Learnable Anchor and a Light Cross-Attention Mechanism. The Shared Learnable Anchor is a dynamic attractor, aligning heterogeneous gallery and query embeddings within a unified embedding space. The Light Cross-Attention Mechanism complements this alignment process by reweighting embeddings relative to the anchor, efficiently refining their alignment within the unified space. Extensive evaluations of several facial benchmark datasets demonstrate LAE’s superior performance, particularly in asymmetric settings. Its robustness and scalability make it an effective solution for real-world applications such as edge-device authentication, cross-platform verification, and environments with resource constraints.

Bioprinted Fibroblast Mediated Heterogeneous Tumor Microenvironment for Studying Tumor-Stroma Interaction and Drug Screening.

Cancer-associated fibroblasts (CAFs) are crucial stromal cells in the tumor microenvironment, affecting cancer growth, angiogenesis, and matrix remodeling. Developing an effective in vitro tumor model that accurately recapitulates the dynamic interplay between tumor and stromal cells remains a challenge. In this study, a 3D bioprinted fibroblast - mediated heterogeneous breast tumor model was created, with tumor cells and fibroblasts in a bionic matrix. The impact of transforming growth factor-β （TGF-β） on the dynamic transformation of normal fibroblasts into CAFs and its subsequent influence on tumor cells is further investigated. These findings reveales a profound correlation between CAFs and several critical biological processes, including epithelial-mesenchymal transition (EMT), extracellular matrix (ECM) remodeling, gene expression profiles, and tumor progression. Furthermore, tumor models incorporating CAFs exhibits reduced drug sensitivity compared to models containing tumor cells alone or models co-cultured with normal fibroblasts. These results underscore the potential of the in vitro fibroblast-mediated heterogeneous tumor model to simulate real-life physiological conditions, thereby offering a more effective drug screening platform for elucidating tumor pathogenesis and facilitating drug design prior to animal and clinical trials. This model's establishment promotes the understanding of tumor-stromal interactions and their therapeutic implications.