arXiv subjects

Peng Feng

Publications and source records attributed to Peng Feng.

9 recordsLinked to original sources

arXiv2025

Intelligent Communication Mixture-of-Experts Boosted-Medical Image Segmentation Foundation Model

Foundation models for medical image segmentation have achieved remarkable performance. Adaptive fine-tuning of natural image segmentation foundation models is crucial for medical image segmentation tasks. However, some limitations exist in existing fine-tuning methods: 1) insufficient representation of high-level features and 2) the fine-tuning process disrupts the structural integrity of pretrained weights. Inspired by these critical problems, we propose an intelligent communication mixture-of-experts boosted-medical image segmentation foundation model, named IC-MoE, with twofold ideas: 1) We construct basic experts, semantic experts, and adaptive experts. Moreover, we implement a pixel probability adaptive voting strategy, which enables expert selection and fusion through label consistency and load balancing. This approach preliminarily enhances the representation capability of high-level features while preserving the structural integrity of pretrained weights. 2) We propose a semantic-guided contrastive learning method to address the issue of weak supervision in contrastive learning. This method further enhances the representation capability of high-level features while preserving the structural integrity of pretrained weights. Extensive experiments across three public medical image segmentation datasets demonstrate that the IC-MoE outperforms other SOTA models. Consequently, the proposed IC-MoE effectively supplements foundational medical image segmentation models with high-level features and pretrained structural integrity. We also validate the superior generalizability of the IC-MoE across diverse medical image segmentation scenarios.

cs.CV↗

arXiv2025

A Novel Pipeline for the Identification of New Gamma-Ray Blazars from the 4FGL-Xiang-DR2 Catalog Based on Multi-wavelength Flux Distributions

The identification and classification of Fermi blazars are core topics in high-energy astrophysics. To enable precise spatial cross-identification, we constructed two high-precision catalogs: the updated 4FGL-Xiang-DR2 (DR2) and a supplementary version of the fifth edition of Roma-BZCAT (\texttt{5BZCAT\_err}). We then developed and applied a novel four-step analytical pipeline combining cross-matching with the statistical analysis of multi-band flux distributions to identify new Fermi blazars. The analytical pipeline has yielded several key results in the systematic comparison of BZBs and BZQs. We found that among single statistical metrics, kurtosis is the most powerful discriminator (MAD~$>$~1.64). At the overall distribution level, the 1.4~GHz, 843~MHz, 5~GHz, 0.1--2.4~keV, and 0.3--10~keV bands show significant divergence (JSD~$>$~0.3). Building on these findings, our proposed ``Box-Cox$+$TND'' model successfully fits the observed flux distributions between BZBs and BZQs. Applying this entire pipeline, we successfully identified 17 new blazars. The validity of these associations is strongly supported by our multi-wavelength flux model, which confirms that 15 of the 17 candidates are statistically consistent with the known blazar population, falling within the $2\sigma$ confidence interval. Although the two remaining sources exhibit some statistical deviation in the gamma-ray band, their strong consistency in other wavebands, coupled with high spatial association probabilities, leads us to conclude that their associations are also reliable and should not be readily excluded.

astro-ph.HE↗

arXiv2025

Immunological mechanisms and immunoregulatory strategies in intervertebral disc degeneration

Intervertebral discs are avascular and maintain immune privilege. However, during intervertebral disc degeneration (IDD), this barrier is disrupted, leading to extensive immune cell infiltration and localized inflammation. In degenerated discs, macrophages, T lymphocytes, neutrophils, and granulocytic myeloid-derived suppressor cells (G-MDSCs) are key players, exhibiting functional heterogeneity. Dysregulated activation of inflammatory pathways, including nuclear factor kappa-B (NF-kappaB), interleukin-17 (IL-17), and nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome activation, drives local pro-inflammatory responses, leading to cell apoptosis and extracellular matrix (ECM) degradation. Innovative immunotherapies, including exosome-based treatments, CRISPR/Cas9-mediated gene editing, and chemokine-loaded hydrogel systems, have shown promise in reshaping the immunological niche of intervertebral discs. These strategies can modulate dysregulated immune responses and create a supportive environment for tissue regeneration. However, current studies have not fully elucidated the mechanisms of inflammatory memory and the immunometabolic axis, and they face challenges in balancing tissue regeneration with immune homeostasis. Future studies should employ interdisciplinary approaches such as single-cell and spatial transcriptomics to map a comprehensive immune atlas of IDD, elucidate intercellular crosstalk and signaling networks, and develop integrated therapies combining targeted immunomodulation with regenerative engineering, thereby facilitating the clinical translation of effective IDD treatments.

q-bio.BM↗

arXiv2025

In-depth Investigation of Conduction Mechanism on Defect-induced Proton-conducting Electrolytes BaHfO$_3$

This study utilizes first-principles computational methods to comprehensively analyze the impact of A-site doping on the proton conduction properties of BaHfO$_3$. The goal is to offer theoretical support for the advancement of electrolyte materials for solid oxide fuel cells. Our research has uncovered that BaHfO$_3$ demonstrates promising potential for proton conduction, with a low proton migration barrier of $0.28$ eV, suggesting efficient proton conduction can be achieved at lower temperatures. Through A-site doping, particularly with low-valence-state ions and the introduction of Ba vacancies, we can effectively decrease the formation energy of oxygen vacancies ($ E_{\text{vac}} $), leading to an increase in proton concentration. Additionally, our study reveals that the primary mechanism for proton migration in BaHfO$_3$ is the Grotthuss mechanism rather than the vehicle mechanism. Examination of the changes in lattice parameters during proton migration indicates that while doping or vacancy control strategies do not alter the mode of H$^+$ migration, they do influence the migration pathway and barrier. These findings provide valuable insights into optimizing the proton conduction properties of BaHfO$_3$ through A-site doping and lay a solid theoretical foundation for the development of novel, highly efficient solid oxide fuel cell electrolyte materials.

cond-mat.mtrl-sci↗

arXiv2025

Hydrogen Bond Strength Dictates the Rate-Limiting Steps of Diffusion in Proton-Conducting Perovskites:A Critical Length Perspective

Identifying the rate-limiting step of proton migration in proton-conducting oxides is essential for assessing and regulating proton conductivity. Proton migration based on the Grotthuss mechanism involves both proton rotation and proton transfer, with the latter typically regarded as the rate-limiting step. However, a universal criterion for identifying the rate-limiting step remains to be established. Here, we perform a quantitative decomposition of the rotation and transfer barriers, revealing that the hydrogen bond to the acceptor oxygen dictates their energy barrier difference via the O$_i$-B-O$_f$ bending mechanism. Based on the energy difference associated with a one-order-of-magnitude variation in residence time, we propose the hydrogen bond length criterion for identifying the rate-limiting step across operating temperatures. Taking the 500 K criterion as an upper limit, when the hydrogen-bond length of systems falls below 2.05~\AA, proton rotation becomes competitive with transfer. Applied to a wider range of perovskite materials, this criterion predicts comparable rotation and transfer rates in cubic structures with small lattice constants, low-valent B-site doped systems with moderate ionic radii, and distorted orthorhombic structures. Our findings provide an atomic-scale insight into the proton migration mechanisms in perovskites, and offer practical guidance for optimizing and designing advanced proton-conducting electrolytes.

cond-mat.mtrl-sci↗

arXiv2024

Moisture Diffusion in Multi-Layered Materials: The Role of Layer Stacking and Composition

Multi-layered materials are everywhere, from fiber-reinforced polymer composites (FRPCs) to plywood sheets to layered rocks. When in service, these materials are often exposed to long-term environmental factors, like moisture, temperature, salinity, etc. Moisture, in particular, is known to cause significant degradation of materials like polymers, often resulting in loss of material durability. Hence, it is critical to determine the total diffusion coefficient of multi-layered materials given the coefficients of individual layers. However, the relationship between a multi-layered material's total diffusion coefficient and the individual layers' diffusion coefficients is not well established. Existing parallel and series models to determine the total diffusion coefficient do not account for the order of layer stacking. In this paper, we introduce three parameters influencing the diffusion behavior of multi-layered materials: the ratio of diffusion coefficients of individual layers, the volume fraction of individual layers, and the stacking order of individual layers. Computational models are developed within a finite element method framework to conduct parametric analysis considering the proposed parameters. We propose a new model to calculate the total diffusion coefficient of multi-layered materials more accurately than current models. We verify this parametric study by performing moisture immersion experiments on multi-layered materials. Finally, we propose a methodology for designing and optimizing the cross-section of multi-layered materials considering long-term moisture resistance. This study gives new insights into the diffusion behavior of multi-layered materials, focusing on polymer composites.

physics.app-ph↗

arXiv2022

Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference

Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials. The limited number of photons within narrow energy bins leads to imaging results of low signal-noise ratio. The existing supervised deep reconstruction networks for CT reconstruction are difficult to address these challenges because it is usually impossible to acquire noise-free clinical images with clear structures as references. In this paper, we propose an iterative deep reconstruction network to synergize unsupervised method and data priors into a unified framework, named as Spectral2Spectral. Our Spectral2Spectral employs an unsupervised deep training strategy to obtain high-quality images from noisy data in an end-to-end fashion. The structural similarity prior within image-spectral domain is refined as a regularization term to further constrain the network training. The weights of neural network are automatically updated to capture image features and structures within the iterative process. Three large-scale preclinical datasets experiments demonstrate that the Spectral2spectral reconstructs better image quality than other the state-of-the-art methods.

eess.IV↗

arXiv2019

TBC-Net: A real-time detector for infrared small target detection using semantic constraint

Infrared small target detection is a key technique in infrared search and tracking (IRST) systems. Although deep learning has been widely used in the vision tasks of visible light images recently, it is rarely used in infrared small target detection due to the difficulty in learning small target features. In this paper, we propose a novel lightweight convolutional neural network TBC-Net for infrared small target detection. The TBCNet consists of a target extraction module (TEM) and a semantic constraint module (SCM), which are used to extract small targets from infrared images and to classify the extracted target images during the training, respectively. Meanwhile, we propose a joint loss function and a training method. The SCM imposes a semantic constraint on TEM by combining the high-level classification task and solve the problem of the difficulty to learn features caused by class imbalance problem. During the training, the targets are extracted from the input image and then be classified by SCM. During the inference, only the TEM is used to detect the small targets. We also propose a data synthesis method to generate training data. The experimental results show that compared with the traditional methods, TBC-Net can better reduce the false alarm caused by complicated background, the proposed network structure and joint loss have a significant improvement on small target feature learning. Besides, TBC-Net can achieve real-time detection on the NVIDIA Jetson AGX Xavier development board, which is suitable for applications such as field research with drones equipped with infrared sensors.

cs.CV↗

arXiv2013

Analytic Comparison between X-ray Fluorescence CT and K-edge CT

X-ray fluorescence computed tomography (XFCT) and K-edge computed tomography (CT) are two important modalities to quantify a distribution of gold nanoparticles (GNPs) in a small animal for preclinical studies. It is valuable to determine which modality is more efficient for a given application. In this paper, we report a theoretical analysis in terms of signal-to-noise ratio (SNR) for the two modalities, showing that there is a threshold for the GNPs concentration and XFCT has a better SNR than K-edge CT if GNPs concentration is less than this threshold. Numerical simulations are performed and two kinds of phantoms are used to represent multiple concentration levels and feature sizes. Experimental results illustrate that XFCT is superior to K-edge CT when contrast concentration is lower than 0.4% which coincides with the theoretical analysis.

physics.med-ph↗