Multiple Losses Research Articles

Cytomorphologic and molecular characterization of spindle cell carcinoid tumors of the lung.

Spindle cell carcinoid tumor (SCCT) is a rare variant of lung carcinoid tumor consisting predominantly or exclusively of spindle cells. To the authors' knowledge, this is the first study to date investigating the molecular characteristics of SCCTs. Eighty-five carcinoid tumors initially diagnosed by fine-needle aspiration over a period of 10 years were reviewed. The final diagnostic classification was based on resection specimens. Six SCCTs were identified and characterized based on cytomorphology, and immunohistochemical and molecular features. Most patients with SCCT were Caucasian (100.0%), women (83.3%), asymptomatic (66.7%), and nonsmokers (83.3%). The median age at diagnosis was 78.0 years (range, 58.2-80.3 years). A higher proportion of patients who had SCCT were diagnosed with distant metastasis. The smears were cellular and demonstrated clean backgrounds without necrosis or mitotic activity. SCCTs comprised of bipolar-to-elongated cells with finely granular chromatin, inconspicuous nucleoli, scant cytoplasm, and minimal atypia or pleomorphism. The tumor cells sometimes appeared boomerang-shaped and might mimic granulomas or blood vessels. SCCTs showed strong expression for pan-cytokeratin, synaptophysin, chromogranin, and CD56, with weak TTF-1 and a very low Ki-67 proliferation index. All SCCTs had low tumor mutational burden and were microsatellite-stable. One case showed multiple whole-gene losses in chromosome 11, whereas another harbored duplication in ARID1A. Two cases demonstrated gains in chromosomes 17, one of which also showed gains in chromosome18. None had a single nucleotide mutation. SCCT is a rare subset of lung carcinoid tumors. These tumors harbor unique cytologic, prognostic, and molecular features that may have significant diagnostic and clinical implications.

Robust Fuzzy Adaptive Fault‐Tolerant Control for a Class of Second‐Order Nonlinear Systems

ABSTRACTIn this brief, an optimal active fuzzy fault‐tolerant control (OAFFTC) scheme is proposed for a class of unknown perturbed multi‐input multi‐output (MIMO) nonlinear systems with nonaffine nonlinear actuator faults and time‐varying sensor faults. The control strategy can handle automatically (online updating) with three additives (bias, drift, and loss of accuracy) along with one multiplicative (loss of effectiveness) sensor faults and nonlinear state‐dependent actuator faults. In order to deal with uncertain system dynamics, sensor and actuator faults, and external disturbances, fuzzy systems (FSs) and backstepping techniques were combined to provide the adaptive control term as well as a robust control term. Butterworth filter is used to get rid the algebraic loop problem. The suggested robust term, can deal with approximation errors of the fuzzy systems (FSs). To automatically optimize the adaptive parameters and the starting conditions, particle swarm optimization (PSO) approach is introduced. The Lyapunov approach is employed to demonstrate the closed‐loop system's stability. To assess the efficacy and correctness of the suggested scheme, quadrotor dynamic model is performed on the simulation part.

Enhancing Autism Spectrum Disorder identification in multi-site MRI imaging: A multi-head cross-attention and multi-context approach for addressing variability in un-harmonized data

Multi-site MRI imaging poses a significant challenge due to the potential variations in images across different scanners at different sites. This variability can introduce ambiguity in further image analysis. Consequently, the image analysis techniques become site-dependent and scanner-dependent, implying that adjustments in the analysis methodologies are necessary for each scanner configuration. Further, implementing real-time modifications becomes intricate, particularly when incorporating a new type of scanner, as it requires adapting the analysis methods accordingly. Taking into account the aforementioned challenge, we have considered its implications for an Autism spectrum disorder (ASD) application. Our objective is to minimize the impact of site and scanner variability in the analysis, aiming to develop a model that remains effective across different scanners and sites.This entails devising a methodology that allows the same model to function seamlessly across multiple scanner configurations and sites. ASD, a behavioral disorder affecting child development, requires early detection. Clinical observation is time-consuming, prompting the use of fMRI with machine/deep learning for expedited diagnosis. Previous methods leverage fMRI’s functional connectivity but often rely on less generalized feature extractors and classifiers. Hence, there is significant room for improvement in the generalizability of detection methods across multi-site data, which is acquired from multiple scanners with different settings. In this study, we propose a Cross-Combination Multi-Scale Multi-Context Framework (CCMSMCF) capable of performing neuroimaging-based diagnostic classification of mental disorders for a multi-site dataset. Thus, this framework attains a degree of internal data harmonization, rendering it to some extent site and scanner-agnostic. Our proposed network, CCMSMCF, is constructed by integrating two sub-modules: the Multi-Head Attention Cross-Scale Module (MHACSM) and the Residual Multi-Context Module (RMCN). We also employ multiple loss functions in a novel manner for training the model, which includes Binary Cross Entropy, Dice loss, and Embedding Coupling loss. The model is validated on the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset, which includes data from multiple scanners across different sites, and achieves promising results.

An ultrathin ultralight electromagnetic absorber based on shortcut glass-coated amorphous magnetic Fiber/Salisbury-like screen

A structural design methodology is proposed for an ultrathin, ultralight, and absorption-adjustable electromagnetic absorber. The proposed absorber (SFSL) consists of an absorbing layer with shortcut glass-coated amorphous magnetic fiber and a substrate layer with transmitting material. This absorber features a Salisbury-like screen structure and incorporates multiple loss mechanisms. By investigating the influence of fiber distribution, length, content, and substrate layer thickness on absorption performance, it has been determined that the weight per square meter and thickness of a single-layer SFSL can be lowered within 50 g/m2 and 1.5 mm respectively. Furthermore, the absorption intensity and bandwidth can be adjusted by manipulating these parameters. The SFSL exhibits resonant behavior similar to that of a metamaterial absorber; however, SFSL with randomly distributed fibers demonstrates broader and stronger absorption characteristics in the frequency range from 2 to18 GHz. Additionally, the thicknesses of the substrate layer and surface covering affect the electromagnetic response characteristics. This work provides a simple strategy for constructing an ultrathin and ultralight composite to achieve efficient absorption of electromagnetic waves.

An interpretable unsupervised capsule network via comprehensive contrastive learning and two-stage training

Limited attention has been given to unsupervised capsule networks (CapsNets) with contrastive learning due to the challenge of harmoniously learning interpretable primary and high-level capsules. To address this issue, we focus on three aspects: loss function, routing algorithm, and training strategy. First, we propose a comprehensive contrastive loss to ensure consistency in learning both high-level and primary capsules across different objects. Next, we introduce an agreement-based routing mechanism for the activation of high-level capsules. Finally, we present a two-stage training strategy to resolve conflicts between multiple losses. Ablation experiments show that these methods all improve model performance. Results from linear evaluation and semi-supervised learning demonstrate that our model outperforms other CapsNets and convolutional neural networks in learning high-level capsules. Additionally, visualizing capsules provides insights into the primary capsules, which remain consistent across images and align with human vision.

Multiple independent losses of crossover interference during yeast evolutionary history.

Meiotic recombination is essential for the accurate chromosome segregation and the generation of genetic diversity through crossover and gene conversion events. Although this process has been studied extensively in a few selected model species, understanding how its properties vary across species remains limited. For instance, the ancestral ZMM pathway that generates interference-dependent crossovers has undergone multiple losses throughout evolution, suggesting variations in the regulation of crossover formation. In this context, we first characterized the meiotic recombination landscape and properties of the Kluyveromyces lactis budding yeast. We then conducted a comprehensive analysis of 29,151 recombination events (19, 212 COs and 9, 939 NCOs) spanning 577 meioses in the five budding yeast species Saccharomyces cerevisiae, Saccharomyces paradoxus, Lachancea kluyveri, Lachancea waltii and K. lactis. Eventually, we found that the Saccharomyces yeasts displayed higher recombination rates compared to the non-Saccharomyces yeasts. In addition, bona fide crossover interference and associated crossover homeostasis were detected in the Saccharomyces species only, adding L. kluyveri and K. lactis to the list of budding yeast species that lost crossover interference. Finally, recombination hotspots, although highly conserved within the Saccharomyces yeasts are not conserved beyond the Saccharomyces genus. Overall, these results highlight great variability in the recombination landscape and properties through budding yeasts evolution.

Construction of rGO/MOF-derived CNTs aerogel with multiple losses for multi-functional efficient electromagnetic wave absorber

Graphene aerogels have attracted increasing attention in electromagnetic wave (EMW) absorption due to their ultra-low density and excellent dielectric performance. However, pure graphene aerogels have limited loss mechanisms, and it remains a challenge to introduce multiple loss mechanisms while maintaining lightweight. Here, reduced graphene oxide/metal organic framework (MOF)-derived carbon nanotubes (rGO/MD@CNTs) aerogels are successfully constructed by uniformly compositing GO and MOF particles by hydrothermal treatment and further in-situ growing CNTs by chemical vapour deposition method. GO and MOF particles form strong bonding sites due to their interaction to construct the aerogel with excellent mechanical property, while magnetic loss is also introduced. The MOF-catalyzed CNTs not only contribute to the formation of interface and dipole polarizations, but also effectively regulate the conductivity to improve impedance matching. rGO/MD@CNTs aerogel integrates multiple losses and optimal impedance matching. With a filling ratio of 6 wt%, the aerogel exhibits an excellent reflection loss of −51.6 dB and a wide effective absorption bandwidth of 5.9 GHz. In addition, the aerogel shows good thermal stability and antifreeze properties. Therefore, this work provides an effective strategy for obtaining the aerogel with multiple losses for efficient EMW absorption, demonstrating the potential applications of rGO/MD@CNTs aerogel in complex environments.

Multi-branch evolutionary generative adversarial networks based on covariance crossover operators

Generative Adversarial Networks (GANs) have brought surprises in image generation and processing, particularly excelling in alleviating the problem of sparse samples in few-shot learning. Evolutionary Generative Adversarial Networks (EGANs) aim to convert the generation task into an optimization problem by integrating multiple loss functions to minimize the gap between the generated distribution and the data distribution. However, EGANs’ heavy reliance on mutation operations introduces excessive randomness, resulting in unstable generator updates and affecting the diversity of generated samples. This leads to challenges such as model collapse and gradient vanishing. In this paper, inspired by human grafting mechanism, we propose a novel Multi-branch Evolutionary Generative Adversarial Network (ME-GAN) based covariance crossover operator, which includes two distinct branches – Evolutionary GAN (E-GAN) and Conditional Evolutionary GAN (CE-GAN). Specifically, the E-GAN branch involves a mutation process to introduce randomness to the generated samples, while the CE-GAN branch aims to provide more realistic and diversify generated samples through conditional enhancement and relevant mutation processes. We propose a crossover operation that utilizes the covariance similarity metric to transfer different feature attributes between offspring of different generations, thereby generating a diverse sample. Extensive experiments on CIFAR-10, STL-10, and CelebA datasets demonstrate the effectiveness and superiority of the proposed framework.

Defect-rich carbon nanosheets derived from p(C3O2)x for electromagnetic wave absorption applications

Defect modulation strategies have been shown to be an effective way to design efficient EMW absorbing materials, but the coexistence of multiple loss mechanisms due to the complexity of the existing models makes it difficult to elucidate the mechanism by which defect-induced dielectric losses dominate. In this work, p(C3O2)x is applied for the first time in the field of EMW absorption and the concentration of defects in the sample is controlled by changing the pyrolysis temperature. In addition, the unique molecular structure of p(C3O2)x enables the prepared samples to completely eliminate the interference of interfacial polarization and magnetic loss on EMW dissipation. The results show that the dielectric loss induced by defects significantly enhances the EMW absorption performance as the concentration of defects increases, but excessive defects lead to a sudden drop in the conductivity of the sample and reduce the EMW absorption performance. In which, the RLmin of OC-800 can reach −51.0 dB, and the EAB of OC-900 can go up to 5.6 GHz at only 1.6 mm. Finally, CST simulation verified the potential application of the prepared absorber in real scenarios. This work has improved the theoretical basis of the effect of defect-induced dielectric loss on EMW absorbing properties, and the simple synthetic raw materials and routes have made the industrialized production of highly efficient EMW absorbing materials possible.

Achieving excellence with interdisciplinary approaches in complex orthodontic adult patients

Over the last decades the percentage of adult orthodontic patients has substantially increased. Undeniably, an important motif for seeking orthodontic care at an older age is smile improvement, but this is not all. Frequently, impaired dentofacial aesthetics are combined with several other issues: severe dentoskeletal malocclusions; multiple tooth loss due to caries or endodontic failure; dental agenesis or trauma; periodontal breakdown; or functional problems, such as temporomandibular disorders or obstructive sleep apnoea. Therefore, comprehensive adult treatment mostly requires close collaboration of a team of equally well-trained dental specialists to select and execute the most suitable treatment option for the individual patient from day one. With joint planning, intensive communication with the patient and the involved specialists, and continuous monitoring of the treatment process, true patient-centred care can be delivered. In order to serve the individual patient best, the treating orthodontist does not only need thorough speciality training and continuing education, but furthermore, should be well-equipped with sufficient knowledge of the other dental fields of expertise. In combination with digital technology as an important tool for enhancing communication and efficient exchange of information between all involved team members, excellent joint clinical skills will take comprehensive interdisciplinary treatment to the next level.

Rational construction of dicarbonous CB/CNT@PI composites toward low filler loading and low-frequency electromagnetic wave absorption.

With the application of low frequency radar and the demand for stealth of high temperature resistant components, it is increasingly urgent to develop absorbing materials with both low frequency and high temperature resistant properties. Here, we successfully prepared various carbon/polyimide composites as low-frequency electromagnetic wave absorbing materials by simple blending method. The well-designed mesh lap structure introduces a large amount of free space, further optimizing the impedance matching of the material. At the same time, the multiple loss mechanism formed by the combination of carbon black dominated polarization and carbon nanotube dominated conductive loss enhances the loss of incident electromagnetic wave. The results showed that only 10 wt% filler loading of the CB/CNT@PI is achieved in the low frequency range (1-4 GHz) with a minimum RL strength of - 18.3 dB, which has obvious advantages compared with other works in recent years. This study provides a way for the design and preparation of resin-based absorbing materials.

Cryo-EM structure and polar assembly of the PS2 S-layer of Corynebacterium glutamicum.

The polar-growing Corynebacteriales have a complex cell envelope architecture characterized by the presence of a specialized outer membrane composed of mycolic acids. In some Corynebacteriales, this mycomembrane is further supported by a proteinaceous surface layer or 'S-layer', whose function, structure and mode of assembly remain largely enigmatic. Here, we isolated ex vivo PS2 S-layers from the industrially important Corynebacterium glutamicum and determined its atomic structure by 3D cryoEM reconstruction. PS2 monomers consist of a six-helix bundle 'core', a three-helix bundle 'arm', and a C-terminal transmembrane (TM) helix. The PS2 core oligomerizes into hexameric units anchored in the mycomembrane by a channel-like coiled-coil of the TM helices. The PS2 arms mediate trimeric lattice contacts, crystallizing the hexameric units into an intricate semipermeable lattice. Using pulse-chase live cell imaging, we show that the PS2 lattice is incorporated at the poles, coincident with the actinobacterial elongasome. Finally, phylogenetic analysis shows a paraphyletic distribution and dispersed chromosomal location of PS2 in Corynebacteriales as a result of multiple recombination events and losses. These findings expand our understanding of S-layer biology and enable applications of membrane-supported self-assembling bioengineered materials.

Regulating the phase composition and microstructure of Fe3Si/SiC nanofiber composites to enhance electromagnetic wave absorption

The rational introduction of multi-components to fabricate electromagnetic wave absorbing (EMA) materials with synergistic conductive/dielectric/magnetic losses promises lightweighting and excellent EMA performance, but it remains a challenge. In this work, multicomponent Fe3Si/SiC nanofibre composites with network structure were constructed by electrostatic spinning method and in-situ carbothermal reduction strategy. Design of microstructures and multicomponent modulation by controlling the carbothermal reduction temperature. As a result, the presence of a large number of non-homogeneous interfaces, three-dimensional (3D) conductive network structures and defect structures in Fe3Si/SiC nanofibre composites induces a combination of multiple loss mechanisms that significantly improve the EMA performance. When the filling amount of Fe3Si/SiC in the paraffin transmission matrix is 20 wt%, the maximum effective absorption bandwidth (EABmax) of the fabricated material reaches 5.84 GHz with a thin thickness of 2.02 mm. Moreover, the minimum reflection loss (RLmin) value at 10.96 GHz is as low as −67.57 dB. Meanwhile, the radar cross-section (RCS) simulation verifies that the F-4 peak RCS is reduced to −30.37 dB in the range of −60°<θ < 60°. It indicates that the Fe3Si/SiC nanofiber composites have a good radar-wave dissipation capability in practical applications. In summary, the comprehensive performance of lightweight multicomponent Fe3Si/SiC nanofiber composites can meet the new application requirements and is expected to become an emerging multifunctional wave-absorbing material suitable for harsh environments.

Masked contrastive graph representation learning for age estimation

Age estimation of face images is a crucial task with various practical applications in areas such as video surveillance and Internet access control. While deep learning-based age estimation methods like Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Transformers have shown remarkable performance, they have limitations when modeling complex or irregular objects, and often contain redundant information. Although existing graph neural networks (GNNs) can alleviate this issue, they only considers the structural information of the image and ignores the semantic features, thus the feature representation capability of graph nodes is limited. To address these issues, this paper proposes a novel Masked Contrastive Graph Representation Learning (MCGRL) method for accurate age estimation of face images. Our approach leverages CNN to extract semantic features of the image, which are then partitioned into patches that serve as nodes in the graph. We use a masked graph convolutional network (GCN) to derive image-based node representations that capture rich structural information. Finally, we incorporate multiple losses to explore the complementary relationship between structural information and semantic features, which improves the feature representation capability of GCN. Experimental results on real-world face image datasets demonstrate the superiority of our proposed method over other state-of-the-art age estimation approaches. Our code is available at https://github.com/yuntaoshou/MCGRL

Enhanced electromagnetic wave absorption performance of Ta₄C₃Tx MXenes with optimized interlayer spacing via hydrofluoric acid etching

Ta4C3Tx MXene, a novel two-dimensional material, shows significant application potential for electromagnetic wave (EMW) absorption because of its unique layered structure and high conductivity. In this study, hydrofluoric acid etching was used to obtain Ta4C3Tx MXenes with varying interlayer spaces by adjusting the etching duration, which enhanced the EMW absorption performance. The impact of interlayer spacing on the EMW absorption performance of Ta4C3Tx MXene was systematically evaluated for the first time. The results show that the sample etched with hydrofluoric acid for 48 h (S2-48 h) exhibited the best EMW absorption performance. For S2-48 h, the optimal reflection loss (RL) value was −33.53 dB at a frequency of 9.74 GHz and a thickness of 1.4 mm. In addition, the widest effective absorption bandwidth was 2.97 GHz (10.73–13.7 GHz), which was achieved at a frequency of 11.72 GHz and a thickness of 1.2 mm. This outstanding performance is mainly attributed to good impedance matching and multiple loss mechanisms induced by the etching process, including conduction loss, dipolar polarization, interfacial polarization, and multiple RLs. Furthermore, this research expands the application potential of two-dimensional MXene materials in EMW absorption.

EnhancedNet, an End-to-End Network for Dense Disparity Estimation and its Application to Aerial Images

Recent developments in deep learning technology have boosted the performance of dense stereo reconstruction. However, the state-of-the-art deep learning-based stereo matching methods are mainly trained using close-range synthetic images. Consequently, the application of these methods in aerial photogrammetry and remote sensing is currently far from straightforward. In this paper, we propose a new disparity estimation network for stereo matching and investigate its generalization abilities in regard to aerial images. First, we propose an end-to-end deep learning network for stereo matching, regularized by disparity gradients, which includes a residual cost volume and a reconstruction error volume in a refinement module, and multiple losses. In order to investigate the influence of the multiple losses, a comprehensive analysis is presented. Second, based on this network trained with synthetic close-range data, we propose a new pipeline for matching high-resolution aerial imagery. The experimental results show that the proposed network improves the disparity accuracy by up to 40% in terms of errors larger than 1 px compared to results when not including the refinement network, especially in areas containing detailed small objects. In addition, in qualitative and quantitative experiments, we are able to show that our model, pre-trained on a synthetic stereo dataset, achieves very competitive sub-pixel geometric accuracy on aerial images. These results confirm that the domain gap between synthetic close-range and real aerial images can be satisfactorily bridged using the proposed new deep learning method for dense image matching.

Research on the Development of Agricultural Products Logistics under the Value Chain Optimization Concept of Logistics Management

The value chain of logistics management is the key to meeting the needs of circulation entities, improving logistics efficiency, optimizing logistics division of labor, and promoting the upgrading of logistics enterprises. It plays a positive role in promoting the development of agricultural product logistics. However, it is understood that there are still many problems in China's agricultural product logistics system, such as opaque information, multiple circulation links, high costs, high losses, and low profits. These will reduce the optimization of logistics management value chain, leading to low efficiency in agricultural product circulation and entering a low consumption cycle mode. Therefore, based on the concept of optimizing the value of logistics management, this article analyzes the development difficulties of the current agricultural product reverse logistics model, explores the design and operation mode of the agricultural product reverse logistics system, proposes corresponding countermeasures to maximize the value chain of agricultural product logistics management, in order to promote the stable and sustainable development of agricultural product logistics and improve the circulation efficiency of agricultural products.

Terrabacteria: redefining bacterial envelope diversity, biogenesis and evolution.

The bacterial envelope is one of the oldest and most essential cellular components and has been traditionally divided into Gram-positive (monoderm) and Gram-negative (diderm). Recent landmark studies have challenged a major paradigm in microbiology by inferring that the last bacterial common ancestor had a diderm envelope and that the outer membrane (OM) was lost repeatedly in evolution to give rise to monoderms. Intriguingly, OM losses appear to have occurred exclusively in the Terrabacteria, one of the two major clades of bacteria. In this Review, we present current knowledge about the Terrabacteria. We describe their diversity and phylogeny and then highlight the vast phenotypic diversity of the Terrabacteria cell envelopes, which display large deviations from the textbook examples of diderms and monoderms, challenging the classical Gram-positive-Gram-negative divide. We highlight the striking differences in the systems involved in OM biogenesis in Terrabacteria with respect to the classical diderm experimental models and how they provide novel insights into the diversity and biogenesis of the bacterial cell envelope. We also discuss the potential evolutionary steps that might have led to the multiple losses of the OM and speculate on how the very first OM might have emerged before the last bacterial common ancestor.

Heterostructure engineering of N-doped Co@ carbon nanotubes toward broadband efficient electromagnetic absorption

The synergistic effect of magnetic loss and dielectric loss, as well as the reasonable construction of microstructure, have a significant effect on improving the absorption performance of electromagnetic wave absorbers. In this paper, N-doping Co@ carbon nanotubes (NCC) composites are successfully grown in situ on cobalt particles using a cobalt source as a catalyst. The metal particle Co is encapsulated within the carbon nanotubes and N is doped in the carbon lattice. This special structure introduces a large number of heterogeneous interfaces and defects, leading to optimized impedance matching and strong electromagnetic attenuation. The results showed that the minimum reflection loss is −69.33 dB for NCC800-1 with a thickness of 1.6 mm, and NCC900-4 possesses the widest effective absorption bandwidth of 7.70 GHz at a thickness of 2.1 mm with other NCC composites. Furthermore, the radar scattering cross section simulation results confirm that the prepared absorbing coatings can well suppress radar waves in different directions. The outstanding absorption performance is attributed to the defects and heterogeneous interfaces in the cobalt-catalyzed carbon nanofibers, the conductive network formed by carbon fibers, and the synergistic effects of magnetic and dielectric losses. This work presents a novel engineering approach for synthesizing absorbers with multiple loss mechanisms and heterogeneous structures, offering a promising pathway for the development of high-performance electromagnetic wave absorbing materials.

MADMFuse: A multi-attribute diffusion model to fuse infrared and visible images

In the field of deep learning vision, infrared and visible image fusion (IVIF) has received significant attention due to its ability to enhance scene comprehension by combining the complementary features of both types of images. Existing methods based on generative models are either unstable in training, such as generative adversarial network, or only have a single denoising object, such as diffusion models, resulting in poor fused results that cannot fully contain multi-modal features. To solve these problems, we propose a multi-attribute diffusion model to fuse infrared and visible images, termed MADMFuse. Specifically, to preserve the salient information in infrared images and the texture details in visible images, we have designed a forward diffusion process with shared noise for multiple independent attributes, ensuring that the denoising network learns complementary features of different attributes simultaneously during training. Subsequently, our reverse process with multi-attribute as conditions employs the denoising network that iteratively reparameterizes noise, gradually adjusting and achieving fine image fusion. Furthermore, to address the issue of feature degradation resulting from solely focusing on a single denoising object, we derive a multiple diffusion object loss function based on pixel-level fusion target, the fidelity and luminance term in this loss function aim to guide the fused results towards visual similarity with the source image while preserving its brightness. Extensive experiments indicate that MADMFuse is more effective than other state-of-the-art image fusion methods.