Multilayer Architecture Research Articles

A Flexible Organomagnetic Single-Layer Composite Film with Built-In Multistimuli Responsivity.

Materials possessing multiple properties and functionalities, that can be controlled or modulated by external stimuli, are a central focus of current research in materials sciences due to their potential to significantly enhance various future technological applications. Herein, we report a significant advancement in this field through the development of a smart, multifunctional organomagnetic composite material. By utilizing a thin layer of polydimethylsiloxane (PDMS) and polypyrrole (PPy) precursors, doped with nickel nanoparticles (NiNPs), we have created an innovative organomagnetic, PDMS/PPy/NiNPs (PPN), single-layer composite film that displays multistimuli responsivity. The study presents the first demonstration of a multifunctional flexible, three-component film structure integrating the structural and flexible PDMS component, together with a conductive polymer component and metal-based nanoparticles into a single-layer design, which displays enhanced and unprecedented responsivity properties against multiple different stimuli. Unlike typical stacked multilayered structures, that exhibit one or two functionalities at most, this novel configuration exhibits multiple functionalities, including magnetoresistance, mechanical stress response, piezoresistivity, and temperature change sensitivity. The as-prepared film demonstrates notable magnetoresistance responsivity, with a relative electrical resistance, ΔR/R0, changing under a weak magnetic field and under ambient conditions. The significance of our study lies in the film's versatility, stability, and sensitivity, especially within the physiological temperature range, making it highly relevant for future biomedical applications. Furthemore, the film's sensitivity to mechanical deformation reveals an impressive piezoresistance behavior. Unlike existing multilayer architectures of higher complexity, our single-layer thin film offers a simpler, more flexible, and reliable solution with a broad range of stimuli-sensing capabilities. The significance of this novel multiresponsive composite material is underscored by the growing demand for advanced materials in biomedical devices, magnetic switches, sensors, electronic skin, transistors, and organic spintronic devices. These promising organomagnetic self-standing layers provide a robust platform for future technological innovations.

MIDAS: Multi-layered attack detection architecture with decision optimisation

The proliferation of cyber attacks has led to the use of data-driven detection countermeasures, in an effort to mitigate this threat. Machine learning techniques, such as the use of neural networks, have become mainstream and proven effective in attack detection. However, these data-driven solutions are limited by: a) high computational overhead associated with data pre-processing and inference cost, b) inability to scale beyond a centralised deployment to cope with environmental variances, and c) requirement to use multiple bespoke detection models for effective attack detection coverage across the cyber kill chain. In this context, this paper introduces MIDAS, a cost-effective framework for attack detection, which introduces a dynamic decision boundary that is used in a multi-layered detection architecture. This is achieved by modelling the decision confidence of the participating detection models and judging its benefits using a novel reward policy. Specifically, a reward is assigned to a set of available actions, corresponding to a decision boundary, based on its cost-to-performance, where an overall cost-saving is prioritised. We evaluate our approach on two widely used datasets representing two of the most common threats today, i.e., phishing and malware. MIDAS shows that it effectively reduces the expenditure on detection inference and processing costs by controlling the frequency of expensive detection operations. This is achieved without significant sacrifice of attack detection performance.

Advances in Electrospun Poly(ε-caprolactone)-Based Nanofibrous Scaffolds for Tissue Engineering.

Tissue engineering has great potential for the restoration of damaged tissue due to injury or disease. During tissue development, scaffolds provide structural support for cell growth. To grow healthy tissue, the principal components of such scaffolds must be biocompatible and nontoxic. Poly(ε-caprolactone) (PCL) is a biopolymer that has been used as a key component of composite scaffolds for tissue engineering applications due to its mechanical strength and biodegradability. However, PCL alone can have low cell adherence and wettability. Blends of biomaterials can be incorporated to achieve synergistic scaffold properties for tissue engineering. Electrospun PCL-based scaffolds consist of single or blended-composition nanofibers and nanofibers with multi-layered internal architectures (i.e., core-shell nanofibers or multi-layered nanofibers). Nanofiber diameter, composition, and mechanical properties, biocompatibility, and drug-loading capacity are among the tunable properties of electrospun PCL-based scaffolds. Scaffold properties including wettability, mechanical strength, and biocompatibility have been further enhanced with scaffold layering, surface modification, and coating techniques. In this article, we review nanofibrous electrospun PCL-based scaffold fabrication and the applications of PCL-based scaffolds in tissue engineering as reported in the recent literature.

Upconversion/Downshifting Circularly Polarized Luminescence over 1200 nm in a Single Nanoparticle for Optical Anticounterfeiting and Information Encryption.

Multimodal upconversion and downshifting circularly polarized luminescent materials hold significant potential for optical anticounterfeiting applications due to their exceptional chiroptical properties. However, constructing these materials within a single emitter remains challenging. In this study, a conceptual model of multimodal up-conversion/downshifting circularly polarized luminescence (CPL) is realized within a single nanoparticle. A new type of nanoparticles with multilayer core-shell architecture is fabricated, capable of delivering upconversion/downshifting luminescence, when excited by a 980 nm laser. Utilizing a co-assembly strategy, multimodal upconversion/downshifting CPL emission, covering a broad emission range from ultraviolet (UV) to the second near-infrared (NIR-II) region, can be realized at the supramolecular level. These chiroptical properties closely follow the chirality of host matrix and are strongly dependent on the distribution mode of nanoparticles within the matrix films. The multimodal upconversion/downshifting CPL behavior enabled cutting-edge encryption applications including optical anticounterfeiting and information encryption. This work introduces a novel approach to designing multimodal upconversion/downshifting CPL materials and opens new avenues for the development of chiroptical functional materials.

Block Medcare: Advancing Healthcare Through Blockchain Integration

In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings underscore the transformative potential of blockchain technology in healthcare, pointing towards a new era of patient-centric and secure healthcare services. By addressing current limitations and exploring future enhancements, such as integration with IoT devices and AI-driven analytics, this research contributes to the ongoing evolution of secure, efficient, and interoperable healthcare systems.

SNN using color-opponent and attention mechanisms for object recognition

The current spiking neural network (SNN) relies on spike-timing-dependent plasticity (STDP) primarily for shape learning in object recognition tasks, overlooking the equally critical aspect of color information. To address this gap, our study introduces an unsupervised variant of STDP that incorporates principles from color-opponency mechanisms (COM) and classical receptive fields (CRF) found in the biological visual system, facilitating the integration of color information during parameter updates within the SNN architecture. Our approach initially preprocesses images into two distinct feature maps: one for shape and another for color. Then, signals derived from COM and intensity concurrently drive the STDP process, thereby updating parameters associated with both color and shape feature maps. Furthermore, we propose a channel-wise attention mechanism to enhance differentiation among objects sharing similar shapes or colors. Specifically, this mechanism utilizes convolution to generate an output spike-wave, identifying a winner based on earliest spike timing and maximal potential. The winning kernel computes attention, which is then applied via convolution to each input image feature map, generating post-feature maps. A STDP-like normalization rule compares firing times between pre- and post-feature maps, dynamically adjusting channel weights to optimize object recognition during the training phase.We assessed the proposed algorithm using SNN with both single-layer and multi-layer architectures across three datasets. Experimental findings highlight its efficacy and superiority in complex object recognition tasks compared to state-of-the-art (SOTA) algorithms. Notably, our approach achieved a significant 20% performance improvement over the SOTA on the Caltech-101 dataset. Moreover, the algorithm is well-suited for hardware implementation and energy efficiency, leveraging a winner-selection mechanism based on the earliest spike time.

Integration of experimental and intelligent modeling for optimizing iron electrocoagulation-flocculation recovery of aquafarm effluent

The present study integrates experimental and intelligent models in the modeling and optimization of the iron electrode-based electrocoagulation-flocculation (EC-FLC) process for the treatment of aquafarm effluent (AFE). The modeling was done using Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), while the optimization was performed with Particle Swarm Optimization (PSO). The physicochemical properties of AFE were determined before and post-treatment to assess the effectiveness of the process. Central composite design (CCD) embedded in RSM was employed for experimental design. ANFIS model utilized the grid partition and ANN employed a multi-layer perceptron-based feed-forward architecture. The statistical analysis revealed superior prediction accuracy in the decreasing order ANFIS (R2: 0.9968, AAD: 0.0009, RMSE: 0.2373), ANN (R²: 0.9896, AAD: 0.0023, RMSE: 0.4388), and RSM (R²: 0.9655, AAD: 0.0067, RMSE: 0.7764), demonstrating the superiority of ANFIS model. Models’ reliability were validated by the strong correlation between the actual/predicted values of turbidity removal. The experimentally validated optimization solutions for turbidity were RSM numerical (96.71 %), RSM-PSO (97.46 %), ANN-PSO (98.83 %), and ANFIS-PSO (99.01 %). Post-treatment analysis revealed significant reductions in turbidity, nutrient content, and organic matter. These findings demonstrated the effectiveness of the intelligent tools in the modeling/optimization of EC-FLC recovery of AFE.

Which classifiers are connected to others? An optimal connection framework for multi-layer ensemble systems

Ensemble learning is a powerful machine learning strategy that combines multiple models e.g. classifiers to improve predictions beyond what any single model can achieve. Until recently, traditional ensemble methods typically use only one layer of models which limits the exploration of different aspects in the classifiers’ predictions. On the other hand, the rise of deep learning has introduced multi-layer architectures that can learn complex functions by transforming data into multiple levels of representation. This characteristic of deep learning suggests that multi-layer ensembles may potentially provide better performance compared to single-layer ensembles. However, a problem which might arise is that in the subsequent layers, not all the inputs to a classifier are desirable, leading to lower performance. In this paper, we introduce a novel multi-layer ensemble of classifiers named COME in which each classifier at a specific layer is connected to multiple classifiers in the previous layer. These connections signify the use of the previous-layer-classifiers’ outputs as inputs for training the current layer's classifier. Each classifier can be connected to different classifiers in the previous layer, which allows inputs in each layer to be optimally selected. We propose a binary encoding scheme to encode the topology of the proposed multi-layer ensemble with defined connections between layers. Differential Evolution, a popular evolutionary computation method, is used as the optimisation algorithm to search for the optimal set of connections. Experimental results on 30 datasets from the UCI Machine Learning Repository and OpenML demonstrate that our proposed ensemble outperforms many state-of-the-art ensemble learning algorithms.

Multilayer neuroadaptive constraint-handling control architecture for a family of nonlinear systems with uncertainty compensation

In practical applications, there are some requirements for time-varying constraints on full system states. Currently, Barrier Lyapunov Function is a popular tool for constraint control. However, how to handle various uncertainties while satisfying constraint requirements is worth further research. Accordingly, a high-performance multilayer neuroadaptive constraint-handling controller for a class of nonlinear systems with uncertainty compensation will be developed. Significantly, asymmetric time-varying constraints for full system states can be implemented. Furthermore, a set of novel multilayer neuroadaptive disturbance observers will be proposed to address modeling uncertainties. Moreover, by introducing a set of nonlinear command filters, the intelligent control algorithm will be designed via the command filtered backstepping method. The stability of the whole closed-loop system is strictly proved. Additionally, the proposed algorithm is applied to different nonlinear systems including a hydraulic robotic manipulator system to verify its feasibility.

A three-tier approach to Internet of Things software architecture design

The Internet of Things (IoT) rapidly evolves, presenting challenges and opportunities. This study emphasises the critical role of software in advancing IoT technologies, focusing on machine learning (ML), fog computing, and process optimisation for security and resiliency. ML is pivotal in IoT for predicting equipment failures, evaluating process efficiency, and enabling informed decision- making through real-time data analysis. By integrating ML models directly into IoT devices (edge computing), latency is reduced, and data transmission needs are minimised. Fog computing and cloud computing address latency issues by moving computational resources closer to IoT devices, enhancing scalability and optimising network resource usage. Security remains a paramount concern due to the increasing number of connected devices and their vulnerabilities. IoT software must balance efficiency, security, and performance, employing deep learning for anomaly detection, blockchain for data transparency, and optimised encryption protocols. The trend towards distributed architectures like Edge Computing enhances system resilience by reducing latency and increasing fault tolerance. The proposed IoT system architecture is a three-tier structure consisting of Edge, Fog, and Cloud levels. At the Edge level, initial data processing occurs directly on IoT devices, reducing latency and network load. The Fog level processes data within the local network, utilising more powerful computational resources for complex tasks and ensuring security through advanced machine learning and encryption. The Cloud level serves as a central repository for long-term storage and global data analysis, leveraging containerisation and orchestration technologies for scalability and reliability. This multi-layered architecture ensures efficient data processing, high security, and adaptability, making it suitable for real-time applications. The study highlights the importance of software in optimising data processing across these levels, ensuring the IoT system’s resilience, scalability, and long-term sustainability.

Soft robotic artificial left ventricle simulator capable of reproducing myocardial biomechanics.

The heart's intricate myocardial architecture has been called the Gordian knot of anatomy, an impossible tangle of intricate muscle fibers. This complexity dictates equally complex cardiac motions that are difficult to mimic in physical systems. If these motions could be generated by a robotic system, then cardiac device testing, cardiovascular disease studies, and surgical procedure training could reduce their reliance on animal models, saving time, costs, and lives. This work introduces a bioinspired soft robotic left ventricle simulator capable of reproducing the minutiae of cardiac motion while providing physiological pressures. This device uses thin-filament artificial muscles to mimic the multilayered myocardial architecture. To demonstrate the device's ability to follow the cardiac motions observed in the literature, we used canine myocardial strain data as input signals that were subsequently applied to each artificial myocardial layer. The device's ability to reproduce physiological volume and pressure under healthy and heart failure conditions, as well as effective simulation of a cardiac support device, were experimentally demonstrated in a left-sided mock circulation loop. This work also has the potential to deliver faithful simulated cardiac motion for preclinical device and surgical procedure testing, with the potential to simulate patient-specific myocardial architecture and motion.

Numerical analysis of an advanced infrared-based metasurface surface plasmon resonance sensor for COVID-19 detection

The SARS-CoV-2 pandemic has highlighted the critical need for rapid and precise diagnostic methodologies. Conventional testing approaches often exhibit limitations in terms of speed, accessibility, and sensitivity. To address these constraints, we propose an advanced graphene-based biosensing platform for SARS-CoV-2 detection. This sensor integrates advanced nanophotonic principles, metamaterial physics, and two-dimensional material properties to achieve enhanced sensitivity through the detection of refractive index perturbations induced by SARS-CoV-2 biomolecules. Electromagnetic simulations were conducted to optimize the sensor design. The resulting configuration demonstrates a sensitivity of 1800 nmRIU−1 and a detection limit of 0.007 RIU. The sensor exhibits multi-wavelength sensing capabilities at 3.942 μm, 4.018 μm, 4.088 μm, and 4.201 μm, with a high figure of merit of 104.167 RIU−1 and quality factors ranging from 65.766 to 251. 750.The sensor's multilayer architecture, comprising tungsten, graphene, and strontium titanate synergistically enhances both sensitivity and selectivity. Its compact form factor and capacity for real-time analysis render it a promising candidate for point-of-care COVID-19 diagnostics.

Interplay of Circularly Polarized Light with Molecular and Structural Chirality: Chiral Lanthanide Complexes and Cellulose Nanocrystals

AbstractThe interaction of circularly polarized (CP) light with chiral matter at different scales opens several possibilities of light manipulation in photonic and electronic devices. Here it is shown that in a multilayer architecture, it is possible to take advantage of the polarization‐selective reflection of the nematic arrangement of cellulose nanocrystals and the strong intrinsic CP luminescence (CPL) of the various bands of chiral Eu complexes. In this way, both the intrinsic CPL and total emission of the complex are modified depending on the enantiomer applied and on the detection geometry. This concept may apply for polarization control in electronic and photonic devices and polarized optical cavities.

Towards Mine 4.0: A Proposed Multi-Layered Architecture for Real-Time Surveillance and Anomaly Detection in an Open-Pit Phosphate Mine

Given the global importance and limited availability of phosphate, optimizing the use of this critical resource and minimizing its wastage are of paramount importance. In this context, this paper proposes an innovative architecture for the implementation of an intelligent video surveillance system specifically designed for open-pit phosphate mines. The proposed architecture is designed to meet the overall functional requirements of a surveillance system in the challenging environment of open-pit mining, while aligning with the guidelines of the Mine 4.0 revolution. It incorporates advanced technologies that address the critical challenges of latency, data security, and transparency commonly encountered in traditional monitoring systems. By adopting a multi-layered approach that leverages edge, fog, and cloud computing, coupled with blockchain technology and expert collaboration, our architecture offers a comprehensive framework for efficient data processing at every stage, from initial data acquisition to real-time anomaly detection and decision-making.

Pursuing Extreme Descriptive Power and Generality in Chemical Bond Theories: A Method to Decipher "Interatomic Genomes" from Interatomic Electron Structures.

The description and analysis of chemical bonds have been difficult following the popularization of electronic structure calculations. Although many attempts have been made from the perspective of electronic structure, the sheer volume of information in the electronic structure has left contemporary chemical bond analysis methods grappling with an inescapable "Trilemma" where the model briefness, generality, and descriptiveness (descriptive power) cannot be obtained simultaneously. To push the generality and descriptiveness to their extremes, herein a general machine learning-based framework is introduced to compact chemical bonds into a detailed residue-by-residue "genome" with matched encoding/decoding tools. The framework fuses the quantum mechanical aspects, auto feature extraction, nanostructures and/or simulations, and generative models. The encoded genomes are information-dense and decodable, where 100% generality is guaranteed. The descriptiveness of genomes appears to be broader than most known models. As a proof of concept, the realization presented in this work compacts the complete information regarding two critical chemical bonds in thiolate-protected gold nanoclusters, the S-Au and Au-Au bonds, from a Bosonic-Fermionic character perspective into 8-valued genomes. The machine learning component is trained based on 26,528 density functional theory simulated electron localization function images. With an exploration of the space span for the genome, bond polarization, hybridization, intrusion of other atoms, alignments, crystal orientation, atomic motions, and more details are observed. Furthermore, it has emerged from extensive generation tests that molecules and solids can be integrated in such a concise manner than is typically achieved with purely geometric representations. To showcase the intraclass complexity of S-Au and Au-Au bonds visually, a roadmap is plotted by summarizing and correlating the similarities of 8-value-genomes. Furthermore, genomes can be associated with realistic indices easily with a simple multilayer perception architecture as a simple calculating tool. Besides, there are 3 sets of applications, including a set of chemisorption, a set of molecular dynamical analysis, and a set of ultrafast processes, showcasing the interpretability potentials of interatomic genomes in the geometric structures, kinetic properties, and vibration characteristics of molecular systems. As the framework rose to the challenge of nanoclusters from a complicated mesoscopic family of material, the displayed generality and comprehensiveness indicate that the model may "understand" chemical bonds in a machine's way.

Multi-layer resource scheduling architecture and algorithm for a service-oriented optical network based on a fine grain OTN

With the development of service, there are demands for flexible connection scheduling based on customer-side services, fast connection response, and efficient scheduling of line-side resources in an optical transport network (OTN). This article proposes a service-oriented multi-layer resource scheduling architecture and algorithm based on the introduction of OTN fine grain technology. Specifically, we propose a service awareness scheme that enables OTNs to better match service requirements. Based on a centralized distributed collaborative architecture, we extend a path computation element communication protocol (PCEP) to achieve more efficient connection distribution. We further design a resource scheduling strategy and an algorithm based on multi-layer collaboration to perform differentiated scheduling for services with different priorities. Through the mechanisms and algorithms proposed, it is technically feasible to achieve precise matching of customer-side services, flexible scheduling of connections and resources, and efficient service response, and promote the evolution of the OTN service supply from traditional fixed point-to-point connections to flexible service-oriented optical network (SOON) connections.

Multilevel Hollow-Structured Particles through Halogen-Bond Regulated Polymer Assembly under 3DConfinement.

Engineering of hollow particles with tunable internal structures often requires complicated processes and/or invasive cleavage. Halogen-bond driven 3D confined-assembly of block copolymers has shed light on the engineering of polymer organization along with the fabricating of unique nanostructures. Herein, a family of multilevel hollow-structured particles (e.g., fully porous, multi-chamber, multi-shell, and concentric multi-layer architectures) is reported via halogen-bond regulated 3Dconfined-assembly of amphiphilic polymer networks. To do so, polystyrene-b-poly(2-vinyl pyridine)-b-poly(ethylene oxide) (PS-b-P2VP-b-PEO) amphiphilic triblock copolymer is selected, where P2VP blocks act as halogen acceptor. Meanwhile, poly(3-(2,3,5,6-tetrafluoro-4-iodophenoxy) propyl acrylate) (PTFIPA) is employed as halogen donor. Halogen-bond driven donor-acceptor linking between PTFIPA and P2VP block presented in PS-b-P2VP-b-PEO, can lead to the formation of supramolecular polymeric networks, along with the increased P2VP domain and tunable hydrophobic volume. Therefore, an adjustable packing parameter (p) is thus anticipated, which can enable the morphology transformation sequence until an equilibrium state is reached. Moreover, computer simulations are further utilized as the tool to interpret such morphologies transition and identify the precise distribution of each component. Benefiting from the tunable hollow structure and a substantial surface for transporting purpose, these structurally novel particles open perspectives toward promising applications including encapsulation, nanoreactor, and catalyst support.

Graph Attention Networks: A Comprehensive Review of Methods and Applications

Real-world problems often exhibit complex relationships and dependencies, which can be effectively captured by graph learning systems. Graph attention networks (GATs) have emerged as a powerful and versatile framework in this direction, inspiring numerous extensions and applications in several areas. In this review, we present a thorough examination of GATs, covering both diverse approaches and a wide range of applications. We examine the principal GAT-based categories, including Global Attention Networks, Multi-Layer Architectures, graph-embedding techniques, Spatial Approaches, and Variational Models. Furthermore, we delve into the diverse applications of GATs in various systems such as recommendation systems, image analysis, medical domain, sentiment analysis, and anomaly detection. This review seeks to act as a navigational reference for researchers and practitioners aiming to emphasize the capabilities and prospects of GATs.

Data fusion integrated network forecasting scheme classifier (DFI-NFSC) via multi-layer perceptron decomposition architecture

The Massive Access Problem of the Internet of Things stands for the access problem of the wireless devices to the Gateway when the device population in the coverage area is excessive. We develop a hybrid model called Data Fusion Integrated Network Forecasting Scheme Classifier (DFI-NFSC) using a Multi-Layer Perceptron (MLP) Decomposition architecture specifically designed to address the Massive Access Problem. We utilize our custom error metric to display throughput and energy consumption results. These results are obtained by emulating the Joint Forecasting–Scheduling (JFS) system on a single IoT Gateway and distinguishing between ARIMA, LSTM, and MLP forecasters of the JFS system. The outcomes indicate that the DFI-NFCS method plays a notable role in improving performance and mitigating challenges arising from the dynamic fluctuations in the diversity of device types within an IoT gateway’s coverage zone.

Spatiotemporal transcriptomic mapping of regenerative inflammation in skeletal muscle reveals a dynamic multilayered tissue architecture.

Tissue regeneration is orchestrated by macrophages that clear damaged cells and promote regenerative inflammation. How macrophages spatially adapt and diversify their functions to support the architectural requirements of actively regenerating tissue remains unknown. In this study, we reconstructed the dynamic trajectories of myeloid cells isolated from acutely injured and early stage dystrophic muscles. We identified divergent subsets of monocytes/macrophages and DCs and validated markers (e.g., glycoprotein NMB [GPNMB]) and transcriptional regulators associated with defined functional states. In dystrophic muscle, specialized repair-associated subsets exhibited distinct macrophage diversity and reduced DC heterogeneity. Integrating spatial transcriptomics analyses with immunofluorescence uncovered the ordered distribution of subpopulations and multilayered regenerative inflammation zones (RIZs) where distinct macrophage subsets are organized in functional zones around damaged myofibers supporting all phases of regeneration. Importantly, intermittent glucocorticoid treatment disrupted the RIZs. Our findings suggest that macrophage subtypes mediated the development of the highly ordered architecture of regenerative tissues, unveiling the principles of the structured yet dynamic nature of regenerative inflammation supporting effective tissue repair.