Fusion Applications Research Articles

Influence of heat treatments on low-power-LPBFed CuCrZr for nuclear fusion applications

In this study, the processability of CuCrZr alloy with additive manufacturing (AM) technology and the performance achievable with Direct Age Hardening treatments for nuclear fusion applications were investigated. This copper alloy is one of the most interesting for the field: it is easier to manufacture through Laser-based additive manufacturing technology and mechanically superior compared to pure copper, and it ensures values of thermal conductivity high enough to be considered a valid substitute for pure copper in many applications.The investigation on CuCrZr alloy was carried out in order to examine the influence of Direct Age Hardening (DAH) treatments on physical and mechanical properties. Laser Powder Bed Fusion technology was used to produce samples with CuCrZr alloy. The additive manufacturing process involved a machine provided with a 370 W IR laser and a preliminary process optimization was carried out to find the printing parameters that assured the highest density (99.15 %), which confirmed the processability of CuCrZr alloy also with low IR laser power. Then, three different DAH treatments were tested and the performance of DAHed material was compared to that of the alloy in as-built conditions. Precipitation phenomena were investigated with DSC analyses, revealing the effectiveness of the treatment already after 1 h. A deep microstructural investigation revealed a fine cellular structure formed during solidification and the presence of nanometric precipitates starting from the as-built condition. The presence of microstructural defects was also investigated. Mechanical performance and thermal conductivity were tested, too: the as-built samples showed limited properties, while very promising results for the use of additively manufactured CuCrZr components have been obtained after the DAHs. The ultimate tensile strength (UTS) and yield strength (YS) doubled the as-built values after 1 h treatment at 550 °C. The thermal conductivity reached three times the initial condition (from 100 W/mK to 300 W/mK).

Tailoring metal powders by dry nanoparticle coating for powder bed fusion applications

The influence of high intensity mixing of graphene platelets with stainless steel particles in the scope of additive manufacturing is investigated. Therefore, 0.75 vol% graphene platelets were coated onto conventional stainless steel powder as a model material, resulting in very homogenously distributed graphene layers. The specific energy input during mixing was found to be the main factor for increasing the flowability and decreasing the reflectivity of the powder. Furthermore, the resulting surface roughness of the coated powders could be correlated with a theoretical consideration of van der Waals forces. In this way, the mixing process can be controlled regarding its specific energy input to achieve tailored powders for the application in PBF-LB/M. Cubic parts were produced with different laser parameters and analysed. As a result, the admixing of graphene at lower specific energies resulted in a wider process window and parts with a relative density of 99.99% were achieved.

A novel attribute reduction approach using coverage-credibility-based rough decision entropy for interval-valued data

Attribute reduction is an important method in data analysis and machine learning, and it usually relies on algebraic and informational measures. However, few existing informational measures have considered the relative information of decision class cardinality, and the fusion application of algebraic and informational measures is also limited, especially in attribute reductions for interval-valued data. In interval-valued decision systems, this paper presents a coverage-credibility-based condition entropy and an improved rough decision entropy, further establishes corresponding attribute reduction algorithms for optimization and applicability. Firstly, the concepts of interval credibility, coverage and coverage-credibility are proposed, and thus, an improved condition entropy is defined by virtue of the integrated coverage-credibility. Secondly, the fused rough decision entropy is constructed by the fusion of improved condition entropy and roughness degree. By introducing the coverage-credibility, the proposed uncertainty measurements enhance the relative information of decision classes. In addition, the nonmonotonicity of the improved condition entropy and rough decision entropy is validated by theoretical proofs and experimental counterexamples, with respect to attribute subsets and thresholds. Then, the two rough decision entropies drive monotonic and nonmonotonic attribute reductions, and the corresponding reduction algorithms are designed for heuristic searches. Finally, data experiments not only verify the effectiveness and improvements of the proposed uncertainty measurements, but also illustrate the reduction algorithms optimization through better classification accuracy than four comparative algorithms.

Injectable bioactive poly(propylene fumarate) and polycaprolactone based click chemistry bone cement for spinal fusion in rabbits.

Degenerative spinal pathology is a widespread medical issue, and spine fusion surgeries are frequently performed. In this study, we fabricated an injectable bioactive click chemistry polymer cement for use in spinal fusion and bone regrowth. Taking advantages of the bioorthogonal click reaction, this cement can be crosslinked by itself eliminating the addition of a toxic initiator or catalyst, nor any external energy sources like UV light or heat. Furthermore, nano-hydroxyapatite (nHA) and microspheres carrying recombinant human bone morphogenetic protein-2 (rhBMP-2) and recombinant human vascular endothelial growth factor (rhVEGF) were used to make the cement bioactive for vascular induction and osteointegration. After implantation into a rabbit posterolateral spinal fusion (PLF) model, the cement showed excellent induction of new bone formation and bridging bone, achieving results comparable to autograft control. This is largely due to the osteogenic properties of nano-hydroxyapatite (nHA) and the released rhBMP-2 and rhVEGF growth factors. Since the availability of autograft sources is limited in clinical settings, this injectable bioactive click chemistry cement may be a promising alternative for spine fusion applications in addressing various spinal conditions.

The inviscid incompressible limit of Kelvin–Helmholtz instability for plasmas

Introduction: The Kelvin–Helmholtz Instability (KHI) is an interface instability that develops between two fluids or plasmas flowing with a common shear layer. KHI occurs in astrophysical jets, solar atmosphere, solar flows, cometary tails, planetary magnetospheres. Two applications of interest, encompassing both space and fusion applications, drive this study: KHI formation at the outer flanks of the Earth’s magnetosphere and KHI growth from non-uniform laser heating in magnetized direct-drive implosion experiments. Here, we study 2D KHI with or without a magnetic field parallel to the flow. We use both the GAMERA code, which solves the compressible Euler equations, and the STRATOSPEC code, which solves the Navier-Stokes equations under the Boussinesq approximation, coupled with the magnetic field dynamics. GAMERA is a global three-dimensional MHD code with high-order reconstruction in arbitrary nonorthogonal curvilinear coordinates, which is developed for a large range of astrophysical applications. STRATOSPEC is a three-dimensional pseudo-spectral code with an accuracy of infinite order (no numerical diffusion). Magnetized KHI is a canonical case for benchmarking hydrocode simulations with extended MHD options.Methods: An objective is to assess whether or not, and under which conditions, the incompressibility hypothesis allows to describe a dynamic compressible system. For comparing both codes, we reach the inviscid incompressible regime, by decreasing the Mach number in GAMERA, and viscosity and diffusion in STRATOSPEC. Here, we specifically investigate both single-mode and multi-mode initial perturbations, either with or without magnetic field parallel to the flow. The method relies on comparisons of the density fields, 1D profiles of physical quantities averaged along the flow direction, and scale-by-scale spectral densities. We also address the triggering, formation and damping of filamentary structures under varying Mach number or Atwood number, with or without a parallel magnetic field.Results: Comparisons show very satisfactory results between the two codes. The vortices dynamics is well reproduced, along with the breaking or damping of small-scale structures. We end with the extraction of growth rates of magnetized KHI from the compressible regime to the incompressible limit in the linear regime assessing the effects of compressibility under increasing magnetic field.Discussion: The observed differences between the two codes are explained either from diffusion or non-Boussinesq effects.

Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction.

Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil; aerial photos taken by UAVs display the vegetative index (NDVI); and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors.

Novel Multimedia Feature Fusion Classification (Mffc) Model for Sports Game Design Enhancement

Multimedia feature fusion is reshaping the landscape of sports game design, offering immersive and dynamic experiences for players and spectators alike. By integrating various multimedia elements such as audio, video, graphics, and interactive interfaces, game designers can create engaging environments that simulate the excitement and intensity of real-life sporting events. From lifelike player animations to realistic crowd reactions, multimedia feature fusion enhances the realism and authenticity of sports games, transporting players into the heart of the action. Furthermore, interactive interfaces enable players to customize their gaming experience, from choosing team strategies to controlling player movements, adding depth and complexity to gameplay. With advancements in technology, such as virtual reality and augmented reality, multimedia feature fusion is poised to revolutionize sports game design, offering unprecedented levels of immersion and interactivity for players worldwide. This paper explores the application of multimedia feature fusion in sports game design, utilizing the Multimedia Feature Fusion Classification (MFFC) framework alongside key feature extraction with the genetic model. By integrating various multimedia elements including audio, video, graphics, and interactive interfaces, the MFFC framework enhances the realism and excitement of sports games, offering immersive experiences for players. Through the genetic model, key features are extracted and optimized to improve gameplay dynamics, player interactions, and overall gaming experience. The effectiveness of the proposed approach is evaluated through simulated experiments and empirical validations, demonstrating significant enhancements in game realism, user engagement, and satisfaction. Results indicate a 30% increase in player immersion and a 25% improvement in gameplay dynamics compared to traditional game design methods.

Sound Art of Internet of Things Explore: Sound Sensors in the Fusion of Painting and Music

INTRODUCTION: The combination of sound art and Internet of Things (IoT) technology offers new possibilities for artistic creation. With the rapid development of the Internet of Things (IoT), sound sensors have become a powerful tool for capturing environmental sounds. This study aims to explore the application of suitable sensors in the fusion of painting and music by utilizing IoT technology to create more prosperous and interactive artworks for artists. OBJECTIVES: The study's primary purpose is to explore the potential uses of sound sensors in art creation, especially in integrating painting and music. By deeply analyzing the interaction between sound and visual art, the researchers aim to discover new creative possibilities, thus expanding the boundaries of artistic creation. METHODS: The study utilized a comprehensive methodology that included a literature review, field research, and artistic practice. First, the literature on sound art, IoT, and art fusion was extensively reviewed to provide a theoretical foundation for the study. Second, through field research, the researchers collected practical examples of sound sensors applied in the field of art. Finally, through hands-on art creation in the artist's studio, the researchers verified the potential applications of suitable sensors in the fusion of painting and music. RESULTS: The study found that sound sensors have many applications in painting-music fusion. By embedding suitable sensors into painting tools and music devices, artists can capture the sounds of their surroundings and transform them into visual and auditory artistic expressions. Examples from practice demonstrate how this technological innovation can bring a richer, more sensual experience to artistic creation while engaging the audience in interactive participation. CONCLUSION: The results of this study show that the combination of sound sensors and IoT technology offers new possibilities for artistic creation. By integrating proper elements in painting and music, artists can create more profound and engaging works. This innovation expands the boundaries of art and provides the audience with a more intimate and interactive experience with the artwork. In the future, further research can delve into the application of sound sensors in other artistic fields, thus promoting the integration of art and technology to move forward.

Integrating multisensory information fusion and interaction technologies in smart healthcare systems

<p>The advent of intelligent medical systems has heralded a new era in healthcare, promising enhanced diagnostic accuracy, treatment efficacy, and personalized patient care. Central to these advancements is the application of multisensory information fusion and interaction technology, which integrates diverse data types—from imaging to auditory signals and electronic health records—to facilitate comprehensive patient assessments. This study examines the efficacy of such multisensory integration within an intelligent medical system framework, focusing on its impact on diagnostic accuracy and treatment effectiveness. A hypothetical dataset encompassing various sensory inputs for a cohort of patients was analyzed, revealing a significant improvement in diagnostic precision (average accuracy of 92.3%) and treatment outcomes, with a majority of interventions rated as highly effective. These findings underscore the potential of multisensory data fusion in revolutionizing medical diagnostics and treatment planning. Despite the promising results, limitations such as sample size and data quality were acknowledged, pointing towards the necessity for further research. This study not only corroborates the value of multisensory information fusion in enhancing healthcare delivery but also highlights the pathway for future advancements in intelligent medical systems. The article’s novelty lies in its approach to integrating multisensory data with AI technologies, leading to a more nuanced understanding of patient health. This method transcends traditional diagnostic techniques, allowing for a multifaceted analysis of medical conditions. It emphasizes the potential of this technology to detect diseases earlier and more accurately, tailor treatments to individual patient needs, and improve overall healthcare efficiency.</p>

Corrosion resistance of 9–12Cr ODS steels in pressurized water for fusion application

The blanket design of Chinese fusion engineering test reactor (CFETR) may utilize pressurized water as coolant. The fusion structural materials will endure high temperature, high pressure and result in corrosion in the pressurized water. It is necessary to verify corrosion resistance of the materials. In the present study, three kinds of advanced fusion structural materials with excellent mechanical properties and neutron irradiation resistance, i.e., 9Cr-ODS steel, 9CrODS-Al steel, and 12CrODS-Al steel were immersed in pressurized water at 325 °C under the pressure of 15.5 MPa up to 859 h. Room-temperature tensile properties were investigated for the materials before and after corrosion. The oxidation layers of the steels were analyzed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The ODS steels have good corrosion resistance in the pressurized water, especially the 9CrODS-Al and 12CrODS-Al steels. The outer oxide layer is identified as Fe2O3 for all the steels. The inner layer is mainly FeCr2O4 for the 9CrODS steel, and Fe(Al,Cr)2O4 for the ODS steels with Al element which better protect the steels from further corrosion. The relevant properties data can provide reference for the blanket materials choice of CFETR and the future fusion reactors.

Enhanced Seamless Video Fusion: A Convolutional Pyramid-Based 3D Integration Algorithm.

Video fusion aims to synthesize video footage from different sources into a unified, coherent output. It plays a key role in areas such as video editing and special effects production. The challenge is to ensure the quality and naturalness of synthetic video, especially when dealing with footage of different sources and qualities. Researchers continue to strive to optimize algorithms to adapt to a variety of complex application scenarios and improve the effectiveness and applicability of video fusion. We introduce an algorithm based on a convolution pyramid and propose a 3D video fusion algorithm that looks for the potential function closest to the gradient field in the least square sense. The 3D Poisson equation is solved to realize seamless video editing. This algorithm uses a multi-scale method and wavelet transform to approximate linear time. Through numerical optimization, a small core is designed to deal with large target filters, and multi-scale transformation analysis and synthesis are realized. In terms of seamless video fusion, it shows better performance than existing algorithms. Compared with editing multiple 2D images into video after Poisson fusion, the video quality produced by this method is very close, and the computing speed of the video fusion is improved to a certain extent.

Finite element models for radiation effects in nuclear fusion applications

Deuterium-tritium fusion reactions produce energy in the form of 14.1 MeV neutrons, and hence fusion reactor components will be exposed to high energy neutron irradiation while also being subjected to thermal, mechanical and magnetic loads. Exposure to neutron irradiation has numerous consequences, including swelling and dimensional changes, comparable in magnitude to the peak transient thermal deformations occurring in plasma-facing components. Irradiation also dynamically alters the various thermo-mechanical properties, relating temperature, stress and swelling in a strongly non-linear way. Experimental data on the effect of neutron exposure spanning the design parameter space are very sparse and this highlights the relevance of computer simulations. In this study we explore the equivalence between the body force/surface traction approach and the eigenstrain formalism for treating anisotropic irradiation-induced swelling. We find that both commercial and massively parallelised open source software for finite element method (FEM) simulations are suitable for assessing the effect of neutron exposure on the mechanically loaded reactor components. We demonstrate how two primary effects of irradiation, radiation swelling and the degradation of thermal conductivity, affect the distributions of stress and temperature in the divertor of the ITER tokamak. Significant uncertainties characterising the magnitude of swelling and models for treating it suggest that on the basis of the presently available data, only an order of magnitude estimate can be given to the stress developing in reactor components most exposed to irradiation during service.

Experimental activities in view of hydrogen- deuterium equilibrium reaction in dielectric barrier discharge reactor: Effects of plasma treatment parameters and reactor design

Cryogenic distillation (CD) is a crucial process for separating and concentrating tritium in the tritium fuel cycle of fusion reactors. The equilibrator plays a vital role in decomposing HD, DT, and HT to improve the separation efficiency of hydrogen isotopes in CD. As the equilibrium reaction temperature decreases, a larger proportion of HD, HT, and DT decomposes into H2, D2, and T2, which enhances the performance of the equilibrator. While Pt/Al2O3 catalyst exhibits high activity at 303 K in the H2-D2 equilibrium reaction, it is almost inactive at 77 K. Non-thermal plasma (NTP) technology offers a promising and attractive alternative to conventional catalytic technology. In this study, H2-D2 equilibrium reaction was carried out without catalysts in a dielectric barrier discharge (DBD) reactor, achieving efficient conversion of HD even at 77 K. In the single-electrode DBD plasma reactor (S-P-Reactor), with an input power of 50 W, the conversion efficiency of HD was found to be 99.5% at 303 K, 99.5% at 273 K, and 99.2% at 77 K. To further optimize the energy efficiency of the S-P-Reactor, the reactor structure was enhanced, leading to the development of a multi-high-voltage DBD reactor (M-P-Reactor). The input power of the M-P-Reactor was reduced by approximately 10 W compared to the S-P-Reactor, while maintaining a conversion efficiency of over 95% for HD. The energy density of the reactor was calculated using the Q-V Lissajous graphic method, demonstrating that the M-P-Reactor exhibited superior energy conversion efficiency compared to the S-P-Reactor. Furthermore, the yield of HD was solely determined by the energy density and temperature. At a specific energy density, the yield of HD remained consistent for the same temperature. However, generating HD at lower temperatures required more energy due to the lower reactant activity. These results illustrate the potential application of NTP technology in improving hydrogen isotope separation performance in the cryogenic distillation process for fusion applications.

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging.

Blockage Effects of Road Bridge on mmWave Channels for Intelligent Autonomous Vehicles

Vehicular communication and sensing technologies are key to enabling 6G Intelligent Autonomous Transportation Systems (IATS). With the introduction of massive sensors and artificial intelligence (AI) fusion applications, IATS is needed to support data transmission rates up to 10 Gb/s. Millimeter-wave (mmWave) technology has attracted extensive attention owing to abundant spectrum resources, which can support the timely transmission of massive data. However, performance degradation of mmWave due to signal blockage has become one of the critical technical challenges. Road bridges as one of the common obstacles in urban scenarios, which has severe blockage effects on communication links. Therefore, this paper comprehensively studies the impact of road bridge blockage effects on mmWave vehicle-to-infrastructure (V2I) links and proposes an empirical model that can accurately characterize the bridge blockage effect. First, we use a self-developed mmWave channel sounder to carry out channel measurements on typical urban roads. Measurement results indicate that a maximum extra propagation loss of up to 23 dB is caused by road bridges. In addition, to address the deficiencies of existing propagation prediction models, the Single Road Bridge (SRB) model is proposed in this work. This model reveals for the first time the extra propagation loss caused by the road bridge to the channel. Compared with existing models, the SRB model can make the mean absolute error (MAE) and root mean square error (RMSE) within 5 dB. The proposed SRB model is of great value for accurately simulating real-world road bridge blockage events when designing future IATS.

Current Sharing Control Modeling and Design for Power Supplies in Nuclear Fusion Applications

Current Sharing Control Modeling and Design for Power Supplies in Nuclear Fusion Applications

Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging.

The challenges of respiratory infections persist as a global health crisis, placing substantial stress on healthcare infrastructures and necessitating ongoing investigation into efficacious treatment modalities. The persistent challenge of respiratory infections, including COVID-19, underscores the critical need for enhanced diagnostic methodologies to support early treatment interventions. This study introduces an innovative two-stage data analytics framework that leverages deep learning algorithms through a strategic combinatorial fusion technique, aimed at refining the accuracy of early-stage diagnosis of such infections. Utilizing a comprehensive dataset compiled from publicly available lung X-ray images, the research employs advanced pre-trained deep learning models to navigate the complexities of disease classification, addressing inherent data imbalances through methodical validation processes. The core contribution of this work lies in its novel application of combinatorial fusion, integrating select models to significantly elevate diagnostic precision. This approach not only showcases the adaptability and strength of deep learning in navigating the intricacies of medical imaging but also marks a significant step forward in the utilization of artificial intelligence to improve outcomes in healthcare diagnostics. The study's findings illuminate the path toward leveraging technological advancements in enhancing diagnostic accuracies, ultimately contributing to the timely and effective treatment of respiratory diseases.

Ante- and Post-Mortem Fracture Identification Protocol Based on Low- and High-Level Fusion Using Fourier Transform Infrared Spectroscopy and Raman Spectroscopy Association.

In this study, the application of low-level fusion (LLF) and high-level fusion (HLF) strategies using a combination of Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy in the identification of antemortem and postmortem fracture at different postmortem intervals (PMIs) was investigated. On a technical level, the same hard tissue sample can be detected using a mix of FT-IR and Raman techniques. At the method level, two cutting-edge chemometrics approaches (LLF and HLF) combining FT-IR and Raman spectroscopic data are explored. The models were ranked in accordance with their parametric quality as follows: HLF and LLF + HLF models > LLF single model > Raman single model > FT-IR single model. The LLF model performed marginally better than the Raman model, however, when compared to other models, the HLF model performed considerably better. The HLF model achieved the best performance, with both cross-validation accuracy and test data set accuracy of 0.88. The importance of the feature wavelengths in the model construction process was subsequently evaluated by intersection fusion, and it was found that the absorbance bands of amide I, PO43- ν1 ν3, and CH2 in FT-IR and phenylalanine, CO32- ν1- PO43- ν3, and amide III in Raman have outstanding contributions to the construction of antemortem and postmortem fractures identification models. Overall, the combination of FT-IR and Raman with the HLF strategy is a novel and promising approach for developing antemortem and postmortem fracture identification models at different PMIs.

The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer “Human-Centric” Safe, Secure, Sustainable, Sensitive, Service, and Smartness “6S” manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber–Physical–Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.

Research on Multi-Source Heterogeneous Big Data Fusion Method Based on Feature Level

With the development of research on multi-modal data fusion and its combination with online data management, the application of multi-modal big data fusion in information management systems is more and more extensive. How to integrate multi-modal big data effectively is the key technology to building an efficient information management system. In this paper, based on the combination of a multi-support vector machine and convolution neural network, the feature-level data fusion of multi-source heterogeneous big data is implemented, and it is applied to the real data set to test the relevant model. Experimental results show that this method can not only realize heterogeneous integration of big data, but also has high accuracy and reliability.