Abstract:To investigate the current development of multimodal visual perception and manipulation for space manipulators and the pressing technical challenges, this paper conducts an analysis and summary of the existing literatures. Multimodal visual perception refers to a vision approach that integrates heterogeneous sensors and multi-source data, including visible, infrared, depth cameras, and LiDAR. Space manipulation is on-orbit activities conducted with robotic manipulators and other actuators, encompassing approach, grasping, assembly, and maintenance. This paper first reviews representative space manipulator systems that have been deployed domestically and internationally, summarizing their developmental path and application characteristics. Building on this foundation, we adopt a perception-planning-control framework to systematically review three technologies essential for autonomous on-orbit servicing. We first address multimodal visual perception, focusing on heterogeneous data fusion and multimodal pose estimation. We then examine trajectory planning under complex constraints, covering model-based, optimization-based, and learning-based methods and their applicability to free-floating bases and strongly coupled dynamics. Last, we discuss compliant grasping for free-floating moving targets to ensure operational safety. Finally, the paper highlights major challenges faced by space manipulators in autonomous on-orbit servicing, including limited onboard computational resources, scarcity of on-orbit data, difficulties in multimodal coordination, and the need for long-term reliability. Future directions are then outlined from the perspectives of hardware, algorithms, and system-level integration. Research indicates that autonomous on-orbit servicing for space manipulators is still immature, with multiple bottlenecks persisting in key technical components and practical deployment. Advances in multimodal vision, learning-based trajectory planning, and compliant grasping control will be critical to enhancing autonomous performance.

Abstract:To systematically summarize the theoretical foundations, design methodologies, fabrication strategies, and engineering translation of multiphysical coupled programmable metamaterials, and to elucidate the key scientific questions and emerging trends in current research, this review proposes an integrated research framework that connects “theory-design-manufacturing-characterization-application-engineering”. The framework aims to provide a solid theoretical insights and engineering guidance for the on-demand functional design and intelligent responsiveness of metamaterials. Based on effective-medium and homogenization theories, we elucidate the modeling principles of Bloch-wave analysis and topological phases, and discuss the integration of nonlinear multistability with physics-constrained machine learning to achieve hybrid data-physics-driven modeling. Furthermore, we compare advanced design paradigms, such as topology optimization, Bayesian optimization, reinforcement learning, and generative modeling, and highlight the importance of explicitly incorporating manufacturability constraints and tolerance robustness at the design stage. Subsequently, the development routes of additive manufacturing and 4D printing from micro/nano to macro scales are summarized, together with multi-material and time-varying programmable strategies. Finally, cross-domain characterization metrics and standardized protocols are consolidated, and a unified framework is proposed based on data assimilation and parameter inversion. The study reveals that metamaterials research is evolving from the discovery of isolated exotic properties toward integrated, reconfigurable, and programmable multifunctionality. By integrating intelligent optimization and additive manufacturing, metamaterials have achieved remarkable performance enhancements and prototype demonstrations in vibration isolation, energy absorption, electromagnetic absorption and cloaking, acoustic focusing and noise control, thermal management, flexible sensing, biomedical applications, and lightweight aerospace systems. The proposed framework provides a generalizable technical roadmap for functional design and engineering translation of programmable metamaterials. Future research should focus on understanding multiscale coupling mechanisms, improving fabrication precision, enhancing reliability evaluation, and promoting system-level integration to bridge the gap between laboratory demonstrations and real-world engineering applications.

Abstract:As a complex electrochemical system with strong multiphysics coupling, the performance of lithium batteries is affected by the intricate interplay of physicochemical processes spanning atomic, mesoscopic, and macroscopic scales. Conventional single-scale simulations are insufficient to elucidate such intricate couplings or fully explain the complex failure mechanisms. To systematically characterize the internal multi-field couplings, it is necessary to establish a comprehensive multiscale computational modeling framework that integrates these key physicochemical processes. Therefore, multiscale computational modeling has emerged as an essential approach for understanding electrochemical-mechanical interactions, predicting performance degradation, and guiding the design of next-generation materials and structures. This review summarizes the overall research framework and recent advances in multiscale modeling of lithium-ion batteries, with a focus on the key scientific questions and mainstream methodologies across three hierarchical levels, i., microscopic, mesoscopic, and macroscopic. Key methodologies include density functional theory, molecular dynamics, phase-field modeling, Kinetic Monte Carlo, and the foundational porous electrode theory. Models at each scale play crucial roles in elucidating electrode material structure and mechanical behavior, ion transport and solvation, the formation and evolution of solid-electrolyte interphases, dendrite growth, and the multiphysics coupling phenomena within battery cells. Density Functional Theory, at the quantum level, serves as the cornerstone for resolving intrinsic material structures, thermodynamic properties, and mechanical characteristics. At the atomic scale, Molecular dynamics is crucial for clarifying ion transport mechanisms within electrolytes and the complex dynamics of solvation structures. Moving to the mesoscopic scale, phase-field modeling shows unique advantages in simulating the dynamic formation and evolution of the solid electrolyte interphase and the complex morphological growth of lithium dendrites. Finally, macroscopic porous electrode theory provides the core framework for linking all these microscopic mechanisms to the overall cell-level multiphysical coupling behavior. Furthermore, key information transfer strategies for bridging these different scales-such as hierarchical (top-down) and concurrent (handshaking) modeling, as well as emerging machine-learning surrogate models-are discussed in detail. We emphasize that, despite the power of multiscale approaches, current simulations still face substantial challenges in reconciling high fidelity with computational efficiency. Key limitations include the prohibitive computational cost of fine-grained models, persistent difficulties in validating model consistency across scales, and the limited ability to accurately represent the complex, heterogeneous microstructures of practical electrodes. We conclude that the deep integration of physics-informed machine learning, the development of high-fidelity real-time battery digital twins, and the closed-loop integration of computation with advanced characterization techniques will be the key pathways to overcoming these challenges and accelerating the design of next-generation high-performance batteries.

Abstract:To systematically analyze the state-of-the-art, core challenges, and future trends of the consensus problem for linear homogeneous multi-agent systems, this paper presents a comprehensive, multi-dimensional review of consensus protocols, offering a clear theoretical landscape and forward-looking guidance for researchers in the field. The review begins with foundational classic linear time-invariant protocols, analyzing their design philosophy of extending single-agent observer-control theory to networked environments and proposing a unified analytical framework. Building on this, the paper investigates advanced protocols developed to address complex real-world constraints. These include: fully distributed protocols designed to eliminate reliance on global information; attack-immune protocol that eliminates information exchange between controllers; time-delay compensation protocols for overcoming communication bottlenecks; finite-time/prescribed-time consensus protocols for achieving faster convergence and stronger robustness; and attack-resilient and fault-tolerant protocols to ensure reliability in adversarial environments. Finally, current research achievements in consensus protocols and the challenges regarding their future development are summarized, followed by an outlook on potential directions for the next stage of exploration. The findings indicate that research on consensus for linear multi-agent systems has established a relatively complete theoretical framework. However, limitations persist: a unified theory for addressing coexisting, compound challenges remains underdeveloped, and traditional model-based control methods lack adaptability in highly dynamic and uncertain environments. In conclusion, key future breakthroughs are expected in: developing unified frameworks to handle compound challenges, advancing the theory and application of data-driven and learning-based control, and shifting the research paradigm from state-level “consensus” to task-level “coordination”.

Abstract:Prussian blue (PB) and its analogues, as classic inorganic electrochromic materials, have shown great application potential in fields such as smart Windows, automotive sunroofs and low-power displays due to their advantages of low cost, high coloration efficiency and neutral color. This work systematically reviews the multicolor electrochromic mechanism of PB-based analogues, elucidates the role of water molecules in the electron-transfer process, and summarizes the characteristics and applicability of common preparation methods. On this basis, it focuses on outlining the electrochromic properties of PB-based composites and their applications in smart Windows, automotive sunroofs, and display fields. It further discusses the key challenges and future development trends facing PB-based electrochromic materials in practical applications. Overall, this review provides theoretical references and technical support for the performance optimization and application expansion of PB-based analogues, while outlining future directions and potential breakthroughs in the field of intelligent optoelectronics.

Abstract:To meet the dual requirements of structural performance and lightweight design in modern high-end equipment, high-reliability joining technologies for dissimilar metals have become one of the core challenges in the field of advanced manufacturing. Diffusion bonding, as an advanced solid-state joining process, enables interfacial atomic interdiffusion through thermo-mechanical coupling to achieve metallurgical bonding. It is particularly suitable for joining of dissimilar materials with significantly different physical and chemical properties, and holds unique value in high-end industrial fields such as aerospace, new energy, and nuclear industry. This paper systematically reviews the current state of research and progress in diffusion bonding of dissimilar metals. Firstly, based on the metallurgical compatibility of material combinations, dissimilar metal diffusion bonding systems are categorized into three types: systems with good compatibility (e.g., dissimilar steels, steel/nickel, etc.), systems with poor compatibility (e.g., copper/iron, tungsten/copper, etc.), and systems prone to forming brittle intermetallic compounds (IMCs) (e.g., steel/titanium, titanium/nickel, aluminum/steel, etc.). The bonding mechanisms and principal technical challenges for each category are discussed in detail. Secondly, the study focuses on combinations of key engineering materials such as steel, nickel, titanium, copper, aluminum, and magnesium alloys, and examines strategies for controlling interfacial reactions, IMC growth, and residual stresses through optimized process parameters, innovative process methods, interlayer design, and surface pretreatment. Research shows that selecting appropriate interlayers (e.g., Ni, Cu, Ag, high-entropy alloys, etc.) can effectively suppress the formation of harmful IMCs and alleviate thermal stresses caused by differences in thermal expansion coefficients, thereby significantly improving joint performance. Pretreatment techniques such as surface nanocrystallization enable high-quality low-temperature bonding while minimizing thermal damage to materials. Finally, this paper highlights future key research directions in the field, including multi-scale simulations of interfacial reaction kinetics, development of low-stress and defect-free new processes, and the application of artificial intelligence in material design and process optimization, aiming to provide theoretical references and technical support for promoting the wider application of diffusion bonding technology in high-end industrial fields.

Abstract:Under the "dual-carbon" goals, urban water systems, characterized by substantial energy consumption, chemical dosage, and carbon emissions, have emerged as a critical field for carbon footprint management and control, urgently requiring the exploration of low-carbon development pathways and sustainable operation and maintenance strategies. This study focuses on the key infrastructures of urban water systems, including drinking water treatment plants, wastewater treatment plants, and water supply and drainage networks. The main sources, characteristics, and formation mechanisms of carbon footprints within urban water systems are systematically reviewed. The core approaches and feasible strategies for carbon mitigation across drinking water production, wastewater treatment, and water conveyance processes are summarized. Drinking water treatment plants should focus on controlling operational energy consumption and chemical consumption by advancing material development, optimizing treatment processes, implementing intelligent operation, and adopting clean energy alternatives. Wastewater treatment plants, in addition to these measures, are required to incorporate additional energy recovery and resource utilization modules, ensuring the implementation of carbon offset strategies. Water supply and drainage networks should be anchored in scientific planning and rational spatial layout, supplemented by source wastewater reduction, routine pipeline maintenance, and dynamic pressure regulation to limit pumping energy. In addition, an implementation framework and recommendations for coordinated carbon footprint management and control in urban water systems are proposed, providing theoretical and technical support for the low-carbon transition and sustainable development of urban water systems.

Abstract:Polymer-based conductive composites have garnered increasing attention in the field of transportation, owing to their outstanding electrical conductivity, synergistic deformation characteristics with matrix structures, and in-situ monitoring capabilities. To facilitate their broader application, elucidate prevailing research challenges as well as prospective development trajectories, this research provided a comprehensive review of recent advances in polymer-based conductive composites within the transportation domain. Firstly, viewing from the material composition and preparation process, the influences of various matrix materials (such as polypropylene and epoxy resin) and conductive fillers (such as carbon nanotubes, graphene and metal particles et.) on the comprehensive performances of composite materials were analyzed, and the regulatory effects of different preparation techniques, such as solution mixing and melt blending method, on the comprehensive performance of composite materials were discussed. Secondly, the performances characterization of polymer-based conductive composites were reviewed, delineating how functional properties, such as temperature sensitivity and mechanical responsiveness, vary under complex environmental factors like temperature and force fields. Thirdly, the conductive mechanisms underlying polymer-based composites were then explored, with a focus on how the dispersion and interaction of distinct conductive fillers contribute to enhanced conductivity. Finally, the practical applications of these composites as sensors were presented, emphasizing their deployment in transportation infrastructure and intelligent traffic management, along with an assessment of their operational efficacy. Based on the current research findings and technical challenges, it is evident that polymer-based conductive composites hold substantial promise for widespread application in transportation. Nevertheless, the challenges concerning sensors durability and functionality warrant further innovation and improvement.

Abstract:To promote the application of two-dimensional transition metal carbides/nitrides MXene materials in the transportation field, this research reviews the preparation procedures, material classifications, and recent applications progress related to the transportation-engineering. Various synthesis techniques are introduced, including hydrofluoric acid etching, in-situ hydrofluoric acid etching, molten salt etching, electrochemical etching, and alkali etching. The mechanisms underlying the formation and control of porous gels, films, fabrics, and spherical particles, and their impact on the multifunctional performance of MXene-based materials, are discussed thoroughly. Key breakthroughs in MXene materials applications are summarized, highlighting their roles in developing efficient photothermal de-icing coatings for roads, improving the corrosion resistance of transportation infrastructure, and constructing lightweight, high-performance electromagnetic shielding materials for transport systems. Results show that, MXene materials can achieve ultra-high photothermal de-icing efficiency of 73.1%, reduce metal corrosion rates by two orders of magnitude, and attain electromagnetic interference shielding effectiveness exceeding 10⁵ dB·cm²·g^-1. However, their large-scale application in the transportation sector still faces challenges such as high costs of green preparation, poor long-term stability, and a lack of intelligent response. Future research should focus on developing intelligent response and adaptive functionalities to facilitate the transition of MXene materials from laboratory research to practical engineering applications. This literature review can provide the theoretical references and technical support for promoting the intelligent upgrading and green sustainable development of transportation infrastructure.

Abstract:The prevention and control of major diseases, such as cancer, cardiovascular and cerebrovascular diseases, and neurodegenerative disorders, remain core challenges in modern medicine. Their precise diagnosis and treatment critically rely on the integrative analysis of heterogeneous multi-source data, including medical imaging, electronic health records, and genomics. Traditional unimodal approaches, however, suffer from information silos and struggle to comprehensively characterize the complex biological mechanisms and clinical phenotypes of diseases. In response to this challenge, this paper systematically reviews the progress of multimodal large models (MLM) in major disease prevention and control. First, we summarize the transformer-centered technical paradigm, elucidating the underlying architecture and synergistic mechanisms that enable fusion of multimodal medical data. Second, we systematically survey applications across core clinical scenarios-early diagnosis, precise subtyping, and prognostic prediction, while deeply analyzing its technical potential and empirical value. Furthermore, we summarize common challenges encountered in practice, including data heterogeneity, the model “black box” problem, and ethical, legal, and data security issues. Finally, we outlook future development trends and propose key breakthrough directions, emphasizing clinically task-oriented model optimization, causal reasoning and enhanced interpretability, federated learning and privacy-preserving computation, and human-AI collaborative intelligent diagnostics. This review aims to provide a systematic reference for researchers, clinicians, and policymakers, promoting the clinical translation of multimodal large models in the prevention and treatment of major diseases, thereby empowering the high-quality development of precision medicine.

Abstract:Towards to general intelligentization of autonomous driving systems, the world models as a cognitive engine that internally models, infers, and predicts the environment, is becoming a critical technical pathway to break bottlenecks in traditional perception-decision paradigms and address long-tail scenarios. To synthesize the research progress and key issues of the world models in autonomous driving, and explore their technical routes for advancing the implementation of general intelligent driving, the research status and development trends in autonomous driving are reviewed. Firstly, the basic concept of world models and their core functionalities in autonomous driving are clarified, mainstream technical architectures are summarized, and the merits and drawbacks of various paradigms are comparatively analyzed. Secondly, the latest progress of world models in three key application directions are summarized including of future scene generation and understanding, end-to-end driving policy learning, and data-driven closed-loop simulation systems, and practical value in enhancing the system’s forward-looking capabilities and interaction understanding is revealed. Thirdly, the evaluation metrics of world models and the application scopes of public datasets are organized, which lays a foundation for the subsequent analysis of their technical challenges. Overall, despite achieving phased breakthroughs in multi-scale spatiotemporal representation and complex scene generation, the world models still face the challenges in adhering to physical laws, safe and credible reasoning, long-term temporal stability, and lightweight deployment. Accordingly, it is suggested that future research should focus on efficient computing architectures, long-term generation consistency, uncertainty modeling, and self-supervised representation integrated with physical knowledge, so as to promote the effective function of world models in various traffic scenarios.

Abstract:As a core component of rocket and missile engines, the forming process of composite nozzle preforms directly determines the performance and reliability of the final product. The design flexibility of composite materials has resulted in numerous forming techniques for rocket nozzle preforms, but a systematic summary is still lacking. This work reviews three typical forming processes (1D winding, 3D needling, and 3D braiding), comparing their principles, technical features, and key factors affecting preform quality. The advantages and limitations of different processes are discussed in depth, the types of components suitable for each process are clarified, and we offer a brief outlook on future development in composite nozzle preform manufacturing. This study provides a comprehensive reference for selecting suitable forming processes for composite rocket nozzle preforms. It offers technical support for the high-performance application of composite materials in high-end aerospace power equipment and helps drive the upgrading and iteration of next-generation aircraft power systems.

Abstract:The aerodynamic database construction for conventional aircraft configurations typically employs full sampling across inflow dimensions. Given the substantial distance and weak interference between control surfaces, the strategy of superimposing control effectiveness increments is widely used, which avoids the need for a full combinatorial sampling of all control surface deflections. However, for flying-wing aircraft, the presence of strong aerodynamic interference among multiple closely-spaced control surfaces makes full combinatorial sampling that accounts for these interactions prohibitively expensive. This paper focuses on the critical challenge of constructing an aerodynamic model capable of capturing nonlinear interference between control surfaces under limited combinatorial sample. Firstly, an intelligent aerodynamic modeling method is proposed that integrates a convolutional neural network (CNN) with an engineering model, specifically for scenarios with dense angle-of-attack sampling such as wind tunnel tests. Secondly, to characterize the aerodynamic forces generated by 3 trailing-edge control surface combinations on a low-speed flying-wing configuration, high-fidelity CFD simulations are used to obtain aerodynamic data for single and dual control surface deflections. Finally, a low-fidelity engineering model is constructed using a method that linearly superimposes individual control effectiveness and interference increments between adjacent control surfaces. Then, by introducing an angle-of-attack sequence modeling mechanism, a CNN is applied to further characterize nonlinear interference effects both among the control surfaces and across the angle-of-attack dimension. Results indicate that the proposed method not only improves accuracy by approximately 40% compared to the engineering model, but also reduces the standard deviation of prediction error by over 50% relative to a direct deep neural network model without embedded engineering knowledge. This research significantly enhances both accuracy and robustness of strongly nonlinear aerodynamic modeling for flying-wing aircraft under high-angle-of-attack and large-control-surface-deflection conditions.

Abstract:To elucidate the micro-mechanisms of matrix crack evolution in continuous fiber reinforced ceramic-matrix composites (FRCMCs), this study systematically investigates the influences of the initial matrix crack length, interfacial properties, thermal residual stresses, and radial stress on the matrix cracking stress. First, a simplified Coulomb-friction model that comprehensively accounts for Coulomb friction, Poisson’s effect, residual stresses and radial stress, is introduced to derive the analytical formula between the fiber bridging stress and the crack opening displacement. Then, a theoretical model for the matrix cracking stress is established by combining this bridging law with linear-elastic fracture mechanics. By this theoretical model, a quantitative relationship between the cracking stress and the initial crack length is obtained. Subsequently, the roles of constituent properties and the external factors including the ambient temperature and the radial stress are studied. The results show that long initial cracks propagate at low stress levels, whereas short initial cracks require high stress levels to grow, and the distribution of initial crack lengths thus governs the evolution of matrix cracking. Moreover, in addition to increasing matrix fracture toughness, interface friction coefficient and interface debonding energy, the application of radial compressive stress can significantly raise the matrix cracking stress. Therefore, the effect of radial stress must be fully considered in the structural design of continuous-fiber-reinforced ceramic matrix composites, and a radial compressive state should be maintained as far as possible to suppress crack propagation and enhance structural performance.

Abstract:Unmanned aerial vehicle (UAV) swarms have become a normalized element of modern warfare-as exemplified by the Russia-Ukraine conflict-serving as critical assets for strikes and reconnaissance. To satisfy the requirements of future maritime swarm operations, it is imperative to investigate dense-obstacle avoidance, cooperative bypass of non-cooperative targets, and three-dimensional reconstruction for UAV swarms operating in adversarial environments. To enhance autonomous perception and cooperative mapping in complex maritime scenarios, this work proposes an integrated approach for swarm intelligence planning and 3D reconstruction tailored to dynamic environments. Initially, in the cooperative trajectory planning stage, a leader-based UAV cooperative tracking method is proposed. The leader UAV is responsible for real-time locking and tracking of high-value non-cooperative targets such as warships, while the follower UAVs maintain formation stability through a formation-keeping mechanism. Furthermore, when encountering mobile obstacles from other unmanned swarms, the swarm avoids threats through path re-planning and maneuvering actions, and quickly resumes target tracking after safely passing through. Ultimately, in the dynamic 3D reconstruction phase, after the UAV swarm approaches the target of interest, a multi-UAV cooperative circumnavigation and 3D reconstruction method is designed to achieve multi-perspective observation and dynamic 3D reconstruction of non-cooperative maneuvering targets. Sea-based simulation experiments demonstrate that the methods proposed in this paper enable the UAV swarm to autonomously complete real-time tracking and efficient reconstruction of warships in complex adversarial environments, balancing mission completion rate and flight safety. This provides an effective solution for the reconnaissance, surveillance, and intelligence gathering tasks of UAV swarms in future battlefields.

Abstract:To address the challenge of characterizing the dynamic properties of bolted-rabbet joint structures in aero-engine rotors under high rotational speeds and multi-load excitation, a rotor dynamic modeling method considering multiple bolted-rabbet joints is established, and the influence of joint interfaces on the rotor’s natural characteristics, critical speeds, and unbalance response is systematically revealed in this study. Firstly, SOLID186 solid elements are employed to model the disks and shafts, COMBI214 elements to simulate the bearings, and MATRIX27 elements to characterize the multi-dimensional stiffness (tensile, lateral, bending, and torsional) of the joint interfaces. By combining the MPC (multi-point constraint) contact algorithm to achieve degree-of-freedom coupling with the bolted-rabbet assembly surfaces, a high-precision dynamic model is established for aero-engine rotors with bolted-rabbet joints. Secondly, a rotor test rig with multiple bolted-rabbet joints is constructed. Ultrasonic preload testing, modal testing, and excitation experiments are conducted to acquire bolt preloads, natural characteristics under different boundaries, and vibration responses under constant-frequency and sweep-frequency excitation, thus verifying the model’s correctness. Finally, the influence of bolted-rabbet joints on the rotor system’s critical speeds and unbalance response is analyzed. The results show that: the bolted-rabbet joint interfaces significantly affect the rotor’s free modes, and ignoring interface stiffness results in the overestimation of natural frequencies; under bearing support, the joint interfaces have minimal impact on translational and pitching modes but a pronounced influence on bending modes, and notably alter the critical speeds associated with bending modes; in the unbalance response, the amplitude of bending modes increases significantly due to interface stiffness loss, and discontinuities in lateral and rotational displacements emerge in the speed interval between the 2nd and 3rd critical speeds. This study provides theoretical support for the design optimization of joint structures in aero-engine rotors.

Abstract:To investigate the seepage erosion characteristics commonly found between soil layers under cyclic loading and to reveal the micromechanical mechanisms of contact erosion under water-flow coupling, a three-dimensional computational model for soil contact erosion was developed, based on the coupling of computational fluid dynamics (CFD) and discrete element method (DEM), considering the influence of cyclic loading amplitude. Firstly, the movement patterns and spatial distribution characteristics of the particles were analyzed within different cyclic loading periods. Secondly, the macroscopic deformation characteristics resulting from particle erosion were explored. At the same time, two localized deformation regions were selected to study two typical particle migration modes. Finally, the evolution mechanism of particle contact mechanics during the erosion process was discussed, combined with force chain analysis. The results show that during a single cycle of loading, the compression of coarse particles caused by loading and the stress relaxation induced by unloading are the primary factors responsible for the migration of fine particles. Cyclic loading induces intense particle migration at the soil layer interface, resulting in significant axial deformation of the sample. Simultaneously, the seepage field generates an upward hydraulic gradient that promotes the pump-driven migration of fine particles. A threshold value for the loading amplitude exists, and the soil rapidly compacts when the cyclic loading amplitude exceeds this threshold, leading to a reduction in erosion-induced deformation.

Abstract:The generation and editing of floor plans are critical in intelligent architectural planning, requiring a high degree of flexibility and efficiency. In response to the limitations of existing methods—such as excessive reliance on input information, lack of interactivity, and insufficient support for precise local modifications—this paper presents ChatHouseDiffusion. The proposed approach first utilizes a large language model to parse natural language instructions into structured JSON prompts, then applies Graphormer to capture the topological relationships between rooms, and finally adopts a diffusion model to generate floor plans under room boundary constraints. The results indicate that during the editing phase, the cross-attention maps replacement mechanism enables localized modifications without reconstructing the entire layout. Experiments conducted on the RPLAN dataset demonstrate that ChatHouseDiffusion outperforms existing models in both Micro-IoU and Macro-IoU, especially when accurate input prompts are used, achieving results highly consistent with ground truth, which exhibits strong practical utility and generalization performance. The model not only strictly follows user requirements but also enables intuitive and iterative design through interactive operations, providing a novel pathway for intelligent floor plan design. Based on this method, we further developed a visual platform enabling the drawing of outlines, inputting text prompts, and generating and editing floor plans, enhancing the practicality and usability of floor plan design.

Abstract:To enhance the synergistic effects of denitrifying phosphate accumulating organisms (DPAOs) and denitrifying glycogen accumulating organisms (DGAOs) in the denitrification phosphorus removal technology, three identical SBR reactors (R1, R2, R3) were set up in the experiment. These reactors operated under a periodic aerobic/anoxic (O/A)_n aeration mode with n values of 1,2, and 3, respectively, to run a simultaneous nitrification endogenous denitrification and phosphorus removal (SNEDPR) system. By comparing the operational conditions of the three reactors over 70 days, the pollutant treatment performance and the activity of functional bacteria were investigated. The results show that under the condition of similar internal carbon storage during the anoxic phase, the R3 reactor operated with (O/A)₃ achieved the highest pollutant removal rates on the 71st day, with 89.38% for total nitrogen (TN), 91.78% for total phosphorus (TP), and 90.20% for chemical oxygen demand (COD), demonstrating the best pollutant removal effect, and also had a higher TP removal rate during the anoxic phase. The typical cycles indicated that on the last aerobic stage of the 70st day, the ratio of NO-₂-N to total nitrogen was 42.41%, 49.83%, and 52.33%, respectively, confirming that increasing the n value in (O/A)_n operation enhances the system′s NTR. Analysis of sludge characteristics revealed that in the R3 reactor, MLSS(mixed liquor suspended solids) and MLVSS(mixed liquor volatile suspended solids) increased steadily, with a low SVI(sludge volume index). The unit VSS extracellular polymeric substances (EPS) content reached 85.27 mg/g by the 70st day, indicating good sludge structure and settling performance. Microbial community analysis showed a higher relative abundance of DPAOs and DGAOs in R3, suggesting that increasing the aeration cycle of (O/A)_n is beneficial for the enrichment of functional bacteria. Increasing the n value in (O/A)_n operation can effectively enhance the synergistic action between DPAOs and DGAOs, improve the nitrogen and phosphorus removal efficiency of the SNEDPR system, and reduce the reliance on external carbon sources.

Abstract:The trend towards larger offshore wind turbines has led to increasingly severe vibration issues for floating wind turbines operating in harsh sea conditions. The complex fluid-structure interaction and multi-mode vibration characteristics of such structures highlight the limitations of conventional passive vibration absorbers, which can only be tuned to a single frequency. To address this issue, this paper proposes the application of a nonlinear energy sink (NES) with broadband vibration reduction capability to the tension leg platform floating wind turbine (TLP-FWT), aiming to develop a novel vibration reduction strategy suitable for multi-mode responses. A dynamic model of the TLP-FWT-NES was first established based on D′Alembert′s principle, and its parameters were corrected using the Leven-Marquardt algorithm. Subsequently, the NES parameters were optimized through a combination of grid search and Bayesian optimization, and its performance was compared with that of a tuned mass damper (TMD). Finally, fully coupled numerical simulations under various sea states were carried out using the OpenFAST to comprehensively evaluate the vibration reduction performance of the NES. The results demonstrate that the NES exhibits greater robustness to stiffness variation than the TMD, through the mechanism of resonance capture cascade and targeted energy transfer, it can effectively suppress multiple vibration modes when high-order modes are selected as the optimization target. However, in single-mode control scenarios, the NES does not outperform the TMD, while under extreme sea states, both devices are capable of reducing structural vibration, with the TMD showing superior overall effectiveness. This study provides new insights into multi-mode vibration control of floating wind turbines and offers a useful reference for enhancing the operational safety and structural reliability of large-scale floating wind turbines under complex marine conditions.

Abstract:Sodium-cooled fast reactor (SFR) is an advanced nuclear reactor that uses liquid sodium as a coolant and relies on fast neutrons for nuclear fission, with passive safety characteristics. However, it may lead to core melting when a severe accident occurs. In order to improve the safety response performance of SFR in severe accidents, the ATHLET (analysis of thermal-hydraulics of leaks and transients) code is provided to conduct a safety analysis of the temperature of sodium pool in SFR under severe accidents in this article. Firstly, the SFR core structure was reasonably simplified and a nodalization diagram was developed based on the established model. Then, the ATHLET code was used to simulate the core melting accident of SFR, the key parameters including the decay heat of molten matter and the power of decay heat exchangers (DHX) in the cold/hot pools were input. Finally, based on the simulation results, the impact of different operating conditions of four DHXs located in the cold/hot pools on the thermal parameters of the main regions in SFR was analyzed. The results showed that the closure of DHX in the hot pool would cause backflow in the flow channel between the lower cooling pool and the lower chamber, resulting in a significant increase in the temperature of the descending section of the lower chamber. The closure of DHX in the cold pool would cause a severe increase in the temperature of the coolant in various parts of the SFR. In comparison, the closure of one DHX in the cold pool is more dangerous for SFR in severe accidents and it is necessary to ensure that both DHXs in the cold pool are in operation under severe accidents, which can timely remove the heat of core melting. In summary, the results on the role of DHX in core melting accidents in this article can provide valuable references for optimizing structural design and validating thermal hydraulic models of SFRs.

Abstract:The advancement of spaceborne inverse synthetic aperture radar (ISAR) imaging for on-orbit target represents a critical technology for space situational awareness (SSA). While conventional two-dimensional (2D) range-Doppler (RD) imaging provides valuable scattering intensity distributions, it inherently is the projection of the target’s three-dimensional (3D) structure, thereby losing critical geometric information essential for predicting orbital maneuvers and enabling non-cooperative target recognition. Current multi-views reconstruction methods based on image sequences face inherent limitations in spaceborne scenarios: the relative orbital motion between the spaceborne platform and target satellite induces limited observation time and unstable imaging projection plane. To address these challenges, a target reconstruction algorithm with variable observation mode is proposed. First, the structural information contained in range migration (RM) trajectories is directly exploited to avoid the error-prone image alignment process. Second, we derive a unified geometric model characterizing the range migration (RM) evolution under both 2D and 3D rotation patterns, and extract discriminative features for rotation pattern classification. Finally, we develop a high-precision RM estimation algorithm based on higher-order Doppler coefficient estimation. For different rotation modes, a truncated nuclear norm (TNN) regularization combined with factorization framework enables the reconstruction of 2D or 3D targets under observation error conditions. Simulation results demonstrate that the proposed target reconstruction algorithm effectively achieves scattering point extraction and RM matrix reconstruction. It further ensures the regularized convergence of the RM matrix, thereby obtaining spatial target reconstruction results under various rotation states. This validates the effectiveness and flexibility of the proposed algorithm.

Abstract:Single-cell RNA sequencing (scRNA-seq) and single-cell T-cell receptor sequencing (scTCR-seq) are pivotal for deciphering immune cell characteristics, offering complementary insights into the immune system′s complexity through gene expression and antigen recognition, respectively. However, conventional analytical methods are often confined to a single modality, hindering the effective integration of complementary information from the two omics. To overcome this limitation and achieve efficient integration of cross-omics data, this study proposes a novel data integration framework named scRTIA (single-cell RNA and TCR integrative analysis). Based on deep learning theory, the model employs a multimodal variational autoencoder and a Transformer as its core architecture, jointly embedding the scRNA-seq gene expression matrix and scTCR-seq TCR sequence features into a unified low-dimensional latent space, thereby constructing a fused cell representation that simultaneously preserves transcriptomic features and immune repertoire information. Experimental validation on real datasets demonstrates that the cell representations generated by scRTIA exhibit significantly superior resolution in identifying cell subpopulations, enabling the discovery of rare T-cell populations with specific functional states that are difficult to detect using traditional methods. By effectively integrating transcriptomic and immunome data, our work transcends the limitations of single-modal analysis and enables a multidimensional characterization of T-cell identity and function, offering valuable insights for immune-related diseases research and precision medicine.

Abstract:According to China′s code for design of concrete structures (GB 50010—2010), the punching calculation for slabs with unbalanced bending moments is currently based on elastic theory to calculate the equivalent concentrated reaction force. This force is then used to evaluate the punching shear capacity of the plate-column joint under the action of vertical loads only. However, previous studies have revealed that the shear stress along the control perimeter exhibits plastic characteristics when the slab with unbalanced bending moments reaches its ultimate state. Therefore, to address the limitation of elastic methods in neglecting the plastic behavior of slabs, this paper presents a formula for calculating the punching of slabs with unbalanced bending moments, considering the influence of plasticity on shear stress. Initially, to assess the plastic development of shear stress along the control perimeter when slabs with unbalanced bending moments undergo shear failure, the ratio of the maximum shear stress along the control perimeter calculated by elastic methods in slabs with vertical loads and unbalanced bending moments to the ultimate shear stress along the control perimeter in slabs with vertical loads only is defined as the plastic development coefficient. Subsequently, through analysis of 76 sets of punching test data of slabs under the combined action of vertical load and unbalanced bending moment, it was observed that the plastic development coefficient decreases with the increase of concrete tensile strength, while the ratio of the eccentric shear stress induced by unbalanced bending moment to the average shear stress induced by vertical load increases. Consequently, by considering concrete tensile strength and shear stress ratio as the two horizontal coordinates and the plastic development coefficient as the vertical axis, the relationship between the three was fitted. Finally, the method for calculating the equivalent concentrated reaction force for slabs with unbalanced bending moments is revised to plastic theory, utilizing the derived formula for calculating the plastic development coefficient.

Abstract:Pavement distresses are typically characterized by irregular morphology, weak texture features, dense small targets, and large-scale variations. Traditional detection heads suffer from limited spatial modeling, poor scale perception, and insufficient semantic sharing, making it difficult to balance detection accuracy and computational efficiency. To address these issues, this study proposed three lightweight detection head optimization schemes based on the YOLOv11 framework, establishing an intelligent and efficient detection architecture for pavement distresses. Specifically, the DyHead module introduces a multi-dimensional attention mechanism to achieve dynamic feature modeling, significantly enhancing scale and spatial perception; the LSCD structure adopts a lightweight shared convolution design, reducing the number of parameters by 30% and FLOPs by 22% while maintaining feature representation capacity, thereby improving deployment efficiency; and the GLSA module integrates a global-local spatial attention mechanism to strengthen feature discrimination and multi-scale adaptability under complex road conditions. Experimental results demonstrate that all three improved models achieve notable gains in both accuracy and efficiency: DyHead achieves an mAP@0.5 of 64.95%, LSCD performs best under resource-constrained conditions, and GLSA exhibits the most balanced overall performance. The proposed methods provide new insights for lightweight model design and embedded deployment of pavement distress detection, offering valuable technical support for the intelligent maintenance of smart transportation infrastructure.

Abstract:To reveal the intrinsic structure-property relationships between the inherent "genetic" characteristics of multi-source base asphalt and its performance, this study selected ten commonly used base asphalts from different crude oil sources. Through systematic characterization of the macroscopic properties (such as the three major indicators), chemical composition (saturates, aromatics, resins, and asphaltene content), and elemental composition of the base asphalts, combined with compositional analysis and correlation analysis, it was revealed that the resin-to-asphaltene ratio (I_R/A) exhibits a very strong correlation with the rotational viscosity at 135 ℃, while the resin-to-aromatics ratio (I_R/A) only shows a strong negative correlation with the softening point after aging. Furthermore, combined with gel permeation chromatography test results, it was found that the synergistic effect between molecular weight distribution characteristics and the four fractions significantly influences the rheological behavior of asphalt. Among these, the weight-average molecular weight demonstrates a strong negative correlation with ductility after aging. The polydispersity index exhibits strong or very strong positive or negative correlations with the penetration index (PI), softening point after aging, standard viscosity, and rotational viscosity at 135 ℃. This indicates that a broader molecular weight distribution leads to higher temperature susceptibility and viscosity of asphalt, while reducing its ductility. Through partial least squares regression analysis, the influence of the four fractions and molecular weight parameters on macroscopic properties was further validated, and structure-property equations between these "genetic" characteristics and asphalt performance were established. To some extent, the constructed structure-property relationship model allows for the prediction of corresponding performance indicators based on genetic characteristic parameters. This study provides a theoretical foundation for the precise design and performance prediction of asphalt materials in the future.