Search Results (384)

The feasibility of employing the dynamic substructure approach for seismic response analysis of complex structures has been widely recognized. However, the analytical accuracy of this method is affected by several factors, including the element type, the structural configuration, and the analysis method. To address these issues, four types of reticulated shell structures were designed and analyzed using the mode superposition response spectrum method (MSRSM) and the time history analysis method (THAM). The displacements of the key nodes and all member stresses were extracted to compare the simplified finite element models with the original models. The relative errors of nodal displacements calculated by the models with reduced degree of freedom (DOF) were within 1.62%. For the member stresses of the single-layer reticulated shells, the relative errors of the simplified models were within 14.35%. In the simplified models of double-layer reticulated shells, several members exhibited a relative error greater than 30%; however, these members were mainly located near the substructure boundaries and accounted for less than 0.62% of the entire structure. Three strategies are proposed to mitigate the influence of the member stress errors on the structural analysis conclusions for double-layer reticulated shell structures. In addition, the dynamic substructure method was extended to the coupled system of large-span spatial structures and point-supported glass facades. The seismic response results confirmed that this method effectively reduces computational costs while maintaining satisfactory accuracy, indicating that it is a useful tool for simplifying large-span spatial structures in extensive numerical analyses. Full article

The convergence of artificial intelligence (AI), explainable AI (XAI), and neuroscience is fostering new opportunities for understanding both machine and biological intelligence through interpretable and human-centered learning paradigms. In this Perspective, we introduce XAI2Brain as a conceptual framework for brain–AI alignment, positioning mechanistic interpretability as an intermediate layer connecting neural network representations, human understanding, and neuroscience-inspired AI design. Rather than viewing XAI solely as a post hoc transparency tool, we emphasize its emerging role in enabling mechanistic analysis of internal model representations, concept-level reasoning, and interactive human–AI alignment. We define XAI2Brain as a multi-level conceptual framework rather than a deployable system, explicitly aimed at structuring brain–AI alignment across representation-level, mechanism-level, and interaction-level perspectives. We survey the evolution of XAI methodologies—from feature attribution and concept-based explanations to mechanistic and human-centric interpretability approaches—and discuss how these methods may support bidirectional knowledge transfer between AI systems and cognitive neuroscience. Importantly, we adopt a cautious stance on brain–AI analogy, explicitly recognizing that artificial neural representations are not equivalent to biological neural representations, and instead focusing on functional and informational correspondences rather than structural equivalence. Unlike conventional human-in-the-loop or reinforcement learning from human feedback paradigms that primarily optimize behavioral outputs, XAI2Brain focuses on cognitively interpretable and mechanistically grounded alignment between AI systems and human reasoning processes. This alignment promotes interactive human-in-the-loop intelligence, empowering humans to comprehend, guide, and refine AI systems, while enabling AI systems to better interpret human instructions, intentions, and contextual reasoning. We further discuss the challenges of scaling explainability to large generative and multimodal models, including issues of interpretability robustness, cognitive compatibility, evaluation, and ethical accountability. We also highlight key limitations of current mechanistic interpretability methods, including explanation instability, representation superposition, and lack of causal guarantees, underscoring that these challenges remain open research problems. Rather than proposing a complete artificial brain architecture, this Perspective outlines a research roadmap toward more interpretable, adaptive, and neuroscience-inspired AI systems capable of supporting future brain–AI integration and collaborative intelligence. We additionally clarify that this work follows a narrative perspective review methodology with structured thematic synthesis of the literature. By framing explainability as a bridge between mechanistic AI understanding, cognitive science, and human-centered interaction, XAI2Brain highlights the importance of interpretable alignment for the next generation of brain-inspired AI systems. Full article

The counterfeiting of official documents and banknotes represents a critical threat to global security and requires robust and low-cost protection techniques. This work presents an innovative information security system that uses photoluminescent inks for chromatic multiplexing of QR codes. Unlike conventional cryptographic methods, the proposed approach employs physical-layer information hiding through the superposition of two QR codes encoded in magenta and cyan colors on a white background. The controlled interaction between these codes generates an additional logical state that enables a third representation of information through pixel-level operations. The resulting chromatic QR code remains visually imperceptible under ambient illumination and can be reliably recovered through chromatic demultiplexing and thresholding process. Additionally, its visibility can be enhanced under ultraviolet (UV) excitation due to photoluminescent behavior and spectral response variations. The experimental results demonstrate that both encoded data layers can be extracted independently with high fidelity using standard CMOS sensors, while preserving structural integrity and decodability. The proposed scheme increases information density within a single optical tag while improving resistance against unauthorized replication and visual forgery. Full article

Loading fluctuations cause structural responses such as deformations and vibrations on the propeller. Structural response of propellers results in vibrations on the shaft system or even the hull. Considering the demand for structural safety, the correlation between structural response of propellers and inflow conditions is numerically studied in the present paper. The interaction between the propeller and turbulence structures and vortex shedding from upstream structures is considered. Loading fluctuations on the propeller blade are obtained by a turbulence model of improved delayed detached eddy simulations (IDDESs). The deformations and vibrations of propeller blades fixed at their roots are captured considering fluid–structure interaction. Results show that the loading fluctuations and vibrations on the propeller contain tonal components occurring at harmonics of shaft frequency and broadband components. Inhomogeneous inflow amplifies pressure fluctuations as a product of space frequency and shaft frequency (SF). Inhomogeneous inflow also results in more intense fluctuations of velocity in the tip vortex at SF and blade wake at blade passing frequency and encounter frequency. As a result of loading fluctuations, the vibration of the blade is a superposition of excited vibrations and natural vibrations. Inhomogeneous inflow amplifies the vibrations at the encounter frequency. Resonance of the blade can be observed when the excited frequency approaches the first natural frequency. Full article

To address the issues of pronounced resonance, limited control bandwidth, and insufficient trajectory tracking accuracy in piezoelectric-driven elliptical vibration-assisted cutting (EVC) systems under high-frequency vibration, this paper proposes a trajectory tracking control strategy combining damping control with frequency-domain shaping. First, a damping-control strategy is integrated into the control system to refine the plant’s inherent dynamic properties, suppressing the resonance peak and elevating the system’s stability margin. Second, to enhance the system bandwidth and dynamic response, a high-gain PID controller is designed via frequency shaping. Additionally, given that the nominal model becomes high-order after implementing the damping controller, proportional gain is used for approximate equivalence with the system transfer function, lowering the model order and streamlining controller design. Next, a disturbance observer (DOB) is introduced to estimate and compensate for the unmodeled dynamics in the feedforward path in real time, further improving the trajectory tracking accuracy. Finally, taking the designed piezoelectric-driven EVC device as the controlled plant, the system frequency response is obtained through sweep excitation experiments, based on which the nominal model is identified, and the controller parameters are determined. The experimental results demonstrate that the proposed control strategy effectively suppresses resonance effects, increases system bandwidth, and reduces the trajectory tracking error. In the complex harmonic superposition trajectory tracking experiment, the steady-state tracking error is maintained within ±0.09 μm. These results demonstrate that the proposed approach markedly improves the system’s dynamic response and trajectory tracking performance, thereby providing technical support for high-precision fabrication of micro/nano-structured surfaces. Full article

Metamaterials with tailored structural architectures enable negative thermal expansion through geometric mechanisms that counteract constituent-level positive expansion. This study evaluates the thermomechanical performance and structural limits of polymer and hybrid NTE lattices. We systematically classify the dominant kinematic mechanisms, including bimetallic bending, rotational squares, and re-entrant honeycombs, and quantify the inherent trade-offs between effective thermal contraction, structural stiffness, and mass efficiency. The analysis demonstrates that reliance on idealized linear–elastic and rigid-lever models leads to significant predictive discrepancies when evaluating the physical response of polymeric and hybrid prototypes. We establish that these deviations are fundamentally governed by localized stress singularities at multi-material interfaces and the profound thermoviscoelastic softening of polymers as they approach the glass transition temperature ($T_g$). We conclude that accurate prediction of the cyclic lifespan and dimensional stability of these systems requires a transition to coupled multiphysics frameworks. Specifically, integrating temperature-dependent cohesive zone modeling and time–temperature superposition principles is essential for capturing interfacial delamination and thermal ratcheting in high-performance polymeric NTE metamaterials. Full article

To address core challenges involving severe reservoir heterogeneity, complex fracture systems, and rapid energy depletion encountered in the development of tight oil reservoirs in the G5 block of the Nanpu Sag, this study performs a systematic analysis of geological characteristics and optimizes an integrated geology–engineering development strategy. Through the integration of 3D seismic and well-logging data, the “sandwich-style” superposition architecture of sand bodies in the Es₃⁴ sub-member is quantitatively characterized. It reveals that productivity is co-controlled by high-quality main channel sand bodies (permeability: 0.5–1 mD) and high-density fracture zones (linear density: 3.2 fractures·m⁻¹) along structural ridges. Consequently, a comprehensive technical system is established, incorporating trajectory optimization for high-angle wells, differential stimulated reservoir volume (SRV) fracturing based on the Reservoir Quality Index (RQI), and CO₂ huff-n-puff for energy supplementation. Field applications demonstrate that optimized well placement increased the drilling encounter rate of high-quality reservoirs from 42% to 78%, while CO₂ huff-n-puff technology successfully restored the formation pressure coefficient from 0.65 to 0.82. The implementation of this integrated approach extended the stable production period of typical wells to 18 months, significantly mitigating production decline and increasing the ultimate recovery factor of the block to 14.5%, which provides a favorable recovery level for a complex fault-block tight oil reservoir compared with the generally low primary-recovery performance reported for analogous tight oil systems in rift-basin settings. This study confirms that the coupling zone of fracture systems along structural ridges and high-quality sand bodies represents the optimal target for economic development. The proposed geology–engineering synergy model provides a transferable technical paradigm for the efficient development of similar complex fault-block tight oil reservoirs. Full article

The double-slit experiment is commonly interpreted as evidence that a single electron must be described by a spatially extended wavefunction whose path amplitudes interfere. Here, we present an alternative geometric formulation within the Bivector Standard Model, in which the observed far-field interference pattern is reproduced while the phase is attributed to an internal bivector clock carried by the electron. In this approach, each electron remains localized and produces a single detection event, while the familiar fringe pattern emerges statistically from the accumulation of many impacts. Interference arises from the comparison of clock phases associated with geometrically distinct paths, rather than from the superposition of spatial waves. The resulting probability distribution recovers the standard two-slit interference factor, the single-slit diffraction envelope, and the usual fringe-spacing relation in the Fraunhofer regime. The de Broglie wavelength emerges as the spatial manifestation of this transported phase. This formulation provides a geometric account of the origin of phase in single-electron interference, consistent with standard results while offering a distinct physical interpretation. Full article

Deep phosphate mining operations face complex, dynamic working conditions characterized by the superimposed disturbances of high temperature, high stress, and high strain. The occurrence of rockburst disasters demonstrates a clear pattern of dynamic evolution. Traditional rockburst risk assessment methods mostly adopt a static analysis approach, making it difficult to accurately grasp the dynamic characteristics of the entire process of a rockburst from inception and development to occurrence, and also making it hard to meet the practical work requirements of deep phosphate mining safety management. To address this engineering problem, this study constructs a superimposed analysis model for the risk of underground rockburst accidents in deep phosphate drilling based on a dynamic fault tree, and strives to tackle the complex dynamic issues in rockburst risk analysis and prediction. This model retains the technical advantages of traditional fault tree logical reasoning, integrates the time-series analysis function of dynamic fault trees, and organizes and describes various risk factors of deep phosphate rockbursts, as well as the concurrent, selective, and time-overlapping correlations among each factor. Finally, by introducing dynamic logic gates such as priority gates and standby gates, combined with the quantitative representation of rockburst risk stacking effects, it achieves dynamic risk assessment and accurate prediction of rockburst disasters. The model construction strictly follows the core processes of top event definition, hierarchical decomposition of risk factors, and dynamic logic structure construction, and organically integrates risk stacking theory with the dynamic fault tree method, forming an emergency rockburst risk prediction system that can provide technical support for reducing the probability of deep phosphate rockburst accidents. The rock fracture risk superposition model developed in this study aims to provide a tool for risk identification and spatial superposition analysis in deep phosphate mining, minimizing disturbances to the mine’s ecological environment, and offering theoretical support and technical methods for safe and green mining, sustainable development, and high-quality exploitation of deep phosphate resources. Full article

Structural health monitoring of thick-walled industrial components remains challenging due to modal superposition and dispersion effects that limit conventional time-of-flight-based guided-wave analysis. This study proposes an active excitation-based monitoring framework using a self-developed flexible Macro Fiber Composite (MFC) transducer for defect characterization in 25 mm thick aluminum plates. Controlled three-cycle tone-burst excitation at 350 kHz was introduced, and the resulting elastic wave responses were analyzed using Short-Time Fourier Transform (STFT)-based time–frequency energy and spectral bandwidth metrics. Artificial V-shaped notches with depths of 10% and 30% of the plate thickness were introduced to evaluate defect severity. Compared to the intact specimen, the 10% notched plate exhibited a 22.5% reduction in STFT-based energy and a 3.37% decrease in spectral bandwidth, while the 30% notched specimen showed reductions of 37.5% and 7.78%, respectively. The results demonstrate that defect-induced structural discontinuities in thick plates not only attenuate overall guided-wave energy but also alter frequency distribution characteristics. The proposed approach enables quantitative defect evaluation without explicit modal separation and validates the dual actuation and sensing capability of the flexible MFC transducer, supporting the feasibility of transitioning from passive acoustic emission monitoring to an active, self-diagnostic structural health monitoring framework for thick industrial structures. Full article

Two techniques of the Boundary Elements Method (BEM) are coupled for solving piecewise homogeneous elastic problems with body forces. The Domain Superposition Technique (DST), which is an alternative to the classical sub-regions approach, is used to approach the sectorial homogeneities, modeling the domain as a sum of a homogeneous surrounding sector and other complementary ones with different constitutive properties. The Galerkin tensor, with the adoption of a primitive function of the fundamental solution, transforms domain integrals. A classical boundary element matrix system is formed in which, unlike the sub-region idea, no interfaces exist between regions, and compatibility and equilibrium conditions are not imposed. The necessary correlation between the surrounding domain and all sub-domains is performed through the classic boundary element procedure of scanning, in which source points are used as the basis for integration along the boundaries. This research contributes to BEM literature by addressing a specific and non-trivial gap: the simultaneous treatment of body forces and material heterogeneity in a simplified and computationally efficient manner, without resorting to classical sub-region formulations. Full article

As a complex structural form capable of simultaneously bearing upper-deck highway traffic, lower-deck highway traffic, and rail transit, the curved chord stiffened double-deck continuous steel truss bridge is distinct from traditional single-deck bridges. The spatial superposition of multiple traffic types within this structure may result in multiple components approaching their critical states concurrently. Despite prior research efforts on this structural type, the failure evolution process from local yielding to global collapse under mixed traffic loads remains ambiguous. This study addresses these questions through systematic numerical investigation of a nine-span bridge with a 300 m main span. A two-stage analytical approach is employed: a Midas/Civil analysis first identifies critically stressed regions, then ABAQUS multi-scale modeling enables refined analysis of critical components while maintaining computational efficiency. Twenty-nine combined traffic loading cases encompassing dual- and triple-category configurations are systematically analyzed. The results show that the ultimate load-carrying capacity coefficients range from approximately 7 to 18, with a minimum of 7.137, and the dual-level highway combinations exert greater influence than road–rail combinations. More importantly, three failure path convergence characteristics were discovered. First, the initial failure position under each working condition tends to be consistent, initiating at the lower chord near the top of the mid-span pier, which confirms that inherent structural defects exist at this location. Second, the gusset plate at the top of pier W6 appears as the second failure location in 48% of cases and ranks within the first four locations across all cases. Third, path similarity progressively increases with traffic diversity. Additionally, Q370qE steel exhibits 5–22% stress exceedance with variable critical locations depending on traffic conditions. Based on these convergence characteristics, a safety monitoring scheme is proposed: monitoring points need to be arranged symmetrically on both sides of the bridge on the top chords, bottom chords, web members, and wedge plates near the tops of the piers. Full article

This study investigates the time-periodic electroosmotic flow and solute transport of Maxwell fluid in a parallel microchannel with modulated surface charges. The Poisson–Boltzmann equation and the linearized momentum equations are solved using a superposition-based analytical approach. The influences of oscillation intensity, fluid elasticity, and electrokinetic parameters on the velocity and concentration distributions are examined. The results show that wall-potential modulation combined with a time-periodic electric field generates recirculating motion and oscillatory velocity patterns. Moderate oscillation strengthens both flow and solute transport, whereas stronger oscillation weakens transport efficiency. This work provides a quantitative analysis the interplay between oscillatory electroosmotic flow and solute transport in Maxwell fluid and clarifies the role of oscillation strength in controlling solute dispersion. Full article

This study develops an efficient fatigue prediction framework for offshore wind turbine (OWT) monopile foundations under coupled wind–wave conditions using four surrogate models: XGBoost, Random Forest (RF), Support Vector Regression (SVR), and Gaussian Process Regression (GPR). A finite element model (FEM) incorporating soil–pile interaction is established to accurately capture structural responses under realistic environmental loading. Fatigue damage is evaluated through time-domain simulations based on this model. A surrogate modeling approach is employed to capture the nonlinear mapping between environmental variables and fatigue damage using 60 representative samples. Results show that the proposed framework significantly improves computational efficiency while maintaining predictive reliability. Among the models evaluated, GPR yields the highest prediction accuracy, while SVR shows comparable performance. In contrast, XGBoost and RF exhibit relatively larger deviations. Parametric analysis reveals that fatigue damage is positively correlated with wind speed and significant wave height, but inversely correlated with peak wave period. Further, wind-induced loading dominates fatigue accumulation, and conventional load superposition methods underestimate fatigue damage due to nonlinear wind–wave coupling effects. Furthermore, fatigue damage exhibits pronounced circumferential variation, with maximum values occurring in the fore-aft directions. Full article

Quantum computing is increasingly seen as a disruptive technology capable of expanding the computational limits of advanced manufacturing systems within the emerging Industry 5.0 framework. By utilizing quantum mechanical principles such as superposition, entanglement, and quantum parallelism, quantum computation enables new approaches to solving complex optimization, simulation, and data-intensive problems that are challenging or impractical for classical computers. This paper offers a comprehensive and critical review of the potential impacts of quantum computing on advanced manufacturing, focusing on intelligent production planning, supply chain optimization, materials discovery, predictive maintenance, and human–machine collaboration, key aspects of Industry 5.0. The originality of this review lies in its integrated analysis of quantum computing alongside artificial intelligence, digital twins, and cyber–physical systems, highlighting how these technologies, when combined, improve decision-making speed, process efficiency, and sustainability. Despite these opportunities, the integration of quantum computing into Industry 5.0 systems faces critical challenges, including hardware limitations, algorithm scalability, data security concerns, workforce readiness, and the complexity of integrating quantum solutions with existing industrial infrastructures. The role of hybrid quantum-classical architectures is examined as a feasible and transitional approach for near-term manufacturing applications. By critically assessing both technological strengths and practical constraints, this review positions quantum computing as a promising enabler of resilient, human-centered, and sustainable manufacturing ecosystems. The insights aim to assist researchers, industry players, and policymakers in strategically managing the integration of quantum technologies as manufacturing systems advance toward Industry 5.0. Full article