The year 2024 marked the 50th anniversary of the discovery of the J/ψ particle, which unveiled the charm quark and the charmonium spectrum, instigating the “November Revolution” in particle physics. This discovery catalyzed the development of quenched potential models, most notably the Cornell model, which provided a foundational quantitative description of the hadronic spectrum. However, the landscape of hadron spectroscopy has been profoundly transformed since the turn of the 21st century with the observation of numerous charmonium-like states, such as X(3872), which exhibit properties starkly at odds with quenched model predictions. These discrepancies, exemplified by the “X(3872) low-mass puzzle” and the “Y problem” associated with vector states like Y(4260), underscore the critical limitations of the quenched approximation and signal the necessity for a new theoretical paradigm. This review synthesizes recent advances in hadronic spectroscopy, arguing that the unquenched picture, which incorporates coupled-channel effects such as hadronic loops, is essential for a unified description of these new states and associated anomalies. We demonstrate how unquenched effects provide compelling solutions to long-standing puzzles in charmonium decays (e.g., the “ρπ puzzle” and anomalous dipion transitions), predict and explain the existence of exotic charged states like Zc(3900) and Zb(10610) via mechanisms such as Initial Single Pion Emission, and offer a framework for understanding interactions between charmonia and with nucleons. Furthermore, we emphasize the universality of unquenched effects, extending their application to bottomonium and light-flavor sectors. As experimental precision continues to improve, we advocate for the systematic development of unquenched hadronic spectroscopy, which heralds a new era of high-precision theoretical and experimental studies, moving decisively beyond the quenched approximation to achieve a more complete understanding of non-perturbative behavior of the strong interaction.
Achieving broadband infrared radiation performance with thermal robustness remains a critical challenge for extreme-condition heat management. Here, we propose a dual-regulation strategy coupling configurational entropy engineering with targeted Mn enrichment to synchronously tailor the electronic transitions and lattice vibrations of spinel oxides. Guided by density functional theory (DFT) screening, the Mn-rich high-entropy (HE) spinel(Fe1/8Co1/8Ni1/8Cu1/8Cr1/8Mn3/8)(3)O-4 (HEO-M3) was identified as the optimal composition. Moderate Mn incorporation narrows the band gap to strengthen short-wave carrier absorption, while Mn3+-induced Jahn-Teller (JT) distortions intensify mid-to-long-wave lattice emission. Notably, electron-phonon coupling bridges the broad transition region, creating a continuous near-blackbody spectrum. Experimentally, HEO-M3 delivers outstanding optical performance, achieving high solar-band absorptance (alpha(0.25-2.5 mu m) = 0.914) and mid-to-long-wave emissivity (epsilon(2.5-15 mu m) = 0.885). It demonstrates exceptional high-temperature stability, maintaining epsilon = 0.913 at 10 0 0 degrees C with negligible degradation after 1200 degrees C thermal shocks due to the sluggish diffusion effect. Furthermore, driven by synergistic sol-gel kinetics and thermodynamic entropy stabilization, a single-phase spinel is synthesized at an ultralow 400 degrees C. When applied as a coating, HEO-M3 reaches a 0.948 solar-band absorptance. This work offers a mechanistic framework for entropy-element synergy and an energy-efficient pathway toward highperformance infrared emitters. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Environmentally friendly bio-based surfactants demonstrate substantial potential for enhancing coal wettability and improving coalbed methane extraction efficiency. This study employed dynamic contact angle measurements, scanning electron microscopy, and Fourier transform infrared spectroscopy to investigate the dynamic evolution of coal wettability under four representative bio-based surfactants. Results demonstrate that all surfactants significantly reduce surface tension relative to pure water, with surface tension exhibiting an initial rapid decrease followed by stabilization as concentration increases. RL exhibits the lowest critical micelle concentration and the maximum surface tension reduction. Dynamic contact angles on coal surfaces decrease substantially after surfactant treatment, with RL achieving the most rapid droplet spreading (contact angle from 50.76 degrees to 23.81 degrees within 3 s). Owens-Wendt surface free energy calculations reveal that RL increases total surface free energy by 15.7 mJ/m2, with polar component enhancement up to 2.6-fold. FTIR peak-fitting analysis indicates that hydrophilic hydroxyl-ether oxygen bond content increases significantly, while aromatic hydrophobic skeleton proportion decreases by 40-60%. SEM analysis indicates that the modified coal surface forms a dense adsorption layer. The wetting enhancement mechanism involves synergistic "functional group reconstruction-surface free energy elevation-interfacial spontaneous imbibition" effects. RL, owing to its dualrhamnosering hydrophilic head groups and dual beta-hydroxy fatty acid hydrophobic tails, achieves optimal performance. This study provides support for optimal wetting agent selection in coal seam water injection.
Accurate and adaptive vehicle dynamics modeling is critical for the safety of autonomous driving systems, particularly under extreme maneuvers and time-varying parameters. Although the deep Koopman model provides a global linear representation, its online adaptation in high-dimensional lifted spaces can be sensitive to rank-deficient excitation and parameter drift. To address these issues, this paper proposes a tire-force-driven adaptive deep Koopman framework with offline physics-informed training and online normalized-projection adaptation. In the offline stage, longitudinal-lateral planar force equilibrium residuals derived from a 7DOF vehicle model are embedded into the learning objective to improve the physical consistency of the learned dynamics. In the online stage, the Koopman operator is updated via a Physics-Informed Variable Step-Size Normalized Least Mean Squares (PI-VSS-NLMS) algorithm with offline-prior anchoring regularization. The update avoids explicit covariance-matrix inversion and admits an explicit bound on the adaptive operator deviation. Extensive Car-Sim simulations and dSPACE real-time tests validate the framework's robust prediction accuracy across various conditions. Furthermore, the algorithm achieves an average execution time of 0.421 ms, demonstrating strong potential for onboard deployment.
In Sequential Recommendation Systems (SRSs), Transformer models have demonstrated remarkable performance but face computational and memory cost challenges, especially when modeling long-term user behavior sequences. Due to its quadratic complexity, the dot-product attention mechanism in Transformers becomes expensive for processing long sequences. By approximating the dot-product attention using elaborate mapping functions, linear attention provides a more efficient option with linear complexity. However, existing linear attention methods face three limitations: 1) they often use learnable position encodings, which incur extra computational costs in long-term sequence scenarios, 2) limited by the low-rank deficiency, they may not sufficiently account for user's fine-grained local preferences (short-lived burst of interest), and 3) they try to capture some temporary activities, but often confuse these with stable and long-term interests. This can result in unclear or less effective recommendations. To remedy these drawbacks, we propose a long-term sequential Recommendation model with Gated Rotary Enhanced Linear Attention (RecGRELA). Specifically, we first propose a Rotary-Enhanced Linear Attention (RELA) module to efficiently model long-range dependency within the user's historical information using rotary position encodings. Then, to address the low-rank deficiency of linear attention, we introduce an Adaptive Rank Modulator. It incorporates a rank augmentation branch to explicitly inject local token mixing and a Gated Rank Selector to dynamically balance stable long-term preferences and transient short-term interests. Experimental results on four public benchmark datasets show that our RecGRELA achieves state-of-the-art performance compared with existing SRSs based on Recurrent Neural Networks, Transformer, and Mamba while keeping low memory overhead.
