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.
This work concerns about stochastic Burgers type equations with reflection. First of all, by means of the equicontinuous uniform Laplace principle, we prove the Freidlin-Wentzell uniform large deviation principle for these equations uniformly on bounded sets. Then based on this result, we establish the Dembo-Zeitouni uniform large deviation principle for these equations uniformly on compact sets. Finally, an averaging principle result for these equations is obtained through the time discretization approach.
The stochastic generalized Burgers-Huxley equation (SgBHE) introduces a power law parameter in its nonlinear terms, offering a robust framework for modeling reaction-convection-diffusion processes under uncertainty. While existing research has mainly focused on theoretical aspects, this work represents the first attempt to study the strong convergence of an explicit full-discrete scheme for the SgBHE driven by an additive fractional Gaussian noise. To approximate the continuous problem, we propose a temporal nonlinear-tamed exponential integrator combined with the spatial spectral Galerkin method. We derive strong error estimates and examine convergence rates for the fully discrete scheme, depending on the regularity of both time and spatial variables. In contrast to the stochastic Burgers-Huxley equation (SBHE), we develop a progressive technique to overcome the challenges posed by the generalized nonlinear terms, ensuring the boundedness of moments for the numerical solution. A numerical experiment is conducted to validate the theoretical results.
Living in a changing climate, human society now faces more frequent and severe natural disasters than before. As a consequence, rapid disaster response during the “Golden 72 Hours” of search and rescue becomes a vital humanitarian necessity and community concern. However, traditional disaster damage analysis routinely fails to generalize across distinct urban morphologies and new disaster events. Effective damage mapping typically requires exhaustive and time-consuming manual data annotation. To address this issue, we introduce Smart Transfer, a novel Geospatial Artificial Intelligence (GeoAI) framework, leveraging state-of-the-art vision Foundation Models (FMs) for rapid building damage mapping with post-earthquake Very High Resolution (VHR) imagery. Specifically, we design two novel model transfer strategies: first, Pixel-wise Clustering (PC), ensuring robust prototype-level global feature alignment; second, a Distance-Penalized Triplet (DPT), integrating patch-level spatial autocorrelation patterns by assigning stronger penalties to semantically inconsistent yet spatially adjacent patches. Extensive experiments and ablations on the recent 2023 Türkiye-Syria earthquake show promising performance in multiple cross-region transfer settings, namely Leave One Domain Out (LODO) and Specific Source Domain Combination (SSDC), achieving an average F1-score of 0.64 and mIoU of 0.69. Moreover, Smart Transfer provides a scalable, automated GeoAI solution to accelerate building damage mapping and support rapid disaster response, offering new opportunities to enhance disaster resilience in climate-vulnerable regions and communities. The data and code are publicly available at https://github.com/ai4city-hkust/SmartTransfer.
This study develops an event-driven simulation framework to analyze congestion-induced service degradation in timetable-based urban rail transit systems (URTSs) under time-dependent demand. Rather than representing service deterioration as a static outcome or an isolated disturbance, service degradation is represented as an operational process that emerges from passenger accumulation, extended dwell times, and headway constraints. Using the Shenzhen URTS as a case study, the model is evaluated against smart-card observations and then used to characterize baseline congestion regimes and system responses to controlled operational perturbations. Results show that congestion-induced service degradation is strongly localized in both time and space, with impacts remaining largely confined even under severe service gaps. Reduced-model comparisons further show that simplified settings can reproduce the broad timing of degradation onset and buildup, but systematically underestimate degradation intensity, persistence, and recovery lag when endogenous feedback becomes important. The proposed framework provides an operationally explicit basis for analyzing how service degradation forms, persists, and is absorbed in timetable-based URTSs.
