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Ground Delay Programs (GDPs) are widely used to mitigate demand-capacity imbalances in air traffic operations. However, existing studies typically address GDP planning from either the perspective of air traffic control or that of individual airlines, with limited attention to their coordination. This study develops a collaborative framework for the airline-driven GDP that integrates proactive slot reduction and airline precancellation. The problem is formulated as a two-stage stochastic program. The first determines slot reductions and flight precancellations in anticipation of various potential capacity scenarios, whereas the second adjusts slot allocations and executes airline recovery actions based on the realized slot capacity. To address the computational challenges of this large-scale problem, we propose an efficient Lagrangian dual decomposition algorithm, which reduces the computation time by 49.06% on average compared with the standard column generation approach. Numerical experiments based on real-world data show that the proposed framework consistently achieves lower expected total recovery costs than rule-based deterministic sequential strategies commonly used in current GDP practice. Moreover, when fairness constraints are imposed, allowing slot exchanges among airlines further reduces total costs by an average of 1.37%. These findings highlight the importance of collaborative decision-making between air traffic managers and airlines, providing a robust and practical tool to enhance system-wide efficiency under capacity disruptions.
Streaming EEG fatigue monitoring is challenging because fatigue signatures are both frequency-dependent and spatially distributed across electrodes, while deployment requires causal updates with bounded latency and memory. Existing Transformers weakly exploit electrode geometry and incur history-dependent cost, whereas graph models typically rely on fixed or unconstrained connectivity that is difficult to adapt as cognitive state changes. We propose HASTE, a hybrid-attention streaming topology-aware estimator that separates where interactions are allowed from how strongly they adapt: a physical k-nearest-neighbor (kNN) graph constrains spatial attention to anatomically feasible neighborhoods, and an online band-aware functional prior biases within-neighborhood attention through an additive log-bias. Causal spectral fusion tracks time-varying band relevance, and causal local temporal attention with a fixed look-back window enables constant-memory inference with respect to session length. Under leave-one-subject-out evaluation with causal normalization on SEED-VIG, SADT, and MPD-DF, HASTE achieves Pearson correlation coefficients (PCCs) of 0.776, 0.705, and 0.669, respectively, outperforming the six baselines evaluated across all three datasets. On SEED-VIG, HASTE also outperforms the added Mamba-DE baseline on all three metrics. On an NVIDIA RTX 3090, its neural forward pass on a precomputed window feature takes ∼2.8ms and uses ∼47MB of graphics processing unit (GPU) memory.
Current benchmarks for Large Language Models (LLMs) focus mainly on classification tasks or require manual evaluation, limiting the accurate assessment of generative capabilities. Inspired by voting games, we propose VotingBench, an automated evaluation framework that measures LLMs' generative performance through default automated peer voting in interactive games, covering both text and multimodal tasks. We position these outputs as scalable and reproducible preference signals rather than human-annotated ground truth. The framework reduces reliance on manual scoring and optionally allows human participation for comparative analysis. Extensive experiments across multiple domains and languages demonstrate strong alignment with existing benchmarks. Human-model comparisons reveal systematic differences in decision-making strategies, highlighting VotingBench as an objective and scalable evaluation tool.
Acoustic vortices carrying orbital angular momentum, often generated using metagratings, provide an effective approach for wavefront engineering, particle manipulation, and underwater information transfer. However, most existing acoustic metagratings rely on fixed structural configurations, which inherently restrict their tunability and multifunctionality. Here, we propose a three-dimensional reconfigurable reflective acoustic metagrating for dynamic manipulation of vortex fields. The device consists of two gradient-depth air-groove supercells, in which the reflection response can be actively reconfigured by tuning the depth of paired grooves. Based on a generalized conservation principle of topological charge in metagrating diffraction, we demonstrate controllable conversion of incident acoustic vortex beams into reflected vortex states with different diffraction channels. In particular, near-perfect switching between anomalous reflection and specular reflection is achieved with high conversion efficiency. In addition to structural reconfiguration, frequency-dependent modulation provides an additional degree of freedom for dynamically tailoring the reflected vortex field. Our results reveal a simple yet robust mechanism for tunable vortex-beam reflection and offer a compact platform for multifunctional acoustic devices, with potential extensions to other wave systems such as electromagnetic and elastic waves.
Intervertebral disc degeneration (IVDD) represents a leading cause of chronic low back pain worldwide, imposing considerable public health and socioeconomic burdens. Current clinical interventions remain largely palliative, unable to reverse the progressive pathological cascade. This cascade is driven by a core self-amplifying triad of oxidative stress, chronic inflammation, and nutrient deprivation within the unique avascular, high-load disc niche, and ultimately progresses to end-stage terminal phenotypes such as biomechanical failure and neurovascular ingrowth. Over recent years, biomaterial-based regenerative strategies have evolved from passive structural fillers toward multifunctional platforms capable of active microenvironmental reprogramming. This review provides a systematic, critical synthesis of state-of-the-art advances in the field, organized within a hierarchical framework spanning immunometabolic modulation of the degenerative niche and targeted delivery of cellular and molecular therapeutics with further elaboration of precision-stratified design and whole-disc tissue engineering. We critically appraise evidence maturity across different strategy classes and dissect disc-specific translational barriers, noting that most candidate platforms remain at preclinical validation stages. Building on this analysis, we outline priority research directions and realistic near-term translational milestones to guide rational development of next-generation regenerative platforms toward durable functional disc repair.
