We propose and analyze a monotone finite element method for an elliptic distributed optimal control problem constrained by a convection-diffusion-reaction equation in the convection-dominated regime. The method is based on the edge-averaged finite element (EAFE) scheme, which is known to preserve the discrete maximum principle for convection-diffusion problems. We show that the EAFE discretization inherits the monotonicity property of the continuous problem and consequently preserves the desired-state bounds at the discrete level, ensuring that the numerical optimal state remains stable and free of nonphysical oscillations. The discrete formulation is analyzed using a combination of the EAFE consistency result and a discrete inf-sup condition, which together guarantee well-posedness and yield the optimal convergence order. Comprehensive numerical experiments are presented to confirm the theoretical findings and to demonstrate the robustness of the proposed scheme in the convection-dominated regimes.
We present results from two test beam campaigns that investigate the performance of straw tube detectors as potential candidates for an FCC-ee straw tracker. These studies were carried out at CERN using 150 GeV muon beams. Dedicated algorithms were developed to determine both single tube spatial resolution for the primary coordinate in the r−ϕ plane and spatial resolution for the secondary coordinate along the tube direction within a straw chamber. Detection efficiency was also evaluated as a function of the extrapolated hit position for each tube. Both datasets showed consistent results for spatial resolutions and efficiency. The average single-tube spatial resolution in the primary r−ϕ coordinate is approximately 110 μm, while the average spatial resolution in the secondary coordinate along the tube direction is approximately 2 mm. Most tubes show a single-tube detection efficiency above 98%.
Active earth pressure acting on retaining walls can be significantly affected by tension cracks that develop within unsaturated marginal backfills. Existing analytical approaches typically idealize these cracks as a continuous zone of uniform depth, neglecting their stochastic nature and the resulting variability in active thrust. This study presents a physics-informed stochastic framework for evaluating active thrust in unsaturated backfills containing spatially variable tension cracks. A physically admissible random field is developed in which crack depth is characterized by its mean, coefficient of variation, and correlation length, while crack location is governed by mean crack spacing. The generated crack field is integrated into a limit-equilibrium formulation incorporating suction stress and a log-spiral failure mechanism. Deterministic analyses first identify an Influence Zone (IZ) along the backfill surface where tension cracks affect active thrust. Monte Carlo simulations are then performed to quantify the influence of crack variability on the 95th percentile of the active earth pressure coefficient, Ka,0.95. Results reveal the existence of a Transitional Regime Domain (TRD) within the IZ where active thrust exhibits maximum sensitivity to crack variability and cracking-zone extent. The location and extent of the TRD depend on the statistical characteristics of the crack field, particularly mean crack spacing and depth variability. Among the stochastic parameters considered, the coefficient of variation of crack depth exerts the strongest influence on Ka,0.95, whereas correlation length has a comparatively minor effect over the conditions and autocorrelation models investigated. The proposed framework provides a practical tool for probabilistic assessment and risk-informed design of retaining walls with unsaturated cracked backfills.
The persistence of living systems depends on their capacity to sense, distribute, and resolve stress. Across evolution, this pressure shaped increasingly integrated architectures that align local perception with collective regulation, enabling the emergence of multicellular form. Here, I argue that chronic, unresolvable stress exposes a fundamental vulnerability of these architectures and drives the breakdown of tissue-level coordination. Synthesizing perspectives from evolutionary and developmental biology, cancer biology, and trauma psychology, I propose that tumorigenesis represents a morphogenetic trauma response: a stress-induced dissociation in which cells lose access to shared regulatory memory and enact self-reinforcing, anatomically intrusive behaviors. This claim is advanced as a structural analogy rather than a claim of psychological causation, situating trauma as a general biological phenomenon that can manifest across distinct substrates and scales. This view carries therapeutic implications, suggesting a reintegrative approach that seeks to return cancer cells to the homeostatic control of the surrounding tissue.
Audio comprehension—including speech, non-speech sounds, and music—is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio understanding to qualify as generally intelligent. However, evaluating auditory intelligence comprehensively remains challenging. To address this gap, we introduce MMAU-Pro, the most comprehensive and rigorously curated benchmark for assessing audio intelligence in AI systems. MMAU-Pro contains 5,305 instances, where each instance has one or more audios paired with human expert-generated question-answer pairs, spanning speech, sound, music, and their combinations. Unlike existing benchmarks, MMAU-Pro evaluates auditory intelligence across 49 unique skills and multiple complex dimensions, including long-form audio comprehension, spatial audio reasoning, multi-audio understanding, among others. All questions are meticulously designed to require deliberate multi-hop reasoning, including both multiple-choice and open-ended response formats. Importantly, audio data is sourced directly ``from the wild" rather than from existing datasets with known distributions. We evaluate 22 leading open-source and proprietary multimodal AI models, revealing significant limitations: even state-of-the-art models such as Gemini 2.5 Flash and Audio Flamingo 3 achieve only 57.33% and 45.9% accuracy, respectively, approaching random performance in multiple categories. Our extensive analysis highlights specific shortcomings and provides novel insights, offering actionable perspectives for the community to enhance future AI systems' progression toward audio general intelligence.
