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.
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.
This article is a review of the public service announcements (PSAs) disseminated with the G.I. JOE: A Real American Hero cartoon series which aired 95 episodes in the 1980s: 35 PSAs were identified and tabulated. The topics addressed ranged from specific health situations to moral guidance. Most of the PSAs (N = 25, 71.4%) were relevant to childhood safety and reflected the theoretical underpinnings of educational messaging likely to promote the most significant change in children's health and health communication targeted to children. The G.I. JOE series can serve as an exemplar for the development of contemporary PSAs intended to improve children's health.
Scientists estimate that humanity has exceeded seven of nine planetary boundaries, threatening the entire planet with potentially catastrophic consequences for all species. We therefore have a moral imperative for future generations and other species to return to the safe side of those boundaries. Threats to these boundaries take the form of social dilemmas, defined as situations in which individuals acting in their own interest undermine collective welfare, which can only be solved through cooperation. Western economic theory has conditioned us to believe that humans are inherently selfish. This assumption has led economists, scientists, and policymakers to increasingly pursue market-based solutions to conservation approaches, which have yielded limited success. In contrast, this article argues that humans are inherently cooperative. We employ Multi-Level Selection Theory (MLS) to depict the evolutionary advantages of cooperation and to define morality as putting the group ahead of the individual. We examine two examples of MLS in action: Territories of Life (TOL) and Ubuntu. The paper provides guidance for pathways of Ecozoic governance, planning, and restoration. Applied in a Western context in Burlington, Vermont, the philosophies hold true, showing that social norms and group identity already shape ecological behavior in Burlington residents' lawn care practices. Ultimately, providing an alternative economic model built on these ethical foundations, we introduce the Neighbor's Goodwill that reframes social dilemmas in a game theory context. The Neighbor's Goodwill demonstrates how loyalty, reciprocity, and social belonging alter payoff structures. This research is founded on the fact that humans are inherently social and tend to make decisions in the interest of the whole group over their own.
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.
