A bstract: We present a novel possibility that a network of domain walls bounded by cosmic strings generates a stochastic gravitational wave background (SWGB) signal originating from the spontaneous breaking of a gauged U(1) _F flavor symmetry and the subsequent breaking of discrete Z ₂ symmetry that accommodates dark matter. The gravitational wave (GW) spectrum produced by the string-bounded-wall network can be detected for high U(1) _F breaking scales in forthcoming GW detectors including LISA, ET and SKA. The GW signal exhibits a distinctive frequency slope, in the infrared, compared to the standard cosmic-string case, in the frequency range between micro-hertz and hertz. We develop a possible strategy to distinguish and characterize GW spectrum of the hybrid defect from other defects, such as stable cosmic strings, via employing the exact calculation with a machine-learning surrogate based on a multilayer perceptron (MLP), trained on spectra obtained from the full numerical treatment. This is then used for rapid inference in the detector-specific signal-to-noise ratio (SNR) computation which also makes the process fast and efficient. We also discuss some possible complementarity between GW searches and Flavor observables in the laboratory.

Large-scale artificial intelligence systems increasingly disclose energy and carbon metrics, but their freshwater costs remain less consistently measured. This paper introduces the Water Cost of Intelligence (WCI), a per-query metric combining direct water consumed for on-site data-center cooling with indirect water consumed during electricity generation, weighted by local scarcity using Aqueduct 4.0 Baseline Water Stress (BWS) scores. We first reconstruct Google’s disclosed Gemini direct-water figure from Google’s own reported parameters, an internal consistency check on the implementation rather than an independent validation. Expanding the accounting boundary to include electricity-generation water raises the estimate for a median large language model (LLM) prompt by 179% under a uniform national water-intensity value. Parameterizing that intensity by the regional generation mix instead changes the estimate substantially and reverses the regional ordering, depending on whether hydroelectric reservoir evaporation is allocated to generation: the same grid is the least water-intensive of those studied under one convention and the most water-intensive under the other. A region cannot be characterized as water-efficient in terms of electricity without first establishing that convention. Direct-only reporting can be internally accurate yet boundary-incomplete, and regional scarcity can change the interpretation of identical physical water use by an order of magnitude.