This paper investigates the role of viscosity in the error upper bounds of a consistent splitting scheme for the Navier–Stokes equations proposed by Huang and Shen [1]. In their original analysis the viscosity is fixed to unity. By following and extending their proof methodology while keeping the viscosity ν symbolic, we obtain an H1 velocity error bound that contains negative powers of ν, indicating that the scheme is not robust as ν → 0. To establish this bound we refine a theorem in [2] on the constant in the Stokes pressure estimate, which is crucial to the error analysis. A targeted numerical experiment based on a perturbation of the Kovasznay flow corroborates this analytical prediction: the scheme of [1] blows up at high Reynolds number, and a comparison with a fully implicit Newton solver and with the time-dependent Stokes counterpart of the same scheme localizes the failure to the explicit treatment of the convection term.
In this review article, we present the current state of artificial intelligence and advanced technologies in air and water pollution monitoring in the USA. Recent studies in this field show promising outcomes, including the ability to generate more granular data in real-time, using predictive analytics to identify and prevent pollution, and better modeling of complex exposure. Compared to traditional monitoring systems, these next-generation technologies show improvements in timeliness and sensitivity. Despite these promising developments, some gaps and challenges remain, including calibration and standardization, interoperability, regulatory fragmentation, funding limitations, digital divide, and stakeholder trust, which impede their broad and equitable adoption. This article points to some best-practice examples, conducts a structured, evidence-focused narrative review and comparative assessment of the different technologies using quantitative findings from existing meta-analyses and large comparative studies, and discusses barriers to implementation. The article concludes by providing some recommendations for practice and policy. Recommendations from this review are primarily related to the development of consistent technical standards and specifications, workforce and community engagement, participatory governance, and ethical frameworks to address the responsible use of AI and the implications of data bias with the need for accountability and transparency. To fully harness the potential of next-generation monitoring systems in pursuit of environmental justice, public health protection, and informed policymaking will require further research and concerted efforts in policy and investment to address these issues.
Forecasting the wide variety of high-impact weather events experienced globally is a challenge for both Artificial Intelligence (AI) and Numerical Weather Prediction (NWP) models and it is critical that such models be properly verified before deployment. Although AI weather models are rapidly evolving, much of their evaluation is currently done either with a global-scale evaluation or by hand-picking a small number of case studies or a region. A widely-used open-source benchmark suite focusing on high-impact weather will help to drive the science forward for all scales of weather models, as it has for other AI fields. Here we introduce Extreme Weather Bench (EWB), a new community-driven benchmark suite that facilitates model validation and verification on a variety of high-impact hazards that matter to people around the globe. EWB provides a standard set of case studies (spanning across multiple spatial and temporal scales and different parts of the weather spectrum), observational data, impact-based metrics, and open-source code for users to evaluate their models. Verifying that a model works against a standard set of case studies, especially events that are high-impact for the general public, is a key piece of improving the trustworthiness of AI models. EWB will help to drive the science forward for all weather models, enabling true comparisons across models and evaluating models on specific high-impact phenomena through the use of case studies. EWB is a free open-source community-driven system and will continue to evolve to include additional phenomena, test cases and metrics in collaboration with the worldwide weather and forecast verification community.
The experimental pursuit of neutrinoless double-beta decay (0νββ) constitutes one of the most compelling avenues for probing lepton-number violation and exploring physics beyond the Standard Model. Within this landscape, ^76Ge has consistently ranked among the most promising isotopes for current and next-generation bolometric and liquid-scintillator experiments, notably GERDA and LEGEND. In the present work, we adapt a rigorous statistical protocol previously established for ^48Ca and ^136Xe to the ^76Ge system, utilizing a valence configuration that aligns with our recent investigation of ^82Se . Our methodology introduces systematic, bounded fluctuations to the two-body matrix elements of established effective interactions, subsequently monitoring how these perturbations propagate through a suite of low-energy nuclear observables. Special emphasis is placed on the 0νββ nuclear matrix element (NME), whose theoretical uncertainty currently dominates the interpretation of experimental half-life limits. By integrating these simulated variations into a Bayesian Model Averaging framework and benchmarking against empirical spectroscopic data, we derive a constrained probability distribution for the NME. The resulting analysis yields a central value of 2.46 with an associated standard deviation of 0.25, thereby quantifying the intrinsic theoretical spread within the interacting shell model approach. Furthermore, we perform a comprehensive correlation analysis across all computed observables to evaluate internal consistency, identify non-trivial structural dependencies, and establish benchmarks that may guide the refinement of future effective interactions.
ABSTRACT Smart contact lenses (SCLs) have emerged as a cutting‐edge wearable platform for health monitoring and therapeutic applications. Graphene's exceptional electrical conductivity and high optical transparency make it particularly well‐suited for SCL development. This has driven research\leading to advancements in SCLs with graphene, including graphene films for shielding electromagnetic interference (EMI) and preventing eye dehydration, graphene‐based field‐effect transistors (FETs) for electrochemical diagnostics, graphene‐enabled intraocular pressure (IOP) biosensors for improved glaucoma diagnosis, transparent graphene‐based contact lens electrodes (GRACE) for electroretinography (ERG), and graphene‐based hybrid electrodes for wireless data transmission between SCLs and external devices. This comprehensive review summarizes the fabrication of high‐quality graphene sheets on SCLs, with an emphasis on chemical vapor deposition (CVD) using Cu foils followed by solution‐based transfer. It discusses graphene‐enabled diagnostic innovations in SCLs, focusing on FETs, IOP biosensors, ERG electrodes, and hybrid electrodes. The review also highlights graphene‐based therapeutic lenses, particularly for drug delivery, and summarizes ocular safety issues associated with graphene. Finally, challenges and future directions for integrating graphene into SCL technology are discussed.
