Oct 2026· Journal of water resources planning and management· 0 citations· 10 references
Abstract
Dead-end sections of water distribution systems often experience long residence times and stagnation, causing chlorine residuals to fall below regulatory standards and leading utilities to run continuous blow-offs that increase nonrevenue water. This study proposes a hybrid advection–reaction (AR) and advection–dispersion–reaction (ADR) screening–validation framework to identify cost-effective continuous blow-off configurations that satisfy minimum chlorine residual and pressure requirements across multiple operating scenarios. Candidate configurations are screened in EPANET, a public-domain hydraulic and water quality modeling software, using an AR formulation, within nondominated sorting genetic algorithm II (NSGA-II) to minimize worst-case chlorine deficit penalty while maximizing worst-case treatment-cost savings across multiple scenarios. Shortlisted nondominated configurations are then reevaluated in Washington University dead end simulator (WUDESIM), a dead-end water quality model that uses an ADR formulation and incorporates stochastic water demands, to confirm compliance in dispersion-dominated dead ends and to quantify AR–ADR discrepancy. The framework is tested on a multisource Canadian municipal system with 63 operational blow-offs supported by calibrated hydraulic and chlorine models and stochastic household demands. EPANET screening produced 14 configurations; ADR validation confirmed two configurations satisfied chlorine and pressure criteria, enabling closure of approximately 68%–73% of blow-offs while maintaining compliance, and yielding annual treatment-cost savings of approximately 64%–70% compared with baseline operation with all blow-offs open. AR screening underestimated the mean dead-end chlorine deficiency by up to 14% relative to ADR validation, indicating the importance of ADR confirmation for conservative closure decisions. Sensitivity analysis identifies nodal demand as the dominant source of uncertainty, followed by bulk decay, with wall decay having a smaller influence. Overall, the proposed hybrid workflow provides an efficient and practical approach to ensuring that safe chlorine levels are maintained.
MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.
James C. Davis, Kelechi G. Kalu, Huiyun Peng et al.· 1 citation
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
Albérick Euraste Djiré, Iyiola E. Olatunji, Melissa Tessa et al.· 1 citation
An audit-and-placebo protocol is proposed that separates verifier artifacts, interaction scaffolding, and grounded feedback credit in evaluations of self-evolving test generators in evaluations of self-evolving test generators.
Yunhao Liang, Chengguang Gan, Ruixuan Ying et al.· 0 citations
A diagnostic support system based on a unified web platform that classifies patients according to the risks of developing three diseases based on regularly collected clinical or audio data using classical supervised learning algorithms is presented.
Vedamurthy D R, Dr. Anup Ritti, A. Bibi et al.· International Journal for Re...· 0 citations
This prototype MRG image translocation software was helpful to 69% of patients with binocular diplopia, but limited by large angle strabismus because of the limited instrument field of view.
Edsel B Ing, Kevin Sha, Sarosh Dandoti et al.· Journal of neuro-ophthalmolo...· 0 citations
The findings of the present study indicated that potential complications such as delayed union, nonunion, and osteomyelitis in the intramedullary nailing method are approximately comparable to those of the external fixator method.
Reza Noktesanj, Ali Nami, F. Amani et al.· journal of Health Research a...· 0 citations
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