Oct 2026· Journal of Structural Engineering· 0 citations· 17 references
Abstract
Conventional contact-based inspection techniques face significant challenges in accurately modeling the complex 3D geometry of locally buckled steel members, which hinder reliable assessment of their residual load-carrying capacity. To overcome these limitations, this study proposes a method for analyzing the bearing capacity of locally buckled steel members using geometric reconstruction models, enabling precise evaluation of their load-carrying capacity. The research methodology encompasses three primary aspects: (1) model preprocessing; (2) load-bearing capacity analysis of specimens; and (3) experimental validation. Model preprocessing involves three key tasks: point cloud model reconstruction and optimization; evaluation of the effect of external factors on model accuracy; and parametric modeling with verification of geometric accuracy. The load-bearing capacity of damaged specimens was analyzed by predicting the residual capacity of the corresponding parametric models using finite element software. Finally, axial compression tests on equal-leg single-angle steel specimens were conducted to validate the accuracy of the finite element analysis results, thereby demonstrating the effectiveness of the proposed method. Key findings include: (1) the overlap ratio has the most significant influence on model accuracy; at an overlap level of 18, the comprehensive mean absolute error is below 0.005, and model-specimen similarity between the angle steel parametric model and the angle steel specimen reaches 0.99 (no significant difference at 95% confidence level); (2) among the extracted key feature data, the elastic stiffness, peak load, and peak displacement all exhibit relative errors less than 10%, while peak strain in deformation zones shows larger deviations; and (3) among the 44 statistically key characteristic data points, 41 exhibit relative errors less than 10%, confirming the method’s high reliability for practical engineering applications.
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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