Oct 2026· Horttechnology· 0 citations· 21 references
Abstract
To address the problems of low sorting accuracy and poor operation stability caused by physical leaf entanglement in traditional drum screening of fresh tea leaves, a multiscale fresh tea leaf sorting system with drum-axial airflow coupling based on intelligent control was designed. The axial moving distances of fresh tea leaves of different scales at wind speeds of 5, 7, and 9 m/s were calibrated through bench tests, and the optimal wind speed parameter for secondary fine screening was determined. A numerical model of the axial airflow field inside the drum was established via Fluent software, and a coupled sorting test platform was built to compare the sorting performance of the traditional pure drum screening mode and that of the coupled intelligent sorting mode. The results showed that 7 m/s is the optimal axial airflow velocity for secondary fine screening of multiscale fresh tea leaves during this test, which can realize effective back-blowing of small-scale materials and accurate screening of large-scale materials. At this velocity, the flow field is evenly distributed, and the effective thrust area highly matched the sorting demand. The average sorting efficiency of the coupled intelligent sorting mode reached 84.8%, which is 25.6% higher than that of traditional pure drum screening, with favorable sorting accuracy and operation stability. These findings can provide a theoretical basis and technical reference for the optimization, upgrading, and intelligent transformation of high-efficiency fresh tea leaf sorting equipment.
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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