Jie Shi

DELP-YOLOv12: a lightweight deployable model for maize pest and disease detection

Jie Shi, Xinrui Zhang, Zhi Li, L Yang et autres

Abstract Background Maize is one of the world’s most important cereal crops for both food and feed, yet its yield is severely threatened by pests and diseases. The complexity of field environments, the variability of illumination, and the diversity of pest and …

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Additional file 1 of DELP-YOLOv12: a lightweight deployable model for maize pest and disease detection

Jie Shi, Xinrui Zhang, Zhi Li, L Yang et autres

Supplementary Material 1.

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Additional file 1 of DELP-YOLOv12: a lightweight deployable model for maize pest and disease detection

Jie Shi, Xinrui Zhang, Zhi Li, L Yang et autres

Supplementary Material 1.

cn (code pays fourni par la source)

DELP-YOLOv12: a lightweight deployable model for maize pest and disease detection

Jie Shi, Xinrui Zhang, Zhi Li, L Yang et autres

Abstract Background Maize is one of the world’s most important cereal crops for both food and feed, yet its yield is severely threatened by pests and diseases. The complexity of field environments, the variability of illumination, and the diversity of pest and …

cn (code pays fourni par la source)

The fungal stronghold: biofilms in hemodialysis catheters, diagnostic pitfalls, and the challenge of catheter salvage

Jie Shi, Naiying Lan, Fanzhou Zeng, Nanmei Liu et autres

The management of fungal catheter-related bloodstream infections (CRBSIs) in the hemodialysis population represents a critical collision between rigorous infectious disease guidelines and the grim clinical reality of vascular access exhaustion. While guidelines from the IDSA and KDIGO unequivocally recommend immediate catheter removal …

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Fast SAR Imaging Method Based on Overlapping Subband Stitching

Y. Chen, H. Wang, Y. Zhang, Jie Shi

To address the computational complexity of traditional frequency-domain broadband synthesis methods, their inability to meet the low-latency requirements of SAR imaging, and the problem of amplitude and phase jumps at the splicing point caused by sub-band overlap, this paper proposes a fast …

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Frequency-Modulated Stepped Signal DDC Design Based on Fast Symmetric FIR

Jie Shi, Hong Wang, Yilei Chen, Yewen Zhang

Aiming at the digital down-conversion (DDC) processing requirements of the intermediate frequency (IF) echo signal of the frequency-modulated stepped radar under high-speed sampling conditions, this paper proposes an efficient DDC architecture based on the fast symmetric FIR algorithm. This architecture combines 4-way …

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A novel synthetic MRI for discrimination of oral cavity and oropharynx malignancy

Rui Wang, Qifan Ma, Yong Zhang, Jie Shi et autres

PURPOSE: To evaluate the discriminative performance of morphologic and quantitative images derived from synthetic MRI in oral cavity and oropharynx lesions. METHODS: A total of 65 patients with 69 lesions and 5 healthy volunteers underwent preoperative MRI scans using the pre-contrast synthetic …

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Clinical efficacy observation and safety evaluation of acupuncture for intractable facial paralysis: a single-blinded randomized controlled pilot trial

Hongyu Xie, Yu Xia, Lele Zhang, Jinsong Yan et autres

Introduction Acupuncture can effectively improve the clinical symptoms of intractable facial paralysis (IFP). However, it is not yet clear whether the use of acupuncture manipulation affects the therapeutic effect. This study aims to conduct a preliminary exploration of the clinical efficacy and …

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End-to-End Prediction of Massive Hemorrhage During Dilatation and Curettage in Cesarean Scar Pregnancy Patients Using a 3D VGG Model

Jie Shi, Jiejun Cheng, Feng Gao, Le Fu

Motivation: Dilation and Curettage (D&C) is the standard treatment for cesarean scar pregnancy (CSP). However, existing quantitative approaches for preoperative assessment of intraoperative bleeding require manual feature selection, introducing uncertainty and hindering automation. Goal(s): To develop an end-to-end deep-learning model for automatic …

Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation

Tingyun Li, Shisong Chen, Jie Shi, Xinyi Wang et autres

While large language models (LLMs) have demonstrated remarkable performance across diverse tasks, they fundamentally lack self-awareness and frequently exhibit overconfidence, assigning high confidence scores to incorrect predictions. Accurate confidence estimation is therefore critical for enhancing the trustworthiness and reliability of LLM-generated outputs. …

Effect of Acupuncture on Clinical Symptoms of Patients with Intractable Facial Paralysis: A Multicentre, Randomized, Controlled Trial

Hongyu Xie, Zehua Wang, Wen-Jing Kan, Ai-Hong Yuan et autres

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