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  1. 341

    YOLOv11-GSF: an optimized deep learning model for strawberry ripeness detection in agriculture by Haoran Ma, Qian Zhao, Runqing Zhang, Chunxu Hao, Wenhui Dong, Xiaoying Zhang, Fuzhong Li, Xiaoqin Xue, Gongqing Sun

    Published 2025-08-01
    “…To overcome these limitations, this paper introduces YOLOv11-GSF, a real-time strawberry ripeness detection algorithm based on YOLOv11, which incorporates several innovative features: a Ghost Convolution (GhostConv) convolution method for generating rich feature maps through lightweight linear transformations, thereby reducing computational overhead and enhancing resource utilization; a C3K2-SG module that combines self-moving point convolution (SMPConv) and convolutional gated linear units (CGLU) to better capture the local features of strawberry ripeness; and a F-PIoUv2 loss function inspired by Focaler IoU and PIoUv2, utilizing adaptive penalty factors and interval mapping to expedite model convergence and optimize ripeness classification. …”
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  2. 342

    Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept by Eli Garlick, Nourhan Hesham, MD. Zoheb Hassan, Imtiaz Ahmed, Anas Chaaban, MD. Jahangir Hossain

    Published 2025-01-01
    “…Due to the vast nature of interference signals in the frequency bands used by cognitive TWNs, it is non-trivial to acquire manually labeled data sets of all interference signals. Detecting the presence of an unknown and remote interference source in a frequency band from the transmitter end is also challenging, especially when the received interference power remains at or below the noise floor. …”
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  3. 343

    Mid-Water Ocean Current Field Estimation Using Radial Basis Functions Based on Multibeam Bathymetric Survey Data for AUV Navigation by Jiawen Liu, Kaixuan Wang, Shuai Chang, Lin Pan

    Published 2025-04-01
    “…This makes unknown ocean currents a significant error source that leads to substantial cumulative positioning errors. …”
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  6. 346

    Functional and Genomic Evidence of L-Arginine-Dependent Bacterial Nitric Oxide Synthase Activity in <i>Paenibacillus nitricinens</i> sp. nov. by Diego Saavedra-Tralma, Alexis Gaete, Carolina Merino-Guzmán, Maribel Parada-Ibáñez, Francisco Nájera-de Ferrari, Ignacio Jofré-Fernández

    Published 2025-06-01
    “…Although nitric oxide (NO) production in bacteria has traditionally been associated with denitrification or stress responses in model or symbiotic organisms, functionally validated L-arginine-dependent nitric oxide synthase (bNOS) activity has not been documented in free-living, non-denitrifying soil bacteria. …”
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  7. 347

    Does the island area also need to insist on salt iodization to prevent iodine deficiency disorders? a cross-sectional survey in Hainan Province, China by Hongying Wu, Shizhu Meng, Dingwei Sun, Yuting Hu, Tingou Wu, Xiaohuan Wang, Xingren Wang, Ying Liu, Chuyan Peng, Chuyan Peng, Bin He, Fangang Meng, Fangang Meng

    Published 2025-02-01
    “…The overall prevalence of thyroid diseases was 27.01%. The detection rates of subclinical hypothyroidism and subclinical hyperthyroidism in males were significantly higher than those in females, and the detection rates of thyroid nodules and goiter were significantly lower than those in females. …”
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  8. 348
  9. 349

    Design of a Non-Destructive Seed Counting Instrument for Rapeseed Pods Based on Transmission Imaging by Shengyong Xu, Rongsheng Xu, Pan Ma, Zhenhao Huang, Shaodong Wang, Zhe Yang, Qingxi Liao

    Published 2024-12-01
    “…Human–machine interaction software based on PyQt5 was developed, integrating functions such as communication between upper and lower machines, image acquisition, storage, and processing. …”
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  10. 350
  11. 351

    Using an ensemble approach to predict habitat of Dusky Grouse ( Dendragapus obscurus ) in Montana, USA by Elizabeth A Leipold, Claire N Gower, Lance McNew

    Published 2024-12-01
    “…Consensus between the resource selection function and random forest models was high (93%) and the ensemble map had higher predictive accuracy when classifying the independent dataset than the other two models. …”
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  12. 352
  13. 353

    Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data by Fatimah Alzahrani, Steve Maddock, Heidi Christensen

    Published 2025-06-01
    “…Video-based analysis offers a promising, low-cost alternative to resource-intensive clinical assessments. This paper investigates visual features (eye blink rate (EBR), head turn rate (HTR), and head movement statistical features (HMSFs)) for distinguishing between neurodegenerative disorders (NDs), mild cognitive impairment (MCI), functional memory disorders (FMDs), and healthy controls (HCs). …”
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  14. 354

    Effects of virtual reality-based interventions on cognitive function, emotional state, and quality of life in patients with mild cognitive impairment: a meta-analysis by Xiaohan Li, Yuting Zhang, Lifeng Tang, Lifeng Tang, Lin Ye, Min Tang

    Published 2025-04-01
    “…ObjectivesThis meta-analysis aims to systematically evaluate the effects of virtual reality (VR)-based interventions on cognitive function, emotional state, and quality of life in patients with mild cognitive impairment (MCI).MethodsA comprehensive literature search was conducted using five databases from their inception to June 2024. …”
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    Hyper CLS-Data-Based Robotic Interface and Its Application to Intelligent Peg-in-Hole Task Robot Incorporating a CNN Model for Defect Detection by Fusaomi Nagata, Ryoma Abe, Shingo Sakata, Keigo Watanabe, Maki K. Habib

    Published 2024-10-01
    “…In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. …”
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  18. 358

    SDES-YOLO: A high-precision and lightweight model for fall detection in complex environments by Xiangqian Huang, Xiaoming Li, Limengzi Yuan, Zhao Jiang, Hongwei Jin, Wanghao Wu, Ru Cai, Meilian Zheng, Hongpeng Bai

    Published 2025-01-01
    “…These results indicate that SDES-YOLO successfully combines efficiency and precision in fall detection. Through these innovations, SDES-YOLO not only improves detection accuracy but also optimizes computational efficiency, making it effective even in resource-constrained environments.…”
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  19. 359
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    Image acquisition technology for unmanned aerial vehicles based on YOLO - Illustrated by the case of wind turbine blade inspection by Zhenjun Dai

    Published 2024-12-01
    “…Wind energy, as a renewable energy source, is becoming increasingly important. The maintenance and damage detection of wind turbine blades are particularly crucial. …”
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