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学者姓名：叶大鹏
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Abstract ：
Precision operational amplifiers (op-amps) adopt dynamic offset compensation (DOC) techniques to achieve microvolt-level offset and nanovolt-level noise. However, the switches in DOC introduce charge injection, which is a major source of current noise and residual offset. Charge-injection-induced glitches need to be attenuated via low-pass filtering, which decreases the usable signal bandwidth. This article proposes a six- channel precision op-amp with multiphase switch reuse, designed and simulated in a 0.18 mu m CMOS process with a 1.032 mm2 active area. Compared with the traditional ping-pong architecture, the six-channel op- amp effectively reduces the required switch width, thereby decreasing the amount of charge injection. The multiphase switch reuse technique reduces the frequency of charge injection per channel and shifts the noise spikes generated by the switches to higher frequencies. Therefore, the proposed circuit allows the cutoff frequency of the post-low-pass filter to be increased, effectively removing the limitations on the overall system's signal transmission bandwidth i mposed by the front-end amplifier. These techniques results in an root input-referred noise density of 8 nV/ Hz, an input offset voltage of 2.46 mu V, an offset drift of 24.65 nV/degrees C, and a gain-bandwidth product of 5.3 MHz.
Keyword ：
Auto-zeroing Auto-zeroing Charge injection Charge injection Chopper Chopper Dynamic offset compensation (DOC) Dynamic offset compensation (DOC) Glitch reduction Glitch reduction Multiphase switch reuse Multiphase switch reuse
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| GB/T 7714 | Dai, Haiyan , Xu, Chengye , Ye, Dapeng et al. Low-noise, zero-drift six-channel precision operational amplifier with multiphase switch reuse [J]. | MICROELECTRONICS JOURNAL , 2025 , 156 . |
| MLA | Dai, Haiyan et al. "Low-noise, zero-drift six-channel precision operational amplifier with multiphase switch reuse" . | MICROELECTRONICS JOURNAL 156 (2025) . |
| APA | Dai, Haiyan , Xu, Chengye , Ye, Dapeng , Luo, Zhicong , Li, Jinghu . Low-noise, zero-drift six-channel precision operational amplifier with multiphase switch reuse . | MICROELECTRONICS JOURNAL , 2025 , 156 . |
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BackgroundRice blast is one of the most destructive diseases in rice cultivation, significantly threatening global food security. Timely and precise detection of rice panicle blast is crucial for effective disease management and prevention of crop losses. This study introduces ConvGAM, a novel semantic segmentation model leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM). This design aims to enhance feature extraction and focus on critical image regions, addressing the challenges of detecting small and complex disease patterns in UAV-captured imagery. Furthermore, the model incorporates advanced loss functions to handle data imbalances effectively, supporting accurate classification across diverse disease severities.ResultsThe ConvGAM model, leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM), achieves outstanding performance in feature extraction, crucial for detecting small and complex disease patterns. Quantitative evaluation demonstrates that the model achieves an overall accuracy of 91.4%, a mean IoU of 79%, and an F1 score of 82% on the test set. The incorporation of Focal Tversky Loss further enhances the model's ability to handle imbalanced datasets, improving detection accuracy for rare and severe disease categories. Correlation coefficient analysis across disease severity levels indicates high consistency between predictions and ground truth, with values ranging from 0.962 to 0.993. These results confirm the model's reliability and robustness, highlighting its effectiveness in rice panicle blast detection under challenging conditions.ConclusionThe ConvGAM model demonstrates strong qualitative advantages in detecting rice panicle blast disease. By integrating advanced feature extraction with the ConvNeXt-Large backbone and GAM, the model achieves precise detection and classification across varying disease severities. The use of Focal Tversky Loss ensures robustness against dataset imbalances, enabling accurate identification of rare disease categories. Despite these strengths, future efforts should focus on improving classification accuracy and adapting the model to diverse environmental conditions. Additionally, optimizing model parameters and exploring advanced data augmentation techniques could further enhance its detection capabilities and expand its applicability to broader agricultural scenarios.
Keyword ：
ConvNeXt ConvNeXt FocalTverskyLoss FocalTverskyLoss GAM GAM Rice blast Rice blast Semantic segmentation Semantic segmentation
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| GB/T 7714 | Lin, Shaodan , Huang, Deyao , Wu, Libin et al. UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism [J]. | PLANT METHODS , 2025 , 21 (1) . |
| MLA | Lin, Shaodan et al. "UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism" . | PLANT METHODS 21 . 1 (2025) . |
| APA | Lin, Shaodan , Huang, Deyao , Wu, Libin , Cheng, Zuxin , Ye, Dapeng , Weng, Haiyong . UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism . | PLANT METHODS , 2025 , 21 (1) . |
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The advancement of biosensing devices based on field effect transistor (FET) has been rapid, largely due to the simplicity of their operational mechanism, rapid response, ease of miniaturization, and integration. The preparation of field effect transistors using inorganic nanomaterials as channel materials has been extensively employed in biosensing applications, including assessing food quality and safety, environmental monitoring, and diagnosing biological diseases. The detection of disease-causing microorganisms, antibiotics, heavy metals, and harmful gases in modern agricultural breeding environments also necessitates the utilization of sensors that are able to achieving label-free, miniaturized, rapid, and specific detection. Biosensing devices based on field effect transistors are able to rapidly and specifically detect, meeting the needs of modern agricultural breeding environments for low-cost, accurate, miniaturized, and portable devices.
Keyword ：
Agricultural breeding environment Agricultural breeding environment Application prospect Application prospect Field effect transistor Field effect transistor Structure and principle Structure and principle
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| GB/T 7714 | Long, Bo , Xing, Qiongqiong , Zhang, Qian et al. Exploring field effect transistor sensing devices in agricultural breeding environment: application prospects [J]. | ADVANCED COMPOSITES AND HYBRID MATERIALS , 2025 , 8 (1) . |
| MLA | Long, Bo et al. "Exploring field effect transistor sensing devices in agricultural breeding environment: application prospects" . | ADVANCED COMPOSITES AND HYBRID MATERIALS 8 . 1 (2025) . |
| APA | Long, Bo , Xing, Qiongqiong , Zhang, Qian , Deng, Liying , Liu, Qi , Zhang, Lintong et al. Exploring field effect transistor sensing devices in agricultural breeding environment: application prospects . | ADVANCED COMPOSITES AND HYBRID MATERIALS , 2025 , 8 (1) . |
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The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the grasping force based on individual mushroom sizes. To address this, we propose an improved method that integrates visual feedback to dynamically adjust the execution end, enhancing cut precision. Our approach enhances YOLOv8n-seg with Star Net, SPPECAN (a reconstructed SPPF with efficient channel attention), and C2fDStar (C2f with Star Net and deformable convolution) to improve feature extraction while reducing computational complexity and feature loss. Additionally, we introduce a mask ownership judgment and merging optimization algorithm to correct positional offsets, internal disconnections, and boundary instabilities in grasping area predictions. Based on this, we optimize grasping parameters using an improved centroid-based region width measurement and establish a region width-to-PWM mapping model for the precise conversion from visual data to gripper control. Experiments in real-situation settings demonstrate the effectiveness of our method, achieving a mean average precision (mAP50:95) of 0.743 for grasping area segmentation, a 4.5% improvement over YOLOv8, with an average detection speed of 10.3 ms and a target width measurement error of only 0.14%. The proposed mapping relationship enables adaptive end-effector control, resulting in a 96% grasping success rate and a 98% qualified cutting surface rate. These results confirm the feasibility of our approach and provide a strong technical foundation for the intelligent automation of enoki mushroom cutting systems.
Keyword ：
Flammulina filiformis Flammulina filiformis machine vision machine vision multi-target recognition multi-target recognition serial communication serial communication servo control servo control YOLO v8 YOLO v8
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| GB/T 7714 | Xie, Limin , Jing, Jun , Wu, Haoyu et al. MPG-YOLO: Enoki Mushroom Precision Grasping with Segmentation and Pulse Mapping [J]. | AGRONOMY-BASEL , 2025 , 15 (2) . |
| MLA | Xie, Limin et al. "MPG-YOLO: Enoki Mushroom Precision Grasping with Segmentation and Pulse Mapping" . | AGRONOMY-BASEL 15 . 2 (2025) . |
| APA | Xie, Limin , Jing, Jun , Wu, Haoyu , Kang, Qinguan , Zhao, Yiwei , Ye, Dapeng . MPG-YOLO: Enoki Mushroom Precision Grasping with Segmentation and Pulse Mapping . | AGRONOMY-BASEL , 2025 , 15 (2) . |
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Biomass monitoring of mushroom liquid strains during the fermentation process demands real-time analysis with minimal manual intervention, highlighting the urgent need for intelligent surveillance. This study introduced a soft sensor method based on edge computing machine vision, termed Edge CV, for in situ non-invasive estimation of biomass. In our experiment, the hardware of the Edge CV system includes the Jetson Nano with 4 GB RAM, 64 GB ROM, and a 128-core Maxwell GPU for executing intelligent machine vision tasks, along with embedded cameras for image data acquisition. Furthermore, a cascaded machine vision model was developed to enable biomass evaluation on the Edge CV system. The cascaded machine vision model mainly consists of three steps: first, the object detection task to locate the observation window, achieving a mean Average Precision (mAP50:95) of 82.3% with 78.7 GFLOPs; then, the segmentation task to extract liquid strain data within the observation window, yielding a mean intersection over union (MIoU) of 85.9% with 110.4 GFLOPs; and finally, calculating mycelium biomass indices via the morphological image processing task. The correlation between Edge CV inference and manual measurement showed an R2 of 0.963 and an RMSE of 0.027 for normalized biomass indices, demonstrating a robust and consistent trend. Therefore, this study illustrates the practical application of edge computing-based machine vision for biomass soft sensing during the fermentation process.
Keyword ：
biomass biomass edge computing edge computing liquid strain liquid strain machine vision machine vision soft sensing soft sensing
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| GB/T 7714 | Wu, Libin , Xiao, Guimiao , Huang, Deyao et al. Edge Computing-Based Machine Vision for Non-Invasive and Rapid Soft Sensing of Mushroom Liquid Strain Biomass [J]. | AGRONOMY-BASEL , 2025 , 15 (1) . |
| MLA | Wu, Libin et al. "Edge Computing-Based Machine Vision for Non-Invasive and Rapid Soft Sensing of Mushroom Liquid Strain Biomass" . | AGRONOMY-BASEL 15 . 1 (2025) . |
| APA | Wu, Libin , Xiao, Guimiao , Huang, Deyao , Zhang, Xiandong , Ye, Dapeng , Weng, Haiyong . Edge Computing-Based Machine Vision for Non-Invasive and Rapid Soft Sensing of Mushroom Liquid Strain Biomass . | AGRONOMY-BASEL , 2025 , 15 (1) . |
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The unclear aspects of the continuous microwave drying process for wheat bran were addressed, aiming to improve uniformity and enhance the efficiency of microwave energy absorption during the drying process. A mathematical model for the dielectric properties, temperature, and moisture content of wheat bran was established. Subsequently, a multi-physics coupling model integrating the electromagnetic field, heat transfer, and mass transfer was developed. A moving simulation strategy was implemented to achieve continuous microwave drying. This study identified the waveguide arrangement, layer thickness, and conveyor belt height as key factors influencing drying uniformity. Through single-factor and orthogonal experiments, the optimal parameters for the drying equipment were determined, yielding a waveguide arrangement (b), material thickness of 20 mm, and conveyor height of 135 mm. The electric field uniformity coefficient was 0.25, and the microwave energy absorption efficiency reached 87.4 %. The bench experiment results showed that, under the optimal conditions, the temperature and moisture content trends aligned well with simulations. The root mean square errors were 3.44 degrees C for temperature and 1.75 % for moisture content, affirming the model's accuracy and reliability. This study provides valuable insights for analyzing microwave drying processes and supports the development of effective drying equipment.
Keyword ：
Finite element Finite element Heat and mass transfer Heat and mass transfer Microwave drying Microwave drying Multi-physics coupling Multi-physics coupling Wheat bran Wheat bran
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| GB/T 7714 | Wang, Hao , Fang, Bing , Chen, Ying et al. Numerical simulation and optimization of microwave drying process of wheat bran [J]. | FOOD AND BIOPRODUCTS PROCESSING , 2025 , 151 : 84-102 . |
| MLA | Wang, Hao et al. "Numerical simulation and optimization of microwave drying process of wheat bran" . | FOOD AND BIOPRODUCTS PROCESSING 151 (2025) : 84-102 . |
| APA | Wang, Hao , Fang, Bing , Chen, Ying , Ye, Dapeng , Xie, Limin . Numerical simulation and optimization of microwave drying process of wheat bran . | FOOD AND BIOPRODUCTS PROCESSING , 2025 , 151 , 84-102 . |
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UAV image acquisition and deep learning techniques have been widely used in field hydrological monitoring to meet the increasing data volume demand and refined quality. However, manual parameter training requires trial-and-error costs (T&E), and existing auto-trainings adapt to simple datasets and network structures, which is low practicality in unstructured environments, e.g., dry thermal valley environment (DTV). Therefore, this research combined a transfer learning (MTPI, maximum transfer potential index method) and an RL (the MTSA reinforcement learning, Multi-Thompson Sampling Algorithm) in dataset auto-augmentation and networks auto-training to reduce human experience and T&E. Firstly, to maximize the iteration speed and minimize the dataset consumption, the best iteration conditions (MTPI conditions) were derived with the improved MTPI method, which shows that subsequent iterations required only 2.30% dataset and 6.31% time cost. Then, the MTSA was improved under MTPI conditions (MTSA-MTPI) to auto-augmented datasets, and the results showed a 16.0% improvement in accuracy (human error) and a 20.9% reduction in standard error (T&E cost). Finally, the MTPI-MTSA was used for four networks auto-training (e.g., FCN, Seg-Net, U-Net, and Seg-Res-Net 50) and showed that the best Seg-Res-Net 50 gained 95.2% WPA (accuracy) and 90.9% WIoU. This study provided an effective auto-training method for complex vegetation information collection, which provides a reference for reducing the manual intervention of deep learning.
Keyword ：
auto-DL method auto-DL method data augmentation automatic data augmentation automatic network training automatic network training automatic reinforcement learning for DL reinforcement learning for DL segmentation deep learning segmentation deep learning vegetation detection vegetation detection
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| GB/T 7714 | Chen, Yayong , Zhou, Beibei , Chen, Xiaopeng et al. A method of deep network auto-training based on the MTPI auto-transfer learning and a reinforcement learning algorithm for vegetation detection in a dry thermal valley environment [J]. | FRONTIERS IN PLANT SCIENCE , 2025 , 15 . |
| MLA | Chen, Yayong et al. "A method of deep network auto-training based on the MTPI auto-transfer learning and a reinforcement learning algorithm for vegetation detection in a dry thermal valley environment" . | FRONTIERS IN PLANT SCIENCE 15 (2025) . |
| APA | Chen, Yayong , Zhou, Beibei , Chen, Xiaopeng , Ma, Changkun , Cui, Lei , Lei, Feng et al. A method of deep network auto-training based on the MTPI auto-transfer learning and a reinforcement learning algorithm for vegetation detection in a dry thermal valley environment . | FRONTIERS IN PLANT SCIENCE , 2025 , 15 . |
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Trace amounts of antibiotics in water can accumulate in the human body through the food chain, posing significant health risks. Therefore, there is an urgent need to develop simple and effective methods for detecting antibiotics in water. In this study, we prepared electrochemical aptamer sensors based on carbon nanotubes@polystyrene sulfonate-gold nanoparticles/reduced graphene oxide (CNT@PSS-AuNPs/rGO) layered thin films for real-time, on-site detection of ciprofloxacin (CIP) in aquaculture environments, utilizing a portable sensing detection device. The CNT@PSS-AuNPs/rGO layered film offers an excellent specific surface area, providing ample binding sites for the aptamer. The functionalized CNT@PSS-AuNPs enhance the dispersibility and conductivity of the substrate material and increase the surface area of the electrode when loaded with rGO. Under optimal experimental conditions, the developed sensor exhibits a dynamic range from 4 ng/mL to 1.0 x 103 ng/mL and a limit of detection of 4 ng/mL (S/N = 3), demonstrating satisfactory sensitivity. The sensor also shows good stability, with a relative standard deviation of less than 1% after 100 repeated measurements. Moreover, when combined with a portable detection platform, CIP levels in aqueous environments can be analyzed intelligently, rapidly, and timely. Our study aims to promote simple and effective detection strategies, potentially extending their practical applications.
Keyword ：
CIP CIP Electrochemical sensor Electrochemical sensor Layered film Layered film Portability Portability Reduced graphene oxide Reduced graphene oxide
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| GB/T 7714 | Long, Bo , Zhang, Qian , Zhang, Lintong et al. Sensors based on CNT@PSS-AuNPs/rGO layered films for portable detection of ciprofloxacin [J]. | ADVANCED COMPOSITES AND HYBRID MATERIALS , 2025 , 8 (1) . |
| MLA | Long, Bo et al. "Sensors based on CNT@PSS-AuNPs/rGO layered films for portable detection of ciprofloxacin" . | ADVANCED COMPOSITES AND HYBRID MATERIALS 8 . 1 (2025) . |
| APA | Long, Bo , Zhang, Qian , Zhang, Lintong , Liu, Qi , Xing, Qiongqiong , Qu, Fangfang et al. Sensors based on CNT@PSS-AuNPs/rGO layered films for portable detection of ciprofloxacin . | ADVANCED COMPOSITES AND HYBRID MATERIALS , 2025 , 8 (1) . |
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Water is essential for life on Earth, but increased human activity has led to water pollution. The international community has been highly concerned about this issue, making it crucial to develop methods for detecting water pollutants. Recently, electrochemical sensors have emerged as effective tools for detecting environmental pollutants in water. Covalent organic frameworks (COFs), a quickly developing type of crystalline linked organic polymers, possess highly structured frameworks, significant specific surface areas, durable chemical properties, and customizable pore microenvironments, which confer great versatility. Notably, electrochemical sensors based on COFs have garnered significant attention due to their outstanding analytical performance. Herein, a comprehensive overview of the basic characteristics is provided and synthesis techniques of COFs in the field of electrochemical detection are widely used, emphasizing their role in the development of electrochemical biosensors and high response detection devices. The design principles, preparation methods, and detection mechanisms of COF-based electrochemical sensors are also detailed. Recent scientific advancements have been examined, highlighting the application of COF as a functional material in electrochemical sensors for detecting various water pollutants, such as antibiotics, heavy metals, insecticides, bacteria, and fungi. Additionally, the challenges and future development prospects of COF-based electrochemical detection technology have been outlined.
Keyword ：
antibiotics antibiotics covalent organic frameworks covalent organic frameworks electrochemical sensors electrochemical sensors heavy metal ions heavy metal ions pesticides pesticides
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| GB/T 7714 | Long, Bo , Liu, Qi , Zhang, Qian et al. Analysis and Study of Covalent Organic Frameworks in Electrochemical Sensors for Water Environment Pollutant Detection [J]. | SMALL STRUCTURES , 2025 , 6 (9) . |
| MLA | Long, Bo et al. "Analysis and Study of Covalent Organic Frameworks in Electrochemical Sensors for Water Environment Pollutant Detection" . | SMALL STRUCTURES 6 . 9 (2025) . |
| APA | Long, Bo , Liu, Qi , Zhang, Qian , Xing, Qiongqiong , Deng, Liying , Qu, Fangfang et al. Analysis and Study of Covalent Organic Frameworks in Electrochemical Sensors for Water Environment Pollutant Detection . | SMALL STRUCTURES , 2025 , 6 (9) . |
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为探索熔融沉积制造(FDM)工艺参数对玻璃纤维增强丙烯腈-丁二烯-苯乙烯共聚物(ABS/GF)复合材料力学性能的影响，为其应用和性能优化提供理论依据，本文通过Plackett-Burman筛选实验、单因素实验以及正交试验，探讨了各工艺参数对材料力学性能的影响，并识别出对拉伸强度和弯曲强度有显著影响的关键参数。在此基础上，基于拉丁超立方采样方法获取实验数据，通过反向传播(BP)神经网络建立工艺参数与力学性能之间的非线性预测模型。最后，通过非支配排序遗传算法II(NSGA-II)多目标遗传算法，对拉伸强度和弯曲强度进行同步优化，得到了Pareto前沿解集，展示了不同参数组合下的优化权衡。结果表明，喷嘴温度、打印层高、打印线宽和打印速度是影响材料拉伸强度和弯曲强度的最显著因素。通过多目标优化，得到了能够同时最大化拉伸强度和弯曲强度的最佳参数组合，拉伸强度和弯曲强度分别提高7.6%和7.2%以上。实验验证结果显示，优化模型的预测值与实验测得值的偏差在可接受范围内，进一步验证了所提出代理模型和多目标优化方法的有效性。
Keyword ：
反向传播神经网络 反向传播神经网络 多目标优化 多目标优化 正交试验 正交试验 玻璃纤维增强丙烯腈-丁二烯-苯乙烯共聚物 玻璃纤维增强丙烯腈-丁二烯-苯乙烯共聚物 遗传算法 遗传算法
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| GB/T 7714 | 林峰 , 叶大鹏 . 基于FDM的ABS/GF复合材料的力学性能分析及工艺参数优化 [J]. | 塑料工业 , 2025 , 53 (04) : 77-85 . |
| MLA | 林峰 et al. "基于FDM的ABS/GF复合材料的力学性能分析及工艺参数优化" . | 塑料工业 53 . 04 (2025) : 77-85 . |
| APA | 林峰 , 叶大鹏 . 基于FDM的ABS/GF复合材料的力学性能分析及工艺参数优化 . | 塑料工业 , 2025 , 53 (04) , 77-85 . |
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