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学者姓名：陈学永
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Abstract ：
The Ti-based amorphous alloy system was designed based on the principles of "near mixing enthalpy" and "effective atomic size difference," leading to a series of compositions formulated as Ti(76-2x)Mn(7 + x)Cu(17+x) (x = 0, 5, 10, 15). Among these, the Ti56Mn17Cu27 composition exhibited the highest degree of amorphization after 60 h of mechanical alloying. Accordingly, this study systematically investigated the influence of TiC powder addition on the glass-forming ability, microstructure, and thermal stability of Ti56Mn17Cu27 alloy powders. The results revealed that in the absence of TiC, residual crystalline phases, mainly Mn solid solution, remained throughout the mechanical alloying process. In contrast, the addition of TiC significantly accelerated the disappearance of residual Mn diffraction peaks, facilitating the formation of Ti-Mn-Cu/TiC amorphous composite powders after 60 h of milling. The incorporation of TiC promoted dislocation multiplication within and around the residual crystalline regions, disrupted local lattice ordering, and enhanced non-equilibrium atomic diffusion, collectively promoting the amorphization process. Furthermore, TiC addition contributed to particle size refinement and improved uniformity in size distribution. Among all studied compositions, the Ti56Mn17Cu27 + 10 wt.% TiC powder demonstrated the highest amorphous phase fraction, the broadest supercooled liquid region (Delta Tx = 27.09 degrees C), and a relatively low crystallization enthalpy, indicating that the optimal TiC content effectively enhances both amorphous phase formation and thermal stability.
Keyword ：
Amorphous alloy Amorphous alloy Glass-forming ability Glass-forming ability Mechanical alloying Mechanical alloying Thermal stability Thermal stability
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| GB/T 7714 | Chen, Zhihui , Nie, Jilong , Chen, Xin et al. Preparation and characterization of Ti-Mn-Cu/TiC amorphous alloy composite powders via mechanical alloying [J]. | JOURNAL OF NON-CRYSTALLINE SOLIDS , 2026 , 673 . |
| MLA | Chen, Zhihui et al. "Preparation and characterization of Ti-Mn-Cu/TiC amorphous alloy composite powders via mechanical alloying" . | JOURNAL OF NON-CRYSTALLINE SOLIDS 673 (2026) . |
| APA | Chen, Zhihui , Nie, Jilong , Chen, Xin , Li, Haibo , Chen, Xueyong , Tang, Cuiyong . Preparation and characterization of Ti-Mn-Cu/TiC amorphous alloy composite powders via mechanical alloying . | JOURNAL OF NON-CRYSTALLINE SOLIDS , 2026 , 673 . |
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Rice pests critically threaten rice cultivation by inflicting direct physiological damage, spreading diseases, and potentially causing catastrophic field extinction, leading to significant agricultural losses. To address challenges such as dense pest clusters, subtle morphological variations, and frequent small-target missed detections in pest detection lamp images, an intelligent recognition method was proposed by using an enhanced YOLO v8 STSF model. Key innovations included integrating a Swin Transformer module to boost backbone network multi-scale feature extraction, optimizing neck network feature fusion via distribution shift convolution (DSConv), and adopting the Focal EIoU loss function to enhance small-target localization. Validated on a 7 000-image multi-species pest dataset, the improved model achieved 95. 45% of precision, 90. 45% of recall, and 90. 03% of F1-score, surpassing the original YOLO v8 by 2. 13, 0. 33, and 3. 09 percentage points, respectively, while operating at 32 f/ s for real-time PC-based monitoring. A dual-platform system (Web and Android mobile) demonstrated field performance with 1. 38 s average response time, 96. 34% of accuracy, and 3. 86% of missed detection rate. This system can provide an efficient solution for precision pest control and advance intelligent agricultural monitoring. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.
Keyword ：
Cultivation Cultivation Feature extraction Feature extraction Pest control Pest control
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| GB/T 7714 | Wang, Xingwang , Zha, Hainie , Lu, Haonan et al. Multi-category Pest Identification Algorithm and Monitoring System Based on YOLO v8 STSF [J]. | Transactions of the Chinese Society for Agricultural Machinery , 2025 , 56 (6) : 228-236 . |
| MLA | Wang, Xingwang et al. "Multi-category Pest Identification Algorithm and Monitoring System Based on YOLO v8 STSF" . | Transactions of the Chinese Society for Agricultural Machinery 56 . 6 (2025) : 228-236 . |
| APA | Wang, Xingwang , Zha, Hainie , Lu, Haonan , Wang, Yubin , Wu, Dongsheng , Wang, Xufeng et al. Multi-category Pest Identification Algorithm and Monitoring System Based on YOLO v8 STSF . | Transactions of the Chinese Society for Agricultural Machinery , 2025 , 56 (6) , 228-236 . |
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Abstract ：
This study addresses challenges in agricultural pest detection, such as false positives and missed detections in complex environments, by proposing an enhanced Mask-RCNN model integrated with a Convolutional Block Attention Module (CBAM). The framework combines three innovations: (1) a CBAM attention mechanism to amplify pest features while suppressing background noise; (2) a feature-enhanced pyramid network (FPN) for multi-scale feature fusion, enhancing small pest recognition; and (3) a dual-channel downsampling module to minimize detail loss during feature propagation. Evaluated on a dataset of 14,270 pest images from diverse Chinese agricultural regions (augmented to 7,000 samples and split into 6:1:3 training/validation/test sets), the model achieved precision, recall, and F1 scores of 95.91%, 95.21%, and 95.49%, respectively, outperforming ResNet, Faster-RCNN, and Mask-RCNN by up to 2.67% in key metrics. Ablation studies confirmed the CBAM module improved F1 by 5.5%, the FPN increased small-target recall by 6%, and the dual-channel downsampling boosted AP@50 by 3.1%. Despite its compact parameter size (63.87 MB, 1.39 MB lighter than Mask-RCNN), limitations include reduced accuracy in low-contrast scenarios (e.g., foggy fields) and GPU dependency. Future work will focus on lightweight deployment for edge devices and domain adaptation, offering a robust solution for intelligent pest monitoring systems that balance accuracy with computational efficiency.
Keyword ：
attention mechanism attention mechanism deep learning deep learning dual channel downsampling dual channel downsampling feature enhanced pyramid network feature enhanced pyramid network Mask-RCNN-CBAM Mask-RCNN-CBAM pest extraction pest extraction
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| GB/T 7714 | Wang, Xingwang , Hu, Can , Wang, Xufeng et al. Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM [J]. | FRONTIERS IN AGRONOMY , 2025 , 7 . |
| MLA | Wang, Xingwang et al. "Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM" . | FRONTIERS IN AGRONOMY 7 (2025) . |
| APA | Wang, Xingwang , Hu, Can , Wang, Xufeng , Zha, Hainie , Chen, Xueyong , Yuan, Shanshan et al. Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM . | FRONTIERS IN AGRONOMY , 2025 , 7 . |
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本实用新型涉及一种无人机对中夹紧装置，包括底板以及设置于底板上方的停机坪，所述停机坪上方前、后对称设置有可同步反向运动的X向夹紧机构，所述X向夹紧机构上方两侧对称设置有可同步反向运动的Y向夹紧机构。本装置结构简单，设计合理，通过设置X向夹紧机构、Y向夹紧机构来调整无人机的停放位置，解决无人机降落至停机坪存在误差的问题，增强停放稳定性。
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| GB/T 7714 | 陈霄鹄 , 游宇轩 , 王咏静 et al. 一种无人机对中夹紧装置 : CN202422408569.3[P]. | 2024-09-30 . |
| MLA | 陈霄鹄 et al. "一种无人机对中夹紧装置" : CN202422408569.3. | 2024-09-30 . |
| APA | 陈霄鹄 , 游宇轩 , 王咏静 , 胡泽贵 , 陈学永 . 一种无人机对中夹紧装置 : CN202422408569.3. | 2024-09-30 . |
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水稻害虫危害十分巨大，不仅对水稻造成直接的生理破坏，还传播病害，严重时导致稻田绝收，造成难以估量的损失。水稻害虫精准识别与实时监测是减少农业损失的关键，针对虫情测报灯图像中害虫密集、体态差异细微及小目标漏检等问题，提出一种基于YOLO v8-STSF模型的水稻害虫智能识别方法。通过引入Swin Transformer模块增强骨干网络的多尺度特征提取能力，结合分布移位卷积(DSConv)优化颈部网络特征融合，并采用Focal EIoU损失函数提升密集小目标定位精度。构建了包含多类水稻害虫的7 000幅图像数据集进行识别验证，YOLO v8-STSF模型在测试集上的精确率为95.45%、召回率为90.45%、F1值为90.03%,较原YOLO v8模型分别提升2.13、0.33、3.09个百分点，在PC端的推理速度为32 f/s,满足实时需求。同时以Web端监测系统为基础，设计基于Android移动端的虫情监测系统，在田间测试中系统平均响应时间为1.38 s,识别准确率为96.34%,漏检率为3.86%。研究结果可为水稻害虫精准防控提供高效技术支持，推动农业病虫害监测智能化发展。
Keyword ：
Android Android YOLO v8-STSF YOLO v8-STSF 多类别害虫 多类别害虫 害虫识别 害虫识别 监测系统 监测系统
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| GB/T 7714 | 王兴旺 , 查海涅 , 卢浩男 et al. 基于YOLO v8-STSF的多类别害虫识别算法与监测系统研究 [J]. | 农业机械学报 , 2025 , 56 (06) : 228-236 . |
| MLA | 王兴旺 et al. "基于YOLO v8-STSF的多类别害虫识别算法与监测系统研究" . | 农业机械学报 56 . 06 (2025) : 228-236 . |
| APA | 王兴旺 , 查海涅 , 卢浩男 , 王禹彬 , 吴东昇 , 王旭峰 et al. 基于YOLO v8-STSF的多类别害虫识别算法与监测系统研究 . | 农业机械学报 , 2025 , 56 (06) , 228-236 . |
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Orah mandarin (Citrus reticulata cv. Orah) lacks systematic grading treatment after harvesting, resulting in a high fresh fruit loss rate and affecting the economic value. There are many drawbacks to traditional manual and mechanical sorting. Therefore, intelligent, rapid, non-destructive surface defect detection technology is significant. In addition to the fruit size, surface defects (e.g., canker, sunburn) are another important criterion for grading fruit. To overcome the challenges in detecting surface defects of orah mandarin, like multi-scale features, significant size differences, and slow convergence speed, we propose the YOLOv8-Orah detection model based on YOLOv8n. Path Aggregation Network (PANet) is replaced by a Focusing Diffusion Pyramid Network (FDPN), and the Diffusion and Spatial Interaction (DASI) module is introduced to effectively fuse and enhance features of different scales and improve detection accuracy. The Bottleneck in the C2f module is replaced by the Hybrid Dilated Residual Attention Block (HDRAB) module to reduce missed detections and false detections. We also introduce the NWD-CIoU joint bounding box loss to accelerate the convergence speed and improve the detection accuracy of small defects. The experimental results show that the improved YOLOv8-Orah model performs well in terms of precision, recall, and average precision, reaching 81.9%, 78.8%, and 84.2%, respectively. Compared with the original YOLOv8n, the improved model increased by 4.0%, 1.7%, and 3.0%, respectively. Meanwhile, the parameter count decreased by 7.76%. Compared with other mainstream models, YOLOv8-Orah achieves a good balance between detection accuracy and computational efficiency. The results technically support defect detection in postharvest orah mandarin and real-time grading of their quality. Meanwhile, it can promote the intelligent development of the bergamot industry.
Keyword ：
defect detection defect detection orah mandarin orah mandarin post-harvest classification post-harvest classification YOLOv8n YOLOv8n
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| GB/T 7714 | Li, Hongda , Wang, Xiangyu , Bu, Yifan et al. YOLOv8-Orah: An Improved Model for Postharvest Orah Mandarin (Citrus reticulata cv. Orah) Surface Defect Detection [J]. | AGRONOMY-BASEL , 2025 , 15 (4) . |
| MLA | Li, Hongda et al. "YOLOv8-Orah: An Improved Model for Postharvest Orah Mandarin (Citrus reticulata cv. Orah) Surface Defect Detection" . | AGRONOMY-BASEL 15 . 4 (2025) . |
| APA | Li, Hongda , Wang, Xiangyu , Bu, Yifan , David, Chiaka Chibuike , Chen, Xueyong . YOLOv8-Orah: An Improved Model for Postharvest Orah Mandarin (Citrus reticulata cv. Orah) Surface Defect Detection . | AGRONOMY-BASEL , 2025 , 15 (4) . |
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Abstract ：
Featured Application This study is primarily oriented towards the industrialized Orah sorting process.Abstract This study proposes a grading algorithm for Orah sorting lines based on machine vision and deep learning. The original ShuffleNet V2 network was modified by replacing the ReLU activation function with the Mish activation function to alleviate the neuron death problem. The ECA attention module was incorporated to enhance the extraction of Orah appearance features, and transfer learning was applied to improve model performance. As a result, the ShuffleNet_wogan model was developed. Based on the operational principles of the sorting line, a time-sequential grading algorithm was designed to improve grading accuracy, along with a multi-sampling diameter algorithm for simultaneous Orah diameter measurement. Experimental results show that the ShuffleNet_wogan model achieved an accuracy of 91.12%, a 3.92% improvement compared to the original ShuffleNet V2 network. The average prediction time for processing 10 input images was 51.44 ms. The sorting line achieved a grading speed of 10 Orahs per second, with an appearance grading accuracy of 92.5% and a diameter measurement compliance rate of 98.3%. The proposed algorithm is characterized by high speed and accuracy, enabling efficient Orah sorting.
Keyword ：
appearance inspection appearance inspection computer vision computer vision deep learning deep learning Orah grading Orah grading
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| GB/T 7714 | Bu, Yifan , Liu, Hao , Li, Hongda et al. Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2 [J]. | APPLIED SCIENCES-BASEL , 2025 , 15 (8) . |
| MLA | Bu, Yifan et al. "Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2" . | APPLIED SCIENCES-BASEL 15 . 8 (2025) . |
| APA | Bu, Yifan , Liu, Hao , Li, Hongda , Murengami, Bryan Gilbert , Wang, Xingwang , Chen, Xueyong . Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2 . | APPLIED SCIENCES-BASEL , 2025 , 15 (8) . |
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Abstract ：
采用激光熔覆工艺在45钢基材表面对Fe
Keyword ：
显微硬度 显微硬度 涂层 涂层 激光熔覆 激光熔覆 耐腐蚀性能 耐腐蚀性能 非晶合金 非晶合金
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| GB/T 7714 | 唐翠勇 , 谢文彬 , 邹泽昌 et al. 激光熔覆Fe [J]. | 热加工工艺 , 2025 , (10) : 74-77,83 . |
| MLA | 唐翠勇 et al. "激光熔覆Fe" . | 热加工工艺 10 (2025) : 74-77,83 . |
| APA | 唐翠勇 , 谢文彬 , 邹泽昌 , 刘娟 , 陈学永 . 激光熔覆Fe . | 热加工工艺 , 2025 , (10) , 74-77,83 . |
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本实用新型涉及一种带展开平台的无人机收纳机巢装置，包括无人机巢外壳以及设置于无人机巢外壳表面的对中夹紧装置；对中夹紧装置包括停机坪，所述停机坪上方前、后对称设置有可同步反向运动的X向夹紧机构，X向夹紧机构上方两侧对称设置有可同步反向运动的Y向夹紧机构；无人机巢外壳包括无人机巢底座以及设置于无人机巢底座两侧且可开合的展开平台，停机坪与两个展开平台表面共同形成无人机识别区域。本装置结构简单，设计合理，通过两个可收折的展开平台来增大无人机降落的识别区域，避免发生误识现象，导致无人机损坏，通过设置X向夹紧机构、Y向夹紧机构来调整无人机的停放位置，解决无人机降落至停机坪存在误差的问题，增强停放稳定性。
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| GB/T 7714 | 陈霄鹄 , 游宇轩 , 王咏静 et al. 一种带展开平台的无人机收纳机巢装置 : CN202422408535.4[P]. | 2024-09-30 . |
| MLA | 陈霄鹄 et al. "一种带展开平台的无人机收纳机巢装置" : CN202422408535.4. | 2024-09-30 . |
| APA | 陈霄鹄 , 游宇轩 , 王咏静 , 胡泽贵 , 陈学永 . 一种带展开平台的无人机收纳机巢装置 : CN202422408535.4. | 2024-09-30 . |
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Abstract ：
本实用新型涉及一种带展开平台的无人机巢外壳，包括无人机巢底座以及设置于无人机巢底座两侧且可开合的展开平台，所述无人机巢底座表面与呈打开状态的展开平台表面共同形成无人机识别区域。本装置结构简单，设计合理，通过两个可收折的展开平台来增大无人机降落的识别区域，避免因识别区域小，导致发生误识现象，导致无人机损坏。
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| GB/T 7714 | 游宇轩 , 陈霄鹄 , 王祥宇 et al. 一种带展开平台的无人机巢外壳 : CN202422408625.3[P]. | 2024-09-30 . |
| MLA | 游宇轩 et al. "一种带展开平台的无人机巢外壳" : CN202422408625.3. | 2024-09-30 . |
| APA | 游宇轩 , 陈霄鹄 , 王祥宇 , 洪涛 , 陈学永 . 一种带展开平台的无人机巢外壳 : CN202422408625.3. | 2024-09-30 . |
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