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Aging Mongolian pine plantations face high risks of drought-induced growth decline:evidence from both individual tree and forest stand measurements

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Journal of Forestry Research 2024年第2期35卷 109-120页

作者： mingyong li Leilei Yang Yu Cao Dedong Wu Guangyou Hao CAS Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016People’s Republic of China Key Laboratory of Terrestrial Ecosystem Carbon Neutrality Liaoning ProvinceShenyang 110016People’s Republic of China Daqinggou Ecological Research Station Institute of Applied EcologyChinese Academy of SciencesShenyang 110016People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People’s Republic of China Research Institute of Sand Control and Utilization Liaoning ProvinceFuxin 123000People’s Republic of China

Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas.An important species for afforestation in dry environments of northern China,Mongolian pine(Pinus sylvestris var.mongolica litv.)has recently exhibited growth decline and dieback on many sites,particularly pronounced in old-growth plantations.However,changes in response to drought stress by this species with age as well as the underlying mechanisms are poorly understood.In this study,tree-ring data and remotely sensed vegetation data were combined to investigate variations in growth at individual tree and stand scales for young(9-13 years)and aging(35-52 years)plantations of Mongolian pine in a water-limited area of northern China.A recent decline in tree-ring width in the older plantation also had lower values in satellited-derived normalized difference vegetation indices and normalized difference water indices relative to the younger plantations.In addition,all measured growth-related metrics were strongly correlated with the self-calibrating Palmer drought severity index during the growing season in the older plantation.Sensitivity of growth to drought of the older plantation might be attributed to more severe hydraulic limitations,as reflected by their lower sapwood-and leaf-specific hydraulic conductivities.Our study presents a comprehensive view on changes of growth with age by integrating multiple methods and provides an explanation from the perspective of plant hydraulics for growth decline with age.The results indicate that old-growth Mongolian pine plantations in water-limited environments may face increased growth declines under the context of climate warming and drying.

关键词： Tree age Drought stress Mongolian pine plantation Tree rings Remote sensing Plant hydraulics

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Online diagnosis platform for tomato seedling diseases in greenhouse production

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International Journal of Agricultural and Biological Engineering 2024年第1期17卷 80-89页

作者： Xin Jin Xiaowu Zhu Jiangtao Ji mingyong li Xiaolin Xie Bo Zhao College of Agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471003HenanChina Science&Technology Innovation Center for Completed Set Equipment Longmen LaboratoryLuoyang 471023HenanChina Institute of Mechatronics Technology and Application China Mechanized Agriculture Science Research Institute Group Co.LtdBeijing 100020China

The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities,an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment.The diagnostic platform consists of three main parts:pest and disease information detection,clustering and decision-making of detection results,and platform diagnostic display.Firstly,based on the You Only Look Once(YOLO)algorithm,the key information of the disease was extracted by adding attention module(CBAM),multi-scale feature fusion was performed using weighted bi-directional feature pyramid network(BiFPN),and the overall construction was designed to be compressed and lightweight;Secondly,the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications;Finally,a detection platform was designed and developed using Python,including the front-end,back-end,and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases.The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP(mean Average Precision)of 92.7%,weights of 12.8 Megabyte(M),inference time of 33.6 ms.Compared with the current mainstream single-stage detection series algorithms,the improved algorithm model has achieved better performance;The accuracy rate of the platform diagnosis output pests and diseases information of 91.2%for images and 95.2%for videos.It is a great significance to tomato pest control research and the development of smart agriculture.

关键词： pest and disease detection YOLO diagnosis platform k-means clustering facility production base

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Cross-upgrading of biomass hydrothermal carbonization and pyrolysis for high quality blast furnace injection fuel production:Physicochemical characteristics and gasification kinetics analysis

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International Journal of Minerals,Metallurgy and Materials 2024年第2期31卷 268-281页

作者： Han Dang Runsheng Xu Jianliang Zhang mingyong Wang Jinhua li State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China School of Chemical Engineering The University of QueenslandQueensland 4072Australia

The paper proposes a biomass cross-upgrading process that combines hydrothermal carbonization and pyrolysis to produce high-quality blast furnace injection fuel.The results showed that after upgrading,the volatile content of biochar ranged from 16.19%to 45.35%,and the alkali metal content,ash content,and specific surface area were significantly reduced.The optimal route for biochar pro-duction is hydrothermal carbonization-pyrolysis(P-HC),resulting in biochar with a higher calorific value,C=C structure,and increased graphitization degree.The apparent activation energy(E)of the sample ranges from 199.1 to 324.8 kJ/mol,with P-HC having an E of 277.8 kJ/mol,lower than that of raw biomass,primary biochar,and anthracite.This makes P-HC more suitable for blast furnace injection fuel.Additionally,the paper proposes a path for P-HC injection in blast furnaces and calculates potential environmental benefits.P-HC of-fers the highest potential for carbon emission reduction,capable of reducing emissions by 96.04 kg/t when replacing 40wt%coal injec-tion.

关键词： blast furnace injection biomass cross-upgrading hydrothermal carbonization pyrolysis physicochemical properties gasific-ation properties

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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

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Computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi li Longfei Ma Zheng Jiang mingyong li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low storage.Cross-modal retrieval technology can be applied to search engines,crossmodalmedical processing,etc.The existing main method is to use amulti-label matching paradigm to finish the retrieval tasks.However,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal results.To avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal data.First,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for processing.Second,this method uses the inference capabilities of the transformer encoder to generate global fine-grained features.Finally,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching datasets.This article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous classicalmethods.The experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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ViT2CMH:Vision Transformer Cross-Modal Hashing for Fine-Grained Vision-Text Retrieval

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Computer Systems Science & Engineering 2023年第8期46卷 1401-1414页

作者： mingyong li Qiqi li Zheng Jiang Yan Ma College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

In recent years,the development of deep learning has further improved hash retrieval technology.Most of the existing hashing methods currently use Convolutional Neural Networks(CNNs)and Recurrent Neural Networks(RNNs)to process image and text information,respectively.This makes images or texts subject to local constraints,and inherent label matching cannot capture finegrained information,often leading to suboptimal results.Driven by the development of the transformer model,we propose a framework called ViT2CMH mainly based on the Vision Transformer to handle deep Cross-modal Hashing tasks rather than CNNs or RNNs.Specifically,we use a BERT network to extract text features and use the vision transformer as the image network of the model.Finally,the features are transformed into hash codes for efficient and fast retrieval.We conduct extensive experiments on Microsoft COCO(MS-COCO)and Flickr30K,comparing with baselines of some hashing methods and image-text matching methods,showing that our method has better performance.

关键词： Hash learning cross-modal retrieval fine-grained matching transformer

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A PERT-BiLSTM-Att Model for Online Public Opinion Text Sentiment Analysis

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Intelligent Automation & Soft Computing 2023年第8期37卷 2387-2406页

作者： mingyong li Zheng Jiang Zongwei Zhao Longfei Ma College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

As an essential category of public event management and control,sentiment analysis of online public opinion text plays a vital role in public opinion early warning,network rumor management,and netizens’person-ality portraits under massive public opinion data.The traditional sentiment analysis model is not sensitive to the location information of words,it is difficult to solve the problem of polysemy,and the learning representation ability of long and short sentences is very different,which leads to the low accuracy of sentiment classification.This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model of the disordered language model,bidirectional long-term and short-term memory network and attention mechanism.The model first uses the PERT model pre-trained from the lexical location information of a large amount of corpus to process the text data and obtain the dynamic feature representation of the text.Then the semantic features are input into BiLSTM to learn context sequence information and enhance the model’s ability to represent long sequences.Finally,the attention mechanism is used to focus on the words that contribute more to the overall emotional tendency to make up for the lack of short text representation ability of the traditional model,and then the classification results are output through the fully connected network.The experimental results show that the classification accuracy of the model on NLPCC14 and weibo_senti_100k public data sets reach 88.56%and 97.05%,respectively,and the accuracy reaches 95.95%on the data set MDC22 composed of Meituan,Dianping and Ctrip comment.It proves that the model has a good effect on sentiment analysis of online public opinion texts on different platforms.The experimental results on different datasets verify the model’s effectiveness in applying sentiment analysis of texts.At the same time,the model has a strong generalization ability and can achieve good results for sentiment analysis of datasets in different

关键词： Natural language processing PERT pre-training model emotional analysis BiLSTM

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An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 2349-2371页

作者： mingyong li lirong Tang Longfei Ma Honggang Zhao Jinyu Hu Yan Wei College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331China

The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.

关键词： Deep learning fatigue detection facial expression recognition sentiment analysis information fusion

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螺旋藻肽亚铁螯合物的制备及结构表征

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食品工业科技 2024年第10期45卷 165-175页

作者：张雨李崴王丽娟李方巍曾名湧中国海洋大学三亚海洋研究院海南三亚572000 中国海洋大学食品科学与工程学院山东青岛266003

为了提高螺旋藻的高值化利用,本文以钝顶螺旋藻蛋白肽为实验原料,氯化亚铁为铁源,采用初步优化螺旋藻肽亚铁螯合物的螯合条件,并对螯合物通过分子量测定、氨基酸组成实验、荧光光谱、扫描电镜、量子化学计算进行结构表征。结果表明:初... 详细信息

为了提高螺旋藻的高值化利用,本文以钝顶螺旋藻蛋白肽为实验原料,氯化亚铁为铁源,采用初步优化螺旋藻肽亚铁螯合物的螯合条件,并对螯合物通过分子量测定、氨基酸组成实验、荧光光谱、扫描电镜、量子化学计算进行结构表征。结果表明:初步合适的螯合条件为pH6、肽亚铁质量比6:1、肽液浓度1%、温度60℃、时间40 min,在此条件下螯合物得率为44.13%。螺旋藻蛋白肽与亚铁盐螯合反应后产生了新结构,铁主要与谷氨酸和天冬氨酸的羧基、精氨酸的氨基、赖氨酸的胍基结合,且镶嵌在氨基酸电负性末端形成的空腔中,形成稳定的结构,证明了螯合物的形成。研究结果将为螺旋藻补铁剂的开发提供一定的理论依据。

关键词：螺旋藻蛋白肽多肽亚铁螯合物结构表征量子化学计算

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螺旋藻多酚复合纳米微胶囊的制备与稳定性分析

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食品工业科技 2024年第10期45卷 198-206页

作者：钟秋帆李崴孙玮鸿李方巍曾名湧中国海洋大学三亚海洋研究院海南三亚527000 中国海洋大学食品科学与工程学院山东青岛266000

为通过微胶囊化提高螺旋藻多酚的稳定性。利用玉米醇溶蛋白(Zein)和多孔淀粉(porous starch,PS)对螺旋藻多酚(polyphenol,Phe)进行包埋,制备了螺旋藻多酚-多孔淀粉(Phe-PS),螺旋藻多酚-多孔淀粉-玉米醇溶蛋白(Phe-PS-Zein)两种微胶囊,并... 详细信息

为通过微胶囊化提高螺旋藻多酚的稳定性。利用玉米醇溶蛋白(Zein)和多孔淀粉(porous starch,PS)对螺旋藻多酚(polyphenol,Phe)进行包埋,制备了螺旋藻多酚-多孔淀粉(Phe-PS),螺旋藻多酚-多孔淀粉-玉米醇溶蛋白(Phe-PS-Zein)两种微胶囊,并对Phe,Phe-PS和Phe-PS-Zein进行了微观结构和稳定性、抗氧化性及体外模拟消化性质的比较。结果表明,Phe-PS微胶囊在芯壁比1:10、温度80℃、磁力搅拌时间120 min条件下,包埋率为60.29%;Phe-PS-Zein微胶囊在芯壁比1:10、壁材比1:1、搅拌时间90 min、温度60℃和pH7时包埋率最高,达86.93%。XRD和XPS图谱结果表明,添加Zein后,晶体衍射峰强度有所降低,主要与Zein的无定型结构有关。DSC分析结果表明,蛋白做壁材改善了微胶囊热稳定性。通过对抗氧化性、稳定性以及体外缓释效果的测定发现,与游离Phe和Phe-PS相比,Phe-PS-Zein对DPPH自由基清除力和Fe3+的还原力提高。综上,Phe-PSZein颗粒可以作为螺旋藻多酚的有效递送载体。本研究结果有望为Phe的高值化利用提供理论和实验基础。

关键词：螺旋藻多酚多孔淀粉玉米醇溶蛋白微胶囊结构表征稳定性抗氧化活性体外消化

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Obstacle avoidance transplanting method on Kinect visual processing

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International Journal of Agricultural and Biological Engineering 2021年第5期14卷 72-78页

作者： Xin Jin Ruoshi li Jiangtao Ji Yanwei Yuan mingyong li College of Agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471003HenanChina Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province Luoyan 471003HenanChina Chinese Academy of Agricultural Mechanization Sciences Beijing 100020China

In modern facility agriculture,to improve the quality and efficiency of transplanting,the application of transplanting robots based on visual processing is becoming more and more widespread.In order to reduce the damage to plants during the transplanting process and reduce the damage rate of plant stems,leaves and substrates,a transplanting method based on Kinect visual processing combined with an inclined transplanting manipulator was proposed.In the research,the Kinect visual processing was used to obtain and process the seedling height information and leaf edge information,and the working coordinate system of the transplanting manipulator was established and applied to plan the obstacle avoidance path.Combined with the oblique manipulator,the obstacle avoidance transplanting method was proposed.Through the structural design and force analysis of the seedling transplanting device,the key parameters that affect the transplanting quality were obtained,and the optimal transplanting performance parameters were obtained through experiments.In the experiment,with the aid of the Kinect vision processing system,the designed obstacle avoidance transplanting manipulator had a leaf damage degree of 4.70%,a stem bending rate of 16.67%,substrate integrity of 83.45%and a transplanting quality parameter of 87.36%.The time for a single seedling transplanting was(8.32±0.40)s.The experiment result proves that the obstacle avoidance transplanting method based on Kinect visual processing can effectively reduce the damage to seedlings when ensuring the transplanting efficiency.

关键词： Kinect vision processing oblique-type transplanting robot path planning obstacle avoidance

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