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Study on the Nutrition Characteristics of Different K Use Efficiency Cotton Genotypes to K Deficiency Stress

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Agricultural Sciences in China 2008年第6期7卷 740-745页

作者： JIANG Cun-cang CHEN Fang GAO Xiang-zhao LU Jian-wei WAN Kai-yuan nian fu-zhao WANG Yun-hua Plant Nutrition Lab Huazhong Agricultural University Wuhan 430070 P.R.China Wuhan Botanical Garden Chinese Academy of Sciences Wuhan 430074 P.R.China National Agro-Tech Extension and Service Center Beijing 100026 P.R.China

To study the mechanism of plant K use efficiency, both K high and low use efficiency cotton genotypes, 103 and 122, respectively, were selected from 86 cotton cultivars （Gossypium hirsutum L.）. The research was conducted using pot experiment for planting cotton on without K （0.0 g kg-1 soil） and with K （0.4 g kg-1 soil） treatments in 2005. Experimental result showed that, with K deficiency stress, genotype 103 grew much better than genotype 122 except that its lower leaves showed the symptoms of K deficiency, whereas all the leaves of genotype 122 showed the symptoms of K deficiency. Root dry matter weights of treatments for genotype 103 with and without K application were 1.07 and 1.25 times of genotype 122. It indicated that the root system of genotype 103 was well developed and has better nutrition uptake capability than that of genotype 122. The result also showed that the cotton shoots of genotype 103 were 1.07 and 1.13 times over genotype 122 on treatments of with and without K application. It indicated that genotype 103 has stronger transport organs. In genotype 103, plants, dry matter, and potassium were mainly transported to cotton bolls. The boll dry weight of genotype 103 was 2.58 times in without K treatment and 1.90 times in with K treatment over genotype 122. The potassium accumulation in bolls of genotype 122 was only 49.3% of that in genotype 103. Potassium accumulation in the other organs of genotype 103 was relatively low compared with in bolls. This indicated that the distribution of K and organic matter in genotype 103 was more efficient than genotype 122. The main differences between high K efficiency cotton genotype 103 and low K efficiency genotype 122 lie in their potassium nutrition and organic matter using efficiency on uptake, transportation, accumulation, distribution, and utilization.

关键词： cotton (Gossypium hirsutum L.) potassium use efficiency genotype uptake capability distribution ability

The chlorine evolution reaction promoted by organocatalysts with amide functional groups

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Science China Chemistry 2024年第3期67卷 749-750页

作者： Lan zhao Liang-nian He State Key Laboratory and Institute of Elemento-Organic Chemistry College of ChemistryNankai UniversityTianjin 300071China

Since the nineteenth century, the chlor-alkali process entails the electrochemical oxidation of sodium chloride solutions to Cl_(2) by the chlorine evolution reaction(CER) and the simultaneous production of sodium hyd... 详细信息

关键词： chlorine hydroxide promoted

Effects of individual heterogeneity on social contagions

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Chinese Physics B 2024年第5期33卷 737-747页

作者：年福忠杨宇 School of Computer and Communication Lanzhou University of TechnologyLanzhou 730000China

Despite having significant effects on social contagions,individual heterogeneity has frequently been overlooked in earlier *** better understand the complexity of social contagions,a non-Markovian model incorporating heterogeneous social influence and adoption thresholds is *** theoretical analysis,a generalized edge-based compartmental theory which considers the heterogeneities of social influence and adoption thresholds is *** on the final adoption size,the critical propagation probability,and the phase transition type,social contagions for adoption thresholds that follow normal distributions with various standard deviations,follow various distributions,and correlate with degrees are *** thresholds follow normal distributions,a larger standard deviation results in a larger final adoption size when the information propagation probability is relatively ***,when the information propagation probability is relatively high,a larger standard deviation results in a smaller final adoption *** thresholds follow various distributions,crossover phenomena in phase transition are observed when investigating the relationship of the final adoption size versus the average adoption threshold for some threshold *** thresholds are correlated with degrees,similar crossover phenomena occur when investigating the relationship of the final adoption size versus the degree correlation ***,we find that increasing the heterogeneity of social influence suppresses the effects of adoption threshold heterogeneity on social contagions in three *** theory predictions agree well with the simulation results.

关键词： complex networks social contagions heterogeneity phase transition

A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems

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Control Theory and Technology 2024年第2期22卷 213-221页

作者： Yanxin fu Wenxiao zhao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing100049China

We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (SG) algorithm is applied to obtain initial estimates of the unknown parameter matrix and in the second step an optimization criterion is introduced for the sparse identification of multivariate ARX systems. Under mild conditions, we prove that by minimizing the criterion function, the zero elements of the unknown parameter matrix can be recovered with a finite number of observations. The performance of the algorithm is testified through a simulation example.

关键词： ARX system Stochastic gradient algorithm Sparse identification Support recovery Parameter estimation Strong consistency

Nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in aerospace community:a comparative analysis

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International Journal of Extreme Manufacturing 2024年第2期6卷 190-271页

作者： Guolong zhao Biao zhao Wenfeng Ding Lianjia Xin Zhiwen nian Jianhao Peng Ning He Jiuhua Xu College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016People’s Republic of China

The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex *** machining is the main material removal process for the vast majority of aerospace ***,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface *** energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical *** provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool ***,systematic reviews of this technology are lacking with respect to the current research status and development *** paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace *** addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical ***,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.

关键词： difficult-to-cut materials geometrically complex components nontraditional energy mechanical machining aerospace community

Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum Science 2024年第4期21卷 2329-2344页

作者： Jiu-Qiang Yang nian-Tian Lin Kai Zhang Yan Cui Chao fu Dong Zhang College of Earth Sciences and Engineering Shandong University of Science and Technology Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology Transportation Institute of Inner Mongolia University Key Laboratory of Gas Hydrate Qingdao Institute of Marine Geology Ministry of Natural Resources

Conventional machine learning(CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning(DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM mode

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction

Predicting impact forces on pipelines from deep-sea fluidized slides:A comprehensive review of key factors

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International Journal of Mining Science and Technology 2024年第2期34卷 211-225页

作者： Xingsen Guo Ning Fan Defeng Zheng Cuiwei fu Hao Wu Yanjun Zhang Xiaolong Song Tingkai nian State Key Laboratory of Coastal and Offshore Engineering Dalian University of TechnologyDalian 116024China Department of Civil Environmentaland Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUnited Kingdom Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering Ocean University of ChinaQingdao 266100China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu 610059China College of Civil Engineering and Architecture Wenzhou UniversityWenzhou 325035China Key Laboratory of Engineering and Technology for Soft Soil Foundation and Tideland Reclamation of Zhejiang Province Wenzhou 325035China Zhejiang International Science and Technology Cooperation Base of Ultra-soft Soil Engineering and Smart Monitoring Wenzhou 325035China School of Geography Liaoning Normal UniversityDalian 116029China China Railway First Survey and Design Institute Group Co. Ltd.Xi’an 710043China Department of Geotechnical Engineering Nanjing Hydraulic Research InstituteNanjing 210024China Wuhan Center China Geological Survey(Central South China Innovation Center for Geosciences)Wuhan 430205China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and TechnologyHuainan 232001China

Deep-sea pipelines play a pivotal role in seabed mineral resource development,global energy and resource supply provision,network communication,and environmental ***,the placement of these pipelines on the seabed surface exposes them to potential risks arising from the complex deep-sea hydrodynamic and geological environment,particularly submarine *** incidents have highlighted the substantial damage to pipelines due to ***,deep-sea fluidized slides(in a debris/mud flow or turbidity current physical state),characterized by high speed,pose a significant *** assessing the impact forces exerted on pipelines by fluidized submarine slides is crucial for ensuring pipeline *** study aimed to provide a comprehensive overview of recent advancements in understanding pipeline impact forces caused by fluidized deep-sea slides,thereby identifying key factors and corresponding mechanisms that influence pipeline impact *** factors include the velocity,density,and shear behavior of deep-sea fluidized slides,as well as the geometry,stiffness,self-weight,and mechanical model of ***,the interface contact conditions and spatial relations were examined within the context of deep-sea slides and their interactions with *** upon a thorough review of these achievements,future directions were proposed for assessing and characterizing the key factors affecting slide impact loading on pipelines.A comprehensive understanding of these results is essential for the sustainable development of deep-sea pipeline projects associated with seabed resource development and the implementation of disaster prevention measures.

关键词： Deep-sea fluidized slides Pipes Impact forces Shear behavior of slides Interface contact conditions Spatial relation

Optimized Auxiliary Frequency Control of Wind Farm Based on Piecewise Reduced-order Frequency Response Model

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 791-802页

作者： Xu Zhang Chen zhao Junchao Ma Long Zhang Dan Sun Chenxu Wang Yan Peng Heng nian the School of Electrical Engineering Zhejiang UniversityHangzhou 310058China Electric Power Research Institute of State Grid Zhejiang Electric Power Corporation Hangzhou 310014China

With the increasing wind power penetration in the power system,the auxiliary frequency control(AFC)of wind farm(WF)has been widely *** traditional system frequency response(SFR)model is not suitable for the wind power generation system due to its poor accuracy and *** this paper,a piecewise reduced-order frequency response(PROFR)model is proposed,and an optimized auxiliary frequency control(O-AFC)scheme of WF based on the P-ROFR model is ***,a full-order frequency response model considering the change in operating point of wind turbine is established to improve the *** order to simplify the fullorder model,a P-ROFR model with second-order structure and high accuracy at each frequency response stage is *** on the proposed P-ROFR model,the relationship between the frequency response indexes and the auxiliary frequency controller coefficients is expressed ***,an OAFC scheme with the derived explicit expression as the optimization objective is proposed in order to improve the frequency support capability on the premise of ensuring the full release of the rotor kinetic energy and the full use of the effect of time delay on frequency ***,the effectiveness of the proposed P-ROFR model and the performance of the proposed OAFC scheme are verified by simulation studies.

关键词： Frequency response piecewise reduced-order wind farm auxiliary frequency control

Echo State Network Based on Improved Knowledge Distillation for Edge Intelligence

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Chinese Journal of Electronics 2024年第1期33卷 101-111页

作者： Jian ZHOU Yuwen JIANG Lijie XU Lu zhao fu XIAO College of Computer Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks

Echo state network(ESN) as a novel artificial neural network has drawn much attention from time series prediction in edge intelligence. ESN is slightly insufficient in long-term memory, thereby impacting the prediction performance. It suffers from a higher computational overhead when deploying on edge devices. We firstly introduce the knowledge distillation into the reservoir structure optimization, and then propose the echo state network based on improved knowledge distillation(ESN-IKD) for edge intelligence to improve the prediction performance and reduce the computational overhead. The model of ESN-IKD is constructed with the classic ESN as a student network, the long and short-term memory network as a teacher network, and the ESN with double loop reservoir structure as an assistant network. The student network learns the long-term memory capability of the teacher network with the help of the assistant network. The training algorithm of ESN-IKD is proposed to correct the learning direction through the assistant network and eliminate the redundant knowledge through the iterative pruning. It can solve the problems of error learning and redundant learning in the traditional knowledge distillation process. Extensive experimental simulation shows that ESN-IKD has a good time series prediction performance in both long-term and short-term memory, and achieves a lower computational overhead.

关键词： Knowledge engineering Training Performance evaluation Computational modeling Time series analysis Artificial neural networks Reservoirs

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