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Mathematical model for predicting fungal growth and decomposition rates based on improved Logistic equations

International Journal of Agricultural and Biological Engineering 2023年第1期16卷 60-65页

作者： Jiayong Pan Wenxin Le Zi’ao Wang Jian Chen College of Engineering China Agricultural UniversityBeijing 100083China Shenzhen Key Laboratory of Intelligent Microsatellite Constellation Shenzhen 518107GuangdongChina Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 200063China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518000GuangdongChina Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment Jiangsu UniversityZhenjiang 212013China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural AffairsGuangzhou 510642China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou 310013China

As the function of the decomposition of fungi has been clearly researched in the global carbon cycle,it is obviously of value to explore the decomposition rate of fungal *** study analyzed the relationship between environmental factors and biodiversity step by *** order to explore the interaction between the fungi and the relationship between the decomposition rate of fungi with time,the model based on the Logistic model was built and the Lotka-Volterra model was employed in the condition of two kinds of fungi existing in an environment with limited *** changing trend of population number and decomposition rate of several fungi under different environmental conditions can be predicted through the *** illustrate the applicability of the model,Laetiporus conifericola and Hyphoderma setigerum were applied as *** results showed that the higher the degree of population diversity,the greater the decomposition rate,and the higher the decomposition efficiency of the *** rich species diversity is conducive to accelerating the decomposition of litter,lignocellulose,and the circulation of the entire *** on the above model and using the data from measuring the mycelial elongation rate of each isolate at 10℃,16℃,and 22℃ under standardized laboratory conditions,the growth patterns of the five fungi combinations were *** results revealed a general increase in growth rate with increasing temperature,which verifies the accuracy of the ***,it also revealed that the total decomposition rate after fungal incorporation was negatively correlated with the decomposition rate of a fungal single *** on the above model,predictions can be made for fungal growth in different environments,and suitable environments for fungal growth can be *** the future,the model can be further optimized,and lignin and cellulose decomposition factors can be added to fit the decomposition of *** application

关键词： Logistic equations fungal decomposition mathematical model

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation

Multi-scale cyclical similarity prototype refinement for few...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Yingfeng Ou Xing Yang Jian Zhang Caiqing Jian Lihui Wang Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%.

关键词： Image segmentation Ultrasonic imaging Breast tumors Semantics Prototypes Signal processing data mining Few shot learning Residual neural networks

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A Study on Application of Artificial Intelligence in College English Teaching Management and Evaluation 3

A Study on Application of Artificial Intelligence in College...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Feng, Tao Deng, Jianjie Liu, Yiwen Gao, Yanxia Peng, Ling Fu, Jinrong School of Foreign Languages Huaihua University Hunan Huaihua418000 China School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China School of Computing Science University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9798400716775

With the rapid development of artificial intelligence, AI technology has been widely applied in various industries, including education. In English teaching, AI technology provides teachers with objective and instructive feedback, offers students a more personalized learning experience, helping them improve their English language skills, etc., and brings new development opportunities and challenges to English teaching as well. However, there are still some problems that need to be solved in college English teaching in our university. For this reason, based on the application of AI technology in college English teaching, this paper mainly explores the impact and application countermeasures of AI on college English teaching from the following four aspects: individuals with differences, teaching management, classroom teaching and teaching evaluation. In addition, the profound integration of college English education and AI will foster the advancement and transformation of college English instruction, ultimately serving as a catalyst for its reform. © 2023 ACM.

关键词： Artificial intelligence

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Helicity controlled spin Hall angle in the 2D Rashba altermagnets

arXiv

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arXiv 2024年

作者： Chen, Weiwei Zeng, Longhai Zhu, W. Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Sciences China Jiliang University Hangzhou310018 China School of Science Westlake University Hangzhou310024 China

We investigate the efficiency of charge-to-spin conversion in two-dimensional Rashba altermagnets, a class of materials that merge characteristics of both ferromagnets and antiferromagnets. Utilizing quantum linear response theory, we quantify the longitudinal and spin Hall conductivities in this system and demonstrate that a substantial enhancement of the spin Hall angle is achieved below the band crossing point through the dual effects of relativistic spin-orbit interaction and nonrelativistic altermagnetic exchange interaction. Additionally, we find that skew scattering and topology-related intrinsic mechanisms are almost negligible in this system, which contrasts with conventional ferromagnetic Rashba systems. Our findings not only advance the understanding of spin dynamics in Rashba altermagnets but also pave the way for novel strategies in manipulating charge-to-spin conversion via the sophisticated control of noncollinear in-plane and collinear out-of-plane spin textures. © 2024, CC0.

关键词： Spin dynamics

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MULTIPLE HEADS ARE BETTER THAN ONE: MIXTURE OF MODALITY KNOWLEDGE EXPERTS FOR ENTITY REPRESENTATION LEARNING

arXiv

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arXiv 2024年

作者： Zhang, Yichi Chen, Zhuo Guo, Lingbing Xu, Yajing Hu, Binbin Liu, Ziqi Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC). The main challenge is to collaboratively model the structural information concealed in massive triples and the multi-modal features of the entities. Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies, yet they overlook the utilization of multi-perspective features concealed within the modalities under diverse relational contexts. To address this issue, we introduce a novel framework with Mixture of Modality Knowledge experts (MOMOK for short) to learn adaptive multi-modal entity representations for better MMKGC. We design relation-guided modality knowledge experts to acquire relation-aware modality embeddings and integrate the predictions from multi-modalities to achieve joint decisions. Additionally, we disentangle the experts by minimizing their mutual information. Experiments on four public MMKG benchmarks demonstrate the outstanding performance of MOMOK under complex scenarios. Our code and data are available at https://***/zjukg/MoMoK. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation

arXiv

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arXiv 2024年

Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples and multi-modal information of the entities to overcome the inherent incompleteness. Existing MMKGC methods usually extract multi-modal features with pre-trained models, resulting in coarse handling of multi-modal entity information, overlooking the nuanced, fine-grained semantic details and their complex interactions. To tackle this shortfall, we introduce a novel framework MYGO to tokenize, fuse, and augment the fine-grained multi-modal representations of entities and enhance the MMKGC performance. Motivated by the tokenization technology, MYGO tokenizes multi-modal entity information as fine-grained discrete tokens and learns entity representations with a cross-modal entity encoder. To further augment the multi-modal representations, MYGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. Experiments on standard MMKGC benchmarks reveal that our method surpasses 19 of the latest models, underlining its superior performance. Code and data can be found in https://***/zjukg/MyGO. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Caiqing Jian Yongbin Qin Lihui Wang Chen Ye Xinyu Cheng GState Key Laboratory of Public Big Data College of Computer Science and Technology Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma.

关键词： Instance segmentation Measurement Visualization Adhesives Shape Semantic segmentation Glands Signal processing algorithms Signal processing Market research

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Study on Aerodynamic Breakup of Non-Newtonian Liquid Droplets and the Distribution Characteristics of Their Sub Droplets

SSRN

引用

SSRN 2024年

作者： Qian, Lijuan Cheng, Lijun Sun, Chengbin Lv, Li College of Mechanical and Electrical Engineering China Jiliang University Hangzhou310018 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China jiliang University Hangzhou310018 China

In industrial applications, the secondary breakup of droplets and the size distribution of their sub droplets are crucial for the atomization performance indicators. In this work, a high-speed digital camera is utilized to experimentally and theoretically study the secondary breakup process of xanthan gum (XG) droplets and the size distribution changes of resulting sub droplets in continuous airflow by changing the Weber number and effective Ohnesorge number. In addition, a detailed study is conducted on the liquid ring with the highest liquid content. The results show that at low Weber numbers, the number of nodes generated at the XG droplet ring is the same as that of the water droplet, following "the combined R-T/aerodynamic drag" mechanism. However, the final diameter of the nodes differs significantly from that of the water droplet. The particle size of XG liquid ring nodes is not only affected by Weber number, but also decreases with the increase of effective Ohnesorge number. The particle size of the remaining broken sub droplets in the liquid ring decreases with the increase of effective Ohnesorge number. Finally, the spatial range of droplet breakup under different parameters is described by using the liquid ring breakup angle, and it is found that the breakup angle of the liquid ring is mainly related to the breakup mode. © 2024, The Authors. All rights reserved.

关键词： Non Newtonian flow

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Visualization Study on the Dynamic Behavior of Shear Thinning Droplets Impacting Superhydrophobic Spheres

SSRN

引用

SSRN 2024年

作者： Qian, Lijuan Xu, Liujun Sun, Chengbin Lv, Li College of Mechanical and Electrical Engineering China Jiliang University Hangzhou310018 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China jiliang University Hangzhou310018 China

The surface characteristics and droplet characteristics significantly affect the dynamic behavior of droplet impact on solid surfaces. A visualization platform is utilized in this work to experimentally study the dynamic behavior of shear thinning fluid droplets impacting superhydrophobic spheres. By calculating the diffusion arc angle and diffusion coefficient corresponding to the two-dimensional wetting arc in the maximum diffusion state, the effects of different concentrations, We numbers, particle size ratios on droplets impact are studied. The results show that on the superhydrophobic sphere, as the shear thinning droplet concentration increases, the diffusion diameter increases, and the contact time decreases. Under the same curvature, the contact time between the droplet and the sphere decreases with the increase of We number. After the We number reaches a specific value, the contact time remains unchanged with the increase of We number. Then under different curvature conditions, the contact time increases with the increase of curvature. When the diameter ratio of the sphere to the droplet is D*≥6, the maximum diffusion coefficient remains almost unchanged. However, when D*＜6, the maximum diffusion dimensionless diameter significantly increases as the particle size ratio decreases. Importantly, a theoretical model considering gravity effect is established to predict the maximum dimensionless diameter of shear thinning droplets on superhydrophobic spheres on the basis of energy conservation. The deviation between the maximum dimensionless diameter obtained by this theoretical model and the experimental results is within ± 10%. © 2024, The Authors. All rights reserved.

关键词： Shear thinning

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Some Thoughts on the Classification Standard of intelligent Autonomous Level of Measurement and Control System

Some Thoughts on the Classification Standard of Intelligent ...

引用

International Conference on big data and Information Analytics (bigDIA)

作者： Guangfu Yuan Liu Yang Haoran Han Yong Li Cong Shao Siyu Zhan Gansu Photoelectric Measurement Technology Research Laboratory Gansu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China

Intelligence is an important trend in the development of measurement and control systems. However, there is still a lack of unified understanding of the intelligent autonomous classification of the measurement and control system. The establishment of intelligent evaluation standards in this field has become an urgent research topic. This paper first gives an overview of the aviation measurement and control system, and then takes the classification standards of autonomy in the fields of automobile driving automation in China and the United states, the autonomy and autonomy level of typical ships in China, and the automation of civil unmanned aircraft system in China as a reference. A 5-level intelligent grading standard based on task adaptability, environmental complexity and human-computer interaction is proposed. The classification standard is practical and easy to understand. It can provide reference for the establishment of intelligent standards for measurement and control systems, improve the application speed of intelligent autonomous technology in the field of measurement and control, and broaden the application prospect of intelligent measurement and control systems.

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