检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Rtr-Cnn: Rotated Tray Region Selection and Seedling Health Information Detection by Cnn

SSRN

引用

SSRN 2024年

作者： Li, Yatao Lin, Hao Tong, Junhua Wang, Xiaoqin Jiang, Jiahang School of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou310018 China Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province Hangzhou310018 China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Wuyishan354300 China

Transplanting seedlings is the primary technique employed in fruit and vegetable production facilities. Utilizing mobile inspection transplanting machines in seedbed technology could effectively reduce manual labor. The detection of plug seedlings and the acquisition of health information are crucial for achieving automated seedling transplantation. Herein, a two-stage detection algorithm called RTR-CNN was developed to locate and identify multiple rotating trays with varying angles on seedbeds within greenhouses while differentiating the growth status of individual seedlings. This study focused on a seedling tray containing 200 cells. Corresponding matching templates were designed on the basis of the binary morphological characteristics of the seedling trays to identify trays at various angles. The recognition results were then segmented and distorted for each cell before being inputted into the backbone network for classification. Masked generative distillation was utilized to optimize the performance of the deployed network for edge computing hardware. The accuracy rate of the deployed network using the WideResNet101_2 teacher network reached 88.80%, and the two-classification accuracy rate reached 98.95%. The MobileNetV2 lightweight network was used for deployment, reaching an accuracy rate of 87.45% through distillation learning, and the two-classification accuracy rate reached 99.15% at a test speed of 159.30 fps with a batch size of 200. Compared with the training accuracy rate without distillation method, that with using distillation learning had a 0.95% increase, and the two-classification accuracy rate exhibited a 0.5% increase. © 2024, The Authors. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Surface wave imaging with stationary-phase noise sources based on ultrashort traffic-induced data: An application in karst cave detection 11

Surface wave imaging with stationary-phase noise sources bas...

引用

11th International Conference on Environmental and Engineering Geophysics, ICEEG 2024

作者： Ning, Ling Dai, Tianyu Liu, Ya Zhang, Hao Xi, Chaoqiang Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education Jiangxi Normal University Nanchang330022 China School of Mathematics and Computer Sciences Nanchang University Nanchang330031 China Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province School of Earth Sciences Zhejiang University Hangzhou310030 China GBA Branch of Aerospace Information Research Institute Chinese Academy of Sciences Guangzhou510700 China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines School of Earth and Environment Anhui University of Science and Technology Anhui Huainan232001 China

In urban environments, abundant traffic-induced noise data are analyzed through crosscorrelation to retrieve high-frequency (> 1 Hz) surface waves, providing a cost-effective technique for detecting near-surface structures. The isotropic noise source distribution is an essential prerequisite for the correct reconstruction of the Green's function. The azimuth of traffic noise sources, however, can change with human activities in highly populated urban areas, resulting in non-random distributions in time and space. Due to the uneven distribution of traffic noise sources, spurious signals are generated in the noise crosscorrelation functions and phase velocities calculated from the retrieved surface waves are overestimated, leading to incorrect S-wave velocity profiles. By analyzing the noise source distribution of each segment, we selected the stationary-phase segments to improve the retrieval of surface waves. We processed approximately one-day ultrashort continuous recordings to obtain virtual shot gathers with larger multichannel-coherency coefficients and dispersion images with more surface-wave dispersion data. S-wave velocity profiles for different arrays, including a 3D S-wave velocity model, were produced by inverting the surface-wave dispersion data to reveal the distribution of karst caves beneath the surface. The results demonstrate the effectiveness of the strategy of the stationary-phase segment selection and the great potential of traffic-induced surface waves in monitoring subsurface changes in urban areas. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Caves

来源：评论

学校读者我要写书评

暂无评论

Impact of a Compound Droplet on a Solid Surface: The Effect of the Shell on the Core

SSRN

引用

SSRN 2024年

作者： Du, Yuying Dai, Li Qian, Lijuan Zhou, Fang Ma, Yuechao College of Mechanical and Electrical Engineering China Ji liang University Hangzhou310018 China Key Laboratory of intelligent Manufacturing Quality Big Data Tracing and Application of Zhejiang Province China Ji Liang University Hangzhou310018 China

The dynamic behavior of compound droplets impacting a solid surface was studied via experiments over κ (defined as the ratio of the compound droplet shell thickness h to the diameter D0 of compound droplet) ranging from 0 to 0.34, We ranging from 25 to 325 and the viscosity of the shell ranging from 5.92mPa·s to 21.1mPa·s. The spreading diameter ratio, the maximum spreading dynamic contact angle and spreading speed of the core were investigated. Four modalities of the core of compound droplets were observed on the solid surface, including a) core rebound, b) no rebound, c) core rebound splitting, d) core splitting. The results revealed that the thickness of the shell, We, and the viscosity of the shell have a significant effect. The high viscosity oil shell is conducive to its spreading. As the thickness of the oil shell increases, its cushioning effect on the water core also increases. In addition, the boundary between four modalities of the core was identified as a result of the quantified relation between modalities and the shell thickness on the core. Two equations were also proposed for predicting the maximum spreading diameter ratio of the core, which include dimensionless parameters of the compound droplets. © 2024, The Authors. All rights reserved.

关键词： Viscosity

来源：评论

学校读者我要写书评

暂无评论

MISO hierarchical inference engine satisfying the law of importation with aggregation functions

arXiv

引用

arXiv 2021年

作者： Li, Dechao Guo, Qiannan School of Information and Engineering Zhejiang Ocean University Zhoushan316000 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

Fuzzy inference engine, as one of the most important components of fuzzy systems, can obtain some meaningful outputs from fuzzy sets on input space and fuzzy rule base using fuzzy logic inference methods. In multi-input-single-output (MISO) fuzzy systems, in order to enhance the computational efficiency of fuzzy inference engine, this paper aims mainly to investigate three MISO fuzzy hierarchial inference engines employed fuzzy implications satisfying the law of importation with aggregation functions (LIA). We firstly find some aggregation functions for well-known fuzzy implications such that they satisfy LIA. The fuzzy implication satisfying LIA with respect to a given aggregation function is then characterized. Finally, three fuzzy hierarchical inference engines in MISO fuzzy systems are constructed according to aforementioned theoretical developments. Three examples are also provided to illustrate our theoretical arguments. Copyright © 2021, The Authors. All rights reserved.

关键词： Fuzzy inference

来源：评论

学校读者我要写书评

暂无评论

Training on the Benchmark Is Not All You Need

arXiv

引用

arXiv 2024年

作者： Ni, Shiwen Kong, Xiangtao Li, Chengming Hu, Xiping Xu, Ruifeng Zhu, Jia Yang, Min Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology CAS China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University China University of Science and Technology of China China Shenzhen MSU-BIT University China Harbin Institute of Technology Shenzhen China

The success of Large Language Models (LLMs) relies heavily on the huge amount of pre-training data learned in the pre-training phase. The opacity of the pre-training process and the training data causes the results of many benchmark tests to become unreliable. If any model has been trained on a benchmark test set, it can seriously hinder the health of the field. In order to automate and efficiently test the capabilities of large language models, numerous mainstream benchmarks adopt a multiple-choice format. As the swapping of the contents of multiple-choice options does not affect the meaning of the question itself, we propose a simple and effective data leakage detection method based on this property. Specifically, we shuffle the contents of the options in the data to generate the corresponding derived data sets, and then detect data leakage based on the model’s log probability distribution over the derived data sets. If there is a maximum and outlier in the set of log probabilities, it indicates that the data is leaked. Our method is able to work under gray-box conditions without access to model training data or weights, effectively identifying data leakage from benchmark test sets in model pre-training data, including both normal scenarios and complex scenarios where options may have been shuffled intentionally or unintentionally. Through experiments based on two LLMs and benchmark designs, we demonstrate the effectiveness of our method. In addition, we evaluate the degree of data leakage of 35 mainstream open-source LLMs on four benchmark datasets and give a ranking of the leaked LLMs for each benchmark, and we find that the Qwen family of LLMs has the highest degree of data leakage. Copyright © 2024, The Authors. All rights reserved.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data

引用

BRIEFINGS IN BIOINFORMATICS 2022年第6期23卷 bbac448-bbac448页

作者： Yao, Yuhua Lv, Yaping Tong, Ling Liang, Yuebin Xi, Shuxue Ji, Binbin Zhang, Guanglu Li, Ling Tian, Geng Tang, Min Hu, Xiyue Li, Shijun Yang, Jialiang School of Mathematics and Statistics Hainan Normal University Haikou 570100 China Key Laboratory of Data Science and Intelligence Education Ministry of Education Hainan Normal University Haikou China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University Haikou China Genies Beijing Co. Ltd. Beijing 100102 China Chifeng Municipal Hospital Chifeng Inner Mongolia 024000 China Qingdao Geneis Institute of Big Data Mining and Precision Medicine Qingdao 266000 China Basic Courses Department Zhejiang Shuren University Hangzhou 310000 China School of Life Sciences Jiangsu University Zhenjiang 212013 China Dept. of Colorectal Surgery National Cancer Center/ Cancer Hospital Chinese Academy of Medical Science 17 Panjiayuan Nanli Chaoyang District Beijing China 100021

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 x 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

关键词： breast cancer recurrence and metastasis multi-modality residual network attention module

来源：评论

学校读者我要写书评

暂无评论

Adam revisited:a weighted past gradients perspective

引用

Frontiers of Computer Science 2020年第5期14卷 61-76页

作者： Hui Zhong Zaiyi Chen Chuan Qin Zai Huang Vincent W.Zheng Tong Xu Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei230027China Zhejiang Cainiao Supply Chain Management Co.Ltd Hangzhou311122China Advanced Digital Sciences Center Singapore138602Singapore

Adaptive learning rate methods have been successfully applied in many fields,especially in training deep neural *** results have shown that adaptive methods with exponential increasing weights on squared past gradients(i.e.,ADAM,RMSPROP)may fail to converge to the optimal *** many algorithms,such as AMSGRAD and ADAMNC,have been proposed to fix the non-convergence issues,achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these *** this paper,we propose a novel adaptive method weighted adaptive algorithm(WADA)to tackle the non-convergence *** AMSGRAD and ADAMNC,we consider using a milder growing weighting strategy on squared past gradient,in which weights grow *** on this idea,we propose weighted adaptive gradient method framework(WAGMF)and implement WADA algorithm on this ***,we prove that WADA can achieve a weighted data-dependent regret bound,which could be better than the original regret bound of ADAGRAD when the gradients decrease *** bound may partially explain the good performance of ADAM in ***,extensive experiments demonstrate the effectiveness of WADA and its variants in comparison with several variants of ADAM on training convex problems and deep neural networks.

关键词： adaptive learning rate methods stochastic gradient descent online learning

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare

SSRN

引用

SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Excited-state quantum phase transitions in Kerr nonlinear oscillators

引用

Physical Review A 2020年第6期102卷 063531-063531页

作者： Qing-Wei Wang Sha Wu School of Mathematics Physics and Information Science Zhejiang Ocean University Zhoushan Zhejiang 316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan Zhejiang 316022 China

Excited-state quantum phase transitions (ESQPTs) generalize the concept of quantum phase transitions to excited levels. Here we investigate how such ESQPTs can emerge in Kerr nonlinear oscillators, where a bosonic mode with a Kerr nonlinearity is subject to both coherent (one-photon) and parametric (two-photon) driving. We determine the structure and properties of the complete phase diagram by defining proper dimensionless parameters. Rich phases and phase transitions exist in the phase diagram, including phases with two or four critical energies above the ground-state level and first-order quantum phase transitions. We characterize the ESQPTs by singularities in the density of states, which show universal logarithmic divergence or discontinuity at the critical energies. Furthermore, we find that observables such as the photon number also exhibit singular behaviors at the critical energies and obey scaling laws. These results may extend our understanding of the driven Kerr oscillator and ESQPTs in finite-component systems.

关键词： Nonlinear optics Optical parametric oscillators & amplifiers Quantum phase transitions Semiclassical physics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：