检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Lu, Yang Yang, Jingyi Zhang, Yong Wu, Lin State Key Laboratory for Food Nutrition and Safety College of Food Science and Engineering Tianjin University of Science and Technology Tianjin300457 China State Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

To determine allergen β-lactoglobulin (β-LG) in foods, a method by a coffee-ring effect (CRE)-based paper sensor chip combined with a smartphone has been developed. The strategy was based on the principle of fluorescence quenching by the quantum dot-aptamer-graphene oxide (QD-Apt-GO) and where the degree of quenching was inversely proportional to the concentration of β-LG. First, the prepared β-LG solution was added to the QD-aptamer fluorescent probe solution followed by an appropriate amount of GO. After the reaction, a certain amount of the mixture was dispensed onto the detection zone of the sensor chip, resulting in the spread of the analyte solution by evaporation-driven flow. The QD-aptamer and the QD-Apt-β-LG in the mixed solution formed a visible fluorescent coffee-ring. For signal quantitation, an image of the coffee-ring was taken by a smartphone and imported into self-programmed software for analysis. The quantitative results for β-LG were acquired rapidly based on the magnitude of the generated fluorescent coffee-ring. The limit of detection (LOD) for β-LG was 0.048 mg L -1 and the linear range for the optimized assay was 0.39–1000 mg L -1 . The method was validated for analysis of real food samples and the results were consistent with those of a commercial ELISA assay kit. The smartphone-based assay provides a simple, economic, compact, and visual detection platform for the determination of β-LG in foods. © 2022, The Authors. All rights reserved.

关键词： Graphene

来源：评论

学校读者我要写书评

暂无评论

FC-NT: A crime prediction algorithm incorporating formal context and neural networks 3

FC-NT: A crime prediction algorithm incorporating formal con...

引用

3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Zao, Feifei Lv, Lingling Ding, Feng Jin, Shixian Guo, Xiaoding Henan Province Engineering Research Center of Spatial Information Processing Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Zhengzhou450046 China School of Electrical Engineering North China University of Water Resources and Electric Power Zhengzhou450046 China School of Internet of Things Engineering Jiangnan University Wuxi214122 China Henan Provincial Educational Resources Guarantee Center Zhengzhou450003 China

ISBN: (纸本)9781510668522

The task of crime prediction is to predict the charges of a case based on the given description of the case, which has become a research hot spot. Most of the existing methods use neural networks and machine learning algorithms to predict. The prediction results have high accuracy, but the prediction results are often not well interpretable. In addition, some early traditional methods can explain the prediction results, but their accuracy is often very low. Formal concept analysis is an effective data extraction tool. Therefore, this paper combines formal concept analysis with neural network to improve the accuracy of the algorithm. The experimental results show that our method has higher prediction results and can better explain the prediction results than the original neural network model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Accelerating Intelligent Microfluidic Image Processing with Transfer Deep Learning: A Microchannel Droplet/Bubble Breakup Case Study

SSRN

引用

SSRN 2023年

作者： Zhang, Shuyuan Li, Haoran Wang, Kai Qiu, Tong Department of Chemical Engineering Tsinghua University Beijing100084 China Beijing Key Laboratory of Industrial Big Data Systems and Applications Tsinghua University Beijing100084 China State Key Laboratory of Chemical Engineering Department of Chemical Engineering Tsinghua University Beijing100084 China

Deep learning has recently been leveraged in the microfluidic research for intelligent image processing. However, it usually requires massive human efforts to label sufficient images for obtaining a precise deep learning model. We herein propose a transfer deep learning method for the rapid deployment of Mask R-CNN, which is capable of reutilizing models trained in previous microfluidic domains to accelerate the analysis of droplets and bubbles in the images of the target microfluidic domain. The transfer deep learning is validated in the microfluidic research with a constrictive microchannel, which is commonly used to break large droplets or bubbles to enhance emulsification. With only 40 labeled images and about 10 min training, the transfer deep learning achieves precise recognition results for both droplets and bubbles in the microfluidic experiments. Applying the trained Mask R-CNN to unlabeled images, the breakup of droplets is found to be highly dependent on the low flow rate, but the bubble breakup is much easier to happen in the same microfluidic equipment. Critical conditions of droplet breakup and size laws of droplet and bubble are precisely determined based on the recognition results of the trained Mask R-CNN. © 2023, The Authors. All rights reserved.

关键词： Microfluidics

来源：评论

学校读者我要写书评

暂无评论

Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Isothiocyanate Extracted from Moringa oleifera Improves Insulin Resistance of C2C12 Myotube Cells

引用

Journal of Chinese Institute of Food Science and Technology 2023年第1期23卷 32-40页

作者： Mao, Jiaying Bai, Yuying Peng, Linjie Xie, Jing Tian, Yang College of Food Science and Technology Yunnan Agricultural University Kunming650201 China Yunnan Key Laboratory of Biological Big Data Kunming650201 China Development and Utilization of Food and Drug Homology Resources Engineering Center Ministry of Education Kunming650201 China

Objective: A C2C12 myotube cell insulin resistance (C2C12-IR) model was established to explore the effect of isothiocyanate extracted from Moringa oleifera (MIC-1) on C2C12 myotube cell insulin resistance. Methods: Palmitic acid (PA) was used to induce C2C12 myotube cells to establish a stable C2C12-IR model, and the cells were treated with different concentrations of MIC-1 (0, 2, 4, 8, 16 μmol/L) for 16 h. The cell viability of C2C12-IR cells was detected by MTT assay, and the effect of MIC-1 on glucose metabolism of C2C12-IR cells was observed. Cellular oxidative damage [nitric oxide (NO), malondialdehyde (MDA) and glutathione peroxidase (GSH)] were tested. The expression levels of related proteins in the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway were detected by using Western blot assay. Results: The optimal modeling conditions for the C2C12-IR model were determined, that is, C2C12 myotube cells were treated with 0.5 mmol/L PA for 16 h. Compared with the C2C12-IR group, MIC-1 significantly increased the glucose consumption and glycogen content of C2C12-IR cells in a dose-dependent manner. MIC-1 significantly decreased the level of MDA and NO, and significantly increased the level of GSH in C2C12-IR cells. In addition, MIC-1 significantly increased the expression levels of phosphoinositide-dependent protein kinase-1 (PDK1), p-AKT, and glucose transporter 4 (GLUT4). Conclusion: MIC-1 improves insulin resistance by inhibiting oxidative stress. © 2023 Chinese Institute of Food Science and Technology. All rights reserved.

关键词： Nitric oxide

来源：评论

学校读者我要写书评

暂无评论

Privacy Law Enforcement Under Centralized Governance: A Qualitative Analysis of Four Years’ Special Privacy Rectification Campaigns

arXiv

引用

arXiv 2025年

作者： Jing, Tao Li, Yao Ye, Jingzhou Wang, Jie Wang, Xueqiang School of Cyber Science and Engineering Huazhong University of Science and Technology China JinYinHu Laboratory China University of Central Florida United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

In recent years, major privacy laws like the GDPR have brought about positive changes. However, challenges remain in enforcing the laws, particularly due to under-resourced regulators facing a large number of potential privacy-violating software applications (apps) and the high costs of investigating them. Since 2019, China has launched a series of privacy enforcement campaigns known as Special Privacy Rectification Campaigns (SPRCs) to address widespread privacy violations in its mobile application (app) ecosystem. Unlike the enforcement of the GDPR, SPRCs are characterized by large-scale privacy reviews and strict sanctions, under the strong control of central authorities. In SPRCs, central government authorities issue administrative orders to mobilize various resources for market-wide privacy reviews of mobile apps. They enforce strict sanctions by requiring privacy-violating apps to rectify issues within a short timeframe or face removal from app stores. While there are a few reports on SPRCs, the effectiveness and potential problems of this campaign-style privacy enforcement approach remain unclear to the community. In this study, we conducted 18 semi-structured interviews with app-related engineers involved in SPRCs to better understand the campaign-style privacy enforcement. Based on the interviews, we reported our findings on a variety of aspects of SPRCs, such as the processes that app engineers regularly follow to achieve privacy compliance in SPRCs, the challenges they encounter, the solutions they adopt to address these challenges, and the impacts of SPRCs, etc. We found that app engineers face a series of challenges in achieving privacy compliance in their apps. For example, they receive inconsistent app privacy review reports from multiple app stores and have difficulties confirming the issues flagged by these reports;they also lack institutional support for studying privacy laws, self-validating privacy compliance of their apps, communicating effectiv

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

LSU-NET: LIGHTWEIGHT AUTOMATIC ORGANS SEGMENTATION NETWORK FOR MEDICAL IMAGES

arXiv

引用

arXiv 2025年

作者： Ding, Yujie Teng, Shenghua Li, Zuoyong Chen, Xiao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou350121 China

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

引用

IEEE Transactions on Evolutionary Computation 2025年

作者： Xue, Xingsi Mei, Yi Zhao, Baozhong Zhang, Mengjie Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand Taiyuan University of Technology School of Electrical and Power Engineering Shanxi Taiyuan030024 China

Ontology is a kernel technique of the semantic web, which defines concepts, properties, and their relationships to establish a shared understanding of domain knowledge. Ontology matching identifies semantically similar entities across different ontologies, which uses similarity features to measure their similarity from different perspectives. However, due to the complexity of the entity heterogeneity, no single similarity feature is universally effective. In recent years, genetic algorithms have proven effective in constructing similarity features for ontology matching, but their potential is limited by the reliance on default classification strategies, empirical determination of the number of high-level features, the requirement for manually selecting, combining these features, and tuning the associated combination parameters. To overcome these drawbacks, we propose a multi-layer hybrid genetic programming approach to automatically construct high-level similarity features. This approach includes three novel components. First, a new multi-layer individual representation is designed, which faciliates the algorithm to adaptively explore the search space of constructing high-level similarity features. Second, to enhance the search effectiveness, a new initialization method and a mutation operator are developed, which use a weight-based strategy to adaptively select and construct a more diverse set of similarity features. Third, a compact genetic algorithm-based optimizer is designed to refine the tree structures of elite individuals. The experimental results on the ontology alignment evaluation initiative's benchmark show that our algorithm can generate high-quality ontology matching results across various matching tasks, significantly outperforming the state-of-the-art ontology matching methods. © 2025 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

A Method of Extracting Discipline Inspection Cases Based on Deep Learning 3

A Method of Extracting Discipline Inspection Cases Based on ...

引用

3rd International Conference on Education, knowledge and Information Management, ICEKIM 2022

作者： Liu, XiaoLing Chen, JunJie Gao, Jing Fan, Hao Dong, JianMin Inner Mongolia Agricultural University College of Computer and Information Engineering China Inner Mongolia Autonomous Reg. Key Lab. of Large Data Res. and Applic. of Agric. and Anim. Husbandry China Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Hohhot China

ISBN: (纸本)9781665416665

Event extraction is one of the key tasks in information extraction tasks, and it has been widely used in various fields in recent years. In the field of disciplinary inspection and supervision, disciplinary inspection case text has the characteristics of multiple types, multiple professional terms, and strong correlation between event types and event arguments. In the face of huge data, relying on manual analysis by manpower has seriously affected the efficiency of disciplinary inspection. However, there is no corpus in the field of discipline inspection currently available. This article uses BIO annotation to construct a corpus of discipline inspection to lay the foundation for subsequent work. Proposed BERT-bigRU-CRF event joint extraction model. Use the BERT model to train the discipline inspection corpus, and Combine bigRU network and CRF network to realize event type recognition and argument extraction. The experimental results show that the model can effectively extract event information in the discipline inspection field. At the same time, in order to better facilitate the work of disciplinary inspection personnel, a disciplinary inspection and supervision event extraction system is constructed to systematically extract all event information contained in the case. © 2022 IEEE.

关键词： Inspection

来源：评论

学校读者我要写书评

暂无评论

Secondary Reheat Steam Temperature Prediction Based on Hybrid Deep Learning 11

Secondary Reheat Steam Temperature Prediction Based on Hybri...

引用

11th IEEE data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Gao, YuQing Xue, Yali Sun, Li School of Energy and Environment Southeast University Jiangsu Nanjing210096 China State Key Lab of Power Systems Tsinghua University Department of Energy and Power Engineering Beijing100084 China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua University Beijing100084 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

An accurate prediction model of the secondary reheat steam temperature in the ultra-supercritical double-reheat coal-fired power plant is difficult to build due to the characteristics of strong nonlinearity, multi-variable coupling, large inertia, and various disturbances. This paper proposed a joint data-driven prediction method based on GRU-Pooling-Attention to improve the prediction accuracy and training speed. Firstly, CatBoost algorithm is taken to calculate the importance of features, and the input feature set is selected combined with expert knowledge. Then the GRU is adopted to learn the time series relationship between data, the pooling method is embedded to reduce the number of parameters and to highlight key values, and the attention mechanism is introduced to assign different weights to the output of each time step of GRU to reflect the large delay and large inertia of the system. The hybrid deep learning method is applied to the prediction of the secondary reheat steam temperature based on historical data, and the results show that it can obtain accurate and fast multi-step prediction. The trained model can be used to explore the dynamic characteristics of the processes. © 2022 IEEE.

关键词： Forecasting

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：