Effect of phosphate salts on the gluten network structure and quality of wheat noodles

2021 ◽  
pp. 129895
Author(s):  
Juan Sun ◽  
Min Chen ◽  
Xiaoxiao Hou ◽  
Tingting Li ◽  
Haifeng Qian ◽  
...  
Keyword(s):  
Network Structure ◽  
Phosphate Salts

scholarly journals Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1906
Author(s):  
Jia-Zheng Jian ◽  
Tzong-Rong Ger ◽  
Han-Hua Lai ◽  
Chi-Ming Ku ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Network Structure ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Multi Scale ◽  
Imbalance Problem ◽  
Average Accuracy ◽  
Significant Difference ◽  
Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

scholarly journals Pediatric health-related quality of life and school social capital through network perspectives

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242670
Author(s):  
Tomoya Hirota ◽  
Michio Takahashi ◽  
Masaki Adachi ◽  
Kazuhiko Nakamura
Keyword(s):  
Quality Of Life ◽  
Social Capital ◽  
Network Structure ◽  
Population Sample ◽  
Health Related Quality ◽  
Pediatric Health ◽  
Related Quality ◽  
Health Related ◽  
School Social Capital

Background Despite their importance in population health among children and adolescents, our understanding of how individual items mutually interact within and between pediatric health-related quality of life (HRQOL) and school social capital is limited. Methods We employed network analysis in a general population sample of 7759 children aged 9–15 years to explore the network structure of relations among pediatric HRQOL and school social capital items measured using validated scales. Furthermore, network centrality was examined to identify central items that had stronger and more direct connections with other items in the network than others. Network structure and overall strength of connectivity among items were compared between groups (by sex and age). Results Our analysis revealed that the item related to school/academic functioning and the item related to shared enjoyment among students had the highest strength centrality in the network of HRQOL and school social capital, respectively, underpinning their critical roles in pediatric HRQOL and school social capital. Additionally, the edge connecting “I trust my friends at school” and “trouble getting along with peers” had the strongest negative edge weight among ones connecting school social capital and pediatric HRQOL constructs. Network comparison test revealed stronger overall network connectivity in middle schoolers compared to elementary schoolers but no differences between male and female students. Conclusion The network approach elucidated the complex relationship of mutually influencing items within and between pediatric HRQOL and school social capital. Addressing central items may promote children’s perceived health and school social capital.

Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set

2017 ◽  
Vol 13 (4) ◽  
pp. 38-55 ◽  
Author(s):  
Han Ke
Keyword(s):  
Breast Cancer ◽  
Extreme Learning Machine ◽  
Rough Set ◽  
Network Structure ◽  
High Efficiency ◽  
Breast Cancer Dataset ◽  
Intelligent Technique ◽  
Tolerance Rough Set ◽  
Learning Machine

In this paper, we present a new extreme learning machine network structure on the basis of tolerance rough set. The purpose of this paper is to realize the high-efficiency and multi-dimensional ELM network structure. Various published algorithms have been applied to breast cancer datasets, but rough set is a fairly new intelligent technique that applies to predict breast cancer recurrence. We analyze Ljubljana Breast Cancer Dataset, firstly, obtain lower and upper approximations and calculate the accuracy and quality of the classification. The high values of the quality of classification and accuracy prove that the attributes selected can well approximate the classification. Rough sets approach is established to solve the prolem of tolerance.

Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set

2020 ◽  
pp. 263-282
Author(s):  
Han Ke
Keyword(s):  
Breast Cancer ◽  
Extreme Learning Machine ◽  
Rough Set ◽  
Network Structure ◽  
High Efficiency ◽  
Breast Cancer Dataset ◽  
Tolerance Rough Set ◽  
Learning Machine ◽  
Lower And Upper Approximations

In this paper, we present a new extreme learning machine network structure on the basis of tolerance rough set. The purpose of this paper is to realize the high-efficiency and multi-dimensional ELM network structure. Various published algorithms have been applied to breast cancer datasets, but rough set is a fairly new intelligent technique that applies to predict breast cancer recurrence. We analyze Ljubljana Breast Cancer Dataset, firstly, obtain lower and upper approximations and calculate the accuracy and quality of the classification. The high values of the quality of classification and accuracy prove that the attributes selected can well approximate the classification. Rough sets approach is established to solve the prolem of tolerance.

Study on the Calculation of Power Grid Asset Utilization Index

2013 ◽  
Vol 860-863 ◽  
pp. 2122-2126
Author(s):  
Guang Yu Lei ◽  
Yuan Zeng ◽  
Cong Zhao
Keyword(s):  
Network Structure ◽  
Power Grid ◽  
Distribution Lines

As the using quality of power grid asset becomes the focus of concern, the reasonable power grid asset utilization index is needed. The paper discusses the researching level of domestic and foreign power grid assets utilization and gives a brief analysis of the typical factors which have great influential on the utilization of power grid asset. According to safety and network structure requirements during the operation, the traditional power grid asset utilization indices were improved. Finally, the paper selects the typical 10kV distribution lines to conduct the calculation of utilization, and gives some analysis and advice according to the results.

scholarly journals The Impact of Salt on the Quality of Fresh Wheat Noodle

2017 ◽  
Vol 21 (2) ◽  
pp. 53-61 ◽  
Author(s):  
Yunfeng Hu ◽  
Jinjin Wei ◽  
Yuanyuan Chen
Keyword(s):  
Tensile Properties ◽  
Network Structure ◽  
Tensile Force ◽  
Salt Content ◽  
Cooking Time ◽  
Rheological Parameters ◽  
Internal Network ◽  
Stability Time ◽  
The Impact

AbstractThe purpose is to analyze the concentration-response relationship of salt on the rheological properties, cooking characteristic and microstructure of fresh noodle and investigate the influence rules of salt on rheological characteristics, cooking characteristics and microstructure of fresh noodle. The change rules of rheological parameters, cooking characteristics and microstructure were analyzed using the refined wheat flour as the experimental material, adding different proportion of salt (0 up to 5% weight on flour basis), making fresh noodles. Results showed that the dough formation time, stability time, the maximum tensile force and tensile range increased gradually, weakening degree and the best cooking time decreased gradually, in addition, the internal network structure was fine-meshed with the increase of salt content. But the tensile distance began to decline, the network structure became loose and the hole enlarged when adding amount surpassed 3%. Taken together, adding 3% of the salt can improve the quality of fresh noodle. Research conclusions: the right amount of salt can improve the opaque quality index and tensile properties, reduce water absorption and optimum cooking time, enhance the internal network structure; but excessive salt will reduce the tensile properties of noodles and cooking characteristics, black or even destroy the production of internal network structure.

The Performance Analysis of Different Topology Structures of High-Speed Train Based on Industrial Ethernet

2014 ◽  
Vol 687-691 ◽  
pp. 2379-2382
Author(s):  
Chong Hui Ren ◽  
De Qiang He
Keyword(s):  
Network Topology ◽  
Network Structure ◽  
High Speed ◽  
Reference Value ◽  
Industrial Ethernet ◽  
High Speed Train ◽  
Network Topologies ◽  
Application Data ◽  
Ladder Network

The industrial Ethernet technology has been gradually applied to the high-speed train. However, different network topologies can have big impact on the quality of communication. The real-time and reliability performances of the high-speed train to which three kinds of network topology structures of the linear, the ring and the ladder are applied based on industrial Ethernet are analyzed in theory according to IEC61375(2009). Four kinds of application data are built to modify the data transmission in this paper. The three structures are simulated with the OPNET modeler. Afterwards, the network performances are compared in the simulation. At last, a conclusion that the performance of the ladder network structure is superior to that of the linear and the ring network structure is drew. The results of the study could provide the reference value of the construction and majorization of the network topology structure based on the industrial Ethernet.

scholarly journals The Influence of Network Structural Preference on Link Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yongcheng Wang ◽  
Yu Wang ◽  
Xinye Lin ◽  
Wei Wang
Keyword(s):  
Network Structure ◽  
Private Information ◽  
Link Prediction ◽  
Low Cost ◽  
Small Degree ◽  
Privacy And Security ◽  
Protein Protein Interaction ◽  
Prediction Algorithms ◽  
The Social

Link prediction in complex networks predicts the possibility of link generation between two nodes that have not been linked yet in the network, based on known network structure and attributes. It can be applied in various fields, such as friend recommendation in social networks and prediction of protein-protein interaction in biology. However, in the social network, link prediction may raise concerns about privacy and security, because, through link prediction algorithms, criminals can predict the friends of an account user and may even further discover private information such as the address and bank accounts. Therefore, it is urgent to develop a strategy to prevent being identified by link prediction algorithms and protect privacy, utilizing perturbation on network structure at a low cost, including changing and adding edges. This article mainly focuses on the influence of network structural preference perturbation through deletion on link prediction. According to a large number of experiments on the various real networks, edges between large-small degree nodes and medium-medium degree nodes have the most significant impact on the quality of link prediction.

scholarly journals Capturing the interplay of dynamics and networks through parameterizations of Laplacian operators

2016 ◽  
Vol 2 ◽  
pp. e57 ◽  
Author(s):  
Xiaoran Yan ◽  
Shang-hua Teng ◽  
Kristina Lerman ◽  
Rumi Ghosh
Keyword(s):  
Network Topology ◽  
Network Structure ◽  
Graph Partitioning ◽  
Laplacian Matrix ◽  
Dynamical Process ◽  
Dynamical Processes ◽  
Quality Function ◽  
The Impact ◽  
Laplacian Operators

We study the interplay between a dynamical process and the structure of the network on which it unfolds using the parameterized Laplacian framework. This framework allows for defining and characterizing an ensemble of dynamical processes on a network beyond what the traditional Laplacian is capable of modeling. This, in turn, allows for studying the impact of the interaction between dynamics and network topology on the quality-measure of network clusters and centrality, in order to effectively identify important vertices and communities in the network. Specifically, for each dynamical process in this framework, we define a centrality measure that captures a vertex’s participation in the dynamical process on a given network and also define a function that measures the quality of every subset of vertices as a potential cluster (or community) with respect to this process. We show that the subset-quality function generalizes the traditional conductance measure for graph partitioning. We partially justify our choice of the quality function by showing that the classic Cheeger’s inequality, which relates the conductance of the best cluster in a network with a spectral quantity of its Laplacian matrix, can be extended to the parameterized Laplacian. The parameterized Laplacian framework brings under the same umbrella a surprising variety of dynamical processes and allows us to systematically compare the different perspectives they create on network structure.

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