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Article: Effect of Milling Processing Parameters on the Surface Roughness and Tool Cutting Forces of T2 Pure Copper.

Lai, Fuqiang / Hu, Anqiong / Mao, Kun / Wu, Zhangbin / Lin, Youxi

2023 Volume 14, Issue 1

Abstract: In this paper, the responses of machined surface roughness and milling tool cutting forces under the different milling processing parameters (cutting ... ...

Abstract	In this paper, the responses of machined surface roughness and milling tool cutting forces under the different milling processing parameters (cutting speed
Language	English
Publishing date	2023-01-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2620864-7
ISSN	2072-666X
ISSN	2072-666X
DOI	10.3390/mi14010224
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Anti-Disturbance of Scale-Free Spiking Neural Network against Impulse Noise.

Guo, Lei / Guo, Minxin / Wu, Youxi / Xu, Guizhi

Brain sciences

2023 Volume 13, Issue 5

Abstract: The bio-brain presents robustness function to external stimulus through its self-adaptive regulation and neural information processing. Drawing from the advantages of the bio-brain to investigate the robustness function of a spiking neural network (SNN) ... ...

Abstract	The bio-brain presents robustness function to external stimulus through its self-adaptive regulation and neural information processing. Drawing from the advantages of the bio-brain to investigate the robustness function of a spiking neural network (SNN) is conducive to the advance of brain-like intelligence. However, the current brain-like model is insufficient in biological rationality. In addition, its evaluation method for anti-disturbance performance is inadequate. To explore the self-adaptive regulation performance of a brain-like model with more biological rationality under external noise, a scale-free spiking neural network(SFSNN) is constructed in this study. Then, the anti-disturbance ability of the SFSNN against impulse noise is investigated, and the anti-disturbance mechanism is further discussed. Our simulation results indicate that: (i) our SFSNN has anti-disturbance ability against impulse noise, and the high-clustering SFSNN outperforms the low-clustering SFSNN in terms of anti-disturbance performance. (ii) The neural information processing in the SFSNN under external noise is clarified, which is a dynamic chain effect of the neuron firing, the synaptic weight, and the topological characteristic. (iii) Our discussion hints that an intrinsic factor of the anti-disturbance ability is the synaptic plasticity, and the network topology is a factor that affects the anti-disturbance ability at the level of performance.
Language	English
Publishing date	2023-05-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651993-8
ISSN	2076-3425
ISSN	2076-3425
DOI	10.3390/brainsci13050837
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Article: Complex spiking neural networks with synaptic time-delay based on anti-interference function.

Guo, Lei / Zhang, Sijia / Wu, Youxi / Xu, Guizhi

Cognitive neurodynamics

2022 Volume 16, Issue 6, Page(s) 1485–1503

Abstract: The research on a brain-like model with bio-interpretability is conductive to promoting its information processing ability in the field of artificial intelligence. Biological results show that the synaptic time-delay can improve the information ... ...

Abstract	The research on a brain-like model with bio-interpretability is conductive to promoting its information processing ability in the field of artificial intelligence. Biological results show that the synaptic time-delay can improve the information processing abilities of the nervous system, which are an important factor related to the formation of brain cognitive functions. However, the synaptic plasticity with time-delay of a brain-like model still lacks bio-interpretability. In this study, combining excitatory and inhibitory synapses, we construct the complex spiking neural networks (CSNNs) with synaptic time-delay that more conforms biological characteristics, in which the topology has scale-free property and small-world property, and the nodes are represented by an Izhikevich neuron model. Then, the information processing abilities of CSNNs with different types of synaptic time-delay are comparatively evaluated based on the anti-interference function, and the mechanism of this function is discussed. Using two indicators of the anti-interference function and three kinds of noise, our simulation results consistently verify that: (i) From the perspective of anti-interference function, an CSNN with synaptic random time-delay outperforms an CSNN with synaptic fixed time-delay, which in turn outperforms an CSNN with synaptic none time-delay. The results imply that brain-like networks with more bio-interpretable synaptic time-delay have stronger information processing abilities. (ii) The synaptic plasticity is the intrinsic factor of the anti-interference function of CSNNs with different types of synaptic time-delay. (iii) The synaptic random time-delay makes an CSNN present better topological characteristics, which can improve the information processing ability of a brain-like network. It implies that synaptic time-delay is a factor that affects the anti-interference function at the level of performance.
Language	English
Publishing date	2022-04-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2276890-7
ISSN	1871-4099 ; 1871-4080
ISSN (online)	1871-4099
ISSN	1871-4080
DOI	10.1007/s11571-022-09803-4
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Article ; Online: OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series.

Wu, Youxi / Hu, Qian / Li, Yan / Guo, Lei / Zhu, Xingquan / Wu, Xindong

IEEE transactions on cybernetics

2023 Volume 53, Issue 5, Page(s) 3288–3300

Abstract: Traditional sequential pattern mining methods were designed for symbolic sequence. As a collection of measurements in chronological order, a time series needs to be discretized into symbolic sequences, and then users can apply sequential pattern mining ... ...

Abstract	Traditional sequential pattern mining methods were designed for symbolic sequence. As a collection of measurements in chronological order, a time series needs to be discretized into symbolic sequences, and then users can apply sequential pattern mining methods to discover interesting patterns in time series. The discretization will not only cause the loss of some important information, which partially destroys the continuity of time series, but also ignore the order relations between time-series values. Inspired by order-preserving matching, this article explores a new method called order-preserving sequential pattern (OPP) mining, which does not need to discretize time series into symbolic sequences and represents patterns based on the order relations of time series. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath time series. We propose an OPP-Miner algorithm to mine frequent patterns in time series with the same relative order. OPP-Miner employs the filtration and verification strategies to calculate the support and uses the pattern fusion strategy to generate candidate patterns. To compress the result set, we also study to find the maximal OPPs. Experimental results validate that OPP-Miner is not only efficient but can also discover similar subsequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/OPP-Miner.
Language	English
Publishing date	2023-04-21
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2022.3169327
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Top-k Self-Adaptive Contrast Sequential Pattern Mining.

Wu, Youxi / Wang, Yuehua / Li, Yan / Zhu, Xingquan / Wu, Xindong

IEEE transactions on cybernetics

2022 Volume 52, Issue 11, Page(s) 11819–11833

Abstract: For sequence classification, an important issue is to find discriminative features, where sequential pattern mining (SPM) is often used to find frequent patterns from sequences as features. To improve classification accuracy and pattern interpretability, ...

Abstract	For sequence classification, an important issue is to find discriminative features, where sequential pattern mining (SPM) is often used to find frequent patterns from sequences as features. To improve classification accuracy and pattern interpretability, contrast pattern mining emerges to discover patterns with high-contrast rates between different categories. To date, existing contrast SPM methods face many challenges, including excessive parameter selection and inefficient occurrences counting. To tackle these issues, this article proposes a top- k self-adaptive contrast SPM, which adaptively adjusts the gap constraints to find top- k self-adaptive contrast patterns (SCPs) from positive and negative sequences. One of the key tasks of the mining problem is to calculate the support (the number of occurrences) of a pattern in each sequence. To support efficient counting, we store all occurrences of a pattern in a special array in a Nettree, an extended tree structure with multiple roots and multiple parents. We employ the array to calculate the occurrences of all its superpatterns with one-way scanning to avoid redundant calculation. Meanwhile, because the contrast SPM problem does not satisfy the Apriori property, we propose Zero and Less strategies to prune candidate patterns and a Contrast-first mining strategy to select patterns with the highest contrast rate as the prefix subpattern and calculate the contrast rate of all its superpatterns. Experiments validate the efficiency of the proposed algorithm and show that contrast patterns significantly outperform frequent patterns for sequence classification. The algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/SCP-Miner.
MeSH term(s)	Algorithms ; Data Mining ; Pattern Recognition, Automated
Language	English
Publishing date	2022-10-17
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2021.3082114
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Unique Combination of Mn

Cen, Yuwei / Chen, Shujie / Wei, Shuyu / Wu, Shuangshuang / Tao, Mingyang / Fu, Youxi / Wang, Yuncheng / Chen, Jing / Ma, Yixuan / Liu, Hongyan / Song, Baifen / Ma, Jinzhu / Wang, Beiyan / Cui, Yudong

The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale

2024 Volume 2024, Page(s) 7502110

Abstract: Introduction: The development of combinatorial adjuvants is a promising strategy to boost vaccination efficiency. Accumulating evidence indicates that manganese exerts strong immunocompetence and will become an enormous potential adjuvant. Here, we ... ...

Abstract	Introduction: The development of combinatorial adjuvants is a promising strategy to boost vaccination efficiency. Accumulating evidence indicates that manganese exerts strong immunocompetence and will become an enormous potential adjuvant. Here, we described a novel combination of Mn Results: IsdB3 proteins plus Mn Conclusion: These data showed that the combination of Mn
Language	English
Publishing date	2024-04-17
Publishing country	Egypt
Document type	Journal Article
ZDB-ID	1057056-1
ISSN	1712-9532 ; 1180-2332
ISSN	1712-9532 ; 1180-2332
DOI	10.1155/2024/7502110
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 3099: Show issues

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Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Book ; Online: Sequential three-way decisions with a single hidden layer feedforward neural network

Wu, Youxi / Cheng, Shuhui / Li, Yan / Lv, Rongjie / Min, Fan

2023

Abstract: The three-way decisions strategy has been employed to construct network topology in a single hidden layer feedforward neural network (SFNN). However, this model has a general performance, and does not consider the process costs, since it has fixed ... ...

Abstract	The three-way decisions strategy has been employed to construct network topology in a single hidden layer feedforward neural network (SFNN). However, this model has a general performance, and does not consider the process costs, since it has fixed threshold parameters. Inspired by the sequential three-way decisions (STWD), this paper proposes STWD with an SFNN (STWD-SFNN) to enhance the performance of networks on structured datasets. STWD-SFNN adopts multi-granularity levels to dynamically learn the number of hidden layer nodes from coarse to fine, and set the sequential threshold parameters. Specifically, at the coarse granular level, STWD-SFNN handles easy-to-classify instances by applying strict threshold conditions, and with the increasing number of hidden layer nodes at the fine granular level, STWD-SFNN focuses more on disposing of the difficult-to-classify instances by applying loose threshold conditions, thereby realizing the classification of instances. Moreover, STWD-SFNN considers and reports the process cost produced from each granular level. The experimental results verify that STWD-SFNN has a more compact network on structured datasets than other SFNN models, and has better generalization performance than the competitive models. All models and datasets can be downloaded from https://github.com/wuc567/Machine-learning/tree/main/STWD-SFNN.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-03-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Human-Induced Hepatocytes-Derived Extracellular Vesicles Ameliorated Liver Fibrosis in Mice Via Suppression of TGF-β1/Smad Signaling and Activation of Nrf2/HO-1 Signaling.

Liu, Wenjing / Wu, Jiajun / Cao, Huiying / Ma, Chen / Wu, Zhitao / Tian, Youxi / Ma, Chenhui / Qiu, Hong / Pan, Guoyu

Stem cells and development

2023 Volume 32, Issue 19-20, Page(s) 638–651

Abstract: Liver fibrosis is a wound-healing response caused by persistent liver injury and often occurs in chronic liver diseases. Effective treatments for liver fibrosis are still pending. Recent studies have revealed that extracellular vesicles (EVs) derived ... ...

Abstract	Liver fibrosis is a wound-healing response caused by persistent liver injury and often occurs in chronic liver diseases. Effective treatments for liver fibrosis are still pending. Recent studies have revealed that extracellular vesicles (EVs) derived from primary hepatocytes (Hep-EVs) have therapeutic potential for multiple liver diseases. However, Hep-EVs are difficult to manufacture in bulk because of the limited sources of primary hepatocytes. Human-induced hepatocytes (hiHep) are hepatocyte-like cells that can expand in vitro, and their cell culture supernatant is thus an almost unlimited resource for EVs. This study aimed to investigate the potential therapeutic effects of EVs derived from hiHeps. hiHep-EVs inhibited the expression of inflammatory genes and the secretion of inflammation-related cytokines, and suppressed the activation of hepatic stellate cells by inhibiting the transforming growth factor (TGF)-β1/Smad signaling pathway. The anti-inflammatory and antifibrotic effects of hiHep-EVs were similar to those of mesenchymal stem cell-EVs. Furthermore, the administration of hiHep-EVs ameliorated oxidative stress, inflammation, and fibrosis in a CCl
MeSH term(s)	Humans ; Mice ; Animals ; Transforming Growth Factor beta1/metabolism ; NF-E2-Related Factor 2/metabolism ; NF-E2-Related Factor 2/pharmacology ; NF-E2-Related Factor 2/therapeutic use ; Smad Proteins/metabolism ; Liver Cirrhosis/chemically induced ; Liver Cirrhosis/therapy ; Liver/metabolism ; Signal Transduction ; Liver Diseases/metabolism ; Hepatocytes/metabolism ; Hepatic Stellate Cells/metabolism ; Collagen Type I/metabolism ; Inflammation/pathology ; Extracellular Vesicles/metabolism
Chemical Substances	Transforming Growth Factor beta1 ; NF-E2-Related Factor 2 ; Smad Proteins ; Collagen Type I
Language	English
Publishing date	2023-07-24
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2142214-X
ISSN	1557-8534 ; 1547-3287
ISSN (online)	1557-8534
ISSN	1547-3287
DOI	10.1089/scd.2023.0110
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 3616: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: NetNMSP: Nonoverlapping maximal sequential pattern mining.

Li, Yan / Zhang, Shuai / Guo, Lei / Liu, Jing / Wu, Youxi / Wu, Xindong

Applied intelligence (Dordrecht, Netherlands)

2022 Volume 52, Issue 9, Page(s) 9861–9884

Abstract: Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. ...

Abstract	Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.
Language	English
Publishing date	2022-01-10
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1479519-X
ISSN	1573-7497 ; 0924-669X
ISSN (online)	1573-7497
ISSN	0924-669X
DOI	10.1007/s10489-021-02912-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: NOSEP: Nonoverlapping Sequence Pattern Mining With Gap Constraints.

Youxi Wu / Yao Tong / Xingquan Zhu / Xindong Wu

IEEE transactions on cybernetics

2017 Volume 48, Issue 10, Page(s) 2809–2822

Abstract: Sequence pattern mining aims to discover frequent subsequences as patterns in a single sequence or a sequence database. By combining gap constraints (or flexible wildcards), users can specify special characteristics of the patterns and discover ... ...

Abstract	Sequence pattern mining aims to discover frequent subsequences as patterns in a single sequence or a sequence database. By combining gap constraints (or flexible wildcards), users can specify special characteristics of the patterns and discover meaningful subsequences suitable for their own application domains, such as finding gene transcription sites from DNA sequences or discovering patterns for time series data classification. Due to the inherent complexity of sequence patterns, including the exponential candidate space with respect to pattern letters and gap constraints, to date, existing sequence pattern mining methods are either incomplete or do not support the Apriori property because the support ratio of a pattern may be greater than that of its subpatterns. Most importantly, patterns discovered by these methods are either too restrictive or too general and cannot represent underlying meaningful knowledge in the sequences. In this paper, we focus on a nonoverlapping sequence pattern mining task with gap constraints, where a nonoverlapping sequence pattern allows sequence letters to be flexibly and maximally utilized for pattern discovery. A new Apriori-based nonoverlapping sequence pattern mining algorithm, NOSEP, is proposed. NOSEP is a complete pattern mining algorithm, which uses a specially designed data structure, Nettree, to calculate the exact occurrence of a pattern in the sequence. Experimental results and comparisons on biology DNA sequences, time series data, and Gazelle datasets demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence patterns compared to other methods.
Language	English
Publishing date	2017-09-28
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2017.2750691
Database	MEDical Literature Analysis and Retrieval System OnLINE

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