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  1. Article ; Online: Quantization of linear acoustic and elastic wave models in characterizations of isomorphism.

    Yang, Chen

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 8759

    Abstract: From the macroscopic to the microscopic world, quantum mechanical effects in acoustics and elastic waves have become increasingly important. Observations on the quantum effects of acoustic and elastic waves using experimental methods have been reported ... ...

    Abstract From the macroscopic to the microscopic world, quantum mechanical effects in acoustics and elastic waves have become increasingly important. Observations on the quantum effects of acoustic and elastic waves using experimental methods have been reported in the literature. However, the conventional formulations of acoustic and elastic waves are still mainly governed by classical models. In this study, we investigated the quantization of acoustic and elastic waves using generalized Lorenz gauges. The potential variables of acoustic and elastic waves can be quantized in a manner similar to that of electrodynamics. The results include the Schrödinger equation with minimal coupling between the field and particles. The quantization of field variables is established as a consequence of the gauge symmetry property of the Schrödinger equation. Later, we explored the connections between the parallel formulations of mechanics and waves through an algebraic aspect. This highlights the isomorphism pattern from the theoretical characterization within the parallel formulations. To support the results, the derivations of potential formulations based on Lorenz gauges and functional mapping between field variables are presented.
    Language English
    Publishing date 2024-04-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-57092-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Foams : Emerging Technologies

    Xu, Huijin / Yang, Chen / Jing, Dengwei

    2020  

    Keywords Spectrum analysis, spectrochemistry, mass spectrometry ; Colloid chemistry
    Size 1 electronic resource (154 pages)
    Publisher IntechOpen
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021048923
    ISBN 9781839681073 ; 1839681071
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Article ; Online: Study on non-iterative algorithms for center-of-sets type-reduction of Takagi–Sugeno–Kang type general type-2 fuzzy logic systems

    Yang Chen

    Complex & Intelligent Systems, Vol 9, Iss 4, Pp 4015-

    2022  Volume 4023

    Abstract: Abstract The paper performs the center-of-sets (COS) type-reduction (TR) and de-fuzzification for Takagi–Sugeno–Kang (TSK) type general type-2 fuzzy logic systems (GT2 FLSs) on the basis of the $$\alpha$$ α -planes expression of general type-2 fuzzy sets. ...

    Abstract Abstract The paper performs the center-of-sets (COS) type-reduction (TR) and de-fuzzification for Takagi–Sugeno–Kang (TSK) type general type-2 fuzzy logic systems (GT2 FLSs) on the basis of the $$\alpha$$ α -planes expression of general type-2 fuzzy sets. Actually, comparing the popular Karnik–Mendel (KM) algorithms with other non-iterative algorithms is an important question in T2 society. Here the modules of fuzzy inference, COS TR, and de-fuzzification for TSK type GT2 FLSs are discussed by means of non-iterative Nagar–Bardini (NB) algorithms, Nie–Tan (NT) algorithms, and Begian–Melek–Mendel (BMM) algorithms. Simulation instances are constructed to illustrate the performances of three types of non-iterative algorithms compared with the KM algorithms. It is proved that, the proposed non-iterative algorithms can enhance the computational efficiencies significantly, which afford the potential application value for designers of GT2 FLSs.
    Keywords General type-2 fuzzy logic systems ; Computational efficiency ; Center-of-sets type-reduction ; Alpha-planes ; Non-iterative algorithms ; Electronic computers. Computer science ; QA75.5-76.95 ; Information technology ; T58.5-58.64
    Subject code 006
    Language English
    Publishing date 2022-12-01T00:00:00Z
    Publisher Springer
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: TransCrimeNet

    Yang, Chen

    A Transformer-Based Model for Text-Based Crime Prediction in Criminal Networks

    2023  

    Abstract: This paper presents TransCrimeNet, a novel transformer-based model for predicting future crimes in criminal networks from textual data. Criminal network analysis has become vital for law enforcement agencies to prevent crimes. However, existing graph- ... ...

    Abstract This paper presents TransCrimeNet, a novel transformer-based model for predicting future crimes in criminal networks from textual data. Criminal network analysis has become vital for law enforcement agencies to prevent crimes. However, existing graph-based methods fail to effectively incorporate crucial textual data like social media posts and interrogation transcripts that provide valuable insights into planned criminal activities. To address this limitation, we develop TransCrimeNet which leverages the representation learning capabilities of transformer models like BERT to extract features from unstructured text data. These text-derived features are fused with graph embeddings of the criminal network for accurate prediction of future crimes. Extensive experiments on real-world criminal network datasets demonstrate that TransCrimeNet outperforms previous state-of-the-art models by 12.7\% in F1 score for crime prediction. The results showcase the benefits of combining textual and graph-based features for actionable insights to disrupt criminal enterprises.
    Keywords Computer Science - Computers and Society
    Subject code 006
    Publishing date 2023-11-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: CrimeGNN

    Yang, Chen

    Harnessing the Power of Graph Neural Networks for Community Detection in Criminal Networks

    2023  

    Abstract: In this paper, we introduce CrimeGNN, a novel application of Graph Neural Networks (GNNs) specifically designed to uncover hidden communities within criminal networks. As criminal activities increasingly rely on complex network structures, traditional ... ...

    Abstract In this paper, we introduce CrimeGNN, a novel application of Graph Neural Networks (GNNs) specifically designed to uncover hidden communities within criminal networks. As criminal activities increasingly rely on complex network structures, traditional methods of network analysis often fall short in detecting the intricate and dynamic communities within these networks. Leveraging the power of GNNs, CrimeGNN provides an advanced and specialized solution to this problem. The model ingests a graph structure of a criminal network, where vertices represent individuals and edges represent relationships between them. CrimeGNN aims to identify a partition of the vertex set, such that each subset represents a distinct community within the network, maximizing the modularity function. Experimental results on several benchmark datasets demonstrate the effectiveness of CrimeGNN, outperforming existing methods in terms of both accuracy and computational efficiency. The proposed framework offers significant potential for aiding law enforcement agencies in proactive policing and crime prevention measures by providing a more in-depth understanding of the structure and operation of criminal networks.
    Keywords Computer Science - Social and Information Networks
    Subject code 006
    Publishing date 2023-11-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: CrimeGAT

    Yang, Chen

    Leveraging Graph Attention Networks for Enhanced Predictive Policing in Criminal Networks

    2023  

    Abstract: In this paper, we present CrimeGAT, a novel application of Graph Attention Networks (GATs) for predictive policing in criminal networks. Criminal networks pose unique challenges for predictive analytics due to their complex structure, multi-relational ... ...

    Abstract In this paper, we present CrimeGAT, a novel application of Graph Attention Networks (GATs) for predictive policing in criminal networks. Criminal networks pose unique challenges for predictive analytics due to their complex structure, multi-relational links, and dynamic behavior. Traditional methods often fail to capture these complexities, leading to suboptimal predictions. To address these challenges, we propose the use of GATs, which can effectively leverage both node features and graph structure to make predictions. Our proposed CrimeGAT model integrates attention mechanisms to weigh the importance of a node's neighbors, thereby capturing the local and global structures of criminal networks. We formulate the problem as learning a function that maps node features and graph structure to a prediction of future criminal activity. The experimental results on real-world datasets demonstrate that CrimeGAT out-performs conventional methods in predicting criminal activities, thereby providing a powerful tool for law enforcement agencies to proactively deploy resources. Furthermore, the interpretable nature of the attentionmechanism inGATs offers insights into the key players and relationships in criminal networks. This research opens new avenues for applying deep learning techniques in the Aeld of predictive policing and criminal network analysis.
    Keywords Computer Science - Social and Information Networks
    Subject code 006
    Publishing date 2023-11-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: CrimeGraphNet

    Yang, Chen

    Link Prediction in Criminal Networks with Graph Convolutional Networks

    2023  

    Abstract: In this paper, we introduce CrimeGraphNet, a novel approach for link prediction in criminal networks utilizingGraph Convolutional Networks (GCNs). Criminal networks are intricate and dynamic, with covert links that are challenging to uncover. Accurate ... ...

    Abstract In this paper, we introduce CrimeGraphNet, a novel approach for link prediction in criminal networks utilizingGraph Convolutional Networks (GCNs). Criminal networks are intricate and dynamic, with covert links that are challenging to uncover. Accurate prediction of these links can aid in proactive crime prevention and investigation. Existing methods often fail to capture the complex interconnections in such networks. They also struggle in scenarios where only limited labeled data is available for training. To address these challenges, we propose CrimeGraphNet, which leverages the power of GCNs for link prediction in these networks. The GCNmodel effectively captures topological features and node characteristics, making it well-suited for this task. We evaluate CrimeGraphNet on several real-world criminal network datasets. Our results demonstrate that CrimeGraphNet outperforms existing methods in terms of prediction accuracy, robustness, and computational efAciency. Furthermore, our approach enables the extraction of meaningful insights from the predicted links, thereby contributing to a better understanding of the underlying criminal activities. Overall, CrimeGraphNet represents a signiAcant step forward in the use of deep learning for criminal network analysis.
    Keywords Computer Science - Social and Information Networks
    Subject code 006
    Publishing date 2023-11-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: A Novel Disulfidptosis-related lncRNAs Prognostic Signature for Prognosis Predicting and Immune Microenvironment Characterization in Breast Cancer.

    Chen, Xi / Yang, Chen

    Current medicinal chemistry

    2024  

    Abstract: Introduction: Breast cancer (BRCA) is one of the leading causes of cancer-related death in women. The improvement of the BRCA risk assessment method is of positive clinical significance. Although many clues showed the potential role of disulfidptosis in ...

    Abstract Introduction: Breast cancer (BRCA) is one of the leading causes of cancer-related death in women. The improvement of the BRCA risk assessment method is of positive clinical significance. Although many clues showed the potential role of disulfidptosis in BRCA as a novel type of programmed cell death, whether disulfidptosis is involved in BRCA tumorigenesis remains unclear.
    Method: We used LASSO-univariate Cox analysis and multivariate Cox analysis to identify six disulfidptosis-related lncRNAs (DPLs) that correlated with BRCA clinical outcome and confirmed that these DPLs were independent prognostic factors for BRCA (YTHDF3-AS1, AC002398.1, AL451085.2, AC092718.4, AC097662.1 and AC053503.5). The BRCA risk prognosis model was subsequently established based on these DPLs.
    Result: After verifying the model reliability in predicting prognosis, immune infiltration and somatic mutation analysis showed significant differences in the immune microenvironment and mutation of DPLs by risk stratification. Immunotherapy response and drug resistance analysis suggest the reference value of DPLs in clinical individualized therapy.
    Conclusion: The abnormal expressions of selected DPLs were further validated by the BRCA cell line experiment. Our results shed new light on the role of DPLs in BRCA.
    Language English
    Publishing date 2024-04-17
    Publishing country United Arab Emirates
    Document type Journal Article
    ZDB-ID 1319315-6
    ISSN 1875-533X ; 0929-8673
    ISSN (online) 1875-533X
    ISSN 0929-8673
    DOI 10.2174/0109298673294711240405090150
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4432: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
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  9. Article ; Online: Superconducting Two-Dimensional FeSe Grown on the Fe-Enriched Interface.

    Yang, Chen-Kai / Jiao, Liying

    ACS nano

    2024  

    Abstract: Two-dimensional (2D) tetragonal FeSe has sparked extensive research interest owing to its tunable superconductivity, providing valuable insights into the design of high-temperature superconductors. Currently, the intricate Fe-Se phase diagram poses a ... ...

    Abstract Two-dimensional (2D) tetragonal FeSe has sparked extensive research interest owing to its tunable superconductivity, providing valuable insights into the design of high-temperature superconductors. Currently, the intricate Fe-Se phase diagram poses a challenge to the controlled synthesis of superconducting 2D FeSe in a pure tetragonal phase. Here, we exploit the ion-exchange property of fluorophlogopite mica to devise a straightforward approach for the phase-controlled synthesis of tetragonal FeSe on an Fe-enriched mica surface within a molten salt environment. This method successfully produces highly crystalline FeSe in a pure tetragonal phase with adjustable thickness. We investigated the surface composition of the postgrowth mica substrate using various microscopic and spectroscopic characterizations to highlight the importance of the Fe-enriched growth interface in the phase-selective synthesis of 2D tetragonal FeSe. The obtained 2D FeSe exhibited 2D superconductivity, comparable to that of FeSe mechanically exfoliated from bulk crystals, confirming the high quality of our samples. Beyond tetragonal FeSe, 2D antiferromagnetic FeTe and superconducting FeS
    Language English
    Publishing date 2024-05-03
    Publishing country United States
    Document type Journal Article
    ISSN 1936-086X
    ISSN (online) 1936-086X
    DOI 10.1021/acsnano.4c00984
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: An Investigation of the Influencing Factors of Chinese WeChat Users’ Environmental Information-Sharing Behavior Based on an Integrated Model of UGT, NAM, and TPB

    Yang Chen

    Sustainability, Vol 12, Iss 2710, p

    2020  Volume 2710

    Abstract: Sustainable development is a common challenge for all. Under this background, how to promote public participation in environmental communication has become an important topic. The purpose of this study is to understand the motivating mechanism behind ... ...

    Abstract Sustainable development is a common challenge for all. Under this background, how to promote public participation in environmental communication has become an important topic. The purpose of this study is to understand the motivating mechanism behind Chinese WeChat users’ environmental information-sharing behavior by taking China’s unique social and cultural background into account. A comprehensive theoretical model for this study is constructed based on the uses and gratification theory, the norm activation model, and the theory of planned behavior. Through an online survey, data were collected from 526 participants to test the research model. The research results show that Chinese WeChat users’ environmental information-sharing behavior is motivated by both egoistic factors (self-presentation, information seeking, and socializing) and altruistic factors (awareness of consequences and ascription of responsibility). During the behavioral decision-making process, these motivating factors impact people’s actual sharing behavior via their attitudes toward the behavior, subjective norms, personal norms, and behavioral intention in various patterns. The findings are discussed from an interdisciplinary perspective of media usage, prosocial behavior, and behavioral psychology. This article not only proposes a new conceptual framework to explain social media users’ behavior of sharing environmental information but also provides important theoretical and practical implications regarding motivating public participation in environmental communication on social media.
    Keywords environment ; social media ; motivation ; information sharing ; WeChat ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
    Subject code 306
    Language English
    Publishing date 2020-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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