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  1. Article ; Online: The structural stability of Mn

    Zhang, Junran / Lu, Yunhao / Li, Yanchun

    Journal of physics. Condensed matter : an Institute of Physics journal

    2024  Volume 36, Issue 19

    Abstract: Pressure engineering has attracted growing interest in the understanding of structural changes and structure-property relations of layered materials. In this study, we investigated the effect of pressure on the crystal structure of ... ...

    Abstract Pressure engineering has attracted growing interest in the understanding of structural changes and structure-property relations of layered materials. In this study, we investigated the effect of pressure on the crystal structure of Mn
    Language English
    Publishing date 2024-02-15
    Publishing country England
    Document type Journal Article
    ZDB-ID 1472968-4
    ISSN 1361-648X ; 0953-8984
    ISSN (online) 1361-648X
    ISSN 0953-8984
    DOI 10.1088/1361-648X/ad2587
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A Review on Soils Treated with Biopolymers Based on Unsaturated Soil Theory.

    Zhang, Junran / Liu, Jiahao

    Polymers

    2023  Volume 15, Issue 22

    Abstract: Adding different materials to soil can improve its engineering properties, but traditional materials such as cement, lime, fly ash, etc., have caused pollution to the environment. Recently, biopolymers have shown many advantages, such as economy and ... ...

    Abstract Adding different materials to soil can improve its engineering properties, but traditional materials such as cement, lime, fly ash, etc., have caused pollution to the environment. Recently, biopolymers have shown many advantages, such as economy and environmental protection, which make them applicable to geotechnical engineering. This study summarizes the effects of biopolymers on soil's engineering properties and the main directions of current research. Firstly, the advantages and disadvantages of a variety of widely used biopolymer materials and their effects on the specific engineering characteristics of soil (i.e., water retention characteristics, strength characteristics, permeability characteristics, microstructure) are introduced, as well as the source, viscosity, pH, and cost of these biopolymers. Then, based on the theory of unsaturated soil, the current research progress on the water retention characteristics of improved soil is summarized. The key factors affecting the strength of biopolymer-treated soil are introduced. Due to the actual environmental conditions, such as rainfall, the permeability and durability of biopolymer-treated soil are also worthy of attention. In summary, it is necessary to study the variation laws of the engineering properties of biopolymer-treated soil in the full suction range, and to predict such laws reasonably. The relevant results are conducive to the safer and more scientific application of biopolymers in geotechnical engineering practice.
    Language English
    Publishing date 2023-11-16
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2527146-5
    ISSN 2073-4360 ; 2073-4360
    ISSN (online) 2073-4360
    ISSN 2073-4360
    DOI 10.3390/polym15224431
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  3. Article: [Research on migraine time-series features classification based on small-sample functional magnetic resonance imaging data].

    Sun, Ang / Chen, Ning / He, Li / Zhang, Junran

    Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi

    2023  Volume 40, Issue 1, Page(s) 110–117

    Abstract: The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time- ... ...

    Abstract The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time-series signals to characterize the functional state of the brain in migraine patients and healthy controls, which can effectively utilize the temporal information and reduce the computational effort of classification model training. Firstly, Group Independent Component Analysis and Dictionary Learning were used to segment different brain areas for small-sample groups and then the regional average time-series signals were extracted. Next, the extracted time series were divided equally into multiple subseries to expand the model input sample. Finally, the time series were modeled using a bi-directional long-short term memory network to learn the pre-and-post temporal information within each time series to characterize the periodic brain state changes to improve the diagnostic accuracy of migraine. The results showed that the classification accuracy of migraine patients and healthy controls was 96.94%, the area under the curve was 0.98, and the computation time was relatively shorter. The experiments indicate that the method in this paper has strong applicability, and the combination of time-series feature extraction and bi-directional long-short term memory network model can be better used for the classification and diagnosis of migraine. This work provides a new idea for the lightweight diagnostic model based on small-sample neuroimaging data, and contributes to the exploration of the neural discrimination mechanism of related diseases.
    MeSH term(s) Humans ; Time Factors ; Migraine Disorders/diagnostic imaging ; Magnetic Resonance Imaging ; Brain/diagnostic imaging ; Neuroimaging
    Language Chinese
    Publishing date 2023-02-28
    Publishing country China
    Document type English Abstract ; Journal Article
    ZDB-ID 2576847-5
    ISSN 1001-5515
    ISSN 1001-5515
    DOI 10.7507/1001-5515.202206060
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6887: Show issues Location:
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  4. Article ; Online: Identifying autism spectrum disorder using edge-centric functional connectivity.

    Sun, Ang / Wang, Jiaojian / Zhang, Junran

    Cerebral cortex (New York, N.Y. : 1991)

    2023  Volume 33, Issue 13, Page(s) 8122–8130

    Abstract: Brain network analysis is an effective method to seek abnormalities in functional interactions for brain disorders such as autism spectrum disorder (ASD). Traditional studies of brain networks focus on the node-centric functional connectivity (nFC), ... ...

    Abstract Brain network analysis is an effective method to seek abnormalities in functional interactions for brain disorders such as autism spectrum disorder (ASD). Traditional studies of brain networks focus on the node-centric functional connectivity (nFC), ignoring interactions of edges to miss much information that facilitates diagnostic decisions. In this study, we present a protocol based on an edge-centric functional connectivity (eFC) approach, which significantly improves classification performance by utilizing the co-fluctuations information between the edges of brain regions compared with nFC to build the classification mode for ASD using the multi-site dataset Autism Brain Imaging Data Exchange I (ABIDE I). Our model results show that even using the traditional machine-learning classifier support vector machine (SVM) on the challenging ABIDE I dataset, relatively high performance is achieved: 96.41% of accuracy, 98.30% of sensitivity, and 94.25% of specificity. These promising results suggest that the eFC can be used to build a reliable machine-learning framework to diagnose mental disorders such as ASD and promote identifications of stable and effective biomarkers. This study provides an essential complementary perspective for understanding the neural mechanisms of ASD and may facilitate future investigations on early diagnosis of neuropsychiatric disorders.
    MeSH term(s) Humans ; Autism Spectrum Disorder/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Brain Mapping/methods ; Biomarkers
    Chemical Substances Biomarkers
    Language English
    Publishing date 2023-03-28
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1077450-6
    ISSN 1460-2199 ; 1047-3211
    ISSN (online) 1460-2199
    ISSN 1047-3211
    DOI 10.1093/cercor/bhad103
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3235: 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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  5. Article ; Online: Enlighten the non-illuminated region by phase segregation of mixed halide perovskites.

    Lv, Yan / Zhang, Junran / Chen, Xiaolong / Wang, Lin

    Light, science & applications

    2022  Volume 11, Issue 1, Page(s) 311

    Abstract: The well-known ion migration in mixed halide perovskites has been intensely investigated within the area under uniform light illumination. Here, the authors demonstrate that the anion segregation in these materials is a nonlocal effect of which the ion ... ...

    Abstract The well-known ion migration in mixed halide perovskites has been intensely investigated within the area under uniform light illumination. Here, the authors demonstrate that the anion segregation in these materials is a nonlocal effect of which the ion redistribution may occur at a macroscopic or mesoscopic scale beyond.
    Language English
    Publishing date 2022-10-26
    Publishing country England
    Document type Journal Article
    ZDB-ID 2662628-7
    ISSN 2047-7538 ; 2047-7538
    ISSN (online) 2047-7538
    ISSN 2047-7538
    DOI 10.1038/s41377-022-01019-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Novel evaluation method based on critical arch height as instability criterion for sustaining arch locked-segment-type slopes.

    Wang, Lijin / Jia, Hang / Jiang, Tong / Zhang, Junran / Jia, Yanchang / Li, Longfei / Wan, Li

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 7991

    Abstract: In sustaining arch locked-segment-type slopes, natural soil arches play a key anti-sliding role in the slope's evolution. In this study, a self-developed model test device was used to simulate the whole process of deformation evolution of sustaining arch ...

    Abstract In sustaining arch locked-segment-type slopes, natural soil arches play a key anti-sliding role in the slope's evolution. In this study, a self-developed model test device was used to simulate the whole process of deformation evolution of sustaining arch locked-segment-type slopes, and the formation of natural sustaining arch and its locking control effect on slope stability were studied. The test results show that the continuous formation and progressive destruction of the sustaining arch were observed. The sustaining arch formed in the second time has the best locking effect, and the anti-sliding force reaches its stress peak point. However, the slope is not in a critically unstable state, instead, the stress is continuously adjusted to form a larger range of soil arch to resist the slope thrust. Consequently, the slope destabilizes until the ultimate shear strength of arch foots is exceeded, at which point the critical arch height of the arch is reached. The critical arch height mechanical model for slope stability analysis was developed based on the soil arching effect and limit equilibrium theory. The applicability of the model was demonstrated by the physical test and Xintan slope data, which can provide some guidance for early warning of landslides.
    Language English
    Publishing date 2024-04-05
    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/s41598-024-58737-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Observation and Characterization of Multiple Resonance Modes in a Chiral Helimagnet CrNb

    Li, Liyuan / Li, Haotian / Zhou, Kaiyuan / Xiao, Xiao / Chen, Lina / Ma, Fusheng / Zhang, Junran / Wang, Lin / Zhang, Lei / Liu, Ronghua

    Nano letters

    2023  Volume 23, Issue 20, Page(s) 9243–9249

    Abstract: The chiral helimagnet ... ...

    Abstract The chiral helimagnet CrNb
    Language English
    Publishing date 2023-10-04
    Publishing country United States
    Document type Journal Article
    ISSN 1530-6992
    ISSN (online) 1530-6992
    DOI 10.1021/acs.nanolett.3c02031
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  8. Article ; Online: An innovative short-term multihorizon photovoltaic power output forecasting method based on variational mode decomposition and a capsule convolutional neural network

    Liu, Yunfei / Liu, Yan / Cai, Hanhu / Zhang, Junran

    Applied Energy. 2023 Aug., v. 343 p.121139-

    2023  

    Abstract: Due to its importance regarding the integration, economic dispatch, and operation of PV smart grid systems, infrastructure planning, and budgeting, the accurate forecasting of photovoltaic (PV) power generation has drawn increasing research and industry ... ...

    Abstract Due to its importance regarding the integration, economic dispatch, and operation of PV smart grid systems, infrastructure planning, and budgeting, the accurate forecasting of photovoltaic (PV) power generation has drawn increasing research and industry attention. However, the instability, intermittence, and randomness of solar irradiance impose difficulties on the short-term economic dispatch of a smart integrated power, grid and significantly increase the risks arising from PV generation in a power system, exposing PV generators to potential additional costs. A deep convolutional neural network (CNN)-based method can effectively improve the performance of PV generation point prediction and probabilistic interval prediction by efficiently extracting nonlinear features at each frequency. Nevertheless, existing deep learning (DL) studies have mostly focused on more complex network topologies and data decomposition algorithms, ignoring the importance of simultaneously forecasting the PV power produced over multiple temporal periods. To solve the described challenge, we propose a novel two-stage DL approach for PV generation prediction. The end-to-end trained model used in the proposed PV generation forecasting method is an effort to combine the existing methods and avoid the separation of entire task, such as single time-scale PV generation forecasting, independent point forecasting, and probabilistic interval forecasting. A temporal signal decomposition technique called variational mode decomposition (VMD) is employed in the first stage to construct association mappings from fine-grained features to images. In the second stage, an innovative capsule CNN (ACCNet) is proposed to obtain very short-term multihorizon ahead output power predictions for seven different PV systems based on polycrystalline, monocrystalline, cadmium telluride (CdTe) thin-film, amorphous, copper indium gallium diselenide (CIGS) thin-film, heterojunction with intrinsic thin layer (HIT) hybrid, and concentrated photovoltaic (CPV) technologies. The input parameters for each system include solar radiation (diffuse/global horizontal radiation (DHR/GHR) and radiation diffuse/global tilted (RDT/RGT)) and ambient temperature, while the output parameter is the power output of each PV system. The proposed model is validated with the historical datasets of a PV system downloaded from the Desert Knowledge Precinct in Central Australia (DKASC) homepage. The performance of the developed method is proven in detail based on seven different PV systems over multiple data periods, and the experimental results reveal that the proposed approach displays significant improvements and robustness in point forecasting and probabilistic forecasting tasks. We will release the source code to ensure reproducibility and facilitate future work. Our model is open-sourced at https://github.com/YunDuanFei/ACCNet.
    Keywords ambient temperature ; cadmium ; data collection ; economic dispatch ; electrical equipment ; energy ; gallium ; indium ; industry ; infrastructure ; neural networks ; power generation ; prediction ; solar collectors ; solar energy ; solar radiation ; Australia
    Language English
    Dates of publication 2023-08
    Publishing place Elsevier Ltd
    Document type Article ; Online
    ZDB-ID 2000772-3
    ISSN 0306-2619
    ISSN 0306-2619
    DOI 10.1016/j.apenergy.2023.121139
    Database NAL-Catalogue (AGRICOLA)

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  9. Article ; Online: A Cascade Model with Prior Knowledge for Bone Age Assessment

    Nanxin Li / Bochao Cheng / Junran Zhang

    Applied Sciences, Vol 12, Iss 7371, p

    2022  Volume 7371

    Abstract: Bone age is commonly used to reflect growth and development trends in children, predict adult heights, and diagnose endocrine disorders. Nevertheless, the existing automated bone age assessment (BAA) models do not consider the nonlinearity and continuity ...

    Abstract Bone age is commonly used to reflect growth and development trends in children, predict adult heights, and diagnose endocrine disorders. Nevertheless, the existing automated bone age assessment (BAA) models do not consider the nonlinearity and continuity of hand bone development simultaneously. In addition, most existing BAA models are based on datasets from European and American children and may not be applicable to the developmental characteristics of Chinese children. Thus, this work proposes a cascade model that fuses prior knowledge. Specifically, a novel bone age representation is defined, which incorporates nonlinear and continuous features of skeletal development and is implemented by a cascade model. Moreover, corresponding regions of interest (RoIs) based on RUS-CHN were extracted by YOLO v5 as prior knowledge inputs to the model. In addition, based on MobileNet v2, an improved feature extractor was proposed by introducing the Convolutional Block Attention Module and increasing the receptive field to improve the accuracy of the evaluation. The experimental results show that the mean absolute error (MAE) is 4.44 months and significant correlations with the reference bone age is (r = 0.994, p < 0.01); accuracy is 94.04% for ground truth within ±1 year. Overall, the model design adequately considers hand bone development features and has high accuracy and consistency, and it also has some applicability on public datasets, showing potential for practical and clinical applications.
    Keywords multi-point distribution label ; cascade model ; prior knowledge ; bone age assessment ; deep learning ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Language English
    Publishing date 2022-07-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Ultrathin two-dimensional hybrid perovskites toward flexible electronics and optoelectronics.

    Zhang, Junran / Song, Xuefen / Wang, Lin / Huang, Wei

    National science review

    2021  Volume 9, Issue 5, Page(s) nwab129

    Abstract: Ultrathin hybrid perovskites combine the advantages of 2D morphology and organic-inorganic components. This perspective article provides an updated summary and new insights for their development in flexible electronics and optoelectronics. ...

    Abstract Ultrathin hybrid perovskites combine the advantages of 2D morphology and organic-inorganic components. This perspective article provides an updated summary and new insights for their development in flexible electronics and optoelectronics.
    Language English
    Publishing date 2021-07-19
    Publishing country China
    Document type Journal Article
    ZDB-ID 2745465-4
    ISSN 2053-714X ; 2053-714X
    ISSN (online) 2053-714X
    ISSN 2053-714X
    DOI 10.1093/nsr/nwab129
    Database MEDical Literature Analysis and Retrieval System OnLINE

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