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Artikel ; Online: Abnormal development of the fetal nervous system in a gestational woman with LEOPARD syndrome.

Zhang, Jin-Cheng / Liu, Hong-Qian

2024 Band 47, Heft 4, Seite(n) 2067–2068

Mesh-Begriff(e)	Female ; Humans ; LEOPARD Syndrome/complications ; LEOPARD Syndrome/diagnosis ; LEOPARD Syndrome/genetics ; Fetus ; Nervous System
Sprache	Englisch
Erscheinungsdatum	2024-01-19
Erscheinungsland	Netherlands
Dokumenttyp	Letter
ZDB-ID	1068461-x
ISSN	0219-3108 ; 1015-9584
ISSN (online)	0219-3108
ISSN	1015-9584
DOI	10.1016/j.asjsur.2024.01.017
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Digital twin of wind farms via physics-informed deep learning

Zhang, Jincheng / Zhao, Xiaowei

Energy Conversion and Management. 2023, p.117507-

2023 , Seite(n) 117507–

Abstract: The spatiotemporal flow field in a wind farm determines the wind turbines' energy production and structural fatigue. However, it is not obtainable by the current measurement, modeling, and prediction tools in wind industry. Here we propose a novel data ... ...

Abstract	The spatiotemporal flow field in a wind farm determines the wind turbines' energy production and structural fatigue. However, it is not obtainable by the current measurement, modeling, and prediction tools in wind industry. Here we propose a novel data and knowledge fusion approach to create the first digital twin for onshore/offshore wind farm flow system, which can predict the in situ spatiotemporal wind field covering the entire wind farm. The digital twin is developed by integrating the Lidar measurements, the Navier-Stokes equations, and the turbine modeling using actuator disk method, via physics-informed neural networks. The design enables the seamless integration of Lidar measurements and turbine operating data for real-time flow characterisation, and the fusion of flow physics for retrieving unmeasured wind field information. It thus addresses the limitations of existing wind prediction approaches based on supervised machine learning, which cannot achieve such prediction because the training targets are not available. Case studies of a wind farm under typical operating scenarios (i.e. a greedy case, a wake-steering case, and a partially-operating case) are carried out using high-fidelity numerical experiments, and the results show that the developed digital twin achieves very accurate mirroring of the physical wind farm, capturing detailed flow features such as wake interaction and wake meandering. The prediction error for the flow fields, on average, is just 4.7% of the value range. With the accurate flow field information predicted, the digital twin is expected to enable brand new research across wind farm lifecycle including monitoring, control, and load assessment.
Schlagwörter	administrative management ; energy conversion ; lidar ; prediction ; wind ; wind farms ; Digital twin ; NS equations ; Physics-informed machine learning ; Wind farm wake
Sprache	Englisch
Erscheinungsort	Elsevier Ltd
Dokumenttyp	Artikel ; Online
Anmerkung	Pre-press version
ZDB-ID	2000891-0
ISSN	0196-8904
ISSN	0196-8904
DOI	10.1016/j.enconman.2023.117507
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel ; Online: UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications.

Zhang, Jincheng / Wolek, Artur / Willis, Andrew R

Sensors (Basel, Switzerland)

2024 Band 24, Heft 7

Abstract: This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental ... ...

Abstract	This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental construct is created using highly realistic environments made possible by integrating the AirSim simulator with Google 3D maps models using the Cesium Tiles plugin. Experiments are conducted in this high-realism simulated environment to evaluate the performance of three distinct mapping algorithms: (1) Direct Sparse Odometry (DSO), (2) Stereo DSO (SDSO), and (3) DSO Lite (DSOL). Experimental results evaluate algorithms based on their measured geometric accuracy and computational speed. The results provide valuable insights into the strengths and limitations of each algorithm. Findings quantify compromises in UAV algorithm selection, allowing researchers to find the mapping solution best suited to their application, which often requires a compromise between computational performance and the density and accuracy of geometric map estimates. Results indicate that for UAVs with restrictive computing resources, DSOL is the best option. For systems with payload capacity and modest compute resources, SDSO is the best option. If only one camera is available, DSO is the option to choose for applications that require dense mapping results.
Sprache	Englisch
Erscheinungsdatum	2024-03-29
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s24072204
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Wind farm wake modeling based on deep convolutional conditional generative adversarial network

Zhang, Jincheng / Zhao, Xiaowei

Energy. 2022 Jan. 01, v. 238

2022

Abstract: Modeling of wind farm wakes is of great importance for the optimal design and operation of wind farms. In this work a surrogate modeling method for parametrized fluid flows is proposed for wind farm wake modeling, based on the state-of-the-art deep ... ...

Abstract	Modeling of wind farm wakes is of great importance for the optimal design and operation of wind farms. In this work a surrogate modeling method for parametrized fluid flows is proposed for wind farm wake modeling, based on the state-of-the-art deep learning framework i.e. deep convolutional conditional generative adversarial network. Based on the proposed method and the data generated by high-fidelity large eddy simulations, a novel wind farm wake model is developed. The developed model is first validated against high-fidelity data and the results show that it achieves accurate, efficient, and robust prediction of wind turbine wake flow, at all the streamwise locations including both near wake and far wake, for both streamwise and spanwise velocity components, and at the cases with different inflow wind profiles. Then an extensive parametric study is carried out and the results show that the model generalizes well to unknown flow scenarios. Furthermore, a case study for a wind farm is investigated by the developed model. The prediction results are then compared with high-fidelity simulations, showing that the model can predict the wind farm wake flow (including both the streamwise and spanwise velocity fields) very well.
Schlagwörter	case studies ; energy ; prediction ; wind ; wind farms ; wind turbines
Sprache	Englisch
Erscheinungsverlauf	2022-0101
Erscheinungsort	Elsevier Ltd
Dokumenttyp	Artikel
ZDB-ID	2019804-8
ISSN	0360-5442 ; 0360-5442
ISSN (online)	0360-5442
ISSN	0360-5442
DOI	10.1016/j.energy.2021.121747
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel ; Online: Successful Transvaginal Oocyte Retrieval after Laparoscopic Management of Tubal Pregnancy.

Sa, Sha-Wei / Qiao, Xiao-Yong / Zhang, Jin-Cheng / Ma, Qian-Hong

Journal of minimally invasive gynecology

2024

Sprache	Englisch
Erscheinungsdatum	2024-05-03
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2186934-0
ISSN	1553-4669 ; 1553-4650
ISSN (online)	1553-4669
ISSN	1553-4650
DOI	10.1016/j.jmig.2024.04.022
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Modeling of a hinged-raft wave energy converter via deep operator learning and wave tank experiments

Zhang, Jincheng / Zhao, Xiaowei / Greaves, Deborah / Jin, Siya

Applied Energy. 2023 July, v. 341 p.121072-

2023

Abstract: Model identification for a hinged-raft wave energy converter (WEC) is investigated in this paper, based on wave tank experiments and deep operator learning. Different from previous works which all formulated this issue as a function approximation task, ... ...

Abstract	Model identification for a hinged-raft wave energy converter (WEC) is investigated in this paper, based on wave tank experiments and deep operator learning. Different from previous works which all formulated this issue as a function approximation task, this work, for the first time, formulates it as an operator approximation task (which learns the mapping from a function space to another function space). As such, a continuous-time WEC model is identified from data, greatly expanding the horizon of data-based WEC modeling because previous works were limited to discrete-time model identification. The error accumulation for multi-step predictions in the discrete-time formulation is thus also addressed. The model is developed by first carrying out a set of wave tank experiments to generate the training data, and then the deep operator learning model, i.e. the DeepONet, is constructed and trained based on the experimental data. The validation study shows that the model captures the WEC dynamics accurately. A new set of experimental runs are further carried out and the results show that after training, the model can be used as a digital wave tank, an alternative to the expensive numerical and physical wave tanks, for accurate and real-time simulations of the WEC dynamics.
Schlagwörter	energy conversion ; models ; water power ; Data-based modeling ; Deep learning ; DeepONet ; Wave energy converter ; Wave tank experiment
Sprache	Englisch
Erscheinungsverlauf	2023-07
Erscheinungsort	Elsevier Ltd
Dokumenttyp	Artikel ; Online
Anmerkung	Use and reproduction
ZDB-ID	2000772-3
ISSN	0306-2619
ISSN	0306-2619
DOI	10.1016/j.apenergy.2023.121072
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel ; Online: Serum VEGF, high-sensitivity CRP, and cystatin-C assist in the diagnosis of type 2 diabetic retinopathy complicated with hyperuricemia

Wei Jing / Zhang Jincheng / Shi Yanan / Zhang Huiqin / Wu Yan

Open Medicine, Vol 18, Iss 1, Pp 1816-

2023 Band 60

Abstract: Elevated serum uric acid (UA) level is related to type 2 diabetic retinopathy (DR). Vascular endothelial growth factor (VEGF), high-sensitivity C-reactive protein (hs-CRP), and cystatin C (Cys-C) have involvement in type 2 DR complicated with ... ...

Abstract	Elevated serum uric acid (UA) level is related to type 2 diabetic retinopathy (DR). Vascular endothelial growth factor (VEGF), high-sensitivity C-reactive protein (hs-CRP), and cystatin C (Cys-C) have involvement in type 2 DR complicated with hyperuricemia (HUA) (HUDR), and we explored their clinical values in HUDR. Type 2 DR patients were allocated into HUDR/DR groups, with type 2 diabetes mellitus (T2DM) patients as the control group. Serum VEGF and inflammation markers hs-CRP, and Cys-C levels were assessed by ELISA and immunoturbidimetry. The correlations between serum UA level and VEGF/hs-CRP/Cys-C were analyzed by Pearson tests, diagnostic values of VEGF/hs-CRP/Cys-C were analyzed by receiver operating characteristic curves, and the independent risk factors in HUDR were analyzed by logistic multivariate regression. Serum VEGF/hs-CRP/Cys-C level differences among the T2DM/DR/HUDR groups were statistically significant, with the levels in HUDR > DR > T2DM. Serum UA level in HUDR patients was positively correlated with serum VEGF/hs-CRP/Cys-C. Serum VEGF/hs-CRP/Cys-C assisted in HUDR diagnosis, with their combination showing the greatest diagnostic value. UA/FPG/HbA1C/VEGF/hs-CRP/Cys-C were independent risk factors for HUDR. The incidence of proliferative DR was increased in HUDR patients. Collectively, serum VEGF, hs-CRP, and Cys-C levels in HUDR patients were increased, and HUA might promote DR progression.
Schlagwörter	type 2 diabetes mellitus ; retinopathy ; hyperuricemia ; vascular endothelial growth factor ; inflammation ; high-sensitivity c-reactive protein ; cystatin c ; pearson test ; receiver operating characteristic curve ; logistic multiple factor regression analysis ; Medicine ; R
Sprache	Englisch
Erscheinungsdatum	2023-12-01T00:00:00Z
Verlag	De Gruyter
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Understanding the Heat Transfer Performance of Zeolitic Imidazolate Frameworks upon Gas Adsorption by Molecular Dynamics Simulations.

Cheng, Ruihuan / Wei, Wei / Zhang, Jincheng / Li, Song

The journal of physical chemistry. B

2023 Band 127, Heft 43, Seite(n) 9390–9398

Abstract: Metal-organic frameworks (MOFs) with ultrahigh specific surface area and porosity have emerged as promising nanoporous materials for gas separation, storage, and adsorption-driven thermal energy conversion systems such as adsorption heat pumps. However, ... ...

Abstract	Metal-organic frameworks (MOFs) with ultrahigh specific surface area and porosity have emerged as promising nanoporous materials for gas separation, storage, and adsorption-driven thermal energy conversion systems such as adsorption heat pumps. However, an inadequate understanding of the thermal transport of MOFs with adsorbed gases hampers the thermal management of such systems in practical applications. In this work, an in-depth investigation on the mechanistic heat transfer performance of three topological zeolitic imidazolate frameworks (ZIFs) upon hydrogen, methane, and ethanol adsorption was carried out by molecular dynamics simulations. It is revealed that the trade-off between the additional heat transfer pathway and phonon scattering resulting from adsorbed gases determines the thermal conductivity of ZIFs. It is found that the increased thermal conductivity with the increased number of adsorbed gases is correlated with the overlap energy between the vibrational density of states of gases and Zn atoms, suggesting the additional heat transfer pathways formed between gas molecules and frameworks. Moreover, the gas spatial distribution and diffusion also impose remarkable impacts on the heat transfer performance. Both the homogeneous gas distribution and the fast gas diffusion are conducive to form effective heat transfer pathways, leading to enhanced thermal conductivity. This study provides molecular insight into the mechanism of the improved thermal conductivity of ZIFs upon gas adsorption, which may pave the way for effective thermal management in MOF-related applications.
Sprache	Englisch
Erscheinungsdatum	2023-10-18
Erscheinungsland	United States
Dokumenttyp	Journal Article
ISSN	1520-5207
ISSN (online)	1520-5207
DOI	10.1021/acs.jpcb.3c04372
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: UAV-borne Mapping Algorithms for Canopy-Level and High-Speed Drone Applications

Zhang, Jincheng / Wolek, Artur / Willis, Andrew R.

2024

Abstract: This article presents a comprehensive review of and analysis of state-of-the-art mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on canopy-level and high-speed scenarios. This article presents a comprehensive exploration of ... ...

Abstract	This article presents a comprehensive review of and analysis of state-of-the-art mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on canopy-level and high-speed scenarios. This article presents a comprehensive exploration of sensor technologies suitable for UAV mapping, assessing their capabilities to provide measurements that meet the requirements of fast UAV mapping. Furthermore, the study conducts extensive experiments in a simulated environment to evaluate the performance of three distinct mapping algorithms: Direct Sparse Odometry (DSO), Stereo DSO (SDSO), and DSO Lite (DSOL). The experiments delve into mapping accuracy and mapping speed, providing valuable insights into the strengths and limitations of each algorithm. The results highlight the versatility and shortcomings of these algorithms in meeting the demands of modern UAV applications. The findings contribute to a nuanced understanding of UAV mapping dynamics, emphasizing their applicability in complex environments and high-speed scenarios. This research not only serves as a benchmark for mapping algorithm comparisons but also offers practical guidance for selecting sensors tailored to specific UAV mapping applications.
Schlagwörter	Computer Science - Robotics ; Computer Science - Computer Vision and Pattern Recognition
Thema/Rubrik (Code)	629
Erscheinungsdatum	2024-01-12
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel: Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning

Zhang, Jincheng / Zhao, Xiaowei

Applied energy. 2021 Oct. 15, v. 300

2021

Abstract: In this work, a physics-informed deep learning model is developed to achieve the reconstruction of the three-dimensional (3-D) spatiotemporal wind field in front of a wind turbine, by combining the 3-D Navier–Stokes equations and the scanning LIDAR ... ...

Abstract	In this work, a physics-informed deep learning model is developed to achieve the reconstruction of the three-dimensional (3-D) spatiotemporal wind field in front of a wind turbine, by combining the 3-D Navier–Stokes equations and the scanning LIDAR measurements. To the best of the authors’ knowledge, this is for the first time that the full 3-D spatiotemporal wind field reconstruction is achieved based on real-time measurements and flow physics. The proposed method is evaluated using high-fidelity large eddy simulations. The results show that the wind vector field in the whole 3-D domain is predicted very accurately based on only scalar line-of-sight LIDAR measurements at sparse locations. Specifically, at the baseline case, the prediction errors for the streamwise, spanwise and vertical velocity fields are 0.263 m/s, 0.397 m/s and 0.361 m/s, respectively. The prediction errors for the horizontal and vertical direction fields are 2.84° and 2.58° which are important in tackling yaw misalignment and turbine tilt control, respectively. Further analysis shows that the 3-D wind features are captured clearly, including the evolutions of flow structures, the wind shear in vertical direction, the blade-level speed variations due to turbine rotation, and the speed variations modulated by the turbulent wind. Also, the developed model achieves short-term wind forecasting without the commonly-used Taylor’s frozen turbulence hypothesis. Furthermore it is very useful in advancing other wind energy research fields e.g. wind turbine control & monitoring, power forecasting, and resource assessments because the 3-D spatiotemporal information is important for them but not available with current sensor and prediction technologies.
Schlagwörter	energy ; lidar ; prediction ; turbulent flow ; wind power ; wind speed ; wind turbines
Sprache	Englisch
Erscheinungsverlauf	2021-1015
Erscheinungsort	Elsevier Ltd
Dokumenttyp	Artikel
ZDB-ID	2000772-3
ISSN	0306-2619
ISSN	0306-2619
DOI	10.1016/j.apenergy.2021.117390
Datenquelle	NAL Katalog (AGRICOLA)

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