645 research outputs found
Magmatic architecture within a rift segment: Articulate axial magma storage at Erta Ale volcano, Ethiopia
No full textUnderstanding the magmatic systems beneath rift volcanoes provides insights into the deeper processes associated with rift architecture and development. At the slow spreading Erta Ale segment (Afar, Ethiopia) transition from continental rifting to seafloor spreading is ongoing on land. A lava lake has been documented since the twentieth century at the summit of the Erta Ale volcano and acts as an indicator of the pressure of its magma reservoir. However, the structure of the plumbing system of the volcano feeding such persistent active lava lake and the mechanisms controlling the architecture of magma storage remain unclear. Here, we combine high-resolution satellite optical imagery and radar interferometry (InSAR) to infer the shape, location and orientation of the conduits feeding the 2017 Erta Ale eruption. We show that the lava lake was rooted in a vertical dike-shaped reservoir that had been inflating prior to the eruption. The magma was subsequently transferred into a shallower feeder dike. We also find a shallow, horizontal magma lens elongated along axis inflating beneath the volcano during the later period of the eruption. Edifice stress modeling suggests the hydraulically connected system of horizontal and vertical thin magmatic bodies able to open and close are arranged spatially according to stresses induced by loading and unloading due to topographic changes. Our combined approach may provide new constraints on the organization of magma plumbing systems beneath volcanoes in continental and marine settings
Applications, Evolutions, and Challenges of Drones in Maritime Transport
No full textThe widespread interest in using drones in maritime transport has rapidly grown alongside the development of unmanned ships and drones. To stimulate growth and address the associated technical challenges, this paper systematically reviews the relevant research progress, classification, applications, technical challenges, and possible solutions related to the use of drones in the maritime sector. The findings provide an overview of the state of the art of the applications of drones in the maritime industry over the past 20 years and identify the existing problems and bottlenecks in this field. A new classification scheme is established based on their flight characteristics to aid in distinguishing drones’ applications in maritime transport. Further, this paper discusses the specific use cases and technical aspects of drones in maritime rescue, safety, navigation, environment, communication, and other aspects, providing in-depth guidance on the future development of different mainstream applications. Lastly, the challenges facing drones in these applications are identified, and the corresponding solutions are proposed to address them. This research offers pivotal insights and pertinent knowledge beneficial to various entities such as maritime regulatory bodies, shipping firms, academic institutions, and enterprises engaged in drone production. This paper makes new contributions in terms of the comprehensive analysis and discussion of the application of drones in maritime transport and the provision of guidance and support for promoting their further development and integration with intelligent transport
A data-driven risk model for maritime casualty analysis: a global perspective
No full textMaritime casualty analysis needs to be addressed given the increasing safety demand in the field due to the accidents’ low-frequency and high-consequence features. This paper aims to delve deeper into the factors that affect maritime accident casualties by establishing a new database and conducting an accident casualty evolution analysis. Based on the refined dataset, a pure data-driven Bayesian Network (BN) model is developed to conduct the casualty analysis of maritime accidents that occurred under different ship operational conditions. Methodologically, it introduces new risk factors to improve maritime casualty analysis accuracy through the enriched updated maritime accident database. Furthermore, the new database is categorised into five new datasets based on temporal development trends to better analyse the evolution of the casualty. Five risk analysis models are individually constructed based on different timeframes to illustrate the dynamics of the casualties and compared by seven evaluation indexes to demonstrate the effectiveness of the proposed data-driven BN model. It, for the first time, investigates the changing roles of different risk factors on maritime casualties with time. The insights gained from this model are invaluable, contributing to improved risk prediction and maritime safety strategies by acknowledging the changing patterns of maritime accidents
Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships
No full textIt is critical to have accurate ship trajectory prediction for collision avoidance and intelligent traffic management of manned ships and emerging Maritime Autonomous Surface Ships (MASS). Deep learning methods for accurate prediction based on AIS data have emerged as a contemporary maritime transportation research focus. However, concerns about its accuracy and computational efficiency widely exist across both academic and industrial sectors, necessitating the discovery of new solutions. This paper aims to develop a new prediction approach called Deep Bi-Directional Information-Empowered (DBDIE) by utilising integrated multiple networks and an attention mechanism to address the above issues. The new DBDIE model extracts valuable features by fusing the Bi-directional Long Short-Term Memory (Bi-LSTM) and the Bi-directional Gated Recurrent Unit (Bi-GRU) neural networks. Additionally, the weights of the two bi-directional units are optimised using an attention mechanism, and the final prediction results are obtained through a weight self-adjustment mechanism. The effectiveness of the proposed model is verified through comprehensive comparisons with state-of-the-art deep learning methods, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-LSTM, Bi-GRU, Sequence to Sequence (Seq2Seq), and Transformer neural networks. The experimental results demonstrate that the new DBDIE model achieves the most satisfactory prediction outcomes than all other classical methods, providing a new solution to improving the accuracy and effectiveness of predicting ship trajectories, which becomes increasingly important in the era of the safe navigation of mixed manned ships and MASS. As a result, the findings can aid the development and implementation of proactive preventive measures to avoid collisions, enhance maritime traffic management efficiency, and ensure maritime safety
ASN910957 Supplemental Figure1 - Supplemental material for VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury
No full textSupplemental material, ASN910957 Supplemental Figure1 for VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury by Zian Xia, Wenbin Liu, Fei Zheng, Wei Huang, Zhihua Xing, Weijun Peng, Tao Tang, Jiekun Luo, Lunzhao Yi and Yang Wang in ASN Neuro</p
ASN910957 peak areas - Supplemental material for VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury
No full textSupplemental material, ASN910957 peak areas for VISSA-PLS-DA-Based Metabolomics Reveals a Multitargeted Mechanism of Traditional Chinese Medicine for Traumatic Brain Injury by Zian Xia, Wenbin Liu, Fei Zheng, Wei Huang, Zhihua Xing, Weijun Peng, Tao Tang, Jiekun Luo, Lunzhao Yi and Yang Wang in ASN Neuro</p
A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping
No full textAccurate vessel traffic flow (VTF) prediction can enhance navigation safety and economic efficiency. To address the challenge of the inherently complex and dynamic growth of the VTF time series, a new hierarchical methodology for VTF prediction is proposed. Firstly, the original VTF data is reconfigured as a three-dimensional tensor by a modified Bayesian Gaussian CANDECOMP/PARAFAC (BGCP) tensor decomposition model. Secondly, the VTF matrix (hour ✕ day) of each week is decomposed into high- and low-frequency matrices using a Bidimensional Empirical Mode Decomposition (BEMD) model to address the non-stationary signals affecting prediction results. Thirdly, the self-similarities between VTF matrices of each week within the high-frequency tensor are utilised to rearrange the matrices as different one-dimensional time series to solve the weak mathematical regularity in the high-frequency matrix. Then, a Dynamic Time Warping (DTW) model is employed to identify grouped segments with high similarities to generate more suitable high-frequency tensors. The experimental results verify that the proposed methodology outperforms the state-of-the-art VTF prediction methods using real Automatic Identification System (AIS) datasets collected from two areas. The methodology can potentially optimise relation operations and manage vessel traffic, benefiting stakeholders such as port authorities, ship operators, and freight forwarders
Correction: Li et al. Optimizing Soil Health and Sorghum Productivity through Crop Rotation with Quinoa. <i>Life</i> 2024, <i>14</i>, 745
No full textThe author Wenbin Bai has been changed to the second corresponding author [...
Erratum: Immune landscape in Burkitt lymphoma reveals M2-macrophage polarization and correlation between PD-L1 expression and non-canonical EBV latency program (Infect Agents Cancer (2020) 15: 28 DOI: 10.1186/s13027-020-00292-w)
No full textFollowing publication of the original article [1], the authors identified an error in the author name of Wenbin Wei The incorrect author name is: Wenbin Wi The correct author name is: Wenbin Wei The author group has been updated above and the original article [1] has been corrected
Unknown input and state estimation for linear discrete-time stochastic systems in the presence of constraints
No full textThis thesis presents an unknown input and state estimation algorithm for linear discrete-time stochastic systems with inequality constraints on the inputs and states. The proposed algorithm consists of optimal Bayesian estimation and information aggregation. The optimal estimation provides minimum-variance unbiased (MVU) estimates, and then they are projected onto the constrained space in the information aggregation step. It is shown that the estimation errors and their covariances from the proposed algorithm are strictly less than those from the unconstrained algorithm when projected. Moreover, the expected state estimation errors of the proposed estimation algorithm are proved to be practically exponentially stable.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-08-01The student, Wenbin Wan, accepted the attached license on 2020-05-11 at 10:50.The student, Wenbin Wan, submitted this Thesis for approval on 2020-05-11 at 10:53.This Thesis was approved for publication on 2020-05-13 at 07:35.DSpace SAF Submission Ingestion Package generated from Vireo submission #15308 on 2020-10-02 at 15:30:37Made available in DSpace on 2020-10-07T22:07:09Z (GMT). No. of bitstreams: 2
WAN-THESIS-2020.pdf: 532458 bytes, checksum: 6549b50f3b2b114edcdb5f284c830947 (MD5)
LICENSE.txt: 4207 bytes, checksum: 6537fd658a9ddb127e353fdd48de5085 (MD5)
Previous issue date: 2020-05-13Embargo set by: Seth Robbins for item 116182
Lift date: 2022-10-07T22:07:19Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 116182
Lift date: 2022-10-07T22:44:53Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Onl
- …
