47,704 research outputs found
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 [...
A new species of Sweltsa (Plecoptera: Chloroperlidae) from Sichuan Province of southwestern China
No full textDong, Wenbin, Cui, Jianxin, Li, Weihai (2018): A new species of Sweltsa (Plecoptera: Chloroperlidae) from Sichuan Province of southwestern China. Zootaxa 4418 (4): 388-392, DOI: 10.11646/zootaxa.4418.4.
Hua Ying fan yi jin zhen : er bian /
No full textHas added title: Translation exercises, by Li Wenbin. Book I. From Chinese into English; Book II. From English to Chinese.Xia bian, 1914 zai ban.Mode of access: Internet
FIGURE 1 in A new species of Sweltsa (Plecoptera: Chloroperlidae) from Sichuan Province of southwestern China
No full textFIGURE 1. Sweltsa brevihamula sp. nov. (holotype male): 1. Adult habitus, dorsal view.Published as part of Dong, Wenbin, Cui, Jianxin & Li, Weihai, 2018, A new species of Sweltsa (Plecoptera: Chloroperlidae) from Sichuan Province of southwestern China, pp. 388-392 in Zootaxa 4418 (4) on page 389, DOI: 10.11646/zootaxa.4418.4.5, http://zenodo.org/record/124494
Assessment of Self-Archiving in Institutional Repositories: Depositorship and Full-Text Availability
No full textThis research evaluates the success of open access self-archiving in several well-known institutional repositories. Two assessment factors have been applied to examine the current practice of self-archiving: depositorship and the availability of full text. This research discovers that the rate of author self-archiving is low and that the majority of documents have been deposited by a librarian or administrative staff. Similarly, the rate of full-text availability is relatively low, except for Australian repositories. By identifying different practices of self-archiving, repository managers can create new strategies for the operation of their repositories and the development of archiving policies
FIGURE 3 in New species and new records of Amphinemurinae (Plecoptera: Nemouridae) from Shaanxi Province of China
No full textFIGURE 3. Amphinemura longihamita sp. nov. (male). a. Adult habitus, dorsal view. b–c. Epiproct, dorsal view. d. Paraproct apex, anterodorsolateral view. e. dorsolateral view. c & e showing variation after KOH treatment.Published as part of Li, Weihai, Dong, Wenbin & Yang, Ding, 2018, New species and new records of Amphinemurinae (Plecoptera: Nemouridae) from Shaanxi Province of China, pp. 149-162 in Zootaxa 4402 (1) on page 153, DOI: 10.11646/zootaxa.4402.1.7, http://zenodo.org/record/120829
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
FIGURES 10–12 in A new species of Sweltsa (Plecoptera: Chloroperlidae) from Sichuan Province of southwestern China
No full textFIGURES 10–12. Sweltsa brevihamula sp. nov. (holotype male): 10. Terminalia, lateral view. 11. Epiproct, closer lateral view. 12. Epiproct, lateral view. 11–12 showing epiproct before KOH treatment.Published as part of Dong, Wenbin, Cui, Jianxin & Li, Weihai, 2018, A new species of Sweltsa (Plecoptera: Chloroperlidae) from Sichuan Province of southwestern China, pp. 388-392 in Zootaxa 4418 (4) on page 391, DOI: 10.11646/zootaxa.4418.4.5, http://zenodo.org/record/124494
Learning robotic motor skills for dynamic grasping, catching, and dexterous in-hand manipulation
No full textEndowing robots with human-level grasping and manipulation skills is an appealing yet challenging research topic over decades. Towards more extensive functionalities, the robots should interact with the objects and environment in a more robust, efficient and intelligent way. With the rapid development of deep learning, merging the learning-based algorithms into the perception and control loop of the robot exhibits promising performances. In this thesis, we explore the implementation of model-free deep reinforcement learning (DRL) and learning from demonstrations (LfD) in acquisition of dynamic grasping and manipulation motor skills of a robotic hand-arm system.
We propose a systematic framework for end-to-end learning robotic control policies with model-free DRL, including simulation set-up, reward design and sim-to-real transfer. The DRL-based training framework is evaluated by three case studies with different robotic tasks and research emphases.
We first introduce the framework in Chapter 3, with a focus on the design of reward function and special initial training states. The trained policy coordinately controls the robotic hand and arm for dynamic reaching, grasping and re-grasping of objects sliding on the ground.
We further propose a multi-modular structure consisting of three control policies trained with proposed framework (Chapter 5). With seamless cross-module integration achieved by the gating policy network, the robot can catch in-flight objects with mitigated impact forces.
In Chapter 6, dexterous in-hand manipulation skills with tactile feedback is trained from scratch in simulation and directly transferred to real robot. We focus on the exploitation of tactile perception and sim-to-real transfer methods.
Moreover, in Chapter 4 we propose a low-cost method to collect human demonstration data for supervised learning. Using only proprioceptive sensing, the trained neural network based controllers can grasp property-unknown objects with adaptive grasping forces
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
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