Video_2_Deep learning-based control framework for dynamic contact processes in humanoid grasping.MP4

Humanoid grasping is a critical ability for anthropomorphic hand, and plays a significant role in the development of humanoid robots. In this article, we present a deep learning-based control framework for humanoid grasping, incorporating the dynamic contact process among the anthropomorphic hand, the object, and the environment. This method efficiently eliminates the constraints imposed by inaccessible grasping points on both the contact surface of the object and the table surface. To mimic human-like grasping movements, an underactuated anthropomorphic hand is utilized, which is designed based on human hand data. The utilization of hand gestures, rather than controlling each motor separately, has significantly decreased the control dimensionality. Additionally, a deep learning framework is used to select gestures and grasp actions. Our methodology, proven both in simulation and on real robot, exceeds the performance of static analysis-based methods, as measured by the standard grasp metric Q1. It expands the range of objects the system can handle, effectively grasping thin items such as cards on tables, a task beyond the capabilities of previous methodologies.</p

Video_3_Deep learning-based control framework for dynamic contact processes in humanoid grasping.MP4

Humanoid grasping is a critical ability for anthropomorphic hand, and plays a significant role in the development of humanoid robots. In this article, we present a deep learning-based control framework for humanoid grasping, incorporating the dynamic contact process among the anthropomorphic hand, the object, and the environment. This method efficiently eliminates the constraints imposed by inaccessible grasping points on both the contact surface of the object and the table surface. To mimic human-like grasping movements, an underactuated anthropomorphic hand is utilized, which is designed based on human hand data. The utilization of hand gestures, rather than controlling each motor separately, has significantly decreased the control dimensionality. Additionally, a deep learning framework is used to select gestures and grasp actions. Our methodology, proven both in simulation and on real robot, exceeds the performance of static analysis-based methods, as measured by the standard grasp metric Q1. It expands the range of objects the system can handle, effectively grasping thin items such as cards on tables, a task beyond the capabilities of previous methodologies.</p

Video_1_Deep learning-based control framework for dynamic contact processes in humanoid grasping.MP4

Humanoid grasping is a critical ability for anthropomorphic hand, and plays a significant role in the development of humanoid robots. In this article, we present a deep learning-based control framework for humanoid grasping, incorporating the dynamic contact process among the anthropomorphic hand, the object, and the environment. This method efficiently eliminates the constraints imposed by inaccessible grasping points on both the contact surface of the object and the table surface. To mimic human-like grasping movements, an underactuated anthropomorphic hand is utilized, which is designed based on human hand data. The utilization of hand gestures, rather than controlling each motor separately, has significantly decreased the control dimensionality. Additionally, a deep learning framework is used to select gestures and grasp actions. Our methodology, proven both in simulation and on real robot, exceeds the performance of static analysis-based methods, as measured by the standard grasp metric Q1. It expands the range of objects the system can handle, effectively grasping thin items such as cards on tables, a task beyond the capabilities of previous methodologies.</p

HbA1c is Positively Associated with Serum Carcinoembryonic Antigen (CEA) in Patients with Diabetes: A Cross-Sectional Study

<p><br></p> <p><b>Article full text</b></p> <p><br></p> <p>The full text of this article can be found <a href="https://link.springer.com/article/10.1007/s13300-017-0356-2"><b>here</b>.</a></p> <p><br></p> <p><b>Provide enhanced content for this article</b></p> <p><br></p> <p>If you are an author of this publication and would like to provide additional enhanced content for your article then please contact <a href="http://www.medengine.com/Redeem/”mailto:[email protected]”"><b>[email protected]</b></a>.</p> <p> </p> <p>The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.</p> <p><br></p> <p>Other enhanced features include, but are not limited to:</p> <p><br></p> <p>• Slide decks</p> <p>• Videos and animations</p> <p>• Audio abstracts</p> <p>• Audio slides</p

High-Coverage H₂ Adsorption on the Reconstructed Cu₂O(111) Surface

The adsorption of H₂ on the Cu₂O­(111) surface has been studied by spin-polarized density functional theory (DFT+U) calculations and atomic thermodynamics. It has been found that there exists reconstruction on a stoichiometric Cu₂O­(111) surface. The probability distribution of the reconstructed Cu₂O­(111) surfaces as a function of temperature has been analyzed using Boltzmann statistics. It has been found that the molecular H₂ prefers to adsorption on the uncoordinated Cu_CUS atom at low coverages (1/4 or 1/2 monolayer), while totally dissociative H₂ is preferred on the reconstructed Cu₂O­(111) surface at higher coverages (3/4 or 1 monolayer). For H₂ splitting on the Cu₂O­(111) surface, homolytical dissociative adsorption on two surface-uncoordinated Cu_CUS atoms is preferred which is a new mechanism for H₂ on metal oxides. More interesting is that the surface reconstruction will be recovered for eight hydrogen atoms binding on four uncoordinated Cu_CUS and four uncoordinated O_CUS atoms at saturation coverage. It has been found that the adsorbed H atoms will put out the lattice oxygen to the surface at higher coverage (five and six H₂), which agrees well with the experimental findings. The phase diagrams of H₂ binding on ideal and reconstructed Cu₂O­(111) surfaces were plotted and analyzed. In addition, we compared and analyzed the adsorption mechanisms of H₂ splitting on different metal oxides

Photocatalytic degradation of PCP with TiO₂, graphene-TiO₂ and without catalyst under different pH values: (a) pH = 1; (b) pH = 4; (c) pH = 10; (d) pH = 13.

<p>Photocatalytic degradation of PCP with TiO₂, graphene-TiO₂ and without catalyst under different pH values: (a) pH = 1; (b) pH = 4; (c) pH = 10; (d) pH = 13.</p

Comparation of photocatalytic rate constants (k) of PCP with different photocatalyst conditions under 3 kinds of UV irradiation.

<p>Comparation of photocatalytic rate constants (k) of PCP with different photocatalyst conditions under 3 kinds of UV irradiation.</p

Comparation of phenol production under different photocatalyst conditions after 2 hours UV irradiation and its percentage of initial PCP.

<p>Comparation of phenol production under different photocatalyst conditions after 2 hours UV irradiation and its percentage of initial PCP.</p

Schematic experimental system for photo degradation experiments.

<p>Schematic experimental system for photo degradation experiments.</p

Supplementary figure -Supplemental material for Hydrogen peroxide promotes the activation of preeclampsia peripheral T cells

<p>Supplemental material, Supplementary figure for Hydrogen peroxide promotes the activation of preeclampsia peripheral T cells by Jingzhu Lv, Xiaojie Zhang, Caizhi Wang, Hongtao Wang, Ting Wang and Zhongqing Qian in Innate Immunity</p