Xiamen University

About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.

First-Principles Study of Lithium Adsorption and Diffusion on Graphene with Point Defects

Abstract: To understand the effect of point defects on the Li adsorption on graphene, we have studied the adsorption and diffusion of lithium on graphene with divacancy and Stone–Wales defect using the first...

Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment

Abstract: Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.

Shape Sensing Techniques for Continuum Robots in Minimally Invasive Surgery: A Survey

Abstract: Continuum robots provide inherent structural compliance with high dexterity to access the surgical target sites along tortuous anatomical paths under constrained environments and enable to perform complex and delicate operations through small incisions in minimally invasive surgery These advantages enable their broad applications with minimal trauma and make challenging clinical procedures possible with miniaturized instrumentation and high curvilinear access capabilities However, their inherent deformable designs make it difficult to realize 3-D intraoperative real-time shape sensing to accurately model their shape Solutions to this limitation can lead themselves to further develop closely associated techniques of closed-loop control, path planning, human–robot interaction, and surgical manipulation safety concerns in minimally invasive surgery Although extensive model-based research that relies on kinematics and mechanics has been performed, accurate shape sensing of continuum robots remains challenging, particularly in cases of unknown and dynamic payloads This survey investigates the recent advances in alternative emerging techniques for 3-D shape sensing in this field and focuses on the following categories: fiber-optic-sensor-based, electromagnetic-tracking-based, and intraoperative imaging modality-based shape-reconstruction methods The limitations of existing technologies and prospects of new technologies are also discussed

Mechanism investigation and suppression of self-discharge in active electrolyte enhanced supercapacitors

Abstract: The self-discharge (SDC) process of active electrolyte enhanced supercapacitors (AEESCs) was investigated systematically. The AEESC with hydroquinone as an active electrolyte showed higher specific capacitance but much faster SDC compared with electronic double layer supercapacitors. The electrode process of the above AEESC was studied, and the mechanism of the SDC process was investigated quantitatively. The migration of the active electrolyte between two electrodes of the device was found to be the primary reason for the fast SDC. Two strategies were designed to suppress the migration of the active electrolyte. Following these strategies, two new AEESCs were fabricated, with a Nafion® membrane as the separator and CuSO4 as the active electrolyte. The two AEESCs showed both high specific capacitances and longer SDC times, demonstrating that the problem of poor energy retention of AEESCs was successfully solved.