Browsing by Author "Zhang, Weiwei"
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Item Benchmark of ReaxFF force field for subcritical and supercritical water(2018-06-21) Manzano, Hegoi; Zhang, Weiwei; Raju, Muralikrishna; Dolado, Jorge S.; López-Arbeloa, Iñigo; Van Duin, Adri C.T.; Tecnalia Research & InnovationWater in the subcritical and supercritical states has remarkable properties that make it an excellent solvent for oxidation of hazardous chemicals, waste separation, and green synthesis. Molecular simulations are a valuable complement to experiments in order to understand and improve the relevant sub- and super-critical reaction mechanisms. Since water molecules under these conditions can act not only as a solvent but also as a reactant, dissociative force fields are especially interesting to investigate these processes. In this work, we evaluate the capacity of the ReaxFF force field to reproduce the microstructure, hydrogen bonding, dielectric constant, diffusion, and proton transfer of sub- and super-critical water. Our results indicate that ReaxFF is able to simulate water properties in these states in very good quantitative agreement with the existing experimental data, with the exception of the static dielectric constant that is reproduced only qualitatively.Item Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis(2021-07) Zhang, Weiwei; Yang, Guang; Zhang, Nan; Xu, Lei; Wang, Xiaoqing; Zhang, Yanping; Zhang, Heye; Del Ser, Javier; de Albuquerque, Victor Hugo C.; IAIn general, artery-specific calcification analysis comprises the simultaneous calcification segmentation and quantification tasks. It can help provide a thorough assessment for calcification of different coronary arteries, and further allow for an efficient and rapid diagnosis of cardiovascular diseases (CVD). However, as a high-dimensional multi-type estimation problem, artery-specific calcification analysis has not been profoundly investigated due to the intractability of obtaining discriminative feature representations. In this work, we propose a Multi-task learning network with Multi-view Weighted Fusion Attention (MMWFAnet) to solve this challenging problem. The MMWFAnet first employs a Multi-view Weighted Fusion Attention (MWFA) module to extract discriminative feature representations by enhancing the collaboration of multiple views. Specifically, MWFA weights these views to improve multi-view learning for calcification features. Based on the fusion of these multiple views, the proposed approach takes advantage of multi-task learning to obtain accurate segmentation and quantification of artery-specific calcification simultaneously. We perform experimental studies on 676 non-contrast Computed Tomography scans, achieving state-of-the-art performance in terms of multiple evaluation metrics. These compelling results evince that the proposed MMWFAnet is capable of improving the effectivity and efficiency of clinical CVD diagnosis.