South China University of Technology

About: South China University of Technology is a education organization based out in Guangzhou, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 62343 authors who have published 69468 publications receiving 1251592 citations. The organization is also known as: SCUT & Huánán Lǐgōng Dàxué.

Nonfullerene Acceptor Molecules for Bulk Heterojunction Organic Solar Cells

Abstract: The bulk-heterojunction blend of an electron donor and an electron acceptor material is the key component in a solution-processed organic photovoltaic device. In the past decades, a p-type conjugated polymer and an n-type fullerene derivative have been the most commonly used electron donor and electron acceptor, respectively. While most advances of the device performance come from the design of new polymer donors, fullerene derivatives have almost been exclusively used as electron acceptors in organic photovoltaics. Recently, nonfullerene acceptor materials, particularly small molecules and oligomers, have emerged as a promising alternative to replace fullerene derivatives. Compared to fullerenes, these new acceptors are generally synthesized from diversified, low-cost routes based on building block materials with extraordinary chemical, thermal, and photostability. The facile functionalization of these molecules affords excellent tunability to their optoelectronic and electrochemical properties. Within t...

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

Abstract: PurposeTo develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).Patients and MethodsThe prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous–phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive p...

Zn/MnO2 Battery Chemistry With H+ and Zn2+ Coinsertion

Abstract: Rechargeable aqueous Zn/MnO2 battery chemistry in a neutral or mildly acidic electrolyte has attracted extensive attention recently because all the components (anode, cathode, and electrolyte) in a Zn/MnO2 battery are safe, abundant, and sustainable. However, the reaction mechanism of the MnO2 cathode remains a topic of discussion. Herein, we design a highly reversible aqueous Zn/MnO2 battery where the binder-free MnO2 cathode was fabricated by in situ electrodeposition of MnO2 on carbon fiber paper in mild acidic ZnSO4+MnSO4 electrolyte. Electrochemical and structural analysis identify that the MnO2 cathode experience a consequent H+ and Zn2+ insertion/extraction process with high reversibility and cycling stability. To our best knowledge, it is the first report on rechargeable aqueous batteries with a consequent ion-insertion reaction mechanism.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Deep High-Resolution Representation Learning for Visual Recognition

Abstract: High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions in series (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams in parallel and (ii) repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at https://github.com/HRNet .