Protic Amine Carboxylic Acid Ionic Liquids Additives Regulate α-FAPbI Phase Transition for High Efficiency Perovskite Solar Cells.

The α-phase formamidinium lead tri-iodide (α-FAPbI ) has become the most promising photovoltaic absorber for perovskite solar cells (PSCs) due to its outstanding semiconductor properties and astonishing high efficiency. However, the incomplete crystallization and phase transition of α-FAPbI substantially undermine the performance and stability of PSCs. In this work, a series of the protic amine carboxylic acid ion liquids are introduced as the precursor additives to efficiently regulate the crystal growth and phase transition processes of α-FAPbI .

Low-Cost Hydroxyacid Potassium Synergists as an Efficient In Situ Defect Passivator for High Performance Tin-Oxide-Based Perovskite Solar Cells.

Perovskite solar cells (PSCs) based on SnO electron transport layers have attracted extensive research due to their compelling photovoltaic performance. Herein, we presented an in situ passivation of SnO with low-cost hydroxyacid potassium synergist during deposition to optimize the interface carrier extraction and transport for high power conversion efficiency (PCE) and stabilities of PSCs. The orbital overlap of the carboxyl oxygen with the Sn atom alongwith the homogenous nano-particle deposition effectively suppresses the interfacial defects and releases the internal residual strains in the perovskite.

Gender Differences in the Correlations Between Immune Cells and Organ Damage Indexes of Acute Myocardial Infarction Patients.

Background: There are clear gender differences in the pathological process and outcome in acute myocardial infarction (AMI) patients but inflammatory responses remain clarified. Here, we aimed to analyse the correlations between inflammatory cells and organ injury parameters in AMI patients and compared between male and female groups.

Methods: A total of 603 AMI patients who underwent percutaneous coronary intervention (PCI) within 24 hours of the onset were analysed retrospectively.

Ultrasound to X-ray synthesis generative attentional network (UXGAN) for adolescent idiopathic scoliosis.

Standing X-ray radiograph with Cobb's method is the gold standard for scoliosis diagnosis. However, radiation hazard restricts its application, especially for close follow-up of adolescent patients. Compared with X-ray, ultrasound imaging has advantages of being radiation-free and real-time.

Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning.

Carotid atherosclerosis is one of the leading causes of cardiovascular disease with high mortality. Multi-contrast MRI can identify atherosclerotic plaque components with high sensitivity and specificity. Accurate segmentation of the diseased carotid artery from MR images is very essential to quantitatively evaluate the state of atherosclerosis.

Graph Convolutional Network Discrete Hashing for Cross-Modal Retrieval.

With the rapid development of deep neural networks, cross-modal hashing has made great progress. However, the information of different types of data is asymmetrical, that is to say, if the resolution of an image is high enough, it can reproduce almost 100% of the real-world scenes. However, text usually carries personal emotion and it is not objective enough, so we generally think that the information of image will be much richer than text.

Self-Supervised Enhancement for Named Entity Disambiguation via Multimodal Graph Convolution.

Named entity disambiguation (NED) finds the specific meaning of an entity mention in a particular context and links it to a target entity. With the emergence of multimedia, the modalities of content on the Internet have become more diverse, which poses difficulties for traditional NED, and the vast amounts of information make it impossible to manually label every kind of ambiguous data to train a practical NED model. In response to this situation, we present MMGraph, which uses multimodal graph convolution to aggregate visual and contextual language information for accurate entity disambiguation for short texts, and a self-supervised simple triplet network (SimTri) that can learn useful representations in multimodal unlabeled data to enhance the effectiveness of NED models.

SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning.

Coronavirus disease (COVID-19) broke out at the end of 2019, and has resulted in an ongoing global pandemic. Segmentation of pneumonia infections from chest computed tomography (CT) scans of COVID-19 patients is significant for accurate diagnosis and quantitative analysis. Deep learning-based methods can be developed for automatic segmentation and offer a great potential to strengthen timely quarantine and medical treatment.

Siamese Implicit Region Proposal Network With Compound Attention for Visual Tracking.

Recently, siamese-based trackers have achieved significant successes. However, those trackers are restricted by the difficulty of learning consistent feature representation with the object. To address the above challenge, this paper proposes a novel siamese implicit region proposal network with compound attention for visual tracking.

[CT Image-based Surgery Assist System for Left Atrial Appendage Occlusion].

At present, the standard left atrial appendage occlusion procedure mainly involves two-dimensional imaging methods such as X-ray fluoroscopy and transesophageal echocardiography to guide the operation, which will lead to underestimation of the three dimensional structure of the left atrial appendage and the surrounding tissue, thus adversely affects the surgery. To solve this problem, a surgery assist system for left atrial appendage occlusion based on preoperative cardiac CT images is developed. The proposed system realizes the left atrial appendage parameter measurement based on cardiac CT image, and realizes the calculation of optimal delivery sheath trajectory and three-dimensional simulation of the delivery sheath movement on the basis of a novel delivery sheath trajectory model.

LSCIDMR: Large-Scale Satellite Cloud Image Database for Meteorological Research.

People can infer the weather from clouds. Various weather phenomena are linked inextricably to clouds, which can be observed by meteorological satellites. Thus, cloud images obtained by meteorological satellites can be used to identify different weather phenomena to provide meteorological status and future projections.

Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images.

Accurate and automatic carotid artery segmentation for magnetic resonance (MR) images is eagerly expected, which can greatly assist a comprehensive study of atherosclerosis and accelerate the translation. Although many efforts have been made, identification of the inner lumen and outer wall in diseased vessels is still a challenging task due to complex vascular deformation, blurred wall boundary, and confusing componential expression. In this paper, we introduce a novel fully automatic 3D framework for simultaneously segmenting the carotid artery from high-resolution multi-contrast MR sequences based on deep learning.

Multi-branch fusion network for Myocardial infarction screening from 12-lead ECG images.

Background And Objective: Myocardial infarction (MI) is a myocardial anoxic incapacitation caused by severe cardiovascular obstruction that can cause irreversible injury or even death. In medical field, the electrocardiogram (ECG) is a common and effective way to diagnose myocardial infarction, which often requires a wealth of medical knowledge. It is necessary to develop an approach that can detect the MI automatically.

Skeletal bone age assessments for young children based on regression convolutional neural networks.

Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations pertaining to genetic disorders, hormonal complications and abnormalities in the skeletal system maturity of children. Conventional methods dating back to 1950 were often tedious and suscep-tible to inter-observer variability, and preceding attempts to improve these traditional techniques have inadequately addressed the human expert inter-observer variability so as to significantly refine bone age evaluations. In this paper, an automated and efficient approach with regression convolutional neu-ral network is proposed.

Weighted Mixed-Norm Regularized Regression for Robust Face Identification.

Face identification (FI) via regression-based classification has been extensively studied during the recent years. Most vector-based methods achieve appealing performance in handing the noncontiguous pixelwise noises, while some matrix-based regression methods show great potential in dealing with contiguous imagewise noises. However, there is a lack of consideration of the mixture noises case, where both contiguous and noncontiguous noises are jointly contained.

Neutrophil to lymphocyte ratio as a predictor of myocardial damage and cardiac dysfunction in acute coronary syndrome patients.

Background: Neutrophil to lymphocyte ratio (NLR) in peripheral blood is established to correlate with the morbidity and mortality of heart disease patients. We aimed to define the severity of inflammation (NLR) by observing the association of NLR with cardiac functions or myocardial damage parameters in patients with acute myocardial infarction.

Methods: Data from 715 patients who underwent percutaneous coronary intervention (PCI) within 72 hours of incidence in 2016 were analysed retrospectively.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times.

Online glocal transfer for automatic figure-ground segmentation.

This paper addresses the problem of automatic figure-ground segmentation, which aims at automatically segmenting out all foreground objects from background. The underlying idea of this approach is to transfer segmentation masks of globally and locally (glocally) similar exemplars into the query image. For this purpose, we propose a novel high-level image representation method named as object-oriented descriptor.