Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to design a prediction model for patient no-shows, thereby assisting hospitals in making relevant decisions, and reducing the probability of patient no-show behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 = 286,503), and a validation set (N2 = 95,501). We used machine learning algorithms such as logistic regression, k-nearest neighbor (KNN), boosting, decision tree (DT), random forest (RF) and bagging to design prediction models for patient no-show in online outpatient appointments. The patient no-show rate of online outpatient appointment was 11.1% (N = 42,224). From the validation set, bagging had the highest area under the ROC curve and AUC value, which was 0.990, followed by random forest and boosting models, which were 0.987 and 0.976, respectively. In contrast, compared with the previous prediction models, the area under ROC and AUC values of the logistic regression, decision tree, and k-nearest neighbors were lower at 0.597, 0.499 and 0.843, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations

RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature Dictionar

Thriving underwater applications demand efficient extreme compression technology to realize the transmission of underwater images (UWIs) in very narrow underwater bandwidth. However, existing image compression methods achieve inferior performance on UWIs because they do not consider the characteristics of UWIs: (1) Multifarious underwater styles of color shift and distance-dependent clarity, caused by the unique underwater physical imaging; (2) Massive redundancy between different UWIs, caused by the fact that different UWIs contain several common ocean objects, which have plenty of similarities in structures and semantics. To remove redundancy among UWIs, we first construct an exhaustive underwater multi-scale feature dictionary to provide coarse-to-fine reference features for UWI compression. Subsequently, an extreme UWI compression network with reference to the feature dictionary (RFD-ECNet) is creatively proposed, which utilizes feature match and reference feature variant to significantly remove redundancy among UWIs. To align the multifarious underwater styles and improve the accuracy of feature match, an underwater style normalized block (USNB) is proposed, which utilizes underwater physical priors extracted from the underwater physical imaging model to normalize the underwater styles of dictionary features toward the input. Moreover, a reference feature variant module (RFVM) is designed to adaptively morph the reference features, improving the similarity between the reference and input features. Experimental results on four UWI datasets show that our RFD-ECNet is the first work that achieves a significant BD-rate saving of 31% over the most advanced VVC

A Contamination Sensor Based on an Array of Microfibers with Nanoscale-Structured Film

A contamination sensor based on an array of microfibers with nanoscale-structured film using evanescent field is proposed and demonstrated theoretically and experimentally. When the molecular contaminants deposit on the nanoscale-structured film, the refractive index of the film will change and the additional loss will be produced due to the disturbance of evanescent field. The possibility of the sensor is demonstrated theoretically by using three-dimensional finite-difference time domain (3D-FDTD). The corresponding experiments have also been carried out in order to demonstrate the theoretical results. Microfibers are fabricated by using hydrogen-oxygen flame-heated scanning fiber drawing method and the nanoscale-structured film coated on the surface of microfibers is deposited by using dip coating process. Then an array of microfibers is assembled to demonstrate the feasibility of the device. The experimental results show that contaminants detection with the device can agree well with the results measured by the laser-scattering particle counter, which demonstrates the feasibility of the new type of contaminant sensor. The device can be used to monitor contaminants on-line in the high-power laser system

Application of graphene in fiber-reinforced cementitious composites: A review

Graphene with fascinating properties has been deemed as an excellent reinforcement for cementitious composites, enabling construction materials to be smarter, stronger, and more durable. However, some challenges such as dispersion issues and high costs, hinder the direct incorporation of graphene-based reinforcement fillers into cementitious composites for industrial production. The combination of graphene with conventional fibers to reinforce cement hence appears as a more promising pathway especially towards the commercialization of graphene for cementitious materials. In this review paper, a critical and synthetical overview on recent research findings of the implementation of graphene in fiber-reinforced cementitious composites was conducted. The preparation and characterization methods of hybrid graphene-fiber fillers are first introduced. Mechanical reinforcing mechanisms are subsequently summarized, highlighting the main contribution of nucleation effect, filling effect, interfacial bonding effect, and toughening effect. The review further presents in detail the enhancements of multifunctional properties of graphene-fiber reinforced cementitious composites, involving the interfacial properties, mechanical properties, durability, electrical conductivity, and electromagnetic interference shielding. The main challenges and future prospects are finally discussed to provide constructive ideas and guidance to assist with relevant studies in future

Tunable Correlated Chern Insulator and Ferromagnetism in Trilayer Graphene/Boron Nitride Moir\'e Superlattice

Studies on two-dimensional electron systems in a strong magnetic field first revealed the quantum Hall (QH) effect, a topological state of matter featuring a finite Chern number (C) and chiral edge states. Haldane later theorized that Chern insulators with integer QH effects could appear in lattice models with complex hopping parameters even at zero magnetic field. The ABC-trilayer graphene/hexagonal boron nitride (TLG/hBN) moir\'e superlattice provides an attractive platform to explore Chern insulators because it features nearly flat moir\'e minibands with a valley-dependent electrically tunable Chern number. Here we report the experimental observation of a correlated Chern insulator in a TLG/hBN moir\'e superlattice. We show that reversing the direction of the applied vertical electric field switches TLG/hBN's moir\'e minibands between zero and finite Chern numbers, as revealed by dramatic changes in magneto-transport behavior. For topological hole minibands tuned to have a finite Chern number, we focus on 1/4 filling, corresponding to one hole per moir\'e unit cell. The Hall resistance is well quantized at h/2e2, i.e. C = 2, for |B| > 0.4 T. The correlated Chern insulator is ferromagnetic, exhibiting significant magnetic hysteresis and a large anomalous Hall signal at zero magnetic field. Our discovery of a C = 2 Chern insulator at zero magnetic field should open up exciting opportunities for discovering novel correlated topological states, possibly with novel topological excitations, in nearly flat and topologically nontrivial moir\'e minibands.Comment: 16 pages, 4 figures, and 2 extended figure

A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention

Deep neural networks (DNNs) have been widely employed in recommender systems including incorporating attention mechanism for performance improvement. However, most of existing attention-based models only apply item-level attention on user side, restricting the further enhancement of recommendation performance. In this paper, we propose a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information, and is built with a co-attention mechanism on attribute-level to achieve performance gains. Specifically, each user and item in ACAM are represented by a set of attribute embeddings at first. Then, user representations and item representations are augmented simultaneously through capturing the correlations between different attributes by a co-attention module. Our extensive experiments over two realistic datasets show that the user representations and item representations augmented by attribute-level co-attention gain ACAM's superiority over the state-of-the-art deep models