A simple and accurate discontinuous Galerkin scheme for modeling scalar-wave propagation in media with curved interfaces

Conventional high-order discontinuous Galerkin (DG) schemes suffer from interface errors caused by the misalignment between straight-sided elements and curved material interfaces. We have developed a novel DG scheme to reduce those errors. Our new scheme uses the correct normal vectors to the curved interfaces, whereas the conventional scheme uses the normal vectors to the element edge. We modify the numerical fluxes to account for the curved interface. Our numerical modeling examples demonstrate that our new discontinuous Galerkin scheme gives errors with much smaller magnitudes compared with the conventional scheme, although both schemes have second-order convergence. Moreover, our method significantly suppresses the spurious diffractions seen in the results obtained using the conventional scheme. The computational cost of our scheme is similar to that of the conventional scheme. The new DG scheme we developed is, thus, particularly useful for large-scale scalar-wave modeling involving complex subsurface structures

All Inkjet-Printed Graphene-Silver Composite Ink on Textiles for Highly Conductive Wearable Electronics Applications

© 2019, The Author(s). Inkjet-printed wearable electronic textiles (e-textiles) are considered to be very promising due to excellent processing and environmental benefits offered by digital fabrication technique. Inkjet-printing of conductive metallic inks such as silver (Ag) nanoparticles (NPs) are well-established and that of graphene-based inks is of great interest due to multi-functional properties of graphene. However, poor ink stability at higher graphene concentration and the cost associated with the higher Ag loading in metal inks have limited their wider use. Moreover, graphene-based e-textiles reported so far are mainly based on graphene derivatives such as graphene oxide (GO) or reduced graphene oxide (rGO), which suffers from poor electrical conductivity. Here we report inkjet printing of highly conductive and cost-effective graphene-Ag composite ink for wearable e-textiles applications. The composite inks were formulated, characterised and inkjet-printed onto PEL paper first and then sintered at 150 °C for 1 hr. The sheet resistance of the printed patterns is found to be in the range of ~0.08–4.74 Ω/sq depending on the number of print layers and the graphene-Ag ratio in the formulation. The optimised composite ink was then successfully printed onto surface pre-treated (by inkjet printing) cotton fabrics in order to produce all-inkjet-printed highly conductive and cost-effective electronic textiles

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant

Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant

Scalable Production of Graphene-Based Wearable E-Textiles

© 2017 American Chemical Society. Graphene-based wearable e-textiles are considered to be promising due to their advantages over traditional metal-based technology. However, the manufacturing process is complex and currently not suitable for industrial scale application. Here we report a simple, scalable, and cost-effective method of producing graphene-based wearable e-textiles through the chemical reduction of graphene oxide (GO) to make stable reduced graphene oxide (rGO) dispersion which can then be applied to the textile fabric using a simple pad-dry technique. This application method allows the potential manufacture of conductive graphene e-textiles at commercial production rates of ∼150 m/min. The graphene e-textile materials produced are durable and washable with acceptable softness/hand feel. The rGO coating enhanced the tensile strength of cotton fabric and also the flexibility due to the increase in strain% at maximum load. We demonstrate the potential application of these graphene e-textiles for wearable electronics with activity monitoring sensor. This could potentially lead to a multifunctional single graphene e-textile garment that can act both as sensors and flexible heating elements powered by the energy stored in graphene textile supercapacitors

Engineering Graphene Flakes for Wearable Textile Sensors via Highly Scalable and Ultrafast Yarn Dyeing Technique

© 2019 American Chemical Society. Multifunctional wearable e-textiles have been a focus of much attention due to their great potential for healthcare, sportswear, fitness, space, and military applications. Among them, electroconductive textile yarn shows great promise for use as next-generation flexible sensors without compromising the properties and comfort of usual textiles. However, the current manufacturing process of metal-based electroconductive textile yarn is expensive, unscalable, and environmentally unfriendly. Here we report a highly scalable and ultrafast production of graphene-based flexible, washable, and bendable wearable textile sensors. We engineer graphene flakes and their dispersions in order to select the best formulation for wearable textile application. We then use a high-speed yarn dyeing technique to dye (coat) textile yarn with graphene-based inks. Such graphene-based yarns are then integrated into a knitted structure as a flexible sensor and could send data wirelessly to a device via a self-powered RFID or a low-powered Bluetooth. The graphene textile sensor thus produced shows excellent temperature sensitivity, very good washability, and extremely high flexibility. Such a process could potentially be scaled up in a high-speed industrial setup to produce tonnes (∼1000 kg/h) of electroconductive textile yarns for next-generation wearable electronics applications

Highly scalable, sensitive and ultraflexible Graphene‐based wearable E‐Textiles sensor for bio‐signal detection

Abstract: Graphene‐based wearable electronic textiles (e‐textiles) show promise for next‐generation personalized healthcare applications due to their non‐invasive nature. However, the poor performance, less comfort, and higher material cost limit their wide applications. Here a simple and scalable production method of producing graphene‐based electro‐conductive yarn that is further embroidered to realize piezoresistive sensors is reported. The multilayer structures improved the conductivity of the piezoresistive sensors, exhibiting good sensitivity with high response and recovery speed. Additionally, the potential applications of such wearable, ultraflexible and machine‐washable piezoresistive sensors as pressure and breathing sensors are demonstrated. This will be an important step toward realizing multifunctional applications of wearable e‐textiles for next‐generation personalized healthcare applications

IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

Infrared small target detection occupies an important position in the infrared search and track system. The most common size of infrared images has developed to 640×512. The field-of-view (FOV) also increases significantly. As the result, there is more interference that hinders the detection of small targets in the image. However, the traditional model-driven methods do not have the capability of feature learning, resulting in poor adaptability to various scenes. Owing to the locality of convolution kernels, recent convolutional neural networks (CNN) cannot model the long-range dependency in the image to suppress false alarms. In this paper, we propose a hierarchical vision transformer-based method for infrared small target detection in larger size and FOV images of 640×512. Specifically, we design a hierarchical overlapped small patch transformer (HOSPT), instead of the CNN, to encode multi-scale features from the single-frame image. For the decoder, a top-down feature aggregation module (TFAM) is adopted to fuse features from adjacent scales. Furthermore, after analyzing existing loss functions, a simple yet effective combination is exploited to optimize the network convergence. Compared to other state-of-the-art methods, the normalized intersection-over-union (nIoU) on our IRST640 dataset and public SIRST dataset reaches 0.856 and 0.758. The detailed ablation experiments are conducted to validate the effectiveness and reasonability of each component in the method