A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. Specifically, we define a so-called pill-prescription matching task, which attempts to match the images of the pills taken with the pills' names in the prescription. We then propose PIMA, a novel approach using Graph Neural Network (GNN) and contrastive learning to address the targeted problem. In particular, GNN is used to learn the spatial correlation between the text boxes in the prescription and thereby highlight the text boxes carrying the pill names. In addition, contrastive learning is employed to facilitate the modeling of cross-modal similarity between textual representations of pill names and visual representations of pill images. We conducted extensive experiments and demonstrated that PIMA outperforms baseline models on a real-world dataset of pill and prescription images that we constructed. Specifically, PIMA improves the accuracy from 19.09% to 46.95% compared to other baselines. We believe our work can open up new opportunities to build new clinical applications and improve medication safety and patient care.Comment: Accepted for publication and presentation at the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2022

An Improved MobileNet for Disease Detection on Tomato Leaves

Tomatoes are widely grown vegetables, and farmers face challenges in caring for them, particularly regarding plant diseases. The MobileNet architecture is renowned for its simplicity and compatibility with mobile devices. This study introduces MobileNet as a deep learning model to enhance disease detection efficiency in tomato plants. The model is evaluated on a dataset of 2,064 tomato leaf images, encompassing early blight, leaf spot, yellow curl, and healthy leaves. Results demonstrate promising accuracy, exceeding 0.980 for disease classification and 0.975 for distinguishing between diseases and healthy cases. Moreover, the proposed model outperforms existing approaches in terms of accuracy and training time for plant leaf disease detection

Simulation Study of Mid-infrared Supercontinuum Generation at Normal Dispersion Regime in Chalcogenide Suspended-core Fiber Infiltrated with Water

We report simulation results of supercontinuum generation in the suspended-core optical fibers made of chalcogenide (As2S3) infiltrated with water at mid-infrared wavelength range. Applying water-hole instead of the air-hole in fibers allows improving the dispersion characteristics, hence, contributing to supercontinuum generations. As a result, the broadband supercontinuum generation ranging from 1177 nm to 2629 nm was achieved in a 10 cm fiber by utilizing very low input pulse energy of 0.01 nJ and pulse duration of 100 fs at 1920 nm wavelength

Mindsponge-based investigation into the non-linear effects of threat perception and trust on recycled water acceptance in Galicia and Murcia, Spain

The water scarcity crisis is becoming more severe across the globe and recycled water has been suggested as a feasible solution to the crisis. However, expanding the use of potable and recycled public water has been hindered by public acceptance. Previous studies suggest threat perception and trust of provided information have positive linear relationships with recycled water acceptance. However, given the complex filtering role of trust in the human mental process, we argue that the effects of threat perception and trust may have non-linear relationships with acceptance of recycled water for drinking. To support and validate this argument, we employed Bayesian Mindsponge Framework analytics on 726 Spanish residents. We found that individuals more concerned about water shortage are less likely to accept using recycled water for drinking if their trust in the water quality and safety is low. Meanwhile, people more concerned about water shortage are more likely to accept using recycled water for drinking if they trust the water quality and safety. The findings suggest the non-linear relationships between threat perception, trust, and recycled water acceptance while validating mindsponge-based reasoning. Moreover, the results also highlight the importance of trust in influencing the mental process’s outcome: recycled water acceptance

Fabrication of Electrochemical Electrodes Based on Platinum and ZnO\text{ZnO} Nanofibers for Biosensing Applications

Platinum (Pt) electrodes were designed in imitation of screen-printed electrodes, and prepared by microelectronic techniques. These electrodes were then modified with zinc oxide (ZnO) nanofibers for biosensing applications. ZnO nanofibers with average length $\sim 20-30\; \mu$ m and diameter $\sim 150$ nm in hexagonal crystalline structure are prepared using electrospinning method. Their surface characteristics were analyzed by field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. Electrochemical properties of modified Pt electrodes were investigated in comparison with commercial carbon screen-printed electrodes. The results showed that the cyclic voltammogram of modified Pt electrodes was stable, but has much lower resistance compared to that of carbon screen-printed electrodes

A Two-Stage Filter for High Density Salt and Pepper Denoising

Image restoration is an important and interesting problem in the field of image processing because it improves the quality of input images, which facilitates postprocessing tasks. The salt-and-pepper noise has a simpler structure than other noises, such as Gaussian and Poisson noises, but is a very common type of noise caused by many electronic devices. In this article, we propose a two-stage filter to remove high-density salt-and-pepper noise on images. The range of application of the proposed denoising method goes from low-density to high-density corrupted images. In the experiments, we assessed the image quality after denoising using the peak signal-to-noise ratio and structural similarity metric. We also compared our method against other similar state-of-the-art denoising methods to prove its effectiveness for salt and pepper noise removal. From the findings, one can conclude that the proposed method can successfully remove super-high-density noise with noise level above 90%. (c) 2020, Springer Science+Business Media, LLC, part of Springer Nature

Synthesis and Optical Characterization of Building-Block Plasmonic Gold Nanostructures

Plasmonics, the field involves manipulating light at the nanoscale, has been being an emergent research field worldwide. Synthesizing the plasmonic gold nanostructures with controlled morphology and desired optical properties is of special importance towards specific applications in the field. Here, we report the chemical synthesis and the optical properties of various plasmonic Au nanostructures, namely Au nanoparticles (AuNPs), Au nanorods (AuNRs) and random Au nano-islands (AuNI) that are the building blocks for plasmonic research. The results show that the AuNPs exhibited a single plasmonic resonance, the AuNRs displayed two identical and separated modes of the resonance, and the random Au nano-islands presented a very broad resonance. Specifically, tailoring the anisotropy of the Au nanorods enabled extending their resonant frequencies from the visible to the near infrared ones, which is in accordance with the finite different time domain simulations

Numerical simulation of all-normal dispersion visible to near-infrared supercontinuum generation in photonic crystal fibers with core filled chloroform

This study proposes a photonic crystal fiber made of fused silica glass, with the core infiltrated with chloroform as a new source of supercontinuum (SC) spectrum. We numerically study the guiding properties of the fiber structure in terms of characteristic dispersion and mode area of the fundamental mode. Based on the results, we optimized the structural geometries of the CHCl3-core photonic crystal fiber to support the broadband SC generations. The fiber structure with a lattice constant of 1 μm, a filling factor of 0.8, and the diameter of the first-ring air holes equaling 0.5 μm operates in all-normal dispersion. The SC with a broadened spectral bandwidth of 0.64 to 1.80 μm is formed by using a pump pulse with a wavelength of 850 nm, 120 fs duration, and power of 0.833 kW. That fiber would be a good candidate for all-fiber SC sources as cost-effective alternative to glass core fibers

Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics

Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation

Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset – a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation quality of the proposed method are better than ones of the compared methods.This research was funded by University of Economics Ho Chi Minh City, Vietnam