Investigating car purchasing decision-making process using Multi- Objective Optimization Ratio Analysis based Analytical Hierarchy Process Model: An empirical case from Vietnam

This study aims to define and quantify the factors affecting selecting the best car among the market's available alternatives. Many criteria are involved while deciding to purchase the best car from various car models; therefore, car purchasing behaviour is a multi-criteria decision-making problem (MCDM). Proposed criteria are based on the customers’ survey when they are willing to purchase the cars, including Price, Branding, Safety, Performance, Exterior, Fuel efficiency, Maintainance cost, After-Sale Service, and Resale. In this study, the AHP technique calculates each criterion's weight, and then Multi-Objective Optimization Ratio Analysis (MOORA) is employed to rank the car models in a numerical example from Vietnam. The results show that this proposed model can minimize the consumer effort to select a car and make accurate decisions. Furthermore, this study's findings could provide car manufacturers with valuable insight into the criteria that reflect the customer's assessment of the car selection process. JEL: C02, C61, D53, Q1

Contribution to the study on chemical constituents of Hedyotis auricularia L., (Rubiaceae)

From the whole plant of Hedyotis auricularia L., a new glycoside, 1’-deoxy-6’-O-(1-hydroxymethyl-2-hydroxy-1-methoxy)ethylglucopyranoside (1) was isolated along with 1’-O-ethyl-β-D-galactopyranoside (2), 2-formyl-5-hydroxymethylfuran (3), stigmasta-5,22-diene-3-O-β-D-glucopyranoside (4), ursolic acid (5) and oleanolic acid (6). Among them (1), (2), (3), (4)were the first time known to be present in this plant

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

Comparative Study of Image Denoise Algorithms

Denoising is a pre-processing step in digital image processing system. It is also typical image processing challenges. Many works proposed to solve problem with new approaching. They can be divided into two main categories: spatial-based or transform-based. Some denoising methods apply in both spatial and transform domains. The goal of this paper focuses on reviewing denoise methods, classifying them into different categories, and identifying new trends. Moreover, we do experiments to compare pros, cons of methods in survey

IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis

Our study focuses on the potential for modifications of Inception-like architecture within the electrocardiogram (ECG) domain. To this end, we introduce IncepSE, a novel network characterized by strategic architectural incorporation that leverages the strengths of both InceptionTime and channel attention mechanisms. Furthermore, we propose a training setup that employs stabilization techniques that are aimed at tackling the formidable challenges of severe imbalance dataset PTB-XL and gradient corruption. By this means, we manage to set a new height for deep learning model in a supervised learning manner across the majority of tasks. Our model consistently surpasses InceptionTime by substantial margins compared to other state-of-the-arts in this domain, noticeably 0.013 AUROC score improvement in the "all" task, while also mitigating the inherent dataset fluctuations during training

Numerical and Experimental Study on the Grinding Performance of Ti-Based Super-Alloy

The experiments of the surface grinding of Ti-6Al-4V grade 5 alloy (Ti-64) with a resin-bonded cubic Boron Nitride (cBN) grinding wheel are performed in this research to estimate the influence of cutting parameters named workpiece infeed speed, Depth of Cut (DOC), cooling condition on the grinding force, force ratio, and specific energy. A finite element simulation model of single-grain grinding of Ti-64 is also implemented in order to predict the values of grinding forces and temperature. The experimental results show that an increase of workpiece infeed speed creates higher intensified cutting forces than the DOC. The grinding experiments under wet conditions present slightly lower tangential forces, force ratio, and specific energy than those in dry grinding. The simulation outcomes exhibit that the relative deviation of simulated and experimental forces is in the range of 1-15%. The increase in feed rate considerably reduces grinding temperature, while enhancement of DOC elevates the heat generation in the cutting zone

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.Comment: Accepted for presentation at the 51st International Conference on Parallel Processin

The prevalence and risk factors of Spirocerca lupi in domestic dogs in the Mekong Delta of Vietnam

Spirocercosis is caused by Spirocerca spp., which is a chronic disease and might cause life-threatening due to forming cancer in oesophagus in canid carnivores. There are limited studies involving spirocercosis in domestic dogs. Thus, this study aims to investigate the prevalence and analyse risk factors involved in the S. lupi infection in Mekong Delta in Vietnam. In total, 400 fecal samples from domestic dogs were collected from May 2020 to May 2021. The overall prevalence of spirocercosis in domestic dogs in the Mekong Delta was 10.50% by copromicroscope and PCR methods. PCR targeted to the housekeeping gene cytochrome c oxidase I (cox-1) was applied to identify species of Spirocerca spp. and analyse the phylogenetic tree. Outdoor dogs had 5.48 times (CI 95% = 2.45-11.690, p < 0.001) higher risks of S. lupi infection compared to indoor dogs. Besides, seasons and age showed a correlation to the increase the risk of S. lupi infection, while neither dog breeds nor gender influenced the prevalence of this species. The cytochrome c oxidase I (cox-1) gene sequence of S. lupi in the Mekong Delta showed the high homologues to the S. lupi isolates in India, Israel, and the North of Vietnam and belonged to the S. lupi genotype 2

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{https://github.com/thanhtrunghuynh93/estimate}.Comment: Technical report for accepted paper at WSDM 202

Study on the influence of various base agents in K2TiF6 hydrolysis on morphology, structure and photocatalytic activity of TiO2

K2TiF6 was hydrolyzed by various base agents including solutions of NH3, NaOH, and KOH at the same concentration of 3 M. TiO2 material was obtained after filtering, washing, drying and calcination at 500 °C of Ti(OH)4 precipitate. The experimental results showed that there was a significant influence of base agent nature on the morphology, structure, surface area of TiO2 product. Using NH3 agent could obtain uniform TiO2 particles with about 15 nm in size and surface area of 98.93 m2.g-1, while using NaOH or KOH led to TiO2 with varying particle sizes (in the range of 15-500 nm), and decreasing surface area (less than 20 m2.g-1). The nature of base agent also significantly influenced photocatalytic activity of TiO2 product for methylene blue (MB) decomposition. The prepared TiO2 from NH3-using process showed the highest phtocatalytic activity in comparison with TiO2 from KOH/NaOH-using process or commercial TiO2 (P25). Keywords. K2TiF6, TiO2, Ti(OH)4, photocatalyst