A Tourists' Travel Intention in the Context of Covid-19 in Viet Nam

The novel coronavirus pneumonia has seriously affected the development of tourism in Vietnam and even the whole world. Combining the current of VietNam's mitigation and the gradual recovery of tourism, this paper puts the research perspective on tourists' travel intention, and constructs a new theoretical structure by using TPB and TRA theory, This study presents the findings of a research which examined the relationship Subject norms and perceived behavioral control have a significant impact on Attitude and tourists' desire to travel;  the Attitude and desire to travel has a significant positive impact on tourists' travel intention, a little empirical study investigates these relationships together. This article investigates these relationships using SEM with data 437 tourists in the Vietnam. Findings of the study revealed that Attitude mediates the relationship between Subject norms, Perceived Behavioral Control, and Travel intention. Desire to travel mediates the relationship between Subject Norm, Perceived Behavioral Control and Travel intention. Based on the study findings, implications for theory and practice are discussed. Keywords: COVID-19, Theory of planned behaviour(TPB), Theory of reasoned action(TRA),Viet Nam DOI: 10.7176/EJBM/13-14-07 Publication date:July 31st 202

Effect of Silica Nanoparticles on Properties of Coatings Based on Acrylic Emulsion Resin

Effect of nanosilica size on physic-mechanical properties, thermal stability and weathering durability of coating based on acrylic emulsion. Nanocomposite coating formulas were filled by 2 wt.% nanosilica particles which were used in this study, namely: nanosilica from Sigma (15-20nm), nansilica from rice husk (~70-200 nm) and nanosilica from Arosil – Belgium (7-12 nm). Obtained results showed that viscosity flow of coating formula containing nanosilica from Arosil saw the highest flow-time while coating formulas filled other nanosilica and unfilled nanosilica experienced similar flow-time. In presence of nanosilica, coating properties were improved in comparison with neat coating. However, coating filled by nanosilica from rice husk indicated the best properties in studied coating formula. It may explained that size of nanosilica from rice husk was the largest in studied nanosilica particles and thus easily dispersing into coating formula

Deep heterogeneous ensemble.

In recent years, deep neural networks (DNNs) have emerged as a powerful technique in many areas of machine learning. Although DNNs have achieved great breakthrough in processing images, video, audio and text, it also has some limitations such as needing a large number of labeled data for training and having a large number of parameters. Ensemble learning, meanwhile, provides a learning model by combining many different classifiers such that an ensemble of classifiers is better than using single classifier. In this study, we propose a deep ensemble framework called Deep Heterogeneous Ensemble (DHE) for supervised learning tasks. In each layer of our algorithm, the input data is passed through a feature selection method to remove irrelevant features and prevent overfitting. The cross-validation with K learning algorithms is applied to the selected data, in order to obtain the meta-data and the K base classifiers for the next layer. In this way, one layer will output the meta-data as the input data for the next layer, the base classifiers, and the indices of the selected meta-data. A combining algorithm is then applied on the meta-data of the last layer to obtain the final class prediction. Experiments on 30 datasets confirm that the proposed DHE is better than a number of well-known benchmark algorithms

Multi-label classification via incremental clustering on an evolving data stream.

With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings

Fractional flow reserve in assessment of intermediate non-culprit lesions in acute myocardial infarction

Context: Percutaneous coronary intervention (PCI) of intermediate non-culprit arteries can reduce death or heart attack risk in patients with acute myocardial infarction and multivessel coronary artery disease. Aims: To compare the effectiveness of fractional flow reserve (FFR)-guided PCI with angiography-guided PCI for intermediate non-culprit lesions in patients with acute myocardial infarction and multivessel coronary artery disease. Methods: In this cohort study, acute myocardial infarction patients with multivessel coronary artery disease who had successful percutaneous coronary intervention of the culprit artery were divided into group of patients receiving FFR-guided PCI (FFR≤0.80, n = 31) and group of patients receiving angiography-guided PCI (diameter stenosis of 50-90%, n = 62) for lesions in non-culprit arteries. These two groups were followed for at least 1 year for major adverse cardiovascular events. Results: There was no statistically significant difference in major cardiovascular events between FFR-guided percutaneous coronary intervention group and angiography-guided percutaneous coronary intervention group. However, FFR-guided percutaneous coronary intervention group had a lower mortality rate compared to the angiography-guided percutaneous coronary intervention group (3.2% vs. 4.8%). Additionally, there were no reported cases of myocardial infarction in angiography-guided PCI group, while angiography-guided PCI group had a rate of 1.6%. Conclusions: This study found that it remains uncertain whether FFR-guided PCI is superior than angiography-guided PCI for intermediate non-culprit lesions in patients with acute myocardial infarction and multivessel coronary artery disease

Evolving interval-based representation for multiple classifier fusion.

Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation. In the former, the optimal set of base classifiers, meta-classifier, original features, or meta-data is selected to obtain a better ensemble than using the entire classifiers and features. In the latter, the base classifiers or combining algorithms working on the outputs of the base classifiers are made to adapt to a particular problem. The adaptation here means that the parameters of these algorithms are trained to be optimal for each problem. In this study, we propose a novel evolving combining algorithm using the adaptation approach for the ensemble systems. Instead of using numerical value when computing the representation for each class, we propose to use the interval-based representation for the class. The optimal value of the representation is found through Particle Swarm Optimization. During classification, a test instance is assigned to the class with the interval-based representation that is closest to the base classifiers’ prediction. Experiments conducted on a number of popular dataset confirmed that the proposed method is better than the well-known ensemble systems using Decision Template and Sum Rule as combiner, L2-loss Linear Support Vector Machine, Multiple Layer Neural Network, and the ensemble selection methods based on GA-Meta-data, META-DES, and ACO

Multi-layer heterogeneous ensemble with classifier and feature selection.

Deep Neural Networks have achieved many successes when applying to visual, text, and speech information in various domains. The crucial reasons behind these successes are the multi-layer architecture and the in-model feature transformation of deep learning models. These design principles have inspired other sub-fields of machine learning including ensemble learning. In recent years, there are some deep homogenous ensemble models introduced with a large number of classifiers in each layer. These models, thus, require a costly computational classification. Moreover, the existing deep ensemble models use all classifiers including unnecessary ones which can reduce the predictive accuracy of the ensemble. In this study, we propose a multi-layer ensemble learning framework called MUlti-Layer heterogeneous Ensemble System (MULES) to solve the classification problem. The proposed system works with a small number of heterogeneous classifiers to obtain ensemble diversity, therefore being efficiency in resource usage. We also propose an Evolutionary Algorithm-based selection method to select the subset of suitable classifiers and features at each layer to enhance the predictive performance of MULES. The selection method uses NSGA-II algorithm to optimize two objectives concerning classification accuracy and ensemble diversity. Experiments on 33 datasets confirm that MULES is better than a number of well-known benchmark algorithms

Study on synthesis of carboxymethyl cellulose from pineapple leaf waste and its potential applications as a thickener

In this study, cellulose and hemicellulose were successfully extracted from pineapple leaf waste at yields of 58.8 and 16.1% by dried weight, respectively. Carboxymethyl cellulose (CMC) was synthesised from pineapple leaf cellulose by an esterification process using sodium hydroxide (NaOH) and monochloroacetic acid (MCA) with isopropanol as the supporting medium. Preparation of CMC was investigated by varying three free factors, namely, NaOH concentration, MCA dose, and cellulose size. The carboxymethylation process was optimised to produce CMC with differing degrees of substitution (DS). The highest DS of CMC (0.86) was obtained with 15% (w/v) NaOH solution, 0.6 g of MCA/g cellulose, and 50 μm cellulose. The obtained CMC were characterised by FTIR spectroscopy, SEM images and XRD diffractions. Moreover, the thickening performance of obtained CMC was also determined. The influence of the CMC’s molecular weight and degree of substitution on the viscosity of 1% (w/v) aqueous solution was tested. The experimental results suggest that the viscosity of the solution increases with increasing molecular weight and degree of substitution of CMC