Synthesis of anti-inflamamtory lipid mediators using soybean flour lipoxygenase

This thesis describes the application of soybean flour as an inexpensive, natural enzyme source in the synthesis of anti-inflammatory lipid-based mediators from omega-3 fatty acids. Soybean flour has been demonstrated to be a more versatile biocatalyst than a commercial soybean enzyme in forming product efficiently and catalysing complex lipid mixtures

THE FACTORS OF CUSTOMER EXPERIENCE THAT AFFECT LOYALTY AT NOVOTEL HANOI THAI HA HOTEL, VIETNAM

Customer experience is gradually becoming important in improving the efficiency of hotel business operations because it contributes to attracting, retaining customers and increasing loyalty to the hotel. Loyalty plays an important role in creating a competitive advantage and increasing hotel brand value. Therefore, this research has built a model to evaluate experiential factors that affect customer loyalty at Novotel Hanoi Thai Ha Hotel. This research uses secondary data collection methods and expert interviews to determine the evaluation model. The method of surveying customers using questionnaires to collect opinions on the effect of their experiences when using the service has contributed to increasing their loyalty to the hotel. These data were processed quantitatively using SPSS 25.0 software to test Cronbach's Alpha reliability and EFA exploratory factor analysis. Pearson correlation and linear regression analysis show that the factors of customers' experience at Novotel Hanoi Thai Ha Hotel, such as service product, quality service, service price, brand image, service space, customer loyalty program and customer care are all positively affect their loyalty. Among them, service product factors have the strongest effect, and customer care has the least effect on loyalty. This research is the scientific basis for hotels to refer to when proposing solutions to improve customer experience and increase loyalty. At the same time, the established model can also be applied to studies on the effect of experience on customer loyalty in hotel businesses.  Article visualizations

Transfer AdaBoost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features

AbstractIn signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer AdaBoost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time

Parameter Estimation and Predictive Speed Control of Chopper-Fed Brushed DC Motors

This paper presents an effective speed control method for brushed DC motors fed by a DC chopper using the concept of Finite Control Set-Model Predictive Control (FCS-MPC). As this control algorithm requires the parameters of the controlled object, the estimation of motor parameters is first performed by using two types of data. The first data includes the output speed response corresponding to the step input voltage to obtain the transfer function in the no-load regime. The second data consists of the motor speed and armature current when a load torque is applied to the motor shaft. The discrete-time equation of the motor armature circuit is used to obtain the future values of the armature circuit current and the motor speed. A cost function is defined based on the difference between the reference and predicted motor speed. The optimal switching states of the DC chopper are selected corresponding to the maximum value of the cost function. The performance of the proposed speed control algorithm is validated on an experimental system. The simulation and experimental results obtained show that the MPC controller can outperform the conventional proportional-integral (PI) controller

Optimizing conditions to improve polyphenol content and screening antioxidant capacity with DNA protection activity of Perilla frutescens

Polyphenols are among the natural antioxidants that have been exploited in recent years for their safe and effective ability against oxidative stress. This project aimed to optimize 3 factor-conditions affecting total polyphenols extracted from Perilla frutescens (L.) Britt as well as DNA protection activity of polyphenols for applications in the fields of dietary supplements, pharmaceuticals, and health care. All extracts contained phenolic compounds and exhibited good antioxidant ability through a ferric reducing antioxidant power assay. The total polyphenolic compounds varied from 6.056 ± 0.08 to 9.630 ± 0.127 mg of gallic acid equivalents (GAE) per 1 g dry weight (dw). The highest phenolic content yield was extracted at 70°C for 60 minutes at a pH of 7.0. However, a sample with the highest polyphenol content had a lower residual DNA concentration than the extract (55°C, 60 min, and pH 6.0) with the greatest reducing power. The result of the DNA protection assay also indicated that the extraction concentration and pH condition had a significant effect on preventing DNA from being damaged by free radicals. The study found the conditions for improving polyphenol in the extraction of P. frutescens (L.) Britt with the aid of Box-Behnken Design. This research also proposed that P. frutescens (L.) Britt is a good source showing DNA protection and antioxidant activity for healthcare

A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction (FLSVR TSP ). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR's tuning parameters, the FLSVR TSP incorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that the FLSVR TSP has achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction

Survey on Vietnamese teachers’ perspectives and perceived support during COVID-19

The COVID-19 pandemic has caused unprecedented damage to the educational system worldwide. Besides the measurable economic impacts in the short-term and long-term, there is intangible destruction within educational institutions. In particular, teachers – the most critical intellectual resources of any schools – have to face various types of financial, physical, and mental struggles due to COVID-19. To capture the current context of more than one million Vietnamese teachers during COVID-19, we distributed an e- survey to more than 2,500 randomly selected teachers from two major teacher communities on Facebook from 6th to 11th April 2020. From over 373 responses, we excluded the observations which violated our cross-check questions and retained 294 observations for further analysis. This dataset includes: (i) Demographics of participants; (ii) Teachers' perspectives regarding the operation of teaching activities during the pandemic; (iii) Teachers' received support from their schools, government bodies, other stakeholders such as teacher unions, and parents' associations; and (iv) teachers' evaluation of school readiness toward digital transformation. Further, the dataset was supplemented with an additional question on the teachers' primary source of professional development activities during the pandemic