Linking Relationship Marketing to Customer Loyalty in The E-Banking Context: The Central Role of Customer Satisfaction

This research examines the interrelationships between relationship marketing, customer satisfaction and customer loyalty in e-banking’s context. The study was conducted in two phases: In-depth interviews and a quantitative survey with a sample of 690 Vietnamese individual and corporate e-banking customers. The research results show that customer satisfaction mediates the impact of relationship marketing on customer loyalty among individual customers. Meanwhile, instead of customer satisfaction, relationship marketing significantly and directly contributes to the loyalty of the corporate customer group. The significance and magnitude of the effects that the five dimensions of relationship marketing’s effectiveness have on customer satisfaction and loyalty, which include the banks’ commitment, customer experience, process-driven approach, service reliability and application of technology, are also different between the two customer segments. This study theoretically contributes to the research stream regarding the mechanism underlying the relationship between relationship marketing’s effectiveness and customer loyalty in the e-banking context, and proposes practical implications for commercial banks to effectively apply relationship marketing in the virtual business environment

Speed profile optimization of an electrified train in Cat Linh-Ha Dong metro line based on pontryagin's maximum principle

An urban railway is a complex technical system that consumes large amounts of energy, but this means of transportation still has been obtained more and more popularity in densely populated cities because of its features of high-capacity transportation capability, high speed, security, punctuality, lower emission, reduction of traffic congestion. The improved energy consumption and environment are two of the main objectives for future transportation. Electrified trains can meet these objectives by the recuperation and reuse of regenerative braking energy and by the energy - efficient operation. Two methods are to enhance energy efficiency: one is to improve technology (e.g., using energy storage system, reversible or active substations to recuperate regenerative braking energy, replacing traction electric motors  by energy-efficient traction system as permanent magnet electrical motors; train's mass reduction by lightweight material mass...); the other is to improve operational procedures (e.g. energy efficient driving including: eco-driving; speed profile optimization; Driving Advice System (DAS); Automatic Train Operation (ATO); traffic management optimization...). Among a lot of above solutions for saving energy, which one is suitable for current conditions of metro lines in Vietnam. The paper proposes the optimization method based on Pontryagin's Maximum Principle (PMP) to find the optimal speed profile for electrified train of Cat Linh-Ha Dong metro line, Vietnam in an effort to minimize the train operation energy consumption

Multitask Aspect_Based Sentiment Analysis with Integrated Bidirectional LSTM & CNN Model

International audienceSentiment analysis or opinion mining used to understand the community's opinions on a particular product. Sentiment analysis involves building the opinion collection and classification system. Aspect-based sentiment analysis focuses on the ability to extract and summarize opinions on specific aspects of entities within sentiment document. In this paper, we propose a novel supervised learning approach using deep learning techniques for multitask aspect-based opinion mining system that support four main subtasks: extract opinion target, classify aspect-entity (category), and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of entity. Using extra POS layer to identify morphological features of words combines with stacking architecture of BiLSTM and CNN with word embeddings achieved by training GloVe on Restaurant domain reviews of the SemEval 2016 benchmark dataset in our proposed method is aimed at increasing the accuracy of the model. Experimental results showed that our multitask aspect-based sentiment analysis model has extracted and classified main above subtasks concurrently and achieved significantly better accuracy than the state-of-the-art methods

NGHIÊN CỨU THU NHẬN PROTEIN TỪ CÁM GẠO

Rice bran protein, a plant protein, has been recognized as nutritionally superior to other proteins due on its reported hypoallergenicity and anti-cancer activity. Therefore, it is considered as a promising protein resource applicable in variety of fields such as functional food, cosmetics, livestock and medicine. However, as of now, commercial rice bran protein is not widely available on the market, especially in Vietnam, because of a lack of extraction methods currently in use. In particular, the available methods can not be used to obtain protein isolates of high quality at affordable commercial price. Vietnam is one of the bigest rice export countries in the world, that makes rice bran an abundant agricultural by-product and thus, a readily sufficient source for protein extraction. This study aimed to establish a simple processing method for extraction of high content of protein isolates from rice bran. The obtained results indicated that rice bran was effectively hydrolysed in 20 minutes with α-amylase (Ternamyl) at concentration of 0.25%, pH 7.0 and 90oC. A procedure of 8 steps for protein extraction was given: i) Suspend rice bran in water and stir for 30 minutes at room temperature; ii) Adjust the suspension to pH 9.0 with NaOH 1N and stir for 4 hours; iii) Adjust the suspension to pH 7.0 with HCl 1N, add 0,25% Ternamyl at 90 oC and hydrolyse for 20 minutes; iv) Centrifuge at 4000 rpm for 20 minutes to collect the supernatant; v) Precipitate protein isolates at pH 4.0 by adding HCl 1N; vi) Centrifuge at 4000 rpm for 20 minutes to collect protein isolates; vii) Wash protein isolates twice with water; viii) Dry the isolates at 50oC. The content of protein isolates from this procedure was 41.77% and the yield of processing was 13,41%. The technological indexes including foaming capacity and emulsion activity were 20% and 73.50, respectively, which were higher compared to the same  product from China.Protein cám gạo là loại protein thực vật có giá trị dinh dưỡng vượt trội do có khả năng chống ung thư và không gây dị ứng cho người sử dụng. Vì thế, nó được xem là một protein lương thực cao cấp, có thể ứng dụng trong nhiều lĩnh vực như chăn nuôi, thực phẩm chức năng, thực phẩm dinh dưỡng, mỹ phẩm và y học. Protein này vẫn chưa được thương mại phổ biến trên thị trường, đặc biệt là ở Việt Nam, vì những hạn chế của các phương pháp tách chiết đang sử dụng hiện nay chưa cho phép thu được sản phẩm có chất lượng cao với giá thành phù hợp. Việt Nam là nước sản xuất lúa gạo đứng thứ hai trên thế giới nên nguồn nguyên liệu phụ thải cám gạo cho mục đích tách chiết protein là vô cùng phong phú. Bài báo này trình bày nghiên cứu về xây dựng một quy trình tách chiết protein cám gạo tương đối đơn giản, cho phép thu nhận được protein có hàm lượng tương đối cao. Kết quả nghiên cứu cho thấy α-amylase (Ternamyl) ở nồng độ 0,25%, pH 7.0, nhiệt độ 90­oC, thời gian thủy phân 20 phút có khả năng loại bỏ hiệu quả  tinh bột từ nguyên liệu. Quy trình công nghệ thu nhận protein từ cám gạo xây dựng được gồm 8 bước chính: i) Dịch cám gạo trong nước cất (1:7) được khuấy trong 30 phút; ii) Điều chỉnh dịch cám gạo tới pH 9.0 bằng NaOH 1N và tiếp tục khuấy trong 4 giờ ở nhiệt độ phòng; iii) Điều chỉnh dịch cám gạo về pH 7.0 bằng HCl 1N, bổ sung Termamyl 0,25% ở 90oC và tiến hành thủy phân trong 20 phút; iv) Ly tâm 4000 vòng trong 20 phút để thu dịch trong; v) Tủa protein ở dịch ly tâm bằng HCl 1N tại pH 4.0; vi) Ly tâm thu cặn tủa ở 4000 vòng trong 20 phút; vii) Rửa cặn tủa 2 lần bằng nước khử trùng; viii) Sấy khô mẫu ở 50oC thu protein. Protein thu được từ quy trình này có hàm lượng đạt 41,77% và hiệu suất là 13,41%. Các chỉ số công nghệ của chế phẩm bao gồm độ tạo bọt đạt 20%, độ tạo nhũ tương đạt 73,45, đều cao hơn so với protein đối chứng của Trung Quốc

Multivalvular involvement associated with Libman-Sacks endocarditis detected by multimodality imaging: A case report

Libman-Sacks endocarditis accounts for 6–11 percent of systemic lupus erythematosus patients and is associated with varying degrees of valvular dysfunction, increased risk for stroke and transient ischemic attacks, and increased mortality. In previous studies, left-sided valvular Libman-Sacks vegetations were more frequently detected than right sided vegetations; reported cases of bilateral involvement is very rare. A comprehensive clinical assessment and the multimodality imaging is of utmost importance in the management of systemic lupus erythematosus. In this case report, we describe a 31-year-old female patient with uncontrolled systemic lupus erythematosus initially presented with gastrointestinal symptoms but eventually had a vegetation-like structure on the posterior leaflet of the mitral valve which was revealed during routine echocardiography. Two-dimensional/three-dimensional transthoracic and transesophageal echocardiography, cardiac magnetic resonance, and cardiac computed tomography further characterized the mitral valve vegetation and revealed an additional vegetation of the pulmonary valve. Echocardiography remains the cornerstone for the detection of Libman-Sacks vegetations. Cardiac MRI and cardiac CT are useful in characterizing lesion size and effects and may prove particularly helpful in the assessment of right-sided or multivalvular endocarditis. The presence of focal brain lesions on brain MRI prompted antithrombotic therapy

Characterization of Ag-Doped p-Type SnO Thin Films Prepared by DC Magnetron Sputtering

Crystalline structure and optoelectrical properties of silver-doped tin monoxide thin films with different dopant concentrations prepared by DC magnetron sputtering are investigated. The X-ray diffraction patterns reveal that the tetragonal SnO phase exhibits preferred orientations along (101) and (110) planes. Our results indicate that replacing Sn2+ in the SnO lattice with Ag+ ions produces smaller-sized crystallites, which may lead to enhanced carrier scattering at grain boundaries. This causes a deterioration in the carrier mobility, even though the carrier concentration improves by two orders of magnitude due to doping. In addition, the Ag-doped SnO thin films show a p-type semiconductor behavior, with a direct optical gap and decreasing transmittance with increasing Ag dopant concentration

Targeting effect of folate on cancer cell through curcumin carrier nano-system

Folate receptor (FR) is well known for its overexpression on surface of various cancer cell lines, which is identical to normal tissue. Folic-based targeting drug delivery systems, therefore, are one of the most effective targeting carriers that effectively bind to FR up-regulated cancer cells. Curcumin was used both for labeling and chemotherapy. The materials were characterized and structurally confirmed by FT-IR spectra, fluorescent images and FE-SEM images. Bioassays were conducted on HeLa and HT29 cancer cell lines after 4 and 12 hours. Results show that folic acid significantly enhanced both targeting efficiency and internalization of curcumin to FR-expressing cancer cells

Light Absorption and Luminescence Properties of the Blend Poly(N-vinylcarbazole)/Poly(N-hexylthiophene)

We have investigated the enhancement absorption light and luminescence properties of the blend conducting polymers using poly(N-vinylcarbazole) and poly(N-hexylthiophene). The optimized material showed a broad absorption in the region of ultra violet to near infra-red and the better of luminescence ability than the pristine conducting polymers. The remarkable improvements in photoluminescences of the blends provide useful information to the application of this material in fabrication of optical – electronic devices.

Investigation of Thermal Annealing Condition on the Optical and Electrical Properties of Hybrid Silver Nanowires/Reduced Graphene Oxide (AgNWS/rGO) Films

Herein we report a electrode in the hybrid  structure of the silver nanowires (AgNWs) with reduced graphene oxide (rGO) deposited on glass or PET substrate. The assembly and contacting in procedures of this hybrid film have been strongly affected by preparation conditions, especially annealing temperature. In this work, we have investigated the effects of thermal annealing on interconnected at nanowire junctions and between AgNWS network and rGO films via their sheet resistance and transmission. The annealing temperature was varied from 120$^{\circ}$C to 180$^{\circ}$C with 10$^{\circ}$C step to find out optimal temperature at which the wires can be joined together. The results show that at 170$^{\circ}$C, the sheet resistance and transmission of the hybrid rGO/Ag NW film are $R_{s} = 10.7\;\Omega$ sq$^{ - 1 }$ and $T = 77$% (at wavelength 550 nm) corresponding to the ratio of direct conductivity to optical conductivity $\sigma _{DC}/\sigma _{OP} = 126$ which is the best obtained value. It is expected that the hybrid AgNWS/rGO film can replace ITO film in the near future

Long short-term memory (LSTM) neural networks for short-term water level prediction in Mekong river estuaries

This study firstly adopts a state-of-the-art deep learning approach based on a Long Short-Term Memory (LSTM) neural network for predicting the hourly water level of Mekong estuaries in Vietnam. The LSTM models were developed from around 8,760 hourly data points within 2018 and were evaluated using the Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). The results showed that the NSE values for the training and testing steps were both above 0.98, which can be regarded as very good performance. Furthermore, the RMSE were between 0.09 and 0.11 m for the training and between 0.10 and 0.12 m for the testing, while MAE for the training ranged from 0.07 to 0.08 m and varied from 0.08 to 0.10 m for the testing. The LSTM networks appear to enable high precision and robustness in water level time series prediction. The outcomes of this research have crucial implications in river water level predictions, especially from the viewpoint of employing deep learning algorithms