TRA VINH UNIVERSITY JOURNAL OF SCIENCE; p-ISSN: 2815-6072; e-ISSN: 2815-6099

This research aims to survey the factors in the process of quality of lucuma jam (Pouteria lucuma). The main contents included (i) Investigating the effects of added water (v/w) (60%, 80%, 100%, 120%, 140%); (ii) Optimizethe ratio of sugar (50%, 60%, 70%), acid citric (0.2%, 0.3%, 0.4%) and pectin (0.5%, 1.0% and 1.5%). The results showed that by adding 100% water, the jam product achieved the highest sensory score and attractive color for L*, b* of 30.12 , and 30.96, respectively. The ratio of sugar (70%), citric acid (0.2%) and pectin (1.0%) in the supplementary mixture resulted in a total polyphenol of 21.53 mgGAE/L and 62.67oBx of the final product and gained the highest scores from the sensory panel, which evaluated the jam by perceptive color, flavor, taste , and texture.

Nghiên cứu nhằm đánh giá sự biến đổi về chất lượng nước mặt và tần suất giám sát tại ba trạm giám sát liên tục trên sông Tiền (cụm MT1) và sông Hậu (cụm MH1 và MH2) qua giai đoạn 10 năm (2009-2018), với tần suất giám sát 12 lần trong một năm (hàng tháng). Các biến số chất lượng nước bao gồm nhiệt độ (oC), pH, oxy hòa tan (DO, mg/L), chất rắn lơ lửng tổng số (TSS, mg/L), nitrat (N-NO-3, mg/L), orthophosphat (P-PO3 4-, mg/L), nhu cầu oxy hóa học (COD, mg/L), và coliforms (MPN/100 mL). Các thông số chất lượng nước cá nhân đã được đánh giá bằng cách so sánh với Quy chuẩn kỹ thuật quốc gia về chất lượng nước mặt (QCVN 08-MT: 2015/BTNMT). Tần suất giám sát được đánh giá bằng phương pháp Phân Tích Nhóm (CA). Kết quả cho thấy nước mặt trên cả sông Tiền và sông Hậu luôn bị ô nhiễm bởi chất rắn lơ lửng, chất hữu cơ, dưỡng chất và vi sinh vật. Phân Tích Nhóm xác định rằng tần suất giám sát nước hiện tại có thể giảm từ 12 lần xuống còn 5 lần mỗi năm, giảm 58% chi phí giám sát. Nghiên cứu tương lai nên tập trung vào đánh giá các thông số chất lượng nước để bao trùm tất cả các đặc tính chất lượng nước trên sông Tiền và sông Hậu. Hợp tác quốc tế với các quốc gia có ảnh hưởng đến dòng sông trước khi đổ vào Việt Nam trong quản lý nước cần được tăng cường để giải quyết vấn đề nước tiếp tục.

This study proposes a research model of the factors affecting customer satisfaction with the service quality of San Ha Retail Group. The study proposes a service quality measurement model with five factors and 20 observed variables. The study used the method of testing the reliability of the model scale by Cronbach’s Alpha coefficient, the exploratory factor analysis, and the multivariate regression analysis to explore the results of interviews with 130 customers who have used products and services of San Ha Retail Group. The results show that customer satisfaction with the service quality of San Ha Retail Group is influenced by five factors, including empathy, responsiveness, tangibles, reliability, and service capacity. The findings from the research suggest solutions to increase customer satisfaction when using products and services for San Ha Retail Group.

With the exponential growth of information, it is no surprise that we are in a period of history as the Information Age. The rapid growth of data has presented challenges regarding storage and processing technology. This article refers to Apache Spark, an ecosystem that provides many integrated technologies in Big Data processing, including machine learning libraries and data storage platforms. Apache Spark provides distributed data processing for open source applications, loading data in-memory and making operations for analyzing data of any size, with efficient support for popular programming languages like Java, Scala, R, and Python. The article aims to compare the superior computing power of Saprk compared to Hadoop and how to connect Spark with today's popular data processing tools such as the R language.

This paper analyzes the impact  of base erosion and profit shifting (BEPS)on tax revenue in Southeast Asian countries for the period of 2012-2017. ASEAN countries are mostly developing countries with  little experience dealing with the aggressive tax strategies of multinational corporations (MNEs). Accordingly, measuring the impact of BEPS on tax revenue in Southeast Asian countries is essential and urgent. Specifically, this paper measures BEPS by offshore index using the Offshore Investment Matrix approach proposed by UNCTAD (2015). According to this approach, 3.6% of inflow foreign direct investment in six Southeast Asian countries for the period of 2012-2017 originated from tax havens and specialpurpose tax countries. The empirical simulation shows that about $ 2.195 billion of the tax revenue of Southeast Asian countries in2017 is lost because of BEPS.

Ash in biochar from the sugarcane bagasse (SCB) powder has been researched into many practical applications, contributing to limiting environmental pollution. This article used the slow pyrolysis method at 450oC and 500oC to create biochar from the sugarcane bagasse and was based on ASTM D 2866-89 Standard to determine the ash content in this biochar. The results showed that the total ash (TA) content ranged from 4% to 8%, depending on the slow pyrolysis process.

This paper proposes a novel approach for predicting rice yield using rice field images. This approach utilizes the ability of Vision Transformer (ViT) architecture to extract meaningful features from field images for rice yield prediction. The model was first trained with a classification task. The standard Vision Transformer model is modified by replacing the classification layer with a custom regression layer designed to predict rice yield. This modified Vision Transformer model is then trained on field images with corresponding yield data. Various regression models, such as random forests (RF), support vector regressors (SVR), and multi-layer perceptrons (MLP), were employed to find the best regression model for rice yield prediction. Over 11,000 digital images were collected during the ripening stage of rice plants in An Giang Province and Tra Vinh Province (Vietnam), with the rough grain yield recorded after harvest in these areas ranging from 5 to 12 t ha-1. The experimental results indicate that Vision Transformer – Random forests model achieved the lowest mean absolute error is 75.96.

Current hospital management struggles to adapt to fluctuating patient volumes, leading to inefficiencies and crowding. Traditional paper or spreadsheet-based methods lack real-time responsiveness. This research proposes a novel approach utilizing data analytics and prediction using long short-term memory models to modernize medical processes and optimize patient experience. The study aims to develop a data-driven system that aggregates and visualizes information on patient numbers, medical visits, and clinic status. A data warehouse information system was built by leveraging large datasets from Tien Giang Provincial General Hospital (Vietnam). Data analytics tools and long shortterm memory models were then employed to analyze trends and predict future patient volumes and disease patterns. This system offers several advantages: improved scheduling, regulated patient flow, optimized resource allocation, and a more convenient and efficient medical experience. It not only empowers managers with realtime insights and predictive capabilities but also paves the way for broader applications of data analytics and prediction models in healthcare management.

The electronic disaster response operations monitoring information system is a web-based platform designed to help collect, consolidate, organize, and present post-disaster data. It aimed to address the increasing need for accurate and timely data as a reference for decision-makers in disaster response operations. The iterative waterfall model was used to develop the system. ISO 25010 software quality evaluation results indicate that the system has met the users’ needs and requirements and improved their productivity, job performance, and effectiveness. The high ratings for user acceptance indicate high acceptability. To enhance thedeveloped system, integrating short messaging system notifications for aid requests is recommended to facilitate timely receipt and prompt response. Moreover, expanding this functionality to encompass a broader scope, including regional and national levels, can further optimize disaster response efforts.

African swine fever poses a tough challenge to the swine industry, particularly in regions such as the Philippines, where its impact has been devastating. This paper introduces the development of a web-based GIS simulator tailored to model real-time African swine fever spread dynamics. Through iterative processes within the system development life cycle, the system was meticulously designed to address the specific needs of small-scale backyard farmers and government agencies. A thorough requirement analysis and stakeholder engagement defined the system’s core functionalities, including realtime disease monitoring, interactive mapping, and customized best practices dissemination. Implementation involved integrating these features into a user-friendly platform, ensuring usability and effectiveness. Rigorous testing confirmed the reliability and functionality of the system, with high system usability scale scores reflecting positive user perception. Deployment in a pilot municipality showcased the system’s potential to empower stakeholders with timely information and proactive measures against African swine fever outbreaks. The newly developed system for simulating African swine fever spread dynamics and mapping confirmed cases has received positive feedback and a high system usability scale score. It is recommended to further explore and integrate the newly developed system for simulating African swine fever spread dynamics and mapping confirmed cases. To build on this success, stakeholders are encouraged to consider gradually expanding the use of the system, gathering ongoing feedback for improvement, offering  user support and training, nurturing partnerships with research institutions and system usability scale control agencies, and raising awareness of the system’s benefits.