Tạp Chí Khoa học Trường Đại học Quốc tế Hồng Bàng

Improving productivity of production line using lean manufacturing

Tran Quynh Le — 2025-06-24

Reducing waste, cutting production costs, and raising productivity are the most important objectives for fast-moving consumer goods manufacturing to maintain competitiveness in the global market and aim for stable and long-term development. Lean manufacturing is a set of tools, processes, and concepts for implementing continuous improvement and eliminating operations that do not bring value to the business. This study emphasizes defining and evaluating overburden, monitoring process efficiency, and formulating improvement plans using the DMAIC framework in terms of lean implementation. Lean techniques such as 3M (Muda, Muri, Mura), and NVA (non-value-added) analysis are used to eliminate waste, reduce cost, and increase line productivity. After applying Lean Manufacturing with the suggested DMAIC framework, the packing system automation will replace the current system. The advantages of this innovation will increase the productivity of the production line. In addition, it can also improve industrial safety and ergonomic work conditions for workers and reduce labor.

Effective forecasting method for determining production demand: A case study

Nguyen Thi Thu Hao — 2025-06-24

Forecasting has long been essential for improving enterprise performance, especially in inventory management, production planning, and overall economic efficiency. This study aims to predict production demand for a local stationery manufacturer with the aim of comparing and finding effective methods between three classical forecasting methods - Moving Average, Exponential Smoothing, and ARIMA - with a modern deep learning approach, Long Short-Term Memory (LSTM), to optimize production or inventory planning and management, and support enterprises in reducing waste and saving many expenses. Despite the ease of implement, the classical forecasting methods have difficulties in capturing and remembering economic trends, while machine learning gives more effective results in this circumstance. The data set includes quantities of demand and production collected and pre-processed by normalizing and stationarity testing. Classical methods are applied to capture the trends and variations in the data. At the same time, the LSTM is built to learn models with more complex patterns or some essential elements that traditional methods might overlook. The efficiency of the model is evaluated through forecasting errors such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). According to experimental results, classical methods provide a sustainable baseline. Still, the LSTM model shows high accuracy, especially in catching up with subtle fluctuations and unusual patterns in the dataset. It is ensured that using advanced deep learning techniques, such as LSTM, could improve forecasting error and reliability and significantly benefit manufacturing planning and inventory control. This study contributes to this field by providing a comprehensive comparative analysis of forecasting methods and practical insights for industrial companies to upgrade their capabilities in forecasting production demand effectively.

State management institutions for import-export procedures in Vietnam

Nguyen Diep Thinh — 2025-06-24

Drawing on Institutional Theory and Transaction Cost Economics, this study investigates how procedural inefficiencies and bureaucratic barriers within state management institutions affect the competitiveness of businesses in Vietnam's import-export sector. Through a mixed-methods approach combining qualitative interviews and quantitative surveys, the research examines the relationships between institutional inefficiencies, transaction costs, and business performance. The findings reveal significant negative impacts: procedural inefficiencies reduce business competitiveness (β = -0.45, p < 0.01), while bureaucratic barriers increase operational costs (β = 0.39, p < 0.05), particularly affecting SMEs. These challenges manifest through lengthy customs clearance processes (averaging 20 hours delay), excessive documentation requirements (12 documents versus the regional standard of 7), and frequent regulatory changes. Digital solutions demonstrate significant moderating effects (β = 0.28, p < 0.05), potentially reducing negative impacts by up to 20%. Based on these findings, the study recommends expanding the National Single Window system, implementing standardized documentation templates, enhancing digital infrastructure, and establishing clear performance metrics. These evidence-based reforms are essential for improving Vietnam's trade competitiveness and creating a more efficient, transparent business environment in the import-export sector.

New evidence between cost of debt and information disclosure level

Le Vinh Quang — 2025-06-24

This study examines the factors that influence the cost of debt (CoD) among companies listed on the Vietnamese stock market, with a particular focus on the level of information disclosure and financial distress prediction indicators. The samples consist of companies included in the VN100 index, which represents the top 100 largest and most liquid stocks in Vietnam. Data was collected from audited financial statements and annual reports published between 2019 and 2023. The study employs multiple regression analysis to explore the relationship between CoD and various explanatory variables, including firm-specific characteristics (such as firm size, profitability, and governance structures), financial distress indicators (e.g Altman z-score), and the level of information disclosure. The findings indicate a statistically significant correlation between the level of information disclosure, financial distress prediction indicators, and the CoD. Specifically, the results reveal that companies with higher levels of information disclosure tend to face higher debt costs. The counterintuitive outcome suggests that firms may disclosure more detailed information when they are under financial stress or attempting to signal transparency to mitigate lender concerns, which in turn may rise the perceived risk among creditors.

Development of a predictive model for FIC index of meropenem-colistin combination against carbapenem-resistant Acinetobacter baumannii

Lam Kien Thanh — 2025-06-24

Background: Acinetobacter baumannii represents a critical antimicrobial resistance challenge in Vietnamese healthcare settings, with carbapenem resistance rates reaching 55-90%. Traditional checkerboard methodology for evaluating antibiotic combinations requires substantial time and resources, limiting routine application in research settings. Objective: To develop a predictive model for the FIC index of meropenem-colistin combination based on MIC values. Methods: A cross-sectional analytical study on 61 carbapenem-resistant A. baumannii strains (6/2022 - 12/2022). MICs were determined by broth microdilution, and synergy was evaluated through FIC index. Eight regression models were compared using 80/20 train-test split with 5-fold cross-validation for stability assessment. Results: All strains were resistant to meropenem but intermediate to colistin. The combination showed synergy in 77% and additivity in 23%. A logarithmic model FIC = 0.649 - 0.155 × ln(Mero) - 0.227 × ln(Col) achieved high accuracy (R² = 0.554). The model demonstrated 95% sensitivity and 75% specificity for synergy prediction with an overall classification accuracy of 88.5%. Conclusion: This is the first study applying machine learning for quantitative FIC prediction. While offering potential research applications, clinical implementation requires extensive validation, given recent evidence against therapeutic efficacy of this combination.

Balancing tradition and innovation: The impacts of AI on university students' language learning journey

Lam Binh En — 2025-06-24

This article examines traditional language learning methods within the context of AI's growing influence. It acknowledges the challenges students face in learning new languages and explores AI tools, such as ChatGPT, highlighting both their opportunities and challenges. While the benefits of AI, such as personalized feedback, are highlighted, a deeper critique of potential drawbacks, such as oversimplification and reduced interaction, is necessary. The article calls for a balance between AI and traditional methods, suggesting that AI should complement, not replace, traditional teaching methods to strengthen the argument.

Impact of blended learning (OMO) on Japanese language students at Hong Bang International University

Nguyen Minh Hung — 2025-06-24

This study explores learners' opinions and their experiences on Online Merging Offline (OMO) blended learning model on the quality of student learning in the Japanese Language Department at Hong Bang International University. Implemented with a 70% offline and 30% online blended learning model during the first semester of the 2023-2024 academic year, the study evaluates student perceptions and identifies areas for improvement. Data were collected through surveys, in-depth interviews, and observations involving second to fourth-year students. The findings reveal a generally positive reception towards the OMO model, with students appreciating the flexibility, convenience, and enhanced interaction provided by the blended format. Access to diverse learning resources and support from instructors and institutional facilities improved learning experiences. However, challenges such as group dynamics, self-discipline, and effective utilization of AI tools were noted. About 30% of students reported no significant improvement in self-directed learning, and some felt overwhelmed by the abundance of resources. Recommendations include ongoing implementation with improvements based on feedback, additional training for instructors, and ensuring robust technological support. Addressing scheduling issues and establishing a continuous feedback mechanism are also crucial. Encouraging extracurricular activities and promoting creativity among both instructors and students can further enrich the learning environment. By adopting these recommendations, Hong Bang International University can refine its blended learning approach, ensuring it meets educational goals and enhances the overall learning experience. This study underscores the importance of adapting to technological advancements and leveraging them to optimize higher education in the 4.0 era.

Evaluating the effectiveness of English language learning supported by artificial intelligence: A Survey at Hong Bang International University

Nguyen Thi Bich Thao — 2025-06-24

The purpose of this study is to determine whether chatbots are likely to improve Hong Bang International University students' English language proficiency. A survey was conducted to investigate 229 English majors' opinions and experiences with chatbots. The results showed the advantages as well as disadvantages of using chatbots. Most of the students like using chatbots since chatbots provide quick feedback, are mobile-friendly and offer conversation and grammar practice. Nevertheless, difficulties in managing complicated language and guaranteeing information authenticity surfaced. Furthermore, restrictions in advanced grammar practice were found. According to the study, chatbots can be useful resources, especially for cultivating fundamental skills and getting quick feedback. Ultimately, the study highlights that chatbots can supplement conventional techniques and human interaction, not replace traditional methods and human interaction for comprehensive language acquisition. In short, the study emphasizes the importance of taking accessibility into account when including chatbots as tools for language acquisition.

Antiglycemic and antihypertension potential of extract from Syzygium malaccense (L.) Merr. & L.M.Perry leaf

Tran Huu Thanh — 2025-06-24

Background: Diabetes and hypertension are diseases of an increased rate in humans. The target enzymes of two diseases are α-glucosidase, α-amylase, dipeptidyl peptidase-IV (DPP-IV), and angiotensin-converting enzyme (ACE). Objective: The aim of this study is to determine the inhibition ability of inhibition ability α-glucosidase, α-amylase, dipeptidyl peptidase-IV (DPP-IV), and angiotensin-converting enzyme (ACE) of extract from S. malaccense leaves. Materials and method: S. malaccense leaves were extracted with 96% alcohol at a ratio of 1:25 (w/v) to obtain total extract (TP). A quantity of total extract was fractionated with ethyl ether, ethyl acetate, and n-butanol solvents to obtain ethyl ether extract (EE extract), ethyl acetate extract (EA extract), n-butanol (BU extract), and the remaining aqueous solution (WA extract). TP extract and fractionated extracts were determined for enzyme inhibitory activity. Results: BU extract has the most potent inhibitory activity on α-amylase enzymes with an IC50 value of 265.23 µg/mL. The ethyl acetate (EA) extract was found with the highest inhibitory activity on α-glucosidase, DPP-IV, and ACE activity at IC50 values of 107.34 µg/mL, 160.07 µg/mL, and 186.32 µg/mL, respectively. Conclusion: The research indicates that leaf extracts can be applied to support the treatment of hypertension and diabetes.

Micromorphology and phytochemical screening of Piper betle L. (Piperaceae)

Do Thi Anh Thu — 2025-06-24

Piper betle L., a common species in many countries, is used in many traditional medicines of countries and in many social, cultural, and religious ceremonies. P. betle has many traditional effects such as colds, bronchial asthma, cough, abdominal pain, rheumatism, bad breath, constipation, conjunctivitis, swollen gums, abscesses, and wound healing... Identifying P. betle species correctly is important for these activities, so this study aims to strengthen the criteria for accurate identification of P. betle species further. P. betle leaves were described microbiologically by staining sections, examining P. betle leaf powder and preliminary phytochemical composition of the leaves by modified Ciuley method. Microscopic characteristics of P. betle roots, stems, and leaves, microscopic characteristics of leaves include xylem-phloem in the leaf microstructure, a layer of palisade tissue present in the leaf anatomy, leaves powder (characterized by soft tissue fragments with colored masses, spiral veins), and preliminary chemical composition including essential oils, coumarins, flavonoids, organic acids, reducing agents and polyuronics. The study provides additional data on microscopic characteristics and chemical composition in the leaves of P. betle species.

Antibacterial activity of Piper betle L. (Piperaceae) and its products

Ho Thi Thach Thuy Thuy — 2025-06-24

Introduction: Piper betle L. is a common species in many countries and is used in the production of natural products mainly for its antibacterial activity. Objective: To investigate the antibacterial ability of 96%, 70%, 50% ethanol extracts, essential oils, and products from P. betle. Research methods: agar disc diffusion and dilution methods to determine MIC (minimum inhibitory concentration). Results: The extracts and essential oils of P. betle have good effects on 2 strains of bacteria E.coli and S.aureus, in which the extract with the best MIC is 96% ethanol extract (0.312 mg/mL and 0.078 mg/mL for E. coli and S. aureus, respectively). The MICs of essential oils were 0.5% and 0.0312% for E. coli and S. aureus, respectively, while products from P. betle were only active against S. aureus but had no effect on E.coli strains. Conclusion: Extracts, essential oils, and preparations from P.betle have antibacterial activity against two strains of bacteria, E.coli and S.aureus. Research should be directed to other strains of bacteria for more comprehensive evaluation.

Botanical characteristics, matK sequence and preliminary chemical composition of Manuka - Leptospermum scoparium, Myrtaceae

Nguyen Do Lam Dien — 2025-06-24

Background: Leptospermum scoparium is an introduced plant, used as an ornamental in Vietnam. Leptospermum scoparium has anti-inflammatory, antibacterial effects, treats cough, fever, and diarrhea. Objectives: The aim of this study is to investigate the botanical characteristics, matK sequence for identification of Manuka, and preliminary phytochemical screening. Materials and Methods: Fresh trees collected in Bao Loc - Lam Dong; the morphology was evaluated through detailed observation of fresh stem, leaf, and flower specimens. Anatomical studies involved preparing cross-sections with a razor, staining them with carmine and iodine green, and capturing microphotographs using a Samsung digital camera mounted on the microscope. The matK sequence was examined, and the chemical composition of the stem and leaf powder was analyzed using the modified Ciulei method. Results: L. scoparium was identified as Leptospermum scoparium J.R. Forst. & G.Forst., based on morphological characteristics and matK sequence with the following featured morphological characteristics: Simple leaf, alternate, 5 sepals free, 5 petals free, the subpart of the petals is present, numerous stamen arranged in the same circle, 5 carpels, inferior ovary and data of anatomy: Sclereid in endodermis; inner phloem, secretory bags and calci oxalate crystals in the cross-section of the stems and leaves, parenchyma tissue in the cross-section of the leaves have a symmetrical structure; phytochemical constituents of the stems and leaves include: Essential oil, carotenoids, triterpenoids, tannins, reducing agents and organic acids; additionally, the leaves contain anthraquinones, flavonoids, and saponins. Conclusion: The study contributed to providing more data about of morphological characteristics, together with the anatomical characteristics of stems, leaves and the chemical composition of the stems, leaves of Manuka in Vietnam.

Cost-effectiveness of daratumumab in multiple myeloma treatment: A systematic review

Nguyen Thi My Duyen — 2025-06-24

Introduction: The cost-effectiveness of daratumumab in multiple myeloma treatment is a considerable criterion in clinical practice with tight health budgets. This review aimed to evaluate the cost-effectiveness of daratumumab in the treatment of multiple myeloma. Methods: The systematic review method was conducted under the guidance of PRISMA, identifying through a literature search from 3 databases (Embase, PubMed, and Cochrane) with relevant keywords. Nine articles among 95 found articles, satisfying the inclusion criteria, were analyzed. All studies were conducted in the US and China between 2017 and 2021, assessing the cost-effectiveness of adding daratumumab to current regimens. Results: Most of the studies were from a payer perspective, with ICER of $30,893-$1,445,533/QALY. Daratumumab was found to be cost-effective in 2 studies and not cost-effective in comparison with regimens without daratumumab in 7 remaining studies. Conclusions: The systematic review evaluating the cost-effectiveness of daratumumab in multiple myeloma treatment recorded varied ICER results ranging from $30,893/QALY to $1,445,533/QALY with heterogeneous conclusions about cost-effectiveness, The US and China are the only setting countries. More similar studies in developing countries are required to fully evaluate the cost-effectiveness of daratumumab in the treatment of multiple myeloma.