Hue University

Chuối Già lùn (Musa AAA) là loại trái cây hô hấp đột biến, có cường độ trao đổi chất cao nên nhanh hư hỏng và giảm chất lượng và thời gian lưu trữ. Nghiên cứu này đánh giá hiệu quả của chế phẩm nano nhũ tương tinh dầu sả kết hợp với chitosan (nano TDS-Chi), một lớp phủ sinh học, nhằm kéo dài thời gian bảo quản. Chế phẩm gồm 0,05% nhũ tương tinh dầu sả dạng nano và 1,0% chitosan, được áp dụng theo ba phương pháp: phun trước thu hoạch (TTH), nhúng sau thu hoạch (STH) và kết hợp cả hai (TTH + STH), so sánh với đối chứng không xử lý. Kết quả cho thấy nano TDS-Chi làm chậm đáng kể các biến đổi sinh lý, sinh hóa của quả chuối sau thu hoạch. Trong đó, nghiệm thức TTH + STH cho hiệu quả cao nhất, kéo dài bảo quản đến 20 ngày ở 25 °C. Các chỉ tiêu chất lượng được cải thiện rõ rệt: tỷ lệ hao hụt khối lượng 8,62%, tổng chất rắn hòa tan đạt cực đại 18,71%, hàm lượng acid hữu cơ duy trì 0,18%, đỉnh hô hấp đạt 50,68 mL CO₂·kg⁻¹·h⁻¹, và cường độ sản sinh ethylene 15,73 µl C₂H₄·kg⁻¹·h⁻¹. Kết quả này khẳng định tiềm năng ứng dụng của nano TDS-Chi như một giải pháp bảo quản sinh học thân thiện với môi trường, góp phần giảm tổn thất sau thu hoạch và nâng cao giá trị kinh tế cho sản phẩm chuối Già lùn.

Một số acid béo và dẫn xuất ester, gồm myristic acid (C1), palmitic acid (C2), pentadecanoic acid (C3), linoleic acid (C4), ethyl palmitate (C5), methyl linoleate (C6) và ethyl linoleate (C7), được chọn cho nghiên cứu tính toán in silico về ức chế vi khuẩn hô hấp (Streptococcus pneumoniae). Các phương pháp tính và mô phỏng được sử dụng để đánh giá tiềm năng ức chế của các hợp chất nghiên cứu với protein Q8DQF8. Quá trình mô phỏng docking phân tử đã xác định được các vị trí 1, 2 và 3 là vị trí tối ưu cho quá trình docking phân tử khi nghiên cứu tương tác giữa ligand (C1–C7) và protein Q8DQF8 thông qua năng lượng ức chế DS (kcal.mol–1), chỉ số RMSD, số liên kết chính và tương tác van der Waals. Kết quả cho thấy rằng các hợp chất C1–C7 có khả năng ức chế protein Q8DQF8 theo thứ tự sau: C6 (–11,3 kcal.mol–1) > C3 (–10,9 kcal.mol–1) > C5 (–10,7 kcal.mol–1) > C4 (–9,4 kcal.mol–1) > C2 (–9,0 kcal.mol–1 > C1 (–8,7 kcal.mol–1) > C7 (–8,6 kcal.mol–1). Kết quả mô phỏng động lực học phân tử của các hợp chất có năng lượng ức chế tốt (C3, C5 và C6) với protein Q8DQF8 cho thấy các phức hợp đều khá ổn định và tương tác tốt với protein Q8DQF8, trong đó hợp chất C3 có khả năng tương tác tốt nhất. Kết quả nghiên cứu in silico định hướng cho thực nghiệm để phát triển các phương pháp mới hỗ trợ điều trị bệnh về đường hô hấp.

Background: Although researchers have increasingly explored the combination of machine learning based return forecasts with traditional portfolio construction, the discussion about how these predictive models reshape established methods is still developing. One prominent direction involves extending the classical mean variance approach so that it incorporates forward looking estimates, which is often referred to as the Mean Variance with Forecasting (MVF) framework. In parallel, approaches such as Risk Parity Portfolios (RPP) and Maximum Drawdown Portfolios (MDP) continue to gain attention because they represent different perspectives on risk management. However, despite this growing interest, there is still limited empirical evidence on how Support Vector Regression (SVR) and Random Forest (RF) forecasts affect performance within these three frameworks, and this gap is particularly evident in emerging markets. Objective: This study examines how SVR and RF one day ahead return forecasts influence the risk adjusted performance, drawdown control, and diversification outcomes of the MVF, RPP, and MDP frameworks when applied to stocks in the VN-100 index between 2017 and 2024. The choice of these frameworks is intentional, as each reflects a different level of investor tolerance for risk. MVF tends to appeal to investors who place greater weight on potential returns, RPP seeks a more even distribution of risk which suits investors with a moderate stance, and MDP focuses on limiting losses, making it more suitable for investors who are highly cautious about downside risk. Methods: Daily returns of VN 100 stocks were standardized and then used as inputs for the SVR and RF models. The models were tuned through a grid search on data from 2017 to 2021 and evaluated on the remaining period up to 2024. After generating the return forecasts, portfolios were constructed under the MVF, RPP, and MDP frameworks, and their performance was assessed using monthly excess returns, the information ratio, and total returns in comparison with the VN-100 index. Results: The forecasts generated by SVR showed greater reliability than those obtained from the RF model, and this contributed to stronger risk adjusted performance when applied within the MVF framework. The MDP strategy, which places emphasis on limiting drawdowns, delivered solid protection against large losses, whereas the RPP approach produced more moderate returns along with improved consistency. Conclusion: In the end, matching forecasting techniques and portfolio construction methods with an investor’s risk preferences and view of the market is crucial, since overall performance is shaped by the interaction between predictive inputs and allocation rules. Looking ahead, future studies could investigate a wider range of forecasting models, incorporate transaction costs more explicitly, and explore adaptive forms of optimization that are capable of responding to changing market conditions. Keywords: Machine Learning, Maximum Drawdown, Mean-Variance, Portfolio Optimization, Random Forest, Risk Parity, Support Vector Regression, VN-100 Index

This study was conducted to assess the impact of land allocation on the land tenure security, settlement, and land use stability of households, individuals in Central Vietnam. Structural Equation Modeling (SEM) was employed to test the model using survey data from 400 households and individuals in Quy Nhon, Tay Son, and An Lao, administratively under Binh Dinh Province during the 2019–2023 study period and currently under Gia Lai Province following the July 2025 administrative restructuring. The research results show that land allocation has a direct and positive impact on land tenure security, settlement, and land use stability, while also having an indirect impact through a mediating variable, partly land tenure security (shown by the significance level of the research model at 1%, total effect βLA→LTS–SLUS = 0.603). The research results propose several policy implications for land allocation regulations that combine enhanced legal security, actual security, and perceived security, thereby encouraging land users to settle and stabilize their land use.

BACKGROUND: Breast cancer is the most common malignancy among Vietnamese women. While breast ultrasound is widely accessible, mammography remains unavailable in many areas in Vietnam. There is a limited amount of data about breast cancer detection by ultrasound in the community of the country. This study aimed to evaluate the utility of ultrasound as a primary modality for early breast cancer detection at local health centers.METHODS: We recruited women from the age of 30 and above in the community into this descriptive cross-sectional study. In collaboration with the local Vietnam Women’s Union and health centers, participants completed questionnaires and underwent free breast ultrasound examinations conducted by experienced radiologists. Breast lesions were categorized using the ACR BI-RADS. Women with suspicious findings were recalled to Hue University of Medicine and Pharmacy Hospital for further diagnostic evaluation.RESULTS: Among 6073 participating women, the mean age was 47.3±9.9 years (range: 30-84). Suspicious lesions (Breast Imaging-Reporting and Data System 4-5) were detected in 102 women (1.7%), with five cases confirmed as malignant, resulting in a cancer detection rate (CDR) of 0.82 per 1000 women. Additionally, 58% of participants reported sedentary behavior as a modifiable risk factor.CONCLUSIONS: This study underscores the feasibility of using ultrasound for early breast cancer detection in community settings. The recall rate, CDR and high prevalence of sedentary behavior suggest the need for a community-based approach to improve breast CDR in Central Vietnam.

Abstract This study critically examines the intersection of English language teaching (ELT) and global citizenship education (GCE) in higher education (HE) through data from state-funded universities in the Global South (Colombia, Iraq, Thailand, and Vietnam). It examines how students, educators, and administrators perceive and engage with global and intercultural citizenship through the medium of English. Data were collected through 126 semi-structured interviews and analysed using thematic and content analysis to identify cross-contextual patterns and context-specific interpretations. Participants described a two-fold experience in which English facilitated global academic, professional, and personal access and mobility, while generating concerns about the erasure of local identities and knowledge systems, framed by enduring forms of epistemic inequality, cultural hierarchy, and instrumental pressures reinforced by neoliberal frameworks. In response, many educators and learners challenged dominant Anglophone models by adopting decolonial pedagogies that emphasise ethical engagement, foster critical reflexivity, and reflect locally rooted practices. The study calls for reconfiguring GCE and ELT as interconnected domains of transformation that support inclusive, context-responsive, and socially engaged models of global citizenship within Southern HE settings.

ABSTRACT This study evaluates the efficiency of municipal wastewater (MW)–based microalgae cultivation for biofuel production and wastewater treatment using a meta‐analysis with random resampling and bias‐corrected adjustments. Integrated systems achieved mean removal rates of 87.7% for total nitrogen, 86.2% for ammonium, 84.8% for total phosphorus and 81.5% for phosphate, with lower efficiencies for chemical oxygen demand (64.2%) and nitrate (75.3%). Biodiesel conversion efficiencies ranged from 12.4% to 97.5%, averaging 59.2% (95% CI: [43.3, 73.1]), whereas lipid content varied from 2.7% to 61.7%, averaging 29.1% [26.0, 32.1]. Mixotrophic cultivation and pretreated MW performed best under optimal conditions (22°C–25°C, high nutrient availability, low metal concentrations), whereas stress conditions enhanced lipid accumulation. However, most studies remain laboratory‐scale, limiting real‐world applicability. Key scale‐up challenges include wastewater pretreatment, cultivation strategy, environmental stability, biomass harvesting and process integration. Addressing these challenges requires urgent full‐scale validation in operational wastewater treatment plants.

Protein–protein interactions (PPIs) are essential to understand biological processes and cellular functions. Experimental methods for identifying PPIs are often time-consuming and costly, motivating the development of accurate computational prediction approaches. In this paper, we combine embeddings from the state-of-the-art protein language model ESM-2 with handcrafted descriptors including short-linear motifs representation based on the ELM database. The integration of both deep learning-based and biologically meaningful handcrafted features aims to capture global and local characteristics of protein sequences. These extracted features are subsequently fused and used to train machine learning classifiers to determine interaction likelihoods between protein pairs. Experimental results on benchmark PPI datasets demonstrate that our hybrid feature strategy significantly improves prediction performance, highlighting the complementary strengths of learned embeddings and handcrafted descriptors. This work provides a robust and extensible framework for sequence-based PPI prediction, paving the way for scalable and accurate interactome mapping.