The University of Danang - Journal of Science and Technology
Tạp chí của Đại học Đà Nẵng
Research on the mechanical properties of mortar utilizing carbonated steel slag
This study evaluates the performance of cement mortar utilizing 100% carbonated electric arc furnace (EAF) steel slag substitute for sand. Raw slag underwent aqueous carbonation, achieving a 2% CO2 uptake by transfer CaO into CaCO3, verified via XRD and TGA. Three formulations, a reference, untreated slag (100SS), and carbonated slag (100SS-C), were assessed for mechanical and volumetric properties. While 100SS reduced baseline strengths compared to the control, carbonation mitigated these deficits. Specifically, the 100SS-C mixture demonstrated a 4.27% increase in flexural strength and enhanced compressive performance relative to 100SS. Furthermore, precipitated CaCO3 densified the microstructural matrix and obstructed capillary pores, significantly reducing drying shrinkage. These findings confirm viability of carbonated EAF slag in mortar, presenting approach to waste valorization, resource conservation, and carbon sequestration.
Does family really matter for Generation Z student’s digital entrepreneurial intention? Evidence from Vietnam
This study examines the role of family in the digital entrepreneurial intentions of Generation Z (Gen Z) in developing countries. Drawing on survey data from 377 Gen Z students in Vietnam, this study clarifies how family background and family support influence young people’s digital entrepreneurial intention. The results show that family background significantly impacts perceived behavioral control (β = 0.294), while family support positively influences entrepreneurial attitude (β = 0.360) and subjective norms (β = 0.373). The model explains 41.7% of the variance in digital entrepreneurial intention, with family support and family background serving as key upstream antecedents. Notably, the path from subjective norms to intention was not statistically significant and is not supported. This research fills a gap regarding family’s role in digital entrepreneurship in developing countries and extends the Theory of Planned Behavior (TPB) by integrating family-related variables as key antecedents of Gen Z students’ digital entrepreneurial intention.
Sum-rate maximization in D2D-underlaid noma cell-free MEC networks via partial offloading and SCA
Integrating mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) into cell-free massive MIMO (CF-mMIMO) networks is a potential approach for ultra‐reliable and low-latency communication (URLLC) 6G. However, in dense deployments, underlay device-to-device (D2D) communication introduces heterogeneous spatial interference, rendering successive interference cancellation (SIC) based on channel gain and degrading NOMA performance. This paper examines resource allocation and partial computational offloading to maximize the total NOMA-CF-mMIMO MEC sum rate under latency and power constraints, and proposes a flexible offloading framework with SIC criteria based on the effective channel-to-interference ratio (ECIR). The NP-hard non-convex problem is solved using an alternating optimization (AO) algorithm combining ECIR-based NOMA-SIC pairing and successive convex approximation (SCA). Simulations show that the proposed method converges rapidly and consistently achieves a superior sum rate over conventional benchmarks, even under dense D2D interference.
The impact of ChatGPT on the learning effectiveness of students at Hanoi University of Mining and Geology
In the context of digital transformation, artificial intelligence (AI) is increasingly being used in higher education, with ChatGPT becoming a popular tool among students. However, the effects of ChatGPT on students’ learning effectiveness have not been clearly measured. By employing reliability testing using Cronbach’s Alpha, Exploratory Factor Analysis (EFA), and linear regression analysis with SPSS software, this study investigates the impact of ChatGPT on the learning effectiveness of students at Hanoi University of Mining and Geology. The results indicate that ChatGPT has both positive and negative effects on students’ learning effectiveness, among which frequency of use is the most influential factor with β = 0.468. Based on the research findings, the authors propose several recommendations to improve the effective use of ChatGPT in learning, thereby enhancing students’ competencies and contributing to the improvement of educational quality in Vietnam.
Development of a machine learning–driven intelligent mobile health framework for diagnosis and personalized treatment recommendation in type 2 diabetes
Type 2 diabetes (T2D) is one of the fastest-growing chronic diseases, creating an urgent need for automated tools to support early diagnosis and effective disease management. This study proposes a machine learning–driven clinical decision support framework for a three-class classification problem, including normal, prediabetes and T2D. The proposed approach establishes an end-to-end clinical data analysis pipeline, including data collection, preprocessing, feature selection by employing the Fisher Ratio criterion and a probabilistic Naïve Bayes classification model. Experimental results on real-world data from Vietnamese patients demonstrate that the proposed model achieves an accuracy of 96.67%, outperforming baseline methods. Based on these results, the proposed approach is implemented as a cross-platform application (web and mobile), demonstrating the potential of the proposed artificial intelligence–based model as a clinical decision support system (CDSS) to assist physicians in diagnosis and personalized treatment recommendation.
On the upper convergence of solution sets for Stampacchia-type fuzzy strong vector quasi-equilibrium problems and applications
This paper is to investigate the upper convergence of the solution set for strong Stampacchia-type vector quasi-equilibrium problems in fuzzy environments. First, the study introduce the notions of fuzzy sets and fuzzy mappings. Based on these concepts, establish a new model of strong Stampacchia-type vector quasi-equilibrium problems in fuzzy environments (abbreviated as (SVQEP)). Next, by employing sequences of functions and sequences of sets, we derive sufficient conditions for the upper convergence of the solution set to problem (SVQEP). Finally, the study a fuzzy optimization problem as a practical application of the obtained results. The results are new and differ from several previously published works. Some illustrative examples are also presented to clarify the obtained results.
Digital citizenship education in preschool and primary school: a systematic literature review
Digital citizenship education (DCE) has become an important priority beginning in preschool and primary education, as children are engaging with digital technologies at an increasingly young age. This article presents a systematic literature review of 18 international studies published between 2012 and 2023 to identify major approaches, forms of implementation, and emerging trends in DCE at these levels. The findings show three main areas of focus: preventing risks such as cyberbullying and online safety threats; fostering safe, responsible, and ethical online behavior; and developing digital and information literacy. Implementation appears in various forms, including integrated classroom activities, digital storytelling, educational games, augmented reality, animated videos, cybersecurity platforms, and community-based projects. However, evidence at the preschool level remains limited, while civic participation, digital voice, and social critique receive less attention. The review highlights the need for age-appropriate approaches, stronger teacher capacity, and closer school-family-community collaboration.
Surfaces in P^4 defined by three-point subschemes of length 5
Studying algebraic varieties through linear systems of homogeneous forms with prescribed base loci provides valuable insights into their geometric properties and invariants. It is well known that cubic forms in P^2 with six base points in general position define smooth cubic surfaces with 27 lines in P^3. Recent works have focused on linear systems with four or five base points, whose images are surfaces of degree 3 or 4 in P^4. In this paper, we investigate linear systems of cubic forms defined by a three-point subscheme of length 5 involving fat points. We analyze the resulting configurations, including their degrees, singular loci, and the number of lines on the associated surfaces, thereby contributing to the classification of surfaces in P^4 and clarifying the correspondence between linear systems and varieties, with potential applications in information technology and related engineering fields.
Application of Artificial Intelligence (AI) in identifying effective Japanese language learning sequences for junior high school students
To support junior high school students specializing in Japanese in learning more effectively and appropriately, this study employs Artificial Intelligence (AI) to identify the optimal learning sequence. The dataset includes students’ interest levels, current learning sequences, and academic performance in other subjects. A Random Forest machine learning algorithm is used to develop a predictive model of Japanese language achievement. The analysis reveals that Mathematics has the strongest influence on Japanese learning performance. This may be due to the logical reasoning, spatial visualization, and modeling skills cultivated through Mathematics, which help students learn and remember kanji more effectively. Implementation of the proposed learning sequence increases the predicted average score from 8.70 to 8.84. Although this improvement is modest, the findings demonstrate the feasibility of the proposed approach and suggest that it may help improve Japanese learning outcomes.
The impacts of monetary policy and digital transformation on green innovation performance in Asia: a quantile regression analysis
This study investigates the impact of monetary policy and digital transformation on green innovation performance across 40 Asian countries using panel data from 2010 to 2023. Applying the method of moments quantile regression (MMQR), the results show that both tight monetary policy, reflected in rising interest rates, and expansionary monetary policy, reflected in money supply growth, negatively affect green innovation performance in Asian countries. However, digital transformation exerts a positive effect on countries with moderate and high levels of green innovation performance. Based on these findings, the paper proposes several policy implications and highlights the importance of monetary policy oriented toward green innovation in the context of digital transformation in Asia.
Lyapunov-based nonlinear model predictive control for sustainable operation of ship-mounted container cranes
The rapid development of the marine industry has driven the emergence of ultra-large container ships, creating challenges in accessing small ports and ensuring efficient cargo handling. This paper investigates an advanced control framework for a ship-mounted container crane to enable direct transshipment at sea. Specifically, a Lyapunov-based nonlinear model predictive control (NMPC) approach is proposed to address system nonlinearities and environmental disturbances. The proposed method combines the rigorous stability guarantees of Lyapunov-based control with the optimization and constraint-handling capabilities of NMPC, thereby providing stronger theoretical stability guarantees while offering optimization and constraint-enforcement capabilities. Numerical simulations demonstrate that, compared with robust nonlinear control approaches, the proposed method effectively stabilizes the crane states and constrains the control inputs within prescribed domains, thereby ensuring reliable and sustainable operation.
Rice condition classification from UAV RGB imagery using slic superpixel segmentation and convnext
Rice lodging is a common problem in paddy fields that reduces yield and complicates harvesting. This study presents a UAV-based framework for rice condition classification using RGB imagery. First, the Simple Linear Iterative Clustering (SLIC) algorithm is applied to segment each UAV image into compact superpixel regions, which help preserve major field boundaries and reduce pixel-level noise. Next, a ConvNeXt-Small network is used to classify rice conditions based on the segmented regions. A labeled dataset was manually constructed from UAV RGB images with three classes: green rice, lodged rice, and ripened rice. Data augmentation was further applied to improve sample diversity. Experimental results show that the proposed SLIC–ConvNeXt pipeline can effectively distinguish the three rice conditions, achieving an overall accuracy of 98.07% on the validation set of the current dataset. These results indicate that superpixel-guided deep learning is a promising approach for UAV-based rice condition assessment.
The effects of metacognitive strategy instruction on English listening comprehension of Grade 7 students at a lower secondary school in Da Nang City
Listening comprehension has long been considered challenging to language learners, and language teachers have been seeking instructional approaches to enhancing learners’ strategies and performance in listening. Metacognitive strategy instruction (MSI) emerges as a critical approach to improving not only students’ strategic awareness for, but also their performance in, listening comprehension. This study investigates the effects of MSI on Grade 7 students’ English listening comprehension at a lower secondary school in Da Nang City. Using a mixed-methods quasi-experimental design, the study involves two groups of learners: the experimental group receiving MSI based on the Cognitive Academic Language Learning Approach (CALLA) and the control group following the regular curriculum. The findings indicate that MSI integration by means of teacher modelling, guided practice, and scaffolding can foster learners’ metacognitive awareness, thus leading to increased listening comprehension. The findings provide empirical evidence for the effective integration of MSI in language classrooms.
Cytotoxic effects of the ethyl acetate fraction from Schima superba stem bark in KG-1 leukemia cells
This study aimed to evaluate the cytotoxic activity of different fractions of Schima superba stem bark on human leukemia cells. Among the tested fractions, the ethyl acetate fraction (EA) showed the strongest inhibitory effect on cell viability. Flow cytometric analysis revealed that EA treatment induced accumulation of KG-1 cells in the G0/G1 phase, accompanied by a reduction in S and G2/M populations, indicating arrest at the G1/S checkpoint. An increased sub-G1 fraction was also observed, suggesting apoptosis. In addition, EA elevated caspase-3 activity in a time- and concentration-dependent manner. These findings indicate that the cytotoxic effect of the EA fraction of Schima superba is associated with G1/S cell cycle arrest and caspase-dependent apoptosis, supporting further phytochemical and mechanistic investigations of its anticancer activity.
Shaping minds in motion: integrating Artificial Intelligence (AI) and digital transformation in CDIO-based engineering education
In the context of Industry 4.0, higher education institutions are required to redesign training programs to equip graduates with both technical competence and digital adaptability. This paper analyzes key components of CDIO (Conceive-Design-Implement-Operate), highlighting the role of AI-driven and digital tools in enhancing teaching and learning effectiveness. Furthermore, the paper critically discusses the implementation of outcome-based education aligned with societal and labour market demands. Special attention is given to the radical challenges of delivering specialized engineering courses in English, along with proposed optimal solutions to improve student engagement, learning efficiency, and professional readiness in a digitally transformed and globally connected workforce.
Enhancing AI application competence in teaching and education among primary education students at The University of Danang - Campus in Kontum
This article examines the requirements for developing Artificial Intelligence (AI) application competencies among Primary Education students at the University of Danang - Campus in Kontum within the context of educational digital transformation. Survey results indicate that although students have begun using AI in their learning, they remain limited in integrating AI into pedagogical activities, critically verifying information, and using AI responsibly and ethically. Furthermore, primary school teachers in the Northern Central Highlands express a strong demand for AI applications in teaching but face constraints related to digital literacy and AI tool utilization skills. Based on these findings, the study proposes strategic solutions for integrating AI into primary teacher education curricula through practice-oriented training and aligning AI competencies with professional teacher standards. Initial pilot implementation in selected courses and teaching internships provides preliminary evidence of the feasibility and positive impact of the proposed solutions in primary teacher education.
Using Fourier series to improve the accuracy of the nonlinear grey model and its application in forecasting car sales in Vietnam
The accuracy of forecasting models plays a crucial role in supporting decision-making, especially in environments with nonlinear data and complex fluctuations. This study proposes an improvement to the nonlinear Grey model NGM (1,1,k,c) by integrating a Fourier series to adjust residual errors and enhance forecasting performance. The NGM (1,1,k,c) model is used to simulate the developmental trend of the data series, while the Fourier series captures periodic fluctuation components that are not fully modeled. The data used to evaluate the effectiveness of the improved model consist of automobile sales in Vietnam from 2012 to 2025, obtained from the official website of the Vietnam Automobile Manufacturers’ Association (VAMA). Experimental results show that, using the Fourier series to flit out the residual errors of the NGM (1,1,k,c) model increases by 9.34% and 6.24% in the accuracy in term of sample data and testing data.
The impact of climate change sentiment on trade credit level: evidence from Asian firms
This study investigates the profound impact of managerial sentiment regarding climate change on a firm’s ability to secure trade credit. Utilizing an extensive empirical dataset comprising 927 publicly listed firms across 30 Asian countries over the period from 2002 to 2023, our findings demonstrate that climate-related management perspectives play a pivotal role in shaping external financing opportunities. Specifically, firms whose corporate communications reflect a proactive and positive tone toward climate-related issues are granted significantly higher levels of trade credit by their suppliers. These results suggest that positive managerial climate sentiment serves as a powerful signaling mechanism, enhancing a firm's perceived trustworthiness, operational resilience, and long-term sustainability prospects in the eyes of key trade partners.
Performance analysis of an AoI-aware link-selection rule for periodic hybrid FSO/RF status updating under imperfect weather prediction
Status monitoring over hybrid FSO/RF links is governed by data freshness rather than throughput, while common link selection rules usually optimize reliability or capacity. This paper studies periodic status updating over a hybrid FSO/RF link under imperfect binary weather prediction with possibly asymmetric false bad and false good errors. A Markov renewal model is used to capture service quantization, weather evolution, and forecast state correlation, yielding semi analytical average AoI for three schedulers: always RF, always FSO, and forecast following. The results identify an error threshold beyond which forecast following is no longer optimal, especially under high optical availability. The proposed AoI aware selector chooses the lowest AoI policy from model scores. Over 3136 balanced error cases, it matches the best simple policy in over 99% of long period cases and has about 5.8% excess AoI. Additional sweeps examine asymmetric errors, parameter estimation errors, and short source periods.
Egret Swarm Optimization Algorithm for production optimization: a performance evaluation study
This study evaluates the effectiveness of the Egret Swarm Optimization Algorithm (ESOA) by comparing it with Particle Swarm Optimization (PSO), Differential Evolution (DE), and Grey Wolf Optimizer (GWO) on the CEC-2017 benchmark suite. The results demonstrate that ESOA achieves high solution quality, strong stability, and reliable convergence. In addition, the algorithm was applied to a real-world resource allocation problem, using data collected from the Manufacturing Execution System (MES) of a manufacturing enterprise over a three-month period, incorporating nonlinear cost fluctuations and operational constraints. Experimental results show that ESOA maintains stable performance across multiple runs and exhibits efficient search capability in large-scale solution spaces. However, the study also reveals certain limitations related to the characteristics of the dataset and the number of iterations required to achieve convergence.
Technical solutions apply for the design of space on both sides of street axis with priority for pedestrians and sustainable infrastructure (in case of the street axis of Nguyen The Loc and Yen Khe 1)
When the streets system are new upgraded, renovated, expandable and built, especially the space on both sides of the street axis in the areas of centre and canal has not been designed according to technical requirements in accordance with the function, deficiency of diversity and aesthetics should be affecting to spacial quality of street. The design of this space needs to be suitable for each area, guaranteeing technical requirements, and following the orientation to apply sustainable infrastructure facilities, with infrastructural equipment that meets usable requirements will create: High space order, friendly environment; To increase pedestrian safety; To increase aesthetic value; To increase convenience and improve the quality of the modern urban environment. The research content focuses on technical solutions to design these spaces in a way is compatible with each segment on each street; The propose for to integrate sustainable infrastructure engineering solutions and pedestrian safety engineering.
Bending analysis of RC–UHPC panels in semi-precast slab using finite element method
This paper presents a finite element (FE) analysis of the flexural behavior of a semi-precast RC-UHPC panel during the construction stage. The panel consists of a prefabricated assembly comprising a bottom ultra-high-performance concrete (UHPC) layer, an expanded polystyrene (EPS) lightweight concrete core, and a steel truss mesh. The FE model employs flat triangular shell elements for the concrete layers and 3D bar elements for the steel truss, implemented using the CALFEM-Python framework. The model is validated against experimental data from the literature. A parametric study investigates the effects of the UHPC and EPS thicknesses as well as steel reinforcement diameters on panel deflection under self-weight and construction load. Results show that the EPS thickness is the dominant parameter governing flexural stiffness, while reinforcement diameter has a comparatively minor influence. Specific configurations satisfying practical deflection limits are identified, providing design guidance for this emerging slab typology in the construction phase.
A spline-based shape optimization workflow for composite shells using metaheuristic algorithm and CALFEM-Python
This paper presents a workflow for the optimization of the shape of composite shells, where the shell model is parameterized by Splines. To achieve this, Gmsh API is used to establish the shell geometry using Splines passing through control points, where the coordinates of these points serve as the design variables. A self-developed module ‘geometry.py’ is built to ease the modeling task. The CALFEM-Python toolbox, supplemented with the CS-DSG3-A planar triangular shell element, is used as a tool for analyzing shell behavior depending on the geometry from Gmsh. The optimal shell shape is automatically sought through an improved differential evolution optimization framework. The entire optimization workflow is applied to isotropic and laminated composite shell models and evaluated.
Progressive collapse behavior of three-dimensional frame structures subjected to column loss during seismic excitation
This study presents a nonlinear dynamic OpenSees framework for investigating the seismic progressive collapse response of three-dimensional reinforced concrete frames subjected to first-storey corner-column loss. The model incorporates material and geometric nonlinearities, distributed plasticity, rigid diaphragm constraints, and spatial force redistribution while maintaining computational efficiency for nonlinear time-history analysis. Two configurations, namely an intact frame and an initially damaged frame, were analyzed under earthquake ground motion. The results show that corner-column loss significantly increases compressive axial-force demand in the adjacent column, whereas the corresponding shear and bending moment responses increase only moderately. In the beam above the removed column, the response changes from slight compression to tensile axial force, while the bending moment decreases by approximately 50%, indicating a transition toward an axial tension-dominated catenary-like mechanism. The damaged frame also develops substantially larger lateral displacement demand, with peak roof displacement increasing by approximately 275% compared with the intact configuration.
Interfacial crack analysis in layered media using couple-stress elasticity and the displacement discontinuity method
Interfacial cracks between dissimilar materials represent a critical failure mechanism in layered structural systems such as coatings, composite laminates, and micro-structured materials. However, classical elasticity theory cannot adequately capture the size-dependent behavior observed at micro- and nano-scales. This study investigates a pre-existing interfacial crack in a multilayered elastic medium using couple-stress elasticity and the displacement discontinuity method. The crack is modeled as a prescribed normal opening displacement discontinuity along the interface between two bonded layers resting on an elastic half-plane. The corresponding reaction tractions, stress fields, and couple-stress components are determined using Fourier transform techniques and Gauss-Legendre quadrature. The numerical results indicate that the material length-scale parameter and stiffness mismatch significantly influence the near-tip reaction stress field. For a prescribed crack opening, couple-stress effects enhance the microstructural resistance near the crack tip, highlighting the importance of size-dependent modeling in the analysis of layered interfacial fracture problems.
Investigating the intention-behavior gap in source separation of household waste: a case study in Thanh Khe, Da Nang
This study elucidates the intention-behavior gap in household source separation of municipal solid waste in urban Da Nang by concurrently measuring intentions (INT), self-reported behavior (SrB), and objective behavior (OB). Descriptive results and correlation analyses reveal a weak correlation between SrB and OB, while the gap between INT/SrB and OB is predominantly positive with substantial magnitude, confirming a significant discrepancy between "wanting to do/saying one does" and "actually doing". Employing a mediation model (PROCESS Model 4), the study identifies infrastructure and information barriers (IIB) as a pivotal mediator amplifying the intention-behavior gap: INT heightens barrier recognition, whereas IIB diminishes actual behavior, yielding a negative indirect effect. In contrast, institutional and policy factors (IP), perceived behavioral control (PBC), and subjective norms (SN) exhibit limited roles. These findings underscore that narrowing the intention-behavior gap necessitates prioritizing interventions in infrastructure and information conditions over solely enhancing intentions.
A novel adaptive stochastic model for projects scheduling under uncertainty
Project scheduling faces high uncertainty due to fluctuating productivity, weather disruptions, resource shortages, and rework. Existing models such as CPM, PERT, and deterministic optimization lack the capability to dynamically adapt to real-time disturbances. This paper proposes a novel Adaptive Stochastic Resource-Constrained Scheduling Model (ASRCSM) that integrates (i) stochastic task durations, (ii) dynamic resource allocation rules, and (iii) an adaptive re-scheduling mechanism triggered by project state deviations. In addition, this study proposes a novel Adaptive Hybrid Genetic Algorithm (AHGA) which combines Serial Schedule Generation Scheme and local search heuristics to solve the problem. The approach provides a practical, computationally efficient tool for construction planners facing uncertainty.
Circular economy in construction projects in Vietnam: policy context, current practices, and research gaps
The construction sector is a major consumer of natural resources and a significant source of emissions, making the transition toward a circular economy (CE) essential for sustainable development. Although Vietnam has introduced key policy commitments, including the Green Growth Strategy 2021–2030 and the Net Zero target by 2050, the implementation of CE principles in construction projects remains fragmented and lacks a unified assessment framework. This paper reviews the theoretical foundations of CE in the construction sector and examines the current state of application in Vietnam through literature review, policy analysis, and expert consultation. The findings indicate that existing practices mainly focus on waste management and recycled materials, while life-cycle-based design, supply chain integration, and governance mechanisms are insufficiently addressed. The study identifies critical research and practical gaps, providing a foundation for developing quantitative indicators and an assessment framework for CE implementation in construction projects in Vietnam.
Effect of NaCl salinity on strength and stiffness of cement–fly ash–lime treated clayey soils
Unconfined compressive strength (UCS) and elastic modulus (E₅₀) are key parameters governing the load-bearing capacity and deformation behavior of stabilized soft soils. This study investigated the effect of NaCl salinity on clayey soil stabilized with a cement–fly ash–lime binder system. Two experimental schemes were conducted. In Scheme 1, cement and fly ash contents were fixed at 20% and 30% of dry soil weight, respectively, while lime content varied from 2% to 6%. In Scheme 2, NaCl contents of 1–4% were introduced into the corresponding mixtures. Specimens were cured under standard conditions and tested by unconfined compression after 7, 14, 28, and 56 days. The results were used to evaluate the influence of NaCl salinity on UCS and E₅₀, providing an experimental basis for mixture design and performance assessment of stabilized soft soils in saline environments.
Future hydroclimatic variability in the Vu Gia Thu Bon River basin derived from high-resolution CMIP6-VN projections
Heavy rainfall across Central Vietnam in 2025 highlights the increasing intensity of extreme precipitation and the need for reliable regional climate projections. Although CMIP6 global climate models have improved climate simulations, their coarse spatial resolution limits basin-scale applications. This study uses high-resolution (10km) downscaled CMIP6-VN projections from 19 CMIP6 models to assess future rainfall, temperature, and hydrological changes in the Vu Gia Thu Bon River basin during 1985–2099 under four SSP scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5). Mean annual precipitation is projected to increase under all scenarios, reaching up to 6.7% above the historical baseline (1985–2014). Although rainfall and flood-season runoff increase, streamflow responses are substantially moderated by reservoir regulation, emphasizing the critical role of reservoir operations in future water management. These findings demonstrate the value of high-resolution climate projections for reducing uncertainty in hydrological impact assessments and supporting climate adaptation and water resources planning in regulated river basin.