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Enhanced infrared and visible image fusion using a fast guided filter and an improved visual saliency map
Số 97 - Số Tiếng Anh (12/2025)
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trang 02-07
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Tải về (456.75 KB)
Phu-Hung Dinh
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Tóm tắt
The fusion of infrared (IR) and visible images aims to generate a composite image that is both highly informative and visually optimized for human perception or computer vision applications. This paper presents an innovative fusion approach designed to enhance the effectiveness of this process. The method begins by utilizing the Fast Guided Filter (FGF) to decompose input images into base and detail components. An improved visual saliency map technique is introduced to efficiently fuse the base components, while a local energy-based fusion strategy is applied to the detail components. Experimental evaluations demonstrate that the proposed approach outperforms conventional methods, delivering superior results in both subjective and objective assessments.
Keywords: Fast guided filter (FGF), visual saliency map (VSM), image fusion (IF) |
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Calculation of pollution loadings and preliminary estimation of alcohol consumption in Hanoi
Số 97 - Số Tiếng Anh (12/2025)
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trang 08-12
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Tải về (458.53 KB)
Pham Nguyet Anh,*, Nguyen Thi Lan Huong, Nguyen Tuan Minh
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Tóm tắt
Alcohol consumption is a cultural practice in many countries around the world, including Vietnam. Estimating alcohol consumption is essential for making public health recommendations. Wastewater-based epidemiology (WBE) is a tool that can help estimate alcohol consumption within a community based on information such as the concentration of the alcohol biomarker ethyl sulfate (EtS) in wastewater, the population residing in the area, and the wastewater flow rate. This study determined the flow rate of the Kim Nguu River and analyzed its water quality in November 2023. The results indicate that the water quality of the Kim Nguu River is poor, with daily loads of approximately 21.3 tons of COD, 9.6 tons of BOD5, 17.59 tons of TSS, 5.35 tons of TN, and 0.96 tons of TP. Based on EtS concentrations referenced from another study, the estimated alcohol consumption in Hanoi is approximately 2.61±0.33 mL/person/day. These findings contribute to the recommendations for wastewater treatment and environmental pollution reduction in Hanoi, while also providing a preliminary estimate of alcohol consumption among the city's residents, which can inform public health recommendations.
Keywords: Alcohol, wastewater, pollution, loads, Hanoi |
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Periodic solutions to the non-autonomous Oseen-Navier-Stokes equations in exterior domains
Số 97 - Số Tiếng Anh (12/2025)
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trang 13-19
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Tải về (390.14 KB)
Nguyen Ngoc Huy*, Le Thi Minh Hai , Nguyen Thi Ly, Pham Nam Giang
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Tóm tắt
In this paper, we investigate the existence and uniqueness of periodic mild solutions to the non-autonomous Oseen-Navier-Stokes equations (ONSE) in the exterior domain of a rotating obstacle that is translating with a time-dependent velocity. Our method is based on the smoothness of the evolution family corresponding to linearized equations in combination with interpolation spaces and fixed-point theorems.
Keywords: Evolution families, Periodic solutions, Oseen-Navier-Stokes equations, rotating and translating obstacle. |
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Assessing public awareness, attitudes, and behaviors toward domestic solid waste management in mountainous areas of Tuyen Quang province, Vietnam
Số 97 - Số Tiếng Anh (12/2025)
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trang 20-26
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Tải về (612.32 KB)
Hoang Thi Hue, Nguyen Thi Hong Hanh, Bui Thi Thu Trang*, Nguyen Thi Hoai Thuong
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Tóm tắt
Domestic solid waste (DSW) management in mountainous areas faces significant challenges due to limited infrastructure and low levels of public awareness. This study aims to assess the awareness, attitudes, and behaviors of residents in three mountainous communes of Tuyen Quang province (Nhu Khe, Luong Vuong, and Cong Da) regarding the implementation of the 2020 Law on Environmental Protection, particularly provisions on DSW segregation and collection. Using a sociological survey of 660 households, the study analyzes the factors influencing residents' waste management behaviors through a theoretical framework, descriptive statistics, exploratory factor analysis (EFA), and multivariate regression. The results reveal that while local resident attitudes toward DSW management are generally positive, with 80% expressing agreement with the necessity of proper waste management, but knowledge of local resident remains limited, especially regarding the calculation of service fees under new regulations. Key determinants of behavior include perceived behavioral control, legal knowledge, and social influence. Based on these findings, the study proposes policy recommendations aimed at enhancing communication strategies, improving DSW management infrastructure, and strengthening community participation to increase the effectiveness of DSW management in mountainous regions.
Keywords: Domestic solid waste management, public awareness, environmental behavior, mountainous areas, Tuyen Quang, law on environmental protection 2020 |
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A two-stage approach to agricultural products authentication by mining subtle local features
Số 97 - Số Tiếng Anh (12/2025)
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trang 27-31
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Tải về (268.76 KB)
Dat Tran Anh
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Tóm tắt
Counterfeit agricultural products recognition poses a significant challenge due to the high visual similarity between genuine and fake items. Existing methods often struggle to capture the subtle details necessary for reliable differentiation. This paper presents Focus on Detail (FoD), a novel approach that emphasizes the automatic discovery of trustworthy 'authenticity cues' on the products. By employing custom-designed loss functions and a semi-supervised training strategy, FoD learns to suppress distracting background regions and focus exclusively on the most critical local features. Experimental results demonstrate that FoD achieves superior performance on standard benchmark datasets, establishing a new state-of-the-art in both accuracy and speed.
Keywords: Counterfeit Recognition, Local Feature Learning, Weakly-Supervised Learning |
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Study on the use of antiwash high performance concrete for rehabilitating the hydraulic structure submerged in water
Số 97 - Số Tiếng Anh (12/2025)
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trang 32-36
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Tải về (459.05 KB)
Nguyen Viet Duc
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Tóm tắt
Antiwash high performance concrete (AHPC) is a specialized type of concrete used for construction projects where concrete needs to be placed below the waterline. With the aim to extend the service life of the hydraulic structures under water, this paper attempts to study on AHPC and its usage for rehabilitating the structure submerged in water. To do so, four AHPC mixtures have been involved in the experiment, in which superplasticizer dosages varied from 1% to 2% in conjunction with a specialized anti-washout admixture. Besides, a particular AHPC mixture would be used for repairing the structure by underwater concreting. The experimental results have shown that at fresh state the higher superplasticizer dosage, the higher slump flow of the AHPC mixtures, and the higher mixture mass loss inside the water. Meanwhile, at hardened state the higher superplasticizer dosage, the higher compressive strength gains. The single AHPC, which achieved slump flow of 20 cm and strength class of 50MPa, was chosen for successfully rehabilitating the structure submerged in water by mean of tremie method. The proposed future work could involve the study on mechanical performance of the concerned structure after rehabilitation.
Keywords: Antiwash high performance concrete, hydraulic structure, underwater concreting, anti-washout admixture, superplasticizer |
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Methodologies for cavitation prediction on high-head spillways: Review and propose a hybrid approach
Số 97 - Số Tiếng Anh (12/2025)
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trang 37-42
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Tải về (631.50 KB)
Le Thi Thu Hien*, Nguyen Van Chien, Hoang Duc Thuat
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Tóm tắt
Cavitation on high-head spillways can cause rapid concrete damage, posing serious threats to structural safety during flood releases. This review synthesizes methodologies for predicting cavitation inception, intensity, and damage risk across laboratory, field, numerical, and machine learning approaches. Both physical and numerical methods have been widely applied; however, they present limitations such as high cost, time consumption, and applicability restricted to individual case studies. The literature indicates that for spillways with similar configurations, the locations most at risk of cavitation damage are typically the same. Building on this insight, this study introduces a novel procedure for predicting cavitation risk by integrating advanced techniques including machine learning and 3D CFD modeling to develop a rapid prediction tool for quantifying the cavitation index.
Keywords: High-head spillway, procedure of prediction cavitation, 3D CFD, ML method |
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Green synthesis of Fe-Ni bimetallic material from guava leaf extract for Cu removal from wastewater
Số 97 - Số Tiếng Anh (12/2025)
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trang 43-48
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Tải về (401.95 KB)
Dinh Thi Lan Phuong*, Tran Thi Thu Ha
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Tóm tắt
This study synthesized Fe-Ni bimetallic material from the guava leaf extract for the removal of copper (Cu) from wastewater. The material composition was characterized using EDS and FT-IR spectra. SEM images indicated that particle sizes ranging from 60 to 90 nm, confirming the nanoscale nature of the material. Batch adsorption experiments were conducted with an initial Cu concentration in solution of 5 mg/L. The results showed that Fe-Ni exhibited Cu removal capability, as evidenced by EDS and FT-IR analyses after the adsorption process. The removal mechanism was proposed to involve adsorption and reduction processes on the material surface. Optimal conditions were found to be pH 6, Fe-Ni dosage of 0.8 g/L, temperature of 25°C, contact time of 150 minutes, achieving a maximum removal efficiency of 79%. These results suggest that Fe-Ni synthesized from guava leaves is a friendly and low-cost adsorbent with strong potential for Cu removal in water treatment applications.
Keywords: Fe-Ni bimetallic material, Cu removal, wastewater treatment, adsorption, green synthesis |
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A cloud-based visualization interface for gama to simplify the simulation of complex socio-environmental systems
Số 97 - Số Tiếng Anh (12/2025)
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trang 49-54
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Tải về (324.49 KB)
Nguyen Tuan Thanh Le, Xuan Truong Nguyen*, Minh Thu Dao, Linh Manh Pham
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Tóm tắt
This paper presents a novel cloud-based architecture for multi-agent simulations, built on top of the GAMA platform, to address computational limitations of the traditional desktop-based version. We propose a distributed system leveraging the cloud computing model, pub/sub messaging, and containerized deployment to enable scalable, parallel execution of complex socio-environmental simulations, especially for pig farming. Experimental results demonstrate significant performance improvements, with the system supporting concurrent simulations across multiple worker nodes. The solution reduces infrastructure costs by 40% compared to physical implementation while providing researchers with an accessible web interface for scenario execution. This work establishes a reusable framework for cloud-based agent-based modeling, with particular applicability to smart agriculture and epidemic management.
Keywords: Multi-agent modeling and simulation, cloud computing, GAMA platform, distributed systems |
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Streamflow forecasting under non-homogeneous and discontinuous data conditions using LSTM: Application to Nam Pan basin
Số 97 - Số Tiếng Anh (12/2025)
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trang 60-65
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Tải về (448.19 KB)
Truong Van Anh*, Huynh Thi Lan Huong, Hoang Thi Nguyet Minh, Tran Duy Kieu
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Tóm tắt
Streamflow forecasting in small mountainous basins faces significant challenges due to non-homogeneous and discontinuous observational data caused by sensor failures, irregular sampling, and missing records. This study presents a Long Short-Term Memory (LSTM) deep learning approach to handle discontinuous time series for water level forecasting in the Nam Pan River basin, northern Vietnam. The methodology integrates multiple data preprocessing techniques including linear and PCHIP interpolation, outlier removal, and Z-score normalization to address data irregularities. Input features comprise rainfall observations from multiple stations, lagged water levels (6–24 hours), and cyclical time encoding. The LSTM model achieves Nash–Sutcliffe Efficiency of 0.86–0.91 for 6-hour forecasts and 0.70–0.78 for 24-hour forecasts, with R² values of 0.88–0.94 and forecast assurance of 82–90%. Results demonstrate the model's robustness in handling imperfect data, confirming its applicability for operational flash flood early warning systems in data-limited mountainous catchments.
Keywords: LSTM, Nam Pan river basin, flow forecasting, small mountainous catchment |
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Research and application of deep learning techniques for forecasting groundwater levels in the Hanoi area
Số 97 - Số Tiếng Anh (12/2025)
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trang 66-71
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Tải về (470.73 KB)
Ta Quang Chieu*, Nguyen Đac Phuong Thao, Ngo Quang Minh, Hoang Van Hung
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Tóm tắt
Groundwater is essential for keeping the ecosystem in balance and meeting the water needs of homes, farms, and businesses, especially in big cities like Hanoi, which are strongly affected by climate change and overuse. In the context of climate change and overexploitation, accurate groundwater level forecasts not only help successful water resource management, but also serve as a foundation for long-term development. However, the intricacy, nonlinearity, and long-term dependency of groundwater levels present significant problems for standard approaches. Ours provides a novel approach for predicting groundwater levels at monitoring station Q64 in the Hanoi area using four deep learning models: RNN, LSTM, Transformer and Autoformer. We forecast short-term (48 hours), medium-term (120 hours) and long-term (360 hours). Experimental results reveal that Autoformer is clearly superior in forecasting situations and performs well in short-term, in the medium term forecasting. This demonstrates that the model based on an attention-based architecture ơan capture long-term properties of groundwater level time series. These findings support the use of deep learning in groundwater level forecasting, paving the way for the creation of intelligent forecasting systems, aiding decision-making in water resource management, and developing ways to adapt to climate change in large cities.
Keywords: Groundwater level forecasting, deep learning, transformer, autoformer |
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Existence result for inverse problems governed by generalized rayleigh-tokes equations
Số 97 - Số Tiếng Anh (12/2025)
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trang 72-78
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Tải về (354.55 KB)
Nguyen Van Dac
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Tóm tắt
In this article, we study an inverse source problem related to the generalized Rayleigh–Stokes equations on Hilbert scales, with weak-valued nonlinearities and memory effects depending on the history of the state function. We establish a representation of the mild solution and then investigate crucial properties of the associated resolvent operators. Based on these analyses, the existence result is obtained by applying Banach’s fixed-point principle. Finally, an illustrative example is provided to demonstrate the theoretical results.
Keywords: Rayleigh-Stokes equations, weak nonlinearity, inverse problems, existence results. |
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Factors influencing residents’ behavior of domestic solid waste classification in Hanoi city
Số 97 - Số Tiếng Anh (12/2025)
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trang 79-84
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Tải về (292.15 KB)
Trang Bui Thi Thu, Hanh Nguyen Thi Hong*
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Tóm tắt
This study investigates residents' awareness, attitudes, and behaviors concerning domestic solid waste management in Hanoi. Statistical analysis identifies "knowledge of domestic solid waste management" as the primary determinant of household waste segregation behavior. Consequently, it is recommended that the Hanoi municipal government implement targeted educational interventions to enhance environmental knowledge. Such measures are essential for promoting waste classification at the source, thereby minimizing environmental discharge and alleviating the operational burden on waste management authorities
Keywords: Domestic solid waste, knowledge of domestic solid waste management, residents’ behavior of domestic solid waste classification |
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