Ho Chi Minh City Open University

Tuna dark muscle protein isolate (TDMPI) derived from yellowfin tuna (Thunnus albacares) represents a sustainable by-product for bioactive peptide production. In this study, TDMPI was hydrolysed using alcalase, an eco-friendly enzymatic approach, to generate antioxidant-rich hydrolysates. These hydrolysates contained a diverse profile of hydrophobic and negatively charged amino acids, which contributed to their antioxidant properties. Ultrafiltration was employed to fractionate the hydrolysates into three peptide fractions, which were tested for 2,2-diphenyl-1-picrylhydrazyl free radical scavenging (DPPH-RSA), lipid peroxidation inhibition (LPIA), and total reducing power (TRPA). Results showed that peptides <10 kDa exhibited higher antioxidant activity than the unfractionated hydrolysates, with peptides <3 kDa exhibiting the highest DPPH-RSA and TRPA. However, LPIA was higher in peptides >10 kDa. Hydrolysis time significantly affected antioxidant activity: DPPH-RSA increased up to 9 h, while LPIA peaked at 6 h and TRPA at 3 h before declining. This study demonstrated that TDMPI hydrolysates and their peptide fractions exhibited significant antioxidant activities, making them promising natural alternatives to synthetic preservatives. Their utilisation potentially supports functional food applications and aligns with green processing strategies for valorising fishery by-products.

Although numerous studies have examined the effects of digital human resource management on organizational performance, the findings have been inconsistent due to the influence of contextual factors. It motivates continued exploration of whether businesses that utilize digital technology to manage people will boost their performance. Data were collected from 360 Vietnamese enterprises using digitalization in human resource management. The partial least squares structural equation modeling (PLS-SEM) was employed to test the path model. Based on research results, there exists a significant correlation between digital human resource management and organizational performance under a mechanism of mediating as well as moderating. Notably, the findings show that personnel strategy moderates the relationship between digital human resource management and employee performance (β = −0.081) at a significance level of 0.1. This result indicates that personnel strategy does not impact the relationship between digital human resource management and organizational performance. Additionally, the study proposes managerial implications regarding the role of digital human resource management in influencing employee engagement and performance as mediating, which emphasizes their participation toward efficiency. Furthermore, the research raises awareness of the importance of digital human resource management in the context of the greenization process and innovation to enhance performance and execution capabilities in an era based on the digitalization landscape.

Accurate time-series classification (TSC) remains a fundamental challenge in deep learning due to the complexity and variability of temporal patterns. While recurrent neural networks (RNNs) such as LSTM and GRU have shown promise in modeling sequential dependencies, they often suffer from limitations like vanishing gradients and high computational cost when handling long sequences. To overcome these issues, convolutional neural networks (CNNs), particularly the Inception architecture, have emerged as powerful alternatives due to their ability to capture multiscale local patterns efficiently. In this study, we propose InceptionResNet, a hybrid deep learning framework that integrates the residual learning mechanism of ResNet into the InceptionTime architecture. By replacing the fully convolutional network (FCN) shortcut module in InceptionFCN with ResNet-50, the model gains deeper representational capacity and improved gradient flow during training. We conduct extensive experiments on the UCR-85 benchmark dataset, comparing our model against state-of-the-art approaches, including InceptionTime, InceptionFCN, ResNet, FCN, and MLP. The results show that InceptionResNet achieves superior accuracy on 49 of 85 datasets, demonstrating its robustness and effectiveness in handling diverse and complex time series data. This work highlights the potential of integrating multiscale feature extraction and deep residual learning to advance the performance of TSC models in practical applications.

This study evaluates the effectiveness of the Harmonic Regression (HR) model for seasonal flood classification using Synthetic Aperture Radar (SAR) and optical data. The HR model reduces noise and extracts SAR features for flood detection, while optical images aid in segmentation and validation. Achieving over 90% accuracy, it effectively classifies shallow and deep floods, non-flooded areas, and other non-seasonal objects in agricultural regions. Compared to traditional flood classification methods, this approach provides a more refined distinction between flood types, enhancing flood mapping precision. The method was tested in Vietnam’s upper Mekong region, a complex and human-influenced zone, demonstrating strong capability in distinguishing floodwater from other water bodies. This improves flood map accuracy and supports agricultural water management and irrigation cost estimation. While HR requires threshold adjustments for varying flood conditions, its straightforward implementation and adaptability make it a valuable approach. Future work should explore deep learning techniques and integrate multiple Sentinel-1 signals to enhance its applicability. This study focuses on detecting and classifying shallow and deep flooding in agricultural landscapes within Vietnam’s Upper Mekong Region by leveraging Harmonic Regression (HR) and Synthetic Aperture Radar (SAR) time series data. Tested in a complex, human-influenced flood-prone area, the proposed approach achieved over 90% accuracy in distinguishing shallow floods, deep floods, non-flooded zones, and other water bodies. Unlike conventional flood mapping techniques, which often struggle to distinguish between non-seasonal permanent water bodies and seasonal flood or irrigation patterns, this method provides a more refined classification tailored to the dynamics of agricultural landscapes. By integrating SAR time series with optical imagery, the data processing workflow significantly reduces noise, enhances flood feature extraction, and improves detection precision. In particular, the model effectively separates floodwater from permanent water bodies, offering valuable insights for agricultural water management and irrigation cost estimation. The findings contribute practical support for policymakers and stakeholders involved in flood-prone agricultural planning and risk mitigation.

Background Big-eyed bugs (Geocoris spp.) are important generalist predators in agricultural ecosystems, playing a crucial role in natural pest control. Methods To better understand their dietary sources, we assessed the plant and animal food sources in the gut of Geocoris ochropterus using multiplex PCR and shotgun metagenomic analysis. The PCR assays targeted genetic markers from both animal (COI) and plant (matK and rbcL) DNA. Results Results revealed the presence of both animal and plant-derived DNA in the gut samples, indicating that Geocoris ochropterus feeds on a mixed diet. Additionally, the results of shotgun metagenomic sequencing of the gut microbiota showed a predominance of Eukaryota, with over 80% of sequences belonging to this domain, while a diverse range of taxonomic groups were identified, including arthropods, plants, bacteria, and fungi. Arthropods particularly insects from the orders Lepidoptera, Hemiptera, Hymenoptera, Coleoptera, Phasmatodea and plants belonging to the orders Brassicales, Cucurbitales, and Poales constituted the most abundant dietary components. At the genus level, notable taxa included Maniola (family Nymphalidae), Carposina (Carposinidae), Helicoverpa (Noctuidae), and Solanum (Solanaceae). Species-level analysis confirmed the dominance of several insect species, including Maniola hyperanthus, Carposina sasakii, and Bombyx mori, alongside plant species such as Cucumis melo, Gossypium hirsutum, and Digitaria exilis. Conclusions These findings provide a comprehensive characterization of the diet of Geocoris ochropterus, highlighting its role as a generalist predator with a diverse diet consisting of both insect and plant food sources. This study contributes to the understanding of Geocoris ochropterus as a potential biocontrol agent in agricultural systems.

The modeling of cross-ply composite laminates using numerical methods has been a difficult task, leading to the development of various finite element method and other analytical solutions. However, as materials science advances, this problem has become more complex, presenting new challenges that require reliable and novel approaches. In this study, we propose the utilization of machine learning, specifically physics informed neural networks (PINN), for the first time to examine the behavior of composite plate. By solving a system of partial differential equations derived from the virtual work equilibrium principle, PINN are employed as a method to solve these equations using a generalized strong-form approach. To address the issue of imbalanced loss functions, we also propose adjusting the loss function in this research. Once trained, PINN serve as a surrogate model capable of predicting displacements and stresses in cross-ply composite laminates. To demonstrate the effectiveness and reliability of PINN, we investigate two examples of laminates with different material distributions and boundary conditions including boundary conditions on displacement and boundary conditions on stress, comparing the results with the benchmark Navier solution. The research and obtained results showcase the performance and accuracy of PINN, highlighting their potential as a surrogate model for solving problems related to cross-ply composite laminates.

Developing sustainable, biodegradable, cost-effective, robust, and recyclable catalysts for treating various hazardous organic compounds is still challenging. Herein, the biosynthesis of silver nanoparticles (AgNPs) mediated by durian shell (DS) extracts as a reducing and stabilizing agent decorated on a three-dimensional cellulose–chitin aerogel composite supports (ACeCh) for reducing methyl orange (MO) with the presence of NaBH4 (NB). More specifically, the DS residue after extracting was used to isolate cellulose (Ce), while the shrimp shell (SS) was used as a precursor for synthesizing Ch. These components were combined to create a novel green ACeCh support with an exceptionally high porosity (98.98%) and a low density (0.0155 g/cm3) that uniformly disperses AgNPs, boosts catalytic performance, and makes the catalyst obtained accessible for recovery and reuse. The catalysts’ physicochemical properties were characterized using XRD, EDS, FTIR, SEM, HRTEM, SAED analysis techniques, and BET measurement. The AgNPs were found to possess an average size of 16.95 ± 10.11 nm and were uniformly distributed on the surface of ACeCh. Strikingly, the catalytic activity of the 2.5Ag/ACeCh(4-1-2.0) composite at a AgNPs content of 2.5% under ideal reaction conditions, in which the catalyst quantity was 1.00 g/L, and the MO to NB molar proportion was 1:250, showed MO conversion efficiency of approximate 96.0% after 30 min, correlating with the first-order rate constant kapp of 0.118 min−1. In the same conditions, the appropriate catalyst demonstrated superior catalytic efficiency in reducing various organic contaminants such as 2-nitrophenol (2-NP), 3-nitrophenol (3-NP), and 4-nitrophenol (4-NP). After five reaction cycles via vacuum filtering from the reaction solution in less than 10 s, a nearly 85% conversion is still attained, demonstrating the catalytic sample’s stability.

This study investigates the relationship between capital structure and firm performance, concentrating on the moderating influence of monetary policy by means of bank credit. By using a sample of 49 real estate firms listed on the Ho Chi Minh City Stock Exchange between 2007 and 2021 and applied some econometric models such as ordinary least squares, fixed-effects, random effects estimation, and two-step system GMM, this research pioneers the concept of a minimum debt threshold or lower-bound debt threshold, addressing theoretical and empirical gaps in the evaluation of the effects of maintaining debt levels below this threshold, in contrast to traditional studies that primarily emphasize the optimal debt level or maximum debt threshold. Moreover, the study highlights the semi-moderating impact of bank credit on the threshold, emphasizing the important interaction between monetary policy and capital structure decisions. Specifically, this study finds that debt financing below the minimum threshold has a negative impact on performance, encouraging firms to seek alternative capital solutions or increase leverage above the minimum threshold, which is defined for each industry in accordance with the context of individual economies. The research offers significant insights for companies in capital budgeting and adjusting to variations in credit policies, particularly within Vietnamese real estate sector, which is highly dependent on debt financing and sensitive to monetary changes. JEL Classification: E51, E52, G32.

Unlabelled: Modification of sgRNA has been considered as a necessary approach to enhance the stability and cleavage efficiency of the CRISPR/Cas9 system. In this study, a rigid G-quadruplex structure was genetically applied to the 3' end of typical sgRNA for protection of RNA from 3'-5' exoribonuclease degradation. The in vitro transcriptional production yields of sgRNAs bearing G-quadruplex structure such as sgRNA3 and sgRNA4 were around 1.4 and 1.5 times higher than the yield of typical sgRNA1, respectively. The results have also shown that appending G-quadruplex motif at the 3' end of typical sgRNAs did minorly affect the cleavage activity of CRISPR/Cas9. Interestingly, cleavage efficiency of CRISPR/Cas9 system with sgRNAs bearing the rigid G-quadruplex was fully retained in the presence of 3'-5' exoribonucleases such as RNase II or RNase R. In contrast, the cleavage activity of CRISPR/Cas9 system with the typical sgRNA1 was significantly decreased in the same condition. This protection of sgRNA through G-quadruplex structure-based modifications might provide a potential approach for improving cleavage efficiency of CRISPR/Cas9 system in the exoribonuclease environment. Supplementary information: The online version contains supplementary material available at 10.1007/s13205-025-04354-x.

The time-fractional wave equations are used to describe anomalous diffusion, sub-diffusion processes and relaxation phenomena in complex viscoelastic materials. In this paper, we study a class of final value problems for a time-fractional wave equations involving Caputo’s fractional derivative of order $1<\alpha <2$ in a bounded domain. Firstly, we establish the ill-posedness of the problem then investigate the filter method of regularization for it. Finally, we propose a numerical example to illustrate our theoretical results.

Background/Objectives: Rapid and accurate assessment of lung diseases, like pneumonia, is critical for effective clinical decision-making, particularly during pandemics when disease progression can be severe. Early diagnosis plays a crucial role in preventing complications, necessitating the development of fast and efficient AI-based models for automated severity assessment. Methods: In this study, we introduce a novel approach that leverages VMamba, a state-of-the-art vision model based on the VisualStateSpace (VSS) framework and 2D-Selective-Scan (SS2D) spatial scanning, to enhance lung severity prediction. Integrated in a parallel multi-image regions approach, VMamba effectively captures global and local contextual features through structured state-space modeling, improving feature representation and robustness in medical image analysis. Additionally, we integrate a segmented lung replacement augmentation strategy to enhance data diversity and improve model generalization. The proposed method is trained on the RALO and COVID-19 datasets and compared against state-of-the-art models. Results: Experimental results demonstrate that our approach achieves superior performance, outperforming existing techniques in prediction accuracy and robustness. Key evaluation metrics, including Mean Absolute Error (MAE) and Pearson Correlation (PC), confirm the model’s effectiveness, while the incorporation of segmented lung replacement augmentation further enhances adaptability to diverse lung conditions. Conclusions: These findings highlight the potential of our method for reliable and immediate clinical applications in lung infection assessment.

This study examines the impact of cross-ownership on bank stability and the moderating role of corporate social responsibility (CSR) disclosure on such impact. By using data from 560 banks across 38 countries and territories in Asia during the 2011-2022 period and applying the fixed-effect and system GMM methods, we provide important findings. Firstly, the broad cross-ownership degree increases bank stability, while significant ownership between banks reduces bank stability. Secondly, CSR disclosure can amplify the positive impact of the broad cross-ownership degree and mitigate the negative effects of significant ownership between banks on bank stability. The importance of CSR disclosure was amplified during the COVID-19 crisis. Thirdly, we found that the broad cross-ownership degree enhances bank stability via improved performance and reduced profit volatility, whereas significant ownership between banks reduces bank stability via decreasing bank profitability and decreasing capital ratios. Furthermore, CSR disclosure increases the positive effects of the broad cross-ownership degree via increasing the negative effects of the broad cross-ownership degree on profit volatility, while CSR disclosure reduces the negative effects of significant ownership between banks via reducing the negative impact of significant ownership between banks on profitability and capital ratios.

Highlights This study reveals significant differences in drought assessment when considering evaporation (SPEI) compared to precipitation alone (SPI), with SPEI indicating more severe future drought levels. Future drought projections indicate that long-term and intense droughts will increase, especially during the period 2071–2100. This study shows a clear difference between the SSP245 and SSP585 climate scenarios in terms of drought trends, with significant improvements in wet conditions according to the SPEI index, and the highest drought variability in zone 2. Abstract This study evaluates the performance of CMIP6 models in simulating drought characteristics in the Mekong region, including drought duration, intensity, and severity, using the SPI and SPEI indices. The results show that CMIP6 models are capable of accurately reproducing past drought conditions, with a high agreement between model data and actual data from ERA5. This study projects that future droughts will become more prolonged and severe which could lead to long-term agricultural and hydrological droughts tending to increase. In the SSP585 scenario, drought intensity will increase sharply in the southern and central regions by the end of the century. The SSP245 and SSP585 climate scenarios have distinct differences in drought trends, with SSP245 showing a strong drought trend, while SSP585 indicates a potential increase in precipitation. The SPEI indices show a clear improvement in wet conditions, with the highest drought variability in zone 2 and stable trends across scenarios. Ecosystems influence drought impacts and management needs. These results highlight the importance of accurately assessing drought characteristics to develop effective water resource and agricultural management measures, especially in the context of climate change. However, this study also points out some limitations, including the imperfect accuracy in future projections and the use of only SPI and SPEI indices without combining them with other indices which may reduce the comprehensiveness of drought impact assessment. This requires future studies to improve and expand to overcome the above limitations, thereby enhancing the reliability of drought forecasts and water resource management strategies.

Previous studies have offered static evidence of free‐riding behavior in military alliances; however, dynamic evidence remains largely unexplored. To address this gap, this study employs a time‐varying parameter vector autoregressive (TVP‐VAR) connectedness approach. This method provides dynamic insights, allowing us to identify unusual shifts in military spending dependence among NATO member states over time. Using this method, we observe a significant anomaly in March 2014, when Crimea's administrative authority shifted from Ukraine to Russia. Our findings indicate that this event substantially curtailed free‐rider behavior in military expenditure among NATO members. Furthermore, consistent with the existing literature, the results show a marked decline in free‐riding behavior following 1975, attributable to the implementation of the flexible response doctrine.

Human visual attention is influenced by multiple factors, including visual, auditory, and facial cues. While integrating auditory and visual information enhances prediction accuracy, many existing models rely solely on visual-temporal data. Inspired by cognitive studies, we propose a computational model that combines spatial, temporal, face (low-level and high-level visual cues), and auditory saliency to predict visual attention more effectively. Our approach processes video frames to generate spatial, temporal, and face saliency maps, while an audio branch localizes sound-producing objects. These maps are then integrated to form the final audio-visual saliency map. Experimental results on the audio-visual dataset demonstrate that our model outperforms state-of-the-art image and video saliency models and the basic model and aligns more closely with behavioral and eye-tracking data. Additionally, ablation studies highlight the contribution of each information source to the final prediction.

This study introduces a novel method to assess the mechanical behavior of the Phu My cable-stayed bridge through correlation analysis of vibration signals collected under normal traffic conditions. This method not just monitors changes in vibration parameters but provides a new framework for detecting structural damage and degradation. By using correlation coefficients and standard deviation, the method enhances the accuracy of structural health assessments, offering a practical alternative to traditional vibration analysis techniques. The main contributions of the study include proposing the correlation spectrum as a feature for damage identification and improving understanding of the bridge’s response to service load conditions. The research results also lay the foundation for the development of automatic monitoring systems, optimizing maintenance processes, and improving the safety use of cable-stayed bridges.

The big‐eyed bug, Geocoris ochropterus Fieber, is a polyphagous predator. Although there have been many publications on the rearing of big‐eyed bugs by using other insects, their ability to use flowers as a source of nutrition or habitat has not been previously reported. The diets included Zinnia elegans (A), 10% honey solution (B) ant pupae and Zinnia elegans (C), mealybug and Zinnia elegans (D) treatments at 10°C, 20°C, and 30°C. Significant differences in survival rates were observed among the diets when the nymphs reached the first and second instars at 10°C. Significant differences in growth and development indicators of big‐eyed bugs were found among the treatments during the second, third, fourth and fifth moults at 30°C. Additionally, body size and dry weight of adult G. ochropterus grown on different diets were measured at 20°C and 30°C. At 20°C, male body size parameters did not significantly differ among the diets, except for head width. At 30°C, body length and dry weight of males showed significant differences among the diets, while head width and dry weight of females also varied significantly among diets. The study indicates that the combination of floral resources and diet can affect the development of G. ochropterus at different temperatures.

Ru nanoparticles (NPs) embedded in functionalized carbon (Ru@FC) were synthesized and employed as a bifuntional catalyst for the catalytic transfer hydrogenation of levulinic acid (LA) to γ‐valerolactone (GVL). The synergistic interaction between the functionalized carbon support and Ru NPs was demonstrated with the carbon support facilitating the dehydration step, while Ru NPs acted as active sites for hydrogenation. The proposed reaction pathway involves the initial transformation of LA into a pseudo‐LA intermediate over Ru@FC catalyst followed by dehydration and hydrogenation steps to produce GVL. Additionally, the response surface methodology (RSM) was used for the first time to predict the optimal reaction conditions and evaluate the influence of reaction parameters on the formation of the desired product. The quadratic model was effectively fitted between experimental and predicted data with a low p‐value (<0.0001) and high R² (0.9967). Among the catalysts tested, 5Ru@FC‐30% exhibited superior catalytic performance achieving a high GVL yield of 98.6% under optimized conditions (10 mg catalyst, 130°C, 5 h). Moreover, the 5Ru@FC‐30% catalyst demonstrated high stability and reusability over five cycles. This work contributes a novel Ru@FC catalyst for sustainable and efficient biomass valorization.