Weedy rice (Oryza sativa f. spontanea) poses significant challenges to rice production, reducing yield and commercial value. This study investigates the inhibitory effects of root, stem, and leaf extracts from cultivated rice OM5930 (60 days after sowing) on two weedy rice lines, WR19 (short awn) and WR20 (long awn). The objectives were to: (i) identify the most potent plant part for inhibition, (ii) determine the optimal treatment duration, and (iii) assess the resistance of the two weedy rice lines. Results demonstrated that extracts from all OM5930 plant parts suppressed seedling shoot and root growth in both weedy rice lines. Leaf extracts exhibited the strongest inhibition, achieving complete suppression (100%) at 0.3 g/mL across all time points (0, 48, and 96 hours). The 48-hour treatment showed the most stable inhibitory effect. Root and stem extracts displayed lower efficacy, reaching only 60–70% inhibition at the same concentration. WR20 was more susceptible than WR19, with a slight stimulation observed at lower concentrations (0.015 and 0.075 g/mL). The strong allelopathic potential of OM5930 leaf extracts suggests their application as an eco-friendly bioherbicide, reducing dependence on synthetic herbicides, minimizing production costs, and promoting sustainable rice cultivation.

The study involved 24 crossbred bucks (New Zealand White × Local) in a factorial design to assess the impact of semen collection frequency and age on semen quality. Two factors were considered: the age of the bucks (5-6 months for young and 9-10 months for mature) and the frequency of semen collection (daily (Fre-1), every three days (Fre-3), every five days (Fre-5), and weekly (Fre-7)). The study was conducted over 12 weeks. Results indicated that weekly semen collection yielded the best results, with a concentration of 346×10⁶ sperm/mL, 66.7% sperm motility, 70.5% membrane integrity, and 66.8% live sperm rate. Furthermore, semen volume and the number of motile sperm increased as the collection interval extended, with mature bucks (9-10 months) showing good semen characteristics compared to younger bucks. Younger bucks had smaller testicular dimensions than mature bucks. In summary, the optimal semen collection frequency for New Zealand White × Local crossbred bucks was once a week, and the recommended age for semen collection was 9-10 months.

The study investigated the impact of probiotics and vitamins (PV) on the reproductive performance and egg quality of Ac chickens' diet. The experiment was conducted using a completely random design with 250 hens, aged 40-50 weeks, housed in cages. There were 5 treatments, each with 5 repetitions, and 10 hens per repetition. The treatments corresponding to the diets were: Control (BD), C250 (BD + 250 mg PV/kg of feed), C500 (BD + 500 mg PV/kg of feed), C750 (BD + 750 mg PV/kg of feed), and C1000 (BD + 1000 mg PV/kg of feed). The results showed that the highest laying rate (39.2%), yolk index (0.45), albumen index (0.08) and yolk colour (7.6) were in C1000. Although the C750 indicated the lowest FCR (3.78), it had the greatest egg weight (36.4 g/egg) and Haugh unit (82.8). In conclusion, probiotics and vitamins at 1,000 mg/kg diet improved Ac chicken laying rate, FCR, and egg weight.

Diverse starch-based foods have been widely consumed. To keep the products in good quality, preservatives added are inevitable. Sodium benzoate is one of the most commonly used food preservatives for starch-based products. As an antimicrobial compound, high concentration of sodium benzoate present in water can pose risks to aquatic indigenous microbial communities. In this study, 27 sodium benzoate degrading bacteria were isolated from wastewater samples collected at three rice noodle production facilities. Isolates SB2.1, SB2.2, SB2.4, SB3.10, and SB3.13 demonstrated effective degradation of sodium benzoate at concentrations of 1250, 2500, and 5000 mg/L. Optimal degradation efficiency (>93%) at 1250 mg/L was observed when the isolates were grown in MM medium at pH ranging from 7 to 9. Notably, SB3.10 exhibited chemotaxis towards sodium benzoate after 24 hours of incubation. Based on 16S rRNA gene sequencing and biochemical characterization, SB3.10 was identified as Acinetobacter calcoaceticus SB3.10.

In this work, ZnAl2O4: Mn4+ phosphor is prepared by the sol-gel method. The phase structure was investigated by X-ray diffraction (XRD), and morphology and element composition were analyzed by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), luminescent performance of the phosphor using photoluminescence (PL) spectra, and Raman spectroscopy. The obtained powder shows a red broad emission band peaking at 676 nm and 692 nm, corresponding to the energy transfer of electrons from 2Eg to 4A2g. The Mn4+ concentration is doped at 0.5 mol% into the ZnAl2O4 host lattice. After that, the powder annealed at 1200℃ for 4 hours in the air revealed an average crystallite size of about 52.833 nm and a high emission intensity with (x, y) CIE color coordinates (0.2705, 0.2741). Therefore, ZnAl2O4: Mn4+ phosphor could be a promising material for the solid-state lighting applications.

This study presents a comparative analysis of state-of-the-art deep learning models–EfficientNetB0, MobileNetV2, and ResNet101–for image classification and content-based retrieval in scientific publications. A dataset of 4,303 images from 11 categories was curated from the Can Tho University Journal of Science and enhanced through tailored data augmentation strategies. The models were fine-tuned using transfer learning with hyperparameters optimized via Grid Search. Features were extracted using GlobalAveragePooling2D, and cosine similarity combined with the FAISS library was employed for efficient similarity search. Experimental results demonstrate a clear performance-efficiency trade-off: ResNet101 achieved the highest classification accuracy, while EfficientNetB0 and MobileNetV2 offered significant advantages in inference speed. A user-friendly web interface was developed to support practical image retrieval applications. These findings highlight the potential of deep learning in enhancing the management and integrity of scientific image resources.

Human-action recognition aims to identify the actions performed by individuals. Due to the broad spectrum of human activities, action recognition covers a wide range. Among all, fall detection is a critical aspect of surveillance, particularly in environments where individuals are at risk. Throughout the years, several sensors, data types, and classification techniques have been investigated to address this issue. This paper proposes a lightweight fall detection designed to process sequences of images in real-time. This system is deployed on an embedded device, specifically the Jetson nano. Our goal is to construct a comprehensive dataset that accurately detects falls in various lighting conditions. The proposed system is constructed using YOLOv8n-pose, which have been trained to identify people using widely recognized dataset. Our methodology includes the design and implementation of the YOLOv8n-pose, data collection, and rigorous testing to ensure the accuracy of fall detection in real-time using surveillance camera. The experimental results show that high detection accuracy and acceptable timing capabilities are achieved.

This literature review synthesizes recent advancements and emerging trends in the field of project management, with a focus on the evolving impact of technology, leadership, and sustainability. Drawing on a wide range of scholarly articles and industry publications, the review explores the foundational principles of project management, such as risk management, leadership effectiveness, and project efficiency, while also highlighting the complexities of applying these practices across various sectors. The integration of cutting-edge technologies like artificial intelligence, data analytics, and hybrid methodologies is identified as a key driver of change in project execution. Furthermore, the increasing emphasis on sustainability and ethics reflects a shift in the industry towards more comprehensive measures of project success, beyond traditional financial outcomes. This review emphasizes the need for continuous adaptation in project management practices and calls for future research to refine leadership strategies, improve technology adoption, and develop more holistic frameworks for assessing project success. The findings underscore the importance of aligning traditional project management principles with the demands of an increasingly complex and fast-evolving global environment.

The survey of plant diversity and vegetation identified in Vam Ho Bird Sanctuary and surrounding areas (Ba Tri and Binh Dai districts, Ben Tre province) was performed in October 2024. The study aimed to build the plant diversity basics for investing and upgrading Vam Ho to the status of a Natural Heritage site. Apply transect survey and sampling, the study recorded 179 species, belonging to 55 vascular plants. Among them, Azima sarmentosa, Oryza rufipogon, Psilotum nudum were listed in the Vietnam Red Book, Avicennia marina var. rumphiana, Phoenix paludosa, Sonneratia ovata, Ceriops decandra were on the IUCN Red List.   In addition, a total of 155 out of 179 recorded species has belonged to at least one of six main use groups, medicine, food, animal/poultry feed, ornamental plants, timber/charcoal/construction materials, and household items/handicrafts. The vegetation was divided into three types, including the natural habitat in Vam Ho Bird Sanctuary, Casuarina equisetifolia coastal plantation and estuarine-coastal mangrove forests. The establishment of the Natural Heritage Site there would create the positive conditions for the activities of the habitat conservation (including waterbird) and coastal protection, which help to reduce the pressure of the environmental degradation for the Ba Lai estuary in the near future.

This paper studied the diversity of phytoplankton species at 9 sampling points of aquaculture ponds in 3 districts of Dak Ha, Sa Thay, Kon Plong, Kon Tum province in 2022  ̶  2023. The results recorded 7 phyla, 98 genera and 142 species, of which 22 genera and 23 species of phytoplankton appeared in all 3 sampling periods. The values ​​of the biodiversity index (H’) and the diversity value index (Dv) at 9 sampling points ranged from 2.51 - 3.66 and 2.39 - 3.58, respectively, indicating that the diversity of phytoplankton in aquaculture ponds is high and very high. The regulation index (J) ranged from 0.95 - 0.99, indicating that phytoplankton species were evenly distributed at all studied sampling points. The similarity coefficient of species composition of phytoplankton populations at 9 sampling points ranged from 0.43 - 0.85. The number of phytoplankton species in water samples collected at 9 studied locations had a negative correlation with the impedance value of the water samples. In particular, the main environmental factors affecting the cell density of phytoplankton communities at the sampling locations in this study were suspended solids, salinity, and conductivity, which had a positive correlation, and a negative correlation with impedance.

As EMI (English-medium instruction) programs have rapidly increased in the context of educational globalization in Vietnam, more research is required in this field. With the aim to promote EMI lecturers’ professional development and enhance teaching quality, this study was conducted to identify needs of content lecturers in EMI setting in Vietnamese higher education in order to have a more comprehensive insight for appropriate support and policy development. Data was collected via questionnaires distributed to EMI lecturers (n=33) and in-depth interviews with seven participants (n=6). The research findings showed that EMI lecturers required professional development support, particularly to improve their pedagogical competences and English language fluency. They also expressed a wish to collaborate with colleagues. In addition, this study identified lecturers' specific needs for institutional support, with particular emphasis on teaching materials and resources. These insights contributed to the existing literature and provided helpful suggestions for supporting EMI lecturers in similar institutions.

This study explores the factors influencing online learning motivation among students at Can Tho University. The research examines five key factors: personal, lecturer-related, institutional, academic, and environmental aspects. A survey was conducted with 892 students across various academic disciplines. The study employed statistical analyses, including Cronbach’s Alpha reliability testing, exploratory factor analysis (EFA), and multiple regression analysis to determine the impact of these factors on students' motivation.The findings indicate that personal and lecturer-related factors have the most significant positive influence, highlighting the importance of self-discipline, time management, and lecturer support. Institutional factors also play a crucial role, particularly in terms of learning infrastructure and support services. However, environmental factors negatively affect motivation, as poor internet connectivity, financial difficulties, and distractions hinder students’ engagement.  Additionally, students with higher academic performance and greater online learning experience show stronger motivation. Differences across academic disciplines suggest the need for tailored teaching methods and institutional support. These findings provide insights into enhancing online learning motivation and contribute to policy recommendations for improving the quality of online education at Can Tho University.

This study probed into how gender and development (GAD) concepts and principles are included in English 10 learning materials through a mixed-methods approach. The examination of lesson plans and self-learning modules through content analysis revealed biases supporting traditional gender roles, such as inadequate representation of women and showing stereotypical gender behaviors. Interviews with English 10 public teachers highlighted diverse strategies employed by teachers, despite limited exposure to GAD concepts, indicating a lack of comprehensive training in this area.; However, teachers still endeavor to integrate these principles into their instruction to foster inclusivity, though challenges persist, such as biases influencing disciplinary measures. The findings of this study highlight the importance of challenging narrow views and promoting inclusive practices in educational materials. This study contributes to the ongoing efforts to promote gender equality and diversity in education highlighted in UNESCOs Education for All initiative (2003), emphasizing the need for continuous awareness, sensitivity, and proactive integration of GAD principles in educational settings. Keywords: content analysis, gender and development, learning materials, inclusivity in education, mixed-methods, SDG 4

This meta-synthesis aimed to determine the technological landscape of English Language Teaching (ELT) in Asia over the past decade. Through meticulous screening of relevant studies, a thematic analysis of 14 qualitative and primary sources unveiled a reliance on internet platforms, mobile devices, gadgets, and robots in facilitating effective English instruction. Yet, a critical evaluation exposed deficiencies within ELT technologies, from source unreliability to technological unfamiliarity, alongside notable strengths like user-friendliness and information contemporaneity. Addressing these shortcomings demands a concerted effort to equip students and educators with requisite devices and training. Furthermore, fostering a conducive learning environment mandate tailored curriculum enhancements to bolster technical proficiency and the seamless integration of emerging technologies. Collaboration among parents, institutional leaders, and the educational community is paramount in navigating the adoption of new technologies, both within and beyond the confines of the traditional classroom. Delineating clear learning objectives, providing adequate skills training, and instituting appropriate curriculum changes are pivotal in harnessing the full potential of technology for English language learning.

This paper aimed to evaluate a parent-teacher partnership project implemented in one laboratory school in Metro Manila.Parent-teacher partnership is a collective effort by parents to work together for the benefit of learners.A project aims to address a particular problem and can be an intervention which ends once it has been implemented and regularly used. Participants are middle-income parents.Convenient sampling was used to identify the participants.After planning and designing a project in support of parent-teacher partnership, it was implemented to equip the parents with the necessary strategies to assist their children in flexible learning. After close collaboration with the parents,the researchers evaluated the project using a survey questionnaire.The results show that the objectives of the project were met, and the content was helpful in supporting parent-teacher partnerships.Also, relevant findings revealed that parents perceived the project as informative, as they gained strategies and tools to support their child's academic learning and were equipped with socio-emotional strategies to manage their child’s socio-emotional needs and to teach them how to navigate the educational technologies used in the school.The results of the study provided insights into revisiting policies to engage parents so they can develop the preparedness to support their children’s learning.

The aim of this study was to investigate the antimicrobial activity against Staphylococcus aureus, Propionibacterium acnes, Escherichia coli, and Candida albicans of the ethanol extract and the n-hexane and ethyl acetate extract of the stingless bee Tetragonula sp. propolis. Additionally, the study also identified the stingless bee species based on morphological characteristics and COI gene sequence. The antimicrobial activity of three stingless bee propolis extracts showed resistance against all three bacterial S. aureus, P. acnes, and E. coli. At a concentration of 1000 μg/mL, the ethanol extract exhibited the highest resistance, with P. acnes showing a zone of inhibition of 13.33 mm, while the inhibition zones were 11.33 mm for S. aureus and 10.33 mm for E. coli. The Minimum Inhibitory Concentration (MIC) for the three bacterial S. aureus, P. acnes, and E. coli were found to be 5, 10, and 15 μg/mL, respectively. The study identified the presence of polyphenols, flavonoids, aglycones, phenolics, and ketones  quantified the total phenolic and flavonoid content, assessed the antibacterial activity of the propolis extract against the three pathogenic bacteria, and provide the foundation for further research.

The application of machine learning models in the analysis of helmet-related images has yielded remarkable results in identifying and classifying helmet wearing behaviors. Previous research has used several pretrained models to predict proper or improper helmet use, achieving the highest accuracy of 98.61% on the Helmet Wearing Image Dataset (2024), a newly introduced dataset designed to improve the ability to classify helmet wearing behaviors. This study aims to improve the prediction performance on helmet datasets by leveraging state-of-the-art deep learning models combined with ensemble techniques. Using ResNet-50, MobileNetV2, and EfficientNet-B0 models, the proposed EnsemHelmet Framework uses soft voting ensemble to optimize the classification results, achieving an outstanding accuracy of 99.18% on the experimental dataset. The results demonstrate the potential of ensemble learning to achieve high performance. This study not only improves the accuracy of the helmet wearing recognition system but also highlights the effectiveness of ensemble techniques in optimizing performance on real-world datasets.