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Unshared Weight combine with Self-attention at Base model for On-The-Fly Fine-Grained SBIR
Nguyễn Hữu TuấnOver the years, on-the-fly fine-grained sketch-based image retrieval (SBIR) has demonstrated significant potential for real-world applications. However, previous methods rely on weight-sharing and attention mechanisms, which increase the computational load and reduce accuracy. This study enhances feature extraction and task differentiation by eliminating weight sharing, thereby preserving strong feature discriminative power. Our model achieves 82.04% Acc@5 and 92.57% Acc@10 on QMUL-Chair-V2, and 66.37% Acc@5 and 80.78% Acc@10 on QMUL-Shoe-V2, outperforming Bi-LSTM, Multi-granularity and LGRL models, demonstrating improved retrieval performance.
Năm:2025
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Chủ đề: Khoa học kỹ thuật và công nghệ khác
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Nhà xuất bản: Signal, Image and Video Processing
Detection of Dental Decay in children using Modern Deep Learning Models
Nguyễn Hữu TuấnDental decay is one of the most common oral health problems worldwide, but datasets about it are very limited. This paper introduced a new dataset for dental decay, which included images of children over 12 years old from many dental facilities in Viet Nam. At the same time, we also introduced a new change learning rate method for SGD optimizer. To verify the effectiveness of the proposed method, we compare the results with the Adam optimizer to evaluate the differences in convergence speed and accuracy based on training 3 models: Faster R-CNN, YOLOv3 and DETR, without using pretrained weights
Năm:2025
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Chủ đề: Khoa học kỹ thuật và công nghệ khác
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Nhà xuất bản: Journal of Information Hiding and Multimedia Signal Processing
Determinants of Digital Banking Adoption Intentions: Evidence from Vietnamese commercial banks
Trần An QuânSince the 1990s, digital banking in Vietnam has grown rapidly due to widespread smartphones
use and internet connectivity, offering convenience and efficiency. However, cash usage and
preference for traditional banking persist as barriers. This study examines key determinants
influencing customers’ intentions to adopt digital banking services in seven Vietnamese
commercial banks to enhance digital transformation strategies. Primary data were gathered using
a standardized questionnaire from 322 customers across seven commercial banks in Vietnam,
analyzed using linear regression. The findings indicate that perceived trust, usefulness, ease of
use, and convenience significantly encourage customers’ intentions to adopt digital banking
services, with trust exerting the most substantial influence. Conversely, perceived risk negatively
affects adoption intentions and other related factors. Although internal validity is supported by
rigorous statistical analysis, the external validity is constrained by the non-probability sampling
method, meaning findings are indicative for similar large commercial banks rather than the
Vietnamese banking system overall. Despite these limitations, this study provides valuable and
insightful contributions, enabling these major banks to refine their digital transformation
strategies, enhance customer experiences, and respond more effectively to diverse customer needs
in the rapidly evolving digital banking landscape.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Application of Machine Learning Algorithms for car type classification in Vietnam Market
Trần An QuânSupervised learning is one of the big problems of machine learning that has been applied in many fields. This paper applies it for classification problem on car type in Vietnam market. It uses the database of car industry in Vietnam in 2023, refers to car specification to make prediction of car types. The paper applies 6 algorithms, including: Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Classification and Regression Tree, Support Vector Machine and Gradient Boosting for model training and testing. After selecting the most appropriate algorithm, the research evaluates efficiency of classification in machine learning by accuracy score and confusion matrix.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản: IJARIIE
Two-stage APT malware propagation model in computer networks
Đỗ Xuân ChợEarly detection and prevention of advanced persistent threats (APT) is a critical challenge in cybersecurity. This paper presents an innovative approach using dual susceptible–infected–recovered (Dual-SIR) model to predict the two-stage spread of APT malware within networks. The first SIR model addresses infections at the first stage—device and user level, serving as a precursor to server compromise. The second SIR model focuses on the second stage of propagation—server infections, where sensitive organizational data is stored. Experimental results demonstrate the effectiveness of our proposed model not only for APT malware but also for other types of malware. Our work significantly contributes to the field of cybersecurity by offering a more accurate and proactive method for predicting malware spread. Additionally, this approach has potential applications in forecasting the dissemination of malware in wireless sensor networks and the spread of malicious information on social media platforms.
Năm:2025
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Chủ đề: Khoa học máy tính và thông tin
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Nhà xuất bản: Neural Computing and Applications
A novel ensemble learning-based model for APT attack detection
Vũ Thành ĐứcImproving the effectiveness of APT attack detection models is one of the most critical and essential tasks today. Following this trend, this paper proposes a new model called ACDF-mLSTM to address two primary challenges currently faced by research in this field: (i) data imbalance and (ii) information aggregation and feature extraction. Specifically, to solve the data imbalance problem, the paper proposes a novel data generation method named ACDF. This method leverages advanced and sophisticated techniques to focus on identifying crucial points in sequential data and analyzing context by considering preceding and succeeding data points. Subsequently, a Diffusion Model is applied to generate synthetic APT attack data, built upon the principle of gradual diffusion. With this approach, the ACDF model can generate more meaningful and realistic data. Next, to address the task of information aggregation and feature extraction, the paper proposes a new deep learning model named mLSTM, based on the optimization of Long Short-Term Memory (LSTM). Thus, the mLSTM model performs two main tasks: (i) extracting information from network flows within traffic and (ii) aggregating and highlighting important information before it enters the classification model. In the experimental section, the paper evaluates the ACDF-mLSTM model for the first time across various scenarios and datasets to demonstrate its effectiveness and adaptability. The evaluation results show that the ACDF-mLSTM model outperformed most other methods by an average of 2 to 12% across all metrics and on all experimental datasets.
Năm:2025
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Chủ đề: Khoa học máy tính và thông tin
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Nhà xuất bản: Memetic Computing
A Study of Multilingual Approaches to Vietnamese Information Retrieval
Nguyễn Thị Tuyết HảiNatural language processing (NLP) applications have an extensive development in various languages. However, information retrieval (IR) for Vietnamese texts has not yet received adequate attention from the research community. In this study, we fne-tune high-quality multilingual embedding models to enhance the effectiveness of document retrieval. Additionally, we investigate several approaches to information retrieval in order to gain deeper insights into their performance and applicability.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Deep Herbal Vision: A Computer Vision Approach for Genuine versus Counterfeit Identification of Ngoc Linh Ginseng (Panax vietnamensis Ha et Grushv.)
Châu Văn VânNgoc Linh Ginseng (Panax vietnamensis Ha et Grushv.) is an endemic and highly valuable medicinal plant of Vietnam, noted for the exceptionally high saponin concentrationover 50 rare ginsenosides, including 26 unique compounds [1]. Majonoside-R2 (MR2) constitutes more than 50% of total saponins and is linked to strong antioxidant and anticancer activities [2]. Rising commercial demand has resulted in increasing adulteration, while traditional authentication techniques remain subjective, costly, or unsuitable for large-scale use. This study introduces Deep Herbal Vision (DHV), a computer-vision framework for distinguishing authentic and counterfeit Ngoc Linh ginseng using visual cues associated with saponin rich tissues. A curated NgocLinh-5k dataset of over 5,000 images is used to fine-tune ViT-S/16 and ResNet-50 models enhanced with ArcFace, Focal loss, and regions of interest (ROI) segmentation. The fine-tuned ViT achieves 96.3% accuracy, 95.1% F1-score, and ROC-AUC=0.982, outperforming CNN and classical baselines [3]. Grad-CAM and attention visualizations confirm the model's focus on biologically relevant regions. The proposed framework provides a non-destructive and scalable AI solution for authenticating high value herbal materials and supporting conservation of Vietnam’s Ngoc Linh ginseng.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Hybrid Neural Network Models for Time-Series Biomedical Signal Forecasting
Châu Văn VânBiomedical signals such as ECG, EEG, and respiratory waveforms are highly nonlinear and temporally dependent, making accurate forecasting difficult. This work introduces a Hybrid Neural Network(HNN) integrating multi-level signal decomposition, deep temporal modeling, and neuromorphic dynamics for biomedical time-series prediction. The framework combines Empirical Mode Decomposition(EMD) for adaptive denoising, a CNN-LSTM attention module for temporal feature learning, and a fine-tuned Liquid Neural Network(LNN) for time dependent forecasting. Bayesian Optimization and self-supervised cross signal pretraining further enhance model performance. Experiments on MIT-BIH Arrhythmia and MIMIC-IV waveform datasets demonstrate that HNN outperforms CNN, LSTM, and Transformer baselines in RMSE and R², while retaining interpretability via attention maps. Findings highlight HNN as a generalized and efficient solution for biomedical signal forecasting and real-time clinical decision support.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Teaching C++ with pleasure in Multimedia by developing a virtual park learning environment
Lê Minh HóaThis study presents the development of a virtual reality (VR) application aimed at supporting the teaching of C++ programming to multimedia technology students. The objective of this research is to create an intuitive learning environment that helps students easily visualize and grasp complex programming concepts through interactive experiences within a virtual park. The application allows students to search for and apply programming commands in a virtual reality context, thereby enhancing their memory retention and understanding of the programming language. The research methodology includes the design and development of the VR application, followed by a survey to gather feedback from students on the effectiveness of the application in improving their programming skills. The results show that students felt more engaged and were better able to absorb knowledge when learning in the VR environment compared to traditional methods. This research not only opens up new directions for the application of VR technology in education but also contributes to improving the quality of programming instruction in the multimedia technology field.
Năm:2025
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Chủ đề: Khoa học máy tính và thông tin
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Nhà xuất bản: Communications in Computer and Information Science (Springer Nature Link)
Hardening the LoRA Ecosystem: Multi-Aspect Security Strategies for LLM Adaptation
Phan Nghĩa HiệpLow-Rank Adaptation (LoRA) has become a common way to fine-tune Large Language Models (LLMs) since it is quick and easy to move about. However, the fact that LoRA modules are lightweight and may be shared makes them more vulnerable to security threats, such as backdoor injection and malicious sharing through open-source repositories. This paper proposes a multi-layer security mechanism to protect LoRA-based fine-tuning from backdoors and hostile threats. This paper uses (i) signature verification, safetensors adoption, and sandboxing to make the supply chain stronger; (ii) matrix norms, eigenvalue spectra, and target-layer inspection to analyze static weight; (iii) automated red-teaming and trigger sweeping to evaluate dynamic weight; and (iv) runtime adapters, policy filters, and continuous monitoring to make deployment safe. This paper also puts these protections into a Continuous Integration/Continuous Deployment (CI/CD) pipeline so that they may be used in the real world. Initial tests on LLaMA-2-7B show that the framework can find backdoored LoRA modules without affecting the performance of benign ones. This study underscores the critical necessity for a secure LoRA ecosystem and offers practical techniques for reliable LLM adaptation.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Summarizing Multi-Aspect Label Opinions Based on the Information Synthesis Model
Nguyễn Ngọc DuyProduct reviews are critical for businesses to understand the needs of their customers. This paper proposes an opinion mining model that generates a multi-aspect opinion summary based on aspect-based sentiment analysis (MAOS) as an information synthesis form. This summary is based on senti-ment analysis according to different aspects of the product using a deep learning method supported by ontology. Both sentiment analysis and aspect detection tasks of the MAOS model depend not on individual words or sen-tences but on a strategy of aspect-based information synthesis from opin-ions. The summary, as the information synthesis form, helps opinion miners capture a significant amount of information without spending as much time reading as the summary based on an extractive or abstractive summarization strategy. This study's experimental results show that embedding the ontology into the corpus as part of the knowledge mining method greatly improves performance in tasks like finding aspects and combining information in opinions, even with a small corpus, when compared to standard methods.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Optimizing Detection Tasks with PySwarms: Applications of Paging Algorithms
Châu Văn VânEfficient detection in large-scale systems often requires balancing accuracy, computational cost, and memory management. This paper explores the integration of paging algorithms with particle swarm optimization (PSO), implemented via the PyCharm’s library, to address detection tasks. By adopting paging-inspired strategies, candidate solutions are dynamically loaded and updated, reducing redundant computations and improving convergence efficiency in high-dimensional search spaces. Experimental results demonstrate that the paging–PSO hybrid framework enhances detection accuracy while lowering runtime overhead compared to standard PSO implementations. The proposed approach shows promise for resource-constrained environments, where optimization must be achieved under limited memory and processing capacity. This work highlights the potential of combining classical operating system principles with swarm intelligence, offering a novel pathway for efficient optimization in detection and classification problems.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
VIT-RBTFIR: A Hybrid Vision Transformer Method For Fast Image Retrieval
Châu Văn VânWe present ViT-RBTFIR, a hybrid content-based image retrieval (CBIR) framework that couples Vision Transformers (ViT) for semantic feature extraction with a Randomized Binary Tree Forest (RBTF) for sublinear indexing in Hamming space. ViT embeddings are converted into compact binary codes by a lightweight hashing head, enabling fast candidate generation via RBTF. Across four benchmarks, the method delivers competitive Top-K accuracy while markedly reducing query latency compared with brute-force search. Complementary comparisons against established approximate nearest-neighbor baselines (FAISS and HNSW) indicate favorable speed-accuracy-memory trade-offs, especially at larger scales, supporting the practicality of ViT-RBTFIR for real-time retrieval.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
AI-Driven Test Generation and Fault Prediction
Châu Văn VânIn modern software engineering, ensuring quality and reliability under rapid release cycles requires intelligent and adaptive testing strategies. Traditional automation remains limited in scalability and predictive capability, while recent AI-based approaches often treat test generation and fault prediction separately. This paper proposes a Hybrid AI Framework that integrates Large Language Models (LLMs) for context-aware automated test generation with Machine Learning (ML) models for predictive fault detection. The framework combines focal method analysis and risk-based prioritization to enhance coverage and defect localization. Using open-source datasets (Defects4J and Methods2Test), a prototype implementation based on LLaMA-2-7B-Chat and XGBoost achieved +12.9 pp coverage (+18.5% relative) and +0.07 recall (+9.1%) over ChatUniTest, and TESTPILOT. The proposed architecture demonstrates how generation and prediction can operate synergistically within continuous integration pipelines, reducing manual effort while improving adaptability to code changes. This study contributes an architectural blueprint and validation workflow for AI-driven quality assurance, paving the way toward scalable, explainable, and hybrid intelligent testing systems.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
BERT-Driven Deep Learning for Enhanced Spam Email Classification with Browser Extension Integration
Châu Văn VânEmail volume continues to surge across business, education, and personal use, with spam composing nearly half of global traffic and driving financial and security risks. Classical filters (blacklists, rules, Naive Bayes, k-Nearest Neighbors) and even deep models like BiLSTM struggle to capture full semantics or resist modern evasion. We propose a BERT-driven spam-detection framework that exploits bidirectional self-attention to model rich context and evaluate it against strong baselines. On a balanced dataset of 5,000 emails with a 226-sample hold-out test set, fine-tuned BERT attains 98.67% accuracy and 98.65% F1, outperforming BiLSTM (96.43%), Naive Bayes (91.59%), and k-NN (90.27%). For practical deployment, we integrate BERT into a Chrome extension with a FastAPI backend, enabling real-time Gmail filtering with 0.5 seconds latency per email. Results indicate BERT delivers superior accuracy and robustness, bridging research and production. Future directions include multilingual detection, adversarially robust training, and multimodal filtering (URLs, images).
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
ViT-RBTF: Integrating Vision Transformers with Randomized Binary Tree Forest for Real-Time Image Search
Châu Văn VânContent-based image retrieval (CBIR) at web and enterprise scale requires features that capture global context while meeting strict
latency targets. Vision Transformers (ViTs) provide strong global representations, yet their computational footprint can hinder real-time deployment [1]. Meanwhile, randomized tree ensembles remain attractive for low-latency indexing and robust generalization [3]. This paper presents ViT-RBTF, a hybrid framework that couples a lightweight ViT extractor with deep hashing and a randomized binary tree forest index. The extractor produces compact binary codes, while the forest prunes candidates in logarithmic time before Hamming re-ranking. Across CIFAR-10, Corel-1K, and ImageNet100 subsets, ViT-RBTF improves mean Average Precision (mAP) by 5.8–7.6% over strong ViT-hashing baselines while reducing median query latency by 35–48% under identical hardware budgets. Ablation studies show that perception-mixing attention and token-aware modules enhance global–local fusion, forest depth and tree count control
the accuracy–speed trade-off, and feature-importance filtering stabilizes cross-domain retrieval. These results indicate that ViT-RBTF is an effective method for real-time image search systems, especially in scenarios requiring both high speed and high accuracy, and provide a simple, scalable path toward privacy-aware cloud environments and multimodal retrieval
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Reconstructing Multicolor and Incomplete Random Pictures
Châu Văn VânReconstructing incomplete and randomly fragmented multicolor images is a core challenge in computer vision with broad relevance
to digital restoration, medical imaging, and reliable data transmission. This study investigates the reconstruction of images that exhibit both missing regions and randomly distributed patterns, conditions under which traditional interpolation or diffusion-based methods often fail. We propose a hybrid framework that integrates probabilistic modeling with deep generative learning to infer missing content while preserving both structural coherence and color fidelity. The aim is to enhance spatial correlations, uncertainty modeling, and context-aware generative priors; the method effectively restores globally consistent images even under significant incompleteness. Experimental results demonstrate the outstanding performance of standalone probabilistic or deep learning approaches, achieving higher accuracy and improved perceptual quality across diverse levels of noise, color complexity, and missing data. The findings underscore the promise of hybrid reconstruction strategies in enhancing robustness and visual quality in practical image restoration scenarios.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Factors Influencing the Development of Green Logistics in Vietnamese Logistics Enterprises
Trần Thị HòaAbstract: Green logistics has become an inevitable trend in the context of cli-mate change, resource depletion, and the global demand for sustainable devel-opment. In Vietnam, the logistics sector plays a critical role in economic growth; however, the adoption of green logistics remains limited due to constraints in resources, technology, and managerial awareness. This study aims to identify and measure the factors influencing the development of green logistics in Vietnamese logistics enterprises. Building on established theories and previous studies, a research model was developed and empirically tested using a quantitative ap-proach. The sample consisted of 250 logistics enterprises in Vietnam, with re-sponses collected from managers who are directly involved in and knowledgea-ble about their firms’ logistics activities. The results reveal five major groups of factors affecting the development of green logistics: (1) technological capability (β = 0.458), (2) managerial awareness (β = 0.262), (3) policies and institutions (β = 0.158), (4) internal resources (β = 0.153), and (5) market pressure (β = 0.102). Based on these findings, the study provides managerial and policy implications to promote sustainable logistics practices. The research contributes both theoret-ically, by extending the literature on green logistics, and practically, by offering insights for policymakers and business managers in Vietnam.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
THE IMPACT OF PRODUCT QUALITY ON GREEN TECHNOLOGY CONSUMPTION BEHAVIOR AMONG VIETNAMESE CONSUMERS
Trần Thị HòaGreen technology products are playing an increasingly vital role in sustainable consumption and the advancement of a green economy. Nevertheless, green technology consumption in Vietnam has not yet become as widespread as anticipated. This study seeks to identify and measure the extent to which product quality factors influence Vietnamese consumers’ behavior toward green technology products. A quantitative approach was employed with a sample of 350 respondents, selected through purposive non-probability sampling, to survey users of green technology products in Vietnam. The findings reveal that product performance, reliability, friendliness, and green certification exert significant positive influences on consumer behavior. These results provide valuable implications for businesses in enhancing product development strategies and for policymakers in promoting sustainable consumption practices
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Research on Factors Affecting Digital Transformation in Accounting Field in Vietnam: Opportunities and Challenges
Nguyễn Thị Chinh LamIn the context of the explosion of the 4.0 industrial revolution, digital transformation has become an
inevitable trend of economic sectors, in which the accounting sector plays an important role in updating
information and ensuring the transparency of financial reports. The explosion of technologies such as
Blockchain, cloud computing, IoT, Big Data and artificial intelligence has been creating opportunities as
well as challenges for the modern accounting sector. Vietnam, with the strategy of "National digital
transformation to 2025 - orientation to 2030", has set the goal of developing digital infrastructure,
narrowing the digital gap and modernizing the operations of agencies and businesses, including the
accounting sector. In that context, this article analyzes the factors affecting the digital transformation
process in the accounting sector in Vietnam, from internal factors such as: technological capacity,
management awareness, human resources or external factors such as State policies, organizational size and
digital infrastructure. In addition, the article will also point out the opportunities that digital transformation
brings to the accounting sector as well as the challenges that need to be overcome, thereby building practical
recommendations for agencies, businesses and managers. The data in the article and the survey data cited
from previous studies as well as the empirical questionnaire serve as the theoretical and empirical basis for
this article.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Thermally stable sol-gel yttrium aluminum garnet cerium phosphors for white light-emitting diodes
Phan Xuan LeThis study aims to develop structurally controlled TiO2-based materials that serve a dual purpose as high-performance photocatalysts and optical scattering agents for white light-emitting diodes (LEDs). Hollow spherical TiO2, TiO2/Ag, and TiO2/Au particles were synthesized via a one-step spray thermolysis process using aqueous titanium citrate and titanium oxalate precursors. The method enables precise control of morphology and crystalline phase composition, producing hollow microspheres with tunable anatase–rutile ratios (10–100%) and crystallite sizes ranging from 12 to 120 nm. Photocatalytic performance, evaluated through the ultraviolet (UV)driven oxidation of methylene blue, showed that as-prepared TiO2 exhibited comparable activity to Degussa P25, while metal doping accelerated the anatase-to-rutile transition with minimal plasmonic enhancement under UV light. For LED applications, incorporating hollow TiO2 particles into YAG:Ce phosphor films improved luminous intensity, reaching a peak of ∼71 lm at 1 wt.% TiO2, and enhanced color uniformity, achieving a D-CCT as low as ∼60 K at 5 wt.%. These results confirm that spray thermolysis provides a scalable route to tailor morphology and phase composition, enabling multifunctional TiO2 materials optimized for both environmental photocatalysis and high-quality LED lighting.
Năm:2025
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Chủ đề: Kỹ thuật điện
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Nhà xuất bản: International Journal of Advances in Applied Sciences (IJAAS),
Influence of potassium bromide phosphor on optical properties of white light-emitting diodes
Pham Hong CongConventional phosphor-converted light-emitting diodes (LEDs) using silicone binders often suffer from yellowing, moisture degradation, and
limited spectral tunability, restricting their performance in high-power street lighting. To overcome these limitations, this study aims to develop an advanced LED illumination system integrating a KBr-doped sol-gel/silica phosphor with total internal reflection (TIR) lenses and a reflective housing,
encapsulated by an atomic layer deposition (ALD)-coated minilens panel.
The sol-gel matrix, synthesized from MTEOS, TEOS, and silica granules,
was engineered to achieve uniform KBr particle dispersion, reduced thermal
quenching, and improved chromatic stability. The ALD laminate provides an additional moisture and heat barrier, sealing micro-defects and minimizing stress-induced cracking. Optical performance was quantitatively assessed using Monte Carlo beam-tracking simulations under various street configurations, including focal, zigzag, and single-plane pole layouts. Results demonstrated enhanced luminous efficacy, precise glare control, and high uniformity in street illumination. Overall, this integrated sol-gel/ALD LED design effectively addresses the durability and color instability problems of traditional silicone systems, offering a scalable and energy efficient solution for next-generation street lighting.
Năm:2025
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Chủ đề: Kỹ thuật điện
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Nhà xuất bản: International Journal of Advances in Applied Sciences (IJAAS),
Computer simulation and software engineering in optical analysis of phosphor-converted white light-emitting diodes utilizing barium sulfate
Le Thi TrangAchieving uniform nanoparticle dispersion in electrospun polymer nanofibers remains a critical challenge, as conventional electrospinning
often leads to particle agglomeration and nozzle clogging, reducing fiber uniformity and functional efficiency. This study explicitly addresses this problem by developing poly (vinyl alcohol) (PVA)/BaSO4 composite nanofibers through both conventional and ultrasonic-assisted electrospinning. Scanning electron microscopy (SEM) revealed that ultrasonication effectively disrupted nanoparticle agglomerates, yielding
smoother and more uniform fiber morphologies. X-ray diffraction (XRD) analysis further confirmed that ultrasonic processing reduced the crystalline intensity of PVA and BaSO4, indicating enhanced polymer–filler interaction and finer BaSO4 distribution. Quantitatively, the agglomeration slope decreased from 0.039 (conventional) to 0.006, and the mean crystallite size was reduced from approximately 470 to 300 nm. These results are consistent with recent advances showing that ultrasonic electrospinning improves nanoparticle dispersion and stability in polymer matrices, thereby enhancing optical and mechanical properties. Ultimately, this work demonstrates that ultrasonic-assisted lectrospinning provides a robust and scalable strategy to fabricate lightweight, flexible, and multifunctional PVA-based radiation shielding materials with superior nanoparticle dispersion and structural homogeneity.
Năm:2025
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Chủ đề: Kỹ thuật điện
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Nhà xuất bản: International Journal of Advances in Applied Sciences (IJAAS),