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Leveraging RIS and QoS-Aware Transmission Rate for Energy-Efficient FSO Non-Terrestrial Networks
Lê Tùng HoaFuture 6G networks envision non-terrestrial net- works (NTNs) featuring various airborne devices, such as un- manned aerial vehicles (UAVs), to enable flexibility, rapid de- ployment, and ubiquitous coverage. Free-space optics (FSO), a promising communication technology, aims to connect network devices across the Earth, sky, and space. This paper focuses on the innovative concept of simultaneously transmitting data and energy to an operational UAV from a core network, with the assistance of reconfigurable intelligent surfaces (RIS) to navigate through cloudy regions. The FSO link employs the simultaneous lightwave information and power transfer (SLIPT) technique, incorporating a power-splitting strategy that allocates received power for data decoding and wireless energy charging. To enhance data transmis- sion, we introduce an adaptive rate design with QoS awareness. The analyses in this study assess four key performance metrics: (1) data transmission, quantified by the transmission rate, (2) energy transmission, evaluated based on harvested power, (3) UAV lifespan, represented by the remaining operational time for different SLIPT- powered UAV types, and (4) energy efficiency, defined as the ratio of data transmitted to total energy expended and harvested. The proposed system, which integrates SLIPT, RIS, and adaptive rate design, demonstrates significant improvements in the lifespan and energy efficiency of the UAV, validating its effectiveness under challenging operational conditions. Finally, the theoretical results are verified with the Monte Carlo simulation.
Năm:2025
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Chủ đề: Kỹ thuật điện tử và viễn thông
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Nhà xuất bản: IEEE Transactions on Aerospace and Electronic Systems
The inheritance and development of journalistic illustration from the Indochina School Of Fine Arts (1945–1975) to online journalism in VietNam: Transformation and influence
Hà Thị Hồng NgânJournalistic illustration plays a crucial role in conveying information, expressing aesthetic style, and shaping popular culture. In Vietnam, the tradition of journalistic illustration is deeply influenced by the École des Beaux-Arts de l’Indochine (Indochina Fine Arts School), notably prominent during the resistance war period (1945–1975), when illustrations simultaneously served artistic and propagandistic functions. Amidst the profound digital transformation of modern journalism, studying the inheritance and evolution of illustration styles has become an urgent need to preserve cultural values while guiding the aesthetic development of electronic newspapers.
This paper focuses on two main aspects: (1) the historical and artistic value of journalistic illustration during the resistance period, examined through representative publications such as Mặt trận Kinh tế, Nhân dân, and Tiến lên..; (2) the stylistic transition of illustration from traditional print newspapers to electronic media, with a research focus on official Vietnamese electronic newspapers: VOV and Dân Việt… The study clarifies the extent to which traditional artistic heritage - especially the Indochina style - influences contemporary illustration trends in the digital era.
The contribution of this study is interdisciplinary, linking fine arts, journalism, and communication technology. Beyond its academic value, the research offers practical implications by orienting the development of modern yet rich journalistic illustrations for digital media. Thus, this paper helps establish a new research foundation on the intersection between traditional art and digital communication in Vietnam.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Optimizing Mixed-Resolution ADC Allocation Under Bit-Budget Constraints in LDPC-Coded Massive MIMO
Đặng Ngọc HùngMassive multiple-input multiple-output (MIMO) receivers with 1-bit analog-to-digital converters (ADCs) are a promising solution to reduce hardware cost and power consumption. However, employing only 1-bit ADCs introduces severe quantization distortion and degrades the performance of channel-coded systems. A practical compromise is the mixed-ADC architecture, where a subset of antennas employs higher-resolution ADCs while the rest use 1-bit converters. In this paper, we study the optimization of mixed-resolution ADC allocation under bit-budget constraints for LDPC-coded massive MIMO systems. Each antenna is equipped with either a 1-bit ADC or a bhigh-bit ADC, with bhigh ∈ {3, 4, 5}, and the total number of quantization bits is restricted by a budget Btotal. The decoding threshold is evaluated using protograph extrinsic information chart algorithm (PEXIT) analysis. Numerical experiments for 16 × 64 and 32 × 128 MIMO configurations indicate an efficient operating region at approximately 58% of the maximum budget, corresponding to only 6.25% of high-resolution antennas. Under finite bit budgets, the 1vs3 mixed-ADC configuration consistently outperforms other mix types, delivering the lowest decoding thresholds across all simulated budgets while maintaining substantially lower quantization cost.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Enhancing Medical Image Classification with Noise-Injected Multi-Head Attention
Nguyễn Năng Hùng VânVision Transformers (ViTs) have shown strong potential in
medical image classification due to their ability to model long-range
dependencies. Despite this advantage, the deterministic nature of the
standard Multi-Head Attention (MHA) mechanism can lead to overfit
ting and reduced robustness, especially when working with noisy and
heterogeneous medical datasets. To address this issue, we introduce a
modified attention mechanism called Noise-Injected Multi-Head Atten
tion (NIMHA), which integrates controlled Gaussian noise into the key
and value projections of MHA. This stochastic regularization approach
enhances feature learning and model generalization while maintaining
computational efficiency and compatibility with existing ViT architec
tures. We evaluate NIMHA on two public datasets: Brain Tumor MRI
and CT Kidney. Experimental results show that ViTs with NIMHA out
perform baseline ViTs in classification accuracy, particularly on the more
complex Brain Tumor MRI dataset. In addition, models with NIMHA
exhibit more stable training behavior and faster convergence. Attention
map analysis further reveals that the proposed method promotes a more
distributed focus, improving the model’s ability to generalize to diverse
clinical data. These findings suggest that incorporating noise-based reg
ularization into attention mechanisms is a practical strategy to enhance
the robustness and reliability of ViT-based models for medical imaging
tasks.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
GT-FID: A Graph-Temporal Fusion Network for Host-Based Intrusion Detection from System Call Sequences
Đỗ Phúc HảoAdvanced Persistent Threats (APTs) pose a significant challenge to cybersecurity, as their sophisticated
strategies often evade traditional detectors that fail to capture complex temporal and structural patterns in
system call sequences. To address this gap, we propose the Graph-Temporal Fusion Network for Intrusion
Detection (GT-FID), a novel dual-branch deep learning architecture. GT-FID synergistically integrates a Long
Short-Term Memory (LSTM) network to model time-ordered dependencies with a Graph Neural Network
(GNN) that analyzes structural relationships within dynamically constructed call graphs. Evaluated on the
public ADFA-LD dataset, GT-FID achieves a test accuracy of 0.9622 and a Macro-Averaged F1-Score of
0.95, significantly outperforming strong baselines including GRU (0.9462) and Transformer (0.9563) models.
These results demonstrate that fusing temporal and structural features provides a more robust and effective
representation for detecting complex attack patterns, establishing a promising direction for future host-based
intrusion detection systems.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Resilient Edge Computing: An Elixir-BEAM Architecture for IoT Gas Leakage Detection
TRIET NGUYENIndustrial gas leakages require real-time, fault-tolerant detection systems to mitigate severe risks to safety
and the environment. This paper presents and evaluates an Internet of Things (IoT) solution that uses an edge
server implemented in the Elixir programming language, running on the BEAM virtual machine. We developed
a complete system with a physical layer of sensors and actuators and benchmarked its performance against a
functionally equivalent Python-based server. Key metrics, including CPU and RAM usage, runtime stability,
and developer experience, were assessed. The results show that the Elixir implementation is significantly
more efficient and reliable. It demonstrated a 32.14% reduction in CPU usage and a 40.85% reduction in
RAM usage compared to the Python server. Critically, the Elixir server operated without any downtime,
whereas the Python implementation failed mid-benchmark. While Elixir’s steeper learning curve and less
mature ecosystem present challenges, its lightweight concurrency model and built-in fault tolerance make it a
compelling platform for building highly resilient, safety-critical IoT applications.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Mind the Gap: On the Practical Utility of SHAP for Deep Learning-Based Intrusion Detection
Đỗ Phúc HảoThe increasing sophistication of cyber threats
necessitates the use of advanced techniques like Deep
Learning (DL) for Network Intrusion Detection Systems
(NIDS. While DL models achieve high accuracy, their
”black-box” nature hinders their adoption in real-world
security operations, where transparency and trust are
paramount. This paper investigates the practical chal
lenges of applying Explainable AI (xAI) to this problem
by critically analyzing the outputs of a standard xAI
framework on a high-performance NIDS model. We use
a Deep Neural Network (DNN) trained on the widely
used CIC-IDS-2017 dataset as a baseline model, achieving
an F1-score of 0.99 for attack detection. We then em
ploy SHAP to scrutinize the operational utility of the
generated explanations. Our analysis reveals a critical
”last-mile” problem in explainability, demonstrating that
raw, unprocessed xAI outputs can be unintelligible to
security practitioners due to standard data preprocessing
steps like feature scaling. The results show that while
xAI can identify influential features, significant post
processing and contextualization are required to translate
these outputs into genuinely transparent and actionable
insights, highlighting a crucial gap between theoretical
explainability and practical application in cybersecurity.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Optimizing Resource Allocation for Dynamic IoT Requests Using Network Function Virtualization
Phạm Tuấn MinhNetwork Function Virtualization (NFV) is essential for ensuring efficient and scalable Internet-of-Things(IoT) networks. However, optimizing resource allocation in an NFV-enabled IoT (NIoT) system is challenging, particularly when IoT functions are distributed as Virtual Network Functions (VNFs). This paper presents an approach for optimizing function placement in a dynamic NIoT system deployed within a hierarchical edge cloud computing environment. We propose an integer linear programming model and approximation algorithms to maximize the number of satisfied requests while minimizing system costs for a given set of service requests. Additionally, we develop a deep reinforcement learning-based algorithm (RTL) to determine the optimal timing for relocating IoT functions as bandwidth requirements change. Our evaluation measures several key metrics, including deployment cost, end-to-end delay, and request acceptance ratio. The results demonstrate that the approximation algorithms achieve nearly optimal results in significantly less time. The RTL algorithm consistently improves operational costs across various traffic demand scenarios compared to a baseline algorithm. Furthermore, our findings suggest an investment strategy for NIoT service providers to enhance system performance and reduce costs.
Năm:2025
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Chủ đề: Khoa học máy tính và thông tin
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Nhà xuất bản: IEEE Transactions on Network Science and Engineering
Optimizing Mixed-Resolution ADC Allocation Under Bit-Budget Constraints in LDPC-Coded Massive MIMO
Đặng Ngọc HùngMassive multiple-input multiple-output (MIMO) receivers with 1-bit analog-to-digital converters (ADCs) are a promising solution to reduce hardware cost and power consumption. However, employing only 1-bit ADCs introduces severe quantization distortion and degrades the performance of channel-coded systems. A practical compromise is the mixed-ADC architecture, where a subset of antennas employs higher-resolution ADCs while the rest use 1-bit converters. In this paper, we study the optimization of mixed-resolution ADC allocation under bit-budget constraints for LDPC-coded massive MIMO systems. Each antenna is equipped with either a 1-bit ADC or a bhigh-bit ADC, with bhigh ∈ {3, 4, 5}, and the total number of quantization bits is restricted by a budget Btotal. The decoding threshold is evaluated using protograph extrinsic information chart algorithm (PEXIT) analysis. Numerical experiments for 16 × 64 and 32 × 128 MIMO configurations indicate an efficient operating region at approximately 58% of the maximum budget, corresponding to only 6.25% of high-resolution antennas. Under finite bit budgets, the 1vs3 mixed-ADC configuration consistently outperforms other mix types, delivering the lowest decoding thresholds across all simulated budgets while maintaining substantially lower quantization cost.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Convex Hull-Based Coreset Selection for Identifying Differentially Expressed Genes in Pediatric Sepsis
Nguyễn Kiều LinhPediatric sepsis is a life-threatening condition characterized
by dysregulated immune responses, often leading to high mortality rates.
Identifying differentially expressed genes (DEGs) is essential for under-
standingitspathophysiologyanddiscoveringreliablebiomarkersforearly
diagnosis and treatment. In this study, we propose an integrative data
analysis method that combines machine learning algorithms with convex
hull to detect DEGs from high-dimensional gene expression datasets of
pediatric sepsis patients. The convex hull approach is applied to enhance
feature selection by geometrically separating relevant gene expression
patterns, while supervised learning models are used to classify and val-
idate the identified gene sets. Utilizing gene expression data from 249
pediatric patients, encompassing 11,574 genes, we propose a 10-gene sig-
nature capable of predicting sepsis-related mortality with an accuracy
of 81%. Comparative experiments against baseline methods, including
Principal Component Analysis (PCA) and Random Forest Feature Im-
portance (RFFI), demonstrate that the proposed method achieves supe-
rior predictive performance with a 2–5% improvement in accuracy. This
proposed method offers a promising tool for biomarker discovery and
advances data-driven research in pediatric sepsis.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Multi-modal sensor fusion and federated learning for TinyML on resource-constrained IoT devices
Đỗ Phúc HảoThe surge in IoT devices demands on-device intelligence for privacy-critical, latency-sensitive tasks like activity recognition. This paper presents a federated learning framework for multi-modal sensor fusion, specifically designed to operate under the tight resource constraints of TinyML platforms. Optimized for ARM Cortex-M microcontrollers with a sub-96 KB memory footprint, our framework employs a communication-efficient protocol using gradient sparsification and 8-bit quantization to drastically reduce uplink data requirements. We conduct a detailed comparative analysis of Early and Late Fusion strategies on the PAMAP2 dataset. Our results reveal a critical trade-off: while Early Fusion can achieve a marginally higher peak accuracy (95.75%), the more resource-efficient Late Fusion architecture ensures significantly faster convergence and greater training stability. This study highlights the feasibility of deploying robust, privacy-preserving TinyML models on low-power IoT devices and provides clear insights into selecting the optimal fusion architecture for such environments.
Năm:2025
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Chủ đề: Kỹ thuật thông tin
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Nhà xuất bản: International Journal of Parallel, Emergent and Distributed Systems
Figurative Language Used in Titles of English Novels: A Study on Literary Stylistic
Nguyễn Thị ThuThis study investigates the use of figurative language in the titles of 201 English novels, employing a literary stylistic approach grounded in Leech’s (1981) classification of figurative devices.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Exploring Linguistic Patterns through Machine Learning: Evidence from Logistic Regression Analysis
Nguyễn Minh TuấnThis study examines how machine learning techniques can detect and inter
pret linguistic patterns in Vietnamese text, with logistic regression used as a
core baseline model. The proposed framework integrates linguistic theory with
computational analysis to uncover phonological, morphological, syntactic, and
semantic structures within a multi-domain Vietnamese text classification corpus.
After data preprocessing, tokenization, and stopword removal, several feature
extraction strategies including TF-IDF, n-grams, and linguistically enriched fea
tures such as part-of-speech and morphological cues were applied to represent
both surface-level and deep linguistic regularities. Multiple models, including
Logistic Regression, CNN, Bi-LSTM with Attention, and a fine-tuned PhoBERT
transformer, were trained and evaluated using standard classification metrics.
Experimental results reveal that the Bi-LSTM with Attention model achieved
the highest F1-score (0.80), outperforming both the baseline and CNN models,
while PhoBERT suffered from overfitting and limited generalization. Analysis of
feature weights and attention distributions further highlights meaningful depen
dencies across linguistic levels, demonstrating the value of machine learning
in uncovering structured linguistic insights. The findings contribute to compu
tational linguistics research by providing a scalable, data-driven approach for
studying linguistic patterns in low-resource languages such as Vietnamese.
Năm:2025
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Chủ đề: Khoa học máy tính và thông tin
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Nhà xuất bản: oeil
Policy Communication on Gender Equality in Information Technology Enterprises in Vietnam
Nguyễn Thị ThoaGender equality is a pivotal goal in sustainable development and corporate culture. In Vietnam, although the Law on Gender Equality (2006) and various related policies have been promulgated, a considerable gap remains between policy documents and actual practice. This study analyzes policy communication on gender equality within small and medium-sized information technology (IT) enterprises, focusing on message content, communication channels, employee feedback, and the impact on organizational awareness. A multi-method qualitative design was employed, combining policy document analysis with 14 semi-structured interviews conducted in two representative enterprises.
Findings reveal that the effectiveness of formal gender equality policies is strongly influenced by informal organizational factors. While compensation, promotion, and training policies appear fair in form, the integrity of the equality message is distorted by implicit “decoding frames” such as gendered occupational stereotypes (e.g., software testing = female, programming = male) and “noise” generated by technically biased organizational cultures. These distortions hinder behavioral change (Level 3) and reinforce gendered organizational stratification (Level 4), thereby undermining overall policy effectiveness. Nevertheless, positive mechanisms including anonymous feedback channels and emerging shifts in gender awareness within training programs demonstrate promising signs toward more substantive equality.
The study contributes by proposing an integrated research framework for policy communication, clarifying the mediating role of communication in narrowing the gap between policy formulation and actual implementation.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Optimizing Mixed-Resolution ADC Allocation Under Bit-Budget Constraints in LDPC-Coded Massive MIMO
Đặng Ngọc HùngMassive multiple-input multiple-output (MIMO) receivers with 1-bit analog-to-digital converters (ADCs) are a promising solution to reduce hardware cost and power consumption. However, employing only 1-bit ADCs introduces severe quantization distortion and degrades the performance of channel-coded systems. A practical compromise is the mixed-ADC architecture, where a subset of antennas employs higher-resolution ADCs while the rest use 1-bit converters. In this paper, we study the optimization of mixed-resolution ADC allocation under bit-budget constraints for LDPC-coded massive MIMO systems. Each antenna is equipped with either a 1-bit ADC or a bhigh-bit ADC, with bhigh ∈ {3, 4, 5}, and the total number of quantization bits is restricted by a budget Btotal. The decoding threshold is evaluated using protograph extrinsic information chart algorithm (PEXIT) analysis. Numerical experiments for 16 × 64 and 32 × 128 MIMO configurations indicate an efficient operating region at approximately 58% of the maximum budget, corresponding to only 6.25% of high-resolution antennas. Under finite bit budgets, the 1vs3 mixed-ADC configuration consistently outperforms other mix types, delivering the lowest decoding thresholds across all simulated budgets while maintaining substantially lower quantization cost
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Linear Detection in One-Bit Mixed-ADC Massive MIMO: Insights into Performance and Energy Efficiency
Đặng Ngọc HùngMixed-ADC architectures are a promising solution to reduce power consumption in massive MIMO systems, yet their efficiency depends critically on the balance between one-bit and high-resolution ADCs. This paper develops a formal multi- objective framework to jointly analyze the trade-off between sum-rate and energy efficiency (EE). Through Pareto-front analysis, we identify a phenomenon termed Pareto Front Degeneration, observed in high-resolution configurations. Simulation results show that, at the knee points, the Mixed-ADC 1-vs-3 system (1-bit + 3-bit ADCs) achieves superior performance, with throughput gains of 5.5% over 1-vs-4 and 9.1% over 1-vs-5, together with nearly double the EE at high SNRs. These findings establish the 1-vs-3 configuration with a 40-50% mixing ratio as a practical operating point for energy-efficient massive MIMO design.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Composition distribution and photoluminescence properties of colloidal Cu – doped Zn0.5Cd0.5S quantum dots
Nguyễn Xuân NghĩaThis study aims to find a solution for preparing Cu-doped colloidal Zn0.5Cd0.5S quantum dots (QDs) with different radial Zn and Cd composition distributions and investigate the effects of these distributions on the QDs’ optical properties. QDs were prepared with a radial gradient composition distribution by rapidly and simultaneously injecting precursor solutions of Zn and Cd into the reaction flask. In contrast, QDs with a homogeneous composition distribution were prepared by injecting small amounts of Zn and Cd precursors alternately after the crystal nuclei had been formed, followed by thermal annealing of the QDs in the reaction solution. Cu doping Zn0.5Cd0.5S QDs decreases their band edge luminescence and significantly increases their broad emission band at lower energy. This emission band is generated by radiative recombination related to the Cu dopant, as well as lattice defects such as interstitial atoms and vacancies. Different composition distributions do not affect the excitation power dependence behavior of band edge luminescence and dopant related emission intensities; however, they cause strong band gap energy renormalizations in Cu-doped ternary QDs with increasing excitation power density. This enhancement is attributed to the optically active region in small gradient alloyed QDs and the existence of a wetting layer surrounding it.
Năm:2025
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Chủ đề: Khoa học kỹ thuật và công nghệ khác
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Nhà xuất bản: Vietnam Journal of Science and Technology
CUSTOMER SATISFACTION WITH CULINARY SERVICE QUALITY: A CASE STUDY AT DOOKKI RESTAURANT CHAIN
Nguyễn Thùy DungThis study aims to evaluate customer satisfaction with culinary service quality, using Dookki
Restaurant as a case study. Theoretical Framework: The research is grounded in theoretical frameworks
on the relationship between service quality and customer satisfaction. Method: By a combination of
qualitative and quantitative research methods, the authors surveyed 261 customers who had experienced
dining services at Dookki Restaurant. The collected data were processed using SMART PLS software.
Results and Discussion: The results show that out of the five factors considered, two have significant
impacts while three do not meet statistical significance. Specifically, the factor "Tangibles" (HH) has the
strongest influence on customer satisfaction, followed by "Responsiveness" (DU). The remaining factors—
"Reliability," "Assurance," and "Empathy"—do not show statistically significant relationships with customer
satisfaction. Research Implications: Based on the analysis, the author discusses several
recommendations to improve service quality at Dookki Restaurant Chain, thereby enhancing customer
satisfaction and encouraging repeat visits.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản: Journal of Tianjin University Science and Technology
INVESTIGATION OF α-RELAXATION PHENOMENA IN THE DIELECTRIC RESPONSE OF THERMOPLASTIC POLYIMIDE ODFA-OOD
Đào Thị HồngThis study investigates the dielectric properties of the aromatic thermoplastic polyimide ODFA–OOD in the region of α-relaxation. The films were obtained via a two-step synthesis followed by thermal treatment at 300 °C. Dielectric spectra were measured in the frequency range of 10⁻¹–10⁶ Hz and temperature interval of 473–573K. Analysis using the Havriliak–Negami function revealed the temperature dependence of relaxation parameters and the relationship between molecular mobility and the chemical structure of the polyimide.
Năm:2025
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Chủ đề: Vật lý
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Nhà xuất bản: Science and World - International scientific journal
3D-BPM simulation design of a compact 3-dB optical power splitter using a 2 × 2 RI-MMI coupler on silicon waveguide
Trần Thị Thanh ThủyMultimode interference waveguides are favored for their wide bandwidth, extensive fabrication tolerance, high stability, effective light confinement, and minimal transmission loss. In this study, we propose a numerical design of an optical power splitter based on restricted interference mechanisms using silicon-on-insulator waveguides, where the precise positioning of input pairs and subsequent adjustment of the multimode interference (MMI) region length are essential aspects. The RI-MMI configuration facilitates the reduction of the MMI length due to the applied interference theory. Our design undergoes rigorous simulation and optimization using the highly accurate 3D-BPM simulation method to ensure optimal performance. Simulation results confirm our high-performance design with low excess loss (<2.7 dB), small relative phase difference (<2%), negligible residual (<-18 dB), excellent coupling ratio (-0.09 dB to 0.05 dB), and a high balance factor (<-17 dB) across a wide range of 100 nm (1500 nm – 1600 nm). Furthermore, our optimized design exhibits a width tolerance of ±2.1 µm and a height tolerance of ±10 nm. Notably, the core component of the splitter is housed within an extremely compact footprint area of 6 µm × 65 µm. These exceptional characteristics position our proposed device as highly promising for large-scale integrated optical circuits as well as photonic neural networks in ultrawideband telecom applications.
Năm:2025
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Chủ đề: Kỹ thuật điện tử và viễn thông
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Nhà xuất bản: Opto-Electronics Review
Design and demonstration of a high-performance, compact 2×2 optical switch based on optimized RI-MMI couplers for fast C-band switching in scalable photonic networks
Trần Thị Thanh ThủyWepresent the design and fabrication of a 2 × 2 optical switch based on a Mach-Zehnder Interferometer (MZI) configuration that exhibits high switching performance, enhancing 3 dB bandwidth at low loss. The advance ment of the switch design is to use two thermo-optic phase shifters (TOPSs), which enable precise switching be tween output ports by adjusting the applied voltage. With these two TOPSs, the device overcomes the challenges of thermal crosstalk and improves switching accuracy, ensuring stable operation even with high thermal sensitiv ity. We provide a comprehensive evaluation of the switch's performance, including passive, DC, and AC charac teristics. The DC characterization demonstrates the ability to route signals to different ports based on the applied voltage, while the AC measurement reveals a fast-switching time of approximately 20 μs at the target wavelength of 1548.6 nm. The passive performance exhibits excellent signal integrity with minimal insertion loss (IL) and crosstalk (XT) within the C-band. Furthermore, this design can potentially be expanded into larger configura tions, such as 8 × 8, 16 × 16, and even 64 × 64, by leveraging MMI-based architectures like Banyan and Benes topologies. This scalability makes it an ideal candidate for high-performance, large-scale optical switching net works, where fast switching, low loss, and minimal crosstalk are essential.
Năm:2025
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Chủ đề: Kỹ thuật điện tử và viễn thông
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Nhà xuất bản: Optics Communications
Design and Optimization of a Cascaded RI-MMI - Based 2×4 Quadrature Phase and Power Splitter with Integrated Thermo-Optic Phase Shifters
Trần Thị Thanh ThủyIn this study, we present the design and optimization of a 2×4 quadrature phase and power splitter based on cascaded RI-MMI couplers integrated with thermo-optic phase shifters on a silicon photonic platform. The device operates under the restricted interference regime to achieve a compact and efficient configuration. The proposed circuit consists of two cascaded 2×2 RI-MMI couplers with integrated Ti-based heaters enabling precise thermo-optic phase control and switching capability. Numerical simulations using the 3D Beam Propagation Method (BPM) show that the splitter performs effectively across a broad wavelength range of 1.5–1.6 µm, with an excess loss of approximately 0.2 dB at the central wavelength. The four output ports exhibit well-balanced power distribution, with a maximum deviation of about 15 dB, and maintain stable 90° phase differences among the outputs. The device also demonstrates robust thermal stability and tolerance to fabrication errors up to ±1 nm, ensuring consistent optical performance. These findings highlight the strong potential of the proposed device for advanced optical signal processing applications such as QPSK modulators, multicarrier phase-coded OFDM systems, optical phasors, and multiphase local oscillators, providing a robust and high-precision platform that operates reliably across multiple wavelength bands.
Năm:2025
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Chủ đề: Kỹ thuật điện tử và viễn thông
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Nhà xuất bản: Opto-Electronics Review
Comparison of Clustering Algorithms for Indoor Environmental Time-Series Data
Ngô Quốc DũngClustering multivariate time-series data from indoor environmental sensors is challenging due to data complexity and noise. This paper compares feature-based, raw data-based, and model-based clustering methods to identify the most effective approach for extracting meaningful patterns. We evaluate these methods using Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. Results show that model-based methods, especially Gaussian Mixture Models, outperform others in cluster compactness and separation, despite some negative Silhouette scores. Feature-based methods provide moderate stability, while raw data-based approaches yield mixed results. Limitations include zone-level data without inter-zone links, absence of benchmark datasets with labels, and a limited range of tested algorithms. Future work will address these by expanding data scope, incorporating benchmarks, and exploring more clustering techniques. This study offers a concise comparison of clustering strategies for indoor multivariate time-series data, aiding future research in environmental monitoring and smart buildings.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
A Comparative Study of Anomaly Detection Approaches in Time-Series Environmental Sensor Data
Phan Lý HuỳnhBuilding sensor anomaly detection is crucial for operational efficiency and equipment reliability, yet standardized evaluation frameworks are lacking, leading to inconsistent method selection. This paper presents a comprehensive comparison of anomaly detection methods using a novel unsupervised evaluation framework that incorporates statistical consistency, temporal coherence, cross-method agreement, and domain logic compliance. We evaluate three paradigms—statistical (STL), machine learning (Isolation Forest), and deep learning (LSTM Autoencoder)—on real-world building sensor data spanning two years with eight sensor types. Our results show that Isolation Forest achieves the best overall performance with a total score of 0.464 and superior computational efficiency of 0.339 seconds, detecting four collective anomalies with excellent temporal coherence. Surprisingly, the LSTM Autoencoder achieves only a 0.263 total score despite 206.444 seconds training time,
challenging assumptions about deep learning superiority. These findings demonstrate that well-designed traditional methods can
outperform complex deep learning approaches when considering computational efficiency and practical deployment requirements,
providing essential guidance for building monitoring system design.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản: