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FA-Net: A Dual-Branch Attention Architecture for Extracting Fine-Grained Anatomical Features of Wood
Ma Công ThànhAccurate identification of wood species is a challenging \textit{Fine-Grained Visual Classification (FGVC)}, playing a crucial role in supply chain management and in combating illegal logging. Conventional Convolutional Neural Networks (CNNs) often fail to capture subtle morphological details due to feature compression (global pooling), even though macro-images inherently contain both global structural context and fine-grained cues. To overcome this limitation, we propose \textbf{FA-Net (Fine-Anatomical Network)}, a novel dual-branch architecture that employs a \textit{global branch} to capture global structural context (e.g. porosity types and vessel distribution) and a \textit{local branch} to preserve local morphological details (e.g. parenchyma patterns and vessel/ray sizes) from macro-scale images. Both branches are enhanced with channel–spatial attention mechanisms and are adaptively fused through a pyramid self-attention module, yielding a highly discriminative representation. Comprehensive experiments across five benchmark datasets demonstrate that FA-Net achieves state-of-the-art accuracy, reaching up to 99.32\%—outperforming the DenseNet121 baseline by 4.0\%—while maintaining near-real-time inference speed. Interpretability analysis via EigenCAM further confirms that FA-Net successfully attends to critical anatomical traits (such as porosity types and parenchyma patterns). FA-Net provides an efficient, transparent and deployment-ready solution for practical applications in forestry and customs inspection.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Optimizing Beamforming for Cell-Free MIMO ISAC Systems with Low-Resolution ADCs
Bùi Văn KiênIntegrated sensing and communication (ISAC) is emerging as a critical technology for 5G and beyond wireless networks. Despite the apparent advantages, real-world applications constantly present performance and energy optimization issues. This paper presents a novel method combining low-resolution analog-to-digital converters (L-ADCs) with optimal beamforming strategies for a free-cellular MIMO ISAC system to tackle the energy saving challenge. The proposed system adopts the additive quantization noise model (AQNM) and thereafter examines four different beamforming scenarios based on sensing and/or communication priority schemes. Numerical simulations indicate that the conjugate beamforming (CB) method outperforms the null space beamforming (NS) method across all power allocation ratios and ADC configurations, particularly at low communication power allocation. The large quantity of antennas enhances spatial capacity and mitigates the performance decrease associated with low-resolution ADCs, enabling the system to maintain efficiency while achieving energy conservation advantages. This research addresses the research gap in optimizing noncellular ISAC MIMO systems with low-resolution ADCs, opening the
way for energy-efficient applications.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Bidirectional AC/AC converter linking two microgrids in a flexible microgrid
Nguyễn Thế VĩnhBidirectional AC/AC converter linking two microgrids in a
flexible microgrid
Năm:2025
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Chủ đề: Kỹ thuật điện tử và viễn thông
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Nhà xuất bản: International Journal of Power Electronics and Drive Systems
An Object Detection Framework Based on Relationship Between Objects in an Open Vocabulary Using Owl-VIT And RelTransformer
Nguyễn Thị NguyệtObject detection has been widely adopted across various applications, but traditional methods mainly provide isolated object locations without capturing their relationships. To address this limitation, we propose a system that detects both objects and their relationships within images based on natural language queries. The approach integrates OWL-ViT for open-vocabulary object detection, RelTR for relationship inference, and Large Language Models (LLMs) for query processing and language understanding. Unlike fixed-vocabulary models, OWL-ViT enables detection from free-text descriptions, improving generalization and supporting flexible user queries. Experimental results show that the proposed framework can localize objects and infer their relations with a recognition accuracy of 27%, demonstrating its potential for intelligent systems such as query-driven surveillance and human–machine interaction. This work is not merely a combination of existing models, but a deliberate integration designed to address the novel challenge of detecting object relationships from natural language queries.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Bidirectional AC/DC converter connecting AC and DC microgrids for smart grids
Nguyễn Văn DũngBidirectional AC/DC converter connecting AC and DC microgrids for smart grids
Năm:2025
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Chủ đề: Kỹ thuật điện tử và viễn thông
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Nhà xuất bản: International Journal of Power Electronics and Drive System (IJPEDS)
The DC/DC/AC bidirectional converter for direct power transmission idependent or on grid flexibly for AC microgrid
Nguyễn Thế VĩnhThe work of this paper is based on the development of microgrids in remote areas,
islands and areas often affected by natural disasters in Vietnam and also in island countries in
Asia. Multi-function DC/DC/AC converters work in conjunction with Boost converters and Hbridge circuits with four basic components: renewable energy sources; storage systems, DC,
AC loads and AC grids to provide energy conversion processes that ensure efficient use of
energy from distributed energy sources (renewable energy) and can work on the grid or
independently and have many flexible energy conversion functions in microgrid systems.
Distributed energy sources affect the parameters of the power supply grid due to changes in
the parameters of the power source and especially sudden changes during the process of
supplying energy to the AC and DC microgrids in the power system. In addition, the energy
variation of the load on the DC and AC microgrids also affects the optimal use of energy from
distributed energy sources. The proposed power circuit solution demonstrates significant
efficiency improvements, especially in handling situations involving switching operating cases
and recalibrating parameters to best suit the overall converter. In simulations and experiments,
the superiority of the proposed solution is confirmed through performance comparisons
between simulations, experiments, and references, as well as impact analyzes of different cases.
Năm:2025
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Chủ đề: Kỹ thuật điện tử và viễn thông
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Nhà xuất bản: Power System Technology
TinyCDAE: Lightweight Convolutional Denoising Autoencoders for Real-Time Image Denoising on Resource-Constrained IoT Devices
Nguyễn Trọng HuânImage denoising is a fundamental task in computer vision, serving as a preprocessing step to enhance image quality and improve the performance of subsequent recognition systems. Its importance is particularly evident in Internet of Things (IoT) applications, where sensors frequently capture noisy data. While autoencoder-based denoising models have achieved strong performance, their evaluation on resource-constrained embedded platforms with real on-device measurements remains largely unexplored. This study addresses this gap by presenting one of the first feasibility investigations of deploying an image-denoising autoencoder on a microcontroller unit (MCU). We introduce TinyCDAE, a deployment-oriented lightweight convolutional denoising autoencoder designed for resource-constrained microcontrollers, and demonstrate its deployment on ESP32. The model is first evaluated on a PC environment and compared with a baseline fully connected denoising autoencoder (DAE). The results show that TinyCDAE, with only 373 parameters compared to 52,064 for the baseline DAE, significantly outperforms the baseline in terms of PSNR and SSIM across multiple noise levels. TinyCDAE is then deployed on an ESP32 microcontroller, achieving real-time denoising with an average inference latency of 133.57 ms per image. Beyond demonstrating technical feasibility, our on-device measurements on ESP32 confirm that a compact denoising autoencoder can operate effectively on resource-constrained IoT devices.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
New flexible bidirectional converter for electric vehicle substations connecting microgrids
Nguyễn Thế VĩnhThis paper proposes a flexible and energy-efficient power conversion system capable of bidirectional
energy flow between AC and DC microgrids, as well as electric vehicles (EVs). The converter is designed
by integrating fundamental DC/DC topologies-namely Push-Pull and Half-Bridge converters-with a
multi-level DC/AC inverter. It supports multiple operating modes, enabling seamless integration of
both fixed and mobile EV charging stations through dedicated DC/DC charging interfaces tailored to
various system configurations. A hierarchical multi-agent control strategy is employed, with clearly
defined roles for each converter control component to enable coordinated operation across diverse
use cases. Simulation results conducted in MATLAB demonstrate a high power factor of up to 96.5%
during both EV charging and discharging processes to the interconnected microgrids. The system
accommodates EV input voltages ranging from 350 to 1500 VDC and currents from 15 to 45 A, covering
a wide range of medium to fast charging levels. The optimal switching frequency is determined through
a detailed power loss analysis across input and output stages. The proposed converter offers a compact
design, supports a wide range of voltage levels with low battery-side ripple, and ensures efficient
bidirectional energy conversion between various grids.
Năm:2025
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Chủ đề: Kỹ thuật điện tử và viễn thông
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Nhà xuất bản: Springer Nature
Flexible Converter for Electric Vehicle Charging Station Using Renewable Energy Efficiently
Nguyễn Thế VĩnhThis article presents the research content of a solution for DC/AC, DC/DC, and isolated
AC/DC converters using three-winding pulse transformers to work flexibly with coils that can be
primary and secondary. The ratio between the pairs of coils is calculated differently to suit the output
voltage requirements for battery charging for EVs and AC microgrid loads with different voltage
levels and frequencies. The number of turns of the coil connected to the EV charging station will be
10 times larger than the coils at other outlets, and the coil operates in either primary or secondary
mode in the transformer. In this pulse transformer, components such as renewable energy sources of
solar panels (PV) DC voltage output, distributed sources, loads, and storage systems in the AC
microgrid capable of bidirectional conversion and connecting to the main grid are connected. The
main load is the batteries of electric vehicles (EV) supplied with energy in DC from PV sources and
from the AC microgrid at the same time or in each case from different energy sources. The converter
performs stable and flexible scenario operation to help the EV charging system use renewable energy
efficiently, increase the continuous supply of electricity to the AC microgrid load, help save
electricity, and stabilize the power system. Simulation results using Orcad software describe the
values of current, voltage can reach 1000 VDC for the EV charging station, average conversion power
of nearly 10 kW, and the average efficiency achieved by the converter of nearly 96% compared with
reference documents and experiments to draw initial conclusions for the research project.
Năm:2025
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Chủ đề: Kỹ thuật điện tử và viễn thông
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Nhà xuất bản: DC/DC Converter; DC/AC Converter; Electric car batteries; Inverter; Pulse transformer
Real-time phishing uniform resource locator detection based on hybrid embedding transformer and retraining-free inferencing
Đàm Minh LịnhPhishing attacks that evade traditional detection mechanisms by exploiting deceptive uniform resource locators (URLs) remain a significant cybersecurity threat. This study proposes an adaptive phishing URL detection framework that integrates Levenshtein distance-based string similarity, a hybrid embedding transformer (HET) encoder-based server-side verification mechanism, and a dynamically updated local blacklist. First, a rapid local lookup is executed to identify known phishing URLs. If the input URL is absent from the blacklist, the Levenshtein distance algorithm detects subtle character-level variations, identifying typosquatting and obfuscation effectively. For ambiguous cases, the HET-based module uses a lightweight post-hoc inference method that classifies URL embeddings via k-nearest neighbor voting based on Euclidean similarity in the latent space, thereby avoiding retraining and enabling real-time adaptation to
emerging phishing threats. Confirmed phishing URLs are added iteratively to the local repository to improve detection continuously, enhancing future classification accuracy. Experimental evaluation on a large-scale dataset comprising 235,795 URLs revealed that the proposed method outperforms state-of-the-art approaches, achieving a detection accuracy of 99.8 %, with a falsepositive rate of 0.441 % and false-negative rate of 0.0617 %. Additionally, real-time validation using a Chrome browser extension confirmed rapid processing, with an average processing time of 4.43–6.84 ms per URL on a dataset comprising 5,000 URLs. These results highlight the efficiency of the proposed framework in real-world cybersecurity contexts, enabling high detection accuracy, fast response times, and adaptability to evolving phishing strategies, and underscore the importance of proactive threat intelligence and real-time phishing mitigation in developing scalable, high-performance security infrastructures.
Năm:2026
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Chủ đề: Khoa học dữ liệu và trí tuệ nhân tạo
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Nhà xuất bản: Computers and Electrical Engineering
Estimation of External Government Debt Thresholds: The Case of Vietnam
Đặng Thị Huyền AnhPurpose: This paper aims to investigate the nonlinear relationship between government debt and economic growth in Vietnam, specifically identifying the threshold levels at which the impact of debt shifts from positive to negative.
Design/methodology/approach: A threshold regression model is employed to analyze the marginal impact of Vietnam's government external debt on GDP growth. The model incorporates various control variables, including total factor productivity, foreign direct investment, private debt, and trade openness.
Findings: This research distinguishes itself by focusing on Vietnam's specific context to identify and quantify a precise, non-linear external debt threshold using a threshold regression model, moving beyond general linear assumptions. It provides a quantifiable estimate of debt's impact on growth across varying levels: 36,61% and 78,94%. Moreover, emphasizing the role of moderating factors like productivity, private debt, and trade openness, this research suggests the optimal threshold of public debt-to-GDP ratio for Vietnam is 36.16% represents a critical threshold. Below this level, government debt positively impacts economic growth. However, exceeding this threshold can lead to negative consequences, as total factor productivity and private debt may hinder growth.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản: GLOBAL BUSINESS & FINANCE REVIEW
Dynamic Customer Experience, Satisfaction, and Word-of-Mouth in Telecom-IT Sector
Nguyễn Quang HưngThis study examines how Dynamic Customer Experience (DCX) affects Customer Satisfaction (CS) and Word-of-Mouth (WOM) intentions among VNPT customers in Vietnam, identifying AI-Driven Service Personalization (AISP), Integrated Service Quality (ISQ), Cultural Resonance (CR), and Sustainable IT-Telecom Practices (SITP) as key antecedents, with Customer Empowerment (CEMP), Perceived Value Co-Creation (PVCC), Emotional Engagement (EE), and CS as mediators, and AI Trust (AIT), Service Innovation Maturity (SIM), and Regional Cultural Dynamics (RCD) as moderators. A multi-theoretical framework (Customer Experience Framework, Social Exchange Theory, Expectancy-Disconfirmation Theory, TAM, SERVQUAL) guided the research. Survey data from 677 VNPT customers were analysed using hybrid PLS-SEM (SmartPLS 4.0) for explanatory power and Artificial Neural Network (ANN) in SPSS 25.0 for predictive accuracy. PLS-SEM confirmed significant positive effects of AISP, ISQ, CR, and SITP on DCX (β = 0.24–0.33, p < 0.01), and DCX on CS (β = 0.43) and WOM (β = 0.30). CS was the strongest mediator (indirect effect = 0.20, VAF = 67%). Moderation analyses showed stronger effects in rural areas due to cultural dynamics. ANN validated results with high predictive power (R² testing = 0.83–0.87), identifying AISP and CS as top predictors. This is the first study to integrate sustainability and cultural resonance into DCX for Vietnam's collectivist telecom market using a hybrid PLS-SEM-ANN approach, outperforming single-method studies and providing VNPT actionable strategies for AI personalization and green 5G deployment.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản: Emerging Science Journal
A Survey on Methods of Applying Transformers to Non-NLP Applications
Nguyễn Trung HiếuTransformers play a key role in the success of large language models
today. The success of the architecture is impressive. In this survey we find out
the adaptability of Transformer models in fields other than natural language pro-
cessing (NLP), thereby giving us an insight into the superiority of Transformer
models in processing various types of data. Although initially introduced for nat-
ural language processing (NLP). Transformers are gradually used to solve prob-
lems related to images, related to the analysis of signals, time series and in the
processing and modeling of spatial data, etc. However, there are still many un-
clear issues regarding the application of Transformers in such domains, such as
whether they are natural or not; whether they are really effective; and whether
transformers are a universal architecture for everything; what innovative tech-
niques have been used to apply Transformers in domains other than NLP and
what challenges remain to be solved. Through our survey, these aspects are grad-
ually clarified. We also found some basic principles for adapting transformers to
non-NLP applications, while also recognizing their limitations and directions for
improvement.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
A Comparative Study of Transformer and Convolutional Neural Network Architectures
Nguyễn Trung HiếuNetwork intrusion detection has become increasingly critical in cybersecurity as cyber threats continue to evolve in sophistication and frequency. This paper presents a comprehensive comparative study between Transformer-based architectures and Convolutional Neural Networks (CNNs) for network intrusion detection using the NSL-KDD dataset. We implement and evaluate both architectures on a multi-class classification task involving 40 different types of network attacks and normal traffic across 119 features. Our experimental results demonstrate hat while the Transformer model achieves slightly higher accuracy (71.22% vs 70.73%), the CNN model shows superior F1-score performance 0.6325 vs 0.6116) and significantly better computational efficiency with 8.7× faster training time (203.31s vs 1761.22s). The study provides insights into the effectiveness of attention mechanisms versus convolutional architectures for capturing complex patterns in network traffic data, while also analyzing computational efficiency and practical deployment considerations. Our findings contribute to the advancement of AI-driven cybersecurity solutions and provide guidance for selecting appropriate deep learning architectures for network intrusion detection systems.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Channel-Aware Power and Rate Control for UOWC with DRL and HARQ Integration
Đỗ Huy TiếnUnderwater optical wireless communication (UOWC) has
difficultiesinfulfillingultra-reliablelow-latencycommunication(URLLC)
standards owing to channel distortions, including absorption, scattering, and oceanic turbulence. This paper presents a deep reinforcement learning (DRL) approach utilizing proximal policy optimization (PPO) to concurrently adjust transmit power and coding rate in a point-to-point UOWC system employing hybrid automatic repeat request (HARQ) protocols chase combining (CC-HARQ) and incremental redundancy (IR-HARQ) capitalizing on statistical channel information and signal-to-noise ratio feedback, structured as a Markov decision process (MDP)with rewards that penalize power consumption and delay infractions. By reducing the long-term average power while adhering to stringent delay constraints (e.g., 99.9% dependability at 13 dBm in pristine marine conditions), the approach enables energy-efficient and dependable UOWC for Beyond 5G (B5G) and 6G applications, such as ocean monitoring.
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Performance Analysis of Coded MIMO Systems Using Superposition 64-Ary Constellation with Protograph LDPC and Low-Resolution ADCs
Lê Nhật ThăngThis paper introduces a new coded modulation framework for massive MIMO systems that leverages low-resolution analog-to-digital converters (ADCs) alongside superposition-based 64-ary QAM modulation to increase spectral utilization efficiency. To address the complexity of signal detection, the proposed approach reformulates the original 64-ary QAM channel into an equivalent BPSK model. This reformulation supports a two-layer Tanner graph structure for the combined tasks of signal detection and channel decoding, with computational complexity that grows quadratically with the number of transmit antennas. Protograph LDPC (PLDPC) codes are adopted in the system due to their excellent error correction performance, and integrates an “equal-weight” superposition constellation, which delivers a 0.7–1 dB gain in performance compared to the traditional “equal-distance” design under coarse quantization. Furthermore, the paper introduces a modified PEXIT analysis method that holistically accounts for the combined effects of MIMO fading, quantization noise from ADCs, and LDPC decoding characteristics. Simulation results confirm the reliability of the analytical approach and highlight the critical role of optimized power allocation in maintaining high energy and spectral efficiency in emerging 5G and 6G networks utilizing lower solution receivers.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
A Novel Network Attack Detection Platform Targeting the AMF Component in the 5G Network Infrastructure
Nguyễn Huy TrungIn the trend of the Internet of Things, 5G technology is one of the important platforms connecting mobile devices to the Internet network. Along with the popularity of 5G network deployment in many countries, the risk of destructive attacks on this infrastructure is increasing. This paper proposes a novel platform for detecting attacks on the AMF in 5G cores using distributed ML. Core innovation: The Attack‐Aware Weighted Aggregation (AAWA) in federated learning enables knowledge sharing without raw data exchange, achieving 99.24% global accuracy (0.77% over FedAvg), 25.6% faster convergence (32 vs. 43 rounds), and robust privacy with differential privacy (ε = 1.0, 0.33% accuracy drop). Experiments on a custom AMF dataset validate superior performance in detection, efficiency, and resilience.
Năm:2025
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Chủ đề: Khoa học máy tính và thông tin
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Nhà xuất bản: Concurrency and Computation: Practice and Experience
LogMerge: improved log parsing based on two-step clustering combined with low-level token processing
Viet Le HaiLogs are a crucial source of data, containing a vast amount of information that reflects the real-time operational status of systems, and are widely used in cybersecurity, system monitoring, and fault diagnosis. Log parsing is the first and most essential step in automated log analysis, aiming to transform semi-structured log data into a structured format. However, due to the large volume and heterogeneous structure of log data, existing parsing methods face significant challenges in accurately identifying log structures and extracting parameters, often leading to over-generalization or over-specialization during the parsing process. To address these limitations, this study proposes a log parsing approach named LogMerge, which integrates heuristic techniques with an efficient two-step clustering strategy. First, LogMerge leverages sets of special delimiters to further split tokens into subtokens, enabling the transformation and handling of variations within tokens. Then, LogMerge applies a two-step clustering process: in the first step, logs are grouped into smaller clusters based on token counts and common substrings; in the second step, a fixed-depth parsing tree is employed to merge these small clusters into larger ones, from which appropriate log templates are extracted. Experimental results on 14 datasets demonstrate that the proposed method achieves superior parsing accuracy, with evaluation metrics exceeding 0.8 on 10 datasets. The extracted log templates are closer to the ground-truth templates while mitigating both over-specialization and over-generalization compared to existing log parsers.
Năm:2025
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Chủ đề: Khoa học máy tính và thông tin
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Nhà xuất bản: International Journal of Information Technology
Optimal control problems governed by the vector quasi-optimization problems
Nguyễn Văn HưngThe aim of this paper is to establish the existence results and generic stability of solutions of optimal control problems governed by the vector quasi-optimization problems. First, we establish some sufficient conditions for the existence of solutions to a class of control systems of vector quasi-optimization problems. Moreover, the generic stability of set-valued mappings where the set of essential points of a map is a dense residual subset of a metric space of the set-valued maps is investigated. Finally, we study a new class of optimal control problems where the systems are defined by vector quasi-optimization problems. Results on the existence conditions and generic stability to these problems are also established.
Năm:2026
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Chủ đề: Toán học và thống kê
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Nhà xuất bản: Journal of Nonlinear and Convex Analysis
Key Determinants of Energy-saving Behaviours
Lê Bảo NgọcHousehold energy-saving behaviour reduces greenhouse gas emissions, which is crucial for combating climate change and contributes to multiple sustainable development goals (SDGs), particularly SDG7 (Affordable and clean energy), SDG12 (Responsible consumption and production), and SDG13 (Climate action). This study investigates how two types of environmental knowledge and two categories of environmental concerns influence consumers’ attitudes towards energy saving, leading to their curtailment behaviour and purchases of energy-efficient appliances. The study also examines the spillover effect from curtailment to purchase behaviour. Survey data collected from 405 Vietnamese consumers were analysed through partial least squares structural equation modelling. The results reveal the role of specific knowledge about energy conservation and concern about energy shortages in promoting energy-saving attitudes. The findings also confirm a positive impact of EEA purchasing behaviour on curtailment behaviour. This study advances the knowledge-attitude-behaviour theory by validating a model that simultaneously examines different types of environmental knowledge and concern. It is among the first to demonstrate a spillover effect between two energy-saving behaviours. This study’s fresh insights offer implications for researchers and practitioners developing education and communication strategies to promote energy conservation.
Năm:2025
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
Predicting virtual goods purchases in Vietnam’s MMOGs with a TAM-PERVAL model using PLS SEM and ANN
Vũ Việt TiếnThis study investigates the factors influencing virtual goods purchase intention among Vietnamese Massively Multiplayer Online Game (MMOG) players by integrating the Technology Acceptance Model (TAM) and PERVAL framework. Using a sample of 539 players, an online survey examines functional (usefulness, ease of use), value-based (emotional, social, price, quality), and context-specific factors (blockchain utility, mobile payment ease, trust, immersion, aesthetics), with satisfaction and social influence as mediators and income and gaming experience as moderators. An integrated approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) methods provides both explanatory and predictive insights. Key findings highlight blockchain utility, mobile payment ease, and perceived usefulness as primary drivers of purchase intention, with social influence playing a significant role in Vietnam’s collectivist culture. The study advances digital consumption research in non-Western contexts by offering a comprehensive framework for understanding virtual goods purchases. It provides practical implications for game developers and marketers to leverage blockchain technology and mobile payment systems, enhancing engagement in Vietnam’s rapidly growing MMOG market.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản: Humanities and Social Sciences Communications
The impact of employee experience on organizational commitment: examining the mediating role of employee performance in improving corporate strategy
Nguyễn Thị HồngThe most successful companies manage their people resources in the most effective and efficient manner. According to Mahfouz et al.
(2021), employee commitment significantly affects employee performance (EP), human resource management (HRM) practices significantly affect employee commitment, and employee commitment partially mediates the relationship between HRM practices and EP. This study aims to analyze the mediating role of EP in the relationship between employee experience (EX) and organizational commitment (OC) in enterprises. Based on survey data from 396 employees working in information technology companies in Vietnam, the research model was tested using
the partial least squares structural equation modeling (PLS-SEM) method. The results show that EX has a significant impact on EP, and
in turn, EP positively influences three forms of OC: affective commitment, continuance commitment, and normative commitment.
Notably, EP serves as a crucial mediator in transforming the impact of EX into OC, with the indirect effect found to be statistically
significant. So, to enhance employee engagement, organizations need to integrate strategies that improve both EX and EP. This study
contributes empirical evidence to organizational behavior theory and provides a practical foundation for developing integrated HRM
models in the modern business context and improving corporate strategy.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản: Corporate and Business Strategy Review
New Various Solutions of (3+1)-dimensional Potential Yu-Toda-Sasa-Fukuyama Equation Using Bilinear Neural Network Method
Nguyễn Minh TuấnThis paper investigates the (3+1)-dimensional potential YuTodaSasaFukuyama (YTSF) equation
using the Bilinear Neural Network Method (BNNM). This novel hybrid framework integrates Hirotas bilinear
formalism with neural network modeling. By reformulating the YTSF equation into its bilinear form and
embedding this structure into the BNNM architecture, multiple classes of exact analytical solutions are derived,
including kink-type, periodic, and rational forms. The proposed method yields closed-form solutions that
preserve mathematical rigor while improving computational efficiency for high-dimensional nonlinear evolution
equations. The results demonstrate the effectiveness of the BNNM in generating diverse solution structures for the
YTSF equation, providing potential applications in fluid dynamics, plasma physics, and other nonlinear physical
models.
Năm:2026
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Chủ đề: Trí tuệ nhân tạo
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Nhà xuất bản:
First principles investigation of the ferromagnetism in TM-doped arsenene monolayer (TM = Mn and Fe)
Phạm Minh TânBecause of lacking intrinsic magnetism, developing efficient methods for the magnetism engineering in two-dimensional (2D) materials is necessary in order to make new spintronic materials. In this work, doping and codoping with transition metals (TMs = Mn and Fe) is proposed to modify the arsenene monolayer electronic and magnetic properties. Bare monolayer is intrinsically nonmagnetic, exhibiting semiconductor character with an indirect gap of 1.60 eV. Mn and Fe substitution induces significant magnetism, giving place to overall magnetic moments of 4.00 and 5.00 mB, respectively, being produced primarily by TM impurities. Moreover, Mn impurity induces the half-metallicity with perfect spin
polarization at the Fermi level, while the magnetic semiconductor nature is obtained by Fe substitution. In both cases, perpendicular magnetic anisotropy (PMA) is confirmed through calculating magnetic anisotropy energy. In addition, the ferromagnetic (FM) phase is energetically stable, exhibiting smaller energy than antiferromagnetic (AFM) and ferrimagnetic (FiM) phases. Robust ferromagnetism is achieved by small TM–TM interatomic distance with high Curie temperature up to 1192.19 K. Further separating transition metal impurities will weaken the ferromagnetism, decreasing significantly Curie temperature. Moreover, it is demonstrated also that Mn–Mn separation switches the electronic nature from magnetic semiconductor to half-metallic, meanwhile the half-metallicity is obtained in the cases of Fe doping and Mn/Fe codoping regardless TM–TM separation. Controlling TM–TM separation is also predicted to effectively regulate the system magnetic anisotropy, inducing the PMA-to-IMA (in-plane magnetic anisotropy) switching and vice verse. Our findings may introduce efficient doping approaches to get ferromagnetism in arsenene monolayer, which can form promising 2D candidates for selective spintronic applications.
Năm:2026
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Chủ đề: Vật lý
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Nhà xuất bản: Nanoscale Advances