HighTech and Innovation Journal
type: channel
description: Fast advances in science and technology have broadened the horizons of innovation and knowledge whilst simultaneously creating a multitude of challenging problems in every aspect of modern life. The HighTech and Innovation Journal is intended as an outlet for individuals conducting research on new ideas and findings in innovation and technology at both the micro and macro levels of analysis. High technology journal is a journal that emphasizes the interconnections among engineering, mathematics, science, technology, and the humanities. HighTech and Innovation Journal is a peer-reviewed, open-access journal devoted to publishing research papers in the fields of integrated engineering and technology. We invite researchers interested in HighTech and Innovation to contribute their original research articles to this journal. Current areas of interest, along with related associate editors, are listed below: Smart Technologies and Applications Dr. Angelica Lo Duca Institute for Informatics and Telematics Pisa, Italy Associate Editor Mechanical Engineering & Adv. Manufacturing Prof. Tong Xin Fujian University of Technology Fujian, China Associate Editor Artificial Intelligence, Big Data, Cognitive & Soft Computing Dr. AleŠ¡ Zamuda University of Maribor Maribor, Slovenia Associate Editor Sustainability, Energy & Clean Technologies Prof. Tetyana Pimonenko Sumy State University Sumy, Ukraine Associate Editor Chemical Engineering and Biotechnology Dr. Anna Ilnicka Nicolaus Copernicus University Torun, Poland Associate Editor Innovation in Materials and Nanotechnology Dr. Ermia Aghaie University of Alberta Alberta, Canada Associate Editor Innovation in Electrical Engineering & Electronic Science Dr. Saadane Rachid Hassania School of Public Works Casablanca, Morocco Associate Editor Infrastructure, Buildings & Construction Tech. Prof. Jamshid Mohammadi University of Illinois at Urbana-Champaign Chicago, United States Associate Editor Medical Technologies Prof. Stéphane Bordas Uni…
publisher: Ital Publication
language: en-US
publication name: HighTech and Innovation Journal
issn: 2723-9535
copyright: - The authors retain all copyrights. It is noticeable that authors will not be forced to sign any copyright transfer agreements. - This work (including HTML and PDF Files) is licensed under a Creative Commons Attribution 4.0 International License.
items: Aerodynamics Analysis of a Novel Multirotor Structure Derived from Generative Design Using Computational Fluid Dynamics
items: Multimodal Text-to-Traditional Painting Generation Via Enhanced VQGAN with CLIP and Transformer Integration
items: Managing Projects Effectively: Lean Six Sigma and Agile Methodologies Through the Eyes of Project Managers
items: RFD Optimization: Mechanical Properties and Durability Analysis and Performance Evaluation of UHPC Bridge Structures
items: Developing a Mobile Application for Community Forest Sequestration Assessment: A Mixed-Methods Study
items: Tourism Ecological Environment Quality Assessment Using Principal Component Analysis and Remote Sensing Data
items: AI and Digital Innovation as Catalysts for Green Product Innovation and Sustainable Performance
items: Design of Miao Embroidery Gene Decoding and Digital Activation Based on CNN-Transformer
items: A Novel Hybrid ViT-CNN Approach for Pneumonia and Lung Opacity Detection in X-Ray Images
items: Product Usability Mediates Cognitive-Purchase Relationship in Elderly Consumers: Urban-Rural Differences
items: Optimizing Oxidative Roasting of Low-Grade Molybdenum Intermediates on Phase-Controlled Process Parameters
items: Style Conversion of Zhuang Brocade Patterns Based on Low Rank Adaptive and Deep Learning
items: A Data-Driven SPC Framework for Monitoring and Forecasting Global Temperature Trends
items: Heterogeneous Digital Music Generation Techniques Incorporating Fine-Grained Controls
items: A Real-Time IoT-Enabled Machine Learning for Quality Prediction of Perishable Beef Product
items: Academic Performance Prediction Models Based on Multi-Head Attention LSTM Mechanisms
items: Advanced Microstructural Engineering of Gas Diffusion Layers for Synergistic Electrochemical Performance and Durability
items: Towards Safer Roads: A Machine Learning Framework for Driver Fatigue Detection
items: Project Management Strategies for Smart City Initiatives: A Framework for Sustainable Urban Development
items: Multi-Dimensional Digital Twin Modeling for Fault Diagnosis of Industrial Centrifugal Pump

Aerodynamics Analysis of a Novel Multirotor Structure Derived from Generative Design Using Computational Fluid Dynamics
type: item
description: This study investigates whether generative design structures can enhance the aerodynamic efficiency and payload capacity of multirotor UAVs compared with conventional stacked-rotor layouts. The methodology integrated structural generative design in Autodesk Fusion 360 and evaluated hover aerodynamics using computational fluid dynamics (CFD) analysis in SimScale, employing a steady, pressure-based incompressible RANS solver and Multiple Reference Frame (MRF) zones to model hover at 4500 rpm. Three configurations were evaluated: a baseline “Initial Model” and two generative-design outcomes (Model 1 and Model 3). The results show that Model 1 provides negligible improvement, whereas Model 3 increases total thrust to 42.36 N, corresponding to a 9.6% gain relative to the initial configuration, and raises the system-average figure of merit by approximately 65%. Flow-field analyses indicate that Model 3 promotes cleaner inflow to the upper tier and a more coherent downwash, with reduced recirculation and interference-related losses. The novelty lies in coupling generative structural synthesis with CFD-based aerodynamic screening to demonstrate that frame geometry can actively enhance stacked-rotor hover performance.
creator: Sane Souvanhnakhoomman
creator: Alvin Y. Chua
rights: Copyright (c) 2026 Sane Souvanhnakhoomman, Alvin Y. Chua
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 1
ending page: 16
doi: 10.28991/HIJ-2026-07-01-01

Multimodal Text-to-Traditional Painting Generation Via Enhanced VQGAN with CLIP and Transformer Integration
type: item
description: With the development of artificial intelligence technology, text-driven image generation has gradually become a research hotspot. In terms of the application of traditional Chinese painting generation, due to the particularity of traditional Chinese painting themes, techniques, and artistic conception, existing models have problems such as cross-modal alignment deviation. To enhance the model's understanding of text semantics and improve the matching degree between text and generated Chinese painting images, a multimodal dataset of Chinese paintings is studied, and the Vector Quantized Generative Adversarial Network (VQGAN) is improved. A new multimodal text generation method is constructed by combining transformer-based bidirectional encoder representation, Transformer decoder, and contrastive language-image pre-training models. The results showed that when trained for 100 rounds on the general object dataset and Flickr30k dataset in context, the Inception Score (IS) values of this method were 39.6 and 5.1, respectively, and the Fréchet Inception Distance (FID) values were 2.3 and 1.7, respectively, which were better than models such as VQGAN. In the ablation experiment, the IS was 5.8, the FID was 10.3, and the cosine similarity was 0.82. The convergence was achieved after 60 rounds, which was better than other variants. The method proposed in the study has good adaptability to the generation of traditional Chinese painting. Although the expansion of complex scenes is slightly weak, the overall performance is excellent, filling the gap in traditional artistic semantic mapping and providing support for the digital dissemination and innovation of traditional Chinese painting.
creator: Nan Zhou
rights: Copyright (c) 2026 Nan Zhou
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 17
ending page: 34
doi: 10.28991/HIJ-2026-07-01-02

Managing Projects Effectively: Lean Six Sigma and Agile Methodologies Through the Eyes of Project Managers
type: item
description: This study investigates the comparative effectiveness of Lean Six Sigma (LSS) and Agile methodologies from a project management perspective within a multinational service and technology corporation, aiming to identify the most influential determinants of project success and assess their methodological variability. A comparative embedded case study design was employed, examining two real-life projects conducted within Company S: an Agile-driven order management initiative and a Lean Six Sigma-based human resource process optimization project. Data collection combined qualitative and quantitative approaches, including a structured expert workshop, a Multi-Dimensional Criteria Analysis (MDCA) encompassing 68 validated project success criteria clustered into eight affinity categories, a Fishbone diagram for causal analysis, and an evaluation of project documentation and performance metrics. The findings reveal that clearly defined objectives, stakeholder engagement, systematic monitoring, and team competence constitute the most critical success factors across both methodologies. Agile demonstrated superior adaptability, collaborative communication, and client responsiveness, whereas LSS provided stronger goal alignment, process control, error minimization, and regulatory compliance. The results indicate that methodological effectiveness is context-dependent rather than universally hierarchical. The study contributes novel empirical insight by conducting a controlled intra-organizational comparison and advances the discourse by evidencing the strategic value of hybrid frameworks integrating Agile flexibility with LSS structural rigor in complex corporate environments.
creator: David A. Procházka
creator: Imran A. Shahzad
creator: Kateřina Bočková
creator: Juraj Lovásik
rights: Copyright (c) 2026 David A. Procházka, Imran A. Shahzad, Kateřina Bočková, Juraj Lovásik
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 35
ending page: 57
doi: 10.28991/HIJ-2026-07-01-03

RFD Optimization: Mechanical Properties and Durability Analysis and Performance Evaluation of UHPC Bridge Structures
type: item
description: To investigate the mechanical properties and durability of UHPC bridge structures under varying environments, this study designed factorial experiments covering three curing temperatures, two sand types and mineral admixtures, then coupled accelerated durability tests (chloride penetration, sulfate attack, freeze–thaw) with on-site monitoring data. A DOA-RF model was developed by optimizing Random Forest parameters through the Dream Optimization Algorithm and validated against experimental results. Compressive strength reached 150–200 MPa, tensile strength 10–20 MPa; high-temperature curing increased flexural strength by ≈8 % yet reduced chloride permeability by 23 %, demonstrating superior marine durability. The DOA-RF algorithm achieved the lowest MSE and highest R² (0.75) among all benchmarked methods. Beyond engineering performance, the study proposes the first framework integrating UHPC with cultural-creative industries for accelerated marketization, branding and internationalization, offering a novel pathway for sustainable infrastructure innovation.
creator: Mengying Liu
rights: Copyright (c) 2026 Mengying Liu
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 58
ending page: 76
doi: 10.28991/HIJ-2026-07-01-04

Developing a Mobile Application for Community Forest Sequestration Assessment: A Mixed-Methods Study
type: item
description: A mobile application for community forest carbon sequestration assessment serves as a critical tool for supporting family forest management amidst climate change. This study aimed to design and evaluate the usability of a specialized carbon calculation application tailored for farmers using a mixed-methods approach. A sample of 29 farmers assessed the application via the System Usability Scale (SUS) and in-depth interviews. Results yielded a good average usability score (M = 76.90, SD = 14.12), with no statistically significant difference between trained and untrained users (t(27) = 0.326, p = 0.746; Mann-Whitney U = 113.0, p = 0.709), and a negligible effect size (Cohen’s d = 0.123). Qualitative insights corroborated these quantitative findings, highlighting high user satisfaction regarding ease of use (82.8%) and interface simplicity (65.5%). While users suggested expanding plant species databases and refining data input systems, the findings overall demonstrate that the application’s intuitive design facilitates practical usability without necessitating prior training. Enhancements to system stability are recommended to further optimize the user experience.
creator: Pornperm Sae-ngow
creator: Nayot Kulpanich
creator: Morakot Worachairungreung
creator: Kunyaphat Thanakunwutthirot
creator: Phonpat Hemwan
rights: Copyright (c) 2026 Pornperm Sae-ngow, Nayot Kulpanich, Morakot Worachairungreung, Kunyaphat Thanakunwutthirot, Phonpat Hemwan
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 77
ending page: 88
doi: 10.28991/HIJ-2026-07-01-05

Tourism Ecological Environment Quality Assessment Using Principal Component Analysis and Remote Sensing Data
type: item
description: Current methods for assessing tourism ecological environment quality suffer from insufficient analysis of complex environmental impact factors and low assessment accuracy. In response to this situation, this study proposes a method for assessing tourism ecological environment quality that takes into account principal component analysis and remote sensing data. The study first combined remote sensing data and principal component analysis to establish an evaluation index system, and then introduced the grey-scale correlation method to rank and assign weights to key factors affecting ecological environment quality. Based on the results of principal component analysis and the grey-scale correlation method, the study constructed a comprehensive assessment model. Subsequently, dynamic simulation and prediction were achieved by combining the meta-cellular automata model and the Markov model. The experimental results indicated that in the ecological assessment of different seasonal environments, the Kappa coefficient and overall accuracy of the proposed method reached 0.93 and 0.93, respectively. Furthermore, the coefficient of determination and mean absolute percentage error were 0.91 and 0.08, respectively, and the assessment accuracy reached 93.88%. The tourism ecological environment quality assessment method proposed in this study can effectively address challenges in different environments. It maintains high-precision assessments and dynamic predictions, providing a reliable basis for decision-making in ecological environment protection.
creator: Ruiping Chen
creator: Xiang Wei
creator: Wei Wang
rights: Copyright (c) 2026 Ruiping Chen, Xiang Wei, Wei Wang
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 89
ending page: 105
doi: 10.28991/HIJ-2026-07-01-06

AI and Digital Innovation as Catalysts for Green Product Innovation and Sustainable Performance
type: item
description: This study investigates the influence of AI and digital innovation on green product innovation, low-carbon behavior, and sustainable performance within small and medium-sized enterprise. Drawing on a mediation framework, it examines the mediating role of digital innovation between AI and green product innovation, and the mediating effect of green product innovation linking AI with low-carbon behavior and sustainable performance. The study further explores the sequential mediation of digital innovation and green product innovation. Using partial least squares structural equation modeling (PLS-SEM), data were collected from 1,336 respondents representing SMEs in developing economies. The results indicate that AI significantly and positively affects digital innovation and green product innovation. Digital innovation acts as a key mechanism through which AI strengthens green product innovation, while green product innovation mediates the effects of AI on low-carbon behavior and sustainable performance. These findings highlight the strategic value of AI-enabled digital innovation in advancing environmental sustainability. The study offers theoretical and practical implications for managers and policymakers, showing how SMEs can leverage AI and digital innovation to foster innovation and sustainable performance.
creator: Huong T. Dinh
creator: Nhung H. Nguyen
creator: Anh N. Nguyen
creator: Thoa B. T. Kim
rights: Copyright (c) 2026 Huong T. Dinh, Nhung H. Nguyen, Anh N. Nguyen, Thoa B. T. Kim
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 106
ending page: 120
doi: 10.28991/HIJ-2026-07-01-07

Design of Miao Embroidery Gene Decoding and Digital Activation Based on CNN-Transformer
type: item
description: To overcome the challenges of decoding pattern genes and the lack of innovative designs in the digital transformation of Miao embroidery as an intangible cultural heritage, a deep learning fusion architecture based on a convolutional neural network Transformer (CNN Transformer) is proposed. This architecture aims to accurately analyze the semantics and generate stylized Miao embroidery patterns. This architecture uses a ResNet-50 backbone network to extract local pattern details and combines an improved atrous spatial pyramid pooling (ASPP) module to preserve edge structures for semantic segmentation. It simultaneously utilizes a progressive Transformer encoder and multi-scale channel attention to dynamically focus on the pattern subject, fuse features, and achieve style transfer. The experimental results showed that the segmentation accuracy of the model on four types of embroidery patterns exceeded 85% (up to 92.68%), and the structural similarity (SSIM) reached 0.957. In the style transfer task, the style fidelity index (KL divergence 0.52, Gram matrix MSE 0.019) and average accuracy (AP 0.926) of this method were significantly better than the comparison algorithms. This research method effectively solves the problems of inaccurate segmentation of complex patterns and distortion of cultural symbols in traditional style transfer methods. It provides a technical reference for analyzing and innovatively activating Miao embroidery culture.
creator: Yuchun Huang
rights: Copyright (c) 2026 Yuchun Huang
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 121
ending page: 137
doi: 10.28991/HIJ-2026-07-01-08

A Novel Hybrid ViT-CNN Approach for Pneumonia and Lung Opacity Detection in X-Ray Images
type: item
description: In order to automatically classify chest X-ray pictures into three diagnostic categories—Normal, Lung Opacity, and Viral Pneumonia—this study presents a novel hybrid deep learning architecture that combines the Vision Transformer (ViT) with a Convolutional Neural Network (CNN). The suggested model successfully addresses the drawbacks of single-architecture systems by fusing the ResNet-18 CNN's expertise in local texture analysis with the ViT's global feature representation capability. According to experimental assessments, the hybrid ViT-CNN architecture outperforms the state-of-the-art methods, achieving 94.2% classification accuracy with precision, recall, and F1-scores continuously above 94% for the majority of categories. Even in complicated situations where traditional methods usually falter, like distinguishing between lung opacity and normal patients, the model exhibits strong performance. Additionally, it performs well in discrimination, with AUC values above 0.95 in every class. The system is ideal for real-time clinical deployment because it maintains a high computational efficiency, generating conclusions in about 0.0012 seconds per image. Grad-CAM visualization makes it evident which areas of the image are important for making diagnostic decisions, hence validating the model's interpretability. All things considered, this work establishes a new benchmark for chest X-ray classification performance and offers a useful foundation for automated diagnostic assistance in resource-constrained healthcare settings.
creator: Taqwa Hariguna
creator: Athapol Ruangkanjanases
rights: Copyright (c) 2026 Taqwa Hariguna, Athapol Ruangkanjanases
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 138
ending page: 150
doi: 10.28991/HIJ-2026-07-01-09

Product Usability Mediates Cognitive-Purchase Relationship in Elderly Consumers: Urban-Rural Differences
type: item
description: Objective: This study investigates how cognitive ability affects elderly consumers' purchase decisions for home products, examining product usability as a chain mediator and urban-rural differences as a moderator. Methods: A stratified sampling survey was conducted among 1,247 adults aged 60 and above across 28 Chinese provinces. Structural equation modeling and multi-group analysis were employed to test the proposed model. Findings: Results demonstrate that cognitive ability significantly influences purchase decisions. Product usability serves as a significant chain mediator between cognitive ability and purchase decisions. Notably, urban-rural differences moderate this mechanism: urban elderly rely more on product usability when making purchase decisions, while rural elderly's cognitive ability directly influences their purchasing behavior. The indirect effect accounts for 78.6% of the total effect, with the chain mediation effect being statistically significant (95% CI [0.35, 0.49]). Contributions: This study extends cognitive processing theory by revealing the threshold activation and cascading amplification characteristics in elderly decision-making. The findings provide practical implications for designing elderly-friendly home products and developing differentiated marketing strategies for urban and rural markets.
creator: Yu Zhou
creator: Ali Khatibi
creator: Jacquline Tham
rights: Copyright (c) 2026 Yu Zhou, Ali Khatibi, Jacquline Tham
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 151
ending page: 162
doi: 10.28991/HIJ-2026-07-01-010

Optimizing Oxidative Roasting of Low-Grade Molybdenum Intermediates on Phase-Controlled Process Parameters
type: item
description: Processing low-grade molybdenum intermediates demands oxidative-roasting regimes that maximize Mo recovery and resource efficiency. This work proposes a phase-oriented optimization approach for a 22.23% Mo intermediate, grounded in the multifactor Protodyakonov-Malyshev model and tabular nomographs as a practical parameter-selection tool. Control experiments varied temperature to 700 °C with roasting limited to 20 min and bed height to 0.010 m. Phase evolution during roasting and Mo recovery after alkaline leaching were quantified from integrated mineralogical-chemical data. A robust process window was identified at 550 °C, achieving 97% Mo recovery. Increasing temperature above 600 °C initiates a mechanism shift that forms MoO₂, reducing selectivity. Using the developed nomographs enables 20 min roasting while favoring MoO₃ formation (88%) and minimizing undesired phases, thereby improving overall resource efficiency for low-grade feed. The study’s novelty is the adaptation of the Protodyakonov-Malyshev model to phase-composition control during roasting, steering the transformation from MoS₂ to MoO₃, and proposing a resource-efficient alternative to conventional regimes by exploiting an exothermic reaction that supplies heat and requires no additional energy input. The results support the development of technological regulations, furnace design and operation, and industrial trials at copper-molybdenum plants for producing molybdenum products (CaMoO₄).
creator: Lyutsiya Karimova
creator: Tansholpan Tussupbekova
creator: Tatyana Oleinikova
creator: Irina Terentyeva
creator: Argyn Korabayev
creator: Guldana Makasheva
rights: Copyright (c) 2026 Lyutsiya Karimova, Tansholpan Tussupbekova, Tatyana Oleinikova, Irina Terentyeva, Argyn Korabayev, Guldana Makasheva
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 163
ending page: 177
doi: 10.28991/HIJ-2026-07-01-011

Style Conversion of Zhuang Brocade Patterns Based on Low Rank Adaptive and Deep Learning
type: item
description: As a key bearer of traditional Zhuang culture, Zhuang brocade patterns face challenges such as low design efficiency and a stylistic disconnect from modern demands. Therefore, this study innovatively proposes an integrated model that combines low rank adaptive fine-tuning, stable diffusion model, and generative adversarial network for intelligent pattern generation and style transformation. This model adopts an image-to-image method based on GAN for pattern generation, and introduces an improved synthesis and cascading transformation method to achieve differentiated style conversion of brocade patterns. In performance validation experiments, the model demonstrates clear clustering boundaries in complex pattern feature recognition, with a peak signal-to-noise ratio of 32.5 dB, an initial style transfer score of 78.3, and an average diversity index of 0.78. When processing 400 data samples, the model requires 1123 MB of memory and responds within 122 ms, significantly outperforming comparison models in both performance and efficiency. The results show that the model proposed in the study has advantages in the intelligent generation of Zhuang brocade patterns and style transfer generation quality, style adaptability and comprehensive use of resources. The research results provide an effective solution for intelligent pattern generation and modern style adaptation, with broader potential applications in protecting and innovating intangible cultural heritage.
creator: Li’na Zhao
creator: Xiaoxuan Nie
rights: Copyright (c) 2026 Li’na Zhao, Xiaoxuan Nie
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 178
ending page: 193
doi: 10.28991/HIJ-2026-07-01-012

A Data-Driven SPC Framework for Monitoring and Forecasting Global Temperature Trends
type: item
description: This study aims to develop an enhanced Statistical Process Control (SPC) framework for the effective monitoring and forecasting of global temperature trends, addressing the limitations of traditional control charts in handling autocorrelated environmental data. The primary objective is to construct a modified Exponentially Weighted Moving Average (EWMA) chart capable of detecting both abrupt and gradual shifts in time series exhibiting seasonal and stochastic dependencies. The proposed approach models global temperature data using a Seasonal Autoregressive Moving Average process with exponential white noise (SARMA(1,1)L) to capture temporal patterns and residual variability. An analytical formulation for evaluating the one sided Average Run Length (ARL) is derived, enabling quantitative assessment of chart performance. The simulation analysis demonstrates that, by applying an optimized smoothing parameter, the proposed EWMA control chart outperforms the traditional EWMA and CUSUM charts, particularly in detecting small shifts with significantly lower ARL1 values. The findings confirm that the proposed model effectively tracks and predicts global temperature anomalies with high sensitivity and accuracy. The novelty of this research lies in integrating SARMA-based modeling with SPC design to improve detection reliability under autocorrelated and nonstationary conditions. This data-driven framework offers a promising tool for real-time environmental monitoring and climate forecasting applications.
creator: Yadpirun Supharakonsakun
creator: Korakoch Silpakob
creator: Yupaporn Areepong
rights: Copyright (c) 2026 Yadpirun Supharakonsakun, Korakoch Silpakob, Yupaporn Areepong
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 194
ending page: 211
doi: 10.28991/HIJ-2026-07-01-013

Heterogeneous Digital Music Generation Techniques Incorporating Fine-Grained Controls
type: item
description: Aiming at the problem of insufficient note-level attribute modulation and the difficulty of fusion of cross-genre musical elements, the study proposes a hierarchical conditional embedding mechanism and a symbolic feature conditional diffusion method. Through the dynamic gated fusion of note-structure and adaptive acoustic modulation guided by symbols, it optimizes the millisecond precision generation of melodic rhythm and the synergistic control efficiency of high-fidelity audio synthesis. This enables fine-grained controlled generation of cross-cultural heterogeneous music. The experimental results indicated that the model achieved 96.2% note localization accuracy in cross-cultural scenarios, which was 12.8% higher than the benchmark. The minimum value of beat synchronization deviation was 1.7 ms, which was 52.9% lower than the optimal comparison model. The average value of polyphony duration was 70.6%, an improvement of 9.8%. Differential scale fusion reached 12.5 tone level, breaking through the limit of twelve equal temperament. The peak memory occupation was 198.3 MB, and the energy consumption of a single song was as low as 0.142 kWh, reducing energy consumption by 29.4% compared with the traditional solution. The professional composition evaluation revealed that the cultural coordination degree of the heterogeneous style fusion fragment was 92.1%. The real-time generation delay stabilized at 2.8 ms, and the generation quality improved by 38.7% compared to the industrial standard. These results proved the model's comprehensive advantages in cross-dimensional control and artistic expression. This model can be integrated into digital audio workstations (DAWs) as either a plug-in or a cloud API. It provides creators with real-time, interactive generation and style transfer capabilities. It provides intuitive control over both macro-level structure and micro-level acoustic details via natural language commands or symbolic input. This significantly lowers the barri…
creator: Qiaomei Ma
rights: Copyright (c) 2026 Qiaomei Ma
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 212
ending page: 228
doi: 10.28991/HIJ-2026-07-01-014

A Real-Time IoT-Enabled Machine Learning for Quality Prediction of Perishable Beef Product
type: item
description: The cold chain industry in Indonesia is experiencing rapid growth, especially for perishable products such as beef. Ensuring product quality during distribution requires accurate monitoring of storage environmental factors, including temperature, humidity, and gas exposure. This study aims to develop an IoT-based quality monitoring system for perishable beef products and implement a machine learning approach for quality prediction. An IoT-enabled e-Sense device was developed to collect real-time environmental parameters and RGB colour information as quality parameter from tenderloin beef samples. The collected data were analysed using three regression-based machine learning algorithms: Random Forest (RF), Decision Tree (DT), and Support Vector Regression (SVR). Data preprocessing and hyperparameter tuning were applied to improve model performance. The results show that SVR consistently outperformed RF and DT in predicting RGB colour in presenting beef quality based on prescribed parameters. SVR achieved an R² of 0.973 for RED and 0.992 for both GREEN and BLUE channels. These findings confirm the effectiveness of integrating IoT technology with machine learning for real-time perishable products quality prediction. This research contributes to combining real-time multi-sensor IoT data with regression-based models to provide improved continuous quality monitoring compared to previous single-parameter or offline approaches.
creator: Aldi Pramana Putra
creator: Muhammad Asrol
rights: Copyright (c) 2026 Aldi Pramana Putra, Muhammad Asrol
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 229
ending page: 249
doi: 10.28991/HIJ-2026-07-01-015

Academic Performance Prediction Models Based on Multi-Head Attention LSTM Mechanisms
type: item
description: This study focuses on accurately predicting student academic performance to support personalized teaching and optimize educational resource allocation. Addressing the limitations of traditional prediction methods in handling long-term dependencies within time-series data and extracting key features, this paper proposes an innovative model that integrates multi-head attention mechanisms with Long Short-Term Memory (LSTM) networks. The method utilizes LSTMs to efficiently process sequential data while employing multi-head attention mechanisms to concentrate on critical information synergistically. Experiments demonstrate that this model achieves significantly higher prediction accuracy than traditional methods on specific datasets, with an MAE of 4.12 and an R² of 0.94, fully showcasing its outstanding performance. This model pioneers new pathways for academic performance prediction in education, supporting scientific educational decision-making and driving high-quality educational development.
creator: Shiyuan Zhou
rights: Copyright (c) 2026 Shiyuan Zhou
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 250
ending page: 264
doi: 10.28991/HIJ-2026-07-01-016

Advanced Microstructural Engineering of Gas Diffusion Layers for Synergistic Electrochemical Performance and Durability
type: item
description: The microstructure of gas diffusion layers (GDLs) is a critical factor in determining the electrochemical behaviours and durability of the proton exchange membrane fuel cells (PEMFCs). The paper will assess the ability of engineered GDL microstructures to improve the synergistic behavior of electrochemical performance. The 3D multi-physics numerical model is created, which considers the anisotropic porous transport, water capillary behavior, electrochemical reaction, and thermal effects, and is tested in accordance with known reference data. Using four GDL architectures, which include a traditional GDL, horizontally aligned, vertically, and an X-pattern GDLs, four GDL architectures are compared. The findings demonstrate that directional microstructural engineering produces a strong influence on performance characteristics. Whilst the GDLs that are horizontally and vertically aligned have a selective effect on in-plane and through-plane transport, respectively, the X-pattern GDL has a more balanced nature and exhibits a 35, 40, 28 and 45% reduction in oxygen transport resistance, a reduction in liquid water saturation, an increase in peak power density, and a 45% reduction in voltage degradation rates, respectively, than the conventional design. The originality of the given work is in showing that multidirectional GDL engineering provides an opportunity to synergize electrochemical performance and durability.
creator: Sa′ed Alrwashdeh
creator: Ahmed M. Salem
rights: Copyright (c) 2026 Sa′ed Alrwashdeh, Ahmed M. Salem
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 265
ending page: 288
doi: 10.28991/HIJ-2026-07-01-017

Towards Safer Roads: A Machine Learning Framework for Driver Fatigue Detection
type: item
description: The goal of this research is to enhance road safety by delivering an affordable, non-intrusive, and effective drowsiness detection solution suitable for integration into mainstream vehicle systems. Driver drowsiness is a critical factor contributing to road accidents, often resulting in severe injury or death. In Malaysia alone, it is estimated that drowsiness causes between 2,000 and 3,000 traffic accidents annually. Conventional vehicle-based drowsiness detection systems, which rely on steering behavior or lane departure, often fail to detect early physiological signs of fatigue. Recent advancements in artificial intelligence (AI), particularly in computer vision and machine learning, offer new opportunities for developing low-cost, real-time drowsiness monitoring systems. This research proposes a driver drowsiness monitoring system (DDMS) that utilizes deep learning and visual behavior analysis to detect signs of fatigue through real-time monitoring of eye and mouth activity. A customized Convolutional Neural Network (CNN) is developed to classify eye states (open vs. closed), while yawning is detected using facial landmark analysis and Mouth Aspect Ratio (MAR) computations. The system is trained and evaluated using the MRL Eye Dataset, which consists of 4,000 annotated images, with data preprocessing and augmentation applied to enhance robustness. Through systematic experimentation and hyperparameter tuning, the model achieves a peak accuracy of 98%, with equally high precision, recall, and F1 Scores, using the Adam optimizer, a learning rate of 0.001, and 50 training epochs. The system also demonstrated strong real-time performance across varied lighting conditions, though challenges remain in scenarios involving occlusion (e.g., sunglasses) and extreme head positions. The results indicate that the proposed method is not only feasible but highly accurate, marking a significant advancement in proactive traffic accident prevention technologies.
creator: Deshinta Arrova Dewi
creator: Tri Basuki Kurniawan
creator: Misinem
creator: Mohd Zaki Zakaria
creator: Shahreen Kasim
rights: Copyright (c) 2026 Deshinta Arrova Dewi, Tri Basuki Kurniawan, Misinem, Mohd Zaki Zakaria, Shahreen Kasim
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 289
ending page: 302
doi: 10.28991/HIJ-2026-07-01-018

Project Management Strategies for Smart City Initiatives: A Framework for Sustainable Urban Development
type: item
description: Rapid advances in artificial intelligence (AI), the Internet of Things (IoT), and digital government platforms are accelerating the transformation of urban systems toward smart and sustainable cities, creating new challenges for managing complex urban development projects. This study proposes an integrated project management framework that aligns digital transformation technologies with sustainability principles to enhance smart city implementation in emerging economies. A qualitative research design combining systematic literature analysis and comparative case study evaluation was employed, examining smart city initiatives in Aqkol, Nur-Sultan (Astana), and Almaty, Kazakhstan. The analysis investigates how technological integration, governance mechanisms, stakeholder collaboration, and sustainability objectives interact within adaptive project management environments. Results indicate that successful smart city development depends on coordinated alignment across these four dimensions, supported by flexible governance models and data-driven decision processes. The findings further demonstrate that AI-enabled digital twin technologies and participatory digital governance significantly improve infrastructure optimization, urban decision-making efficiency, and resilience outcomes. The proposed framework contributes a structured evaluation and implementation model that bridges project management theory with smart city digitalization practices. The novelty of this research lies in integrating sustainability-driven project governance with emerging digital technologies into a unified operational framework, providing practical guidance for policymakers, urban planners, and project managers seeking scalable and resilient smart city solutions in rapidly evolving technological contexts.
creator: Aidana Galymzhanova
creator: Shynara Sarkambayeva
creator: Zhuldyz Kalpeyeva
creator: Abdul Razaque
creator: Satyanand Singh
rights: Copyright (c) 2026 Aidana Galymzhanova, Shynara Sarkambayeva, Zhuldyz Kalpeyeva, Abdul Razaque, Satyanand Singh
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 303
ending page: 329
doi: 10.28991/HIJ-2026-07-01-019

Multi-Dimensional Digital Twin Modeling for Fault Diagnosis of Industrial Centrifugal Pump
type: item
description: Industrial centrifugal pumps operating under complex conditions frequently encounter failures, yet existing diagnostic methods face challenges in effectively fusing multi-source sensor data with physical mechanisms. To address these limitations, this paper proposes a multidimensional digital twin modeling approach that establishes a deep bidirectional synergy between data-driven perception and mechanism-based simulation. First, a Dynamic Sparse Spatio-Temporal Graph Attention Network (DS-STGAT) is designed to capture dynamic local and global dependencies among multi-source signals. Second, unlike conventional unidirectional methods, a novel data-mechanism collaborative adaptive mechanism is introduced. This creates a closed-loop pathway of “data-guided → simulation-refined → data-enhanced,” where the perception model retroactively optimizes simulation parameters (e.g., stiffness coefficients) via consistency constraints, while the simulation model provides physical priors to guide graph construction. Experimental results on multiple bearing datasets and real-world pump conditions demonstrate that the proposed method outperforms baseline models in accuracy, physical consistency, and robustness. Notably, the approach achieves high fault identification rates even in zero-shot scenarios, validating its effectiveness and scalability for the intelligent fault diagnosis of complex rotating machinery.
creator: Zhengqian Duan
rights: Copyright (c) 2026 Zhengqian Duan
date: 2026-03-01
publication date: 2026-03-01
volume: 7
number: 1
starting page: 330
ending page: 351
doi: 10.28991/HIJ-2026-07-01-020