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 University of Luxembourg Esch-sur-Alzette, Luxembourg Associate Editor Human Services Technology Prof. Xingwei Li Sichuan Agricultural University China Associate Editor Open Special Issues Special Issue "Grand Challenges Initiative: Sustainability and Development" - 2022-2023 Aim and Scope Over the next decade, the global scientific community must take on the challenge of delivering the knowledge required to support efforts to achieve sustainable development in the context of global environmental change. Solution-focused, strategic, interdisciplinary, long-term research is needed to improve our knowledge of the social-environmental risks facing humanity and to provide science-based support for actions to achieve sustainable development. We rapidly need to deepen our understanding of how the Earth system operates in response to human pressures, improve our ability to predict future risk patterns, and explore social transformations in the world that can overcome barriers to sustainability. Guest Editor: Prof. M. R. Kavianpour Affiliations: Professor, Civil Engineering Faculty, K. N. Toosi University of Technology, Iran. Interests: Climate Change; Remote Sensing; Drought; Water Treatment; Flood (Hazard, Estimation and Modeling, ...); Urban Drainage System (Design and Modeling). 0000-0003-3546-6663 Guest Editor: Dr. Grigorios L. Kyriakopoulos Affiliations: School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Greece. Interests: Renewable Energy Sources; Urban and Regional Development; Energy; Development Economics; Human Resource Management; Sustainability. 0000-0003-4875-8534 Guest Editor: Prof. Eugen Rusu Affiliations: Department of Applied Mechanics, Head of Laboratory of Computations and Modeling in Applied Mechanics, "Dunarea de Jos" University of Galati, Galați, Romania. Interests: Renewable Energy in Marine Environment; Energy Conversion; Offshore Technologies; Climate Change in Marine Environment; Resource Evaluation and Modeling. 0000-0001-6899-8442 Manuscripts should be submitted online at www.hightechjournal.org by registering and logging in to this website. For more information Click Here. Published Papers - This Issue is now open for submissions. Most Cited & Viewed Articles The most cited articles from HighTech and Innovation Journal published since 2020, extracted from <a class="sc-kvjqii-0 fYuYRT" title="Scopus" href="https://www.scopus.com/results/results.uri?cc=10&sort=cp-f&src=dm&nlo=&nlr=&nls=&sid=897df5e8b737fec30750e5bda23839d8&sot=a&sdt=a&sl=223&s=REFSRCTITLE+%28+%22HighTech+and+Innovation+Journal%22+%29+OR+REFSRCTITLE+%28+%22HighTech+and+Innovation+J.%22+%29+OR+REFSRCTITLE+%28+%22HighTech.+Innov.+J.%22+%29+OR+REFSRCTITLE+%28+%22HighTech+and+Innov.+J.%22+%29+OR+REFSRCTITLE+%28+%22High+Tech+Innov.+J.%22+%29&ss=cp-f&ps=r-f&editSaveSearch=&origin=resultslist&zone=resultslist" target="_blank">Scopus. Citation Article Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures–The Database of Micro Networks by: B. Fazelabdolabadi, and M.H. Golestan New Technologies and Innovative Solutions in the Development Strategies of Energy Enterprises by: Piotr F. Borowski Sustainability Assessment in Housing Building Organizations for the Design of Strategies against Climate Change by: Ulises Mercado Burciaga Comparative Analysis of Implementation of Solar PV Systems Using the Advanced SPECA Modelling Tool and HOMER Software: Kenyan Scenario by: Samuel Kariuki Kibaara, D. K. Murage, P. Musau, and M. J. Saulo Relationship of Smart Cities and Smart Tourism: An Overview by: Nada Jasim Habeeb, and Shireen Talib Weli The Study of Dynamics Heterogeneity in SiO2 Liquid by: Giap Thi Thuy Trang, N. H. Linh, N. T. T. Linh, and P. H. Kien
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ML and DL Models for Stroke Prediction from Bio-Signals: A Systematic Review and Bibliometric Analysis
Strokes continue to be a primary reason for disability and death around the globe. Annually, over 12.2 million new strokes occur, which necessitates the development of early detection and intervention tools to reduce the potential harm. This systematic review and bibliometric analysis aim to review and visualize recent advances in predicting stroke or post-stroke effects using bio-signals, either with machine learning (ML) or deep learning (DL). The included studies were published between 2016 and 2024. A comprehensive search of IEEE, PubMed, MDPI, and ScienceDirect databases was performed using keywords related to stroke prediction, machine learning, deep learning, and bio-signals. From an initial pool of 152 studies, 15 studies met the inclusion criteria through the screening process. South Korea contributed the most to publishing studies on stroke prediction using bio-signals. The results show that Electroencephalography (EEG) is the most used bio-signal in the reviewed studies. The sample size ranged from 3 to 4068. The top ten cited journals in the selected literature are high-ranked journals, which indicates the scientific validity of the concept and its potential for dissemination.
Copyright (c) 2025 May Issa Aldossary, Fatemah H. Alghamedy, Dina A. Alabbad, Renad A. Alnuaim, Maimonah S. Altaweel, Reem A. H. Alshami, Haya A. Alzahim, Shahad F. Alotaibi, Sumayh S. Aljameel, Areej Almalki, Sunday O. Olatunji
A Review on Federated Learning on Sensor-Based Human Activity Recognition
Deep learning has demonstrated exceptional human activity recognition (HAR) performance by extracting complex features from inertial data. However, this centralized training approach aggregates data from multiple user devices into a central server and raises significant privacy concerns. Federated learning (FL) is proposed as an alternative. It provides a privacy-preserving scheme by training data analytics models on local users’ devices rather than transferring raw data to a central server for data processing. Although FL is widely applied to various pattern recognition applications, its use in sensor-based HAR is limited, and reviews of the HAR application are even scarcer. Therefore, this paper provides a comprehensive review of FL in HAR. This paper analyzes FL’s architectural design, data model training strategies, and model aggregation techniques. A comparative analysis between FL-based and machine learning methods is presented. The challenges, including data heterogeneity, data privacy, and communication costs, are identified through the findings, while the potential research direction of FL in HAR is underscored. This paper provides insights into the current state of FL for HAR, pinpoints research gaps, and outlines encountered challenges and potential research directions.
Copyright (c) 2025 Ting Xian Wu, Pang Ying Han, Ooi Shih Yin, Lim Zheng You, Hiew Fu San
Simulated Annealing Algorithm for Vehicle Routing with Stochastic Travel Times and Soft Time Windows
In the context of urban logistics, travel time uncertainty is a critical challenge for efficient route planning. Thus, this study proposes an algorithm based on Simulated Annealing (SA) that addresses this problem through a dual approach. On the one hand, the effectiveness of the algorithm is validated in deterministic VRPTW scenarios, using classical Solomon instances and achieving an average GAP of 3.42% in the most complex cases. On the other hand, a stochastic model fed with empirical data from Google Maps is integrated, designed to capture real-time traffic variability, thus addressing the VRPSTTW problem. The results show that the algorithm not only maintains a standard deviation of 2,048 energy units, consolidating its robustness to fluctuations in the optimal parameters, but also stands out for its ability to generate robust solutions in urban contexts with high temporal uncertainty. This proposal, being based on real data and not on theoretical simulations, positions the algorithm as a strategic tool to optimize logistic operations in dynamic and volatile environments.
Copyright (c) 2025 M. Jiménez-Carrión, Kenin M. Ojeda-Hidalgo
Multi-Objective Biomechanical Optimization of Breaststroke Swimming Using NSGA-II
Advancements in computational modeling and optimization algorithms have opened new possibilities for analyzing and improving sports biomechanics. This study presents a multi-objective optimization framework based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize breaststroke swimming techniques. The framework integrates a biomechanical model that combines hydrodynamic forces, joint kinematics, and energy expenditure to address three conflicting objectives: maximizing swimming velocity, improving energy efficiency, and minimizing joint load. Experimental validation conducted with professional swimmers demonstrated that the optimized stroke techniques achieved up to a 20% reduction in peak joint loads at the shoulder and knee, significantly reducing the risk of overuse injuries. Additionally, energy consumption per stroke cycle decreased by 15%-20%, while propulsion efficiency was notably enhanced. The framework generates Pareto-optimal solutions, offering a spectrum of trade-offs that can be tailored to individual performance goals and physical constraints. This approach provides a quantitative, data-driven alternative to traditional training methods, enabling personalized and informed decision-making for athletes and coaches. Beyond breaststroke, the methodology can be extended to other swimming techniques and athletic disciplines, addressing the interplay between performance, efficiency, and safety. This study bridges the gap between theoretical modeling and practical application, offering a scalable and robust solution for optimizing sports performance and reducing injury risks.
Copyright (c) 2025 Zhongjian Luo, Xinting Yan
Assessing AI-Driven Personalization in Smart Cities Using Hybrid Machine Learning and MCDM Approach
This study aims to assess AI-driven personalization strategies in smart cities, focusing on promoting digital inclusion across diverse urban populations. As artificial intelligence becomes increasingly central to urban service delivery, ensuring equitable and effective personalization is critical to preventing the amplification of digital inequality. To address this challenge, a hybrid evaluation framework is proposed, integrating Multi-Criteria Decision Making (MCDM) techniques, specifically Step-wise Weight Assessment Ratio Analysis (SWARA), Linguistic q-Rung Orthopair Fuzzy Numbers (Lq-ROFNs), and the Multi-Attributive Border Approximation Area Comparison (MABAC) with a Machine Learning (ML) classification model based on Random Forest. The framework is applied to stakeholder input from ten Indonesian smart cities, evaluating personalization readiness across five dimensions: accessibility, affordability, user engagement, privacy, and personalization effectiveness. The results indicate that accessibility and user engagement are the most influential criteria, while affordability and privacy are areas requiring strategic policy focus. The integrated model classifies cities by readiness level and identifies sensitivity patterns relevant to inclusive digital policy-making. The novelty of this research lies in its synthesis of MCDM and ML approaches to produce a transparent, scalable, and data-driven tool for evaluating AI personalization. This contributes to inclusive smart city development by aligning AI implementation with broader social equity objectives.
Copyright (c) 2025 Muhammad Faisal, Sahabuddin, Ery Muchyar Hasiri, Darniati, Titik Khawa Abd Rahman, Ida Mulyadi, Billy Eden William Asrul, Sri Wahyuni, I Dewa Made Widia
Analyzing Online News Dissemination Patterns via Social Network Hypergraph Model
This study aims to develop a novel method for analyzing the complex dissemination patterns of online media news using a social network hypergraph model, addressing the limitations of traditional graph models in capturing many-to-many relationships in news dissemination. The author integrates news content, user nodes, and topic tags into a multi-dimensional hypergraph structure. The approach includes detailed analysis of key elements of news dissemination across four dimensions (subject, content, channel, and effect), construction of the hypergraph model, and design of mechanisms for extracting dissemination paths and evaluating influencing factors. Experiments were conducted on real-world data from multiple social platforms to validate the method's effectiveness. The results demonstrate that the proposed hypergraph model outperforms traditional models (GCN, GAT, and RF) in terms of accuracy, F1 value, and error control. The model effectively reflects the complex structure and dynamic evolution of news dissemination, revealing significant factors such as user activity, topic sensitivity, and structural entropy. This research offers a new perspective on understanding and optimizing online news dissemination by leveraging the hypergraph model's ability to capture multi-dimensional interactions. It provides a more comprehensive and accurate analysis framework, laying a theoretical foundation for constructing efficient information dissemination models.
Evaluating the Performance of NoSQL Databases for Big Data in Cloud Computing Environments
This study aims to evaluate the performance of NoSQL databases in distributed cloud computing environments, addressing the lack of comprehensive benchmarking in this domain. Specifically, it investigates MongoDB and Riak KV, two widely used NoSQL systems, across diverse cloud platforms, including Google Cloud, DigitalOcean, and OpenStack. Using the Yahoo Cloud Serving Benchmark, we designed and implemented a benchmarking model to measure key performance indicators, including latency, throughput, and scalability, under varying workloads and data sizes. The analysis revealed that MongoDB integrated with Google Cloud consistently outperformed other configurations, demonstrating superior throughput and lower latency in read and write operations. In contrast, Riak Key Value generally exhibited higher latency, especially in scan-intensive workloads. To support practical decision-making, a decision tree model was developed based on empirical findings to guide optimal selection of cloud computing platforms and databases. The proposed benchmarking framework is modular and extensible, allowing adaptation to other NoSQL technologies, cloud providers, and performance metrics. This research presents a novel, systematic methodology for evaluating NoSQL database performance in cloud environments, providing actionable insights for selecting high-performing, scalable solutions in big data applications. This modular design enables the addition of more database technologies, deployment options, and performance standards in the future, thereby supporting broader research and real-world applications in distributed systems and cloud computing.
Copyright (c) 2025 Ahmet E. Topcu, Aimen M. Rmis, Yehia I. Alzoubi, Ali O. Çibikdiken, Dababrata Chowdhury, Ersin Elbasi, Mohamed Abdel-Maguid, Salem Almadhun
Virtual Reality Tourism: Connecting Immersive Experiences to Future Travel Choices
This empirical investigation examines the transformative impact of Virtual Reality (VR) implementations on destination accessibility within China's tourism sector. The research examines the interrelated relationships between VR Experience (VREX) determinants, experiential outcomes, and subsequent Visiting Intention (VIS). The theoretical framework encompasses three fundamental VREX antecedents: Telepresence (TLP), VR Application Quality (VAQ), and Perceived Realism (PREA). This research analyzes the impact of VREX on Perceived Enjoyment (PE) and Perceived Advantage (PAD), and in turn, their impact on VIS. Through purposive sampling methodology, the study gathered responses from 307 individuals actively engaging with VR tourism applications across China. Statistical analysis revealed significant associations between VREX and VIS, with all three antecedents demonstrating substantial influence on VREX formation. The findings establish that VREX has a significant impact on both PE and PAD dimensions. Notably, while PE emerged as a significant determinant of VIS, PAD demonstrated no substantial effect on visit intentions. This investigation advances theoretical discourse in virtual tourism by illuminating the crucial role of immersive technological experiences in destination marketing. For practitioners, these findings suggest prioritizing enjoyment-focused VR designs and investing in technologies that enhance telepresence and realism to influence potential tourists' visit intentions effectively.
Copyright (c) 2025 Lei Zhou, Huaqing Zhou, Xiaotang Cui, Jing Zhao
Exact Run Length Sensitivity of DEWMA Control Chart Based on Quadratic Trend Autoregressive Model
One well-known process detection tool that is sensitive to even little shift changes in the process is the Double Exponentially Weighted Moving Average (DEWMA) control chart. The present study aims to provide exact average run length (ARL) on the DEWMA chart under the data that is underlying the quadratic trend autoregressive (AR) model. At that point, the computed ARL via the numerical integral equation (NIE) technique was compared in terms of accuracy to the exact one that was developed by using the percentage accuracy (%Acc). And then, the computational times of both were also compared. The results revealed that the ARL results of exact ARL and ARL via the NIE method show hardly any difference in terms of accuracy, but exact ARL outperformed in terms of computational times that were computed instantly, whereas the other way spent approximately 2-3 seconds computing. Thereafter, the proposed ARL operating on the DEWMA chart was compared to the CUSUM and EEWMA charts. It was found to be more effective in terms of detection performance. Especially when there are little shift changes in the process. The run length formulas, which are the standard deviation run length (SDRL) and the median run length (MRL), were measures of sensitivity evaluation and were used to verify their capability. The sensitivity of detecting changes of exact ARL running on the DEWMA chart was illustrated by the real data utilized in fields of economics about natural gas importing in Thailand (Unit: 100 MMSCFD at heat value of natural gas 1,000 BTU/SCF). Apparently, the exact ARL of the DEWMA chart is an excellent choice to detect small shift changes under this scenario, which represents properties as a quadratic trend AR model.
Copyright (c) 2025 Kotchaporn Karoon, Yupaporn Areepong
FATA-ResNet Network for CAD/CAM Integration in Cloud Manufacturing
This paper focuses on the application of mechanical engineering CAD/CAM integration technology under the cloud manufacturing framework, aiming at solving the current technical integration problems in manufacturing informatization. The study analyzes the demand and current situation of 3D CAD/CAM integration in a cloud manufacturing environment, combines the mirage optimization algorithm (FATA) and residual neural network (ResNet), and proposes a CAD/CAM integration application analysis model based on the FATA-ResNet network. Firstly, the functional requirements of CAD/CAM technology integration in a cloud manufacturing platform are clarified, including 3D model uploading and downloading, process file generation, and cross-platform data sharing. Then, the hyperparameters of the ResNet network are optimized by the FATA algorithm to improve the accuracy and efficiency of the model in integration application analysis. The experimental results show that the FATA-ResNet model outperforms the traditional model in terms of accuracy, recall, and F1 score while possessing faster convergence speed and higher computational efficiency. In addition, the operation modules in the cloud platform, including the task management interface and 3D process editing function, were designed and validated, further demonstrating the practicality of the method. Future research will focus on the validation of multi-scene data, model resource optimization, and real-time collaborative operation to promote the in-depth application of CAD/CAM technology in intelligent manufacturing and provide support for the digital and intelligent development of manufacturing.
Efficient Object Detection with an Optimized YOLOv8x Model
This study developed an efficient object detection model for indoor environments, addressing common challenges such as occlusions, varying lighting, and cluttered scenes. We evaluated several YOLOv8 variants—ranging from nano to extra-large—and introduced an optimized YOLOv8x model. Our approach combines structured pruning, quantization-aware training, and advanced data preprocessing techniques, including augmentation and noise reduction, to improve model performance while reducing computational demands. The models were developed and evaluated using a carefully selected indoor object detection dataset featuring ten common categories. Performance was measured through key metrics like precision, recall, and mean average precision (mAP). Among them, the fine-tuned YOLOv8x clearly outshined the baseline models, reaching a training precision of 0.577, a recall of 0.572, and an mAP@0.50 of 0.537. When tested on new data, it demonstrated even better generalization, delivering a precision of 0.502, a recall of 0.528, and an mAP@0.50 of 0.480—proving robustness and reliability in real-world scenarios. These results demonstrate that pruning and quantization can significantly reduce model complexity without sacrificing accuracy, which helps to detect indoor objects. In essence, it is optimized for indoor object detection, offering promising applications in smart environments, surveillance, and robotics.
Copyright (c) 2025 Nafiz Fahad, Md. Jakir Hossen, Md Shohel Sayeed
A Self-Adaptive Weights for K-Means Classification Algorithm
This paper presents an improved K-means clustering algorithm that addresses the traditional algorithm’s sensitivity to outlier and susceptibility to local optima by introducing an adaptive weight adjustment mechanism. It employs an exponential decay function to dynamically reduce the feature weights of outlier data points, effectively suppressing outliers while preserving the structure of the normal data. The proposed method retains the computational efficiency of standard K-means. Key contributions include: (a) A novel distance-based weighting strategy that progressively reduces the influence of noisy points, mitigating the impact of outliers on clustering performance. (b) An innovative form of "local dimensionality reduction" for outlier points via weight decay, which interferes only with the feature space of noisy regions while preserving the global topological structure of clean data. Extensive experiments on three benchmark datasets Iris (4-dimensional, balanced classes), Wine (13-dimensional, correlated features), and Wisconsin Breast Cancer Diagnosis (30-dimensional, imbalanced data) demonstrate the effectiveness of the approach. Compared to standard K-means, the proposed algorithm achieves accuracy improvements of 7.47% on Iris, 13.89% on Wine, and 19% on WBCD. This adaptive strategy offers a practical and efficient solution for clustering in noisy, high-dimensional environments, without the added complexity of mixture models.
Copyright (c) 2025 Cui Chenghu, Arit Thammano
Using PID-BP Digital Virtual Reality for Non-Heritage Protection: Recognition and Assessment
The objective of this paper is to address the issues of low accuracy and slow real-time performance in the existing algorithm for digitally protecting and evaluating non-heritage culture. To achieve this, we propose an improved method for the identification and assessment of non-heritage digital protection by optimizing the BP neural network using the PID search algorithm. This method aims to enhance the precision and real-time capabilities of the algorithm. We extract a set of feature vectors from the digital protection process of non-heritage culture and construct a recognition and evaluation system. The PID search algorithm is employed to optimize the BP neural network, which helps in establishing a mapping relationship between the feature vectors and the assessment values of non-heritage digital protection. We apply this method to the digital protection of non-heritage culture in Dali Xizhou as a case study. The results show that our method significantly improves the accuracy and real-time performance of the assessment compared to traditional BP and other optimized BP network models. This study provides a novel and effective approach to the digital protection of non-heritage culture.
Copyright (c) 2025 Zhenyu Gao, Yaoben Gong
Unveiling Key Drivers of Citizens' Acceptance of E-Voting: A Quantitative Analysis
The current study examines the broader factors influencing citizens’ trust and adoption of electronic voting (e-voting) systems, extending beyond the conventional focus on trust in government and technology. A conceptual framework was developed by incorporating elements from the TAM, IDT, and trust theory. Data was collected through surveys and investigated using SEM to evaluate the relationships amongst crucial variables. The findings reveal that trust in e-voting is significantly shaped by citizens’ trust in governing bodies, the transparency and reliability of the voting process, trust in the technology, and perceived ease of use. In contrast, perceived public value was not found to significantly impact trust. These results highlight the multifaceted nature of trust in digital governance and underscore the importance of considering both procedural and technological factors in system design. The novelty of this study lies in its broader perspective on trust, emphasizing the role of implementation and process transparency in influencing public perception. The proposed model offers practical insights for policymakers and system developers seeking to improve public confidence and foster wider adoption of e-voting technologies.
Copyright (c) 2025 Valmira Osmanaj, Syed Faizan Hussain Zaidi, Atik Kulakli, Miranda Harizaj
Global Brand Equity Patterns in High-Tech Industries: A 24-Year Analysis
The objective of the study is to investigate major brand equity trends among High-Tech brands. Using Interbrand's Top 100 global brands list from 2001 to 2024 as its population, the methodology focused on 48 extracted High-Tech brands. The analysis employed descriptive statistics, including cumulative brand equity and growth rates, alongside country- and region-specific analyses and cluster formation. This approach allowed for an examination of the 2008 global financial crisis and the recent pandemic's impact. Brands from nine countries were categorized into Hardware, Software, and Internet Services, with the analysis grounded in Interbrand's reported brand equity values and annual growth rates. Analysis of High-Tech brands reveals common strategic lessons alongside unique nuances. Resilience during global financial crises and pandemic COVID’19, the country-of-origin effect (US dominance), maintaining financial thresholds, diversified global earnings, and adapting to evolving market standards are universally important. However, High-Tech particularly emphasizes innovation as core, the dominance of internet services and software, and the rapid rise and fall in niche areas in internet-based services. Hardware faces distinct challenges. The emergence of industry "Giants" and strong niche players underscores diverse success paths. Digital transformation is foundational, and effective brand portfolio management is crucial. This research provides novel strategic lessons for brand managers, emphasizing the crucial role of rapid, continuous innovation and strategic digital transformation for maintaining brand equity.
Optimizing AIGC Technology for IoT Devices with Deep Learning
The present article intends to explore how a deep learning model could be applied to improve the ability of AI-generated content (AIGC) technology in graphic recognition within the IoT ecosystem. Objectives: This research pursues two key objectives: first, the model is compressed to a smaller size and decreased computational cost for on-device deployment on resource-poor IoT devices, and second, it achieves better adaptability through data augmentation and regularization techniques. Methods/Analysis: A purpose-built CNN design was built and trained to solve IoT-specific constraints. Model compression techniques such as weight pruning and quantization were used to reduce resource requirements. To ameliorate this, we applied data augmentation techniques like rotation, shear, and zoom, and regularization techniques like dropout to avoid overfitting. The work was done on MNIST and CIFAR-10 typical datasets using TensorFlow as a deep learning framework. Results: The pattern-recognition accuracy on MNIST and CIFAR-10 datasets achieved are 99.5% and 89.2%, respectively. Moreover, the recognition speed was improved by around 30% since the computational cost of the DL algorithm is effective because of parallel processing, resulting in lower processing time. The compressed model overcame the massive computational complexity, which is more suitable for resource-limited IoT devices. Novelty/Improvement: a new methodology is presented that integrates CNN optimization and model compression in conjunction with sophisticated regularization techniques to develop a suitable solution for the peculiarities of the IoT landscape. Ultimately, overcoming the universal problems like limited resources and real-time processes in this research helps to improve the technological and theoretical support for practical IoT applications and accelerate the practical implementation of AIGC performance optimization across various industries such as smart homes, smart transportation, and smart security.
Copyright (c) 2025 YuShui Xiao, Yucheng Dong
Automated Vocabulary Profiling of TOEIC Listening Materials: A CEFR-Aligned Approach for EFL Learners
This study examines the vocabulary characteristics of TOEIC Listening materials to support the development of more targeted English language teaching resources for EFL learners, particularly in Thai higher education. Using a corpus-based approach, we collected and analyzed a representative dataset of TOEIC preparation texts with a custom-built Python tool for vocabulary profiling. The tool performed key tasks such as frequency analysis, concordance generation, n-gram extraction, collocation detection, and CEFR-level classification. The vocabulary items were categorized using established lists, including the General Service List (GSL), Academic Word List (AWL), and CEFR levels. Results reveal that basic (K1) and function words dominate the materials, while a substantial proportion of off-list and domain-specific vocabulary was also identified. Most words fall within the B1 proficiency level, suggesting intermediate-level accessibility. The study contributes a novel, automated vocabulary profiling framework that integrates linguistic metrics and CEFR-based classification, offering practical implications for curriculum design, test preparation, and vocabulary instruction. This approach enhances the precision and efficiency of material evaluation, bridging the gap between test content and learner needs. The findings highlight the potential of automated tools to improve vocabulary-focused teaching strategies and inform language assessment practices in EFL contexts.
Copyright (c) 2025 Banchakarn Sameephet, Kornwipa Poonpon, Nuanchan Pradutshon, Wirapong Chansanam
Mathematical Approaches and Algorithms in Big Data Architecture and Hybrid System Efficiency
This article presents a formal demonstration of a hybrid big data processing architecture that combines the fault tolerance and storage robustness of Hadoop with the speed and in-memory processing capabilities of Apache Spark. The proposed architecture is evaluated through test execution and performance benchmarking in real-world data centers across three regions in Kazakhstan. The model integrates distributed resource management components, Directed Acyclic Graph (DAG)-based scheduling mechanism, and Resilient Distributed Datasets (RDDs) to enable dynamic workload distribution and rapid failure recovery. The results demonstrate that the hybrid system consistently outperforms standalone Spark and Hadoop architectures under variable workloads, illustrating enhancements in execution time, task recovery, and resource utilization. Quantitative performance metrics allow for a structured comparison of architectures and help optimize deployments for diverse scenarios. The proposed hybrid architecture shows significant improvements, reducing average execution time by up to 38% and increasing resource efficiency by 25% compared to standalone Spark and Hadoop systems.
Copyright (c) 2025 Serik Aliaskarov , Raissa Uskenbayeva , Vassily Serbin, Orazmuhamed Bekmurat, Umit Bazarbayeva, Yelena Bakhtiyarova , Kanibek Sansyzbay
Research on Carbon Emission Estimation of Rural Tourist Attractions Through Digital Management
Objectives: This study aims to estimate the carbon emissions of scenic spots in rural tourism using digital management technology. Methods: The Dashahe National Wetland Park, located along the old course of the Yellow River in Feng County, Jiangsu Province, was taken as a case for analysis. During the analysis process, the carbon emission, carbon absorption, and net carbon emission amount of the park during 2018-2023 were estimated. The correlation between different types of land area and the carbon absorption amount was analyzed. Findings: The carbon emission of the wetland park increased annually, but the carbon absorption amount also showed a consistent upward trend, resulting in relatively stable net carbon emissions over the study period. Moreover, the area of wetlands, water bodies, and grasslands exhibited a significant positive correlation with the carbon absorption amount, whereas the correlation between the area of cultivated lands and garden lands and the carbon absorption amount was insignificant. Innovation: This research applied digital management technology to precisely collect data related to carbon emissions within the scenic spot, enabling a more reliable estimation of its carbon footprint.
Achieving Carbon Neutrality: Strategies in Organizations, Engagement and IT Innovations
This study examines the challenges organizations face in achieving carbon neutrality by analyzing employee awareness, organizational practices, and the role of information technology (IT). It aims to (1) assess employee engagement in sustainability initiatives, (2) evaluate the effectiveness of organizational policies in promoting carbon neutrality, and (3) explore the potential of IT solutions in reducing emissions. A mixed-method approach was used, combining questionnaires and interviews to capture quantitative and qualitative insights. Employees from various industries were surveyed to assess their awareness, while interviews provided deeper insights into organizational strategies and IT adoption. Statistical and thematic analyses identified key gaps and opportunities. The study reveals that limited employee awareness hinders sustainability efforts, emphasizing the need for targeted engagement programs. Organizational effectiveness in achieving carbon neutrality varies, with standardized policies and dedicated sustainability teams playing a crucial role. IT adoption levels differ, but data analytics and emerging digital technologies demonstrate strong potential for optimizing carbon reduction strategies. This research integrates organizational, technological, and behavioral perspectives, highlighting the importance of employee engagement and IT solutions in sustainability efforts. It provides actionable insights for organizations seeking to implement effective carbon neutrality strategies.
Copyright (c) 2025 Chan Rou Qian, Rajermani Thinakaran, Richard Pravin Thomas Adisan, Tarik A. Rashid, Sharmila Devi Ramachandaran, László Koloszár