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317 research outputs found
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Digital Transformation Maturity Measurement (DTMM) for the Oil and Gas Industry
Full text linkThis study develops and validates a Digital Transformation Maturity Measurement (DTMM) framework tailored to the oil and gas industry, addressing sector-specific regulatory and technological challenges. A mixed-methods approach was employed, integrating semi structured interviews with 10 senior executives and a survey of 240 industry professionals. The data were analyzed via structural equation modeling (SEM) and confirmatory factor analysis (CFA) to assess five maturity factors—leaders, staff, organization, technology, and value for stakeholders—across three key success indicators: financial performance, customer/internal user value, and environmental and societal value. The findings highlight Technology and Value for Stakeholders as the most influential transformation drivers, significantly improving operational efficiency and sustainability. Unlike prior models, the proposed DTMM integrates financial, technological, and sustainability dimensions into a unified assessment tool. This study contributes to the digital transformation literature by offering an empirically validated framework for evaluating and guiding strategic digital initiatives. Policymakers and industry leaders can leverage these insights to enhance digital maturity, prioritize high-impact strategies, and drive sustainable growth in an increasingly competitive landscape
A Self-Adaptive Weights for K-Means Classification Algorithm
Full text linkThis 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
ML and DL Models for Stroke Prediction from Bio-Signals: A Systematic Review and Bibliometric Analysis
No full textStrokes 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
Experimental and Numerical Modeling of a Cross-Flow Turbine Runner Made of HDPE: Experimental and Numerical Approach
Full text linkThis study investigates the viability of high-density polyethylene (HDPE) as a sustainable, low-cost alternative to conventional metallic materials for Cross-flow turbine runners in micro-hydropower systems. The primary goal is to design, manufacture, and validate the hydrodynamic and structural performance of an HDPE runner. A three-stage methodology was applied: CAD-based design, thermoforming fabrication, and performance evaluation through computational fluid dynamics (CFD) and finite element analysis (FEA) using ANSYS. Numerical predictions were validated against experimental data obtained from a hydraulic test bench. Mesh refinement and turbulence modeling were included to ensure numerical reliability. Results show that the HDPE runner achieved efficiencies of 80-83% compared to a geometrically identical steel runner under similar operating conditions. Structural analysis confirmed von Mises stresses (8.5 MPa) and deformations (0.12 mm) remained well below HDPE’s yield strength (22 MPa), validating its mechanical integrity. Statistical comparison revealed a deviation of less than 4% between numerical and experimental results. This research provides a validated framework for using recyclable HDPE in turbine manufacturing. It demonstrates that HDPE can deliver comparable power output to steel while reducing manufacturing costs and environmental impact, offering a sustainable pathway for rural electrification
Assessing Concession Period Risks of Public-Private Partnership Infrastructure Projects Using FACULTY
Full text linkThe concession period is critical to Public–Private Partnership (PPP) infrastructure project success because it defines how long private investors operate to recoup costs and earn returns. This study investigates risk factors affecting the concession period in Vietnam’s PPP infrastructure projects and introduces a novel evaluation method called FACULTY (Fuzzy AHP integrating Consequences, Uncertainty, and Likelihood Technology). The research objective is to identify which risks most significantly influence concession duration and to demonstrate an improved risk assessment approach. FACULTY combines fuzzy AHP with a traditional consequence-likelihood analysis to capture uncertainty in expert judgments. By surveying 90 PPP experts and analyzing 27 risk factors across five risk categories: construction, revenue, macroeconomic, political, and legal risks, this study identified the ten most critical concession period risks for PPP infrastructure projects. These include land acquisition, access, and compensation issues; construction cost overruns; schedule delays; design deficiencies; geological and site conditions risks; force majeure; traffic demand risk; environmental risks; population growth; and concession price risk. These findings indicate that land acquisition and construction-related risks dominate, reflecting persistent challenges in Vietnam’s infrastructure delivery. This study provides a comprehensive framework for understanding and addressing risks that influence the concession period, offering valuable insights for policymakers and practitioners aiming to optimize PPP project outcomes
A Novel Cost-Effective Unmanned Ground Vehicle Platform for Robotics Education
Full text linkThis study demonstrates a novel unmanned ground vehicle platform suitable for educational robotics that is cost-effective, modular, and utilizes 3D-printed components. The methodology involved creating three UGV designs using Fusion 360 and implementing Finite Element Analysis (FEA) testing in ANSYS to identify potential failure points. The team tested various configurations, including 3D-printed and aluminium components, to find an appropriate balance between durability and cost-effectiveness. Using GPS accuracy and incline navigation, the authors assessed the UGV's capabilities, feasibility, and educational value. The study peer reviews identified standards the UGV should adhere to develop a modular, cost-effective, and feasible learning platform. The platform demonstrated outdoor capabilities and the capacity to perform efficiently using proper specifications. Students and an instructor evaluated various aspects of the UGV platform through workshops conducted by the authors. The assembly received positive ratings, with an average rating of 4 out of 5 on a Likert scale. Issues pointed out by the participants included loose screw threading and the complexity of the fastening screws and nuts. The seamlessness of electronic connection and modules was also rated, with participants rating the battery capacity and Pixhawk unit with 4.17 to 4.21 out of 5 on the scale. However, the Mission Planner assessment showed a significant drop in learning curve evaluation due to the overwhelming interface of the software for new users. The overall performance of the UGV was rated at 4 out of 5 due to its 3D-printed frame. Participants observed that inclines and turning capability were notable features of the UGV platform. The open-source platform features multiple outdoor-specific components, including a distance sensor, GPS, and wireless telemetry. With the option of adding a bump sensor and a co-processor as needed, the UGV platform achieved its goal of being a cheaper alternative to commercially available robotics kits while offering more features for custom configurations. Doi: 10.28991/HIJ-2025-06-01-020 Full Text: PD
Numerical Modeling on Mechanical Properties of Cemented Phosphogypsum Stabilized Soil
Full text linkPhosphogypsum is an industrial waste with a large stock and will show a great threat to people's lives. The study of its mechanical properties is particularly important for engineering applications. The objective of the work is to discuss the influence mechanism of cemented phosphogypsum-stabilized soil by different parameters and provide a research basis for the engineering application of phosphogypsum as road subgrade. The main analysis methods are as follows. The published mechanical test data of cemented phosphogypsum-stabilized soil are firstly collected in this work, and the numerical models for describing the compaction properties, liquid-plastic limit properties, unconfined compressive strength, and the cracking properties of cemented phosphogypsum-stabilized soil are then established by numerical fitting. Based on the verified model, the effects of different parameter factors on the mechanical behavior of cemented phosphogypsum-stabilized soil are finally carried out. The results show that the numerical model can effectively predict the influence of different factors on the mechanical properties of materials and is in good agreement with the test results. The novelty of this work is establishing the numerical modeling on the mechanical properties of cemented phosphogypsum-stabilized soil, considering the effects of different parameter factors. Doi: 10.28991/HIJ-2025-06-01-01 Full Text: PD
Optimizing AIGC Technology for IoT Devices with Deep Learning
Full text linkThe 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
Achieving Carbon Neutrality: Strategies in Organizations, Engagement and IT Innovations
Full text linkThis 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
Exact Run Length Sensitivity of DEWMA Control Chart Based on Quadratic Trend Autoregressive Model
Full text linkOne 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