Digital Literacy for Business Performance: A Study of Entrepreneurs

This study investigates the relationship between digital literacy levels among entrepreneurs and their impact on business performance. Specifically, it examines how entrepreneurs' digital skills significantly influence financial and marketing efficiency. The study evaluates the effects of digital literacy on business performance within the theoretical frameworks of the Digital Economy (DE), Digital Orientation (DO), Dynamic Capabilities (DC), and Adaptive Capability (AC). Using a quantitative approach and structural equation modeling (SEM), a novel analytical framework was developed on the basis of data collected from 354 members of provincial chambers of commerce across Thailand. The findings reveal that digital literacy positively and significantly impacts both financial and marketing performance, with adaptive capability serving as the most influential indirect factor. These results emphasize the critical importance of fostering digital skills among entrepreneurs to enhance innovation, adaptability, and sustainable growth in a competitive digital economy. This study contributes to the expanding literature on digital transformation by providing actionable insights into the practical applications of digital literacy for entrepreneurial success. Policymakers and business leaders are encouraged to prioritize the development of digital skills as a strategic pillar for achieving growth and competitiveness in the digital era. Doi: 10.28991/HIJ-2025-06-01-018 Full Text: PD

Eco-Friendly Materials for Temporary Use in Architecture and Decorations

This paper introduces the development of ecologically friendly composite materials for decoration and architectural purposes. The composites designed comprised degradable polylactic acid (PLA) and sugarcane bagasse fiber (SC) derived from the bioplastics and sugar industries. The SC reinforcement was examined for impurity treatment and composite formation using hot compression molding at 200 ± 10°C. Two processing methods were studied: (1) random dispersion of SC at 0, 2, 4, 6, 8, and 10 wt%, and (2) single and double-layer SC composite sheets made with 6 wt% SC. The physical and mechanical properties of the PLA-SC composites were evaluated through the morphologies and flexural properties (ASTM C293), thermal conductivity (ASTM C518), and biodegradation assessment (ISO 16929:2021). Results revealed that impurities in SC were effectively removed using an alkaline sodium bicarbonate solution followed by boiling in a 5% vinegar solution. Increasing SC contents reduced the weight, density, and thermal conductivity (k-value) of the PLA-SC composites compared to those representing single and double layers of SC. Additionally, this approach enhanced the flexural properties of the composites. Random dispersion with 10 wt% treated SC yielded the best results among the tested methods, making it the optimal approach for sustainable decoration and architectural materials. Doi: 10.28991/HIJ-2025-06-01-06 Full Text: PD

Quasi-Viral Technologies as the Drivers of the Economy Digital Transformation Towards sustainability

The relevance of the article is related to the phenomenon of quasi-viral technologies, which are the drivers of the phase transition to sustainable development. The study is aimed at defining the category "quasi-viral emerging technology”, as well as the disclosure of their content and form, and the analysis of the features in the conditions of digital transformations. The research method is based on the analysis of transformational changes in the components of the trialectic mechanism of the reproduction of socio-economic systems, which occur under the influence of quasi-viral sustainable technologies. The article defines the quasi-viral process of spreading emerging technologies as a transformational process of the informational component replacement within the technological base by methods imitating the course of viral infection. The signs of quasi-viral processes are formulated on several levels: "infection” due to a change in the information algorithm; substantial user preferences; lack of sufficient barriers; significant potential to increase users; and disruptive efficiency. Signs of quasi-viral technologies have the following types of innovations: renewable energy, 3D printing, electric transport, energy storage, IT technologies, digital recording of information, cloud technologies, etc. The authors hypothesize the possibility of using entropy estimates as the only measure of approximating the results of the implementation of quasi-viral technologies to the state of sustainability in society and nature. The expected results of the spread of quasi-viral technologies can be significant dematerialization of industrial metabolism, provision of functions of self-organization and self-improvement of social systems, preservation of biodiversity and ecosystems of the planet, and formation of the foundations of sustainable development. Doi: 10.28991/HIJ-2025-06-01-013 Full Text: PD

A Novel Cost-Effective Unmanned Ground Vehicle Platform for Robotics Education

This 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

Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization

Within the context of fingerprint database clustering, the density-based spatial clustering of applications with noise (DBSCAN) is notable for its robustness to outliers and ability to handle clusters of different sizes and shapes. However, its high computational burden limits its scalability for dense fingerprint databases. A hybrid two-stage clustering method, the CAP-DBSCAN algorithm, is proposed in this paper, designed to accelerate DBSCAN clustering while ensuring accuracy for fingerprint-based localisation systems. The CAP-DBSCAN algorithm employs the closest access point (CAP) algorithm to pre-cluster the database, while the DBSCAN algorithm performs clustering refinement. It dynamically adjusts the neighborhood radius (Eps) value for each pre-cluster using the k-distance plot method. The performance of the CAP-DBSCAN algorithm is determined across four publicly available received signal strength (RSS)-based fingerprint databases with Euclidean and Manhattan distances as fingerprint similarity metrics. This is benchmarked against the performances of the standard DBSCAN (s-DBSCAN) and k-means++-DBSCAN (k-DBSCAN) algorithms presented in previous research. Simulation results show that the CAP-DBSCAN algorithm consistently outperforms both the s-DBSCAN and k-DBSCAN algorithms, achieving higher silhouette scores, which indicates the generation of more compact and well-defined clusters. Furthermore, the CAP-DBSCAN algorithm demonstrates superior computational efficiency as a result of the CAP algorithm generating well-structured pre-clusters better than those generated by the k-means++ algorithm. This significantly reduces the computational burden of the cluster refinement process. Overall, using Manhattan distance as a fingerprint similarity metric results in the best clustering performance of the CAP-DBSCAN algorithm. These findings underscore the potential of the CAP-DBSCAN algorithm for practical applications in resource-constrained fingerprint-based localization systems. Doi: 10.28991/HIJ-2025-06-01-022 Full Text: PD

Novel Management Model for Leveraging Leadership for Successful Digital Transformation in Telecommunications Enterprises

Digital transformation (DT) is crucial for improving telecommunications efficiency and competitiveness. This study examines the role of change leadership in driving successful DT in Vietnamese telecommunication enterprises, focusing on its impact on employee engagement, employee commitment, DT communication, and DT capacity. A mixed-methods approach was employed, combining qualitative insights with quantitative data from surveys of management personnel overseeing digital transformation projects. Data were analyzed to assess direct and indirect relationships using structural equation modeling. The results indicate that change leadership is a significant driver of DT success with the strongest direct effect on employee commitment. Additionally, employee commitment and digital transformation communication positively influence success through their indirect effects on an enterprise’s DT capacity. Leadership plays a critical role in fostering commitment and aligning effort with DT goals. This study introduces a novel paradigm illustrating the interplay of various interrelated factors influencing the effectiveness of digital transformation, distinguishing it from previous studies that examined these factors in isolation. This approach provides novel insights, especially regarding Vietnamese telecommunications, a domain inadequately examined in previous studies on leadership-driven digital transformation initiatives

IASB Framework: Construction of Data Asset Accounting System Based on PO-BP Model

This study aims to construct a data asset accounting system based on the International Accounting Standards Board (IASB) framework, addressing the challenges in identifying, measuring, and reporting data assets within traditional accounting systems. By integrating the Political Optimization (PO) algorithm with the Back Propagation (BP) neural network, we propose a novel PO-BP model to enhance the accuracy and efficiency of data asset valuation. The PO algorithm optimizes the weights and biases of the BP neural network, improving its global search and local development capabilities. Experimental validation using open-source datasets demonstrates that the PO-BP model outperforms traditional models (e.g., BP, GWO-BP, and SSA-BP) in terms of convergence speed, prediction accuracy, and stability, achieving an average relative error of 0.2292% and a coefficient of determination R² of 0.9957. This study innovatively combines the PO algorithm with BP neural network, offering a robust technical approach for data asset value assessment. The findings provide significant theoretical support for advancing data asset accounting and practical guidance for enterprise decision-making during digital transformation. Future research will explore the model's adaptability to diverse industry data and dynamic market environments

Tourist Destination Recommendations Using Deep Learning

Personalized tourist attraction recommendations present a challenging problem in intelligent travel planning. Bangkok, the capital of Thailand, is a popular tourist destination offering a convenient metro system that enables travelers to plan their journeys easily. Leveraging this infrastructure, this study proposes a deep learning-based model designed to classify tourists into five categories: Nature Tourists, Cultural Tourists, Shopping Tourists, Historical Tourists, and Industrial Tourists. The model employs Neural Collaborative Filtering (NCF), utilizing deep neural networks to capture complex, non-linear patterns between users and destinations, surpassing the limitations of traditional matrix factorization methods. It integrates both user-related data, such as tourists’ opinions on destinations, and location-based data from the attractions themselves. To evaluate the model, data were collected from 30 stations along Bangkok's Pink Line, covering the northern part of the city and Nonthaburi province, and 31 tourist attractions along the route. Experimental results demonstrate high classification accuracy across tourism types: 96.26% for Nature Tourists, 80.59% for Cultural Tourists, 93.78% for Historical Tourists, 70.35% for Industrial Tourists, and 97.66% for Shopping Tourists. Furthermore, the study proposes three optimized travel routes tailored to tourist preferences: one for Nature and Cultural Tourists, another for Cultural Tourists, and a third for Historical and Cultural Tourists. By categorizing tourists based on their interests and recommending destinations accordingly, the model supports more informed and personalized travel decision-making. However, this current study serves as a prototype model and can be further applied to problems related to public transportation systems, such as deployment in mobile applications and integration with GPS positioning systems to enhance convenience and accuracy in providing tourist destination recommendations

Generative AI for Enhancing Accessibility and Inclusion in Higher Education: A Systematic Review

This study reviews existing literature on generative artificial intelligence (AI) and its accessibility for students with visual, hearing, and motor disabilities in higher education. The objective is to identify gaps in the implementation of inclusive education practices. The PRISMA protocol guided the review process, and the Scopus and Web of Science databases were selected for their recognized academic rigor and comprehensive coverage. The first phase involved the review of 54 articles in English from 2023 to 2024. The selection process involved prioritizing articles based on empirical scientific studies on AI applications for students with disabilities, and discarding articles that did not meet the criteria. Ultimately, only five articles were selected. The findings reveal a significant research gap regarding the role of generative AI in supporting these students. Notably, the selected articles tend to focus more on sensory disabilities than on motor disabilities. This study is pioneering in pointing out the lack of research on motor disabilities during the analyzed period, a key aspect of AI in higher education. These findings underscore the necessity of further research that aligns with the UN 2030 Agenda, specifically Goals 4 (Quality Education) and 10 (Reduced Inequalities), promoting the development of AI tools that foster equal opportunities and inclusive education

A Novel Optimization Approach for Revolutionizing Architectural Design in Chinese Cultural Heritage

The preservation of China's cultural heritage architecture, which combines contemporary and ancient building techniques, is difficult because of the aesthetic and structural degradation that has overtaken it. This architecture is a testament to the country's technical, artistic, and cultural achievements. A smokescreen with a resolution of 5192 í— 4153 pixels was used to acquire surface photographs and ground shots of the Dazu Rock Carvings, Nanchan Temple, and Foguang Temple using the Microtrans Maryland 4-1000 program. The research aims to improve fault analysis in images of Chinese cultural heritage structures using an Ensemble Ant Colony Fused Convolutional Capsule Neural Network (EAC-CCNN). Then, using a combination of Augmented Reality (AR) and Building Information Modeling (BIM), the designing model for safety management and decision-making will be enhanced. Steps include collecting and annotating data, developing a hybrid EAC-CCNN model to probe the issue with the architectural building, training the model, connecting it with BIM, inspecting the site, and then analyzing the defects using augmented reality (AR) enhanced BIM models. The results show that this integrated approach works to increase the accuracy of defect identification, promote cooperation, and help maintain and preserve cultural heritage assets. The machine learning model's ability to detect and classify defects in buildings that are considered part of China's cultural heritage is evaluated using metrics such as accuracy and F1 score. "With an F1 Score of 95.47% and an accuracy of 93.29%, the architectural design fault identification and safety management model produces respectable results. Phases of training, validation, and testing measure performance in relation to project objectives. Using this approach, machine learning models may be taught to see patterns, fix errors, and make wise predictions under different conditions. Doi: 10.28991/HIJ-2025-06-01-011 Full Text: PD