HighTech and Innovation Journal
Not a member yet
317 research outputs found
Sort by
Financial Cycle Dependence of Monetary and Exchange Rate Policies in an Open Economy
Full text linkThe deepening of globalization has posed challenges to open economies, such as fluctuations in international capital flows and intensified cross-border risk contagion. To explore the impact mechanism of FC on MP and ERP, this paper adopts TVP-VAR, MS-VAR, and MS-DSGE models, and introduces the SVR model as an auxiliary prediction tool to analyze policy dependency characteristics through standardization and periodic decomposition. The results showed that during the 2008 financial crisis, the growth rate of the broad money supply reached 17.0%-20.0%, the Shanghai Interbank Offered Rate rose to 3.6%-5.2%, and the asset price volatility exceeded 20%. During the COVID-19 pandemic in 2020, the volatility of real estate prices reached 7.2%-9.5%. In terms of policy transmission, the impact of asset price shocks on the consumer price index significantly increased after 3 months and reached its peak after 6 months. The regulatory coefficient of interest rate policy on the financial condition index under the high volatility regime was 1.1862, and the response coefficient of the growth rate of the broad money supply to the output gap under the low volatility regime was 0.2156. The SVR model had a prediction accuracy (R2 of 0.85) for the impact of MP on ERVs, especially in capturing nonlinear relationships during financial expansion periods. This achievement demonstrates the significant effect of FC stages on the effectiveness of MP, providing an FC sensitive policy framework for open economies, helping to enhance macroeconomic resilience and maintain internal and external balance
A Systematic TOGAF-Driven Framework for Blockchain-Based Food Traceability with Access Control Lists
Full text linkThe global food supply chain involves multiple stakeholders, including farmers, manufacturers, distributors, retailers, and consumers, requiring a robust traceability system to ensure food security, transparency, and consumer trust. However, existing systems face significant challenges, such as limited transparency, data tampering risks, and inefficient access control mechanisms, leading to supply chain inefficiencies and regulatory concerns. This framework paper develops a systematic model that integrates The Open Group Architecture Framework (TOGAF), blockchain technology, and Access Control Lists (ACLs) to address these limitations. The TOGAF Architecture Development Method (ADM) is applied to design and implement the framework, focusing on business architecture, data security, and stakeholder collaboration. The framework ensures data immutability, privacy, and secure access control while enhancing scalability and adaptability across diverse supply chains. By integrating these technologies, the proposed framework is expected to enhance traceability, strengthen data security, and improve stakeholder engagement, making food supply chains more reliable and transparent for regulators and consumers. The novelty of this framework lies in its unique integration of TOGAF-driven enterprise architecture, blockchain, and ACLs, creating a privacy-preserving, tamper-proof food traceability system. This integration enhances industry practices and provides a scalable, sustainable solution, contributing to global food security and consumer trust
PRO-BiGRU: Performance Evaluation Index System for Hardware and Software Resource Sharing Based on Cloud Computing
Full text linkThis study aims to address the performance evaluation challenges of computer hardware and software resource sharing in cloud computing environments. To achieve this, we propose an enhanced performance evaluation method by integrating the Poor-Rich Optimization (PRO) algorithm with the Bidirectional Gated Recurrent Unit (BiGRU) network. We first construct a comprehensive multi-dimensional performance evaluation index system that encompasses resource utilization, response time, throughput, and scalability. Subsequently, the PRO algorithm is employed to optimize the hyper-parameter design of the BiGRU network, thereby enhancing the model's learning ability and evaluation accuracy. Performance data is collected using system monitoring tools, and experiments are conducted to validate the model's effectiveness. The results demonstrate that the PRO-BiGRU model achieves an average evaluation accuracy of over 97% across four independent experiments, significantly outperforming traditional algorithms such as CNN, RNN, LSTM, and GRU. The proposed model not only improves the accuracy of performance evaluation but also provides a reliable basis for resource optimization and decision-making in cloud service platforms. The novelty of this research lies in the combination of the PRO algorithm with the BiGRU network, which effectively captures complex data features and enhances the model's reliability and robustness in performance assessment tasks
IASB Framework: Construction of Data Asset Accounting System Based on PO-BP Model
Full text linkThis 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
A Novel Optimization Approach for Revolutionizing Architectural Design in Chinese Cultural Heritage
Full text linkThe 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
Quasi-Viral Technologies as the Drivers of the Economy Digital Transformation Towards Sustainability
Full text linkThe 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
Digital Literacy for Business Performance: A Study of Entrepreneurs
Full text linkThis 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
Strategic Metadata Implementation: A Catalyst for Enhanced BI Systems and Organizational Effectiveness
Full text linkIn today's data-driven business landscape, robust metadata and data documentation practices are essential for enterprises aiming to maximize their data assets. When integrated with Business Intelligence (BI) systems, this architecture empowers data democratization, allowing widespread utilization by stakeholders across the organization. This research explores the critical role of metadata in shaping Business Intelligence (BI) systems and organizational effectiveness within today's data-driven business landscape. Through a systematic literature review, a preliminary study, a quantitative survey with 318 responses, and a focus group discussion, the study identifies key metadata components influencing BI systems effectiveness and organizational outcomes. Findings indicate a direct and positive impact of BI systems effectiveness on organizational effectiveness. Certain metadata components exhibit direct positive effects on both BI systems and organizational effectiveness. The research underscores the importance of strategic metadata implementation for enterprises seeking to optimize data-driven decision-making processes. Overall, the study provides practical implications for organizations and contributes valuable insights to the understanding of metadata's role in enhancing enterprise effectiveness. Doi: 10.28991/HIJ-2025-06-01-02 Full Text: PD
Advancing Network Security: Integrating Salp Swarm Optimization with LSTM for Intrusion Detection
Full text linkOver time, intrusion detection systems have grown essential in ensuring network security by identifying malicious activities within network traffic and alerting security teams. Machine learning techniques have been employed to develop these systems. However, these approaches often face challenges related to low accuracy and high false alarm rates. Deep learning models like Long Short-Term Memory (LSTM) are utilized to address these limitations. Despite their potential, LSTM models require numerous iterations to achieve optimal performance. This study introduces an enhanced version of the LSTM algorithm, termed ILSTM, which integrates the Salp Swarm Optimizer (SSO) to boost accuracy. The ILSTM framework was applied to construct an advanced intrusion detection system capable of binary and multi-class classifications. The approach comprises two phases: The first involves training a standard LSTM model to initialize its weights. In contrast, the second employs the SSO hybrid optimization algorithm to fine-tune these weights, enhancing overall performance. The effectiveness of the ILSTM algorithm and the intrusion detection system was assessed using two publicly available datasets, NSL-KDD and LITNET-2020, across nine performance metrics. Results demonstrated that the ILSTM significantly outperformed the conventional LSTM and other comparable deep learning models in accuracy and precision. Specifically, the ILSTM achieved an accuracy of 93.09% and a precision of 96.86%, compared to 82.74% accuracy and 76.49% precision for the standard LSTM. Moreover, the ILSTM exhibited superior performance on both datasets and was statistically validated to be more robust than LSTM. Furthermore, the ILSTM excelled in multiclass intrusion classification tasks, effectively identifying intrusion types
Automated Vocabulary Profiling of TOEIC Listening Materials: A CEFR-Aligned Approach for EFL Learners
Full text linkThis 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