Technological Transformation Research Articles

Enterprise value creation effects of artificial intelligence technology from the perspective of digital agility: evidence from China

ABSTRACT As a pivotal technology driving the latest wave of technological revolution and industry transformation, artificial intelligence (AI) is permeating the processes of business operations widely. Grounded in the dynamic capability theory, the research use Word2Vec and Python to extract metrics for AI technology from annual reports of Chinese A-share listed companies from 2012 to 2022. This research empirically investigates the impact of AI technology on the level of enterprise value creation. The findings reveal that the application of AI technology by listed companies significantly enhances their value creation, a conclusion that remains robust following various endogeneity and robustness tests. AI technology empowers enterprise value creation through the mechanisms of sensing, integrating, and restructuring digital agility. Furthermore, the value-creation effect of AI technology is more significant in manufacturing and the eastern region. The results elucidate the logic behind the influence of AI technology on enterprise value creation and provide practical support for organisations in implementing AI strategic planning and decision-making.

Toward industry 5.0: Challenges and enablers of intelligent manufacturing technology implementation under the perspective of sustainability

The advancement of intelligent manufacturing technology in the era of Industry 5.0 has propelled the intelligence and automation of manufacturing production, while also exerting a significant impact on sustainable development of the manufacturing industry. However, the challenges and enablers faced by the transformation of intelligent manufacturing technology in the context of sustainable development of Industry 5.0 are still unclear. Based on literature review and expert opinions, this study uses the Likert scale to determine the challenges and enablers of the implementation of intelligent manufacturing technology in social, environmental and economic sustainability. The fuzzy-DEMETAL and AISM are used to analyze the logical relationship and hierarchical relationship between the above factors, and the MICMAC matrix is used to determine the key influencing factors. The research conclusions show that the most important challenges affecting the implementation of intelligent manufacturing technology are cost and funding, and the most important enabler is social benefits and public service improved. This research will provide insights for industry practitioners and decision makers in the management and decision-making process of implementing the transformation and upgrading of manufacturing intelligent manufacturing, thereby enhancing the sustainability of manufacturing development.

Machine Learning for Additive Manufacturing of Functionally Graded Materials.

Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.

Assessing the integration of AI competencies in undergraduate public administration curricula in selected South African higher education institutions

There is an increasing call to include Artificial Intelligence (AI) competencies in academic disciplines such as Public Administration, which are not obviously related to Science, Technology, Engineering and Mathematics (STEM). However, the literature on the integration of AI in non-STEM curricula in South African higher education institutions (HEIs), especially in Public Administration curricula, is limited. To address this lacuna, this research assessed the integration of AI competencies in undergraduate Bachelor of Administration (BAdmin) curricula in three HEIs randomly selected from the six offering such a programme. The chosen qualitative research approach focused on human and technical competencies. The findings show that AI competencies are not adequately integrated into the Public Administration curricula of the assessed HEIs in the form of core modules supporting AI competencies. These competencies are only loosely supported by elective (optional) modules. This general AI competency gap in the Public Administration curricula of the assessed South African HEIs must be bridged to achieve a digital public sector by introducing focused AI competency training into the BAdmin curriculum. This will help to create a public sector workforce equipped to handle the opportunities and challenges presented by AI, and maximise the potential of this transformative technology.

Millimeter wave–3D massive MIMO: Deep prior‐aided graph neural network combining with hierarchical residual learning for beamspace channel estimation

SummaryMillimeter Wave (mmWave) communication has emerged as a transformative technology at the forefront of wireless communication. One of the key challenges in harnessing the potential of mmWave technology is overcoming the increased susceptibility to propagation losses and environmental obstacles. To address these challenges, Three‐Dimensional Massive Multiple‐Input Multiple‐Output (3D Massive MIMO) systems have gained traction. The 3D aspect extends this concept by considering the elevation dimension, allowing for enhanced spatial resolution and coverage. Accurate estimation of the channel in 3D Massive MIMO scenarios is particularly challenging because of the complex propagation characteristics of mmWave signals. This paper introduces an efficient‐Aided Graph Neural Network Combining with Hierarchical Residual Learning (DPrGNN‐HrResNetL), designed specifically for beamspace Channel Estimation (CE)in mmWave‐Massive MIMO environments. The proposed model leverages deep priors and GNN mechanisms to enhance the extraction of spatial features, while hierarchical residual connections facilitate effective information flow through the network. DPrGNN enables the model to capture and understand complex spatial relationships among different antenna elements. The incorporation of deep priors provides a mechanism for leveraging prior knowledge about channel characteristics. This enhances the efficiency of the learning process, allowing the model to learn and adapt more effectively. The integration of hierarchical residual connections facilitates effective information flow through the network. This is particularly important for modeling complex dependencies within the beamspace channel data, enhancing the learning capacity of the network. The performance of the DPrGNN‐HrResNetL model is evaluated across a range of Signal‐to‐Noise Ratios (SNRs), utilizing metrics such as Normalized Mean Squared Error (NMSE) to measure the accuracy of the estimation. The outcomes underscore the resilience and efficacy of the DPrGNN‐HrResNetL approach in achieving precise CE within demanding mmWave scenarios.

Robotic Process Automation in Banking for Better Customer Experience

Robotic Process Automation (RPA) has emerged as a transformative technology in the banking sector, offering opportunities to streamline operations, reduce costs, and enhance customer experiences. This explores the impact of RPA implementation on customer experience within the banking industry. By automating repetitive, rule-based tasks such as account maintenance, loan processing, and customer inquiries, banks can significantly improve operational efficiency and accuracy while freeing up human resources to focus on higher-value activities. Furthermore, we discuss the challenges and considerations associated with RPA adoption in banking, including data security, regulatory compliance, and employee upskilling. Ultimately, this underscores the pivotal role of RPA in reshaping the banking landscape, driving innovation, and delivering superior customer experiences in an increasingly digital world.

Immersive learning platforms: analyzing virtual reality contribution to competence development in higher education—a systematic literature review

BackgroundThis study evaluates the effectiveness and pedagogical integration of Virtual Reality (VR), exploring its application in various educational contexts. In this systematic literature review, the role of virtual reality in enhancing competency development within higher education is examined.MethodsThe search was conducted in two databases (Scopus and Web of Science) following the PRISMA method guidelines. Inclusion criteria were limited to studies that used virtual reality as a tool within the classroom, analyzing the competencies developed through its application.ResultsOut of the 1,671 articles retrieved, 61 full texts were selected for review, resulting in 27 academic articles published in the last five years. The findings highlight the capacity of virtual reality to foster interpersonal skills while simultaneously addressing the challenges of its integration.DiscussionThe adoption of Virtual Reality (VR) in higher education is notable for its immersive learning experiences. Despite VR’s significant contribution to education, its widespread integration faces challenges, including the high costs of VR technology and the lack of specialized educational software, which limits its accessibility across various academic disciplines.ConclusionThe advent of global technological advancements has unveiled numerous opportunities within the educational sector, with VR emerging as a transformative technology that offers immersive learning experiences, propelling educational methodologies beyond traditional boundaries. Through this SLR, it becomes evident that the application of VR in education transcends mere knowledge transfer, facilitating the development of critical competencies.Systematic review registrationhttp://bit.ly/3SyKPhv.

Creating Organizational Resilience through Digital Transformation and Dynamic Capabilities: Findings from fs/QCA Analysis on the Example of Polish CHP Plants

Digital transformation, organizational resilience, and agility are now becoming key to meeting the competitive challenges of modern organizations. It is no surprise that digital transformation and digital technologies have also begun to significantly impact the energy industry, moving towards improving the sector’s profitability and efficiency. However, to move the difficult process of digital transformation in today’s dynamically changing environment, organizations, including those in the energy sector, need to build organizational resilience. Nevertheless, the relationship between digital transformation and organizational resilience has not yet been explained in a satisfactory and sufficient manner. Focusing on the level of digital transformation, and more precisely within the two dimensions of digital maturity, i.e., digital intensity and transformation management intensity, as well as based on the perspective of dynamic capabilities, this study developed a configurational framework and proposed a theoretical model to study the equifinal paths through which digital transformation and dynamic capabilities influence organizational resilience in energy sector companies. Based on a fuzzy set qualitative comparative analysis (fs/QCA) conducted on selected companies in the energy sector, i.e., Polish CHP plants, the relationship among digital transformation, dynamic capabilities, and organizational resilience was investigated. The results show that a high level of organizational resilience is possible to achieve through two main paths based on the dominance of dynamic capabilities and the dominance of digital maturity. The results show that a high level of organizational resilience is possible to achieve through two main paths based on the dominance of dynamic capabilities and the dominance of digital maturity. The study found that digital maturity can significantly influence CHP resilience. Moreover, the transformation management intensity is strongly related to high organizational resilience. The paper concludes by describing theoretical and practical implications, as well as research limitations and prospects for future research.

Evolution of Industrial Robots from the Perspective of the Metaverse: Integration of Virtual and Physical Realities and Human–Robot Collaboration

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the limitations of traditional industrial operations. This paper explores the application and evolution of metaverse technology in the field of industrial robotics, focusing on the realization of virtual–real integration and human–machine collaboration. It proposes a design framework for a virtual–real interaction system based on the ROS and WEB technologies, supporting robot connectivity, posture display, coordinate axis conversion, and cross-platform multi-robot loading. This paper emphasizes the study of two key technologies for the system: virtual–real model communication and virtual–real model transformation. A general communication mechanism is designed and implemented based on the ROS, using the ROS topic subscription to achieve connection and real-time data communication between physical robots and virtual models, and utilizing URDF model transformation technology for model invocation and display. Compared with traditional simulation software, i.e., KUKA Sim PRO (version 1.1) and RobotStudio (version 6.08), the system improves model loading by 45.58% and 24.72%, and the drive response by 41.50% and 28.75%. This system not only supports virtual simulation and training but also enables the operation of physical industrial robots, provides persistent data storage, and supports action reproduction and offline data analysis and decision making.

A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation

This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and motor systems. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly technological support and robotic prosthetics powered by brain activity. This review critically examines the latest strides in BCI technology and its application in motor skill recovery. Special attention is given to prevalent EEG devices adaptable for BCI-driven rehabilitation. The study surveys significant contributions in the realm of machine learning-based and deep learning-based rehabilitation evaluation. The integration of BCI with EEG technology demonstrates promising outcomes for enhancing motor skills in rehabilitation. The study identifies key EEG devices suitable for BCI applications, discusses advancements in machine learning approaches for rehabilitation assessment, and highlights the emergence of novel robotic prosthetics powered by brain activity. Furthermore, it showcases successful case studies illustrating the practical implementation of BCI-driven rehabilitation techniques and their positive impact on diverse patient populations. This review serves as a cornerstone for informed decision-making in the field of BCI technology for rehabilitation. The results highlight BCI’s diverse advantages, enhancing motor control and robotic integration. The findings highlight the potential of BCI in reshaping rehabilitation practices and offer insights and recommendations for future research directions. This study contributes significantly to the ongoing transformation of BCI technology, particularly through the utilization of EEG equipment, providing a roadmap for researchers in this dynamic domain.

A two-level decision-support framework for reverse logistics network design considering technology transformation in Industry 4.0: a case study in Norway

Reverse logistics network design is a complex decision-making problem that involves the reuse, repair, remanufacturing, and recycling of end-of-life (EOL) under the tradeoff among conflicting objectives. The cutting-edge technologies in Industry 4.0 are now leading to an unprecedented and dynamic transformation of reverse logistics systems, which, however, further complicates the initial network design. In this paper, a two-level decision-support framework combined with both optimization and dynamic simulation is proposed to balance the cost, environmental impact, and service level in smart and sustainable reverse logistics network design under a dynamically evolving and stochastic environment. The results of a real-world case study in Norway show that the method can better support robust strategic decisions, eliminate dominated/near-dominated solutions, and yield holistic performance analyses considering smart reverse logistics transformation. The proposed two-level decision-support framework can better analyze the impact of the technology transformation of Industry 4.0 on reverse logistics systems, while it also provides a fundamental structure for digital reverse logistics twin.

An Effective Scheme for Modeling and Compensating Differential Age Errors in Real-Time Kinematic Positioning

In many real-time kinematic (RTK) positioning applications, reference observations are transmitted over wireless links that can experience frequent interruptions or substantial delays. This results in large differential ages between base and rover observations, which, in turn, leads to a deterioration in positioning performance. To bridge the significant age difference, in this work, we propose a simple and effective scheme for modeling and compensating for such errors. Firstly, the overall differential age error was modeled using truncated Taylor expansion. Then, a time-differenced carrier phase (TDCP)-based observation model was established to estimate the errors with the Kalman framework. Since estimating the receiver’s clock error is unnecessary, equivalent transformation and sequential filtering technology were adopted to significantly reduce the computational complexity. Furthermore, a predictor performance monitor was introduced to mitigate the integrity risks that may occur due to model mismatches. The effectiveness of this scheme was validated by static and dynamic field experiments. The static experiment results showed that when the differential age was 60 s, the GPS and BDS satellites’ overall root mean square error (RMSE) with the asynchronous RTK (ARTK) prediction method was 2.8 and 5.5 times that of the proposed method, respectively. Moreover, when the differential age was 120 s, these values were 3.3 and 5.4 times that of the proposed method, respectively. The field experiment results showed that when the differential age was 60 s, the integer ambiguity fixed rate and false fixed rate of the ARTK method were 0.90 and 1.63 times that of the proposed method, respectively. Finally, at a 120 s differential age, these values were 0.78 and 4.78 times that of the proposed, respectively.

Vision transformer-enhanced thermal anomaly detection in building facades through fusion of thermal and visible imagery

ABSTRACT The thermal anomaly detection of building facades is critically important for the evaluation and upkeep of structures. While conventional methods are effective in numerous instances, their capability to discern flaws in complex environments is constrained. This research employs an optimized Vision Transformer (ViT) technology, enhanced by the Line Acquisition, Filtering, and Revision (LAFR) algorithm. By amalgamating the distinctive advantages of ViT’s attention-based mechanisms with the complementary data from thermal and visible imagery, this approach significantly bolsters the detection of thermal anomalies on building facades. The experimental outcomes reveal that our methodology, with an F1 score of 0.847, markedly surpasses traditional thermal imaging (F1 score of 0.466), showcasing its potential for precise anomaly detection in architectural contexts. This validates the method as a more robust and efficient solution for structural evaluation, promising to elevate the quality of architectural inspections and maintenance.

Aprendizagem Baseada em Projetos e Problemas (ABP): Uma Abordagem para o Desenvolvimento de Competências no Século XXI

This article analyzes Problem-Based Learning (PBL) as an innovative approach to developing skills for the 21st century. PBL, by breaking away from the traditional teaching model, places the student at the center of the learning process, stimulating autonomy, critical thinking, creativity, and collaboration. The study discusses the definition, characteristics, and history of PBL, highlighting its relevance in the current context of rapid technological and social transformations. In addition, the article explores the skills of the 21st century, classifying them as cognitive, socio-emotional, and technological, and emphasizing their importance for academic and professional success and for the exercise of citizenship. The relationship between PBL and the development of these skills is analyzed based on studies and evidence, demonstrating the potential of PBL to prepare students for the challenges of the future.

Prospects for improving the tools of the tax ecosystem in the context of systemic and technological transformations

Prospects for improving the tools of the tax ecosystem in the context of systemic and technological transformations

Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

Wireless Body Area Networks (WBANs) have emerged as a transformative technology for health monitoring, leveraging sensor nodes placed on or within the human body. This paradigm offers real-time insights into physiological parameters, enabling remote healthcare applications and enhancing patient well-being. However, the WBANs bring challenges that demand innovative solutions to ensure their seamless integration into healthcare ecosystems. Secondly, trustworthiness becomes paramount in healthcare applications, where the sensitivity of health-related data necessitates secure and reliable communication. Lastly, congestion issues, arising from bandwidth constraints and concurrent data transmissions, affect the performance of WBANs, impacting data integrity, energy consumption, and quality of service. Therefore, this paper proposes a TESLDAR protocol that incorporates the proposed Jellyfish Updated Aquila Narrow ExploExpand (JUANE) method aims to address issues such as hotspot-induced thermal stress, trustworthiness concerns, and congestion challenges. The proposed algorithm incorporates Jellyfish Optimization (JFO), inspired by the adaptable movement of jellyfish, with characteristics inspired by the Aquila bird. The method's innovative integration of jellyfish and bird-inspired behaviors positions it as a promising solution for efficient route maintenance in WBANs addressing thermal, trust, and congestion challenges for enhanced reliability in healthcare monitoring applications.

An imbalanced learning method based on graph tran-smote for fraud detection

Fraud seriously threatens individual interests and social stability, so fraud detection has attracted much attention in recent years. In scenarios such as social media, fraudsters typically hide among numerous benign users, constituting only a small minority and often forming “small gangs”. Due to the scarcity of fraudsters, the conventional graph neural network might overlook or obscure critical fraud information, leading to insufficient representation of fraud characteristics. To address these issues, the tran-smote on graphs (GTS) method for fraud detection is proposed by this study. Structural features of each type of node are deeply mined using a subgraph neural network extractor, these features are integrated with attribute features using transformer technology, and the node’s information representation is enriched, thereby addressing the issue of inadequate feature representation. Additionally, this approach involves setting a feature embedding space to generate new nodes representing minority classes, and an edge generator is used to provide relevant connection information for these new nodes, alleviating the class imbalance problem. The results from experiments on two real datasets demonstrate that the proposed GTS, performs better than the current state-of-the-art baseline.

Unleashing the Potential of Artificial Intelligence: Advancements, Applications, and Ethical Considerations

Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various sectors of society. This qualitative study explores the advancements, applications, and ethical considerations surrounding the use of AI. Through in-depth interviews, focus group discussions, and document analysis, the research investigates the latest developments in AI technology, including machine learning, natural language processing, and computer vision. The study examines the diverse applications of AI across industries such as healthcare, finance, transportation, and education, highlighting its role in streamlining processes, enhancing decision-making, and driving innovation. Furthermore, the research delves into the ethical implications of AI adoption, addressing concerns related to privacy, bias, transparency, and accountability. By engaging with stakeholders from academia, industry, and civil society, the study explores perspectives on responsible AI development and governance frameworks. It also discusses initiatives aimed at promoting ethical AI practices, such as fairness, accountability, and transparency in algorithmic decision-making. The findings underscore the need for a balanced approach to AI deployment that maximizes its benefits while mitigating potential risks and societal harms. Key themes identified include the importance of interdisciplinary collaboration, regulatory oversight, and public engagement in shaping the future trajectory of AI. By unpacking the complexities of AI advancements, applications, and ethical considerations, this study contributes to the ongoing discourse on harnessing AI's potential for the benefit of humanity.

Digital transformation as a factor in reducing labor shortages in Russian agriculture

This article examines the dynamics of the number of employees of agricultural organizations, the dynamics of energy availability, electrical equipment, the level of mechanization and technical equipment as key conditions for the introduction of digital solutions and the reduction of labor shortages in the industry. In addition, an analysis of the dynamics of labor costs in the sectoral and product context was carried out, which allows us to identify the most labor-intensive and requiring the introduction of digital solutions in the agricultural sub-sector. The key digital technologies that contribute to reducing the shortage of labor resources have been studied. A factorial regression model is proposed that allows us to study the impact of digital transformation on reducing the labor costs of producing a unit of production. The results of the study allowed us to propose an approach to assessing the impact of the digital transformation of agriculture on labor costs and reducing the shortage of labor resources of an industry or an individual enterprise (group of enterprises), consisting in analyzing the dynamics of the need for workers, the conditions of technical equipment of production, labor costs and approximation based on a regression model of the impact of digital transformation technologies on labor cost reduction.

A Systematic Review on the Generation of Organic Structures through Additive Manufacturing Techniques.

Additive manufacturing (AM) has emerged as a transformative technology in the fabrication of intricate structures, offering unparalleled adaptability in crafting complex geometries. Particularly noteworthy is its burgeoning significance within the realm of medical prosthetics, owing to its capacity to seamlessly replicate anatomical forms utilizing biocompatible materials. Notably, the fabrication of porous architectures stands as a cornerstone in orthopaedic prosthetic development and bone tissue engineering. Porous constructs crafted via AM exhibit meticulously adjustable pore dimensions, shapes, and porosity levels, thus rendering AM indispensable in their production. This systematic review ventures to furnish a comprehensive examination of extant research endeavours centred on the generation of porous scaffolds through additive manufacturing modalities. Its primary aim is to delineate variances among distinct techniques, materials, and structural typologies employed, with the overarching objective of scrutinizing the cutting-edge methodologies in engineering self-supported stochastic printable porous frameworks via AM, specifically for bone scaffold fabrication. Findings show that most of the structures analysed correspond to lattice structures. However, there is a strong tendency to use organic structures generated by mathematical models and printed using powder bed fusion techniques. However, no work has been found that proposes a self-supporting design for organic structures.