Intelligent Transformation Research Articles

Accelerating smart manufacturing technology adoption: an interdependency perspective on technology adoption

ABSTRACT Adopting smart manufacturing technology (SMT) is critical for the intelligent transformation of manufacturing enterprises. However, an inappropriate SMT adoption (SMTA) path will lead to negligible economic benefits, which seriously restricts intelligent transformation. Using a combined network method based on the Grey-DEMATEL and social network analysis, this paper considers SMTA as a system and identifies the importance of individual SMTA. Firstly, the SMTA network is constructed by identifying 16 SMTs and calculating the interdependencies among individual SMTA. The results show that the SMTA system has a relatively high level of interdependency. The important rankings of SMTs are obtained by single and combined indicators. The roles of SMTA are divided into amplifier, absorber, carrier and neutral. Amplifiers play an important role in the adoption of other SMTs and the paths that successively pass through amplifiers can accelerate the whole SMTA. This paper provides a valuable contribution to SMTA theory from a technological interdependency perspective. This new perspective provides a foundation for designing SMTA paths for manufacturing enterprises and entire industries.

Energy Security, Intelligence and Innovation Quality: Evidence From China's New Energy Demonstration City Policy

ABSTRACTThe development of new energy and the application of intelligent technologies are crucial for ensuring energy security and promoting high‐quality economic growth. This article utilises a sample of A‐share listed enterprises in China from 2008 to 2020, employing a difference‐in‐differences (DID) approach to explore the impact of new energy demonstration cities (NEDC) on innovation quality and focuses on the role of intelligent development. It is found that the construction of NEDC contributes to the improvement of enterprise innovation quality. Corporate intelligence transformation further enhances the effect of the NEDC policy, demonstrating a synergistic relationship with new energy transition. Additionally, compared with collaborative innovation, the improvement effect of NEDC policy on enterprise self‐innovation quality is more significant. Non‐state‐owned enterprises, those in the growth stage, with a solid technological foundation and located in non‐resource‐based cities are more likely to achieve improvements in innovation quality during the energy transition. Our work provides empirical evidence and valuable insights for ensuring energy security and facilitating intelligent innovation development.

Digital and AI transformation in the contemporary art industry in China

This study examines the digital and artificial intelligence (AI) transformation in the contemporary art industry in China. This industry in China is undergoing a distinct digital transition and is “ahead” of other countries, having fully integrated digital technologies and AI (digiAI) into policies, regulations, organizations, and professional practices. A systematic, large-scale national integration of digiAI has led to its widespread adoption by artists and arts professionals. However, little is known about how or when this rapid and extensive integration and subsequent adoption occurred or about its impacts on professional practices. This study draws on research conducted between 2023 and 2024, including 30 interviews with contemporary Chinese visual artists, 23 interviews with arts professionals, a survey of 110 professional contemporary visual artists, and a systematic review of government policy. Findings indicate that the government began integrating digital technology into the contemporary art industry in 2016, further promoted digital technology integration in 2021, and introduced regulations to support AI usage in 2023. The data reveal a significant spike in the adoption of digital technologies by professionals between 2019 and 2020, followed by a rise in AI adoption in 2023. DigiAI has been accepted and now used across different kinds of arts professions, various types of visual artists, and several age groups. Digital and AI tools are now being applied in both creative and non-creative aspects of arts practices.

Analysis of the Application and Prospects of Intelligent Technology in Sustainable Railway Operations

With the rapid development of technology, intelligent technology plays an increasingly important role in the sustainable operation of railways. This paper explores the application and prospects of intelligent technology in sustainable railway operations, emphasizing its key roles in enhancing operational efficiency, strengthening safety assurance, optimizing passenger experience, and promoting environmental protection and energy conservation. Through intelligent dispatching, real-time monitoring, big data prediction, personalized services, and mobile ticketing technologies, the railway system achieves efficient resource allocation and service innovation. The intelligent transformation of railways demonstrates significant economic and social benefits, contributing to emission reduction targets and sustainable development. Future efforts should focus on strengthening top-level design, technological innovation, standard construction, and network security to advance the intelligent railway process and co-create a new era of smart, green, and efficient railway transportation.

Research on the Path to High-Quality Development of Grassroots Libraries in the Era of Artificial Intelligence in Henan

Since the age of digital intelligence, the government has introduced policies to actively promote the intelligent transformation of grassroots public cultural services. Based on the investigation and analysis of the development status quo of grassroots libraries in Henan Province, we put forward the path of high-quality development of grassroots libraries in Henan, namely, improving the mechanism and system, leading with digital intelligence technology, empowering by Yellow River culture (the heritage and values rooted in the history and traditions of the Yellow River region), and driven by users’ demand.

Sustainable Supply Chain Management and Green Logistics Practice Analysis: Case Study of Wahaha and Patagonia

In globalization and digitization, supply chains have become key for companies to gain competitive advantages and achieve sustainable development. This study provides a detailed analysis of the sustainable supply chain management practices of China's leading food and beverage company, Wahaha, and the American outdoor brand, Patagonia. It explores their successes in green production, logistics, packaging, and environmental governance. The research finds that Wahaha has achieved efficient resource utilization and cost reduction through digital and intelligent transformation, enhanced recycling, and the use of clean energy. On the other hand, Patagonia maintains a strong environmental commitment by using organic cotton and recycled materials, supporting environmental projects, and promoting a green supply chain. Despite their different strategies, both companies have achieved significant sustainable supply chain management results, offering valuable insights for other businesses. Future research should include different regions and industries, explore the challenges and solutions in sustainable development, and optimize evaluation systems through quantitative analysis and model building to enhance practical application value.

The Levels of Government Environmental Attention and Enterprises’ Green Technological Innovation

This study utilizes data from listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2023 and employs text analysis methods to investigate the impact of government environmental concerns on corporate green technology innovation and its underlying impact mechanisms. The empirical findings demonstrate that heightened government environmental concerns significantly foster corporate green technology innovation. This relationship remains robust across a series of sensitivity analyses. The impact mechanism analysis reveals that increased government environmental concern leads to a heightened intensity of environmental regulation, drives the intelligent transformation of enterprises, and enhances the implementation of corporate environmental strategies, collectively contributing to the improvement of corporate green technology innovation capabilities. Furthermore, the heterogeneity analysis indicates that the effect of government environmental concerns is more pronounced in state-owned enterprises, mature enterprises, and industries with lower pollution levels. This study underscores that the government’s intensified focus on environmental issues can effectively promote corporate green technology innovation, thereby promoting sustainable economic and social development and supporting the dual goals of environmental protection and economic growth.

When knowledge workers meet AI? The double-edged sword effects of AI adoption on innovative work behavior

Purpose The purpose of this study was to investigate the impact of artificial intelligence (AI) adoption on knowledge workers' innovative work behaviors (IWB), as well as the mediating role of stress appraisal and the moderating role of individual learning abilities. Design/methodology/approach This study analyzed the questionnaire results of 313 knowledge workers, and data analysis was conducted by using SPSS 25.0, SPSS 25.0 macro-PROCESS and AMOS 28.0. Findings This study found that AI adoption has a double-edged sword effect on knowledge workers' IWB. Specifically, AI adoption can promote IWB by enhancing knowledge workers' challenging stress appraisal, while inhibiting IWB by fostering their hindering stress appraisal. Moreover, individual learning ability significantly moderated the relationship between AI adoption and stress appraisal, which further influenced IWB. Originality/value This study integrates the conflicting findings of previous studies and proposes a comprehensive theoretical model based on the theory of cognitive appraisal of stress. This study enriches the research on AI in the field of knowledge management, especially extending the understanding of the relationship between AI adoption and knowledge workers’ IWB by unraveling the psychological mechanisms and behavior outcomes of users' technology usage. Additionally, we provide new insights and suggestions for organizations to seek the cooperation and support of employees in introducing new technologies or driving intelligent transformation.

Improving port state control through a transfer learning-enhanced XGBoost model

With the advancements in modern information technology, Port State Control (PSC) inspections, as a crucial measure to protect ship safety and the marine environment, are undergoing an intelligent transformation. This paper aims to streamline the selection process for inspecting high-risk ships by employing a data-driven model to predict the number of deficiencies in ships arriving at ports. A transfer learning-enhanced eXtreme Gradient Boosting (XGBoost) model is proposed by innovatively incorporating sample similarity calculations to adapt the model to the unique characteristics of the target port. This novel model enables the integration of relevant data from other ports, enhancing the predictive performance of the model to specific port conditions. Utilizing PSC inspection records from ports within the Tokyo Memorandum of Understanding (MoU) and choosing the port of Singapore as the target, numerical experiments demonstrate that the proposed model achieves improvements of 1.81 %, 6.08 %, and 3.60 % in the mean absolute error, mean squared error and root mean squared error, respectively, compared to the standard XGBoost model. Furthermore, across various sizes of training samples, the proposed model outperforms other machine learning models. This work may service as a significant step towards exploring the potential of developing data-driven models based on comprehensive data to assess the risk level of foreign ships arriving at ports, ameliorating the PSC inspection process by aiding PSC officers in identifying substandard ships more effectively.

Application and practice of industrial IoT cloud platform in oilfield intelligent transformation

Abstract Under the context of global energy transformation and low-carbon development, the traditional petroleum industry is facing a series of challenges such as technological innovation, market competition, environment protection, etc. Therefore, there is an urgent demand to realize green, safe, economical, and efficient development of oil and gas through oilfield intelligent transformation. This study develops a novel cloud platform covering the whole business based on the technical architecture of the industrial Internet of Things (IoT) for assisting the intelligent transformation of oilfields. Since the application of the IoT-based cloud platform in Qingcheng Oilfield, functions such as automatic collection of whole-domain data, remote monitoring, early warning, as well as intelligent analysis and decision-making have been realized. Furthermore, compared with the traditional platform, the new platform has improved eight indicators, such as data processing efficiency and sharing capacity, by more than 20%. Consequently, the developed IoT-based cloud platform can significantly improve the production and management level of the oilfield, and efficiently support oilfield intelligent transformation.

Damage Prediction Effect of Reinforced Concrete Column and Beam Structure Improved by Multimedia Technology

In order to improve the damage prediction effect of concrete structural column and beam structure, this paper uses multimedia technology to improve the damage prediction effect of reinforced concrete structural column and beam structure. Moreover, this paper presents the intelligent transformation model and the corresponding solution of the general damage problem, and deduces the error relationship between the result estimator and the importance function according to the error of the importance function. In addition, by analyzing the relationship between the importance function and the dual transport calculation, this paper proposes a complementary dual calculation process that can provide the importance function to each other, and builds an intelligent prediction model. Through the experimental research, it can be seen that the multimedia technology algorithm proposed in this paper can play an important role in the damage prediction of concrete structural columns and beams.

Papermaking-inspired sustainable triboelectric sensors for intelligent detecting system

Digitalization not only drives the intelligent transformation of industries while fostering cultural diversity and innovation. Cutting-edge triboelectric sensing technology inspired by traditional Chinese papermaking fabricates a bamboo paper-based triboelectric nanogenerator (BP-TENG). The addition of high-purity bacterial cellulose improves the structural integrity of the resulting paper. The proposed BP-TENG exhibits excellent electrical performance, generating an output voltage of 88 V and a maximum output power of 0.9 mW at a low-frequency motion of 2 Hz. Moreover, the BP-TENG is combined with machine learning algorithms to build a multi-scene intelligent detecting system, including real-time handwriting-to-speech conversion, personalized jump prediction and smart home automation systems. The machine learning algorithm can achieve a recognition accuracy of up to 99.8 %. This multi-scene intelligent detecting system boasts superior accuracy, environmental sustainability and affordability, ideal for communication security, sports health monitoring and smart building applications.

Construction and Practice of Education and Teaching Quality Assurance Systems in Applied Colleges and Universities under the Background of Digital Intelligence

This paper discusses the optimization strategy of education and teaching quality assurance systems in applied colleges and universities under the background of digital intelligence. It first summarizes the relevant theories of digital intelligence transformation and analyzes the impact of digital intelligence transformation on higher education. Secondly, this paper puts forward the principles of constructing the quality assurance system of applied colleges, including strengthening the quality assurance consciousness, improving teachers’ digital literacy, and implementing digital intelligence governance. From the practical perspective, this paper expounds on strategies such as optimizing educational teaching resource allocation, constructing a diversified evaluation system of teaching quality, strengthening the construction and training of teaching staff, and innovating teaching management methods. Specific optimization measures are put forward, such as improving policies, regulations, and system guarantees, strengthening cooperation between schools and enterprises, integrating industry, school, and research, building an educational information platform, and improving the monitoring and feedback mechanism of educational quality.

Energy system optimization based on fuzzy decision support system and unstructured data

To address the complex challenges in energy systems, this study proposes a novel optimization framework that integrates fuzzy decision support and unstructured data processing technologies. This framework aims to improve efficiency, reduce costs, decrease environmental impact, increase system flexibility, and enhance user satisfaction, thereby promoting sustainable development in the energy industry. The framework combines the innovative Energy Semantic Mapping Model (ESMM) and the advanced deep learning architecture ResNet to process textual and visual data effectively. ESMM enables accurate prediction of energy demand, while ResNet significantly reduces equipment maintenance costs and improves energy distribution efficiency. These advancements are critical as they address the limitations of existing approaches in handling large-scale unstructured data and making informed decisions under uncertainty. The Environmental Impact Assessment (EIA) confirms the model's effectiveness in reducing carbon emissions. A comprehensive economic analysis demonstrates substantial cost savings in energy procurement and operations and maintenance, with overall savings exceeding 25%. Enhanced user satisfaction and reduced system response times further validate the practical utility of the proposed approach. Additionally, a genetic algorithm is used to optimize the fuzzy rule base, enhancing the robustness and adaptability of the model. Experimental results show superior performance compared to traditional systems, providing strong empirical evidence for the intelligent transformation of energy systems. This research contributes to the field by offering a more sophisticated and flexible solution for managing energy systems, particularly in terms of leveraging unstructured data and improving decision-making processes.

Prospective Analysis and Innovative Path Research on Silver Sports Economy in the Context of Aging Society

This study focuses on the prospects and innovative pathways for the development of the silver-haired sports economy in the context of an ageing society. The proportion of the global population over 65 years old is expected to increase from 9% in 2019 to 16% in 2050, highlighting the huge potential of the silver-hair economy. This paper adopts a mixed research methodology, combining spatial econometric modelling and rootedness theory, to explore in depth the current development status, trend forecasts and innovation paths of the silver-haired sports economy. The study constructs a silver-hair sports economy assessment system containing 22 indicators, and uses panel data to analyse the development level of 31 provinces, municipalities and autonomous regions in China from 2010 to 2023, and the results show that the average annual growth rate of silver-hair sports economy reaches 15.7%, but the gap between Beijing, Tianjin, Shanghai and Guangdong and the central and western regions reaches 1.8 times. Analyses based on the Spatial Durbin Model show that every 1 percentage point increase in silver-haired sports consumption can drive 0.3 percentage points of GDP growth in neighbouring regions. Through a Delphi method research of 150 industry experts, it is predicted that the size of China's silver-haired sports market will exceed RMB 3 trillion in 2030, increasing its share of GDP to 2%. The qualitative study reveals three innovative paths: intelligent transformation, ecosystem construction, and cross-border integration, and proposes specific strategies such as "integration of sports and medicine" and "social platform for silver sports". The results of the study provide data support and theoretical basis for the formulation of policies for the development of the silver sports economy, and provide important insights into the industrial transformation of an aging society.

Solid-State Fermentation Engineering of Traditional Chinese Fermented Food.

Solid-state fermentation (SSF) system involves solid, liquid, and gas phases, characterized by complex mass and heat transfer mechanisms and microbial complex interactions. The SSF processes for traditional Chinese fermented foods, such as vinegar, soy sauce, and baijiu primarily rely on experience, and most of the operations are replaced by auto machine now. However, there is still a lack of engineering in-depth study of the microbial process of SSF for complete process control. To meet the demands of smart manufacturing and green production, this paper emphasizes the engineering analysis of the mechanisms behind SSF. It reviews the progress in the engineering aspects of Chinese traditional SSF, including raw material pretreatment, process parameter detection, mathematical model construction, and equipment innovation. Additionally, it summarizes the challenges faced during intelligent upgrades and the opportunities brought by scientific and technological advancements, proposing future development directions. This review provides an overview of the SSF engineering aspects, offering a reference for the intelligent transformation and sustainable development of the Chinese traditional SSF food industry.

Research on the technology path selection of China's manufacturing industry under the high-quality development strategy

As the foundation and leading force of national economic development, manufacturing determines a country's comprehensive strength and international competitiveness, and is the key to achieving transformation, upgrading and high-quality development. Based on the perspective of global value chain embedding, this paper explores the issue of technology path selection and high-end development in my country's manufacturing industry. Against the background of Internet development, this paper analyzes the basis for technology path selection in my country's manufacturing industry to achieve intelligent transformation and high-quality development. By constructing an analytical framework for technology path selection from two aspects of internal and external factors, the theoretical basis for technology path selection in my country is proposed. Guided by high-quality development, this paper studies the internal mechanism of technology path selection that can promote the high-end, intelligent, service-oriented and green development of manufacturing industry, and discusses the technology path selection for manufacturing industry to achieve green and sustainable development. Based on an in-depth analysis of the impact and mechanism of global value chain embedding, Internet development, productive services and environmental constraints on technology path selection, this paper proposes the optimal decision equation for R&D resource allocation in my country based on factor endowment conditions, thereby promoting the transformation of manufacturing industry from factor-driven to innovation-driven, accelerating the process of achieving high-end, intelligent, service-oriented and green development, which is of great significance to promoting the high-quality development of manufacturing industry.

Design and Optimization of Key Parameters for a Machine Vision-Based Walnut Shell–Kernel Separation Device

The separation of walnut kernels and shells has long been regarded as a bottleneck, limiting processing efficiency, product quality, and industry advancement. In response to the challenges of improving separation accuracy and the inadequacy of existing equipment for meeting industry demands, this paper proposes an innovative walnut shell–kernel separation device based on machine vision technology. An experimental system was constructed, and key parameters were optimized to enhance its performance. The device comprises five main modules: material conveyance, image acquisition, control module, sorting module, and frame. Differential separation technology is used to convert the walnut material group into a stable particle flow, addressing the issue of missed selections due to material blockages. An enhanced YOLOv8n algorithm improves small object detection and interference resistance, enabling accurate identification of walnut kernels. The Box–Behnken Design and Artificial Neural Network prediction model was used to determine the optimal operating parameters for the device. Experimental results showed that effective differential separation was achieved when the dual-stage conveyor system operated at speeds of 0.2 m/s and 1 m/s. The improved I-YOLOv8n algorithm reached an accuracy of 98.8%. Using the neural network model, the optimal operational parameters were determined: an air pressure of 0.72 MPa, a jetting component angle of 10.16°, and a sorting height of 105.12 cm. Under these conditions, the device achieved an actual cleaning rate of 93.56%, demonstrating outstanding separation performance. Compared to traditional separation methods, this device offers significant advantages in terms of efficiency, quality, and ease of operation, providing new technological pathways and support for the automation and intelligent transformation of the walnut processing industry. In the future, the device is expected to undergo further improvements to meet broader market demand and serve as a reference for the separation of other agricultural products.

The Impact of Job Insecurity on Miner Safety Behavior—A Study Based on SEM and fsQCA

The intelligent transformation of coal mines is one of the current trends in developing China’s coal mining industry. To explore the impact of miners’ insecurity on their safety behavior under this trend, miners’ psychological resilience was introduced as the mediating variable, and team safety climate was used as the moderating variable to conduct a questionnaire survey of frontline miners. The data analysis was carried out using descriptive statistics, correlation analysis, structural equation modeling (SEM), and the fsQCA method to explore the impact of job insecurity on miners’ risk behavior through psychological resilience from the dimensions of job loss insecurity, job performance insecurity, and interpersonal insecurity. The results show that the sense of insecurity of the miners has a significant negative correlation with security behavior and a significant negative correlation with psychological toughness; miners’ psychological resilience plays an intermediary role in the correlation between job loss insecurity and miners’ risk behavior. Meanwhile, team safety climate has a significant moderating effect on the relationship between job insecurity and psychological resilience, as well as the relationship between psychological resilience and safety behavior; that is, a good team safety climate can effectively reduce the negative impact of job insecurity brought about by the transformation and upgrading of coal mines.

Optimization strategy of property energy management based on artificial intelligence

This study focuses on the design and optimization of property energy management systems, aiming to improve energy efficiency, reduce waste, and enhance user comfort and satisfaction through intelligent means. The research background is based on the urgency of energy conservation and emission reduction, and the rise of smart property management models on a global scale, especially the increasing demand for energy efficiency monitoring, predictive analysis, automated control, and user engagement. To address the urgent need for energy conservation and emission reduction, particularly in the realm of property management, this study designed and optimized a property energy management system. The core of the research is a systematic energy management framework that encompasses efficient monitoring, intelligent predictive analytics using techniques such as Long Short-Term Memory (LSTM) networks for energy consumption forecasting, automated control, user-friendly interfaces, and system safety. An empirical case study was conducted at a large-scale commercial complex, confirming the effectiveness of the system. Through intelligent transformation, specifically the optimization of air conditioning and lighting systems using advanced technologies like frequency modulation and LED lighting, a total energy saving rate of 25% was achieved. The annual economic savings exceeded 1.25 million yuan, and user satisfaction was significantly improved. During the research process, several limitations and challenges were encountered, including data quality issues and scalability concerns. These limitations were addressed through rigorous data preprocessing and validation, ensuring the robustness of the findings and their applicability to similar environments. The results demonstrate the potential of integrating artificial intelligence and machine learning techniques into property energy management systems, paving the way for more sustainable and efficient buildings. This revised abstract includes more specific details about the technologies used, such as LSTM networks, and mentions the limitations and challenges faced during the research. It also emphasizes the practical application and scalability of the system.