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The purpose of this paper is to improve the corrosion resistance of the 6061-Al alloy as the battery pack material for electric vehicles, and the nano-SiC/MAO composite coating was prepared.

The corrosion resistance of coatings was evaluated by the global electrochemical test, and the local electrochemical impedance spectroscopy (LEIS) was used to study the local corrosion mechanism. The laser confocal microscope, scanning electron microscope and X-ray diffractometer (XRD) were used to characterise coatings.

Results showed that the impedance of nano-SiC/MAO coating was 1–2 times higher than MAO coating, and the main corrosion product was Al(OH)₃. LEIS results showed that the impedance of the nano-SiC/MAO coating was two times higher than the MAO coating. The defective SiC/Micro-arc oxidation coating still had high corrosion resistance compared to the MAO coating.

The physical model of the local corrosion mechanism for SiC/MAO composite coating in “cavity-fracture collapse” mode was proposed.

The coefficient of thermal expansion (CTE) and modulus of elasticity (ME) values of mortar and stone from room temperature to cryogenic temperatures provide an experimental basis for the design of liquefied natural gas (LNG) storage tanks.

The CTE and ME of mortar and limestone were measured by resistance strain gauge testing technology at cryogenic temperatures.

The test results showed that CTE values of mortar and stone decreased with the decrease of temperature and CTE values of mortar was greater than that of stone from 0 °C to −165 °C. The ME values of mortar increased significantly at cryogenic temperatures, and less change in stone.

The material at cryogenic temperatures may continue to work in the elastic phase due to the continuous increase of elastic modulus. Therefore, the study of material in the elastic stage may be more important than in the ultimate bearing capacity stage, and it is necessary to carry out further study surrounding the deformation properties of materials at cryogenic temperatures. The CTE and ME values of mortar and stone from room temperature to cryogenic temperatures provide an experimental basis for the design of LNG storage tanks.

Built asset management processes require a long transition period to collect, edit and update asset conditions information from existing data sets. This paper aims to explore and explain whether and how digital technologies, including asset information model (AIM), Internet of Things (IoT) and blockchain, can enhance asset conditions assessment and lead to better asset management.

Mixed methods are applied to achieve the research objective with a focus in universities. The questionnaire aims to test whether the integration of AIM, IoT and blockchain can enhance asset condition assessment (ACA). Descriptive statistical analysis was applied to the quantitative data. The mean, median, mode, standard deviation, variance, skewness and range of the data group were calculated. Semi-structured interviews were designed to answer how the integration of AIM, IoT and blockchain can enhance the ACA. Quantitative data was analysed to define and explain the essential factors for each sub-hypothesis. Meanwhile, to strengthen the evaluation of the research hypothesis, the researcher also obtained secondary data from the literature review.

The research shows that the integration of AIM, IoT and blockchain strongly influences asset conditions assessment. The integration of AIM, IoT and blockchain can improve the asset monitoring and diagnostics through its life cycle and in different aspects, including financial, physical, functional and sustainability. Moreover, the integration of AIM, IoT and blockchain can enhance cross-functional collaboration to avoid misunderstandings, various barriers and enhance trust, communication and collaboration between the team members. Finally, costs and risk could be reduced, and performance could be increased during the ACA.

The contribution of this study indicated that the integration of AIM, IoT and blockchain application in asset assessment could increase the efficiency, accuracy, stability and flexibility of asset assessment to ensure the reliability of assets and lead to a high-efficiency working environment. More importantly, a key performance indicator for ACA based on the asset information, technology and people experience could be developed gradually.

This study can break the gap between transdisciplinary knowledge to improve the integration of people, technology (AIM, IoT and blockchain) and process value-based ACA in built asset management within universities.

This paper aims to explore the promotion decisions of heterogeneous sellers on a decentralized platform under competitive conditions and analyze how seller behaviors impact platform profit, seller revenue, buyer surplus and social welfare.

This paper considers a Cournot model consisting of a platform charging a commission rate and two sellers with different conversion rates and browsing costs. Promotion efforts by sellers can increase traffic, but they also incur promotion costs for sellers. The sellers decide on promotion effort by weighing these two effects. The authors also explore the equilibrium when the platform charges a fixed usage fee.

The seller’s profit improves as its conversion rate increases and worsens as browsing costs increase. Also, increasing the commission rate charged by the platform makes the seller invest less in promotional efforts. Therefore, the platform must consider this trade-off to determine an optimal rate. The analysis shows that the seller with a high conversion rate and high browsing cost plays a greater role in generating more overall revenue. When the market favors such a seller, the platform tends to charge less in order not to impair its profitability.

This paper incorporates conversion rate, buyer’s browsing cost, unit promotion cost and the fee charged by the platform into the model to study sellers’ promotion decisions on decentralized platforms.

COVID-19 has aggregated the need for a non-contact medical logistic system. A non-contact robot with self-navigation ability has greatly enhanced the efficiency of the medical logistic system. This paper aims to design a new medical logistics robot system for the complex environment of hospitals with dynamic obstacles.

Targeting a medical logistics robot system for a large-scale hospital environment, this study proposed a dynamic obstacle avoidance system to reduce the robot’s delay time as well as frequent route switching. In the algorithm, this study proposed a new loop closure detection with an artificial correction factor. Moreover, this study presented enhanced 3D object detection, improving detection accuracy in hospital environments.

Experimental results confirm that the robot can move along its global path and reach its destination without colliding with stationary or moving obstacles.

The medical logistics robot system has safe and stable performance in real hospital scenarios. The implementation verifies that the robot has effectiveness and reliability in both hardware and software design.

As an emerging socio-technical paradigm, high-speed railways profoundly change individuals' lifestyle and allow for the shift toward a green transportation. Digital technologies open an opportunity window for the development of enterprises. This study aims to clarify the impact of firm digitalization on the innovation efficiency of the Chinese high-speed rail industry. In addition, human capital is the important non-physical capital of enterprises. The authors also elucidate the moderating role of human capital on the above relationship.

Based on the data of Chinese high-speed railway listed companies from 2015 to 2021, this study explores the impact of digital transformation on the innovation efficiency, and further clarifies the boundary role of human capital with two-way fixed effect regression models.

The empirical results indicate that digital transformation has a positive impact on the innovation efficiency of the Chinese high-speed railway enterprises. Furthermore, human capital significantly enhances the above relationship. In addition, digital transformation fosters the innovation efficiency of small- and medium-sized enterprises and private-owned enterprises, but the correlation coefficients between digital transformation and the innovation efficiency of large enterprises and state-owned enterprises are not significant.

This is one of the earliest studies to explore how digital technologies shape R&D activities. From the perspective of relative efficiency, this study evaluates the effectiveness of digital transformation and provides empirical evidence for the formulation and implementation of corporate digital strategies. Moreover, this study links human capital with digital transformation and identifies condition factors that affect the effectiveness of digital transformation, thereby supplementing existing knowledge.

To explore the mechanism that shapes firms' supply chain learning (SCL) practices, this study examines the relationship between firms' knowledge network embeddedness and their SCL practice in a supply chain network, as well as the moderating role of supply chain network cohesion in this relationship.

Using patent application data and supply chain partner information from 869 listed firms between 2011 and 2020 in China, this study uses fixed-effect regression models to reduce endogeneity problems by controlling for individual heterogeneity effects that cannot be observed over time.

Firms' knowledge network embeddedness has an inverted U-shaped effect on their SCL, and this non-linear relationship is conditional on supply chain network cohesion, which strengthens (weakens) the positive (negative) effect of knowledge network embeddedness on SCL.

The findings show that managers can reconcile the downsides of knowledge network embeddedness on SCL by fostering greater supply chain network cohesion.

Drawing from the network pluralism perspective, this study contributes to supply chain literature by extending the research context of the antecedents of SCL from a single-network setting to a dual-network setting. It extends the network pluralism perspective by showing that not only positive effects but also negative effects of network embeddedness can transfer from one network to another.

This study aims to assess the impact of functional green advertising receptivity and emotional green advertising receptivity on consumers' green purchase intention. The authors then examine the mediating role of perceived competence and perceived warmth. Furthermore, the authors explore the moderating effect of power distance belief (PDB) on the relationships between green advertising receptivity with different appeals and consumers' perceived competence and perceived warmth respectively.

Based on the online survey platform, a total of 468 responses were obtained in China from January to March 2022. 408 valid replies were collected and analyzed in this study. The research hypotheses were empirically verified with bootstrap approach.

The empirical results indicate that both functional green advertisi\ng receptivity and emotional green advertising receptivity are significantly positively correlated with green purchase intention, and perceived competence and perceived warmth play the mediating role. Besides, PDB significantly strengthens the relationship between functional green advertising receptivity and perceived competence, but weakens the incentive effect of emotional green advertising receptivity on perceived warmth.

The psychological mechanism of the receptivity of green advertising with different appeals affecting green purchase intention remains unclear. This is one of the first studies to uncover how functional green advertising receptivity and emotional green advertising receptivity influence green purchase intention. Besides, the impact of PDB on the formation process of consumer perception is also a black box. By clarifying and comparing the moderating role of PDB on the relationships between advertising receptivity with different appeals and consumers' perceived competence and perceived warmth, this study contributes to the research on the effectiveness of green advertising.

The purpose of this paper is to combine triboelectric nanogeneration technology with ball bearing structure to achieve energy collection and fault monitoring.

In this paper, according to the rotation mode of ball bearings, the freestanding mode of triboelectric nanogeneration is selected to design and manufacture a novel triboelectric nanogeneration device Rolling Ball Triboelectric Nanogenerator (RB-TENG) which combines rotary energy collection with ball bearing fault self-sensing.

The 10,000s continuous operation experiment of the RB-TENG is carried out to verify its robustness. The accurate feedback relationship between the RB-TENG and rotation velocity can be demonstrated by the fitting comparison between the theoretical and experimental electrical signal periods at a certain time. By comparing the output electrical signals of the normal RB-TENG and the rotor spalling RB-TENG and polytetrafluoroethylene (PTFE) balls with different degrees of wear at 500 r/min, it can be concluded that the RB-TENG has an ideal monitoring effect on the radial clearance distance of bearings. The spalling fault test of the RB-TENG stator inner ring and rotor outer ring is carried out.

Through coupling experiments of rotor spalling fault of the RB-TENG and PTFE balls fault with different degrees of wear, it can be seen that when rotor spalling fault occurs, balls wear has a greater impact on the normal operation of the RB-TENG, and it is easier to identify. The fault self-sensing ability of the RB-TENG can be obtained, which is expected to provide an effective scheme for monitoring the radial wear clearance distance of ball bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0295/

Wind power is the most promising renewable energy source in China. The development of digital technologies has brought about unprecedented growth opportunities and prospects for wind power. However, the relationship between digital technology adoption and total factor productivity (TFP) in the wind power industry in China has not been empirically assessed. This study aims to clarify whether and how digital technology adoption affects the TFP of the wind power industry in China.

Based on the data of listed companies in the Chinese wind power industry from 2006 to 2021, this study proposes and verifies relevant hypotheses with two-way fixed effects regression models.

The empirical results indicate that digital technology adoption is the cornerstone of the TFP of China’s wind power industry. Reconfiguration capability and technological innovation serially mediate the above relationship. In addition, the incentive effect of digital technology adoption varies among wind power firms. The impact of digital technology adoption is more significant in firms that are old and located in economically undeveloped regions.

This study is one of the earliest attempts to investigate the relationship between digital technology adoption and TFP in the renewable energy sectors of emerging economies. By integrating dynamic capability theory and the analytical framework of “Capability-Behavior-Performance” into the digital context, this study offers the theoretical insights into how digital technology adoption can enhance organizational reconfiguration capability, thereby stimulating technological innovation and subsequent TFP. Additionally, the impacts of different digital technologies are estimated in entirety, rather than in isolation.