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This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.

The traditional grey relational proximity and grey relational similarity degree are integrated into the novel comprehensive grey evaluation framework. The evaluation system of regional green innovation vitality is constructed from three dimensions: economic development vitality, innovative transformation power and environmental protection efficacy. The weights of each indicator are obtained by the entropy weight method. The GIV of 31 provinces in China is measured based on provincial panel data from 2016 to 2020. The ward clustering and K-nearest-neighbor (KNN) algorithms are utilized to explore the regional green innovation discrepancies and promotion paths.

The novel grey evaluation method exhibits stronger ability to capture intrinsic patterns compared with two separate traditional grey relational models. Green innovation vitality shows obvious regional discrepancies. The Matthew effect of China's regional GIV is obvious, showing a basic trend of strong in the eastern but weak in the western areas. The comprehensive innovation vitality of economically developed provinces exhibits steady increasing trend year by year, while the innovation vitality of less developed regions shows an overall steady state of no fluctuation.

The grey entropy comprehensive relational model in this study is applied for the measurement and evaluation of regional GIV, which improves the one-sidedness of traditional grey relational analysis on the proximity or similarity among sequences. In addition, a three-dimensional evaluation system of regional GIV is constructed, which provides the practical guidance for the research of regional development strategic planning as well as promotion paths.

A comprehensive grey entropy relational model based on traditional grey incidence analysis (GIA) in terms of proximity and similarity is proposed. The three-dimensional evaluation system of China's regional GIV is constructed, which provides a new research perspective for regional innovation evaluation and expands the application scope of grey system theory.

The purpose of this paper is to implement the macro-modeling and passivity enforcement for the equivalent high frequency circuit model of a single-phase winding for an alternating current (AC) three-phase motor. It provides a stable and strictly passive Foster-type circuit macro-model for the winding. Consequently, a stable circuit network is guaranteed when it is connected with an external passive circuit. The equivalent circuit is validated on a three-phase permanent magnet synchronous motor. Furthermore, the corresponding three-phase windings macro-model could be obtained accordingly.

The following techniques are used: the least square method, vector fitting method, the fast residue perturbation method, circuit synthesis, sequence quadratic programming method and simulated annealing method.

This work presents an effective approach to model an equivalent high frequency circuit macro-model for a single-phase winding. Simultaneously, both the characteristics of port passivity and component passivity are guaranteed.

This paper carries out both the port passivity and the component passivity enforcement for a single-phase winding of a motor during the macro-modeling procedure. This equivalent motor winding model can be applied to obtain the conducted electromagnetic interference and the overvoltage performance analysis for an adjustable speed motor drive system.

Copper-graphite (Cu/C) is a type of seal material that has been widely used in liquid rocket engines and with normal metal pairs, has a high wear rate under liquid oxygen lubrication. In this study, bearing steel disks were subjected to a high-temperature oxidization and phosphating progress to create an oxidized tricresyl phosphating film (OTCP) film, using tricresyl phosphate (TCP) as the phosphating solvent. It is hoped that the surface wear rate and friction coefficient can be reduced by this method.

This study aim to form an anti-wear film on the surface of bearing steel, which can significantly improve the lubrication performance of Cu/C and bearing steel. The surfaces of bearing steel disks were dried to remove surface water and then put on a heating plate with a magnetic stirrer and a blue glazed oxide film obtained by heating the disks in the air at 200°C for 1 h. To create the OTCP film, bearing steel disks with a blue glazed oxide film were cleaned ultrasonically three times in ethanol and then placed in baths of TCP. After heating for 2 h at 120°C, excess TCP liquid was removed from the disks using ethanol, leaving an OTCP film on the disk surface.

It was found that steel coated with an OTCP film demonstrated better tribological performance (lower coefficient of fiction and wear rate) when pairing with Cu/C than either oxidized or untreated bearing steel. Additional chemical reactions occur when the oxide disk is immersed in TCP and the FePO₄ film is formed after heating. Additionally, the OTCP film coated steel displayed good corrosion resistance, as confirmed by electrochemical corrosion tests. This finding demonstrates the potential for this process in the aerospace industry.

The preparation of OTCP films via high-temperature oxidization and phosphating of bearing steel was demonstrated, with the tribological properties of the OTCP film being investigated alongside those of the original surface and an oxidized film surface. The fabrication of OTCP films is easily scaled up and exhibits significant advantages as a new technology for applications in mechanical contact seal lubrication.

Based on the socio-emotional wealth and agency theories, this study empirically investigates the impact of family ownership and management on green innovation (GI) in family businesses, as well as the moderating effects of institutional environmental support factors, namely, the technological achievement marketisation index and the market-rule-of law index.

This study empirically tests the hypotheses based on a sample of listed Chinese family companies with A-shares in 14 heavily polluting industries from 2009 to 2019.

There is a U-shaped relationship between the percentage of family ownership and GI, and an inverted U-shaped relationship between the degree of family management and GI. Additionally, different institutional environmental support factors affect these relationships in different ways. As the technological achievement marketisation index increases, the U-shaped relationship between the percentage of family ownership and GI becomes steeper, while the inverted U-shaped relationship between the degree of family management and GI becomes smoother. The market rule-of-law index weakens the U-shaped relationship between family ownership and GI.

First, the authors enrich the research on the driving factors of GI from the perspective of the most essential heterogeneity of family businesses. This study shows nonlinear and opposite effects of family ownership and management on GI in family firms. Second, this study contributes to the literature on family firm innovation. GI, not considered by researchers, is regarded as an important deficiency in research on innovation in family businesses. Therefore, this study fills that gap. Third, the study expands research on moderating effects in the literature on GI from the perspective of institutional environmental support factors.

Aluminum alloy is considered an ideal material in aerospace, automobile and other fields because of its lightweight, high specific strength and easy processing. However, low hardness and strength of the surface of aluminum alloys are the main factors that limit their applications. The purpose of this study is to obtain a composite coating with high hardness and lubricating properties by applying GO–PVA over MAO coating.

A pulsed bipolar power supply was used as power supply to prepare the micro-arc oxidation (MAO) coating on 6061 aluminum sample. Then a graphene oxide-polyvinyl alcohol (GO–PVA) composite coating was prepared on MAO coating for subsequent experiments. Samples were characterized by Fourier infrared spectroscopy, X-ray diffraction, Raman spectroscopy and thermogravimetric analysis. The friction test is carried out by the relative movement of the copper ball and the aluminum disk on the friction tester.

Results showed that the friction coefficient of MAO samples was reduced by 80% after treated with GO–PVA composite film.

This research has made a certain contribution to the surface hardness and tribological issues involved in the lightweight design of aluminum alloys.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0427/

In recent years, the phenomena of “accelerating” and “jumping” during the international expansion of Chinese enterprises have attracted a lot of attention from scholars. However, while a CEO’s career horizon can significantly affect his or her enterprise’s strategic decision-making, few studies have explored the role of CEO career horizon in terms of “accelerating” and “jumping” internationalization.

Based on a sample of China’s A-share listed manufacturing companies from 2008 to 2017, this study explores the impact of CEO career horizon on the internationalization pace and international rhythm of enterprises.

First, the shorter the CEO’s career horizon, the more likely the CEO can avoid risky strategic decisions, which ultimately causes a negative relationship between CEO career horizon and the internationalization pace and rhythm of the enterprise. Second, for larger and older boards of directors, there is a more negative impact of the CEO’s short-term career horizon on the internationalization pace and internationalization rhythm of the company. However, given a larger proportion of female directors and non-executive directors, the CEO’s short-term career horizon has a weaker negative impact on international pace and the rhythm of internationalization.

First, based on upper echelon theory, this study interprets the influence of CEO career horizon on the time dimension of corporate internationalization (including internationalization pace and international rhythm), deepening the theory’s explanatory power. Second, by clarifying the important predictive effect of CEO career horizon on internationalization pace and international rhythm, this research enriches extant research on both variables’ antecedents, as well as that on the influence of CEO career horizon. Finally, by introducing the regulatory role of the board’s supervisory ability, this study clarifies the boundary conditions for the influence of the CEO’s career horizon on international pace and rhythm, and it expands the literature on how CEOs and boards of directors can influence corporate strategic decisions during the internationalization process.

This study aims to investigate the impacts of Generation Z (Gen Z) domestic food tourists’ perceived restaurant innovativeness on destination cognitive food image and examine the impacts of destination cognitive food image on destination brand image and tourists’ revisit intention.

Data were collected through an online survey, and 337 Gen Z domestic food tourists in Guangzhou participated. Partial least squares structural equation modeling was used for the data analysis.

This study found that tourists’ perceptions of restaurant menu, technology-based service and experiential innovativeness played essential roles in enhancing destination cognitive food image. In addition, the destination cognitive food image strengthened the destination brand image and increased tourists’ revisit intentions for the future.

The destination cognitive food image perceived by Gen Z domestic food tourists is affected by the restaurant menu, technology-based service and experiential innovativeness. To build a solid destination food image, restaurant operators and decision-makers should prioritize the allocation of resources to develop their innovation capacity. This study also suggests a path of food tourism destination branding from stakeholders’ perspectives and encourages collaboration with stakeholders. Enhanced food tourism destination competitiveness toward the young tourists’ market will generate an overall win for stakeholders

Food providers’ innovativeness, an important factor attracting young tourists’ attention, has been neglected in the discussion of the food tourism experience. This study fills the research gap, investigates the importance of restaurant innovativeness in building food tourism destination competitiveness and provides valuable suggestions to destination restaurant operators and decision-makers.

Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements.

A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots.

The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system.

Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.