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This paper aims to propose cooperative control strategies for dual-arm robots in different human–robot collaborative tasks in assembly processes. The authors set three different regions where robot performs different collaborative ways: “teleoperate” region, “co-carry” region and “assembly” region. Human holds the “master” arm of dual-arm robot to operate the other “follower” arm by our proposed controller in “teleoperation” region. Limited by the human arm length, “follower” arm is teleoperated by human to carry the distant object. In the “co-carry” region, “master” arm and “follower” arm cooperatively carry the object to the region close to the human. In “assembly” region, “follower” arm is used for fixing the object and “master” arm coupled with human is used for assembly.

A human moving target estimated method is proposed for decreasing efforts for human to move “master” arm, radial basis functions neural networks are used to compensate for uncertainties in dynamics of both arms. Force feedback is designed in “master” arm controller for human to perceive the movement of “follower” arm. Experimental results on Baxter robot platform show the effectiveness of this proposed method.

Experimental results on Baxter robot platform show the effectiveness of our proposed methods. Different human-robot collaborative tasks in assembly processes are performed successfully under our cooperative control strategies for dual-arm robots.

In this paper, cooperative control strategies for dual-arm robots have been proposed in different human–robot collaborative tasks in assembly processes. Three different regions where robot performs different collaborative ways are set: “teleoperation” region, “co-carry” region and “assembly” region.

The purpose of this paper is to explore what organizational adaptability means in the digitized context and to discuss how manufacturing companies achieve organizational adaptability during the digital transformation process.

By conducting semi-structured interviews and acquiring archive data from a typical Chinese manufacturing company, this paper gathers extensive data. Based on this, a single-case study methodology is used to investigate organizational adaptability in digital transformation.

This study identifies the process by which companies achieve organizational adaptability during digital transformation and deconstructs organizational adaptability into three dimensions: structural adaptability, operational adaptability and governance adaptability. This study also explores how organizational adaptability is affected by digital capabilities.

This study proposes a process model to demonstrate how organizational adaptability may be attained during digital transformation and redefines organizational adaptability in the context of digitization.

This study aims to examine the impact of digital transformation on firms’ value and explore the mediating impact of ESG performance and moderating impact of information interaction.

Data was collected from companies listed on the Shanghai and Shenzhen stock exchange between 2012 and 2020 with 21,488 observational samples, featuring a selection of 3,348 companies. Panel data regression techniques were used to test the mediating role of ESG performance and the moderating role of information interaction.

The study found that digital transformation can improve firms’ ESG performance, which in turn positively affects their value. The firms that engage in more interaction with outsiders benefit more from digital transformation and have a higher value.

This study provides new theoretical insight into improving firms’ value through digital transformation and ESG performance. It is the first to discuss and study the moderating role of information interaction in the relationship between digital transformation and firms’ value.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

Enterprises generally change their organizational structures in an era of uncertainty for flexibility, and accordingly, employee entrepreneurship emerges as employees gain more power and responsibility during this process. This paper aims to explore how employee entrepreneurship impacts enterprises’ sustainable innovation capability. The authors consider the mediating effect of value cocreation and the moderating effect of role stress.

The authors adopted a questionnaire survey to test the hypothesis, and 356 questionnaires were collected from an online questionnaire platform.

The results show that employee entrepreneurship has a positive impact on sustainable innovation capability, value cocreation plays a partial mediating role in this relationship between employee entrepreneurship and value cocreation and role stress plays a negative moderating role in the relationship between employee entrepreneurship and value cocreation.

The study makes three main contributions to the literature concerning entrepreneurship and enterprise sustainable innovation capability. First, in relation to the entrepreneurship literature, the authors shift entrepreneurship research from entrepreneurs/leaders to employees in the digital transformation context and specify the connotation of employee entrepreneurship. This shift further enriches and broadens the research boundary of entrepreneurship (Bhide, 2000; Djankov et al., 2006). Second, we add the micro dynamic mechanism of sustainable innovation capability by focusing on the effect of employee entrepreneurship. Employee entrepreneurship positively contributes to enterprise sustainable innovation capability, which is partially mediated by value cocreation, and its impact on value cocreation is moderated by role stress due to the identity change of employees in the digital transformation context. The authors answer calls to clarify the micromechanisms of building enterprise competitive advantage (Terziovski and Sohal, 2020). The research confirms the relationship change between enterprises and consumers in value creation from separation to cooperation (Tu et al., 2020; Costa and Hafto, 2021). Additionally, the authors discuss the dynamic mechanism and factors of value cocreation at the employee level and further verify the positive effect of an active interaction among enterprises, consumers and other value creators (Matarazzo et al., 2021; Heinonen et al., 2013). This approach compensates for the deficiency of previous studies investigating the influence of value cocreation at the level of organization members.

The theoretical analysis and empirical conclusions of this study have important practical implications. First, employee entrepreneurship positively impacts enterprises’ sustainable innovation capabilities. Therefore, enterprises should stimulate their employees’ entrepreneurship to strengthen organizational resilience and adaptability, which are important for enterprises to survive in the changeable age. For example, enterprises can authorize their frontline employees with more resource allocation power and reduce hierarchical control; thus, their employees can address emergencies in a timely manner and catch fleeting market opportunities. Second, since value cocreation is a partial mechanism by which employee entrepreneurship contributes to an enterprise’s sustainable innovation capability, enterprises should help employees improve their capabilities and knowledge for value cocreation. For instance, enterprises should develop decision-making support toolkits, upscale training courses and efficient coordination tools to help employees achieve the necessary skills and knowledge. Only in this way can they qualify to perform an increasing number of autonomous tasks. Finally, employees’ role stress negatively moderates the relationship between employee entrepreneurship and value cocreation. This finding provides a reference for enterprises in position setting, role rule-making, role responsibility-planning, etc. Specifically, enterprises should build a feedback mechanism to manage employees’ role ambiguity, conflict and overload; thus, they can eliminate the potential negative effects on value cocreation and their sustainable innovation capability in a timely manner.

This study expands the boundary and connotation of entrepreneurship in an era of uncertainty, specifies the micromechanism of sustainable innovation capability and provides new insights for enterprises constructing their sustainable innovation capability.

The purpose of this paper is to analyze the influencing factors of patients’ financial burden through a case study of hospital on public‒private partnerships (PPP) model (Chaonan Minsheng Hospital of Guangdong Province) and provide some useful information to policymakers for better development of hospitals on PPP model.

There are total six indicators that are defined as patients’ financial burden, basing on the policy of “indicators of medical quality management and control on the third level large general hospital (2011 edition),” issued by Chinese Government. In total, 23 potentially influencing factors of patients’ financial burden for hospital on PPP model were chosen from the above policy. The five-year (2007‒2011) data for the above 29 indicators come from statistic department of hospital on PPP model. Grey relational analysis (GRA) was applied to analyze the influencing factors of patients’ financial burden for hospital on PPP model.

A clear rank of influencing factors of patients’ financial burden is obtained and suggestions are provided from results of GRA, which provide reference for policymakers of hospital on PPP model. The five main influencing factors of patients’ financial burden for hospital on PPP model, in sequence, are rescuing critical ill patients on emergency, rescuing critical ill inpatients, inpatient bed occupancy rate, working days per bed and medical building area.

The study on the influencing factors of patients’ financial burden for hospital on PPP model not only provides decision-making for policymaker of hospital and controlling of medical expenditure but also contributes to release patients’ financial burden for hospitals on PPP model.

The purpose of this paper is to study the frictional behaviors of CuAl10Fe3 journal bearings sliding against chromium electroplated 42CrMo shafts and diamond-like carbon-coated 42CrMo shafts, respectively, under two different conditions and to compare the two kinds of friction pairs.

All journal bearing samples underwent 24 h running-in and repeatability verification. Then, the journal bearing friction experiments were carried out under two different conditions. After testing, the torques, friction coefficients, power consumptions and other parameters were obtained.

The pair of CuAl10Fe3 journal bearing and diamond-like carbon–coated shaft could drive greater load to start up than the pair of CuAl10Fe3 journal bearing and chromium electroplated 42CrMo shaft, but it had greater power consumption during the steady running period under the identical condition. With the changing of specific pressure or rotational speed, the friction coefficients had different variations. The frictional oscillations appeared at 32 rotations per minute under heavy loads for both kinds of pairs, the oscillation frequencies were equal to rotational frequency of the test shaft and the oscillation amplitude for diamond-like carbon coating was much greater.

These results have guiding significance for practical industrial applications.

The purpose of this study was to solve the problem of most woven-fabric self-lubricating bearings that find it difficult to function at temperatures above 320°C, by designing a new type of new nuclear joint bearing. The results of this study will help designers to achieve accurate stress distribution, displacement deformation, fatigue life and damage of bearings. All of these can be a guide for designing self-lubricating joint bearings.

Finite element analysis is undertaken to simulate the new design bearings. To get the most appropriate and accurate results, the room temperature simulation (Simulation A), the modulus of elasticity that changes with temperature (Simulation B) and the thermal-structure-coupled simulation (Simulation C) are compared. The fatigue simulation is conducted to verify whether the self-lubricating method is reasonable and whether the bearing can function for over 60 years in an enclosed environment.

Stress distribution and displacement deformation of joint bearing can be accurately achieved via the thermal-structure coupled simulation. Work life and damage results have been achieved via the fatigue analysis, and the suggested working loads can be calculated via safety factors.

The newly designed joint bearing in which the graphite is laid on the outside of the inner ring functions and self-lubricates at temperatures above 320°C.

The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources.

Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros.

A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time.

The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques.