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The purpose of this paper is to examine the influence of the innovation ability of universities (IAU) on the efficiency of University–Industry knowledge flow and investigate whether the level of provincial innovative agglomeration (PIA) moderates the relationship between IAU and the efficiency of the University–Industry knowledge flow.

This study uses the super-efficiency data envelopment analysis model to measure knowledge research efficiency (KRE) and knowledge transformation efficiency (KTE) and then studies the influencing mechanism of the two kinds of efficiency using the spatial Tobit model with panel data from 2008 to 2017.

The results show that the overall KRE in Chinese universities is higher than the KTE. IAU has a significantly positive impact on KRE and KTE. PIA has a significantly inverted U-shaped influence on KRE and KTE and positively moderates the promoting effect of IAU on KRE and KTE.

Due to the limitations of the data, this paper only selects several secondary indicators to measure KRE and KTE with reference to previous studies.

This study enriches the future research of University–Industry cooperation and knowledge flow and it is conducive to promoting the efficiency of University–Industry knowledge research and transformation from the perspective of universities, enterprises and local governments.

This study proposes the concept of University–Industry knowledge flow and divides the knowledge flow into the knowledge research stage and the knowledge transformation stage based on the knowledge supply chain theory. Moreover, the paper expands the theoretical framework of the impact of IAU on the efficiency of University–Industry knowledge flow and provides findings on the moderating effect of PIA.

Drawing on self-determination theory, this study reveals the formative and functional mechanism of entrepreneurial leadership and constructs an integrated model that combines objective and subjective career success.

Using data from 189 leaders from China, this study examined the relationship among cognitive style, social norms, entrepreneurial leadership and career success. Using SPSS version 25.0 and AMOS version 23.0, factor analysis, correlation, path analysis and moderation analysis were performed.

The results indicated that innovative cognitive style is positively related to entrepreneurial leadership, and this relationship is reinforced by social norms. Adaptive cognitive style is negatively related to entrepreneurial leadership, but this relationship is not regulated by social norms. Besides, this study found a significantly positive relationship between entrepreneurial leadership and objective career success, while entrepreneurial leadership does not demonstrate a significant relationship with subjective career success.

By combining subjective and the objective career success into entrepreneurial leadership research, the findings provide a new perspective for understanding what other experiences entrepreneurship can bring to leaders. Furthermore, the current study analyzes the informal institutional environment's promoting and impeding roles between cognitive style and entrepreneurial leadership.

This study attempts to uncover the nonlinear relationship between public procurement and corporate total factor productivity (CTFP), and investigates the mediating roles of R&D investment and rent-seeking cost. Additionally, it conducts a heterogeneity analysis for firms with varying levels of political connections and corporate social responsibility (CSR).

Employing Ordinary Least Squares (OLS) and Olley-Pakes (OP) methods, the authors gauge CTFP and manually identify government customers to quantify public procurement. Leveraging panel data from Chinese listed companies, this study explores the relationship between public procurement and CTFP.

This study unveils a U-shaped relationship between public procurement and CTFP, highlighting R&D investment and rent-seeking costs as potential mechanisms. Furthermore, it identifies heterogeneous effects among companies with varying levels of political connections and CSR on the relationship between public procurement and CTFP, including their mediating effects.

This research enhances understanding of demand-side policies and provides crucial insights for the government to further improve public procurement policies.

By offering empirical evidence of how public procurement impacts CTFP, this paper enriches the literature on the behavioral repercussions of public procurement and the determinants of CTFP. It also overcomes the “black box” of the mechanism between public procurement and CTFP, based on the government’s dual role as a pathfinder and customer of enterprises. It broadens the application scenarios of institutional theory and principal-agent theory. Additionally, the heterogeneity analysis of firms with varying political connections and CSR extends the frontiers of related research.

The purpose of this paper is to examine the direct effects of Internet use on rural residents' income growth and the indirect effects of increasing their income by promoting rural residents' entrepreneurial and non-agricultural employment.

Regarding the implementation of the rural revitalization strategy, based on the 2016CFPS data, multiple linear regression analysis and mediation effect analysis are used. To decrease the potential endogeneity of the model, we used the instrumental variable in the model.

The results show that: (1) Internet use has a direct effect on rural residents' income growth; (2) rural residents' entrepreneurial or non-agricultural employment affects the mechanism of Internet use and their income growth, so that can perform an indirect promotion effect; (3) the direct promotion effect of Internet use is stronger than the indirect promotion effect of entrepreneurship and non-agricultural employment.

The effect of using Internet for the income growth of Chinese farmers has been confirmed by some scholars, but the specific mechanism is still relatively vague. The originality is to consider the intermediary transmission effect of entrepreneurship and non-agricultural employment in the study of the impact of Internet use on Chinese farmers' income growth, and use the mediation effect model for empirical analysis. The empirical research results further reveal this mechanism.

The purpose of this paper is to numerically investigate the effect of guide vane unsteady passing wake on the rotor blade tip aerothermal performance with different tip clearances.

The geometry and flow conditions of the first stage of GE-E3 high-pressure turbine have been used to obtain the blade tip three-dimensional heat transfer characteristics. The first stage of GE-E3 high-pressure turbine has 46 guide vanes and 76 rotor blades, and the ratio of the vane to the blade is simplified to 38:76 to compromise the computational resources and accuracy. Namely, each computational domain comprises of one guide vane passage and two rotor blade passages. The investigations are conducted at three different tip gaps of 1.0, 1.5 and 2.0 per cent of the average blade span.

The results show that the overall discrepancy of the heat transfer coefficient between steady results and unsteady time-averaged results is quite small, but the dramatic growth of the instantaneous heat transfer coefficient along the pressure side is in excess of 20 per cent. The change of the aerothermal performance is mainly driven by turbulence-level fluctuations of the unsteady flow field within gap regions. In addition, the gap size expansion has a marginal impact on the variation ratio of tip unsteady aerothermal performances, even though it has a huge influence on the leakage flow state within the tip region.

This paper emphasizes the change ratio of unsteady instantaneous heat transfer characteristics and detailed the mechanism of blade tip unsteady heat transfer coefficient fluctuations, which provide some guidance for the future blade tip design and optimization.

The application of thermal barrier coatings (TBCs) allows aero-engine blades to operate at higher temperatures with higher efficiency. The preparation of the TBCs increases the surface roughness of the blade, which impacts the thermal cycle life and thermal insulation performance of the coating. To reduce the surface roughness of blades, particularly the blades with small size and complex curvature, this paper aims to propose a method for industrial robot polishing trajectory planning based on on-site measuring point cloud.

The authors propose an integrated robotic polishing trajectory planning method using point cloud processing technical. At first, the acquired point cloud is preprocessed, which includes filtering and plane segmentation algorithm, to extract the blade body point cloud. Then, the point cloud slicing algorithm and the intersection method are used to create a preliminary contact point set. Finally, the Douglas–Peucker algorithm and pose frame estimation are applied to extract the tool-tip positions and optimize the tool contact posture, respectively. The resultant trajectory is evaluated by simulation and experiment implementation.

The target points of trajectory are not evenly distributed on the blade surface but rather fluctuate with surface curvature. The simulated linear and orientation speeds of the robot end could be relatively steady over 98% of the total time within 20% reduction of the rest time. After polishing experiments, the coating roughness on the blade surface is reduced dramatically from Ra 7–8 µm to below Ra 1.0 µm. The removal of the TBCs is less than 100 mg, which is significantly less than the weight of the prepared coatings. The blade surface becomes smoothed to a mirror-like state.

The research on robotic polishing of aero-engine turbine blade TBCs is worthwhile. The real-time trajectory planning based on measuring point cloud can address the problem that there is no standard computer-aided drawing model and the geometry and size of the workpiece to be processed differ. The extraction and optimization of tool contact points based on point cloud features can enhance the smoothness of the robot movement, stability of the polishing speed and performance of the blade surface after polishing.

This paper aims to develop an additive manufacturing technique for complex zirconia ceramic dental bridges.

To carry out this study, a dental bridge model was obtained by three-dimensional reverse engineering, and a light-curable zirconia ceramic suspension was formulated. Zirconia bridges were manufactured by stereolithography and then treated by vacuum freeze drying, vacuum infiltration and sintering. The optimal scanning speed was determined according to the shape precision comparison. Then, characteristics of the sintered ceramic parts were tested as size shrinkage, relative density, surface Vickers hardness, surface roughness and microstructure.

The method for preparation of light-curable zirconia suspension (40 volume per cent solid loading) with a viscosity value of 127 mPa·s was proposed. The optimal laser scanning speed for zirconia bridge fabrication was 1200 mm/s. A relative density of 98.58 per cent was achieved; the obtained surface Vickers hardness and surface roughness were 1,398 HV and 2.06 µm, respectively.

This paper provides a potential technical method for manufacturing complex zirconia dental bridges and other small complex-shaped ceramic components which are difficult to be made by other manufacturing techniques.

During the process of the robotic grinding and polishing operations on aero-engine blades, the key problem of calibration error lies in fixture error and uneven margin. To solve this problem, this paper aims to propose a novel method to achieve rapid online calibration of the workpiece coordinate system through laser-based measurement techniques.

The authors propose a calibration strategy based on point cloud registration algorithm. The main principle is presented as follows: aero blade mounted on clamping end-effector is hold by industry robot, the whole device is then scanned by a 3D laser scanner to obtain its surface point cloud, and a fast segmentation method is used to acquire the point cloud of the workpiece. Combining Super4PCS algorithm with trimmed iterative closest point, we can align the key points of the scanned point cloud and the sampled points of the blade model, thus obtaining the translation and rotation matrix for calculating the workpiece coordinate and machining allowance. The proposed calibration strategy is experimentally validated, and the positioning error, as well as the margin distribution, is finally analyzed.

The experimental results show that the algorithm can well accomplish the task of cross-source, partial data and similar local features of blade point cloud registration with high precision. The total time spent on point cloud alignment of 100,000 order of magnitude blade is about 4.2 s, and meanwhile, the average point cloud alignment error is reduced to below 0.05 mm.

An improved point cloud registration method is proposed and introduced into the calibration process of a robotic system. The online calibration technique improves the accuracy and efficiency of the calibration process and enhances the automation of the robotic grinding and polishing system.

The purpose of this paper is to propose a new deep learning-based model to carry out better maintenance for naval propulsion system.

This model is constructed by integrating different deep learning algorithms. The basic idea is to change the connection structure of the deep neural network by introducing a residual module, to limit the prediction output to a reasonable range. Then, connect the Deep Residual Network (DRN) with a Generative Adversarial Network (GAN), which helps achieve data expansion during the training process to improve the accuracy of the assessment model.

Study results show that the proposed model achieves a better prediction effect on the dataset. The average performance and accuracy of the proposed model outperform the traditional models and the basic deep learning models tested in the paper.

The proposed model proved to be better performed naval propulsion system maintenance than the traditional models and the basic deep learning models. Therefore, our model may provide better maintenance advice for the naval propulsion system and will lead to a more reliable environment for offshore operations.

The salinity of the oilfield produced water has a significant effect on steel corrosion. The purpose of this paper is to study the influence of salinity on corrosion behavior of X60 steel and it also provides basic for material selection of gas wells with high salinity.

The weight loss experiment was carried out on steel with high temperature and high pressure autoclave. The surface morphology and composition of corrosion scales were studied by means of scanning electron microscopy, energy dispersive spectroscopy and X-ray diffractometry.

The results show that as salinity increases, the corrosion rate of X60 steel will gradually experience a rapid decline stage and then a slow decline stage. X60 steel is mainly exhibiting uniform corrosion in the first rapid decline stage and pitting corrosion in the second slow decline stage. The increase in salinity reduces gas solubility, which, in turn, changes the morphology and density of the corrosion scales of X60 steel. At low salinity, loose iron oxides generated on the surface of the steel, which poorly protects the substrate. At high salinity, surface of the steel gradually forms protective films. Chloride ions in the saline solution mainly affect the structure of the corrosion scales and initiate pitting corrosion. The increased chloride ions lead to more pitting pits on the surface of steel. The recrystallization of FeCO₃ in pitting pits causes the corrosion scales to bulge.

The investigation determined the critical concentration of pitting corrosion and uniform corrosion of X60 steel, and the new corrosion mechanism model was presented.