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Based on achievement motivation theory and two-factor theory, this research aimed to synergize cooperative goal interdependence (refer to possessing incentive factors) and illegitimate tasks (refer to the absence of security factors) and build a triple interaction model in the process of performance pressure affecting employees’ thriving at work.

This research collected 291 valid data through a two-point time-lagged method to test the direct effect of performance pressure on employees’ thriving at work and its moderating mechanism.

Performance pressure has a significant positive effect on employees’ thriving at work. Cooperative goal interdependence imposes an enhanced moderating effect between performance pressure and employees’ thriving at work. Illegitimate task imposes an interfering moderating effect between performance pressure and employees’ thriving at work and further interferes the enhanced moderating effect of cooperative goal interdependence.

Under the premise of advocating for employees to internalize performance pressure originating from the organizational performance management system into their own achievement motivation, leaders should establish incentive systems and security systems for employees to realize self-achievement through the process of goal management and task management.

This research confirmed the joint determination of incentive effect and insecurity effect on employees’ achievement motivation by cooperative goal interdependence and illegitimate task and revealed the boundary conditions of employees’ choice of thriving at work.

The purpose of this study is to develop new biodegradable magnesium alloy. Magnesium possesses similar mechanical properties to natural bone; it is a potential candidate for resorbable implant applications. However, in physiological conditions, the degradation rate of Mg is too high to be used as an implant material.

In this research, Zn, Sr and Ca were chosen as alloying elements; a coating was deposited on the MgZnSrCa alloy surface by means of a biomimetic technique. The corrosion rates of the uncoated and coated specimens were tested in simulated body fluid.

The hydroxyapatite coating formed on the MgZnSrCa alloy surface and the hydroxyapatite layer markedly decreased the corrosion rate of the MgZnSrCa alloy.

A homogenous hydroxyapatite coating was formed on the MgZnSrCa alloy surface by using a biomimetic coating technique. The biomimetic hydroxyapatite coating markedly reduced the corrosion rate of the MgZnSrCa alloy, and the largest decrease in wastage rate was 44 per cent.

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

This paper aims to present a novel transfer alignment method based on combined double-time observations with velocity and attitude for ships’ poor maneuverability to address the system errors introduced by flexural deformation and installing which are difficult to calibrate.

Based on velocity and attitude matching, redesigning and deducing Kalman filter model by combining double-time observation. By introducing the sampling of the previous update cycle of the strapdown inertial navigation system (SINS), current observation subtracts previous observation are used as measurements for transfer alignment filter, system error in measurement introduced by deformation and installing can be effectively removed.

The results of simulations and turntable tests show that when there is a system error, the proposed method can improve alignment accuracy, shorten the alignment process and not require any active maneuvers or additional sensor equipment.

Calibrating those deformations and installing errors during transfer alignment need special maneuvers along different axes, which is difficult to fulfill for ships’ poor maneuverability. Without additional sensor equipment and active maneuvers, the system errors in attitude measurement can be eliminated by the proposed algorithms, meanwhile improving the accuracy of the shipboard SINS transfer alignment.

Endurance time is an important factor limiting the progress of flapping-wing aircraft. In this study, this paper developed a prototype of a double-wing flapping-wing micro air vehicle (FMAV) that mimics insect-scale flapping wing for flight. Besides, novel methods for optimal selection of motor, wing length and battery to achieve prolonged endurance are proposed. The purpose of this study is increasing the flight time of double-wing FMAV by optimizing the flapping mechanism, wings, power sources, and energy sources.

The 20.4 g FMAV prototype with wingspan of 21.5 cm used an incomplete gear flapping wing mechanism. The motor parameters related to the lift-to-power ratio of the prototype were first identified and analyzed, then theoretical analysis was conducted to analyze the impact of wing length and flapping frequency on the lift-to-power ratio, followed by practical testing to validate the theoretical findings. After that, analysis and testing examined the impact of battery energy density and efficiency on endurance. Finally, the prototype’s endurance duration was calculated and tested.

The incomplete gear facilitated 180° symmetric flapping. The motor torque constant showed a positive correlation with the prototype’s lift-to-power ratio. It was also found that the prototype achieved the best lift-to-power ratio when using 100 mm wings.

A gear-driven flapping mechanism was designed, capable of smoothly achieving 180° symmetric flapping. Besides, factors affecting long-duration flight – motor, wings and battery – were identified and a theoretical flight duration analysis method was developed. The experimental result proves that the FMAV could achieve the longest hovering time of 705 s, outperforming other existing research on double-wing FMAV for improving endurance.

The purpose of this study was to synthesize composite nanoparticles (TiO₂@SiO₂) via the chemical deposition method and investigate their efficacy as additives in polytetrafluoroethylene (PTFE) lubricating grease. The focus was on examining the frictional and conductive properties of the TiO₂@SiO₂ grease using a friction tester.

Composite nanoparticles (TiO₂@SiO₂) were synthesized using the chemical deposition method and incorporated into PTFE grease. Frictional and conductive properties were evaluated using a friction tester. Surface morphology and chemical composition of wear tracks were analyzed using scanning electron microscope and X-ray photoelectron spectroscopy, respectively.

Incorporating TiO₂@SiO₂ at a mass fraction of 1 Wt.% led to a significant reduction in friction coefficient and wear width. The wear depth exhibited a remarkable decrease of 260%, while the contact resistance reached its peak value. This improvement in tribological properties could be attributed to the presence of TiO₂@SiO₂, where TiO₂ served as the core and SiO₂ as the shell during the friction process. The high hardness of the SiO₂ shell contributed to enhanced load-bearing capacity. In addition, the exceptional insulation properties of SiO₂ demonstrated excellent electron-capturing capabilities, resulting in improved friction and insulation performance of the TiO₂@SiO₂ lubricating grease.

This study demonstrates the potential of TiO₂@SiO₂ composite nanoparticles as additives in lubricating greases, offering improved friction and insulation performance. The findings provide insights into the design of advanced lubricating materials with enhanced tribological properties and insulation capacity, contributing to the development of more efficient and durable lubrication systems.

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 paper is to overcome the weakness of the traditional model, in which the grey action quantity is a real number and thus leads to a “unique solution” and to build the model with a trapezoidal possibility degree function.

Using the system input and output block diagram of the model, the interval grey action quantity is restored under the condition of insufficient system influencing factors, and the trapezoidal possibility degree function is formed. Based on that, a new model able to output non-unique solutions is constructed.

The model satisfies the non-unique solution principle of the grey theory under the condition of insufficient information. The model is compatible with the traditional model in structure and modelling results. The validity and practicability of the new model are verified by applying it in simulating the ecological environment water consumption in the Yangtze River basin.

In this study, the interval grey number form of grey action quantity is restored under the condition of insufficient system influencing factors, and the unique solution to the problem of the traditional model is solved. It is of great value in enriching the theoretical system of grey prediction models.

Taking power consumption as an example, the accurate prediction of the future power consumption level is related to the utilization efficiency of the power infrastructure investment. If the prediction of the power consumption level is too low, it will lead to the insufficient construction of the power infrastructure and the frequent occurrence of “power shortage” in the power industry. If the prediction is too high, it will lead to excessive investment in the power infrastructure. As a result, the overall surplus of power supply will lead to relatively low operation efficiency. Therefore, building an appropriate model for the correct interval prediction is a better way to solve such problems. The model proposed in this study is an effective one to solve such problems.

A new grey prediction model with its interval grey action quantity based on the trapezoidal possibility degree function is proposed for the first time.

The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel friction pairs.

A PTFE layer was sintered on the surface of a steel disk, and a lubricant with additives was applied to the surface of the steel disk. A friction and wear tester was used to evaluate the tribological properties and insulation capacity. Fourier transform infrared spectrometer was used to analyze the changes in the composition of the lubricant, and X-ray photoelectron spectroscopy was used to analyze the chemical composition of the worn surface.

It was found that incorporating the PTFE film with PSAIL 2280 significantly enhanced both the friction reduction and insulation capabilities at the electrical contact interface during sliding. The system consistently achieved ultra-low friction coefficients (COF < 0.01) under loads of 2–4 N and elucidated the underlying lubrication mechanisms.

This work not only confirm the potential of PTFE films in insulating electrical contact lubrication but also offer a viable approach for maintaining efficient and stable low-friction wear conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0222/

The purpose of this study is to empirically explore the relationship between a new venture top management team's (NVTMT’s) shared leadership and strategic performance in opportunity recognition and entrepreneurial bricolage by drawing on the upper echelons theory.

Data were collected from 344 new manufacturing ventures located in Eastern China. The hypotheses were tested using structural equation modelling (SEM) through the AMOS 23.0 software package. The confluence of the contextual factors of the new venture is examined by a fuzzy-set qualitative comparative analysis (fsQCA).

The results indicate that NVTMT shared leadership has an indirect and positive effect on strategic performance through opportunity recognition, especially in a highly uncertain environment, while the mediating effect of entrepreneurial bricolage is not significant. Furthermore, although the SEM results show that the impact of NVTMT shared leadership on entrepreneurial bricolage is negative, the fsQCA shows that NVTMT shared leadership can significantly and positively affect entrepreneurial bricolage in an environment with high uncertainty, ultimately enhancing strategic performance.

This study contributes to the shared leadership literature by proposing a model on how shared leadership shapes the strategic performance of new ventures via opportunity recognition and entrepreneurial bricolage. The findings not only enrich relevant research on the upper echelons theory, but also help in understanding the patterns of contextual conditions that facilitate the value-adding properties of NVTMT shared leadership.