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The purpose of this study is to reveal the underlying mechanism in film formation of oil-in-water (O/W) emulsion.

This study focuses on the film forming characteristics of O/W emulsion between the surface of a steel ball and a glass disc coated with chromium. The lubricant film thicknesses of O/W emulsion with various mechanical stirring strength were discussed, which were observed by technique of relative optical interference intensity.

The authors directly observed the oil pool in the contact area, finding the size of oil pool was closely related to the film-forming ability of emulsion. Enrichment phenomenon occurs in oil pool, which was caused by phase inversion. Further investigations revealed that the emulsion is stable with strong stirring strength, resulting in a smaller oil pool size and worse film forming ability.

With the wide usage of O/W emulsion in both biological and industrial systems, the ability of emulsion film formation is considered as an important factor to evaluate the lubrication effectiveness.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2022-0354/

The purpose of this paper is to predict quantitative level of stray current leaking to the buried metallic structure by establishing convolution neural network (CNN) model.

First, corrosion experimental system of buried metallic structure is established. The research object of this paper is the polarization potential within 110 min, CNN model is used to predict the quantitative level of stray current leakage using the data from corrosion experimental system further. Finally, results are compared with the method using BP neural network.

Results show that the CNN model has better predictive effect and shorter prediction time than the BP model, the accuracy of which is 82.5507 per cent, and the prediction time is shortened by more than 10 times.

The established model can be used to forecast the level of stray current leakage in the subway system effectively, which provides a new theoretical basis for evaluating the stray current corrosion hazard of buried metallic structure.

The purpose of this study was to investigate the microstructural evolution and hydrolytic stability of poly(phenylborosiloxane) (PPhBS) to further use and develop the oligomers as heat-resistant modifiers.

PPhBS was synthesized by direct co-condensation of boric acid (BA) and phenyltriethoxysilane (PTEOS). The structural evolution of PPhBS at high temperature was investigated by Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), differential thermal analysis (DTA), in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and 29Si nuclear magnetic resonance (NMR) spectroscopy. In addition, the change in the morphology of the PPhBS powder was examined to demonstrate the evolution of the chemical bonds, and the hydrolytic stability of PPhBS was investigated by a combination of X-ray diffraction (XRD) analysis, measurement of the mass loss in water and FTIR spectroscopy.

The results revealed that a cross-linking network was gradually formed with increasing temperature through the condensation of the residual hydroxyl groups in PPhBS, and the Si-OH and B-OH bonds remained even at a high temperature of 450°C. Furthermore, heat treatment improved the hydrolytic stability of the oligomer. The hydrolysis of the B-O-B bonds in PPhBS was reversible, whereas the Si-O-Si and Si-O-B bonds were highly resistant to hydrolysis.

The prepared PPhBS can be used as a heat-resistant modifier in adhesives, sealants, coatings and composite matrices.

Investigation of the structural evolution of a polyborosiloxane at high temperature by DRIFTS is a novel approach that avoided interference from moisture in the air. The insoluble mass fraction and the FTIR spectrum of PPhBS washed with water were used to investigate the hydrolytic stability of PPhBS.

The purpose of this paper was to improve the frictional wear resistance properties of piston skirts caused by the low viscosity lubricant by studying the tribological performance of three novel coating materials.

Comparative tribological examinations were performed in a tribological tester using the ring-block arrangement under two viscosity lubricants, the loading force was applied as 100 N, the speed was set to 60 r/min and the testing time was 180 min.

Under low viscosity lubricant, the friction coefficient and wear of the three coatings all increase, and the friction coefficient and wear of the PTFE coating are the largest, while the MoS₂ coating has the lowest friction coefficient and wear. Under low viscosity lubricant, the friction coefficient of the MoS₂ coating is 2.1%–5.4% and 20.0%–24.3% lower than that of the SiO2 and PTFE coating, respectively. The friction coefficient and wear fluctuation rate of the MoS₂ coating is the smallest when the lubricant viscosity decreases, which indicates that the MoS₂ coating has excellent stability and adaptability under low viscosity lubricant.

To reduce the piston skirt wear caused by low viscosity lubricant in heavy-duty diesel engines, the friction and wear adaptability of three novel composite coating materials for piston skirts were compared under 0 W-20 low viscosity lubricant, which could provide a guidance for the application of wear-resistant materials for heavy-duty diesel engine piston skirt.

Task recommendation is an important way for workers and requesters to get better outcomes in shorter time in crowdsourcing. This paper aims to propose an approach based on 2-tuple fuzzy linguistic method to recommend tasks to the workers who would be capable of completing and accept them.

In this paper, worker’s capability-to-complete (CTC) and possibility-to-accept (PTA) for a task needs to be recommended are proposed, measured and aggregated to determine worker’s priority for task recommendation. Therein, the similarity between the recommended task and its similar tasks and worker’s performance on these similar tasks are computed and aggregated to determine worker’s CTC quantitatively. In addition, two factors of worker’s active degree and worker’s preferences to a task category are presented to reflect and determine worker’s PTA. In the process of measuring them, 2-tuple fuzzy linguistic method is used to represent, process and aggregate vague and imprecise information.

To demonstrate the implementation process and performance of the proposed approach, an illustrative example is conducted on Taskcn, a widely used Chinese online crowdsourcing market. The experimental results show that the proposed approach outperformed the self-selection approach, especially for complex or creative tasks. Moreover, comparing with task recommendation considering worker’s CTC solely, the proposed approach would be better in terms of workers’ response rate. Additionally, the use of linguistic terms and fuzzy linguistic method facilitates the expression of vague and subjective information and makes recommendation process more practical.

In the study, the authors capture alternative workers, collect workers’ behaviors and compute workers’ CTC and PTA manually. However, as the number of tasks and alternative workers grow, the issue, i.e. how to conveniently collect workers’ behaviors and determine their CTC and PTA, becomes conspicuous and needs to be studied further.

The proposed approach provides an alternative way to perform tasks posted in crowdsourcing platforms. It can assist workers to contribute to right tasks, and requesters to get outcomes with high quality more efficiently.

This study proposes an approach to task recommendation in crowdsourcing that integrates workers’ CTC and PTA for the recommended tasks and can deal with vague and imprecise information.

Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment.

First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively.

Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper.

Only one step climbing motion planning is considered in this method.

With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint.

The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

The purpose of this paper is to explore the effecting mechanism of relational embeddedness on technological innovation performance in the context of China.

By probing into the related theories and five exploratory case studies of Chinese manufacturing firms, this paper establishes a conceptual model about the effects of relational embeddedness on technological innovation performance and proposes nine hypotheses. The authors then investigate 228 Chinese manufacturing firms by questionnaires, and testify the hypotheses and conceptual model by structural equation modeling.

Chinese firm's relational embeddedness in the international manufacturing network has a positive effect on its technological innovation performance through explorative learning. Specifically, trust, information sharing and joint problem solving are beneficial to new knowledge acquisition and application, and then to the improvement of technological innovation performance.

This paper mainly focuses on bilateral relations among firms, regardless of the influence of network structure. Future research can extend to multilateral relations as well.

The paper builds up linkages among theories of network resources, organizational learning and technological innovation to open the black‐box of how relational embeddedness acts on technological innovation. It is a supplement to the existing research on inter‐firm network theories in developing countries.

Online employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.

The authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.

Personality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.

These findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.

This study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.

Recognizing every worker's working status instead of only describing the existing construction activities in static images or videos as most computer vision-based approaches do; identifying workers and their activities simultaneously; establishing a connection between workers and their behaviors.

Taking a reinforcement processing area as a research case, a new method for recognizing each different worker's activity through the position relationship of objects detected by Faster R-CNN is proposed. Firstly, based on four workers and four kinds of high-frequency activities, a Faster R-CNN model is trained. Then, by inputting the video into the model, with the coordinate of the boxes at each moment, the status of each worker can be judged.

The Faster R-CNN detector shows a satisfying performance with an mAP of 0.9654; with the detected boxes, a connection between the workers and activities is established; Through this connection, the average accuracy of activity recognition reached 0.92; with the proposed method, the labor consumption of each worker can be viewed more intuitively on the visualization graphics.

With this proposed method, the visualization graphics generated will help managers to evaluate the labor consumption of each worker more intuitively. Furthermore, human resources can be allocated more efficiently according to the information obtained. It is especially suitable for some small construction scenarios, in which the recognition model can work for a long time after it is established. This is potentially beneficial for the healthy operation of the entire project, and can also have a positive indirect impact on structural health and safety.