Search results

This paper aims to investigate the effect of lubricant viscosity model with improver on friction and lubrication of piston skirt-cylinder liner conjunction.

A dynamic calculation model is established for the piston skirt-cylinder liner conjunction of a heavy-duty commercial diesel engine, to explore the effects of two kinds of lube oil viscosity models named after polyalkyle-metacrylate-1 (PAMA1) and styrene-isoprene-copolymer (SICP) improvers on the maximum oil film viscosity, the minimum oil film thickness, the peak oil film pressure, the maximum shear rate, the friction force and the total friction power loss.

The variation trends with the crank angle of the above parameters are not changed with the difference of improvers, while obvious numerical differences are found except the maximum oil film pressure. The minimum oil film thickness and maximum shear rate of PAMA1 are larger than that of SICP, the maximum oil film viscosity of SICP is larger than that of PAMA1, which indicates that the shear-thinning effect of PAMA1 is greater, the maximum friction force on the piston of SICP is larger than that of PAMA1, and the total friction power consumption is also larger, the average friction power consumptions of SICP and PAMA1 are 385.4 and 262.8 W, respectively, with the relative difference of 31.8 per cent.

The influence of different lubricating oil additive models on the lubrication and friction of piston skirt-cylinder liner conjunction is simulated and analyzed.

It is of great significance to study the influence of subgrade filling on permafrost temperature field in permafrost area for the smooth construction and safe operation of railway.

The paper builds up the model for the hydrothermal coupling calculation of permafrost using finite element software COMSOL to study how permafrost temperature field changes in the short term after subgrade filling, on which basis it proposes the method of calculation for the concave distortion of freezing front in the subgrade-covered area.

The results show that the freezing front below the subgrade center sinks due to the thermal effect of subgrade filling, which will trigger hydrothermal erosion in case of sufficient moisture inflows, leading to the thawing settlement or the cracking of the subgrade, etc. The heat output of soil will be hindered the most in case of July filling, in which case the sinking and the distortion of the freezing front is found to be the most severe, which the recovery of the permafrost temperature field, the slowest, constituting the most unfavorable working condition. The concave distortion of the freezing front in the subgrade area increases with the increase in temperature difference between the filler and ground surface, the subgrade height, the subgrade width and the volumetric thermal capacity of filler, while decreases with the increase of the thermal conductivity of filler. Therefore, the filler chose for engineering project shall be of small volumetric thermal capacity, low initial temperature and high thermal conductivity whenever possible.

The concave distortion of the freezing front under different working conditions at different times after filling can be calculated using the method proposed.

This paper aims to explore the latest study of the emerging data-driven approach in the area of FinTech. This paper attempts to provide comprehensive comparisons, including the advantages and disadvantages of different data-driven algorithms applied to FinTech. This paper also attempts to point out the future directions of data-driven approaches in the FinTech domain.

This paper explores and summarizes the latest data-driven approaches and algorithms applied in FinTech to the following categories: risk management, data privacy protection, portfolio management, and sentiment analysis.

This paper details out comparison between different existed works in FinTech with traditional data analytics techniques and the latest development. The framework for the analysis process is developed, and insights regarding the implementation, regulation and workforce development are provided in this area.

To the best of the authors’ knowledge, this paper is first to consider broad aspects of data-driven approaches in the application of FinTech industry to explore the potential, challenges and limitations of this area. This study provides a valuable reference for both the current and future participants.

The purpose of this study is to analyse Peruvian cuisine in the context of visitors’ overall experience, focusing on the loyalty of tourists in terms of gastronomy, their satisfaction with the destination, and the overall image.

Data were obtained from tourists who visited Lima (Peru). Structural equation modeling was used to verify the hypothesized relationships.

The results obtained confirm that satisfaction and appreciation, such as quality, have a positive influence on tourists’ loyalty and, therefore, they recommend and express their desire to come back to this destination. The traditional gastronomy of Lima stands out as a prominent factor on overall experience.

This paper helps the managers of cities in their decisions to improve the satisfaction and seek loyalty of those who visit a city, emphasizes the role of gastronomy.

The results obtained in this research can be used for the establishment of new strategies for the promotion of the destination in terms of tourism and traditional food.

The gastronomy in Lima, (Peru) is recognized worldwide, as well as a stimulus for tourism because it increases the number of visits to the destination. Several studies carried out in these types of destinations have shown the existence of a relationship between gastronomy, overall experience, satisfaction and loyalty. However, there are no previous studies carried out in Lima that sustain this relationship. This work makes a contribution that completes the academic literature on the study of the emotional bonds between Peruvian gastronomy and the tourist who visits it and its behaviour.

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.

This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.

The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.

This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.