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The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM).

Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia.

The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.

The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics.

The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.

This research focuses on exploring a psychological mechanism between abusive supervision and job engagement via job insecurity and job alternative to understand how frontline construction workers' perceived abusive supervision impacts on their work engagement.

This study draws on the conservation of resources theory (COR) to postulate that workers' job insecurity acts as a mediation between abusive supervision and work engagement, and the perception of job alternative moderates the impact of abusive supervision on job insecurity. This study empirically tests survey data from 357 frontline construction workers and adopts partial least square structural equation modeling (PLS-SEM) to test the proposed model.

This paper proved the dark-side of abusive supervision, dual character of job insecurity and moderating role of job alternative. The game-changing mediation effects of job insecurity were examined, which suggested that work engagement would be higher when abusive supervision stimulates stronger job insecurity. Furthermore, the result indicated that the relationship between abusive supervision and job insecurity should be weaker when job alternative is high.

This research suggests construction project managers (CPMs) must take steps such as enhancing supervisors' emotional management skills to control frequency of abuse and thus lessen compromising engagement. Moreover, overall understanding frontline workers' job insecurity is meaningful for better construction performance management.

This research contributes to the disputed leadership style (abusive supervision) and positive psychology knowledge (work engagement) in construction projects by unveiling the psychosocial mechanism (game-changing effects of job insecurity) of construction frontline workers positive engaged feeling. This study highlights the function of personal and job resources in process of resource conservation.

Compared with the time-consuming numerical method and the complex lumped parameter thermal network method to solve the steady-state heat distribution of the permanent magnet (PM) linear motor, there is no analytical method based on the thermal partial differential equations. This paper aims to propose a two-dimensional (2-D) analytical model for predicting the steady-state temperature distribution of PM linear motors to improve the prediction accuracy and speed up the calculation.

Based on the complex Fourier series theory and Cauchy’s product theorem, this paper presents for the first time a general analytical solution for 2-D temperature field in Cartesian coordinates. Then, by combining the electromagnetic field finite element model (FEM), the copper loss, iron loss and PM eddy current loss are used as the heat sources of the thermal analytical model. Finally, the solution to the temperature field is obtained by solving the system equations under boundary and interface conditions.

The analytical results are in good agreement with those from the thermal FEM, and the calculation speed is significantly faster than that of the thermal FEM.

The multilayer model proposed in this paper can consider heat conduction, convection and radiation. It is not only suitable for PM linear motors but also has significant application value for the thermal analysis of electromagnetic devices modeled in 2-D Cartesian coordinates.

This study aims to develop synthetic ester lubricating oil using renewable sinapic acid as raw material, to explore the structural design and selection of raw materials for green, high-performance synthetic ester oils.

A series of the sinapate ester oils were synthesized through esterification and alkoxylation reactions using renewable source sinapic acid as the raw material. The molecular structures of the oils were characterized by nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy spectroscopy and elemental analysis. The oils were evaluated for safety, viscosity-temperature properties, thermal and oxidative stability, as well as friction reducing and anti-wear characteristics.

Compared to commercial base oil tris (2-ethylhexyl) trimellitate (Phe-3Ci8), the bio-lubricant exhibits superior antifriction and anti-wear properties. Notably, the JCi8-C12 sample performed exceptionally well, reducing the friction coefficient by 11.42% and wear volume (WV) by 54.44% in steel/steel tribo-pairs. In steel/aluminum tribo-pairs, the friction coefficient decreased by 27.48%, while WV was reduced by 85.81%. Mechanistic studies reveal that the introduction of short-chain methoxy groups and stable conjugated systems (aromatic rings and double bonds) inhibit oxidation and decomposition at elevated temperatures. The p-p stacking effect enables lubricant molecules to arrange stably on friction surfaces, forming a durable lubricating film.

The utilization of biomass resources to develop green synthetic lubricating oil with excellent performance not only enhances the added value of waste from agricultural processing but also offers significant benefits in terms of both economic and environmental sustainability.