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This paper aims to develop flame-retardant (FR) polyamide 12 (PA12) nanocomposite from regenerated powder via selective laser sintering (SLS), an additive manufacturing technique.

First, the morphology, processibility, thermal and mechanical properties of PA12 regenerated powder, consisting of 50 wt% new and 50 wt% recycled powder, as well as corresponding printed specimens, were evaluated to characterize the effects of previous SLS processing. Second, flame-retardant PA12 was developed by incorporating both single and binary halogen-free flame retardants into the regenerated powder.

It was found that the printed specimens from regenerated powder had much higher tensile and impact properties compared to specimens made from new powder, which is attributed to better particulate fusion and coalescence realized in higher temperature SLS printing. The effect of FRs on thermal, mechanical and flame retardant properties of the PA12 composites/nanocomposites was investigated systematically. It was found that the nanoclay, as a synergist, improved both flame-retardant and mechanical properties of PA12. UL94 standard rating of V-0 was achieved for the printed nanocomposite by incorporating 1 wt% nanoclay into 15 wt% phosphinates FR. Moreover, on average, the tensile and impact strength of the nanocomposite were increased by 26.13% and 17.09%, respectively, in XY, YZ and Z printing orientations as compared to the equivalent flame retardant composite with 20 wt% of the phosphinates FR.

This paper fulfills the need to develop flame retardant parts via SLS technology with waste feedstock. It also addresses the challenge of developing flame retardant materials without obviously compromising the mechanical properties by making use of the synergistic effect of nanoclay and organic phosphinates.

This study aims to comprehensively investigate the process-structure-property correlation of acrylonitrile butadiene styrene (ABS) parts manufactured by the overheat material extrusion (Mex) method. This study considers the relationships between the tensile and impact strength with temperature profiles, mesostructures and fracture behaviors of the ABS-printed parts.

The overheat printing condition was generated by using the highest possible printing temperature of the Mex printer used in this study together with cooling fan turned off. Temperature profiles of the polymer rasters were measured to characterize the diffusion time of the deposited rasters. Thermogravimetric analysis, differential scanning calorimetry and melt flow index were performed to study the thermal properties of the ABS feedstock. The mesostructures of the printed ABS samples were characterized by using an optical microscope, while their fracture surface was investigated using a field emission scanning electron microscope. The authors performed the tensile and impact tests following ASTM D3039 and D256-10A, respectively.

The use of the overheat Mex printing could offer better raster diffusion with reduced cooling rate and prolonged diffusion time. Consequently, the overheat printed ABS parts possessed a porosity as low as 1.35% with an increase in the weld length formed between the adjacent rasters of up to 62.5%. More importantly, the overheat printed ABS parts exhibited an increase of up to 70%, 84% and 30% in tensile strain at break, tensile toughness and impact strength, respectively, compared to their normal printed counterparts.

This study provides a facile but effective approach to fabricate highly dense and strong polymeric parts printed by Mex method for end-use applications.

The growth of sustainable tourism has emerged as a major topic of discussion on a global scale, and the hospitality industry is playing a crucial part in the achievement of this goal. The hospitality industry in China is experiencing rapid growth, and with the assistance of artificial intelligence (AI), the country may be able to realize sustainable growth in the tourism sector. Within the context of the Chinese hospitality industry, this chapter investigated the role AI plays in promoting sustainable tourism and how the Chinese government is supporting the hospitality industry in doing so. Sustainable tourism practices increase travellers' trip experiences while protecting the environment and local people; however, China could invest more in AI technology to promote sustainable tourism, which might benefit the economy, environment and society. This chapter examines AI deployment in the Chinese hotel industry and its challenges and benefits. AI technology can increase service quality, operational efficiency and hospitality operators' competitiveness, according to the authors. AI application requires careful consideration of employee training, ethics and customer privacy.

As the tourism industry continues to evolve and adapt to changing consumer demands, automated systems and artificial intelligence have gained significant attention. However, concerns related to ethical, social and environmental aspects have arisen alongside the benefits offered by these technologies. This chapter explores the emerging challenges surrounding the integration of robots and self-service technology in responsible and creative tourism.

This paper aims to employ social cognitive theory (SCT) as a theoretical framework to investigate the influencing factors affecting the knowledge transfer effectiveness of construction workers in China. The mediating role of their knowledge transfer willingness is also assessed.

A questionnaire on knowledge transfer among Chinese construction workers is designed and subsequently analyzed by structural equation modeling (SEM), with a total sample of 288 construction workers.

The SEM results show that the knowledge self-efficiency, blood and geographic relationships, and trust relationship promote knowledge transfer willingness and positively influence the knowledge transfer effectiveness of construction workers. However, the effect of organizational culture on knowledge transfer willingness and effectiveness is nonsignificant.

The results are conducive to managers and governments formulating strategies to optimize the learning mechanism of construction workers and facilitate their obtaining of resources from the project, thus easing skill shortages and promoting the transformation of construction workers into industrial workers.

This paper innovatively proposes blood and geographic relationships as research variables, expanding their scope. Furthermore, SCT is applied to enable future research to better understand individual knowledge transfer behavior from both personal and environmental perspectives.