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Laser ablation (LA), which employs a pulsed laser to remove materials from a substrate for generating micro-/nanostructures, has tremendous applications in the fabrication of metals, ceramics, glasses and polymers. It has become a noteworthy approach for achieving various functional structures in engineering, chemistry, biology, medicine and other fields. Polymers are one such class of materials; they can be melted and vaporized at high temperature during the ablation process. A number of polymers have been researched as candidate substrates in LA, and many different structures and patterns have been realized by this method. The current states of research and progress are reviewed from basic concepts to optimal parameters, polymer types and applications. The significance of this paper is to provide a basis for follow-up research that leads to the development of superior materials and high-quality production through LA. In this review, we first introduce the basic concept of LA, including mechanism, laser types (millisecond, microsecond, nanosecond, picosecond and femtosecond) and influential parameters (wavelength, repetition rate, fluence and pulse duration). Then, we focus on several commonly used polymer materials and compare them in detail, including the effects of polymer properties, laser parameters and feature designs. Finally, we summarize the applications of various structures fabricated by LA in a variety of areas along with a perspective of the challenges in this research area. Overall, a thorough review of LA of several polymers is presented, which could pave the way for characterization of future novel materials.

High turnover rate represents one of the most significant challenges the hotel industry faces. High turnover rates mean labor shortages, resulting in high costs of recruiting, staffing and training. Turnover also has a negative impact on service quality. Scholars continue to search for the root causes of turnover and propose solutions. To further understand employees' turnover intention, this study reveals the role of stress on hotel front-line employees' turnover intention through the mediation of burnout. Moreover, the study examines the moderating effect of service climate on the underlying mechanism that links role stress with turnover intention. Using a sample of 583 questionnaires from front-line hotel employees in South China, this study reveals that role stress as a four-dimensional construct (i.e., conflict, ambiguity, qualitative overload and quantitative overload) has a statistically significant impact on burnout, which leads to turnover intention. Burnout completely mediates the relationship between role stress and turnover intention, that is, employees under role stress do not resign immediately unless they experience high levels of burnout. In addition, service climate moderates the influence of role stress on burnout, suggesting a moderated mediation relationship. The study contributes to the organizational management literature by confirming the four dimensions of role stress and demonstrating how role stress impacts employees' turnover intention. Furthermore, the critical effect of service climate is further investigated. Theoretical contributions and managerial implications are discussed based on the findings. the study also investigates the moderating effect of service climate on role stress (challenge-hindrance stressors) and burnout.

Background: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI. Methods: We retrospectively included a total of 242 NPC patients who underwent regular follow-up magnetic resonance imaging (MRI) examinations, including contrast-enhanced T1-weighted and T2-weighted imaging. For each MRI sequence, four non-texture and 10,320 texture features were extracted from medial temporal lobe, gray matter, and white matter, respectively. The relief and 0.632 + bootstrap algorithms were applied for initial and subsequent feature selection, respectively. Random forest method was used to construct the prediction model. Three models, 1, 2 and 3, were developed for predicting the results of the last three follow-up MRI scans at different times before RTLI onset, respectively. The area under the curve (AUC) was used to evaluate the performance of models. Results: Of the 242 patients, 171 (70.7%) were men, and the mean age of all the patients was 48.5 ± 10.4 years. The median follow-up and latency from radiotherapy until RTLI were 46 and 41 months, respectively. In the testing cohort, models 1, 2, and 3, with 20 texture features derived from the medial temporal lobe, yielded mean AUCs of 0.830 (95% CI: 0.823-0.837), 0.773 (95% CI: 0.763-0.782), and 0.716 (95% CI: 0.699-0.733), respectively. Conclusion: The three developed radiomic models can dynamically predict RTLI in advance, enabling early detection and allowing clinicians to take preventive measures to stop or slow down the deterioration of RTLI.