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Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences. The majority of this research was published in prominent journals such as the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. These publications predominantly originate from Europe, the United States, and China. Since 2020, there has been substantial growth in the application of machine learning techniques in predicting forest fires, particularly in estimating fire occurrence probabilities, simulating fire spread, and projecting post-fire environmental impacts. Advanced algorithms, including deep learning and ensemble learning, have shown superior accuracy, suggesting promising directions for future research. Additionally, the integration of machine learning with cellular automata has markedly improved the simulation of fire behavior, enhancing both efficiency and precision. The profound impact of climate change on wildfire prediction also necessitates the inclusion of extensive climate data in predictive models. Beyond conventional studies focusing on fire behavior and occurrence probabilities, forecasting the environmental and ecological consequences of fires has become integral to forest fire management and vital for formulating more effective wildfire strategies. The study concludes that significant regional disparities in knowledge exist, underscoring the need for improved research capabilities in underrepresented areas. Moreover, there is an urgent requirement to enhance the application of artificial intelligence algorithms, such as machine learning, deep learning, and ensemble learning, and to intensify efforts in identifying and leveraging various wildfire drivers to refine prediction accuracy. The insights generated from this field will profoundly augment our understanding of wildfire prediction, assisting policymakers and practitioners in managing forest resources more sustainably and averting future wildfire calamities. [ABSTRACT FROM AUTHOR]

In this article, we consider hybrid manufacturing/remanufacturing inventory systems that produce a single product to satisfy demands over a finite planning horizon. In each period, the firm receives random demand and returns of end-of-life products. A serviceable product can be manufactured from ample raw materials or remanufactured from a returned product. The two operations possess random dedicated capacities. The firm's objective is to minimize the expected total discounted cost over the planning horizon. We partially characterize the firm's optimal inventory policy when the two capacities are positively dependent and completely characterize it when only one capacity is random. When there is ample manufacturing capacity, we connect the model with an auxiliary dual-sourcing inventory model and derive a more detailed structure of the optimal policy. Finally, our numerical study provides actionable insights into the effects of random capacities. Among others, we find that approximating a slightly/moderately variable remanufacturing capacity as its deterministic mean capacity or ignoring the correlation between two random capacities under a multi-period setting incurs a limited cost to the firm. [ABSTRACT FROM AUTHOR]

In this study, erucamide as a non‐toxic and tasteless lubricant was melt blended with thermoplastic polyurethane (TPU) to reduce the coefficient of friction (COF) of TPU and thus to improve the wearing comfort of intelligent wearable devices. The results show that when the content of erucamide reaches percolation, erucamide can migrate to the TPU surface, forming a layer of lubrication film, and thus significantly reduces the COF of TPU. In addition, erucamide with amide groups can form hydrogen bonds with carbamate groups from TPU chains and thus can disrupt the hydrogen bonds between two amide groups of TPU chains. With the introduction of erucamide, the chain mobility of TPU increases, the partial phase mixing of soft phase and hard phase of TPU occurs, and the interchain interaction between TPU chains decreases. Under the appropriate content of erucamide (0.5 wt%), both the elasticity and processability of TPU are also significantly improved, whereas the high tensile strength, tear strength and elongation at break of the TPU blend is still obtained, facilitating its practical application in intelligent wearable devices. © 2022 Society of Industrial Chemistry. [ABSTRACT FROM AUTHOR]

Objectives: To explore the relationship between patient's health literacy and perceived shared decision‐making (SDM) among Chinese cancer patients. Methods: A cross‐sectional study was conducted involving a convenience sample of 458 cancer patients from four public hospitals in Guangzhou, China. Patients' self‐reported data were collected using the Health Literacy Management Scale (HeLMS) and the nine‐item Shared Decision‐Making Questionnaire (SDM‐Q‐9). Hierarchical multiple regressions, controlling for patient–doctor relationship, social support, sociodemographic and clinical variables were conducted to explore the effect of health literacy on perceived SDM. Results: Health literacy itself accounted for 68.0% of the variance in perceived SDM. Higher scores in domains "information acquisition ability," and "communication interaction ability" of HeLMS were significantly associated with a higher level of perceived SDM after controlling the covariates (R2 = 75.7%). Conclusions: Health literacy, especially the information acquisition ability and communication interaction ability, played a prominent role for Chinese cancer patients to be involved in treatment decision making. [ABSTRACT FROM AUTHOR]