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Purpose: The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications. Design/methodology/approach: This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process. Findings: The authors' analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples. Originality/value: While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans. [ABSTRACT FROM AUTHOR]

In complex urban scenarios like intersections without dedicated left-turn signals, the construction of planning systems that maximize efficiency while guarantee safety has been a significant challenge. In this paper, we propose a reinforcement learning approach based on curriculum learning using real world dataset, and we develop a partial end-to-end planning and control model capable of adapting to variable temporal and spatial dimensional state inputs, applying it to autonomous driving task. Our model is compared with mainstream reinforcement learning algorithms to validate that our proposed algorithm can effectively solve complex spatio-temporal planning problems. This significantly enhances the efficiency of passing while maintaining a certain level of safety. [ABSTRACT FROM AUTHOR]

The mineralization within the North China Craton (NCC) is intricately linked to Mesozoic large-scale extension in eastern China and is a consequence of a unified geodynamic tectonic background. Despite previous attempts to elucidate the relationship between large-scale mineralization and magmatic activity in the NCC, a lack of systematic research has hindered the identification of connections among deposits with inconsistent metallogenic ages. This study focuses on the coal measures of the Huanghebei Coalfield (HHBC) in western Shandong, presenting a regional magmatic–hydrothermal metallogenic system with a genetic connection. It delves into the intricate interplay between the multi-mineral enrichment mechanism, metallogenic regularity, and the NCC's destruction. The findings reveal that: (1) Various stages of magmatic intrusion during the Yanshanian period significantly influenced the Late Paleozoic coal measures in the HHBC. The coal measures exhibit distinct ranks, ranging from medium-rank bituminous C to A and high-rank anthracite C, resulting in noticeable differences in gas generation among different coal ranks. The shale between the coal seams C5 and C7 emerges as excellent with a good hydrocarbon-generating capacity during the middle-maturity stage. (2) The "Intrusion along the rock layer type" proves most conducive to shale gas enrichment, while the "laccolith type" is more favorable for shale gas enrichment compared to "dike type" intrusions, which have a limited impact on shale gas enrichment. (3) The mineralization process of CBM, shale gas, and iron ore is influenced by Yanshanian-period magma. The enrichment degree of CBM and shale gas exhibits an inverse correlation with the distance from the magmatic intrusion. Iron deposits demonstrate a close association with the magmatic intrusion, with enhanced enrichment along the rock layer. The results indicate that the destruction of the NCC triggered intense metasomatism in the deep cratonic fluids, serving as the primary driving mechanism for large-scale mineralization during the Yanshanian period. Magmatic intrusions bring hydrothermal fluids conducive to mineralization, and the heat release from these intrusions promotes thermal evolution, hydrocarbon generation, and the enrichment of organic-rich strata. [ABSTRACT FROM AUTHOR]