Remnants of cities and farmlands in China’s hyperarid Tarim Basin indicate that environmental conditions were significantly wetter two millennia ago in a region which is barren desert today. Historical documents and age data of organic remains show that the Loulan Kingdom flourished during the Han Dynasty (206 BCE–220 CE) but was abandoned between its end and 645 CE. Previous archaeological, geomorphological and geological studies suggest that deteriorating climate conditions led to the abandonment of the ancient desert cities. Based on analyses of lake sediments from Lop Nur in the eastern Tarim Basin and a review of published records, we show that the Loulan Kingdom decline resulted from a man-made environmental disaster comparable to the recent Aral Sea crisis rather than from changing climate. Lop Nur and other lakes within the Han Dynasty realm experienced rapidly declining water levels or even desiccation whilst lakes in adjacent regions recorded rising levels and relatively wet conditions during the time of the Loulan Kingdom decline. Water withdrawal for irrigation farming in the middle reaches of rivers likely caused water shortage downstream and eventually the widespread deterioration of desert oases a long time before man initiated the Aral Sea disaster in the 1960s.
Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.
This review provides substantial evidences of apoptosis pathways in cancer cells and the apoptotic proteins modulated by active natural products, which may help to gain a comprehensive understanding for the development of natural drugs for better cancer treatment.
As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students’ learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students’ collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.
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