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Abstract Background Periodontitis is a complex chronic inflammatory disease that is particularly associated with health-related conditions such as smoking, excessive drinking and depression. This research aimed to investigate the interaction between these lifestyles factors on periodontitis risk. Methods This study included participants who participated in the National Health and Nutrition Examination Survey in the United States between 2009 and 2014. They had completed oral health-periodontal examination, Smoking-Cigarette Use Questionnaire, Alcohol Use Questionnaire, and Patient Health Questionnaire. Periodontal clinical attachment loss (CAL) of 3 mm or more and Patient Health Questionnaire-9 (PHQ-9) of 10 scores or more were used to identify periodontitis and depression, respectively. Daily alcohol consumption in the past year was classified into three levels: low (1 drink or less), moderate (between 1 and 3 drinks), and heavy drinking (4 drinks or more), while smoking was defined as having smoked at least 100 cigarettes in one's lifetime. Then, the logistic regression combined with interaction models were used to analyze the independent and combined effects of smoking, drinking and depression on periodontitis risk. Results The results indicated a statistically significant multiplicative interaction between smoking and depression in relation to the development of periodontitis, both in the overall population (P = 0.03) and among male participants (P = 0.03). Furthermore, among individuals experiencing depression, smoking was found to significantly increase the prevalence of periodontitis by 129% in the younger age group compared to non-smokers (odds ratio [OR]: 2.29; 95% confidence interval [CI]: 1.10 to 4.76). However, the interaction between smoking and alcohol consumption was only significant among females (P

To improve students' learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from transformers and generative pretrained transformer-2, to automatically generate Python programming remedial materials; subsequently, learning performance prediction models constructed using various machine learning methods are used to determine students' learning type, enabling the automatic generation of personalized remedial materials. The participants in this study were 78 students from a university in northern Taiwan enrolled in an 8-week Python course. Students in the experimental (n = 36) and control (n = 42) groups engaged in the same programming learning activities during the first 5 weeks of the course, and they completed a pretest of programming knowledge in Week 6. For the review activity in Week 7, the 36 students in the experimental group received personalized intervention, whereas those in the control group received traditional class tutoring. We examined the effect of the self-regulated learning and personalized intervention on the learning performance of students. Compared with the traditional class tutoring, the personalized intervention review activity not only helped students obtain higher learning performance but also prompted greater improvements in the following learning strategies: rehearsal, critical thinking, metacognitive self-regulation, effort regulation, and peer learning. We also observed that students' rehearsal and help-seeking learning strategies indirectly affected learning performance through students' note-taking in the provided e-book.

To improve students’ learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from transformers and generative pretrained transformer-2, to automatically generate Python programming remedial materials; subsequently, learning performance prediction models constructed using various machine learning methods are used to determine students’ learning type, enabling the automatic generation of personalized remedial materials. The participants in this study were 78 students from a university in northern Taiwan enrolled in an 8-week Python course. Students in the experimental (n = 36) and control (n = 42) groups engaged in the same programming learning activities during the first 5 weeks of the course, and they completed a pretest of programming knowledge in Week 6. For the review activity in Week 7, the 36 students in the experimental group received personalized intervention, whereas those in the control group received traditional class tutoring. We examined the effect of the self-regulated learning and personalized intervention on the learning performance of students. Compared with the traditional class tutoring, the personalized intervention review activity not only helped students obtain higher learning performance but also prompted greater improvements in the following learning strategies: rehearsal, critical thinking, metacognitive self-regulation, effort regulation, and peer learning. We also observed that students’ rehearsal and help-seeking learning strategies indirectly affected learning performance through students’ note-taking in the provided e-book.

Abstract Sustainable education is a crucial aspect of creating a sustainable future, yet it faces several key challenges, including inadequate infrastructure, limited resources, and a lack of awareness and engagement. Artificial intelligence (AI) has the potential to address these challenges and enhance sustainable education by improving access to quality education, creating personalized learning experiences, and supporting data-driven decision-making. One outcome of using AI and Information Technology (IT) systems in sustainable education is the ability to provide students with personalized learning experiences that cater to their unique learning styles and preferences. Additionally, AI systems can provide teachers with data-driven insights into student performance, emotions, and engagement levels, enabling them to tailor their teaching methods and approaches or provide assistance or intervention accordingly. However, the use of AI and IT systems in sustainable education also presents challenges, including issues related to privacy and data security, as well as potential biases in algorithms and machine learning models. Moreover, the deployment of these systems requires significant investments in technology and infrastructure, which can be a challenge for educators. In this review paper, we will provide different perspectives from educators and information technology solution architects to connect education and AI technology. The discussion areas include sustainable education concepts and challenges, technology coverage and outcomes, as well as future research directions. By addressing these challenges and pursuing further research, we can unlock the full potential of these technologies and support a more equitable and sustainable education system.

Human-guided machine learning can improve computing intelligence, and it can accurately assist humans in various tasks. In education research, artificial intelligence (AI) is applicable in many situations, such as predicting students’ learning paths and strategies. In this study, we explore the benefits of repetitive practice of short-answer questions could enhance students’ long-term memory for subsequent improvements in learning performance. However, frequent authoring questions and grading requires teachers’ professionalism, experience, and considerable efforts. Therefore, this study using modern AI technologies, specifically natural language processing, to provide Automatic question generation (AQG) solution, a combined semantics-based and syntax-based question generation system: Hybrid automatic question generation (Hybrid-AQG) was proposed in this study. We assessed its functionality and student learning performance by asking 91 students to complete short-answer questions and then applied a process similar to the Turing test to evaluate the question and grading quality. The results demonstrated that modern AI technologies can generate highly realistic short-answer questions because: (1) Compared with the control group, the experimental group exhibited significantly better learning performance, implying that students acquired long-term memory of course knowledge through repetitive practice with machine-generated questioning. (2) The experimental group could better distinguish machine-generated and expert-authored questions. Nevertheless, both groups in distinguishing questions presented like guessing. (3) Machine grading was deficient in some respects; but the way students answer questions can be adapted for machine understanding through repetitive practice.

In order to study the distribution and evolution of the transient heat flux on HL-2A during edge-localized-mode (ELM) bursts, the BOUT++ electromagnetic six-field two-fluid model is used to simulate the pedestal collapse under the lower single-null divertor geometry. The equilibrium profiles of HL-2A ELMy H-mode discharge No. 24 953 are adopted as the initial condition in the original case. In this case, linear analysis shows that the resistive ballooning mode (RBM) and drift-Alfven wave are unstable to this equilibrium, and RBM is the dominant instability. The evolutions of the radial heat fluxes at the outer mid-plane and heat fluxes to the inner and outer targets during the ELM event are presented. Six more equilibria are constructed based on the original case to find out the influence of the pedestal profiles on the peak electron heat flux. The results indicate that the heat flux increases with temperature and/or density, and the theoretical analysis and simulation results consistently show that the heat flux q∥e is proportional to ne0,SEPTe0,SEP32.

Abstract The late Paleozoic ice age was one of the three major glaciations in the Phanerozoic. However, the influence and characteristics of this glaciation and deglaciation remain unclear. In this study, we present lithostratigraphy, biostratigraphy, and chemostratigraphy during the Late Mississippian to the Early Permian in Tarim, Northwestern China. Tarim records two Gondwana glacial events in the Serpukhovian‐Moscovian (Glacial II) and the Asselian‐Sakmarian (Glacial III). Together with the global evidence of isotopes and sea level changes, multipulsed growth and decay of Glacial II and III are confirmed. The Serpukhovian isotopic pattern in Tarim exhibits a negative δ13C excursion of −6.6‰, which is comparable to those recorded in other continents. This negative excursion and contemporaneous sea level fall are triggered by the expansion of the ice sheets of Glacial II. The glaciation maximum is marked by the global sea level fall during the Bashkirian. A two‐stage transgression and positive shifts of δ13C from the Tarim and Laurussia represent evidence of stepwise deglaciation of Glacial II during the Moscovian. Positive δ18O excursions in Tarim and other two global sections from Laurussia and the regression during early Asselian to early Sakmarian are linked to the Glacial III, confirming this event is of global origin. The eustatic rise and the negative δ18O shifts indicate the retreat of this Gondwana glacier in late Sakmarian. These continuous data sets in Tarim coupled with time equivalent stratigraphy and geochemical records provide insights into the multistage evolution of the discrete Glacial II and III events.

Surface spin current in a 3D topological insulator (TI) remains difficult to control and the out-of-plane spin texture is largely unexplored. Here, the authors identify subtle effect of the spin texture on surface photocurrent and demonstrate controlled spin injection from a semiconductor to a TI.

An inexact chance-constrained integer programming (ICIP) method is developed for planning contamination control of fluid power system (FPS). The ICIP is derived by incorporating chance-constrained programming (CCP) within an interval mixed integer linear programming (IMILP) framework, such that uncertainties presented in terms of probability distributions and discrete intervals can be handled. It can also help examine the reliability of satisfying (or risk of violating) system constraints under uncertainty. The developed method is applied to a case of contamination control planning for one typical FPS. Interval solutions associated with risk levels of constraint violation are obtained. They can be used for generating decision alternatives and thus help designers identify desired strategies under various environmental, economic, and system reliability constraints. Generally, willingness to take a higher risk of constraint violation will guarantee a lower system cost; a strong desire to acquire a lower risk will run into a higher system cost. Thus, the method provides not only decision variable solutions presented as stable intervals but also the associated risk levels in violating the system constraints. It can therefore support an in-depth analysis of the tradeoff between system cost and system-failure risk.