Serial Position Effects and Forgetting Curves: Implications in Word Memorization

Word memorization is important in English learning and teaching. The theory and implications of serial position effects and forgetting curves are discussed in this paper. It is held that they help students understand the psychological mechanisms underlying word memorization. The serial position effects make them to consider the application the chunking theory in word memorization; the forgetting curve reminds them to repeat the words in long-term memory in proper time. Meanwhile the spacing effect and elaborative rehearsal effect are also discussed as they are related to the forgetting curve

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

miR-181a increases FoxO1 acetylation and promotes granulosa cell apoptosis via SIRT1 downregulation.

Oxidative stress impairs follicular development by inducing granulosa cell (GC) apoptosis, which involves enhancement of the transcriptional activity of the pro-apoptotic factor Forkhead box O1 (FoxO1). However, the mechanism by which oxidative stress promotes FoxO1 activity is still unclear. Here, we found that miR-181a was upregulated in hydrogen peroxide (

External and Internal Predictors of Student Satisfaction with Online Learning Achievement

Building and testing a framework of interactive and indirect predictors of student satisfaction would help us understand how to improve student online learning experience. The current study proposed that external predictors such as poor technological, environmental, and pedagogical factors would be internalized as negative psychological traits and indirectly predict student satisfaction in online learning. Results of multivariate regressions with 5824 Chinese undergraduate students demonstrated that instructors’ online teaching experience and communication with students had a stronger predictive effect on student satisfaction than wireless network quality and learning environment. Providing after-class reviewing materials to students or having longer self-learning time would not buffer students from negative external factors. Structural equation modeling analysis results showed that inferior technological, environmental, and pedagogical factors would be internalized into negative attitudes and emotions toward online learning and indirectly predict student satisfaction. Our study has implications for better understanding the extensive influence of online learning barriers caused by external conditions and building preventive mechanisms through the improvement of instructors’ teaching experience and communication with students

Artificial Intelligent Diagnosis and Monitoring in Manufacturing

The manufacturing sector is heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. It has been reported that 35% of US manufacturers are currently collecting data from sensors for manufacturing processes enhancement. Nevertheless, many are still struggling to achieve the 'Industry 4.0', which aims to achieve nearly 50% reduction in maintenance cost and total machine downtime by proper health management. For increasing productivity and reducing operating costs, a central challenge lies in the detection of faults or wearing parts in machining operations. Here we propose a data-driven, end-to-end framework for monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to extract hidden degradation features from noisy data. We demonstrate the proposed framework on several representative experimental manufacturing datasets drawn from a wide variety of applications, ranging from mechanical to electrical systems. Results reveal that the framework performs well in all benchmark applications examined and can be applied in diverse contexts, indicating its potential for use as a critical corner stone in smart manufacturing

Selective sodium-glucose cotransporter-2 inhibitors in the improvement of hemoglobin and hematocrit in patients with type 2 diabetes mellitus: a network meta-analysis

ObjectiveTo compare the effects of different selective sodium-glucose cotransporter-2 inhibitors (SGLT2i) on hemoglobin and hematocrit in patients with type 2 diabetes mellitus (T2DM) with a network meta-analysis (NMA).MethodsRandomized controlled trials (RCTs) on SGLT2i for patients with T2DM were searched in PubMed, Embase, Cochrane Library, and Web of Science from inception of these databases to July 1, 2023. The risk of bias (RoB) tool was used to evaluate the quality of the included studies, and R software was adopted for data analysis.ResultsTwenty-two articles were included, involving a total of 14,001 T2DM patients. SGLT2i included empagliflozin, dapagliflozin, and canagliflozin. The NMA results showed that compared with placebo, canagliflozin 100mg, canagliflozin 300mg, dapagliflozin 10mg, dapagliflozin 2mg, dapagliflozin 50mg, dapagliflozin 5mg, empagliflozin 25mg, and dapagliflozin 20mg increased hematocrit in patients with T2DM, while canagliflozin 100mg, canagliflozin 200mg, canagliflozin 300mg increased hemoglobin in patients with T2DM. In addition, the NMA results indicated that canagliflozin 100mg had the best effect on the improvement of hematocrit, and canagliflozin 200mg had the best effect on the improvement of hemoglobin.ConclusionBased on the existing studies, we concluded that SGLT2i could increase hematocrit and hemoglobin levels in patients with T2DM, and canagliflozin 100mg had the best effect on the improvement of hematocrit, while canagliflozin 200mg had the best effect on the improvement of hemoglobin.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/#loginpage, identifier PROSPERO (CRD42023477103)