A Generic Hybrid Model for Bulk Elastodynamics, With Application to Ultrasonic Nondestructive Evaluation

Monolayer two-dimensional transitional metal dichalcogenides, such as MoS₂, WS₂, and WSe₂, are direct band gap semiconductors with large exciton binding energy. They attract growing attentions for optoelectronic applications including solar cells, photodetectors, light-emitting diodes and phototransistors, capacitive energy storage, photodynamic cancer therapy, and sensing on flexible platforms. While light-induced luminescence has been widely studied, luminescence induced by injection of free electrons could promise another important applications of these new materials. However, cathodoluminescence is inefficient due to the low cross-section of the electron–hole creating process in the monolayers. Here for the first time we show that cathodoluminescence of monolayer chalcogenide semiconductors can be evidently observed in a van der Waals heterostructure when the monolayer semiconductor is sandwiched between layers of hexagonal boron nitride (hBN) with higher energy gap. The emission intensity shows a strong dependence on the thicknesses of surrounding layers and the enhancement factor is more than 500-fold. Strain-induced exciton peak shift in the suspended heterostructure is also investigated by the cathodoluminescence spectroscopy. Our results demonstrate that MoS₂, WS₂, and WSe₂ could be promising cathodoluminescent materials for applications in single-photon emitters, high-energy particle detectors, transmission electron microscope displays, surface-conduction electron-emitter, and field emission display technologies

The POA-R and non-POA-R participants’ top five ranked items.

The POA-R and non-POA-R participants’ top five ranked items.</p

Machine Learning in Healthcare: towards Data Structure and Causality

To enhance the quality of medical services, Machine Learning (ML) techniques have been widely applied to model Electronic Health Records (EHRs). Nevertheless, clinical data present two significant challenges: data heterogeneity and complex causality, preventing the further application of ML models. The first challenge comes from the complexity of data structure. EHRs may consist of information from various sources presented in an unstructured format. To address this issue, one viable approach is to transform the raw EHR data into knowledge graphs (KGs) and utilize Graph Neural Networks (GNNs). However, given the imbalanced distribution and inherent heterogeneity of EHR data, the need for more robust GNNs tailored to the specifics of EHRs becomes imperative. In this thesis, we introduce two of my models, HSGNN and MHDP, custom-designed to handle specific EHR data and tackle this challenge. The second challenge is rooted in the intricate latent structure of EHRs and the potential for algorithmic bias. Given the high cost associated with collecting EHR data, there may be inherent selection bias and a missing-not-at-random nature in EHRs. This can further cause algorithmic bias on ML models, especially when data volume is low. Furthermore, as demo- graphics in EHRs often act as confounders, deep learning models may exhibit confounding bias when working with observational data. To tackle these issues, we embrace causal inference theories, including using a deconfounder, to mitigate health disparities and enhance the gen- eralization capabilities of our models. In this paper, we introduce two of my models, PriMeD and FLMD, to achieve fairer predictions and more generalizable models

Scoring criteria of review article.

The purpose of this study is to investigate the efficiency of the production-oriented approach research (POA-R) teaching approach on academic English courses. The six-week study involved thirty-nine postgraduate students from Guilin Medical University studying medical technology. These students were randomly divided into POA-R (19 participants) and non-POA-R (20 participants) groups. The process of study in the POA-R group was divided into three stages, motivating, enabling, and assisting. The instructor gave the class a task at the motivational stage that involved taking the academic IELTS exam, writing a review article and giving an oral presentation about their research topic. At this stage, students are challenged to find relevant information searching PubMed and other literature databases. The teacher served as a facilitator of learning and would not offer information related to the tasks. During the enabling phase, students were encouraged to ask their supervisor for help and guidance. Students ask questions in class, and the instructor discusses the questions with the students and guides them to solve the questions independently. During the assessment stage, students take the academic IELTS exam, finish the review article, given an oral presentation related their research project, and complete an instructional questionnaire. The non-POA-R group was instructed by the teacher lecture method, comprising six lectures and an oral presentation in addition to the completion of a review article. The final grades of course include a review article, an oral presentation, and an academic IELTS test. The results revealed that the students in the POA-R group outperformed the non-POA-R group in terms of mean scores on the IELTS exam, oral presentation, and review article. To further support and demonstrate the advantages of the POA-R teaching approach, an instructional questionnaire using Likert scales and the attitudes of their supervisors was employed. In conclusion, the POA-R teaching approach is a highly successful strategy for enhancing postgraduate students’ academic English proficiency. It greatly enhanced the participants’ academic knowledge, learning interest, and active learning.</div

The comparison of academic IELTS score in the POA-R and the non-POA-R group.

The comparison of academic IELTS score in the POA-R and the non-POA-R group.</p

Scoring criteria of oral presentation.

The purpose of this study is to investigate the efficiency of the production-oriented approach research (POA-R) teaching approach on academic English courses. The six-week study involved thirty-nine postgraduate students from Guilin Medical University studying medical technology. These students were randomly divided into POA-R (19 participants) and non-POA-R (20 participants) groups. The process of study in the POA-R group was divided into three stages, motivating, enabling, and assisting. The instructor gave the class a task at the motivational stage that involved taking the academic IELTS exam, writing a review article and giving an oral presentation about their research topic. At this stage, students are challenged to find relevant information searching PubMed and other literature databases. The teacher served as a facilitator of learning and would not offer information related to the tasks. During the enabling phase, students were encouraged to ask their supervisor for help and guidance. Students ask questions in class, and the instructor discusses the questions with the students and guides them to solve the questions independently. During the assessment stage, students take the academic IELTS exam, finish the review article, given an oral presentation related their research project, and complete an instructional questionnaire. The non-POA-R group was instructed by the teacher lecture method, comprising six lectures and an oral presentation in addition to the completion of a review article. The final grades of course include a review article, an oral presentation, and an academic IELTS test. The results revealed that the students in the POA-R group outperformed the non-POA-R group in terms of mean scores on the IELTS exam, oral presentation, and review article. To further support and demonstrate the advantages of the POA-R teaching approach, an instructional questionnaire using Likert scales and the attitudes of their supervisors was employed. In conclusion, the POA-R teaching approach is a highly successful strategy for enhancing postgraduate students’ academic English proficiency. It greatly enhanced the participants’ academic knowledge, learning interest, and active learning.</div

The class schedule in POA-R group and non-POA-R group.

The class schedule in POA-R group and non-POA-R group.</p

The review article were evaluated by their supervisor.

The review article were evaluated by their supervisor.</p

The teaching contents of POA-R and non-POA-R groups.

There are differences in teaching contents between POA-R and non-POA-R groups during the 6-week academic English class for medical technology postgraduate students.</p

Instructor and students’ activities in the POA-R group.

The actions of the instructor and students in the POA-R group during the 6-week academic English class for medical technology postgraduate students.</p