Analyzing the generalizability of the network-based topic emergence identification method

Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.

Rejoinder: The Future of Reliability

In this rejoinder, we examine some of the issues Peter Bentler, Eunseong Cho, and Jules Ellis raise. We suggest a methodological solid way to construct a test indicating that the importance of the particular reliability method used is minor, and we discuss future topics in reliability research.

786 Virtual Teaching in the COVID Era: Providing Surgical Trainee Teaching Via Online Webinars and Video Conferencing

Introduction Core surgical training is dependent on a balance of lecture-based and procedure-based teaching. Due to the COVID-19 pandemic, from March 2020 teaching provided by the Northern Ireland Medical and Dental Training Agency (NIMDTA) for Core Surgical Trainees (CSTs) was cancelled. In lieu of this, a virtual teaching programme was developed to ensure this vital aspect of training was not neglected. Method Firstly, one-year free Affiliate membership to the Royal College of Surgeons of Edinburgh (RCSEd) was provided for all Northern Ireland CSTs, allowing access to RCSEd online webinars. Second, a weekly teaching schedule was developed with accompanying webinar – this was based on the MRCS curriculum. Third, consultants and senior registrars were recruited to conduct a virtual teaching session via videoconferencing. Feedback was collated and used to guide future topics covered. The teaching sessions were recorded for trainee dissemination with consent from the tutors. Results Ten teaching sessions were conducted over three months. While attendance was variable, overall feedback was very positive with requests for this virtual teaching to continue. As such, NIMDTA adopted the teaching programme as their new primary method of lecture-based teaching for all Northern Ireland CSTs. Conclusions Although prepared in a short space of time, a novel, highly successful teaching programme was developed in Northern Ireland to meet the training needs of CSTs. This has resulted in a sustained change to training in Northern Ireland and may be imperative in supporting surgical training in a foreseeably socially distanced world.

Silica Containing Composite Anion Exchange Membranes by Sol–Gel Synthesis: A Short Review

This short review summarizes the literature on composite anion exchange membranes (AEM) containing an organo-silica network formed by sol–gel chemistry. The article covers AEM for diffusion dialysis (DD), for electrochemical energy technologies including fuel cells and redox flow batteries, and for electrodialysis. By applying a vast variety of organically modified silica compounds (ORMOSIL), many composite AEM reported in the last 15 years are based on poly (vinylalcohol) (PVA) or poly (2,6-dimethyl-1,4-phenylene oxide) (PPO) used as polymer matrix. The most stringent requirements are high permselectivity and water flux for DD membranes, while high ionic conductivity is essential for electrochemical applications. Furthermore, the alkaline stability of AEM for fuel cell applications remains a challenging problem that is not yet solved. Possible future topics of investigation on composite AEM containing an organo-silica network are also discussed.

Tourism during and after COVID-19: An Expert-Informed Agenda for Future Research

With the COVID-19 pandemic reaching a more mature, yet still threatening, stage, the time is ripe to look forward in order to identify the topics and trends that will shape future tourism research and practice. This note sets out to develop an agenda for tourism research post COVID-19. We surveyed several industry and academic experts seeking their opinion on three important questions: What potential future topics are needed to address the impact of COVID-19? What existing research areas/topics will become more relevant? What changes are recommended for data collection? Interpreting and synthesizing the answers yields six focal research avenues that researchers should devote more attention and effort to. For each topic, we present various important research questions. By doing so, this note paves the way and serves as a signpost for countless intriguing future research endeavors that are of high relevance and demanded by the industry.