Using DotNetNuke in development and implementation of marine robotics research at University of Limerick

This thesis explores using the open source content management system DotNetNuke (DNN) for the design and implementation of a portal website for the Mobile & Marine Robotic Research Centre (MMRRC) at University of Limerick. This was done in collaboration with the research team in university research centre. Based on plenty of research material accumulated during years of studies, the MMRRC website would be a platform for visitors or interested researchers to learn about the research activities and projects undertaken by MMRRC team at University of Limerick and keep the latest news updated on the website all the time. During this project, there was an exercise in Shannon Estuary which explored a ship undersea and simulate oil leak from the ship. The MMRRC team played significant role in planning and executing the exercise. Video, pictures, as well as real-time data obtained from this exercise were updated on the website. In contrast to traditional websites, this portal website developed with DNN can be easily and conveniently managed by administrators using role-based approach

Supplemental Material - Diasporic citizen journalism: Exploring the discussion on the 2022 blank paper protests in the Chinese twitter community

Supplemental Material for Diasporic citizen journalism: Exploring the discussion on the 2022 blank paper protests in the Chinese twitter community by Jing Zeng and Calvin Yixiang Cheng in Journalism.</p

Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction

Sufficient dimension reduction (SDR) methods target finding lower-dimensional representations of a multivariate predictor to preserve all the information about the conditional distribution of the response given the predictor. The reduction is commonly achieved by projecting the predictor onto a low-dimensional subspace. The smallest such subspace is known as the Central Subspace (CS) and is the key parameter of interest for most SDR methods. In this article, we propose a unified and flexible framework for estimating the CS in high dimensions. Our approach generalizes a wide range of model-based and model-free SDR methods to high-dimensional settings, where the CS is assumed to involve only a subset of the predictors. We formulate the problem as a quadratic convex optimization so that the global solution is feasible. The proposed estimation procedure simultaneously achieves the structural dimension selection and coordinate-independent variable selection of the CS. Theoretically, our method achieves dimension selection, variable selection, and subspace estimation consistency at a high convergence rate under mild conditions. We demonstrate the effectiveness and efficiency of our method with extensive simulation studies and real data examples.</p

Percentage of papers with at least 3 Twitter mentions by journal.

The dotted line denotes the average percentage of papers with at least 3 Twitter mentions. n denotes number of articles.</p

sj-pdf-1-nms-10.1177_14614448221075759 – Supplemental material for Conspiracy theories in online environments: An interdisciplinary literature review and agenda for future research

Supplemental material, sj-pdf-1-nms-10.1177_14614448221075759 for Conspiracy theories in online environments: An interdisciplinary literature review and agenda for future research by Daniela Mahl, Mike S. Schäfer and Jing Zeng in New Media & Society</p

sj-pdf-2-nms-10.1177_14614448221075759 – Supplemental material for Conspiracy theories in online environments: An interdisciplinary literature review and agenda for future research

Supplemental material, sj-pdf-2-nms-10.1177_14614448221075759 for Conspiracy theories in online environments: An interdisciplinary literature review and agenda for future research by Daniela Mahl, Mike S. Schäfer and Jing Zeng in New Media & Society</p

Characteristics of all included papers by region.

Characteristics of all included papers by region.</p

Bayesian multilevel zero-inflated negative binomial regression: Twitter mentions.

Bayesian multilevel zero-inflated negative binomial regression: Twitter mentions.</p

Bayesian multilevel zero-inflated negative binomial regression: Citations.

Bayesian multilevel zero-inflated negative binomial regression: Citations.</p