Nottingham Trent University

About: Nottingham Trent University is a education organization based out in Nottingham, United Kingdom. It is known for research contribution in the topics: Population & Addiction. The organization has 4702 authors who have published 12862 publications receiving 307430 citations. The organization is also known as: NTU & Trent Polytechnic.

Dual‐Scale Roughness Produces Unusually Water‐Repellent Surfaces

Abstract: Super-hydrophobicity can be achieved on relatively smooth surfaces. Short, wide pillars on slightly rough surfaces are shown to produce super-hydrophobic surfaces (see Figure) where neither the pillars nor the slight roughness suffice alone. This use of two length scales to create super-hydrophobic surfaces directly mimics the mechanism used by some plants including the lotus.

Prevalence of the Addictions: A Problem of the Majority or the Minority?

Abstract: An increasing number of research studies over the last three decades suggest that a wide range of substance and process addictions may serve similar functions. The current article considers 11 such potential addictions (tobacco, alcohol, illicit drugs, eating, gambling, Internet, love, sex, exercise, work, and shopping), their prevalence, and co-occurrence, based on a systematic review of the literature. Data from 83 studies (each study n = at least 500 subjects) were presented and supplemented with small-scale data. Depending on which assumptions are made, overall 12-month prevalence of an addiction among U.S. adults varies from 15% to 61%. The authors assert that it is most plausible that 47% of the U.S. adult population suffers from maladaptive signs of an addictive disorder over a 12-month period and that it may be useful to think of addictions as due to problems of lifestyle as well as to person-level factors.

Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network

Abstract: In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to address the problem, a major limitation of existing work is the lack of comparative evaluations, which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks. We conduct an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available Twitter datasets to date, and show that compared to previously reported results on these datasets, our proposed method is able to capture both word sequence and order information in short texts, and it sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13% in F1. We also extend the existing dataset collection on this task by creating a new dataset covering different topics.