University of Westminster

About: University of Westminster is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2944 authors who have published 8426 publications receiving 200236 citations. The organization is also known as: Westminster University & Royal Polytechnic Institution.

Researching Mental Health in Minority Ethnic Communities: Reflections on Recruitment:

Abstract: In this article we reflect on the recruitment of research participants to two related studies of experiences of mental health problems in Black and minority ethnic communities in the United Kingdom. A total of 65 people were recruited via three main strategies: the employment of bicultural recruiters, intensive information sharing about the studies, and work through local community groups. Three main issues seemed to affect recruitment: gatekeepers’ attitudes, the (non)payment of participants, and reciprocal arrangements with local community groups. The type of strategy employed resulted in recruits with differing characteristics (although our sample was too small to draw generalizable conclusions). We conclude that to ensure that research participation is accessible to all, researchers must employ flexible recruitment methods that permit adaptation to specific needs arising out of health status, level of involvement with services, culture, and socioeconomic status. Systematic research into this part of the research process is needed.

Optimal nutrition and the ever-changing dietary landscape: a conference report.

Abstract: The field of nutrition has evolved rapidly over the past century. Nutrition scientists and policy makers in the developed world have shifted the focus of their efforts from dealing with diseases of overt nutrient deficiency to a new paradigm aimed at coping with conditions of excess—calories, sedentary lifestyles and stress. Advances in nutrition science, technology and manufacturing have largely eradicated nutrient deficiency diseases, while simultaneously facing the growing challenges of obesity, non-communicable diseases and aging. Nutrition research has gone through a necessary evolution, starting with a reductionist approach, driven by an ambition to understand the mechanisms responsible for the effects of individual nutrients at the cellular and molecular levels. This approach has appropriately expanded in recent years to become more holistic with the aim of understanding the role of nutrition in the broader context of dietary patterns. Ultimately, this approach will culminate in a full understanding of the dietary landscape—a web of interactions between nutritional, dietary, social, behavioral and environmental factors—and how it impacts health maintenance and promotion.

Obesity and low vision as a result of excessive Internet use and television viewing.

Abstract: The technological age has resulted in children spending prolonged hours in front of television (TV) and computer screens (on the Internet). The aim of this prospective cross-sectional study is to determine the effect of this phenomenon on both childhood obesity and low vision in the State of Qatar. A total of 3000 school students aged 6 to 18 years were approached from September 2009 to March 2010 and 2467 (82.2%) students agreed to participate. Face-to-face interviews based on a designed questionnaire were conducted. The highest proportion of obese children were aged between 15-18 years (9.4%; p < 0.001); spent ≥ 3 hours on the Internet (5.6%; p < 0.001), and spent between 5-7 hours or less sleeping (4.1%; p < 0.001). Forty-six (1.9%) children spent ≥ 3 hours/day on the Internet, and were either overweight/obese and had low vision. The study findings confirmed a positive association between obesity and low vision as a result of excessive time spent on the TV view and Internet use.

An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks

Abstract: A Network Intrusion Detection System is a critical component of every internet-connected system due to likely attacks from both external and internal sources. Such Security systems are used to detect network born attacks such as flooding, denial of service attacks, malware, and twin-evil intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in 5G and IoT network. We evaluated the algorithm with the benchmark Aegean Wi-Fi Intrusion dataset. Our results showed an excellent performance with an overall detection accuracy of 99.9% for Flooding, Impersonation and Injection type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm.