Gerodontology--how big is the challenge in Ireland?

Population trends suggest that the Irish population is ageing, and that this population will have substantial treatment needs. These patients will be better informed than previous generations, and will demand treatment aimed at preserving a natural dentition. This will impact upon delivery of oral healthcare and manpower planning needs to consider how to address the increased demand for dental care. Poor oral health is associated with systemic health problems, including cardiovascular disease, respiratory disease and diabetes mellitus. It also has a negative impact upon quality of life, and the World Health Organisation has encouraged public healthcare administrators and decision makers to design effective and affordable strategies for better oral health and quality of life of older adults, which, in turn, are integrated into general health management programmes. Treatment concepts such as minimally invasive dentistry and the shortened dental arch concept are discussed in the context of these demographic changes and recommendations.

Conical Horn Antenna with Spiral Phase Plate for Difference Pattern Generation

This paper describes the performance characteristics and experimental validation of a compact conical horn antenna with a dielectric cylinder spiral phase plate attached at its aperture. This performs the function of a spatial phase imprinting device creating a helical wave-front which results in a null in the far field radiation pattern of the antenna assembly.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Big data warehouse framework for smart revenue management

Revenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Management

Scott McElveen LLP presents the South Carolina big 50

This ranks private, public and foreign-affiliated companies by the number of employees on their South Carolina payrolls as of July 1, 2008, and then compares the progress of participating companies from year to year. The South Carolina Big 50 includes financial institutions, insurance companies, retailers, retail establishments, hospitals and healthcare organizations. The South Carolina Big 50, however, does exclude government agencies and organizations. The top company remained the same as in the 2007 issue, with Wal-Mart Stores Inc. continuing to be ranked No. 1. BI-LO LLC and Palmetto Health moved from No. 3 and No. 4 to No. 2 and No. 3 respectively.

Cradling ashes : a surrealist opera : how music and psychology can function together to express depraved heart syndrome : for countertenor, soprano, tenor, violin, viola, horn in F, piano, and tape.

Original wav file in this record was 0 bytes; re-extracted a new wav file from the CD in Special Collections Nov 2016 and replaced the original wav file in this record.

Inferring Big 5 Personality from Online Social Networks

Thesis (Master's)--University of Washington, 2014

Contributos para a compreensão do sucesso da cosmologia do big bang : uma análise themática à luz de Gerald Holton

Tese de doutoramento, História e Filosofia das Ciências, Universidade de Lisboa, Faculdade de Ciências, 2016

Big and beautiful: attractiveness and health ratings of the female body by male "fat admirers"

This study examined the body weight and waist-to-hip ratio (WHR) preferences of “fat admirers” (FAs), that is, individuals who are sexually attracted to heavier partners. Fifty-six heterosexual men involved in the FA community rated a series of line drawings that varied in three levels of body weight and six of WHR for physical attractiveness and health. The results showed significant main effects of body weight and WHR, as well as a significant body weight × WHR interaction for both health ratings. In general, there was a preference for heavyweight figures and high WHRs for ratings of attractiveness and normal-weight figures and mid-ranging WHRs for ratings of health. Limitations of the study and explanations for fat admiration are discussed.