Cardiff University

About: Cardiff University is a education organization based out in Cardiff, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 34188 authors who have published 82643 publications receiving 3046531 citations. The organization is also known as: University of Cardiff & University College of South Wales and Monmouthshire.

Therapeutic drug monitoring (TDM) of antifungal agents: guidelines from the British Society for Medical Mycology

Abstract: The burden of human disease related to medically important fungal pathogens is substantial. An improved understanding of antifungal pharmacology and antifungal pharmacokinetics-pharmacodynamics has resulted in therapeutic drug monitoring (TDM) becoming a valuable adjunct to the routine administration of some antifungal agents. TDM may increase the probability of a successful outcome, prevent drug-related toxicity and potentially prevent the emergence of antifungal drug resistance. Much of the evidence that supports TDM is circumstantial. This document reviews the available literature and provides a series of recommendations for TDM of antifungal agents.

The Consequences of Dysfunctional Customer Behavior

Abstract: Christopher Lovelock (1994) coined the term jaycustomers to refer to dysfunctional customers who deliberately or unintentionally disrupt service in a manner that negatively affects the organization...

Feature selection using Joint Mutual Information Maximisation

Abstract: Two new feature selection methods are proposed based on joint mutual information.The methods use joint mutual information with maximum of the minimum criterion.The methods address the problem of selection of redundant and irrelevant features.The methods are evaluated using eleven public data sets and five competing methods.The proposed JMIM method outperforms five competing methods in terms of accuracy. Feature selection is used in many application areas relevant to expert and intelligent systems, such as data mining and machine learning, image processing, anomaly detection, bioinformatics and natural language processing. Feature selection based on information theory is a popular approach due its computational efficiency, scalability in terms of the dataset dimensionality, and independence from the classifier. Common drawbacks of this approach are the lack of information about the interaction between the features and the classifier, and the selection of redundant and irrelevant features. The latter is due to the limitations of the employed goal functions leading to overestimation of the feature significance.To address this problem, this article introduces two new nonlinear feature selection methods, namely Joint Mutual Information Maximisation (JMIM) and Normalised Joint Mutual Information Maximisation (NJMIM); both these methods use mutual information and the 'maximum of the minimum' criterion, which alleviates the problem of overestimation of the feature significance as demonstrated both theoretically and experimentally. The proposed methods are compared using eleven publically available datasets with five competing methods. The results demonstrate that the JMIM method outperforms the other methods on most tested public datasets, reducing the relative average classification error by almost 6% in comparison to the next best performing method. The statistical significance of the results is confirmed by the ANOVA test. Moreover, this method produces the best trade-off between accuracy and stability.

Constraints on Primordial Gravitational Waves Using Planck, WMAP, and New BICEP2/Keck Observations through the 2015 Season.

Abstract: We present results from an analysis of all data taken by the bicep2/Keck CMB polarization experiments up to and including the 2015 observing season. This includes the first Keck Array observations at 220 GHz and additional observations at 95 and 150 GHz. The Q and U maps reach depths of 5.2, 2.9, and 26 μKCMB arcmin at 95, 150, and 220 GHz, respectively, over an effective area of ≈400 square degrees. The 220 GHz maps achieve a signal to noise on polarized dust emission approximately equal to that of Planck at 353 GHz. We take auto and cross spectra between these maps and publicly available WMAP and Planck maps at frequencies from 23 to 353 GHz. We evaluate the joint likelihood of the spectra versus a multicomponent model of lensed-ΛCDM+r+dust+synchrotron+noise. The foreground model has seven parameters, and we impose priors on some of these using external information from Planck and WMAP derived from larger regions of sky. The model is shown to be an adequate description of the data at the current noise levels. The likelihood analysis yields the constraint r0.05<0.07 at 95% confidence, which tightens to r0.05<0.06 in conjunction with Planck temperature measurements and other data. The lensing signal is detected at 8.8σ significance. Running a maximum likelihood search on simulations we obtain unbiased results and find that σ(r)=0.020. These are the strongest constraints to date on primordial gravitational waves.