Book Review: Jamie Peck and Kevin Ward (eds) City of Revolution: Restructuring Manchester

Manchester, Manchester University Press, 2002, xvi + 256 pp., £14.99 (pbk), ISBN 0719058880

Multivariate Markov Process Models for the transmission of methicillin-resistant Staphylococcus Aureus in a hospital ward

Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.

The impact of surgical ward nurses practicing respiratory assessment on positive patient outcomes

Objective: A literature review to examine the incorporation of respiratory assessment into everyday surgical nursing practice; possible barriers to this; and the relationship to patient outcomes. Primary argument: Escalating demands on intensive care beds have led to highly dependent patients being cared for in general surgical ward areas. This change in patient demographics has meant the knowledge and skills required of registered nurses in these areas has expanded exponentially. The literature supported the notion that postoperative monitoring of vital signs should include the fundamental assessment of respiratory rate; depth and rhythm; work of breathing; use of accessory muscles and symmetrical chest movement; as well as auscultation of lung fields using a stethoscope. Early intervention in response to changes in a patient's respiratory health status impacts positively on patient health outcomes. Substantial support exists for the contention that technologically adept nurses who also possess competent respiratory assessment skills make a difference to respiratory care. Conclusions: Sub-clinical respiratory problems have been demonstrated to contribute to adverse events. There is a paucity of research knowledge as to whether respiratory education programs and associated inservice make a difference to nursing clinical practice. Similarly, the implications for associated respiratory educational needs are not well documented, nor has a research base been sufficiently developed to guide nursing practice. Further research has the potential to influence the future role and function of the registered nurse by determining the importance of respiratory education programs on post-operative patient outcomes.

Nursing practice environment, quality of care and morale of hospital nurses in Japan

The purpose of this study was to describe Japanese hospital nurses’ perceptions of the nursing practice environment and examine its association with nurse-reported ability to provide quality nursing care, quality of patient care and ward morale. A cross-sectional survey design was used including 223 nurses working in 12 acute inpatient wards in a large Japanese teaching hospital. Nurses rated their work environment favorably overall using the Japanese version of the Practice Environment Scale of the Nursing Work Index. Subscale scores indicated high perceptions of physician relations and quality of nursing management, but lower scores for staffing and resources. Ward nurse managers generally rated the practice environment more positively than staff nurses except for staffing and resources. Regression analyses found the practice environment was a significant predictor of quality of patient care and ward morale, whereas perceived ability to provide quality nursing care was most strongly associated with years of clinical experience. These findings support interventions to improve the nursing practice environment, particularly staffing and resource adequacy, to enhance quality of care and ward morale in Japan.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Church's wall of silence on sexual abuse

A SINGLE document was all it took to illuminate a dark secret in the Church of England. The two-page child protection report, unearthed by police in the archives of the diocese of Manchester, was proof, at last, that a former cathedral choirboy -- alleging years of sexual abuse by one of Britain's most senior clergyman -- was not alone. There was another boy. Also a solo soprano, on the other side of the world, who was singing from the same hymn sheet about The Very Reverend Robert Waddington. "There had been a previous referral about sexual impropriety some time ago from Australia, where RW had been the headmaster at a school. An ex-pupil had made a complaint to the Bishop of (north) Queensland who had relayed it to the Archbishop (of York)," the 2003 report says. Eli Ward's family had prompted the secret report when they told church officials, without Ward's knowledge, of the alleged abuse he suffered in the mid-1980s.

Choirboy haunted by painful memories

IT was in the magnificent Manchester Cathedral that Eli Ward's pure soprano attracted the attention of the new dean, the Reverend Robert Waddington. When Waddington called for volunteers to help him polish the gold leaf on the altar railings, several choirboys came forward. Among them was Eli, a working-class 11-year-old from a council estate, who loved singing in the choir and was happy to help...