Cyclone intensity categories

The weather forecast centers in Australia and many other countries use a scale of cyclone intensity categories (categories 1-5) in their cyclone advisories, which are considered to be indicative of the cyclone damage potential. However, this scale is mainly based on maximum gust wind speeds. In a recent research project involving computer modeling of cyclonic wind forces on roof claddings and fatigue damage to claddings, it was found that cyclone damage not only depends on the maximum gust wind speed, but also on two other cyclone parameters, namely, the forward speed and radius to maximum winds. This paper describes the computer model used in predicting the cyclone damage to claddings and investigates the damage potential of a cyclone as a function of all the relevant cyclone parameters, based on which it attempts to refine the current scale of cyclone intensity categories.

Validating a novel, low cost, automated malnutrition screening system as a predictor of nutritional risk in the Oncology Day Care Unit

Background Cancer-related malnutrition is associated with increased morbidity, poorer tolerance of treatment, decreased quality of life, increased hospital admissions, and increased health care costs (Isenring et al., 2013). This study’s aim was to determine whether a novel, automated screening system was a useful tool for nutrition screening when compared against a full nutrition assessment using the Patient-Generated Subjective Global Assessment (PG-SGA) tool. Methods A single site, observational, cross-sectional study was conducted in an outpatient oncology day care unit within a Queensland tertiary facility, with three hundred outpatients (51.7% male, mean age 58.6 ± 13.3 years). Eligibility criteria: ≥18 years, receiving anticancer treatment, able to provide written consent. Patients completed the Malnutrition Screening Tool (MST). Nutritional status was assessed using the PG-SGA. Data for the automated screening system was extracted from the pharmacy software program Charm. This included body mass index (BMI) and weight records dating back up to six months. Results The prevalence of malnutrition was 17%. Any weight loss over three to six weeks prior to the most recent weight record as identified by the automated screening system relative to malnutrition resulted in 56.52% sensitivity, 35.43% specificity, 13.68% positive predictive value, 81.82% negative predictive value. MST score 2 or greater was a stronger predictor of nutritional risk relative to PG-SGA classified malnutrition (70.59% sensitivity, 69.48% specificity, 32.14% positive predictive value, 92.02% negative predictive value). Conclusions Both the automated screening system and the MST fell short of the accepted professional standard for sensitivity (80%) or specificity (60%) when compared to the PG-SGA. However, although the MST remains a better predictor of malnutrition in this setting, uptake of this tool in the Oncology Day Care Unit remains challenging.

Accelerating the Mathematics Learning of Low Socio-Economic Status Junior Secondary Students : an early report

The Accelerating the Mathematics Learning of Low Socio-Economic Status Junior Secondary Students project aims to address the issues faced by very underperforming mathematics students as they enter high school. Its aim is to accelerate learning of mathematics through a vertical curriculum to enable students to access Year 10 mathematics subjects, thus improving life chances. This paper reports upon the theory underpinning this project and illustrates it with examples of the curriculum that has been designed to achieve acceleration.