Experimental studies on the shear behaviour and strength of LiteSteel Beams

This paper presents the details of an experimental study on the shear behaviour and strength of a recently developed, cold-formed steel hollow flange channel beam known as LiteSteel Beam (LSB). The new LSB sections with rectangular hollow flanges are produced using a patented manufacturing process involving simultaneous cold-forming and dual electric resistance welding. They are commonly used as flexural members in buildings. However, no research has been undertaken on the shear behaviour of LSBs. Therefore a detailed experimental study involving 36 shear tests was undertaken to investigate the shear behaviour of 10 different LSB sections. Simply supported test specimens of LSBs with aspect ratios of 1.0 and 1.5 were loaded at midspan until failure using both single and back to back LSB arrangements. Test specimens were chosen such that all three types of shear failure (shear yielding, inelastic and elastic shear buckling) occurred in the tests. Comparison of experimental results with corresponding predictions from the current Australian and North American cold-formed steel design rules showed that the current design rules are very conservative for the shear design of LSBs. Significant improvements to web shear buckling occurred due to the presence of rectangular hollow flanges while considerable post-buckling strength was also observed. Appropriate improvements have been proposed for the shear strength of LSBs based on the design equations in the North American Specification. This paper presents the details of this experimental study and the results. When reduced height web side plates or only one web side plate was used, the shear capacity of LSB was reduced. Details of these tests and the results are also presented in this paper. Keywords: LiteSteel beam, Shear strength, Shear tests, Cold-formed steel structures, Direct strength method, Slender web, Hollow flanges.

Cholecystokinin and leptin: their influence upon the eating behaviour and nutrient intake of dialysis patients

Background We have used serial visual analogue scores to demonstrate disturbances of the appetite profile in dialysis patients. This is potentially important as dialysis patients are prone to malnutrition yet have a lower nutrient intake than controls. Appetite disturbance may be influenced by accumulation of appetite inhibitors such as leptin and cholecystokinin (CCK) in dialysis patients. Methods Fasting blood samples were drawn from 43 controls, 50 haemodialysis (HD) and 39 peritoneal dialysis (PD) patients to measure leptin and CCK. Hunger and fullness scores were derived from profiles compiled using hourly visual analogue scores. Nutrient intake was derived from 3 day dietary records. Results Fasting CCK was elevated for PD (6.73 ± 4.42 ng/l vs control 4.99 ± 2.23 ng/l, P < 0.05; vs HD 4.43 ± 2.15 ng/l, P < 0.01). Fasting CCK correlated with the variability of the hunger (r = 0.426, P = 0.01) and fullness (r = 0.52, P = 0.002) scores for PD. There was a notable relationship with the increase in fullness after lunch for PD (r = 0.455, P = 0.006). When well nourished PD patients were compared with their malnourished counterparts, CCK was higher in the malnourished group (P = 0.004). Leptin levels were higher for the dialysis patients than controls (HD and PD, P < 0.001) with pronounced hyperleptinaemia evident in some PD patients. Control leptin levels demonstrated correlation with fullness scores (e.g. peak fullness, r = 0.45, P = 0.007) but the dialysis patients did not. PD nutrient intake (energy and protein intake, r = -0.56, P < 0.0001) demonstrated significant negative correlation with leptin. Conclusion Increased CCK levels appear to influence fullness and hunger perception in PD patients and thus may contribute to malnutrition. Leptin does not appear to affect perceived appetite in dialysis patients but it may influence nutrient intake in PD patients via central feeding centres.

The highs (B1H) and lows (B1L) of human heart B1-adrenoceptors (AR)

The 1AR has two binding sites which can be activated to cause cardiostimulation. The first, termed, 1HAR (high affinity site of 1AR) is activated by noradrenaline and adrenaline and is blocked by relatively low concentrations of β-blockers including carvedilol (Kaumann and Molenaar, 2008). The other, termed, 1LAR (low affinity site of 1AR) has lower affinity for noradrenaline and adrenaline and is activated by some β-blockers including CGP12177 and pindolol, at higher concentrations than those required to block the receptor (Kaumann and Molenaar, 2008). (-)-CGP12177 is a non-conventional partial agonist that causes modest and transient increases of contractile force in human atrial trabeculae (Kaumann and Molenaar, 2008). These effects are markedly increased and maintained by inhibition of phosphodiesterase PDE3. The stimulant effects of (-)-CGP12177 at human β1ARs was verified with recombinant receptors (Kaumann and Molenaar, 2008). However, in a recent report it was proposed that the positive inotropic effects of CGP12177 are mediated through 3ARs in human right atrium (Skeberdis et al 2008). This proposal was not consistent with the lack of blockade of (-)-CGP12177 inotropic effects or increases in L-type Ca2+ current (ICa-L ) by the β3AR blocker 1 μM LY748,337 (Christ et al, 2010). On the otherhand, (-)-CGP12177 increases in inotropic effects and ICa-L were blocked by (-)-bupranolol 1-10 μM (Christ et al, 2010). Chronic infusion of (-)-CGP 12177 (10 mg/Kg/24 hours) for four weeks in an aortic constriction mouse model of heart failure caused an increase in left ventricular wall thickness, fibrosis and inflammation-related left ventricular gene expression levels. Christ T et al (2010) Br J Pharmacol, In press Kaumann A and Molenaar P (2008) Pharmacol Ther 118, 303-336 Skeberdis VA et al (2008) J Clin Invest, 118, 3219-3227

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

The hydrogeology, hydrology and hydrochemistry of the Condamine River Valley alluvium

The Cainozoic alluvium of the Condamine River valley is interpreted to consist of sediments deposited as floodplain and sheetwash deposits in bedrock valleys eroded into Mesozoic sedimentary rocks and tertiary volcanics. A maximum recorded sediment accumulation of 134 m is centred just south of Dalby. The lower section ofboth the flood plain and sheetwash alluvium is composed of variegated sandy and clayey sediments and the upper section of brown and grey sandy and clayey sediments.