Forecasting bike sharing demand using fuzzy inference mechanism

Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, weekend, holiday and weather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rule base and then only current information like date related information and weather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feed forward neural network in terms of prediction accuracy.

Cross-platform urine metabolomics of experimental hyperglycemia in type 2 diabetes

Hyperglycemia causes diabetic nephropathy, a condition for which there are no specific diagnostic markers thatpredict progression to renal failure. Here we describe a multiplatform metabolomic analysis of urine from individualswith type 2 diabetes, collected before and immediately following experimental hyperglycemia. We used targetednuclear magnetic resonance spectroscopy (NMR), liquid chromatography - mass spectrometry (LC-MS) and gaschromatography - MS (GC-MS) to identify markers of hyperglycemia. Following optimization of data normalisation andstatistical analysis, we identified a reproducible NMR and LC-MS based urine signature of hyperglycemia. Significantincreases of alanine, alloisoleucine, isoleucine, leucine, N-isovaleroylglycine, valine, choline, lactate and taurine anddecreases of arginine, gamma-aminobutyric acid, hippurate, suberate and N-acetylglutamate were observed. GC-MSanalysis identified a number of metabolites differentially present in post-glucose versus baseline urine, but these could not be identified using current metabolite libraries. This analysis is an important first step towards identifying biomarkers of early-stage diabetic nephropathy.

The effectiveness of a brief intervention using a pedometer and step-recording diary in promoting physical activity in people diagnosed with type 2 diabetes or impaired glucose tolerance

Issue addressed: To evaluate the effectiveness of a brief intervention using a pedometer and step-recording diary on promoting physical activity in people with type 2 diabetes or impaired glucose tolerance (IGT). Methods: People with type 2 diabetes or IGT who attended the Illawarra Diabetes Service were invited to participate. Participants in the intervention group received a pedometer and a diary to record their daily steps for a two-week period. Both the intervention and comparison group received advice on physical activity. Physical activity levels were measured using the Active Australia Survey at baseline, and at two and 20 weeks. Results: A total of 226 participants were recruited. At two-week follow-up the mean self-reported minutes of walking was significantly higher in the intervention group than the comparison group (223 minutes versus 164 minutes; p=0.01), as was the percentage of intervention participants achieving recommended levels of moderate-intensity physical activity (63.5% versus 41.8%, p=0.02) and the percentage of intervention participants achieving adequate levels of total physical activity (68.9% versus 48.0%, p=0.04). There were no differences between study groups for any physical activity measure at 20-week follow-up. Conclusions: A pedometer and a step-recording diary were useful tools to promote short-term increase in physical activity in people diagnosed with type 2 diabetes or IGT. Future studies need to examine whether a longer intervention, individualised physical activity counselling and support for achieving step goals could result in increasing physical activity over the long term.

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