The effect of humidity and temperature on Wool Comfort Meter assessment of single jersey wool fabrics

The Wool ComfortMeter provides an objective measurement of the fabric-evoked prickle discomfort rating provided by wearers. This work aimed to quantify the sensitivity of the Wool ComfortMeter over a range of different temperature and humidity conditions to determine the recommended test conditions for its operation. The design was: three temperatures (notionally 20, 25 and 30°C) at three relative humidities (RHs, notionally 50, 65 and 80%) each with two replicates, using six different wool single jersey knits (mean fibre diameter 19.5–27.0 µm). As it was difficult to achieve exactly some of the extreme combinations of temperature and RH, some combinations were repeated, providing a total of 23 different assessment conditions. Data were analysed using restricted maximum likelihood mixed model analysis. The best fixed model included RH, RH2, temperature and the interaction of temperature and RH, accounting for 95% of the variation in Wool ComfortMeter readings. Wool ComfortMeter values were almost constant at 55–60% RH. Generally, the Wool ComfortMeter value reduced with increasing RH > 60% at temperatures of 25°C and 28.5°C as the regain of the fabric increased. However, at 20°C little change was detected as RH was increased from 50 to 80% as there were only small changes in fabric regain. The observed effects were in a good agreement with existing knowledge on the effect of regain on the mechanical properties of wool fibre. Wool ComfortMeter is best operated under standard conditions for textile testing of 65% RH and 20°C.

A data mining framework for electricity consumption analysis from meter data

This paper presents a novel data mining framework for the exploration and extraction of actionable knowledge from data generated by electricity meters. Although a rich source of information for energy consumption analysis, electricity meters produce a voluminous, fast-paced, transient stream of data that conventional approaches are unable to address entirely. In order to overcome these issues, it is important for a data mining framework to incorporate functionality for interim summarization and incremental analysis using intelligent techniques. The proposed Incremental Summarization and Pattern Characterization (ISPC) framework demonstrates this capability. Stream data is structured in a data warehouse based on key dimensions enabling rapid interim summarization. Independently, the IPCL algorithm incrementally characterizes patterns in stream data and correlates these across time. Eventually, characterized patterns are consolidated with interim summarization to facilitate an overall analysis and prediction of energy consumption trends. Results of experiments conducted using the actual data from electricity meters confirm applicability of the ISPC framework.

Improved 2000-meter rowing performance in competitive oarswomen after caffeine ingestion

Eight competitive oarswomen (age, 22 ± 3 years; mass, 64.4 ± 3.8 kg) performed three simulated 2,000-m time trials on a rowing ergometer. The trials, which were preceded by a 24-hour dietary and training control and 72 hours of caffeine abstinence, were conducted 1 hour after ingesting caffeine (6 or 9 mg · kg-1 body mass) or placebo. Plasma free fatty acid concentrations before exercise were higher with caffeine than placebo (0.67 ± 0.34 vs. 0.72 ± 0.36 vs. 0.30 ± 0.10 mM for 6 and 9 mg · kg-1caffeine and placebo, respectively; p < .05). Performance time improved 0.7% (95% confidence interval [CI] 0 to 1.5%) with 6 mg · kg-1 caffeine and 1.3% (95% CI 0.5 to 2.0%) with 9 mg · kg-1 caffeine. The first 500 m of the 2,000 m was faster with the higher caffeine dose compared with placebo or the lower dose (1.53 ± 0.52 vs. 1.55 ± 0.62 and 1.56 ± 0.43 min; p = .02). We concluded that caffeine produces a worthwhile enhancement of performance in a controlled laboratory setting, primarily by improving the first 500 m of a 2,000-m row.