Study of wavelength-shifting chemicals for use in large-scale water Cherenkov detectors

Cherenkov detectors employ various methods to maximize light collection at the photomultiplier tubes (PMTs). These generally involve the use of highly reflective materials lining the interior of the detector, reflective materials around the PMTs, or wavelength-shifting sheets around the PMTs. Recently, the use of water-soluble wavelength-shifters has been explored to increase the measurable light yield of Cherenkov radiation in water. These wave-shifting chemicals are capable of absorbing light in the ultravoilet and re-emitting the light in a range detectable by PMTs. Using a 250 L water Cherenkov detector, we have characterized the increase in light yield from three compounds in water: 4-Methylumbelliferone, Carbostyril-124, and Amino-G Salt. We report the gain in PMT response at a concentration of 1 ppm as: 1.88 $\pm$ 0.02 for 4-Methylumbelliferone, stable to within 0.5% over 50 days, 1.37 $\pm$ 0.03 for Carbostyril-124, and 1.20 $\pm$ 0.02 for Amino-G Salt. The response of 4-Methylumbelliferone was modeled, resulting in a simulated gain within 9% of the experimental gain at 1 ppm concentration. Finally, we report an increase in neutron detection performance of a large-scale (3.5 kL) gadolinium-doped water Cherenkov detector at a 4-Methylumbelliferone concentration of 1 ppm.Comment: 7 pages, 9 figures, Submitted to Nuclear Instruments and Methods

Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR.

Multiple Classification Ripple Down Rules (MCRDR) is a knowledge acquisition technique that produces representations, or knowledge maps, of a human expert's knowledge of a particular domain. However, work on gaining an understanding of the knowledge acquired at a deeper meta-level or using the knowledge to derive new information is still in its infancy. This paper will introduce a technique called Weighted MCRDR (WM), which looks at deriving and learning information about the relationships between multiple classifications within MCRDR by calculating a meaningful rating for the task at hand. This is not intended to reduce the knowledge acquisition effort for the expert. Rather, it is attempting to use the knowledge received in the MCRDR knowledge map to derive additional information that can allow improvements in functionality of MCRDR in many problem domains. Preliminary testing shows that there exists a strong potential for WM to quickly and effectively learn meaningful weightings

Exposure to foods’ non-taste sensory properties: a nursery intervention to increase children’s willingness to try fruit and vegetables

Activities that engage young children with the sensory properties of foods are popular with nursery schools, despite the lack of evidence for their efficacy in increasing children's consumption of healthy foods. This study provides the first empirical exploration of the effectiveness of a non-taste sensory activity program in a nursery school setting. Ninety-two children aged between 12 and 36 months were allocated to either an intervention group, who took part in looking, listening, feeling and smelling activities with unusual fruits and vegetables every day for four weeks, or to a non-intervention control group. In a subsequent mealtime taste test, children touched and tasted more of the vegetables to which they had been familiarized in their playtime activities than of a matched set of non-exposed foods. The results demonstrate that hands-on activities with unfamiliar fruits and vegetables can enhance children’s willingness to taste these foods, and confirm the potential for such activities to support healthy eating initiatives