ABSTRACT
Relative house fly, Musca domestica L., activity at three large dairies in central California was monitored during the peak fly activity period from June to August 2005 by using spot cards, fly tapes, bait traps, and Alsynite traps. Counts for all monitoring methods were significantly related at two of three dairies; with spot card counts significantly related to fly tape counts recorded the same week, and both spot card counts and fly tape counts significantly related to bait trap counts 1-2 wk later. Mean fly counts differed significantly between dairies, but a significant interaction between dairies sampled and monitoring methods used demonstrates that between-dairy comparisons are unwise. Estimate precision was determined by the coefficient of variability (CV) (or SE/mean). Using a CV = 0.15 as a desired level of estimate precision and assuming an integrate pest management (IPM) action threshold near the peak house fly activity measured by each monitoring method, house fly monitoring at a large dairy would require 12 spot cards placed in midafternoon shaded fly resting sites near cattle or seven bait traps placed in open areas near cattle. Software (FlySpotter; http://ucanr.org/ sites/FlySpotter/download/) using computer vision technology was developed to count fly spots on a scanned image of a spot card to dramatically reduce time invested in monitoring house flies. Counts provided by the FlySpotter software were highly correlated to visual counts. The use of spot cards for monitoring house flies is recommended for dairy IPM programs.
Subject(s)
Houseflies , Housing, Animal , Image Processing, Computer-Assisted/methods , Insect Control/methods , Population Density , Population Surveillance/methods , Animals , California , Dairying , Female , Insect Control/instrumentation , Male , Pheromones , Sex DistributionABSTRACT
The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this paper we sketch some of our work over the last ten years in the area of supervised learning, focusing on three interlinked directions of research: theory, engineering applications (that is, making intelligent software) and neuroscience (that is, understanding the brain's mechanisms of learning).
Subject(s)
Brain/physiology , Computer Simulation , Learning , Neural Networks, Computer , Vision, Ocular/physiology , Humans , Learning/physiologyABSTRACT
Nurses as professionals and as individual consumers are affected by health policy and bear a responsibility for participating in the health policy arena. Nursing's growing recognition of the importance of public policy issues is increasingly being translated into action by various groups within the profession. Persuading student nurses of the importance of health policy provides a special challenge to faculty. A clinical practicum was designed that requires senior baccalaureate students in various community settings to identify, analyze, and challenge health policy issues. The course design, integration into the curriculum, and evaluation can be models for faculty considering the challenge of stimulating student interest in health policy issues.