No abstract

In this study, we describe an inexpensive and rapid method of using video analysis and identity tracking to measure the effects of tag weight on insect movement. In a laboratory experiment, we assessed the tag weight and associated context-dependent effects on movement, choosing temperature as a factor known to affect insect movement and behavior. We recorded the movements of groups of flightless adult crickets Gryllus locorojo (Orthoptera:Gryllidae) as affected by no tag (control); by light, medium, or heavy tags (198.7, 549.2, and 758.6 mg, respectively); and by low, intermediate, or high temperatures (19.5, 24.0, and 28.3°C, respectively). Each individual in each group was weighed before recording and was recorded for 3 consecutive days. The mean (± SD) tag mass expressed as a percentage of body mass before the first recording was 26.8 ± 3.7% with light tags, 72 ± 11.2% with medium tags, and 101.9 ± 13.5% with heavy tags. We found that the influence of tag weight strongly depended on temperature, and that the negative effects on movement generally increased with tag weight. At the low temperature, nearly all movement properties were negatively influenced. At the intermediate and high temperatures, the light and medium tags did not affect any of the movement properties. The continuous 3-day tag load reduced the average movement speed only for crickets with heavy tags. Based on our results, we recommend that researchers consider or investigate the possible effects of tags before conducting any experiment with tags in order to avoid obtaining biased results.

Floods belong to the most hazardous natural disasters and their disaster management heavily relies on precise forecasts. These forecasts are provided by physical models based on differential equations. However, these models do depend on unreliable inputs such as measurements or parameter estimations which causes undesirable inaccuracies. Thus, an appropriate data-mining analysis of the physical model and its precision based on features that determine distinct situations seems to be helpful in adjusting the physical model. An application of fuzzy GUHA method in flood peak prediction is presented. Measured water flow rate data from a system for flood predictions were used in order to mine fuzzy association rules expressed in natural language. The provided data was firstly extended by a generation of artificial variables (features). The resulting variables were later on translated into fuzzy GUHA tables with help of Evaluative Linguistic Expressions in order to mine associations. The found associations were interpreted as fuzzy IF-THEN rules and used jointly with the Perception-based Logical Deduction inference method to predict expected time shift of flow rate peaks forecasted by the given physical model. Results obtained from this adjusted model were statistically evaluated and the improvement in the forecasting accuracy was confirmed.

No abstract

No abstract