Sensible and latent heat loss from the body surface of Holstein cows in a tropical environment

The general principles of the mechanisms of heat transfer are well known, but knowledge of the transition between evaporative and non-evaporative heat loss by Holstein cows in field conditions must be improved, especially for low-latitude environments. With this aim 15 Holstein cows managed in open pasture were observed in a tropical region. The latent heat loss from the body surface of the animals was measured by means of a ventilated capsule, while convective heat transfer was estimated by the theory of convection from a horizontal cylinder and by the long-wave radiation exchange based on the Stefan-Boltzmann law. When the air temperature was between 10 and 36 degrees C the sensible heat transfer varied from 160 to -30 W m(-2), while the latent heat loss by cutaneous evaporation increased from 30 to 350 W m(-2). Heat loss by cutaneous evaporation accounted for 20-30% of the total heat loss when air temperatures ranged from 10 to 20 degrees C. At air temperatures > 30 degrees C cutaneous evaporation becomes the main avenue of heat loss, accounting for approximately 85% of the total heat loss, while the rest is lost by respiratory evaporation.

Automatic segmentation of latent fingerprints

Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region. © 2012 IEEE.