Predictive sensor method and apparatus

A microprocessor and electronics package employing predictive methodology was developed to accelerate the response time of slowly responding hydrogen sensors. The system developed improved sensor response time from approximately 90 seconds to 8.5 seconds. The microprocessor works in real-time providing accurate hydrogen concentration corrected for fluctuations in sensor output resulting from changes in atmospheric pressure and temperature. Following the successful development of the hydrogen sensor system, the system and predictive methodology was adapted to a commercial medical thermometer probe. Results of the experiment indicate that, with some customization of hardware and software, response time improvements are possible for medical thermometers as well as other slowly responding sensors

Electronic clinical predictive thermometer using logarithm for temperature prediction

A thermometer that rapidly predicts body temperature based on the temperature signals received from a temperature sensing probe when it comes into contact with the body. The logarithms of the differences between the temperature signals in a selected time frame are determined. A line is fit through the logarithms and the slope of the line is used as a system time constant in predicting the final temperature of the body. The time constant in conjunction with predetermined additional constants are used to compute the predicted temperature. Data quality in the time frame is monitored and if unacceptable, a different time frame of temperature signals is selected for use in prediction. The processor switches to a monitor mode if data quality over a limited number of time frames is unacceptable. Determining the start time on which the measurement time frame for prediction is based is performed by summing the second derivatives of temperature signals over time frames. When the sum of second derivatives in a particular time frame exceeds a threshold, the start time is established

Evaluation of Feeding Varying Levels of Wet Distillers Grains with Solubles as Compared to Dry Distilelrs Grains with Solubles to Finishing Steers

A study was conducted to determine the effects of implants and transportation on the metabolic status of feedlot steers. Steers (n = 28) were sorted by body weight, allocated into light or heavy blocks, and randomly assigned to one of two treatments. Treatments included non-implanted controls (CON) and steers implanted with Synovex Plus 70 d prior to harvest (IMP). Jugular blood and muscle biopsy samples (longissimus dorsi (LD) and semimembranosis (SM)) were collected 70 d post-implant, prior to transit. Steers were transported to Schuyler, NE, where blood and biopsy sampling was repeated. After harvest, carcass data were collected and muscle samples were taken from the LD, SM, Psoas Major (PM), and Illiacus (IL) muscles. Implanting increased (P \u3c 0.05) estradiol levels and improved live animal performance. Carcass weight and rib eye area were increased (P \u3c 0.05) in implanted steers. No dark cutters were found in either treatment. Pre-transit insulin/glucagon ratio and muscle glycogen levels did not differ (P \u3e 0.10) between treatments. Non-esterified fatty acid (NEFA) levels were reduced (P \u3c 0.05) in implanted steers pre-transit. Transit increased (P \u3c 0.05) NEFA levels, but had no effect (P \u3e 0.10) on insulin/glucagon ratio or muscle glycogen levels. Implanting did not affect (P \u3e 0.10) insulin/glucagon ratio, NEFA, or LD glycogen levels post-transit. Implanted steers had lower (P \u3c 0.05) glycogen levels in the SM than did non-implanted steers post-transit. Weight block affected (P \u3c 0.05) insulin and insulin/glucagon ratio levels, with steers in the light block having greater levels of each. Muscle pH and objective color (L*, a*, b*) of the LD were not biologically different between treatments. Implanted steers had greater (P \u3c 0.05) glycolytic potential values in the LD, and tended (P \u3c 0.10) to have higher L* values in the PM. Implanting increased (P \u3c 0.05) shear force of the LD. These data indicate that although implants affect bovine metabolism, other factors are necessary to cause a sufficient reduction in muscle glycogen and to produce a dark cutting carcass