Preliminary OARE absolute acceleration measurements on STS-50

On-orbit Orbital Acceleration Research Experiment (OARE) data on STS-50 was examined in detail during a 2-day time period. Absolute acceleration levels were derived at the OARE location, the orbiter center-of-gravity, and at the STS-50 spacelab Crystal Growth Facility. The tri-axial OARE raw acceleration measurements (i.e., telemetered data) during the interval were filtered using a sliding trimmed mean filter in order to remove large acceleration spikes (e.g., thrusters) and reduce the noise. Twelve OARE measured biases in each acceleration channel during the 2-day interval were analyzed and applied to the filtered data. Similarly, the in situ measured x-axis scale factors in the sensor's most sensitive range were also analyzed and applied to the data. Due to equipment problem(s) on this flight, both y- and z- axis sensitive range scale factors were determined in a separate process (using the OARE maneuver data) and subsequently applied to the data. All known significant low-frequency corrections at the OARE location (i.e., both vertical and horizontal gravity-gradient, and rotational effects) were removed from the filtered data in order to produce the acceleration components at the orbiter's center-of-gravity, which are the aerodynamic signals along each body axes. Results indicate that there is a force of unknown origin being applied to the Orbiter in addition to the aerodynamic forces. The OARE instrument and all known gravitational and electromagnetic forces were reexamined, but none produce the observed effect. Thus, it is tentatively concluded that the Orbiter is creating the environment observed

How well can regional fluxes be derived from smaller-scale estimates?

Regional surface fluxes are essential lower boundary conditions for large scale numerical weather and climate models and are the elements of global budgets of important trace gases. Surface properties affecting the exchange of heat, moisture, momentum and trace gases vary with length scales from one meter to hundreds of km. A classical difficulty is that fluxes have been measured directly only at points or along lines. The process of scaling up observations limited in space and/or time to represent larger areas was done by assigning properties to surface classes and combining estimated or calculated fluxes using an area weighted average. It is not clear that a simple area weighted average is sufficient to produce the large scale from the small scale, chiefly due to the effect of internal boundary layers, nor is it known how important the uncertainty is to large scale model outcomes. Simultaneous aircraft and tower data obtained in the relatively simple terrain of the western Alaska tundra were used to determine the extent to which surface type variation can be related to fluxes of heat, moisture, and other properties. Surface type was classified as lake or land with aircraft borne infrared thermometer, and flight level heat and moisture fluxes were related to surface type. The magnitude and variety of sampling errors inherent in eddy correlation flux estimation place limits on how well any flux can be known even in simple geometries

MODELING NITRATE CONCENTRATION IN GROUND WATER USING REGRESSION AND NEURAL NETWORKS

Nitrate concentration in ground water is a major problem in specific agricultural areas. Using regression and neural networks, this study models nitrate concentration in ground water as a function of iron concentration in ground water, season and distance of the well from a poultry house. Results from both techniques are comparable and show that the distance of the well from a poultry house has a significant effect on nitrate concentration in groundwater.Environmental Economics and Policy, Livestock Production/Industries,

Factors which Affect Job Satisfaction and Dissatisfaction of Male and Female Interscholastic Head Coaches

Statement of Problem This study was conducted in an effort to determine the sources of job attitudes of male and female interscholastic head coaches in the State of New Mexico. The Herzberg Dual-Factor Theory of job satisfaction formed the theoretical framework upon which this study was developed. Procedures A 22 item Job Satisfaction Questionnaires and a Personal Data Sheet, accompanied by an introductory letter, were sent to the entire population of 559 male and female interscholastic head coaches in the State of New Mexico. The introductory lefter, follow-up letter, and fina I post card yielded 469 returns (83.9%) of which 457 were usable (81.8%). Conclusions Based on the findings of this investigation, the following conclusions seem warranted: (1) The findings of this investigation reject the Herzberg postulate that sources of satisfaction ore found only in the work content, i.e, in doing the job, and sources of job dissatisfaction are found only in the work context, i.e., the environment in which the work was done. (2) The Herzberg factor universe is inadequate for an accurate assessment of job attitudes of a population whose role calls for intensive interaction in the public sector and/or extra organizational activity. (3) The summation of job factors (average) is not an accurate index of job satisfaction as perceived by male and female head coaches. (4) Although significant results were not obtained, there was a tendency for successful performance in the coaching role to lead to satisfaction, i.e., performance leads to satisfaction. (5) The methodology employed in this investigation may have influenced the results since a greater number of significant job attitudes were reported. (6) The results of this investigation give reason to doubt the universality of the Herzberg DuaI-Factor Theory

MODELING NITROGEN LOADING RATE TO DELAWARE LAKES USING REGRESSION AND NEURAL NETWORKS

The objective of this research was to predict the nitrogen-loading rate to Delaware lakes and streams using regression analysis and neural networks. Both models relate nitrogen-loading rate to cropland, soil type and presence of broiler production. Dummy variables were used to represent soil type and the presence of broiler production at a watershed. Data collected by Ritter & Harris (1984) was used in this research. To build the regression model Statistical Analysis System (SAS) was used. NeuroShell Easy Predictor, neural network software was used to develop the neural network model. Model adequacy was established by statistical techniques. A comparison of the regression and neural network models showed that both perform equally well. Cropland was the only significant variable that had any influence on the nitrogen-loading rate according to both the models.Environmental Economics and Policy,