James M. Gregory

TEAM: integrated, process-based wind-erosion model

Abstract TEAM (Texas Tech Erosion Analysis Model) is a process-based mathematical model that predicts detachment, movement, and deposition of soil particles associated with wind processes. TEAM also simulates the suspension and movement of dust above and downwind from eroding sites. The development of TEAM has been from an educational perspective to teach principles of soil physics, mathematical modeling, soil erosion, and conservation practices. TEAM is relatively easy to calibrate for a given site and is robust enough to deal with both agricultural and industrial conditions. It works for pure or nearly pure sand as found in desert and beach environments. It also works for agricultural soils with and without vegetative or residue covers. Early components of TEAM were used during Operation Desert Storm to develop stabilization treatments for sand barriers. Later, it was evaluated and adopted by the US Department of Energy in 1996 to develop a safety plan for cleaning up a Superfund site contaminated with nuclear and other contaminates. It generally predicts the rate (mass per unit width per unit time) or amount (mass per unit width for total storm duration) of soil movement with an R 2 of 0.8 or better for field applications.

DROUGHT, WIND, AND BLOWING DUST ON THE SOUTHERN HIGH PLAINS OF THE UNITED STATES

Blowing dust is commonly studied in terms of climatic conditions that affect the amount of erosion. A new measure of blowing dust amounts on a regional scale is derived from meteorological data for Lubbock, Texas, based on visibility records and wind speeds during blowing dust events. Regression analysis suggests that the fraction of time of potential erosion (based on wind speed) and the Palmer Drought Severity Index (an index of regional soil moisture) are poor predictors of blowing dust amounts. Changing agricultural management techniques may account for much of the variability of blowing dust in the region. [Key words: blowing dust, drought, wind erosion, Southern High Plains.]

Sleep: A Good Investment in Health and Safety

ABSTRACT Sleep management is critical to both good health and safety. Most people do not know how much sleep is needed and place themselves and sometime others at risk for medical problems and increased risk for injuries or fatal accidents. Sleep need is discussed and the amount of sleep required as a function of age is provided. Agriculture is a high-risk industry with many injury-related or fatal accidents. SLEEP Model, a Web-based simulation model, was used to predict increase in odds ratio for injuries for farmers and other professionals who tend to work with a buildup of sleep debt because of the nature of their work. Results are given for working the day after a night of no sleep with and without use of caffeine and for a gradual buildup of sleep debt for daily sleep amounts of 0, 2, 4, 5, and 5.5 hours of sleep for a 58-year-old person with predicted sleep need of 6.2 hours per day. Results were also compared to some measurements reported in the literature. Odds ratio of about 10 times normal for average of caffeine and non-caffeine use are associated with working a day after no sleep and no previous sleep debt or with a gradual buildup of sleep debt typical of farmers involved in planting and harvesting seasons. Generally, caffeine use can reduce risks by about 70 times but may have little benefit for a night of no sleep after a buildup of severe sleep debt.

Blowing dust and climate change

In order to better understand some of the weather phenomena which may be responsible for blowing dust in diverse regions of the globe, the meteorological record is closely examined for Lubbock, Texas, which records more blowing dust than anywhere else in the United States. The development of a semi‐empirical model for wind erosion is also mentioned.

E-COACH: a paradigm shift for efficient advising

The College of Engineering has become a leader at Texas Tech University in the development of software tools to help students succeed in college and to make the advising process more efficient and effective. The process has a web-based focus and is quickly evolving into an electronic coach, E-COACH. Access is free. E-COACH serves three primary education functions: general education assessment and management, quick advising, and programs assessment. The first function is especially useful to high school students, perspective students visiting a college campus, and students on campus who are undecided about a major or who struggle in their academic work. This tool and related work is causing a paradigm shift in the advising process on campus, especially in the College of Engineering. Both advising quality and efficiency are improving. This work seems to be increasing both recruitment and retention of students. Work to date has focused on undergraduate engineering education.

Straw to Grain Ratio Equation for Combine Simulation

Purpose: The ratio of straw to grain mass as a function of cutting height affects combine efficiency and power consumption and is an important input parameter to combine simulation models. An equation was developed to predict straw to grain ratios for wheat as a function of cutting height. Methods: Two mass functions, one for straw and one for grain, were developed using regression techniques and measured data collected in west Texas during the summer, and used to predict the straw to grain ratio. Results: Three equations were developed to facilitate the simulation of a combine during wheat harvest. Two mass functions, one for straw and one for grain, were also developed; a quadratic equation describes the straw mass with an of 0.992. An S-shaped curve describes the mass function for grain with an of 0.957. An equation for straw to grain ratio of wheat was developed as a function of cutting height. The straw to grain ratio has an value of 0.947. Conclusions: In all cases, the equations had values above 0.94 and were significant at the 99.9 percent probability level (alpha

Active and passive learning connections to sleep management

Strong evidence exists that active is more effective than passive learning. In fact, passive learning is more sensitive to sleep debt. Efficiencies for passive learning and passive activities, such as driving, are reduced by more than 50 percent with as little as 18 hours of sleep debt. This relationship obviously affects highway safety. Further, the relationship also affects academic success. A sleep model, SLEEP (sleep loss effects on everyday performance) model developed in the College of Engineering at Texas Tech University, is used to predict the growth or decline in sleep debt and to predict resulting performance. It predicts active and passive performance efficiencies, time to fall asleep, and amount of sleep needed as a function of sleep, alcohol, and caffeine inputs. A steady-state form of the sleep model is included in GREG (grade requirements evaluation game). GREG predicts college GPA (grade point average) as a function of several academic management variables including sleep and caffeine. Results from both models are presented.

Building little bridges to help students succeed

The transition from high school to college challenges most students and often produces numerous academic casualties. The Mini-BRIDGE (short course on learning how to learn and understanding factors that interfere with learning) provides students with appropriate learning technology to improve academic performance. It is one of the little bridges that helps engineering students to complete a safe transition to academic life and responsibility in college. The Mini-BRIDGE program was implemented in the College of Engineering at Texas Tech University in January of 1996 to target and help freshmen who experienced law grades the fall semester. Thirty-six students enrolled in the short course. A total of 33 students completed the spring semester and were analyzed in this study. Students who participated in the Mini-BRIDGE increased their grade point average (GPA) by 0.648 points on a 4.0 scale. The number of students with 2.0 or higher GPA increased from 18.2 to 60.6 percent-a 233 percent increase in successful students. Before Mini-BRIDGE all students had less than a 3.0 GPA. After Mini-BRIDGE, 21.2 percent of the students earned a GPA of 3.0 or better, and 6.1 percent earned 3.5 or higher. One student earned a perfect 4.0 GPA.

Modeling Seasonal Patterns of Blowing Dust on the Southern High Plains

The Southern High Plains of the United States are notorious for blowing dust, especially during the “dust bowl” of the 1930s. Dust is an environmental and health hazard that can be mitigated with understanding and management. To aid in the understanding of wind erosion, a process-based mathematical simulation model has been developed at Texas Tech University. This paper overviews the components of the model, and illustrates its use with a long-term simulation of expected dust hours for each month of the year. Predicted values were compared to average monthly dust hours reported for Lubbock, Texas, from 1947 to 1989. Predictions matched measured values well, especially considering that the measured data were not used to calibrate or obtain regression coefficients. It is concluded that the Texas Tech model is a reasonable simulator of soil movement, dust concentration, and visibility.