The integration of cover crops into cotton (Gossypium hirsutum, L.) production remains challenging. One potential negative impact of cover crops on cotton is allelopathy. Proper selection of cover crop species and termination timing could potentially reduce the impacts of allelopathy on cotton seedlings. Two studies were conducted to determine cotton germination and growth sensitivity to cover crop leachate, which were measured using (I) five cover crops species, including: oats (Avena sativa L.), hairy vetch (Vicia Villosa), winter pea (Lathyrus hirsutus), winter wheat (Triticum aestivum), and annual rye (Lolium multiflorum), and (II) a blend of cover crops at four termination timings, including: at planting, three weeks prior to planting, six weeks prior to planting, and a split termination, where a 25 cm band in the top of the bed was terminated six weeks prior to planting, and the remaining cover crop was terminated at planting (referred to as strip 6-wk). Samples for Experiment I were collected on May 24th and for Experiment II on March 22nd (Strip/6-wk and 6-wk), April 30th (3-wk), and May 11th (at planting) in 2018. The effect of 0 (deionized water), 25, and 50 (v/v) cover crop leachate extract on cotton seed germination was evaluated in a series of controlled environmental studies. All cover crop species’ leachates negatively impacted cotton germination and seedling growth (p < 0.05). Germination inhibition rates declined numerically by species, with winter pea ≥ hairy vetch ≥ oats ≥ annual rye ≥ winter wheat at the 50 v/v concentrations. Winter pea germination inhibition on cotton equaled 47.0% and cotton radicle length was decreased by 62.8%. Termination at planting suppressed cotton germination more than the other termination timings, with the 50 v/v treatment resulting in a germination inhibition of 60.0%. Proper selection of cover crop species and termination timing prior to planting cotton will be critical in maximizing the benefits and minimizing the risks of a cover crop.

A security manager's selection of risk-mitigation controls for an information system's security architecture depends on the organization's risk-management process. Current security risk-management processes require security managers to thoroughly analyze their organization's threats, vulnerabilities, and assets before selecting cost-effective risk-mitigation controls. The most common risk-management method, Annualized Loss Expectancy (ALE), expects security managers to assess the probabilistic damage from different types of attacks, investing only in those risk-mitigation controls that cost less than the anticipated loss in asset value. The problem with current risk-mitigation-control cost-benefit analysis methods is that they attempt to give security managers the ability to make precise security investment recommendations or decisions based on imprecise information, such as estimated probabilities or expected economic loss in asset value. This thesis proposes the Security Attribute Evaluation Method (SAEM) as an alternative to current risk-mitigation-control cost-benefit analysis methods. SAEM uses multi-attribute decision analysis techniques from the field of Decision Sciences to guide a security manager in his or her selection of risk-mitigation controls for the organization's information system security architecture. In contrast with current cost-benefit analysis methods, SAEM focuses on the relative benefit of risk-mitigation controls rather than the economic net value of the information system with and without the risk-mitigation control. In addition, SAEM integrates a new coverage-analysis model that allows security mangers to evaluate how a risk-mitigation control contributes to the security architecture's defense-in-depth design, a fundamental security engineering design principle. In this thesis, I present the results of using SAEM with the security managers of three different organizations-a large commercial company, a large government organization, and a small hospital. SAEM provided these security managers with insight into their risk priorities and, in two organizations, SAEM highlighted weaknesses in their security architectures. Overall, the security managers felt that SAEM's coverage-analysis model was very helpful in assessing how risk-mitigation controls support the organization's defense-in-depth security strategy. iv v ACKNOWLEDGMENTS

A security manager's selection of risk-mitigation controls for an information system's security architecture depends on the organization's risk-management process. Current security riskmanagement processes require security managers to thoroughly analyze their organization's threats, vulnerabilities, and assets before selecting cost-effective risk-mitigation controls. The most common risk-management method, Annualized Loss Expectancy (ALE), expects security managers to assess the probabilistic damage from different types of attacks, investing only in those risk-mitigation controls that cost less than the anticipated loss in asset value. The problem with current risk-mitigation-control cost-benefit analysis methods is that they attempt to give security managers the ability to make precise security investment recommendations or decisions based on imprecise information, such as estimated probabilities or expected economic loss in asset value.This thesis proposes the Security Attribute Evaluation Method (SAEM) as an alternative to current risk-mitigation-control cost-benefit analysis methods. SAEM uses multi-attribute decision analysis techniques from the field of Decision Sciences to guide a security manager in his or her selection of risk-mitigation controls for the organization's information system security architecture. In contrast with current cost-benefit analysis methods, SAEM focuses on the relative benefit of risk-mitigation controls rather than the economic net value of the information system with and without the risk-mitigation control. In addition, SAEM integrates a new coverage-analysis model that allows security mangers to evaluate how a risk-mitigation control contributes to the security architecture's defense-in-depth design, a fundamental security engineering design principle.In this thesis, I present the results of using SAEM with the security managers of three different organizations-a large commercial company, a large government organization, and a small hospital. SAEM provided these security managers with insight into their risk priorities and, in two organizations, SAEM highlighted weaknesses in their security architectures. Overall, the security managers felt that SAEM's coverage-analysis model was very helpful in assessing how risk-mitigation controls support the organization's defense-in-depth security strategy.iv v ACKNOWLEDGMENTS

Cotton producers in the U.S. Mid-South often plant in cool, wet conditions to lengthen the growing season and maximize yield potential. Although multiple studies have been conducted to determine optimum planting windows and seeding rates, few studies have evaluated the interaction of these parameters. To make a replant decision, the yield potential of the current stand versus the yield potential of the replant must be estimated. The objective of this study was to determine the impact of plant population and planting date on lint yield and fiber quality. Field experiments were conducted in 10 site-years from 2016 to 2018 in Tennessee, Mississippi, and Missouri. Treatments included five seeding rates (10.5, 6.75, 3, 1.5, and 0.75 seeds m-1) and multiple planting dates (typically early May, mid-May, and early June). Although yields were lowest at later planting dates and low populations, results suggested a uniform population of 74,000 plants ha-1 will not warrant a replant at any date, and uniform populations as low as 49,000 plants ha-1 planted after 5 May also will not warrant replanting. Fiber quality was impacted by environment and planting date, with micronaire decreasing and length, strength, and uniformity increasing as planting date was delayed. These data will assist with replant decisions by providing estimates of the current stand relative to the yield potential of a successful (or unsuccessful) replant. Furthermore, results suggest producers could reduce seeding rates at later planting dates without reducing yield potential.

A g o al of the Studio course in the Master of Software Engineering program at Carnegie Mellon University is to bridge the gap between experience and academics. One way to transfer experience to young software engineers is through case studies designed t o f o cus students on speci c software engineering problems. This paper discusses my experience with developing a case study to improve a student's analytical capabilities and introduce the importance o f c onsidering maintenance and implementation issues in software design. The case study, developed as a classroom assignment, proved an e ective tool to teach software engineering students that there a r e more things to consider than performance s p eci cations.