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Female intercollegiate student athletes continue to lack knowledge of their pregnancy rights. Uninformed athletes may react to pregnancy by concealing it or worse, risking their health and the health of their fetus. This study aimed to determine a) baseline pregnancy rights knowledge in a contemporary sample of female athletes; and b) the extent to which pregnancy rights information, when provided, affected their intent to seek help for pregnancy. A convenience sample of 146 female student athletes from nine teams at one National Collegiate Athletic Association (NCAA) Division I university (mean age 20.2 ± 1.5 years) responded to an anonymous, online, author-designed survey. Halfway through the survey, Title IX information was provided. Prior to the intervention most participants were unaware of pregnancy rights. If they experienced pregnancy and particularly if they decided to remain pregnant to the completion of their pregnancy, participants expected to be cut from the team, lose financial aid, and be unable to return to the team. Following the intervention, participants were significantly less likely to expect negative consequences and significantly more likely to seek help from athletic staff. Those who were aware of federal pregnancy rights were significantly unlikely to be aware of NCAA protections. Recommendations to the NCAA and universities include more vigorous, effective provision of complete, timely pregnancy rights information. Recommendations for student athletes include anticipating unplanned pregnancy and self-education. [ABSTRACT FROM AUTHOR]

Modeling and simulation capabilities help the Department of Defense to organize, train, educate, equip, and employ current and future forces for the full range of operations. Within the military analytic domain, validation activities and the study of the appropriateness of modeling and simulation is a growing area of professional concern. In this article, a recent functional response validation metric, the probability-of-agreement validation metric, is detailed, which enables informed comparisons between military simulation models and the real-world systems or processes they emulate. The metric is the probability that the difference between functional responses at the same design point fall within a range deemed acceptable. The corresponding probability-of-agreement plot summarizes agreement transparently and directly across typical measurand values while accounting for potentially complicated bias and variability structures. We demonstrate its applicability in the military analytic domain using a validation exercise involving the comparison of two multi-resolution, high-fidelity F-16 aircraft simulation models. [ABSTRACT FROM AUTHOR]

Unprecedented demand for unmanned aerial systems (UAS) by the United States Air Force is driving a corresponding demand on training units responsible for producing UAS aircrew. This research develops a simulation model of daily training operations based on historical data from a UAS flying training unit. Current requirements, operations, and instructor manning levels are used to derive a baseline throughput of trained aircrew. Subsequent analyses explore the impact of course frequency, class size, and instructor manning levels on trained aircrew produced. This research shows specific adjustments in staff skill mix and class size can significantly increase student throughput and reduce training time. Results drive novel approaches for course execution, increasing instructor utilization and informing staffing for appropriate manning levels required to meet growing UAS aircrew requirements. [ABSTRACT FROM AUTHOR]

Eligibility for enlistment into the US military is assessed by the United States Military Entrance Processing Command (USMEPCOM), an independent agency that reports to the Office of the Secretary of Defense (OSD) and not to any specific branch of military service. This research develops a discrete-event simulation for applicant processing operations at a Military Entrance Processing Station (MEPS) to investigate the viability of potential alternatives to the current applicant arrival and processing operation. Currently, all applicants arrive to the MEPS at the beginning of the processing day in a single batch. This research models and compares two alternatives with the status quo: split-shift processing, by which applicant arrivals occur in two batches: one at 06:00 and one at 11:00 and appointment-based processing, by which applicants may arrive during one of three, four, six, or eight appointment windows. Express-lane processing is also explored, in which applicants are allowed to bypass select processing stations. Experimental results indicate that split-shift processing is not viable under the current processing model due to an unacceptable decrease in applicant throughput. Results from appointment-based scenarios are mixed, with the critical factors being the time between appointment batches and their associated arrival times. [ABSTRACT FROM AUTHOR]

Long-duration logistical wargames within the Air domain are complex and highly dynamic events that are driven by aircraft availability. In order to gain insight into the impact of aircraft use, this research developed a simulation tool that uses a stepwise approach for adjudication and provides the user many capabilities including, but not limited to, the ability to have multiple bases and types of aircraft. Daily aircraft availability and missions accomplished are two critical metrics of interest. Within the simulation, the user has the ability to control types of part failures, control parts availability, control maintenance capabilities, and control number of mission scheduled. Finally, the user can account for the possibility of attrition along with the effects of numerous major events present in real-life scenarios. This tool is validated through application of a space covering design along with regression modeling and shows that the tool is well-behaved, functions as expected, and can quickly provide meaningful insights into operational scenarios. [ABSTRACT FROM AUTHOR]

Combat, unlike many real-world processes, tends to be singular in nature. This makes statistical analysis of the combat data problematic. Building stochastic simulation models of combat scenarios provides a means of studying in some detail the particulars of a combat scenario provided that the model accurately captures the scenario. The scenario of interest in this paper is the WWII Bay of Biscay U-boat campaign, and the model is an agent-based simulation. The challenge is how to validate, or ensure the adequacy of, the simulation representation of the combat scenario with the actual scenario on the basis of comparing simulation output to typically small-sample-size actual combat data. In this paper we give the details of a new statistical methodology for use in validating a mission-level, agent-based model of a historical combat scenario. We use the Bay of Biscay agent-based simulation and develop a bootstrapping technique applied to the small-sample-size actual data from the Bay of Biscay campaign. Results from this bootstrapping statistical technique are compared with commonly used statistical techniques and are shown to improve the comparison of agent-based simulation output to actual real-world data. [ABSTRACT FROM PUBLISHER]

This paper presents research combining an agent-based modeling and simulation paradigm with game theory for an in silico historical analysis of the Bay of Biscay submarine war during WWII. The U-boat threat was of great concern to the Allies, prompting initial operational research efforts to devise counterstrategies. Focusing search efforts in the Bay of Biscay enabled an effective Allied response. Using the historical record as a means to create a reasonably accurate model of the U-boat campaign, we allow the resulting agents within the model to adapt their strategies to counter-opposition strategies. Model output data are examined with respect to the historical record and game theory. The results hold promise for extending the agent-based modeling paradigm into more complex military-based domains. [ABSTRACT FROM PUBLISHER]

In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven a surge of applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues of high-dimensionality, they all induce a cost in terms of network size or computational runtime. This work examines the use of quaternions, a form of hypercomplex numbers, in neural networks. The constructed networks demonstrate the ability of quaternions to encode high-dimensional data in an efficient neural network structure, showing that hypercomplex neural networks reduce the number of total trainable parameters compared to their real-valued equivalents. Finally, this work introduces a novel training algorithm using a meta-heuristic approach that bypasses the need for analytic quaternion loss or activation functions. This algorithm allows for a broader range of activation functions over current quaternion networks and presents a proof-of-concept for future work. [ABSTRACT FROM AUTHOR]