Over recent decades, the world has experienced a growing demand for and reliance upon unmanned aerial systems (UAS) to perform a broad spectrum of applications to include military operations such as surveillance/reconnaissance and strike/attack. As UAS technology matures and capabilities expand, especially with respect to increased autonomy, acquisition professionals and operational decision makers must determine how best to incorporate advanced capabilities into existing and emerging mission areas. This research seeks to predict which autonomous UAS capabilities are most likely to emerge over the next 20 years as well as the key challenges for implementation for each capability. Employing the Delphi method and relying on subject matter experts from operations, acquisitions and academia, future autonomous UAS mission areas and the corresponding level of autonomy are forecasted. The study finds consensus for a broad range of increased UAS capabilities with ever increasing levels of autonomy, but found the most promising areas for research and development to include intelligence, surveillance, and reconnaissance (ISR) mission areas and sense and avoid and data link technologies.

Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p < 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.

A long-standing problem in the US-Mexico bilateral agenda is migration. Although both countries have important agreements to promote economic exchange and trade, the events of 9/11 and other acts of terrorism have increased concerns about border security. Since the US-Mexico border is one of the most important borders in the world in terms of activity, securing it without interfering with the legitimate flow of people and goods, poses an important challenge. The purpose of this paper is to propose conceptual frameworks and models to facilitate collaboration across national borders, by discussing and considering key factors for collaborative US-Mexico Border Security Infrastructure and Systems. Border security technical solutions pose an interesting domain because there are a myriad of concerns (e.g., political, economic, social and cultural) outside the technical implementation that must be deliberated and examined. In this conceptual study, unique aspects of trust, governance, information sharing, culture, and technical infrastructure are identified as the key ingredients in a cross-border collaboration effort. A bi-national organizational network appears to be an effective institutional design to develop a better understanding of the problem, as well as required policies and technologies. This approach is consistent with experiments, research, and conclusions found in the European Union.

Nearly one-half of all construction projects exceed planned costs and schedule, globally [1] . Owners and construction managers can analyze historical project performance data to inform cost and schedule overrun risk-reduction strategies. Though, the majority of open-source project datasets are limited by the number of projects, data dimensionality, and location. A significant global customer of the construction industry, the Department of Defense (DoD) maintains a vast database of historical project data that can be used to determine the sources and magnitude of construction schedule and cost overruns for many continental and international locations. The selection of data provided by the authors is a subset of the U.S. Federal Procurement Data System-Next Generation (FPDS-NG), which stores contractual obligations made by the U.S. Federal Government [2] . The data comprises more than ten fiscal years (1 Oct 2009 – 04 June 2020) of construction contract attributes that will enable researchers to investigate spatiotemporal schedule and cost performance by, but not limited to: contract type, construction type, delivery method, award date, and award value. To the knowledge of the authors, this is the most extensive open-source dataset of its kind, as it provides access to the contract data of 132,662 uniquely identified construction projects totaling $865 billion. Because the DoD's facilities and infrastructure construction requirements and use of private construction firms are congruent with the remainder of the public sector and the private sector, results obtained from analyses of this dataset may be appropriate for broader application.

Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a 21-feature dataset was analyzed related to 223 active-duty Airmen who participated in a comprehensive mental and social health survey, body composition assessment, and physical performance battery. Graphical analysis revealed pass/fail trends related to body composition and obesity. Logistic regression and limited-capacity neural network algorithms were then applied to predict fitness test performance using these biomechanical and psychological variables. The logistic regression model achieved a high level of significance (p < 0.01) with an accuracy of 0.84 and AUC of 0.89 on the holdout dataset. This model yielded important inferences that Airmen with poor sleep quality, recent history of an injury, higher BMI, and low fitness satisfaction tend to be at greater risk for fitness test failure. The neural network model demonstrated the best performance with 0.93 accuracy and 0.97 AUC on the holdout dataset. This study is the first application of psychological features and neural networks to predict fitness test performance and obtained higher predictive accuracy than prior work. Accurate prediction of Airmen at risk of failing the USAF fitness test can enable early intervention and prevent workplace injury, absenteeism, inability to deploy, and attrition.