USMA Digital Commons (United States Military Academy, West Point)
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Army Officer Corps Science, Technology, Engineering and Mathematics (STEM) Foundation Gaps Place Countering Weapons of Mass Destruction (CWMD) Operations at Risk – Part 2
Get PDFIn Great Power Wars, Americans Could Again Become POWs
Get PDFWith the return of great power competition comes another renewed threat most of us probably have not thought about in a long time: American soldiers could become prisoners of war. To put it in perspective, the last conflict where America suffered hundreds of POWs was the Vietnam War. Today, after two decades of fighting non-state insurgents, Survival, Evasion, Resistance, and Escape, or SERE, training for U.S. service members has been tailored to match the counterinsurgency operational environment. But in a large-scale conflict between peer countries, aircrews bail out over enemy-controlled territory, wounded soldiers are captured by an advancing enemy, logistic convoys are ambushed, and the turmoil that comes with a moving battlefield creates risk for troops being captured by the enemy. If that is the more likely battlespace of the future, then there is a need to change once again how we prepare soldiers for being captured
Military Statecraft and the Rise of Shaping in World Politics
No full textIn today’s complex international environment, how do the United States, China, and Russia manage the return of great power competition as well as the persistent threat of violent non-state actors? This book explores shaping: the use of military power to construct a more favorable environment by influencing the characteristics of other militaries, altering the relationships between them, or managing the behavior of allies. As opposed to traditional strategies of warfighting or coercion, shaping relies less on threats, demonstrations, and uses of violence and more so on attraction, persuasion, and legitimacy. Because shaping relies on less hard power and more on soft power, this counterintuitive way of military statecraft contradicts the conventional wisdom of the purpose militaries serve.
Military Statecraft and the Rise of Shaping in World Politics explores the emergence of shaping in classical strategy and its increased frequency following the end of the Cold War when threats and allies became more ambiguous. The four logics of shaping—attraction, socialization, delegation, and assurance—are illustrated through five case studies of recent major military exercise programs led by the United States, China, India, the United Kingdom, and Russia. Moreover, sentiment analysis and statistics of over 1,000 multinational exercises from 1980-2016 reveal how major powers reacted to a complex international environment by expanding the number and scope of shaping exercises. This book illuminates an understudied but frequently common tool of military statecraft for students, practitioners, and interested readers to understand the varied use of military power in today’s competitive international system
Exertional Rhabdomyolysis Following Non-Contact Collegiate Recreational Activity: A Case Report
No full textA healthy 19-year-old male (body mass = 68.04 kg, height = 175.26 cm) participating in a collegiate intramural flag football tournament presented with unilateral gastrocnemius exercise-associated muscle cramps. He was given electrolytes, stretched, and returned to play. The exercise-associated muscle cramps progressed to his quadriceps bilaterally within 23 min of initial reported symptoms. Emergency medical services was activated and the patient was transported by ambulance to the emergency department, where he was diagnosed with acute exertional rhabdomyolysis. This case report explores the rarity of exertional rhabdomyolysis in a noncontact intramural sport and highlights the necessity for early recognition and treatment
Toward a Zero Trust Architecture Implementation in a University Environment
No full textThe core concepts of Zero Trust Architecture have existed since the Jericho Forum in 1994 and have served as the goal of cyber security specialists for many years. Zero Trust Networks and Architectures are extremely appealing to institutions of higher learning because they offer the flexibility to support research and learning while protecting resources with different protection levels, depending on the sensitivity of the resource. This paper investigates how other universities can employ the Zero Trust Architectures using the West Point model
Adaptive Non-Gaussian Aerial Localization Using Lissajous Search Patterns
No full textThis work presents an adaptive approach to cooperative aerial search and localization (SAL) which implements Lissajous search patterns and non-Gaussian observation likelihoods to preserve high target information. The adaptive component of the framework utilizes a simultaneous estimation and modeling technique to both estimate agent states and correct their motion models. In order to maximize the information available about a target even when it is not observed by a search agent, multi-Gaussian observation likelihoods are continuously generated for each agent and then fused across the search team. Monte Carlo simulation studies show that the proposed adaptive localization framework outperforms standard filtering techniques by significant margins, for a wide range of parameter values. The differential entropies of fused target likelihoods are studied for various multiagent Lissajous pattern configurations, leading to the derivation of optimal Lissajous parameters for cooperative SAL. This work has relevance for SAL applications in rescue, safety, and defense sectors, offering a robust solution to target localization when a priori target motion information is unavailable
Perceived social norms and concussion-disclosure behaviours among first-year NCAA student-athletes: implications for concussion prevention and education.
No full textTimely disclosure and identification of concussion symptoms are essential to proper care. Perceived social norms are a potential driving factor in many health-related decisions. The study purpose was to describe concussion disclosure behaviours and identify the association between perceived social norms and these disclosure behaviours. First-year student-athletes (n = 391) at two NCAA institutions completed a cross-sectional survey about concussion disclosure and disclosure determinants. Log-binomial regression models identified factors associated with concussion disclosure behaviour prevalence for: higher intention to disclose symptoms, disclosed all at time of injury, eventually disclosed all, and never participated with concussion symptoms. More favourable perceived social norms were associated with higher prevalence of intention to disclose (PR = 1.34; 95%CI: 1.18, 1.53) and higher prevalence of never participating in sports with concussion symptoms (PR = 1.50; 95%CI: 1.07, 2.10). Clinicians, coaches, sports administrators, and healthcare practitioners should be mindful of the need to create supportive social environments to improve concussion symptom disclosure
Decomposing loosely coupled mixed-integer programs for optimal microgrid design
No full textMicrogrids are frequently employed in remote regions, in part because access to a larger electric grid is impossible, difficult, or compromises reliability and independence. Although small microgrids often employ spot generation, in which a diesel generator is attached directly to a load, microgrids that combine these individual loads and augment generators with photovoltaic cells and batteries as a distributed energy system are emerging as a safer, less costly alternative. We present a model that seeks the minimum-cost microgrid design and ideal dispatched power to support a small remote site for one year with hourly fidelity under a detailed battery model; this mixed-integer nonlinear program (MINLP) is intractable with commercial solvers but loosely coupled with respect to time. A mixed-integer linear program (MIP) approximates the model, and a partitioning scheme linearizes the bilinear terms. We introduce a novel policy for loosely coupled MIPs in which the system reverts to equivalent conditions at regular time intervals; this separates the problem into subproblems that we solve in parallel. We obtain solutions within 5% of optimality in at most six minutes across 14 MIP instances from the literature and solutions within 5% of optimality to the MINLP instances within 20 minutes
Airplane Seating Assignment Problem
No full textSARS-CoV-2, the virus that causes COVID-19, began infecting humans in late 2019 and has since spread to over 57 million people and caused over 1.75 million deaths, as of December 27, 2020. In response to reduced demand and travel restrictions as a result of COVID-19, airlines experienced a 94% reduction in passenger capacity worldwide in April and an estimated 60% reduction in passengers transported for all of 2020. SARS-CoV-2 has been shown to spread on airplanes by infected passengers, so minimizing the risk of secondary infections aboard aircraft may save lives. We present the airplane seating assignment problem (ASAP) to minimize transmission risks on airplanes, and we provide two models to solve ASAP. We show that both models can be effectively solved using a standard commercial solver and that seating assignments provided by these models have lower aggregate risk than the strategy of blocking the middle seats, given the same number of passengers. The available risk models for aircraft are based on influenza data, and hence risk models based on SARS-CoV-2 should be developed to maximize the benefits of our research
An Adversarial Training Based Machine Learning Approach to Malware Classification under Adversarial Conditions
No full textThe use of machine learning (ML) has become an established practice in the realm of malware classification and other areas within cybersecurity. Characteristic of the contemporary realm of intelligent malware classification is the threat of adversarial ML. Adversaries are looking to target the underlying data and/or models responsible for the functionality of malware classification to map its behavior or corrupt its functionality. The ends of such adversaries are bypassing the cybersecurity measures and increasing malware effectiveness. We develop an adversarial training based ML approach for malware classification under adversarial conditions that leverages a stacking ensemble method, which compares the performance of 10 base ML models when adversarially trained on three data sets of varying data perturbation schemes. This comparison ultimately reveals the best performing model per data set, which includes random forest, bagging and gradient boosting. Experimentation also includes stacking a mixture of ML models in both the first and second levels in the stack. A first level stack across all 10 ML models with a second level support vector machine is top performing. Overall, this work reveals that a malware classifier can be developed to account for potential forms of training data perturbation with minimal effect on performance