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Integrating Autonomous Systems into Military Units
No full textAutonomous systems are the future of military warfare and must be carefully employed to hold the advantage in technological advances. In an effort to measure the capabilities of autonomous systems within military units, this study analyzes the effects an autonomous system has while integrated into an Army Infantry unit. Using combat modeling software, a constructive reality modeled breaching and fires mission scenarios to determine the effects an autonomous system may have within a unit. Within the model limitations, statistical analysis supported that an autonomous system has a general impact on increasing a unit’s lethality and survivability. These statistically significant conclusions support that autonomous systems should be integrated within military units since these systems have a strong, positive impact on unit effectiveness. Additional data analysis and extending the analysis to other combat scenarios is crucial in applying these conclusions outside of the tested scenarios
Trunk and Lower Extremity Movement Patterns, Stress Fracture Risk Factors, and Biomarkers of Bone Turnover in Military Trainees.
No full textCONTEXT: Military service members commonly sustain lower extremity stress fractures (SFx). How SFx risk factors influence bone metabolism is unknown. Understanding how SFx risk factors influence bone metabolism may help to optimize risk-mitigation strategies.
OBJECTIVE: To determine how SFx risk factors influence bone metabolism.
DESIGN: Cross-sectional study.
SETTING: Military service academy.
PATIENTS OR OTHER PARTICIPANTS: Forty-five men (agepre = 18.56 ± 1.39 years, heightpre = 176.95 ± 7.29 cm, masspre = 77.20 ± 9.40 kg; body mass indexpre = 24.68 ± 2.87) who completed Cadet Basic Training (CBT). Individuals with neurologic or metabolic disorders were excluded.
INTERVENTION(S): We assessed SFx risk factors (independent variables) with (1) the Landing Error Scoring System (LESS), (2) self-reported injury and physical activity questionnaires, and (3) physical fitness tests. We assessed bone biomarkers (dependent variables; procollagen type I amino-terminal propeptide [PINP] and cross-linked collagen telopeptide [CTx-1]) via serum.
MAIN OUTCOME MEASURE(S): A markerless motion-capture system was used to analyze trunk and lower extremity biomechanics via the LESS. Serum samples were collected post-CBT; enzyme-linked immunosorbent assays determined PINP and CTx-1 concentrations, and PINP : CTx-1 ratios were calculated. Linear regression models demonstrated associations between SFx risk factors and PINP and CTx-1 concentrations and PINP : CTx-1 ratio. Biomarker concentration mean differences with 95% confidence intervals were calculated. Significance was set a priori using α ≤ .10 for simple and α ≤ .05 for multiple regression analyses.
RESULTS: The multiple regression models incorporating LESS and SFx risk factor data predicted the PINP concentration (R2 = 0.47, P = .02) and PINP : CTx-1 ratio (R2 = 0.66, P = .01). The PINP concentration was increased by foot internal rotation, trunk flexion, CBT injury, sit-up score, and pre- to post-CBT mass changes. The CTx-1 concentration was increased by heel-to-toe landing and post-CBT mass. The PINP : CTx-1 ratio was increased by foot internal rotation, lower extremity sagittal-plane displacement (inversely), CBT injury, sit-up score, and pre- to post-CBT mass changes.
CONCLUSIONS: Stress fracture risk factors accounted for 66% of the PINP : CTx-1 ratio variability, a potential surrogate for bone health. Our findings provide insight into how SFx risk factors influence bone health. This information can help guide SFx risk-mitigation strategies
Engineered Pathogens and Unnatural Biological Weapons: The Future Threat of Synthetic Biology
Get PDFRecent developments in biochemistry, genetics, and molecular biology have made it possible to engineer living organisms. Although these developments offer effective and efficient means with which to cure disease, increase food production, and improve quality of life for many people, they can also be used by state and non-state actors to develop engineered biological weapons. The virtuous circle of bioinformatics, engineering principles, and fundamental biological science also serves as a vicious cycle by lowering the skill-level necessary to produce weapons. The threat of bioengineered agents is all the more clear as the COVID-19 pandemic has demonstrated the enormous impact that a single biological agent, even a naturally occurring one, can have on society. It is likely that terrorist organizations are monitoring these developments closely and that the probability of a biological attack with an engineered agent is steadily increasing
On “Civil-Military Relations and Today’s Policy Environment”
Get PDFThis commentary responds to Thomas N. Garner’s article “Civil-Military Relations and Today’s Policy Environment” published in the Winter 2018–19 issue of Parameters (vol. 48, no. 4)
Geometry of the Set of Synchronous Quantum Correlation Sets
Get PDFWe provide a complete geometric description of the set of synchronous quantum correlations for the three-experiment two-outcome scenario. We show that these correlations form a closed set. Moreover, every correlation in this set can be realized using projection valued measures on a Hilbert space of dimension no more than 16
Multiattribute Decision Modeling in Defense Applications
No full textThe role of decision analysis (DA) within military operations research (OR) is discussed. A technically sound and easy-to-apply approach for multiattribute problems is developed, based on an additive value model. This includes developing a value hierarchy based on stakeholder values, the identification of measures of value and the associated scales, the development of single-attribute value functions, and elicitation of attribute swing weights. Cost issues are addressed, as are issues of multiple stakeholders. An example from a military application is presented and developed from beginning to end. The ramifications of decisions under uncertainty are then considered and limitations and cautions identified for application of a linear model. Decision trees are described as the most straightforward way to model a chancy decision. Utility functions are introduced and contrasted with value functions. Some widely useful methods of sensitivity analysis are given. The emphasis is on practical methods that are technically sound but understandable by clients without special training in OR or DA, and on clear methods of presentation of results. The treatment is designed as a sufficient outline for a practitioner, but texts are recommended for those who want to go into more depth in the material
A Model of Technology Incidental Learning Effects
No full textIncreases in technology use, among youth and adults, are concerning given the volume of information produced and disseminated in the modern world. Conceptual models have been developed to understand how people manage the large volume of information encountered during intentional learning activities with technology. What, if anything, do people learn when they happen upon news and other information while using technology for purposes other than learning? Questions like this highlight the need to understand incidental learning, i.e., learning that occurs when people, who are pursuing a goal other than learning such as entertainment, encounter information that leads to a change in thinking or behavior. In this article, we integrate theory and research from multiple scholarly literatures into the Technology Incidental Learning Effects (TILE) model, which provides a framework for future research on how incidental learning occurs and what factors affect this process. Current research on incidental learning can be informed by educational psychology scholarship on dual-processing, motivation, interest, source evaluation, and knowledge reconstruction. The TILE model points to many promising future directions for research with direct implications for modern society, including the need to better understand how and why people move from merely noticing to engaging with incidentally exposed information as well as how to help people successfully manage the large amounts of information they encounter when using technology for purposes other than learning
Algorithm Selection Framework for Cyber Attack Detection
No full textThe number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised
Threatcasting in a Military Setting
Get PDFThe intersection of digital and physical security is critical to the future security of our military and national defense. Coming technological advances widen the attack plain over the next decade including cyber, physical and kinetic vulnerabilities. Visualizing what the future will hold and what new threat vectors will emerge is a task that traditional military planning mechanisms struggle to accomplish given the wicked problem space.
Helping to understand and plan for the future operating environment is the basis of a research effort known as Threatcasting. Arizona State University’s School for the Future of Innovation in Society in collaboration with the Army Cyber Institute at West Point use the threatcasting process to give researchers a structured way to envision and plan for risks ten years in the future. For many organization the scope of this problem can seem overwhelming. Threatcasting, as an analytic technique, focuses on the intersection between cyber and physical domains and how it can revolutionize or paralyze the future.
Threatcasting uses inputs from social science, technical research, cultural history, economics, trends, expert interviews, and even a little science fiction. These inputs allow the creation of potential futures. By placing the threats into an effects based model (e.g. a person in a place with a problem), it allows organizations to understand what needs to be done immediately and also in the future to disrupt possible threats. The Threatcasting framework also exposes what events could happen that indicate the progression towards an increasingly possible threat landscape.
Threatcasting draws strength from futures studies, a field that provides theoretical and applied tools designed to shed light on deep uncertainties and complexities that futures hold. Foresight tools are rooted in exploratory, rather than predictive, methods of futures thinking, learning, and strategy as a means to prepare and plan for long-term outcomes that are difficult to imagine and impossible to predict. Such methods often stand in contrast to causal, linear, ‘plan and predict’ thinking that characterizes many contemporary practices of making and knowing futures.
As national security and technological possibilities change rapidly, new threats and opportunities become ever present. Threatcasting is a means to make-sense and anticipate military futures so that relevant institutions are able to anticipate, manage, navigate uncertainty and complexity ahead. This chapter will use the weaponization of artificial intelligence as a case study to walk readers through the research technique and results. Specifically, we will outline two case studies where the technique was applied with specific results. One case study focuses on the digital and physical supply chain in private industry (Cisco Systems) and the second investigates similar threats to the military’s supply chain (Military Logistics Officers).
The weaponization of any organization\u27s supply chain and logistics systems poses a significant threat to national and global economic security. The very systems that are the engine of economies and the lifeline of goods and services to the world’s population could and most probably will be turned against the very people and organizations that they serve. This new threat landscape and associated challenges will affect industry, militaries and governments through loss of revenue, productivity and even loss of life. This weaponization will allow adversities whether they are criminal, state sponsored, terrorists or hacktivists to transform these systems from engines of productivity to enemies on the inside.
Upon reading this chapter, the student/practitioner will:
- Have an understanding of the threatcasting methodology so to be able to apply it against other problems of interest
- Appreciate the close ties between the advancement of technology and the effect to society, economies, and national security
- Apply the Threatcasting methodology to the specific problem of supply chains and the weaponization of Artificial Intelligence
- Create powerful narratives and fact-based illustrations to provide decision makers on the resultshttps://digitalcommons.usmalibrary.org/aci_books/1022/thumbnail.jp