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DoD 2017.B STTR Solicitation
NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date. For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.
The official link for this solicitation is: http://www.acq.osd.mil/osbp/sbir/solicitations/sttr2017B/index.shtml
Application Due Date:
Available Funding Topics
- AF17B-T001: AFFF Disposal
- AF17B-T002: Closed-Loop Feedback Control for Transcranial Direct Current Stimulation
- AF17B-T003: Resilient Directional Mesh Enhanced Tactical Airborne Networks
- AF17B-T004: Mission and Information Assurance through Cyber Atomics
- DHA17B-001: Handoff Training for Combat Casualty Care (HTC3)
- DHA17B-002: Handoffs for Joint Service Casualty Care (HJSCC)
- DHA17B-003: Fast Parameter Identification for Personalized Pharmacokinetics
- DHA17B-004: Robust Biochemical and Biomarker Rapid Detection and Assay System for Field Use
- DHA17B-005: Oxygen Production and Delivery on Demand
- N17B-T031: Materials Modeling Tool for Alloy Design to Streamline the Development of High Temperature, High-Entropy Alloys for Advanced Propulsion Systems
- N17B-T032: Techniques to Adjust Computational Trends Involving Changing Data (TACTIC-D)
- N17B-T033: Optimized Build Plate Design Tool for Metal Laser Powder Bed Additive Manufacturing
- N17B-T034: Risk-Based Unmanned Air System (UAS) Mission Path Planning Capability
- N17B-T035: Mission Success Assessment and Mitigation Recommendations Using a Cognitive System
TECHNOLOGY AREA(S): Materials
OBJECTIVE: A novel technology that achieves permanent disposal of Aqueous Film-Forming Foam (AFFF) and associated perfluorocarbon components.
DESCRIPTION: AFFF based on perfluorooctyl-substituted surfactants was for decades the water-based firefighting agent of choice in military and civilian applications until it was determined that two highly persistent, bioaccumulative compounds formed from decomposition of AFFF -- perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) -- pose a health risk and display unacceptable toxicity to aquatic index organisms. As a safety measure, Ansuls literature recommends incineration of post-extinguishment wastewaters containing combustible quantities of fuel hydrocarbons; however, it offers no method for disposal of AFFF itself. The Air Force is ridding itself of a large inventory of type 3 MILSPEC AFFF, for which no satisfactory disposal method has been identified, and switching to a shorter-chain homologue for firefighting. A practical method for disposal of AFFF residues and AFFF concentrate will have to manage several significant challenges: - the concentrate is typically supplied as a mixture of water, soluble organics and anionic, amphoteric, and/or non-ionic hydrocarbon surfactants, with a total fluorosurfactant content of 1-10%; - the C“F bonds of PFOS and PFOA, either attached to the surfactant or as the free acid or salt, are extremely stable; - the high-temperature chemistry of PFOS and PFOA has not been characterized, so there is no precedent to predict products of pyrolysis or combustion, temperatures at which these will occur, or the extent of destruction that will be realized; - many likely byproducts will also be environmentally unsatisfactory -- e.g., any volatile perfluoroalkane will be a greenhouse gas -- or toxic, e.g., HF, fluoroacetates, or perfluoroisobutylene. Water effluent from the treatment process may contain no more than 70 parts per trillion (ppt) combined concentration of PFOS and PFOA in any form. Quantities/concentrations of other treatment byproducts released from the treatment process must comply with applicable regulatory standards. Candidate technologies should balance commercial considerations with DoD requirements. Novelty and economics will be factors in the evaluation process. An advanced oxidation process based on oxidation by persulfate is being evaluated by another government agency for groundwater remediation. Related approaches will not be considered for this topic.
PHASE I: The research & development goals of phase I are to identify and validate a chemistry or physics that converts AFFF into environmentally acceptable products, and to demonstrate this process at a treatment rate of 1 gallon/day or more of AFFF. Deliverables include a tech report detailing design, construction, data, interpretation, and a design for a 10-gallon/hour (gph) treatment system.
PHASE II: During Phase II a scalable prototype treatment system will be designed, built, and demonstrated to process 10 gph of AFFF. System will be demonstrated at AFCEC'sFire Research Facilities to deliver an effluent concentration at 10 gphtype 3 MILSPEC AFFFof<70 ppt total PFOS + PFOA.
PHASE III: Design, assemble and demonstrate continuous, full-scale operation of a system -- including a detailed installation/operator guidance/maintenance manual -- to convert 400 gph of type 3 MILSPEC AFFF concentrate into products that can either be reused or disposed as a nonhazardous waste.
1. Ansul, Environmental aspects of AFFF and ar-AFFF, https://www.ansul.com/en/us/DocMedia/F-2003115.pdf
2. D.M. Lemal, Perspective on Fluorocarbon Chemistry, Journal of Organic Chemistry, vol. 69(1):pp. 1-11 (2004).
3. American Wastewater Association, Treatment and Removal of PFCs, www.awwa.org/Portals/0/files/legreg/documents/AWWAPFCFFactSheetTreatmentandRemoval.pdf
KEYWORDS: AFFF, Destruction, Environmental, Fluorocarbon, PFOA, PFOS
TECHNOLOGY AREA(S): Human Systems
OBJECTIVE: Develop a physiological recording and feedback control system to monitor operator cognitive state and control a small, head mounted transcranial direct current stimulation system.
DESCRIPTION: Due to the increasing need for intelligence, surveillance, and reconnaissance missions in modern warfare, human analysts are in great demand. However, human analyst resources are limited and costly to produce. Multiple trends and technical innovations in ISR continue to increase demands upon analysts, such as: (i) a growing need for persistent operation of ISR platforms; (ii) the use of heterogeneous sensor suites to address more challenging obscured and cluttered environments; (iii) generation of increasingly large volumes of imagery by ISR sensors and platforms; and (iv) the need for efficient fusion of information extracted by different analysts from different segments of data. These trends, together with the perishable nature of information contained in the raw data, create a critical and growing need for novel approaches and techniques that enhance analysts' ability to perform at high levels of proficiency for extended periods of operation. The limits of performance of image analysts are dominantly determined by their neuro- and psycho- physiological characteristics (e.g. learning capacity, fatigue resistance, attention span, motivation, engagement) that are jointly referred to as dynamic brain information processing capacity (dBIPC). Their dBIPC fluctuates under the influence of numerous intrinsic and extrinsic factors, including chronic and acute stressors, pharmacological agents, mood state, and environmental distracters . In theory, operational performance could be improved by continuously monitoring dBIPC to determine an analysts' cognitive state and then modify (boost) dBIPC during operations. Practical implementation of the concept has, however, been hampered by lack of tools for unobtrusive monitoring of relevant aspects of dBICP and lack of validated methods for their augmentation. Recent developments in brain and physiological monitoring hardware may have bridged this gap so that a deployable system can be developed that is effective in boosting dBIPC, easy to use, inexpensive, safe and deployable in operational environments. For example, inexpensive multichannel, portable, battery-powered EEG recording platforms are now available that could be utilized for safe and unobtrusive monitoring of the electrical activity of the brain during relevant tasks. Likewise, modern eye tracking systems are small and can be easily incorporated on or off-body. Numerous studies have correlated changes in EEG and eye tracking features with various aspects of cognition or underlying task performance . Similar findings have been reported for functional near-infrared spectroscopy (fNIRS), another brain monitoring technology that is potentially field-deployable . Transcranial direct current stimulation (tDCS) is one method that can be used to modulate cognitive state  without potentially addictive pharmaceuticals or the addition of additional obligations, such as stress management programs, on the already thinly stretched human analyst resources. The tDCS technique is an attractive option for increasing or sustaining dBIPC during the task because of its comparative technical simplicity and ease of integration with a physiological monitoring platform into a system capable of delivering the stimulation when and where needed to enhance or sustain performance. For example, such system could operate in a closed-loop and (1) detect performance degradation through physiology, (2) deliver targeted stimulation to relevant brain regions to counter the degradation, (3) evaluate the effects of tDCS on performance, and (4) adjust the tDCS parameters accordingly.
PHASE I: Design/develop an innovative concept for monitoring human operator cognitive state and modulating dBIPC with transcranial direct current stimulation. The system/concept designed should be capable of measuring changes in operator fatigue and attention. Develop design requirements and engineering specifications for a fully deployable system.
PHASE II: Develop, test, and evaluate a deployable closed loop tDCS system with physiologic feedback and demonstrate accurate operator state assessment and performance sustainment/improvement compared to a sham control. Sensors and electrodes must be easily applied and suitable for operational settings with real-time data analysis to provide a means for closed-loop integration with the tDCS. The hardware should be small and lightweight.
PHASE III: Ultimately, this effort will result in a neuromodulation device capable of assessing the operator's attentional and fatigue state and apply stimulation when needed to enhance performance or reduce effects of stressors. Military: Image analysis, special operations, cyber defense, etc.
1. Lieberman HR, Tharion WJ, Shukitt-Hale B, Speckman, KL, Tulley, R. Effects of caffeine, sleep loss, and stress on cognitive performance and mood during U.S. Navy SEAL training. Psychopharmacology 164:250-261, 2002.
2. Jerbi K, Ossandon T, Hamame CM, Senova S, Dalal SS, Jung J, Minotti L, Bertrand O, Berthoz A, Kahane P, Lachaux JP. Task-related gamma-band dynamics from an intracerebral perspective: review and implications for surface EEG and MEG. Human Brain Mapping, 2009.
3. James DR, Orihuela-Espina F, Leff DR, Mylonas GP, Kwok KW, Darzi AW, Yang GZ. Cognitive burden estimation for visuomotor learning with fNIRS. MICCAI 2010.
4. Silvanto J, Muggleton N, Walsh V. State-dependency in brain stimulation studies of perception and cognition. Trends Cogn Sci. Dec;12(12):447-54, 2008.
KEYWORDS: Transcranial Direct Current Stimulation (tDCS), Non-invasive Brain Stimulation (NIBS), Attention, Vigilance, Cognition, Fatigue, Neuroscience
TECHNOLOGY AREA(S): Info Systems
OBJECTIVE: Develop reliable and resilient directional airborne networking technologies to support enhanced and assured mission success while maintaining backward compatibility with data link technologies currently in use by airborne platforms.
DESCRIPTION: The United States military faces an operating environment characterized by increased uncertainty, complexity, rapid change, and persistent conflict, such as in an Anti-access and Area Denial (A2AD) environment. The Air Force has established a goal of extending and augmenting space and surface network to connect, reconnect and enable collaboration of warfighters executing specific missions. This goal requires that serial layer networks have the attributes of scalability, flexibility, robustness, and responsiveness. However, current advanced serial data links, such as the Multifunction Advanced Data Link (MADL), can only form a linear network topology (i.e. a daisy chain) and provide limited airborne networking capability. This linear topology is well suited for a network with a small number of nodes; but as network size increases, this topology becomes undesirable due to the excessive increase in latency as well as the amount of bandwidth consumed by relaying traffic over multiple hops of the daisy chain. Moreover, a disruption or breakdown of any link in the delay chain will directly lead to disrupted communication and network partition. Such linear networks are especially vulnerable and fragile in an A2AD environment and can pose severe network reliability issues. The current data link (such as MADL) is not scalable or reliable or flexible, and it cannot perform network self-healing. This inadequacy is detrimental for fighting battles in a highly contested area that requires highly dynamic maneuvers, and would significantly reduce next-generation airborne network connectivity and effectiveness. This topic seeks for innovative directional networking technologies with necessary provable capabilities to address current and future airborne networks challenges. Example capabilities include (but are not limited to) directional routing, Time Division Multiple Access (TDMA), joint power-data adaptation, topology management, and low probability of detection (LPI/LPD) connectivity to improve airborne network communications and effectiveness facing A2AD dynamics. The proposed technologies need to stay compatible with legacy capabilities (such as the ability to form a daisy-chain topology), as well as to offer Partial Mesh (PM) capability, which enables platforms to alter their network formation in response to platform mobility and other dynamics rapidly and reliably. This topic seeks a software-based solution without the need to change the communication hardware of the targeted airborne data link, e.g., MADL., it is anticipated that tactical data link physical layer default settings, such as the allowable range of frequency band, power, apertures, etc. will not be changed to maintain backward compatibility. Mature prototype with relatively higher technology readiness lever (TRL) is expected for potential technology insertion and program integration towards the end of the performance period.
PHASE I: Identify viable approaches to directional airborne networking with reliable self-healing capabilities. Generate the system design of the directional airborne networking technologies that can significantly improve networking reliability, resiliency, and flexibility without negative impact on LPI/LPD of the targeted airborne data link. Quantify the benefits, such as improved networking reliability and reduced end-to-end delay using analysis and simulations/emulations, accounting for practical implementation constraints.
PHASE II: Implement the technology into a software prototype without changing the hardware of the targeted airborne data link (government will provide H/W and S/W specifications of targeted data link). Demonstrated and validatethe prototype system with radio elements in an emulated and operationally relevant environment. Demonstrate conclusively the expected benefits and the interoperability with existing tactical data links.
PHASE III: Demonstrate a field-ready software system with mature implementation in relevant operational environment. Perform technology insertion and program integration. The high reliable directional LPI/LPD networking technologies can benefit the commercial telecommunications world.
1. Pan Li, Chi Zheng, and Yuguang Fang, "The Capacity of Wireless Ad Hoc Networks Using Directional Antennas," http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5674047&tag=1
2. T. Schug, C. Dee, N. Harshman, R. Merrell, "Air Force aerial Layer Networking transformation initiatives," IEEE Military Communications Conference, Nov. 2011. http://ieeexplore.ieee.ord/stamp/stamp.jsp?arnumber=6127605
KEYWORDS: Tactical Airborne Networks, Mesh Networks, Directionaldatalink, Backward Compatibility, Time Division Multiple Access (TDMA), Experimentation
TECHNOLOGY AREA(S): Info Systems
OBJECTIVE: Develop a cyber assurance system that expands the reach of information management and control to the network, and processes operations in an atomic manner to secure operations from harm and manipulation.
DESCRIPTION: DoD Cyber Situational Awareness (CSA) involves the knowledge and understanding of the resident cyber assets and operations taking place, with an emphasis on leveraging this knowledge to protect ongoing mission operations and defend critical assets. Unfortunately, the emergence of future forms of cyber attack, network manipulation, and other infrastructure flaws are inevitable. Currently, the state of practice for mission plans, operations, and post-process evaluations of mission decisions are rigorous and stringent because the level of control can mitigate the risk of non-critical, unplanned, or blacklisted operations from occurring. The reason this has not became common practice for cyber operations is because the same degree of understanding, control, and atomicity of operations is at a finer-grained and more complex Informational, software, and hardware level. Applying transaction-based operations for network and cyber control is a common sense next step for operational control. If network transactions are not performing actions in support of past, current, or future missions, then what are they doing on the network in the first place? In the operational world of mission processes and decisions it is rare or impossible to go off the cuff and simply trigger unplanned, unscoped, random, or unbounded missions or mission essential function (MEF) flows, however, in the cyber/software world, after accreditation, there is a lack of management and control for what can be run, and when. Mission operations have begun to expand into similar transactional levels of control and atomic execution by preventing certain operations without planning, support or resource assets, but cyber operational support is lacking the same breadth and reach of internal capabilities. Just as operational situations are ever evolving, but maintain adaptive command and control for plans, active execution support, and post-mission lessons learned, approaches that could increase the cyber control and mitigation of threats could result in robust technologies that prevent attacks, network clogging, and other manipulative or man-in-the-middle techniques that put operations at risk. Just as banking, TCP, and other critical infrastructures and communications standards enforce protocols guaranteeing delivery and proper transactional control, there are similar advantages to leveraging the approaches within the realm of cyber operations, processes, and transactional controls. Developing such capabilities requires addressing several challenges, including: -Mapping network operations to higher level approved processes and infrastructure support for approved, ongoing missions. -Expanding the reach of information management controls to a more find-grained comprehension of on-the-wire communications. -Metrics and atomic transactional controls that administer cyber assets and tasking in and end-to-end manner. The expected results of this effort include enhanced mission assurance, and strategies and approaches that successfully implement cyber command and control through a transaction-based, atomic, and white-listing paradigm for approved mission operations only. This minimizes the burden of software and network operations that consumer mission resources, and provide the foundation for enterprise-level management tools that gain both better control and awareness of past, present, and future operations.
PHASE I: Design a prototype system that develops cyber command and control features spanning from mission and operational support down to individual network packets and communication streams, approving or disallowing communications dependent on their protocols, intent, and true operational value. The administration and cyber controls should be developed with the above objective in mind.
PHASE II: Development of a prototype system that implements the Phase I design, expands across multiple networks and devices utilizing transactional, atomic controls, and demonstrates/validates the prototypes performance using representative mission and network data.
PHASE III: The resulting system will support the protection of networks and cyber assets agnostic of their military or commercial applications.
1. Lehr, William (2013). Cyber Policy and Economic in an Internet Age. Springer Science and Business Media.
2. Singhai, Anoop (2207). Data Warehousing and Data Mining Techniques for Cyber Security.
3. Zimmerman, Carson (2014). Ten Strategies of a World-Class Cybersecurity Operations Center. MITRE Corporation.
KEYWORDS: Situational Awareness, Mission Awareness, Cyber Command And Control, Information Management, Atomic, Mission Assurance
TECHNOLOGY AREA(S): Bio Medical
OBJECTIVE: Develop a training framework capable of working with current DoD systems that employs virtual, mixed, and/or live simulation training strategies capable of providing caregivers the opportunity to master handoff protocols from the point of injury through the continuum of care.
DESCRIPTION: The Defense Health Agency (DHA) seeks to standardize baseline handoff protocols and associated tools across the services and to create a common approach to the training of these protocols and tools. Opportunities for the medical and non-medical individuals, teams, aircrews, and units to participate in collective training of patient evacuation and transport is often limited which can result in the atrophy of their patient handoff skills. Adding baseline handoff protocol training to current approaches will create a standard, service-agnostic approach to ensuring the transfer of responsibility for patients and their information is properly communicated and understood . Providing care to those wounded in combat requires a coordinated effort from a team of teams which generally operate under suboptimal conditions and are separated by time and often great distances. In order to achieve the best possible patient outcomes, everyone involved in the process of transferring patients from one level of care to the next must receive and understands critical information. Unfortunately, while usually conducted, patient handoffs are error prone and have been shown to be frequently insufficient . Handoffs are fundamental for patient care and should be an opportunity for shared cognition and situation awareness between transferring caregivers. To achieve the best possible coordination and continuity of care, training must ensure effective patient handoffs. Approaches to standardized handoff training across the military services must consider the complex nature of multi-trauma handoffs, interactions between team members , and how these factors can be objectively measured in a variety of conditions and settings (e.g. care under fire, assessment and treatment in austere environments, national and regional customs and language barriers, equipment differences). Compounding the opportunities for error, military healthcare providers often find themselves simultaneously handling several waves of multi-casualty handoffs with minimal preparation time before receiving the next series of casualties. These operating conditions amplify their need to be capable of quickly and accurately exchanging and understanding critical patient information. The handoff protocol training approach should implement an innovative framework that considers compatibility and integration within extant DoD computer network systems as well as interoperability with existing systems such as Virtual Battlespace 3 (VBS3), the Wide Area Virtual Environment (WAVE), and/or the array of technologies used by the Critical Care Air Transport Team (C-CAT1), Center for Sustainment of Trauma and Readiness Skills (C- STARS), and the Army Medical Support Training Centers (MSTC). A successful solution will take advantage of in situ performance data which can be collected and analyzed in order to provide objective assessments of individual and team performance for real time monitoring and feedback. Further, a successful system will need to be capable of providing certification, recertification training, and have the ability to support service-specific training for En Route Care (e.x. individual, team and crew tasks, in-flight aeromedical team communication). Additionally, data collected and analyzed during training will contribute to future approaches to Joint En Route Care. Ultimately, the result of this effort will be service members who are properly trained and certified in standardized handoff protocols in representative environments that will contribute to improved patient care and achieve the best possible patient outcomes .
PHASE I: The team will develop an approach for delivering, as appropriate, a virtual, mixed, and/or live simulation training framework to work within existing DoD systems (e.x. VBS3, C-CAT1, C- STARS, WAVE, MSTC, etc.) during a prolonged combat casualty care scenario. The framework will establish that the proposed concept can feasibly work within the selected system(s). Feasibility will be established through the modeling and analysis of specific simulation strategies and techniques which utilize unique training materials and meets the considerations provided in the description. The Phase I Option, if awarded, will include the initial design specifications and capabilities description to build a prototype in Phase II.
PHASE II: Based on Phase I results and the statement of work for Phase II, a simulation training framework solution will be developed and delivered. The prototype must be capable of assessing trainees proficiency with the handoff protocols and tools based on patient outcome and handoff training objectives. Validation of the prototype will be through testing to demonstrate improved performance, team communication, training engagement, and simulated patient outcomes. A detailed test plan will be developed in order to demonstrate that the deliverable meets the intent of the simulation training framework. A Phase III commercialization plan will be developed during Phase II that addresses transition to industry and/or relevant users. If required, a protocol for the protection of human subjects for this effort will be developed in Phase I for use in Phase II.
PHASE III: The team will be expected to support the DHA in transitioning the technology to the designated service(s) for use. During Phase III, the team will support the DHA and the selected service(s) in the system integration and qualification testing for the software technology developed in Phase II. This will be accomplished through integration and test events in order to transition the technology into the selected system(s), training center(s), and/or school house(s). Integration of this system will require testing to demonstrate improved performance team communication, training engagement, and improved simulated patient outcomes. Private Sector Commercial Potential: The efforts of the research in a simulation framework for handoff training and performance will have direct application to high-risk organizations that involve training personnel to operate in complex domains. These domains include civilian healthcare, nuclear energy, commercial air transportation, and the space industry.
1: Department of Defense Patient Safety Program and Agency for Healthcare Research and Quality. 2006. TeamSTEPPS: Team Strategies and Tools to Enhance Performance and Patient Safety: Pocket Guide (AHRQ Pub. No. 06-0020-2). Rockville, Maryland. http://www.ahrq.gov/professionals/education/curriculum-tools/teamstepps/instructor/essentials/pocketguide.pdf
2: Riesenberg, L. A. (2012). Shift-to-shift handoff research: Where do we go from here? Journal of Graduate Medical Education, 4(1), 4-8. http://doi.org/10.4300/JGME-D-11-00308.1
3: Apker, J., Mallak, L. A., Applegate, E. B., Gibson, S. C., Ham, J. J., Johnson, N. A., & Street, R. L. (2010). Exploring emergency physician“hospitalist handoff interactions: development of the handoff communication assessment. Annals of emergency medicine, 55(2), 161-170.
4: Gakhar, B., & Spencer, A.L. (2010). Using direct observation, formal evaluation, and an interactive curriculum to improve the sign-out practices of internal medicine interns. Academic Medicine, 85(7), 1182-1188. http://dx.doi.org/10.4300/JGME-D-12-00203.1
KEYWORDS: Medical Handoff Training, Healthcare Simulation Training, Medical And Paramedical Training, Medical Technology, Patient Safety, Team Communication
TECHNOLOGY AREA(S): Bio Medical
OBJECTIVE: Develop and validate empirically derived combat casualty handoff protocols and tools which can be used across all military branches of the armed forces with the potential application to other healthcare settings.
DESCRIPTION: Prolific evidence indicates that quality patient care is contingent upon communication. To illustrate, the Joint Commission suggests that approximately 70% of sentinel events are attributed to communication mishaps, and 80% of severe medical errors are attributed to miscommunication during handoffs (i.e., the transferring of information, responsibility, and authority for patient care from one provider to another) . Although handoffs are fundamental for clinical care, frequently conducted, and universally performed across sub-specialties, they are consistently inadequate and error prone. Miscommunications during handoffs have resulted in errors of medication, treatments, tests, and pending consultations . In addition, research has demonstrated that inadequate handoffs are linked to poor outcomes including higher rates of readmission, longer lengths of stay and times to intervention, as well as increases in preventable adverse events . Furthermore, mishaps in handoffs have repeatedly been one of the leading factors for malpractice claims . In order to mitigate these communication breakdowns and issues that result from such failures, the Joint Commissions National Patient Safety Goal specifies that organizations should enhance communication between clinicians by implementing a standardized approach to conducting handoffs. Although numerous approaches to standardized protocols have been developed and have been shown to be beneficial within civilian environments, these protocols are often generic and not evaluated rigorously . Additionally, handoffs become even more complicated when considering the austere environments and the nature of the traumatic injuries that occur in combat. Further, the Army Institute of Surgical Research, suggests that there is a poor translation of evidence-based practices to the battlefield (see http://www.usaisr.amedd.army.mil/09_sccp.html). These issues are compounded when considered in context of the realities of combat casualty care. Caregivers treating complex multi-trauma patients often operate in mass casualty scenarios, work under fire, have cultural communication barriers, and respond to situations of prolonged field care which can abruptly end or become extended as opportunities for transfer to patients emerge or become delayed. Handoff protocols should be flexible enough for providers with varying backgrounds (medical and non-medical individuals, teams, aircrews, and units, required for effective patient evacuation and transport) to rapidly provide the appropriate information that is critical for treating time-sensitive injuries under the variety of less than ideal conditions . Further, the developed protocols should be capable of being modified and easily assessed for accuracy to meet service specific needs and be capable of being integrated with current DoD programs such as Virtual Battlespace 3 (VBS3), the Wide Area Virtual Environment (WAVE), and/or the array of technologies used by the Critical Care Air Transport Team (C-CAT1), the Center for Sustainment of Trauma and Readiness Skills (C- STARS), and the Army Medical Support Training Centers (MSTC).
PHASE I: The team will design, develop a new concept for handoff protocols and associated tools for use through the continuum of care for combat casualty care situations. Handoff protocols and associated tools will be designed to be used by the variety of service members that are involved in patient handoffs in a joint environment. The proposed concept will show that it can be used across a range of challenging conditions set forth in the description. The handoff protocols and associated tools should be capable of being modified and easily assessed for accuracy and to meet service specific needs. Any technological solutions must be interoperable with current DoD programs and computer network systems. The Phase I Option, if awarded will include preliminary design concepts and propose capability descriptions for Phase II. An appropriate protocol for the protection of human subjects will be created and approved prior to research and/or testing involving human participants.
PHASE II: Based on Phase I results and the statement of work for Phase II, the team will apply results from Phase I to develop a comprehensive curriculum and training approach to address the tasks, knowledge, and skills needed to effectively use the handoff protocols and associated tools; employ appropriate methods to test the proposed handoff protocols and tools; demonstrate the handoff protocol design approach in a prolonged combat casualty care scenario; and demonstrate improved performance using the prototype handoff protocols and tools over existing processes used to conduct patient handoffs. A commercialization plan will be developed during Phase II that addresses transition to industry and/or relevant users. A protocol for the protection of human subjects for this effort will be developed in Phase I for use in Phase II.
PHASE III: The team will be expected to perform final testing and prepare any and all necessary documentation such as user's guides and instructor's manuals for transition of the program and protocols to the Defense Health Agency (DHA) and integrate the handoff protocols and associated tools into a training solution for combat casualty care and to applicable schoolhouses and simulation platforms. Private Sector Commercial Potential: Advances in this technology are applicable to the paramedic, emergency medicine and hospital care communities. Methods and technologies developed under this effort could be used by healthcare industry members who use handoff protocols to ward off information handoff-related decrements in performance of patient care. Additionally, organizations specializing in safety and communications in high-risk organizations could employ the tools and techniques developed here to ensure consistent performance outcomes and provide standardized communication of critical information for their clientele.
1: Joint Commission. (2012). Sentinel event statistics data: root causes by event type. http://www.jointcommission.org/assets/1/18/Root_Causes_by_Event_Type_2004-2Q2013.pdf. Accessed June 30, 2014.
2: Riesenberg, L. A. (2012). Shift-to-shift handoff research: Where do we go from here? Journal of Graduate Medical Education, 4(1), 4-8. http://doi.org/10.4300/JGME-D-11-00308.1
3: Catchpole, K.R., De Leval, M.R., Mcewan, A., Pigott, N., Elliott, M.J., Mcquillan, A., & Goldman, A.J. (2007). Patient handover from surgery to intensive care: using Formula 1 pitstop and aviation models to improve safety and quality. Pediatric Anesthesia, 17(5), 470-478. http://doi:10.1111/j.1460-9592.2006.02239.x
4: Gakhar, B., & Spencer, A.L. (2010). Using direct observation, formal evaluation, and an interactive curriculum to improve the sign-out practices of internal medicine interns. Academic Medicine, 85(7), 1182-1188. http://dx.doi.org/10.4300/JGME-D-12-00203.1
5: Santos, E. Jr., Rosen, J., Kim, K.J., Yu, F., Li, Y., Guo, Y., Katona, L. (2012). Reasoning about intentions in complex organizational behaviors: Intentions in surgical handoffs. In E. Salas, S.M. Fiore, M.P. Letsky (Eds.), Theories of team cognition: Cross-disciplinary perspective (pp.51-85). New York, New York: Taylor & Francis.
KEYWORDS: Medical Handoffs, Combat Casualty Care, Medical Technology, Patient Safety, Team Communication
TECHNOLOGY AREA(S): Bio Medical
OBJECTIVE: To develop a novel and fast computing scheme for constructing personalized pharmacokinetic models. The scheme must rely on (i) a limited set of measurements for each individual patient and (ii) a knowledge base of existing well-calibrated models for a collection of diverse individual in order to approximate in silico the structure of metabolic interactions for a given individual patient by solving a parameter identification problem.
DESCRIPTION: The field of pharmacokinetics (PK) is dedicated to the study of the concentration dynamics of substances administered to a living organism. In population PK, models of variability among a population receiving clinically relevant doses of a substance of interest are constructed as a function of observable traits such as demographic, pathophysiological, and therapeutic-relevant features, such as body weight, excretory and metabolic functions. These models are used to understand how certain traits in the population affect the dynamics of metabolic interactions. Individual-specific (or personalized) PK models have a different use as they can assist in designing personalized therapies so as to improve effectiveness and to avoid severe side-effects given individual patient characteristics in drug response (see for example,  and ). However, developing individual specific PK models is a demanding task that may require extensive in vivo sampling or intensive computational effort because the parameter identification problem is underdetermined (see e.g. ). In silico approaches may provide a more cost-efficient way of developing personalized PK models. For example, a PK model for a given individual maybe constructed from a knowledge base of well-calibrated models for a set of representative individuals in a population. Using a limited set of measurements obtained from a new individual (pursuant a controlled dose of the substance of interest) one may aim to find the combination (or ensemble) of existing models that best fits the data. The latter task is tantamount to solving a stochastic optimization problem in which the objective is to maximize a measure of model fit by choosing parameter values that are a convex combination of the parameter values in the collection of well-calibrated individual models. This process can be seen as constructing an avatar, i.e. an artificial representation of the new individual as a function of the existing knowledge base. Nonetheless, this stochastic optimization problem is quite challenging from a computational standpoint. First, evaluating the fit of a given choice of parameters may require non-negligible computational time. Several model runs (for the same parameter combination) must be obtained in order to reduce the effects of noise and thus obtain an adequate estimation of the measure of fit. Running this kind of model repeatedly is an extremely time consuming task. Secondly, the model simulation error is not necessarily well-behaved, i.e. it may not be zero-mean and may also exhibit significant correlation. Finally, since the underlying model is akin to a blackbox, the optimization problem associated to model identification is not necessarily convex. The proposed work should result in a relatively simple to use app that could guide the personalizing of drug therapies with military applications such as severe bleeding control and PTSD treatment.
PHASE I: The STTR performer must conceive, implement and test a new in silico approach to constructing personalized PK models. The proposed design must jointly use a limited set of measurements for a given individual patient and a knowledge base of existing well-calibrated models for a collection of diverse individuals in a population sample. For phase I, the knowledge base will be simulated but in Phase II, the STTR performer must develop the testbed and gather a data set. Personalized model identification must be done by finding the combination of parameter values in the collection of well-calibrated individual models that maximizes model fit with respected the limited set of measurements. Implementation and testing of the proposed scheme must be conducted with a well-known PK model (e.g. glucose metabolism). Given that running PK models could be extremely time consuming, the proposed scheme must be distributed and cloud-based or cluster-based. In this computing infrastructure distributed stochastic gradient algorithms could be implemented in order to speed up model identification. A prototype computing scheme for constructing personalized PK models must be developed in the form of a software library that will be application agnostic and flexible enough to accommodate alternate in-silico technologies. Thus, after selecting a PK/PD application area, the overall applicability of the technology must be demonstrated by adapting the algorithmic scheme to a different type of PK model. In light of the fact that modeling error could be significant and not necessarily well-behaved, a comprehensive methodology for assessing of the robustness of the proposed computational scheme must be proposed.
PHASE II: The first task in this phase consists of developing an experimental testbed needed to construct a knowledge base of PK models. This knowledge base must correspond to representative population of adult individuals from which extensive database of measurements will be gathered. Data collection must be done in an unobtrusive manner avoiding burdens, distractions, or alterations to participants typical lifestyle. Thus, the data collected would reflect typical human behavior. The data must be recorded in such a manner that subjects cannot be identified, directly or through identifiers linked to the subjects. In addition to numerical accuracy of the fit, standard clinical metrics should be used to evaluate the quality of the models by comparing fitted model to datasets in order to understand how clinically relevant are the numerical inaccuracies obtained during the fitting process. A second task in this phase, consists of analyzing the performance of the scheme as it relates to bias and/or correlation in modeling error. In case modeling performance is greatly affected, a suite of alternative variations to the computing scheme proposed must be identified and duly supported with empirical evidence. Finally, the last task consists of developing apps that would make use of the personalized PK models obtained. For example, a fully personalized model can be used to educate patients on how to manage their particular condition more effectively. This could be done for instance by (i) creating (or repurposing) a phone app to record relevant information, (ii) communicating recorded information to a cloud service for computing the personalized PK model, and (iii) developing training modules based on the personalized PK model for the use of practitioners and patients. Other example apps could be used by physicians seeking to understand better (individualized) dosing strategies. A system can take the form of a web-services physicians can access to collect data and submit information to a central facility for model identification, use the identified model to suggest therapeutic use and improved dosing to physicians (see for example ).
PHASE III: In Phase III, the STTR performer's software will be available for military and civilian use. The FDA has recently accepted in silico trials as supporting evidence for approving new drugs and/or medical devices. In this context, the value of in-silico clinical studies is directly related to the quality and consistency of data used to generate and test these models. We expect the STTR performer will lay out the foundations for obtaining FDA approval for potential future applications of the software. For example, the STTR performer will strictly rely on standardized clinical trial data (in compliance with FDA). We envision that the team that develops the software will market it for Government laboratory use, and negotiate commercial licensing with commercial and academic markets. As an alternative, any or all of these artifacts might be released into the open source community through organizations such as the Open Source Electronic Health Record Alliance (OSEHRA) or Open Health IT Tools or similar organizations for open sources licensing. Based on negotiations with the types of government and commercial organizations cited, it is possible that hybrid commercial and open source licensing could occur. In the case where these artifacts are released into the open source community, the STTR awardee would need to develop and provide a plan to state how it would sell additional consulting, software implementation and/or training services around their workflow model, technical implementation guidelines, and/or software controls.
1: Patek D. Lv D., Campos-NaÃ±ez E. and Breton M. (2016) Retrospective Optimization of Daily Insulin Therapy Parameters. Proceedings of 11th IFAC Symposium on Dynamics and Control of Process Systems Trondheim, Norway. http://www.sciencedirect.com/science/article/pii/S2405896316304888
2: Konagaya A. Towards an In Silico Approach to Personalized Pharmacokinetics (2012) in Molecular Interactions A. Meghea, Ed. In Tech. pp. 263-282. http://cdn.intechopen.com/pdfs-wm/30532.pdf
3: Kovatchev B., Breton M., Dalla Man C. and Cobelli C. (2009) In Silico Preclinical Trials: A Proof of Concept in Closed-loop Control of Type I Diabetes. Journal of Diabetes Science and Technology. Vol. 3, pp. 44-55. https://www.ncbi.nlm.nih.gov/pubmed/19444330
4: Aoki Y., Hayami K., De Sterck H., Konagaya A. (2014). Cluster Newton Method for Sampling Multiple Solutions of Underdetermined Inverse Problems: Application to a Parameter Identification Problem in Pharmacokinetics, SIAM Journal of Scientific Computing, Vol. 36 No. 1, pp. 14-44. http://epubs.siam.org/doi/abs/10.1137/120885462
KEYWORDS: Pharmacokinetics, Model Identification, Precision Medicine, Stochastic Optimization
TECHNOLOGY AREA(S): Bio Medical
OBJECTIVE: Devise and develop a rugged diagnostic platform for general biochemical and biomarker analysis, which can be used under prolonged field conditions and in isolated, austere environments.
DESCRIPTION: Warfare can occur in isolated, austere environments with limited medical care at hand, precious treatment supplies, and no sophisticated diagnostics to guide treatment. Future operations are planning for more prolonged field care , as rapid evacuation may be impossible. Moving peripheral blood, saliva, or urine based diagnostic testing to the battlefield requires not only sensitive and rapid test results from a simple objective measurement or a simple set of multiplexed measurements, but also a test system consisting of a physical sensor/reader, chemical reagents, and specimen sampling means that can withstand harsh and prolonged environmental conditions. Such a system does not now exist. A useful system must be light weight, reliable, easy to use, yield results rapidly that are easy to interpret, and have direct bearing on preservation of life, health and function of warfighters. A recent report concerning deployment of diagnostic tests to near-patient settings in the rural developing world provides guidance . Biochemical and biomarker systems are of critical importance to diagnosing the incidence, severity, and progression under treatment of wounded warrior status in prolonged field care conditions. Organ and metabolic status, infection, and traumatic brain injury are examples. Some specific biomarkers are the subject of past and possibly pending small business topics . This topic is focused on the development of a platform that can perform multiple measurements, and potentially become a standard. A potential approach to the objectives of this topic could involve functionalized nanoparticles . However, any approach to a battlefield biochemical and biomarker diagnostic system will be considered if it includes the following characteristics: 1. System sensitivity “ Wide biomarker concentration dynamic range from 1.0 micromolar to 0.1 picomolar 2. Rapid “ Less than 5 minutes from the opening of a test pack to obtaining one or more samples, and generating results. 3. Objectivity “ Automatic calibration. No operator interpretation needed. 4. Potential for Multiplexing “ An array of diagnostic data should be obtainable from a single sample, rather than multiple sampling for multiple tests. Detection of data should be seamlessly integrated with its analysis. 5. High-level, easily interpreted diagnostic output “ Possibility of machine learning guided interpretation of simultaneous test results, and trend analysis from serial test results.
PHASE I: The overall aim of this phase is to develop, test, and demonstrate feasibility of a novel approach that meets the above criteria. Demonstrate at a proof-of-principle level the desired sensitivity, speed, objectivity, stability, and shelf life of the assay and testing system. Demonstrate proof of concept by demonstrating at least one diagnostic test that meet the criteria. Show feasibility by design and/or prototype construction of utility by minimally skilled users in an austere environment. Compare the proposed novel approach with existing standard diagnostic assays in the demonstration system.
PHASE II: Construct and demonstrate the operation of a prototype battlefield-ready assay and testing system that meets the above criteria. The term battlefield-ready specifically includes small size, light weight, portable, and able to withstand the harsh conditions of temperature, humidity, water immersion, dirty environment, and physical abuse that are found in far forward environments. Discussions with the FDA should start in this phase to facilitate regulatory approval when ready. Discussions should include material and process documentation, and verifiable data sets on human samples. The U. S. Army Medical Materiel Development Activity, Division of Regulated Activities & Compliance (USAMMDA/DRAC ) is a potential source of regulatory assistance.
PHASE III: In phase III pivotal clinical studies in military, and civilian medicine shall be completed. The prototypes demonstrated in phase II will be developed into Good Manufacturing Practice (GMP) products manufactured either by the small business or under license. FDA approval will be obtained for the validation of the platform and tests conducted with it, and for the GMP compliant processes to produce them as well as post-market data surveillance. The commercialized product will be of great value for the military when prolonged field care is necessary and/or in isolated, austere environments. It will also be of great value in civilian medicine in resource limited areas, such as remote areas, potentially in third world countries. The product could become an important research tool for studying the dynamics of biomarkers in healthy and impaired individuals. It could also potentially replace some more sophisticated laboratory tests owing to its speed, simplicity, and low cost.
1: Joint Program Committee 6/ Combat Casualty Care Research Program Prolonged Field Care Research Award Funding Opportunity Number: W81XWH-16-DMRDP-CCCRP-PFCRA, http://cdmrp.army.mil/funding/pa/16dmrdpcccrppfcra_pa.pdf
2: Dittrich S, Tadesse BT, Moussy F, Chua A, Zorzet A, TÃ¤ngdÃ©n T, et al. (2016) Target Product Profile for a Diagnostic Assay to Differentiate between Bacterial and Non-Bacterial Infections and Reduce Antimicrobial Overuse in Resource-Limited Settings: An Expert Consensus. PLoS ONE 11(8): e0161721. doi:10.1371/journal.pone.0161721
3: Liu X, Dai Q, Austin L, et al. (2008) A One-Step Homogeneous Immunoassay for Cancer. Biomarker Detection Using Gold Nanoparticle Probes Coupled with Dynamic Light Scattering. Journal of the American Chemical Society: 130:2780-2
KEYWORDS: Prolonged Field Care, Austere Environment, Biomarkers, Rapid Diagnostic Tests, Developing World Medical Diagnostics, Activated Nanoparticles
TECHNOLOGY AREA(S): Bio Medical
OBJECTIVE: To develop an efficient technology for medical grade oxygen generation with water as the feedstock and to provide a potential solution (deliverable prototype hardware) for the Armys medical oxygen requirement (or other DoD requirement).
DESCRIPTION: Oxygen is a necessary substance for human beings and has been widely used for the patient in the battlefield; it is a life-saving capability regulated as a drug by the FDA. In terms of the medical-level oxygen, high purity without any contamination is essentially required. On another hand, the safety and the transportation size have to be considered for the military purpose. For example, the conventional high pressure gas cylinders have safety related issues and concerns. At the present time, oxygen is often produced using pressure swing absorption (PSA) or vacuum swing adsorption (VSA) techniques. The limitations of these techniques include the large foot print, heavy weight, and high power consumption. Moreover, since air is used as the feedstock, the oxygen purity has often remained a concern, particularly on a battlefield. As a result, further purification is often required. Alternatively, high-purity oxygen can be obtained through the electrolysis of water, which can produce ultrapure oxygen at low pressure. One limitation of this technique is the use of deionized water in conventional electrolyzers and the requirement of significant electrical power. Recent advances in photoelectrochemical (PEC) water splitting has shown that it is possible to produce large quantities of oxygen directly from water and sunlight. In general, the reaction of water-splitting includes two half reactions: the oxygen evolution reaction (OER) and the hydrogen evolution reaction (HER), which can be driven by solar energy and/or electrical energy.1-4 In this process, high purity hydrogen is also separately generated and can be used as fuels in the battlefield. Various materials and approaches have been developed for PEC water splitting. The scope of this project is to identify the suitable photoelectrode materials for long-term stable oxygen generation from water and to demonstrate a compact prototype unit that is able to deliver high purity oxygen with a flow rate of 10 to 20 liters per minute. The system weight, power consumption, and maintenance requirements need to compare favorably to the conventional VSA or PSA technologies. Moreover, the water requirement should be no higher purity than typical bottled drinking water. Considering the specificity and complexity of military situation, the long-term stable operation needs to be evaluated and compared to alternative technologies.
PHASE I: To demonstrate oxygen generation with water as the feedstock, and to determine the technical feasibility for achieving a flow rate of 10-20 liter per minute. Detailed analysis of the predicted performance needs to be developed. The water requirement needs to be identified with goal of using purified water (without distilling or deionizing) as a source, and the effect of water on the efficiency of oxygen generation and the purity level of oxygen needs to be thoroughly evaluated. For patient use, the oxygen produced would need to meet US Pharmacopeia standards for medical oxygen.
PHASE II: To develop, test, and demonstrate a prototype to produce medical grade oxygen with typical drinking water as the feedstock, and to further define field test objectives and perform limited testing of the system efficiency and stability. The minimum oxygen generation rate should be in the range of 10 to 20 liter per minute, and the system size, weight, and power consumption should be compared favorably with the conventional VSA and PSA equipment. Compliance with FDA medical regulations would also need to be pursued once the standards on the water quality are developed. Oxygen quality monitoring procedures would be developed in accordance with regulatory authorities.
PHASE III: Continued research and development toward high efficiency (expected up to 20% within 5 years) and reliability. Manufacturable process and epitaxial growth considerations for oxygen generating materials should be made and pursued, beyond prototype stages of development. Life testing and generation rate capabilities should be assessed for production systems, including potential for system scale-up to as much as 120 liters per minute to meet the requirements of a deployed intensive care unit.
1: Hisatomi, T.; Kubota, J.; Domen, K., Recent advances in semiconductors for photocatalytic and photoelectrochemical water splitting. Chem. Soc. Rev. 2014, 43 (22), 7520-7535.
2: Gratzel, M., Photoelectrochemical cells. Nature 2001, 414 (6861), 338-344.
3: Kibria, M. G.; Mi, Z., Artificial photosynthesis using metal/nonmetal-nitride semiconductors: current status, prospects, and challenges, J. Mater. Chem. A 2016, 4, 2801-2820.
4: Hu, S; Shaner, R. M.; Beardslee, J. A.; Lichterman, M.; Brunschwig, R. S.; Lewis, N. S., Amorphous TiO2 coatings stabilize Si, GaAs, and GaP photoanodes for efficient water oxidation, Science, 2014, 344 (6187), 1005-1009.
KEYWORDS: Oxygen, Water Splitting, Photoelectrochemical, Water Oxidation, Photoelectrode, Semiconductor
Materials Modeling Tool for Alloy Design to Streamline the Development of High Temperature, High-Entropy Alloys for Advanced Propulsion Systems
TECHNOLOGY AREA(S): Materials
OBJECTIVE: Develop a materials modeling tool for alloy design to streamline development of high temperature, high-entropy alloys for advanced propulsion systems.
DESCRIPTION: The temperature capability of Ni-base superalloy blades has increased by more than 300°C over the last 50 years [Ref 1] and is approaching 1100°C for single crystal superalloys. In spite of many efforts, however, a further improvement in their capability is becoming more difficult due to the low melting point of Ni, which is 1453°C. Considering the ever-increasing demands for materials with higher temperature capabilities to be used in gas turbines with higher efficacy, it is of vital importance to search for alloys based on the concept of High Entropy Alloy (HEA) development. In general, the concept is based on the idea of producing bulk crystalline alloys composed of multiple components being added in proportion that are far beyond their binary solid solubility limits, yet yielding a single-phase solid solution [Refs 1, 2, 3]. In some cases, the solid solutions formed possess simple crystal structures, such as face-centered cubic (FCC) and body-centered cubic (BCC) [Refs 4, 5], and also fulfill the expectations of combining high strength with good ductility [Ref 6]. However, successful efforts with experimental verification have not been reported in the literature. To enable rapid transition of HEAs with higher temperature capability, an innovative modeling tool for high-entropy alloy design that will enable streamlining towards rapid-alloy screening and property-orientation design is needed. This tool must be able to predict the composition of high temperature HEAs for both equiatomic and non-equiatomic formulations for advanced Mo-Si-B alloys. Algorithms should predict microstructural characteristics such as phase evolution, grain size, grain orientation, and microstructural texture. The results of the analysis should be displayed in a graphical way that allows for understanding the new HEAs compositions easily.
PHASE I: Design, develop and demonstrate the feasibility of algorithms to predict composition of a known high-temperature, high entropy alloy Mo-Si-B. This will include both equiatomic as well as non-equiatomic formulations. Algorithms should include phase evolution, grain size, grain orientation, and microstructural texture.
PHASE II: Down select to one composition (equiatomic or non-equiatomic) for verification through physical comparison between algorithms developed HEA and non-HEA coupons. Investigation should include the microstructural/structural changes related to various thermal processing, deformation mechanisms (room-temperature and high-temperature creep), and thermal stability/oxidation mechanisms under isothermal and cyclic exposures at elevated temperature for the selected composition.
PHASE III: Fully develop a materials modeling tool based upon verified algorithms. Demonstrate and validate the modeling tool against existing high temperature alloys. Transition the modeling tool for use in the development of new HEAs for advanced propulsion systems. The technology developed will have applicability to commercial and military aviation manufacturing firms including alloy manufacturers, casting, and forging companies. Private Sector Commercial Potential: The technology developed will have applicability to commercial and military aviation manufacturing firms including alloy manufacturers, casting, and forging companies.
1: Y. Zhang, T. T. Zuo, Z. Tang, M. C. Gao, K. A. Dahmen, P. K. Liaw, Z. P. Lu (2014). Microstructure and properties of high-entropy alloys, Prog. Mater. Sci. 61, 1-93
2: C. T. Sims, N.S Stoloff, W.C. Hagel (1987). Superalloys II: High Temperature Materials for Aerospace and Industrial Power, Wiley-interscience, USA
3: C. C. Tung, J.W. Yeh, T.T. Shun, S.K. Chen, Y.S. Huang, H.S. Chen (2007). On the elemental effect of AlCoCrCuFeN high-entropy alloy system, Mater. Lett. 61
4: J.-W Yah, S.-J Lin, T.-S Chin, Y.-Y Gan, S.-K. Chen, T.-T Shun, C.-H Tsau, S.-Y Chou (2004). Formation of simple crystal structure in Cu-Co-Ni-Cr Al-Ti-V alloys with multiple metallic elements, Metall. Mater. Trans. A35 (2004) 2533-2536
5: E. Cantor, J. T.H Chang, P. Knight, A. J. B Vincent (2004). Microstructural development in equiatomic multicomponent alloys. Mater. Sci. Eng. A 375-377
6: K. C. Pradep, N. Wanderis, P. Choi, J. Banhart, B. S. Murty, D. Raabe (2013). Atomic scale compositional characterization of a nanocrystalline AlCrCuFeNiZn high-entropy alloy using atom probe tomography, Acta. Mater. 61
KEYWORDS: High Entropy Alloy; Modeling; Super Alloys; Gas Turbines; Propulsion Materials; High Temperature Alloy
TECHNOLOGY AREA(S): Air Platform
OBJECTIVE: Develop technology based on statistical or computational methods to assist in the continued tracking of training performance and proficiency trends as underlying tactical data changes.
DESCRIPTION: There is a push by the DoD and USN to leverage the benefits of qualitative analysis by consuming large data sources (e.g., aviation data logs) and implementing human performance assessment and tracking of tactically relevant data to better understand force proficiency. To support decision making, big data analytics focused on developing trends or predictions based on historical data is desired. Military domains for big data is unique in that the tactics, techniques and procedures used by the fleet shift over time due to changes in capabilities or the need to adapt to novel or updated tactics by opposing forces. This creates a unique challenge for the typical statistics that would be leveraged in big data sources, as taking these changes into account is necessary to ensure that comparisons remain meaningful. The continued push for integrated warfare will likely result in cross-platform, mission-based trends; however, there may be differences in constructs across platforms (e.g., one platform may rely on timeliness and another on accuracy) that if not accounted for in the analysis or development of common construct definitions would skew analysis results. This effort seeks to identify statistical or computational methods that can assist with these adjustments to statistical trends, and implement them in an automated tool that will allow for the timely and continued calculation of trends related to fleet performance and proficiency. Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DSS and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract.
PHASE I: Refine or develop methods for adjusting calculations as data points related to tactics, techniques and procedures change. Test the feasibility of implementing any identified/developed methods and to identify the benefits and limitations of each.
PHASE II: Implement automated support through algorithms or other computational processes for implementing feasible methods for adjusting data. Develop usable computer interfaces that allow end users to make note of data points being adjusted as time shifts. Ensure that data results identify any potential limitations of calculations based on early methodological testing to ensure decision makers understand the comparisons. Implement a safeguard that alerts users to the extent to which trend analysis can be continued before the comparisons are meaningless due to lack of continuity of data sources, and implement tools to assist users with re-base lining data in these situations.
PHASE III: Extend the baseline functionality to include advanced or more robust data analysis techniques, and/or integrate developed capability with existing database and analysis systems. Implement Risk Management Framework (RMF) guidelines to support information assurance compliance, including updates to support installation on stand alone or Navy Marine Corps Intranet (NMCI) systems. Coordinate with partners or customers of commercial applications of the technology solution developed. Big data analytics has been implemented in a range of other domains such as athletics and medical communities. For the latter or other quickly advancing domains due to the pace at which technology support changes, novel techniques developed under this topic or integration of technology solutions such as those proposed here may provide unique insights for other domains leverage big data analytics. Private Sector Commercial Potential: Big data analytics has been implemented in a range of other domains such as athletics and medical communities. For the latter or other quickly advancing domains due to the pace at which technology support changes, novel techniques developed under this topic or integration of technology solutions such as those proposed here may provide unique insights for other domains leverage big data analytics.
Big Data, new epistemologies and paradigm shifts: http://bds.sagepub.com/content/1/1/2053951714528481.full.pdf+html
Challenges of Big Data Analysis: http://nsr.oxfordjournals.org/content/1/2/293.short
Example commercial off the shelf technologies: http://www.predictiveanalyticstoday.com/bigdata-platforms-bigdata-analytics-software/#content-anchor
Challenges and Opportunities with Big Data: http://dl.acm.org/citation.cfm?id=2367572
KEYWORDS: Qualitative Analysis; Big Data Analysis; Human Performance Assessment; Data Trends; Data Predictions; Statistical Analysis
TECHNOLOGY AREA(S): Materials
OBJECTIVE: Develop a software tool capable of optimizing the build plate design for metal powder bed additive manufacturing (AM) systems based on part geometry and features, part location and orientation with respect to the build plate and build direction, as well as the thermal effects inherent in AM. The parts location, orientation, and support structure will be optimized to minimize induced residual stress, control geometric distortion, effectively manage heat dissipation, and mitigate the effort needed in post-process support removal.
DESCRIPTION: Additive manufacturing (AM) processes are a class of manufacturing techniques which build components from the ground up by selectively adding material in layers rather than removing or deforming bulk material. This allows for increased flexibility in part design, but also introduces additional challenges in terms of build planning. Due to the layer-wise character of AM processes, portions of the final part may not be self-supporting during the manufacturing process given the parts features and orientation. In such cases, supporting structures must be printed only to be removed in an additional manufacturing step to achieve design geometry. Additionally, the significant thermal effects inherent in AM can lead to distortion and cracking as a result of high residual stresses if the parts orientation and location on the build plate are not carefully considered. Current techniques for generating support structure rely on iterating predefined support topologies, such as hexagonal honey combs, which are defined by the designer or selected by the AM machine when the toolpath is generated. This approach is primarily focused on minimizing the size and amount of support structure used. Part location and orientation are typically selected based on operator judgment and experience, or are overlooked entirely. Inadequate build plate design may result in failures during manufacture or final parts that do not meet geometric requirements, increasing time and costs as parts must be rebuilt. To address these issues, a robust build plate design optimization tool is sought. This tool should take into consideration a parts geometry and features, its location and orientation with respect to the build plate as well as the build path, and the characteristic thermal effects of the AM process that drive the formation of residual stresses and lead to unwanted distortion. The optimization tool should be able to provide the instructions necessary for the layout and orientation of parts on a build plate as well as the design and placement of support structure to minimize induced residual stress, control geometric distortion, effectively manage heat dissipation, and mitigate the effort needed in post-process support removal.
PHASE I: Demonstrate feasibility of a build plate design optimization tool by providing a sample build plate layout and support design for a complex geometry (e.g. overhangs, internal features, thin walls, holes/cylinders, etc.) and compare to the default or traditional build plate layout and support structure design in terms of induced residual stress, distortion, and removal difficulty using a single AM system.
PHASE II: Develop a prototype of the tool using the framework developed in Phase I optimizing the build plate design to minimize induced residual stress, control geometric distortion, effectively manage heat dissipation, and mitigate the effort needed in post-process support removal. Demonstrate that the optimized build plate layout and support structure design successfully minimized induced residual stress, part deformation, and necessary support structure as well as improved the retention of critical part features for one or more Navy-selected parts using multiple, different AM systems (i.e. different manufacturers.)
PHASE III: Fully develop the optimized build plate layout and support structure design tool and demonstrate it in a scenario representative of Navy implementation (i.e. using similar equipment, skillsets, and selected part(s) that would be available in a Navy application.) Transition the optimization tool into a stand-alone and/or combined product for use in Navy and commercial additive manufacturing applications. The software tool developed through this effort will improve the quality of additively manufactured parts as well as increase the efficiency of the AM process by reducing errors and failures resulting from poor build plate design and support strategies. As these aspects are valuable to all types of AM, this toolset will be directly applicable to wide range of commercial applications. The proposed build plate optimization toolset would provide industry with an effective means of improving part quality during the build process. Private Sector Commercial Potential: The software tool developed through this effort will improve the quality of additively manufactured parts as well as increase the efficiency of the AM process by reducing errors and failures resulting from poor build plate design and support strategies. As these aspects are valuable to all types of AM, this toolset will be directly applicable to wide range of commercial applications. The proposed build plate optimization toolset would provide industry with an effective means of improving part quality during the build process.
1: K. Mumtaz, P. Vora, N. Hopkinson (2011). A Method to Eliminate Anchors/Supports from Directly Laser Melted Metal Powder Bed Processes. Retrieved from http://sffsymposium.engr.utexas.edu/Manuscripts/2011/2011-05-Mumtaz.pdf
2: T.A. Krol, E.F. Zaeh, C. Seidel (2012). Optimization of Supports in Metal-Based Additive Manufacturing by Means of Finite Element Models. Retrieved from https://www.researchgate.net/publication/288148661_Optimization_of_supports_in_metal-based_additive_manufacturing_by_means_of_finite_element_models
3: M. Cloots, A.B. Spierings, K. Wegener (2013). Assessing New Support Minimizing Strategies for the Additive Manufacturing Technology SLM. Retrieved from https://www.researchgate.net/publication/289299663_Assessing_new_support_minimizing_strategies_for_the_additive_manufacturing_technology_SLM
4: G. Strano, L. Hao, R.M. Everson, K.E. Evans (2013). A New Approach to the Design and Optimisation of Support Structures in Additive Manufacturing. Retrieved from http://link.springer.com/article/10.1007/s00170-012-4403-x?no-access=true
5: N. Gardan (2014). Knowledge Management for Topological Optimization Integration in Additive Manufacturing. International Journal of Manufacturing Engineering. Retrieved from http://dx.doi.org/10.1155/2014/356256
KEYWORDS: Cost Reduction; Metal Additive Manufacturing; Part Quality; Support Structure; Residual Stress Mitigation; Build Plate Design
TECHNOLOGY AREA(S): Air Platform
OBJECTIVE: Develop an Unmanned Air System (UAS) pre-flight mission planning capability that utilizes path planning algorithms to minimize risk to personnel and property during UAS flight operations while reducing preparation times.
DESCRIPTION: The Navy continues to increase its UAS fleet with new air vehicle systems of various sizes, capabilities, and maturity. UAS do not meet the airworthiness standards that allow manned aircraft to fly within the National Airspace with minimal restrictions placed on flight plans by real-time air traffic control. As a result, UAS operations are typically limited to very restrictive operational areas (e.g. maritime operations and in Active Restricted and Warning Areas) due to risk to personnel and property on the ground. When missions necessitate operation outside of these areas, it can be particularly challenging and time-prohibitive to develop mission plans that ensure proper levels of safety. These constraints significantly limit Navy UAS operations for research, test and evaluation, and fleet operations; therefore, advanced and robust technologies are needed to efficiently create mission flight path plans that enable safe UAS operations within the US National and Foreign Airspaces. Current mission path planning capabilities are primarily air vehicle (AV) centric and rely on human judgment to assess the appropriateness of a given mission plan within a broader context. The UAS air vehicle operator (AVO) is responsible for: 1) defining a complete mission plan prior to flight for autonomous execution, 2) investigating and assessing the risk to personnel and property on the ground using limited information, and 3) continuously monitoring the mission execution (and potentially intervening) for mission changes, vehicle failures, and/or airspace conflicts. The level of safety for a given mission, therefore, depends largely on an individual AVOs ability to synthesize a myriad of data elements and promptly determine and execute the best course of action. Mission planning tools to assist the AVO in the synthesis of such data will improve the overall level of safety of UAS operations, reduce pre-flight manpower requirements, and enable broader integration of UAS within US National and Foreign Airspaces. Mission path planning capabilities and algorithms are needed to improve and standardize the Navys UAS mission planning process, especially to minimize the risk to personnel and property that is independent of, and dependent on, the air vehicle. Potential technologies exist in the academic and industry communities for robotic control, machine learning, data fusion, and numerical optimization that can reduce the complexity of AVO path planning tasks. The risk-based algorithms, and associated technologies, need to be scalable from basic 2-D assessments (e.g. population data) to multidimensional optimization problems that handle mission/vehicle constraints (e.g. vehicle speeds, weight, size, maneuvering capabilities, atmospheric winds, and sensor requirements) and risk-based information uncertainties (e.g. inferring population densities from FAA Sectionals). Initial algorithm development may start with analysis of alternatives, and/or generic algorithm class representations to support the further development of the chosen technologies. The algorithm(s) will address flight path safety during normal flight and during contingency operations, including robustness to air vehicle failures and risk-based data uncertainties (e.g. population density data).
PHASE I: Develop a risk-based UAS mission path planning capability using innovative algorithm(s) for pre-flight (non-real-time) planning tasks that addresses the latitude/longitude 2-D risk problem for personnel and property on the ground. Develop a visualization method to represent the optimization problem trade space and priorities. Identify available and potential information sources to build the required risk database for the proposed mission planning capability. Incorporate example data sources into an open architecture database format and interface to run preliminary, risk-minimized path planning example scenarios by the end of Phase I.
PHASE II: Develop and demonstrate prototype technology to expand the Phase I capabilities to the multidimensional risk-based mission path planning problem. Include capabilities to minimize risk while incorporating air vehicle constraints (e.g. vehicle speeds, weight, size, maneuvering capabilities, atmospheric winds, sensor requirements) and potentially competing mission parameters (e.g. fuel consumption, time to destination, no-fly zones). Include multiple risk database sources with varying levels of detail from gross information (e.g. population data) to detailed local information (e.g. on-board sensor data). Assess the technologys performance for real-time path planning capabilities in the presence of flight path plan modification triggers like mission objectives, vehicle failures, and airspace conflicts. Identify capability limitations, restrictions, benefits, and growth opportunities for continued development and incorporation of third-party capabilities. Perform a series of integrated mission planning exercises with validation by creating comparative human operator mission plans under the same risk-based goals and assumptions.
PHASE III: Transition the technology to a Navy UAS (e.g. MQ-25, Triton, Fire Scout, RQ-21 Blackjack, RQ-7 Shadow, or RQ-23 TigerShark), applicable Department of Defense or US Government UAS, or other commercial UAS application. UAS are being developed for use across the United States, and the rest of the world, for a multitude of applications: police surveillance, package delivery, movie/TV industry, news, sporting events, recreational and business video recording, and weather monitoring. With an understanding that UAS have safety shortcomings in comparison with manned aircraft, the resulting risk to the population may be mitigated through path planning that minimizes exposure. Risk-based mission planning needs to be used to increase the safety UAS operations to the general population as the systems become more pervasive throughout our communities. Private Sector Commercial Potential: UAS are being developed for use across the United States, and the rest of the world, for a multitude of applications: police surveillance, package delivery, movie/TV industry, news, sporting events, recreational and business video recording, and weather monitoring. With an understanding that UAS have safety shortcomings in comparison with manned aircraft, the resulting risk to the population may be mitigated through path planning that minimizes exposure. Risk-based mission planning needs to be used to increase the safety UAS operations to the general population as the systems become more pervasive throughout our communities.
Gonzalez, Luis Felipe, Lee, Dong Seop, and Periaux, Jacques (December 2-4, 2009). Optimal Mission Path Planning (MPP) for an Air Sampling Unmanned Aerial System, Australasian Conference on Robotics and Automation (ACRA), Sydney, Australia. Retrieved from http://www.araa.asn.au/acra/acra2009/papers/pap107s1.pdf
Griner, Alina (2012). Human-RRT collaboration in Unmanned Aerial Vehicle mission path planning, MIT Dept. of Electrical Engineering and Computer Science, Cambridge, MA. Retrieved from http://dspace.mit.edu/handle/1721.1/76913?show=full; DoD Defense Science Board (July 2012).
Task Force Report: The Role of Autonomy in DoD Systems, Office of the Under Secretary of Defense for Acquisition, Technology and Logistics, Washington, D.C. Retrieved from http://fas.org/irp/agency/dod/dsb/autonomy.pdf;
Rudnick-Cohen, Herrmann, and Azarm (2015). Risk-based Path Planning Optimization Methods for UAVs Over Inhabited Areas, Computers and Information in Engineering Conference, IDETC/CIE 2015, Boston, MA. Retrieved from http://www.isr.umd.edu/~jwh2/papers/Rudnick-Cohen-DETC2015-47407.pdf;
Tompkins, Paul (May 2005). Mission-Directed Path Planning for Planetary Rover Exploration, Tech. Report CMU-RI-TR-05-20, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA. Retrieved from http://www.ri.cmu.edu/pub_files/pub4/tompkins_paul_2005_1/tompkins_paul_2005_1.pdf
KEYWORDS: Mission Planning; Risk Reduction; UAS; Safety; Guidance And Control; Airworthiness
TECHNOLOGY AREA(S): Air Platform
OBJECTIVE: Develop a cognitive system as a selectable Unmanned Aircraft System Control Segment (U CS) Service with potential application in Naval Air Systems Command (NAVAIR) Common Control System (CCS).
DESCRIPTION: The demands on unmanned vehicle operators are increasing with the evolution of autonomous vehicles. As the CCS evolves, it is expected that one operator may need to manage a large number of autonomous manned and unmanned vehicles of varying capabilities and vehicle management needs. It is important that the operator knows where, how and when to place attention on needed issues during the execution of an integrated mission plan, especially where multiple vehicles are making decisions autonomously “ without operator approval, or management by negation. Because of these newly anticipated demands on the operator, there is a need to develop a cognitive system that is offered as a UCS Service that is assessing mission risk with some form of statistical confidence to help ensure mission success within this challenging environment. This cognitive system, should support the operator in knowing where, how and when to intercede with autonomous operations to ensure mission success when controlling multiple/diverse vehicles within a theater of operations, even when the theater of operations is fluid and demanding. In order to determine if a cognitive system is best in class with regard to providing a UCS service, the following is provided as the currently identified, but not necessarily all, the criteria that will be considered with regard to autonomous control systems (ACS) and CCS: 1. How well does the cognitive system candidate conform to ACS based on the real time control system architecture? 2. How difficult/easy can the cognitive system candidate be used within the UCS architecture? With regard to analyzing the cognitive systems quality of design to support best in class determination, the following is provided as the currently identified, but not all of, the criteria that will be considered: 1. From what source was the knowledge acquisition process to develop the cognition performed? a. Were multiple sources used? b. Did the sources have differences in perspective? c. If so, how were they resolved to support an optimal cognitive action? 2. What was the knowledge acquisition process used, including the process of translating expert knowledge to a cognitive system, network or branch structure? 3. With regard to the cognitive system, how was the system tested/proven to be reliable in its assessment and recommendations for mission success? 4. Is the statistical confidence associated with the cognitive system improving with increased sample size? The cognitive system is required to demonstrate reliability in terms of mission risk assessment and providing recommendations based on that assessment for unmanned vehicles. The cognitive system should assess both individual vehicle/mission risk and collaborative vehicle/mission risk, including multiple unmanned vehicles and unmanned vehicles supporting manned aircraft, for example an MQ-25s support of multiple F/A-18s. Included in the stages of developing the cognitive system, it is important that first a simulation and then live demonstration of the cognitive system be demonstrated with six or more autonomous, manned and unmanned vehicles. Of the six vehicles, an ultimate end goal for acceptance of the technology is that a minimum of two vehicle types carrying different sensor payloads need to be included in both the simulation and live demonstration. Threshold capability would be two or more cooperative unmanned systems; objective capability would be two or more cooperative manned and unmanned systems. The cognitive system should be designed as a service module that can be installed within a UCS compliant environment based SAE standard AS6518 to support the control stations three main components: (1) Vehicle Management, (2) Mission Planning, and (3) Mission Management. The cognitive system needs to store enough past flight information associated with all three components to support learning. The learning should be specifically focused on how to better determine success and assess risk from previous related and unrelated missions and platforms, without burdening overall Control Station performance. The cognitive system should utilize UCSs Data Distribution Services (DDS) middleware within the Control Station, including but not limited to sensor information to assess mission success from the vehicle. The cognitive system should also provide recommendations to the operator to improve mission success, along with percent of confidence increases or other risk assessment improvement. The recommendations should be able to be translated into UCS message commands that provide vehicle and payload management. Additionally, the cognitive system should include safety alerts and various safety or risk levels in real time and continuously during mission operations. A goal of the UCS cognitive service is to cause a ground control station (GCS) to become an autonomous station, controlling various autonomous vehicles to successfully complete mission goals under supervision of a single operator. This autonomous GCS is envisioned to integrate available sensor information from one or more unmanned autonomous vehicles under control by the GCS. The cognitive system shall be able to assess with a level of statistical confidence whether a mission plan will be successful. The assessment should include a measure of risk associated with the mission plan. Additionally, the cognitive system should provide safety alerts to the operator based on assessment of risk along with recommendations to mitigate/resolve the safety alerts.
PHASE I: Provide a learning-based, algorithm in the form of a UCS service. The algorithm needs to be able to collect and integrate sensor information from various sources under the operators control. The algorithm should include the cognitive design of a learning system that uses statistical confidence to support success and risk assessment of a mission requiring a variety of unmanned vehicles. The algorithm should show how it can reliably and accurately determine the statistical confidence as to whether the mission plan will be successful, along with the degree of risk, including recommendations and safety alerts associated with each vehicle under the control of the GCS.
PHASE II: Develop and demonstrate a prototype cognitive system in the form of an UCS service within a UCS compliant GCS. The cognitive system should be able to collect available sensor information from various unmanned vehicles under the GCSs control. The prototype demonstration should show how the cognitive system, using the algorithm developed in Phase I, can reliably and accurately determine the statistical confidence as to whether the mission plan will be successful and to what degree is the risk of failure, including recommendations and safety alerts to improve the degree of risk. Before a prototype is developed, a simulation should be developed to successfully show that the algorithms code has been implemented properly. It should integrate various sensor data that supports target and friendly vehicle identification tracking, mission assessment and targeting for potential kill chain solutions. Once the algorithms code is proven within the simulated environment, a live demonstration will be required, where one or more vehicles, controlled by a GCS running the algorithm, are following a complex mission plan. During the execution of the mission plan, data should be collected and processed by the algorithm in the form of one or more UCS services. The algorithm should be able to provide real time risk assessment and mitigation recommendations to the operator of the GCS.
PHASE III: Based on a successful prototype demonstration, further develop and test the cognitive systems algorithm as demonstrated in Phase II for transition to the NAVAIR CCS program. In this phase, both a simulation and live demonstration are required that control a minimum of six vehicles following a multi-stage mission plan, where a minimum of two different vehicle types are used and the algorithm is providing real time assessments and recommendations. This technology will benefit large delivery organizations such as United Parcel Service, FedEx, and others that focus on using autonomous unmanned air vehicle delivery of parcels and other items. The ability for a cognitive system to forecast mission success of one or more vehicles, while also making mitigation recommendations, has applications throughout aviation, robotics and unmanned systems industries, including commercial applications and other ground control systems within airports. Private Sector Commercial Potential: This will benefit large delivery organizations such as United Parcel Service, FedEx, and others that focus on using autonomous umanned air vehicle delivery of parcels and other items. The ability for a cognitive system to forecast mission success of one or more vehicles, while also making mitigation recommendations, has applications throughout aviation, robotics and unmanned systems industries, including commercial applications and other ground control systems within airports.
1: RCS: The Real-time Control Systems Architecture (2011). Rep. NIST, n.d. Web. Specifically review sections on Development Support and Performance Measures. Retrieved from http://www.nist.gov/el/isd/rcs.cfm; Giordano, J., Wurzman, R (2016)
2: Integrative Computational and Neurocognitive Science and Technology for Intelligence Operations: Horizons of Potential Viability, Value and Opportunity. Retrieved from http://www.potomacinstitute.org/steps/featured-articles/85-integrative-computational-and-neurocognitive-science-and-technology-for-intelligence-operations-horizons-of-potential-viability-value-and-opportunity
3: Ernst, R (March 2016). UCS Architecture Overview, NAVAIR presentation. Retrieved from http://www.dtic.mil/ndia/2016GRCCE/Ernst.pdf
4: Sun R (2007). Introduction to computational cognitive modeling. Retrieved from 7 December 2016 from http://www.sts.rpi.edu/~rsun/folder-files/sun-CHCP-intro.pdf
5: Forsythe, F, Giordano, J (2011). On the Need for Neurotechnology in the National Intelligence and Defense Agenda: Scope and Trajectory. Synthesis: A Journal of Science, Technology, Ethics and Policy 2 no 1, (2011): 5-8. Retrieved from http://www.synesisjournal.com/vol2_no2_t1/Forsythe_Giordano_2011_2_1.pdf
6: Unmanned Systems (UxS) Control Segment (UCS) Architecture: UCS Architecture Model. http://www.sae.org/search/?sort=date&content-type=(%22STD%22)&root-code=(%22AS6518%22)
KEYWORDS: Cognitive System; UCS Architecture; Vehicle Management; Mission Management; Mission Planning; Common Control System; Sensor Information; Autonomous Vehicles; Risk Assessment