The United States, together with the United Kingdom, signed the Limited Nuclear Test Ban Treaty in 1963. The Comprehensive Nuclear-Test-Ban Treaty was partially ratified by the United Nations General Assembly in 1996. A multifaceted worldwide monitoring network, in which the United States actively participates, continuously monitors treaty compliance. One of the tools this worldwide network uses is atmospheric sampling of radioxenon. During an underground detonation, noble gases, such as xenon, do not react with soil and can escape into the atmosphere. The detection of radioactive xenon in 2006 provided reliable proof of North Korea's underground testing. Because radioactive xenon is required for calibrating the detectors, the synthesis of high-purity radioxenon is of interest. In light of this interest, a team at the Johns Hopkins University Applied Physics Laboratory (APL) developed a novel production pathway for the 135Xe isotope using APL's new linear accelerator facility.
Studies using chemical warfare agents (CWAs) and explosives are dangerous and are therefore conducted only in specialized laboratories with highly controlled conditions and limited accessibility. Obtaining these materials for study is another challenge, as they are tightly regulated. Because of these challenges, simulants-molecules that mimic key characteristics of a specified CWA or explosive but lack toxicity-are often used in testing, sensor development, and decontamination studies. In the past, simulants have generally been selected on the basis of their historical use (with researchers sometimes simply choosing "convenient" materials that happen to provide a detector alarm, for example) rather than rational design. The Johns Hopkins University Applied Physics Laboratory (APL) created a simulant development approach, based on systems engineering concepts, that takes input from relevant parties and is scoped specifically for a project objective. This methodology can be universally applied to any type of simulant and includes down-selection criteria to identify specific simulant candidates that can be verified and validated according to the project's required fidelity. This article describes the development approach, selection process, and demonstrated use cases for both CWAs and energetic materials.
Target tracking is a critical component in defense and airspace protection. To provide awareness of potential enemy threats through target tracking, dynamic states are repeatedly updated based on observations. Because common dynamic models of moving objects typically use Cartesian coordinates, target tracking systems typically use this coordinate system as well. This presents a statistical challenge, however, when observations are recorded with different coordinate systems. This is the case with radar measurements, which use spherical coordinates (range, bearing, and elevation) instead of Cartesian coordinates (x, y, z). The main problem is integrating the statistics of new measurements with a priori state estimates to provide an updated a posteriori estimate. This article focuses on a converted-measurement approach to compute descriptive Cartesian statistics from spherical measurements for updates in a linear tracking system. Converted-measurement tracking, compared with mixed-coordinate tracking, can facilitate multisensor fusion in complex sensor networks. Various converted-measurement methods were evaluated, including Taylor approximations, unscented transforms, and debiased statistical methods, in a simple tracking scenario. Tracking performance varied across these three methods depending on the geometry of the scenario, so users of converted-measurement methods should evaluate the performance of each method for their given domain and application.
The Johns Hopkins University Applied Physics Laboratory (APL) plays a crucial role in helping the United States anticipate, counter, and prevail against chemical, biological, radiological, nuclear, and explosive (CBRNE) threats. APL researchers create innovative technologies and methodologies that enhance national security and provide decision-makers with actionable intelligence in the face of evolving threats. The counterterrorism and homeland security landscape has dramatically changed since the Johns Hopkins APL Technical Digest last published a comprehensive review on these topics in 2003. In the years since, APL's expertise has expanded significantly, integrating advancements in artificial intelligence, data analytics, autonomous systems, and sensor technologies to address increasingly sophisticated CBRNE challenges. This issue highlights APL's latest contributions to detecting, identifying, and mitigating CBRNE threats to strengthen national and global security.
Odor detection canines play a key role in ensuring our nation's security. For more than 15 years, advancement of the community's efficacy and capabilities through the application of interdisciplinary solutions spanning chemistry, biology, data analytics, and engineering. Not only have APL's contributions resulted in strong collaborations across the research space, but they have also directly informed and impacted strategies and capabilities for operational deployments of odor detection canines.
Biothreat detection strategies have historically focused on cheap, specific, and deployable assays that detect a small but specific nucleic acid or protein component of a threat organism. Genomic sequencing technologies that have emerged over the past 15 years are poised to find their place in the biothreat detection tool kit for military and civilian use. Here we describe efforts to compare and contrast sequencing to traditional polymerase chain reaction (PCR) assays for diagnostics and detection of biothreat agents of concern in military applications. We show that after direct spiking of human blood and serum with biothreat simulants, agnostic sequencing can achieve detection. However, for known agents, PCR is still superior in terms of speed, cost, scale, and reliability for military applications. Although PCR should still be the first choice for diagnostics and detection when an agent is known or suspected, for unknown agents, agnostic sequencing can be a powerful addition to identify causative agents in soils, aerosols, and biothreats in patient samples. APL developed and conducted this work for the Department of Defense to address the basic question of when to use PCR versus when to use sequencing for field-forward infectious disease diagnostics and environmental detection.
This article introduces and reviews some of the principles and methods used in Bayesian reliability. It specifically discusses methods used in the analysis of success/no-success data and describes a simple Monte Carlo algorithm that can be used to calculate the posterior distribution of a system's reliability. This algorithm is especially useful when a system's reliability is modeled through the reliability of its subcomponents, yet only system-level data are available.
Forensics and military investigators often assess sites of interest, searching for evidence of bio-logical hazards. The application of metagenomics provides genomic data for all microorganisms present in a sample, enabling advanced analysis for detection of biological signatures and threat detection from such sites. DNA sequence segments (digitally represented as "reads") from metage-nomics samples are commonly compared with reference libraries in order to identify microor-ganisms present in the sample. However, this approach does not capture the complete biological signature, as there always remains a subset of reads that are unable to be successfully mapped to a known organism. The Johns Hopkins University Applied Physics Laboratory (APL) Machine Learning for Metagenomics (MLM) pipeline characterizes these unidentified reads in terms of composition and alignment with sequences of known organisms. Since these reads are unable to be mapped directly to a known organism, our models classify each read according to one of five threat levels, ranging from 0 to 4 (with threat level 4 the most severe). Our pipeline consists of random forest, Bayesian network, and clustering models. When testing this pipeline against simulated and real sequencing data, we achieved high threat level classification accuracy: 95% for clusters of related reads. Based on these results, we are preparing for deployment of our pipe-line on far-forward devices, providing investigators with real-time threat assessment of biological materials to inform an appropriate rapid response.
This article describes the development of a data-driven approach to map adversarial activity into machine-readable models. Specifically, this approach is grounded in well-structured knowledge graphs and uses a semantic representation of domain-specific pathways implementing formal addition, the article describes a web-based application through which a user can interact with the underlying knowledge graph. The application also allows for development of analytics that use these data to answer questions about adversarial activity.
The Defense Advanced Research Projects Agency SIGMA+ program developed a persistent, realtime, early warning and detection system for the full spectrum of chemical, biological, radiological, nuclear, and explosive weapon of mass destruction threats at the city to region scale. In support of this program, and leveraging technical expertise in modeling and simulation, applied mathematics, and epidemiology, the Johns Hopkins University Applied Physics Laboratory (APL) characterized and quantified the impact a wearables-based human sentinel network would have on the ability to provide advanced detection of a naturally occurring or intentional biothreat event. Modeling results demonstrate that instrumenting as few as 5% of the population could advance detection of seasonal influenza by 5-14 days and an anthrax attack by similar to 1 day as compared with traditional public health surveillance. Early detection and geolocation of individuals exposed to biological threats enables timelier and more effective biothreat countermeasures and mitigation strategies.
In 1983, at the behest of the Johns Hopkins University Applied Physics Laboratory (APL) director, an accomplished group called the APL senior fellows produced a report on the projected state of the Laboratory at the beginning of the 21st century. This article presents a retrospective on that report, which Identified key technologies, relationships, and environmental factors that would be important to APL at the dawn of the 21st century and beyond. In this article, these key items are identified, discussed, and assessed for their relevance (or not) to the current state of the Laboratory.
Mechanical engineering design is a traditional discipline that has advanced with the advent of new technology and techniques. Engineers can now combine traditional concepts with novel technologies and techniques to deliver creative solutions. These techniques include geometric dimensioning and tolerancing (GD&T), reverse engineering, advanced surfacing, haptics, augmented and virtual reality, and new methods of communicating designs. Mechanical design engineers at the make critical contributions to diverse domains, such as space exploration and military dominance.
Prototyping techniques have significantly advanced in the last decade, providing engineers with quick ways to iteratively modify designs of parts and systems with greater precision and at lower cost than ever before. The Research and Exploratory Development Department (REDD) at the ments, using rapid prototyping tools and quick-turn manufacturing that was not possible a decade ago to achieve success in many applications. Examples highlighted in this article include human-machine interfaces conceived through a Navy program called Tactical Advancements for the Next Generation (TANG), confined-area autonomous mapping devices like the Enhanced Mapping and Positioning System (EMAPS), and personal protective equipment to help prevent the spread of COVID-19 during the unprecedented and uncertain times of the early pandemic. These three case studies demonstrate the benefits of rapid prototyping.
Highly nonlinear and dynamic mechanical behavior involving impact, crash, and blast is common eling these behaviors involves finite element analysis (FEA) that reaches beyond typical static analyses. APL researchers are able to model complex nonlinear dynamic behavior without oversimplifying or converting the problem to a so-called equivalent static problem. Presented here is an overview of dynamics and nonlinearity and a brief summary of the options available for modeling these behaviors. The article concludes with several case studies that demonstrate how APL's expertise in this area contributes to the safety of our nation's warfighters and diplomatic personnel.
With their proven performance, unique properties, and manufacturability, composite materials lend themselves to many applications. The Johns Hopkins University Applied Physics Laboratory (APL) uses composite materials for advanced prototypes and flight-worthy assemblies in support of a variety of systems and missions, from spacecraft components and instruments, to groundand air-based communication hardware, to uncrewed aerial vehicles of all shapes and sizes. APL designers and engineers typically use thermoset polymer resins reinforced with a variety of fiber types and architectures to create high-performing composite structures. Leveraging its expertise in several composite molding techniques, APL is able to manufacture parts that meet complex requirements and perform as intended to ensure mission success. This article describes APL's composite fabrication capabilities and contributions.
At the Johns Hopkins University Applied Physics Laboratory (APL), microelectronics packaging includes a wide range of microelectronics fabrication and assembly technologies. Conventional microelectronics packaging integrates electronics on a bare die level. At APL, microelectronics packaging has evolved to include packaging of customized miniature electrical, mechanical, and electromechanical devices. APL's engineers design, fabricate, assemble, inspect, screen, repair, and provide depackaging solutions for diverse projects and sponsors. Because of its technological capabilities and facilities, along with the skill sets of its staff members, APL is able to prototype and produce a broad range of devices, such as sensors, detectors, and communications and computing hardware, for mission-critical projects supporting research and development, defense, near- Earth and deep-space missions, and medicine. This article highlights microelectronics packaging capabilities at APL.
The Johns Hopkins University Applied Physics Laboratory (APL) solves complex research, engineering, and analytical problems that present critical challenges to our nation. Its work requires collaboration across a broad realm of scientific domains and technologies, including manufacturing. APL has established modern fabrication techniques and processes for real-world applications, enabling fabrication of components for a diverse set of systems operating from the depths of the oceans to the farthest parts of the solar system. APL delivers high-quality, cutting-edge hardware by pairing state-of-the-art equipment with knowledgeable manufacturing personnel who directly interact with engineers, designers, and research scientists to achieve creative solutions. This synergy allows for rapid iteration and swift system integration. To highlight the impact of this approach, this article describes a few of APL's critical manufacturing contributions: (1) the rapid redesign of components for the Didymos Reconnaissance and Asteroid Camera for Optical navigation (DRACO), the lone instrument in the Double Asteroid Redirection Test (DART) payload; (2) the close collaboration of engineers, scientists, and fabricators on the Boundary Layer Transition (BOLT) hypersonic flight experiment; (3) the advantages of multiaxis turning for the Interstellar Mapping and Acceleration Probe (IMAP) feed horn; and (4) the use of additive manufacturing to produce novel solutions for fabricating the shielding components for instruments on the Europa Clipper and Martian Moons eXploration missions.
The metal additive manufacturing (AM) process uses high-power lasers to rapidly melt and solidify metal powder into complex 3-D shapes, but unfortunately the rapid solidification process often results in stochastic defect formation and nonequilibrium microstructures. To fully understand the AM process and ensure a high-quality, defect-free manufacturing process, novel high-speed sensing methods that can capture key physical phenomena associated with the AM process at high resolution are needed. A team at the Johns Hopkins University Applied Physics Laboratory (APL) is developing novel spectrometry techniques capable of measurement speed exceeding 50 kHz along the laser path to aid in understanding how materials are formed under different laser inputs. The team is also developing machine learning tools to interpret these signals, thus revealing features and trends that are not apparent to human analysts in the sensor data or physi-cal post mortem inspection results of the printed components.
The COVID-19 pandemic upended normalcy around the world, particularly in the workplace. At the Johns Hopkins University Applied Physics Laboratory (APL), staff members in the Concept Design and Realization Branch had to adjust their work practices to prevent disease outbreaks while continuing to design and fabricate critical components for diverse missions. In addition to adhering to Labwide safety measures such as social distancing and cleaning protocols, the design and fabrication teams modified their workstations and processes; adjusted work schedules; adhered to expanded cleaning protocols; and leveraged digital communication and collaboration tools to ensure continuity of operations. As the pandemic has waned, some of these measures have been phased out. However, some of these tools and practices have proven to be highly valuable under normal operations and have become part of the new normal. Using these tools and methods, the design and fabrication teams successfully delivered major projects over the course of the pandemic, demonstrating resourcefulness, adaptability, and commitment in the face of difficulty.
The Johns Hopkins University Applied Physics Laboratory (APL) is working to realize a variety of nanostructured designs via top-down nanofabrication techniques. We leverage electron beam lithography, nanoimprint lithography, and focused ion beam deposition to pattern nanoscale features on semiconductor and optical material substrates. We combine these with other tra-ditional microfabrication techniques as well as a few unique ones, including an atomic layer deposition-enabled nanomolding process to create high-aspect-ratio nanopillars from materials such as titanium dioxide (TiO2). We apply these techniques on a wide variety of nontraditional substrate and film materials, including optical, phase-change, and superconducting materials, to create novel optical and electronic devices.
