National Security Applications Research Articles

Biosensing technology for detection and assessment of pathogenic microorganisms.

At present, the prevalence of infectious diseases is rising annually, making it an important risk factor for human health that should not be neglected. Consequently, infection control and prevention have become even more important. The key to determining and designing the most effective anti-infectious medication depends upon the immediate and accurate identification of the causative agent. The standard techniques used for routine infection screening and surveillance tests are shifting toward biosensors. Furthermore, biosensors are projected to be employed for microbiological detection to satisfy the higher accuracy required for clinical diagnosis. This is because of their compact size, real-time monitoring and ability to analyze large sample numbers with less sophistication and manpower requirement, which have allowed them to develop quickly with extensive uses. Biosensors have multiple applications in food safety, environmental surveillance, drug sensing and national security because they offer several advantages such as quick response, outstanding sensitivity, remarkable selectivity, high degree of accuracy and precision, ease of use and affordable price. This review highlights the performance aspects of recently developed biosensors for the detection of infectious bacteria and viruses in biological and environmental samples and emphasizes the significance of nanotechnology in signal amplification for enhanced biosensor performance and dependability.

Device response principles and the impact on energy resolution of epitaxial quantum dot scintillators with monolithic photodetector integration

Epitaxial quantum dot (QD) scintillator crystals with picosecond-scale timing and high light yield have been created for medical imaging, high energy physics and national security applications. Monolithic photodetector (PD) integration enables the sensing of photons generated within the waveguiding crystal and allows a wide range of scintillator-photodetector coupling geometries. Until recently, these doubly novel devices have suffered from complex, high variance responses to monoenergetic sources which significantly reduces their precision and accuracy. The principles governing the overall device response have now been discerned and embodied by an expression derived within a geometrical optics framework which considers optical properties, surface roughness and photodetector coupling geometry. Response variation due to these factors was sufficiently reduced to obtain material-related energy resolution values of 2.4% with alpha particles. These findings place energy resolution alongside luminescence timescale, photon yield, and radiation hardness as outstanding properties of these engineered materials.

Precision picric acid detection via a fluorenone-amide functionalized fluorescent micellar probe

Detection of picric acid (PA) in aqueous solutions is crucial for pollution control, extending beyond national security and military applications. Herein, a binary ensemble AM@SDS has been assembled by encapsulating a fluorenone functionalized amide-based probe (AM) within the micelles of an anionic surfactant, sodium dodecyl sulphate (SDS) in an aqueous medium. A range of spectroscopic techniques, including high-resolution transmission electron microscopy (HRTEM), dynamic light scattering (DLS), and fluorescence spectroscopy (FL), have been employed to characterize the formation of micelles of AM@SDS. A remarkable increase in fluorescence intensity was observed upon interactions of AM with the microenvironment of SDS micelles. Conversely, the fluorescence intensity at 358 nm was significantly quenched in the presence of PA compared to other nitroaromatic compounds (NACs). The detection limit for PA was found to be 368 nM. Furthermore, the binary ensemble AM@SDS has demonstrated remarkable efficacy in detecting PA in real water samples from diverse sources such as lake, river, and tap water, achieving an exceptional recovery rate of up to 99.9 %.

SERS-integrated centrifugal microfluidic platform for the detection and quantification of Chemical Warfare Agents in single-component solution and mixtures

Chemical Warfare Agents (CWAs) are toxic chemicals. Among CWAs, nerve agents are lethal compounds at low concentrations, and, thus, there is the need to detect and quantify the danger. The combination of Surface-Enhanced Raman Spectroscopy (SERS) with centrifugal microfluidic (CM) systems is a possibility for a safer handling and a fast analysis of nerve agents.In this work, the SERS signal from single-component solutions and mixtures of Tabun (GA) and VX were analyzed with Au-capped silicon nanopillar (NP) SERS substrates integrated on a CM platform and recorded with a custom-built Raman System (RS). Univariate and multivariate analysis methods were applied for the quantification. Both GA and VX were detected and quantified individually with Partial Least Squares regression (PLSR) models, with LoD and LoQ values of 7.39 ppm and 22.17 ppm respectively for GA, and 7.13 ppm and 21.39 ppm for VX. Detection and quantification of GA and VX in mixtures was achieved with Supported Vector Machine (SVM), obtaining R2 of the prediction 0.87 and 0.95, respectively.The results obtained show the potential of the SERS-integrated CM platform combined with machine learning analysis as a reliable method for CWAs on-site detection and quantification and for national security applications in real-case scenarios.

Enhancement of broadband terahertz trace absorption spectrum based on angular multiplexing of trapezoidal dielectric grating

Terahertz spectroscopy provide a fast, label-free, and non-destructive method for detecting bio-medical molecules, with wide applications in national security, pharmacy, and healthcare. However, detecting terahertz absorption spectra of trace analytes still faces significant challenges. In recent years, metamaterial parameter multiplexing technology has been used to detect the terahertz fingerprint spectra of trace substances. This paper proposes a new structure based on the angle-multiplexing enhancement principle of a trapezoidal dielectric grating to enhance the terahertz absorption spectra of thin-film analytes. By scanning the incident wave angle, a series of resonance absorption peaks can be obtained. The enhanced absorption spectrum formed by the envelope of absorption spectrum peaks increases by 141 times compared to directly measuring the absorption spectrum of the same film. This structure provides a new and effective means for terahertz trace detection using metamaterial parameter multiplexing technology.

Nanomaterial-Based Sensors for Coumarin Detection.

Sensors are widely used owing to their advantages including excellent sensing performance, user-friendliness, portability, rapid response, high sensitivity, and specificity. Sensor technologies have been expanded rapidly in recent years to offer many applications in medicine, pharmaceuticals, the environment, food safety, and national security. Various nanomaterial-based sensors have been developed for their exciting features, such as a powerful absorption band in the visible region, excellent electrical conductivity, and good mechanical properties. Natural and synthetic coumarin derivatives are attracting attention in the development of functional polymers and polymeric networks for their unique biological, optical, and photochemical properties. They are the most abundant organic molecules in medicine because of their biological and pharmacological impacts. Furthermore, coumarin derivatives can modulate signaling pathways that affect various cellular processes. This review covers the discovery of coumarins and their derivatives, the integration of nanomaterial-based sensors, and recent advances in nanomaterial-based sensing for coumarins. This review also explains how sensors work, their types, their pros and cons, and sensor studies for coumarin detection in recent years.

Analysis of High-Performance Parallel Computing using TOPSIS Method

High-performance parallel computing involves the simultaneous execution of multiple tasks or processes, orchestrated to achieve improved computational speed and efficiency. This approach leverages the power of parallelism, exploiting both multi-core CPUs and GPUs, distributed computing clusters, and specialized hardware accelerators. The fundamental idea is to divide a task into smaller sub-tasks that can be executed concurrently, thereby reducing processing time and enhancing overall performance. High-performance parallel computing is a transformative approach that enables us to tackle computationally intensive tasks efficiently. This abstract highlight its significance in contemporary computing and sets the stage for further exploration of the intricacies and innovations within this dynamic field. Researchers and practitioners continue to push the boundaries of what is achievable, making high-performance parallel computing a cornerstone of modern computational science and technology. High-performance parallel computing research is of paramount significance due to its transformative impact across diverse fields. It empowers scientists to tackle complex problems that were once computationally intractable, unlocking new frontiers in scientific discovery. It drives innovation in engineering and design, optimizing product development and manufacturing processes across industries. In healthcare, it accelerates genomics research and drug discovery, offering hope for improved medical treatments. Financial institutions rely on it for data analysis and risk assessment, shaping the global economy. Weather forecasting and environmental modelling are enhanced, aiding disaster preparedness and conservation efforts. In the digital age, parallel computing underpins artificial intelligence, enabling advancements in natural language processing and machine learning. Furthermore, it has vital applications in national security, space exploration, and materials science. In essence, high-performance parallel computing research serves as the backbone of technological progress, fostering innovation, efficiency, and problem-solving across a wide spectrum of disciplines, ultimately shaping the future of our world. TOPSIS, this method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. From the result Scatter- free imaging is got the first rank and object-scatter imaging is having the lowest rank.

Building a Scientific Foundation for Security: Multilayer Network Model Insights for System Security Engineering

AbstractTo help incorporate security into INCOSE's Systems Engineering Vision 2035, the INCOSE systems security engineering working group endorses a paradigmatic shift to reframe systems security as trustworthy, loss‐driven, and capabilities‐based. Similar research out of Sandia National Laboratories has explored cutting‐edge approaches to systems security for national security applications. Together, these efforts highlight the need for—‐and a path toward—‐a scientific foundation for security. Leveraging underlying tenets of systems theory, observed security heuristics, and the concepts emerging from INCOSE's SSE working helps triangulate a set of “first principles” as part of a scientific foundation for security as an emergent systems property that incorporates traditional physical security designs, cyber security architectures, and personnel security programs—‐as well as the (often ignored) interactions between them. These first principles, in turn, are the basis for a set of derived systems security performance axioms that support current INCOSE SSE working efforts. We have demonstrated this approach's logic and designability with a multilayer network model‐based approach for systems security. The structure of this scientific foundation for security offers additional, innovative opportunities to achieve desired levels of trustworthiness, creative mechanisms to meet needs, innovative loss‐driven approaches, and enhanced capabilities—‐all aimed at producing more efficient and effective systems security solutions against current and emerging threats, uncertainties, and complexities.

Advanced radiation detector design for applications in food safety and national security

We describe a new concept for a radiation detector based on extruded scintillator technology and commercially available solid-state photo-detectors. The detector is simple in construction, robust, very efficient, cost-effective and easily scalable in size from tens of cm2 to tens of m2. We describe two possible applications: flagging radioactive food contamination and detection of illicit radioactive materials, such as those potentially used in a dirty bomb.

X-ray Induced Electric Currents in Anodized Ta2O5: Towards a Large-Area Thin-Film Sensor.

We investigated the characteristics of radiation-induced current in nano-porous pellet and thin-film anodized tantalum exposed to kVp X-ray beams. We aim at developing a large area (≫cm2) thin-film radiation sensor for medical, national security and space applications. Large area (few cm2) micro-thin Ta foils were anodized and coated with a counter electrode made of conductive polymer. In addition, several types of commercial electrolytic porous tantalum capacitors were assembled and prepared for irradiation with kVp X-rays. We measured dark current (leakage) as well as transient radiation-induced currents as a function of external voltage bias. Large transient currents (up to 50 nA) under X-ray irradiation (dose rate of about 3 cGy/s) were measured in Ta2O5 capacitors. Small nano-porous Ta and large-area flat Ta foil capacitors show similar current-voltage characteristic curve after accounting for different X-ray attenuation in capacitor geometry. The signal is larger for thicker capacitor oxide. A non-negligible signal for null external voltage bias is observed, which is explained by fast electron production in Ta foils. Anodized tantalum is a promising material for use in large-area, self-powered radiation sensors for X-ray detection and for energy harvesting.

Adversarial Transferability in Embedded Sensor Systems: An Activity Recognition Perspective

Machine learning algorithms are increasingly used for inference and decision-making in embedded systems. Data from sensors are used to train machine learning models for various smart functions of embedded and cyber-physical systems ranging from applications in healthcare, autonomous vehicles, and national security. However, recent studies have shown that machine learning models can be fooled by adding adversarial noise to their inputs. The perturbed inputs are called adversarial examples. Furthermore, adversarial examples designed to fool one machine learning system are also often effective against another system. This property of adversarial examples is called adversarial transferability and has not been explored in wearable systems to date. In this work, we take the first stride in studying adversarial transferability in wearable sensor systems from four viewpoints: (1) transferability between machine learning models; (2) transferability across users/subjects of the embedded system; (3) transferability across sensor body locations; and (4) transferability across datasets used for model training. We present a set of carefully designed experiments to investigate these transferability scenarios. We also propose a threat model describing the interactions of an adversary with the source and target sensor systems in different transferability settings. In most cases, we found high untargeted transferability, whereas targeted transferability success scores varied from 0% to 80%. The transferability of adversarial examples depends on many factors such as the inclusion of data from all subjects, sensor body position, number of samples in the dataset, type of learning algorithm, and the distribution of source and target system dataset. The transferability of adversarial examples decreased sharply when the data distribution of the source and target system became more distinct. We also provide guidelines and suggestions for the community for designing robust sensor systems. Code and dataset used in our analysis is publicly available here. 1

Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms

Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field.

Oligopoly competition between satellite constellations will reduce economic welfare from orbit use

Orbital space enables many essential services, such as weather forecasting, global communication, navigation, Earth observation for environmental and agricultural management, and national security applications. Orbit use is increasingly defined by firms launching coordinated fleets-"constellations"-of satellites into low-Earth orbit. These firms operate in markets with few or no competitors, such as the market for broadband internet provision to rural areas. How will oligopolistic competition shape the allocation of orbital space? We analyze orbital-use patterns and economic welfare when two profit-maximizing firms operate satellite constellations with sophisticated collision avoidance systems. We compare this duopoly equilibrium to public utility constellations designed and regulated to maximize economic welfare from orbit use. We show that imperfect competition reduces economic welfare from orbit use by up to 12%-$1.1 billion USD-per year and distorts the allocation of orbital space. The nature of the distortion depends on the magnitude of constellation-related environmental damages. When damages are low, economic welfare is maximized by larger-than-equilibrium constellations. When damages are high, economic welfare is maximized by smaller-than-equilibrium constellations. Between the growing commercial and national interests in outer space and the importance of low-Earth orbit to space exploration, orbit-use management is likely to be a fruitful and policy-relevant area for economic research. We conclude with a discussion of future research directions in orbit-use management relevant to policymakers around the world.

Dual-layer optical encryption fluorescent polymer waveguide chip based on optical pulse-code modulation technique

Information encryption technique has broad applications in individual privacy, military confidentiality, and national security, but traditional electronic encryption approaches are increasingly unable to satisfy the demands of strong safety and large bandwidth of high-speed data transmission over network. Optical encryption technology could be more flexible and effective in parallel programming and multiple degree-of-freedom data transmitting application. Here, we show a dual-layer optical encryption fluorescent polymer waveguide chip based on optical pulse-code modulation technique. Fluorescent oligomers were doped into epoxy cross-linking SU-8 polymer as a gain medium. Through modifying both the external pumping wavelength and operating frequency of the pulse-code modulation, the sender could ensure the transmission of vital information is secure. If the plaintext transmission is eavesdropped, the external pumping light will be switched, and the receiver will get warning commands of ciphertext information in the standby network. This technique is suitable for high-integration and high-scalability optical information encryption communications.

Change Detection Applications in the Earth Sciences Using UAS-Based Sensing: A Review and Future Opportunities

Over the past decade, advancements in collection platforms such as unoccupied aerial systems (UAS), survey-grade GNSS, sensor packages, processing software, and spatial analytical tools have facilitated change detection analyses at an unprecedented resolution over broader spatial and temporal extents and in environments where such investigations present challenges. These technological improvements, coupled with the accessibility and versatility of UAS technology, have pushed the boundaries of spatial and temporal scales in geomorphic change detection. As a result, the cm-scale analysis of topographic signatures can detect and quantify surface anomalies during geomorphic evolution. This review focuses on the use of UAS photogrammetry for fine spatial (cm) and temporal (hours to days) scale geomorphic analyses, and it highlights analytical approaches to detect and quantify surface processes that were previously elusive. The review provides insight into topographic change characterization with precise spatial validations applied to landscape processes in various fields, such as the cryosphere and geosphere, as well as anthropogenic earth processes and national security applications. This work sheds light on previously unexplored aspects of both natural and human-engineered environments, demonstrating the potential of UAS observations in change detection. Our discussion examines the emerging horizons of UAS-based change detection, including machine learning and LIDAR systems. In addition, our meta-analysis of spatial and temporal UAS-based observations highlights the new fine-scale niche of UAS-photogrammetry. This scale advancement sets a new frontier in change detection, offering exciting possibilities for the future of land surface analysis and environmental monitoring in the field of Earth Science.

ARTIFICIAL INTELLIGENCE AND THE SECURITY DILEMMA

Recent breakthroughs in machine learning and artificial intelligence (A.I.) have prompted breathless speculation about their national security applications. Yet most of that work has focused narrowly on their implications for autonomous weapons systems, rather than on the broader security environment. Apart from Michael Horowitz and a handful of others, few scholars have sketched out how A.I. might affect core questions of international relations and foreign policy. One key challenge stands out: What influence will A.I. have on security dilemmas between great powers? With the two leading producers of A.I., the United States and China, already eyeing each other warily, the question is far from an idle one. If we are to maintain a stable international order, we need to better understand how artificial intelligence may exacerbate the security dilemma and what to do about it.

The Future of Synthesis Chemistry in Energetics

The Future of Synthesis Chemistry in Energetics

Automated SAR Image Segmentation and Classification Using Modified Deep Learning

Synthetic Aperture Radar (SAR) represents a type of active remote sensing technology that uses microwave electromagnetic radiation to produce and send data to the surface of a target location. SAR imaging is frequently used in national security applications since it is unaffected by weather, geographical location, or time. In this system, many approaches are examined, to improve automation for segmentation and classification. The utilization of Deep Neural Networks (DNNs) to classify SAR images has gotten a lot of attention, and it usually requires several layers of deep models for feature learning. With insufficient training data, however, the DNN will get affected by the overfitting issue. The major purpose of this work is to make a development on introducing a new framework for SAR image segmentation and categorization using deep learning. Owing to the coherent nature of the backscattering signal, SARs create speckle noise in their images. If the image has noisy material, classification becomes more challenging. Hence, the pre-processing of the images is employed by linear spatial filtering to remove the noise. Further, the Optimized U-Net is used for the segmentation. For the segmented images, the Binary Robust Independent Elementary Features (BRIEF) concept is adopted as the feature descriptor. These features are inputted to the Convolutional Neural Network (CNN) with Tuned Weight DNN (C-TWDNN) for the classification. In both segmentation and classification, the parameter tuning is employed by the combination of Galactic Swarm Optimization (GSO) and Deer Hunting Optimization Algorithm (DHOA) called the Self-adaptive-Galactic Deer Hunting Optimization (SA-GDHO). Experiments are conducted on a variety of public datasets, demonstrating that our method is capable of outperforming various expert systems and deep structured architectures.

Visualization of X-rays with an Ultralow Detection Limit via Zero-Dimensional Perovskite Scintillators

X-rays play an extremely significant role in medical diagnosis, safety testing, scientific research, and other practical applications. However, as the main sources of radioactive pollution, the hazard of X-rays to human health and the environment has been a major concern. Herein, the explored perovskite scintillator of Cs2Zr1-xPbxCl6 in this work exhibits an ultrahigh radioluminescence intensity owing to the enhanced X-ray absorption for the introduction of Pb2+ ions. The Cs2Zr1-xPbxCl6 crystals are demonstrated as efficient scintillators with a self-trapped exciton emission and extremely high steady-state light yield (∼101,944 photons meV-1). This fascinating scintillator provides a convenient visual tool for X-ray detection even for an indoor lighting environment, reaching a low detection limit of ∼14.2 nGy·s-1, which is about 1/387 of the typical medical imaging dose (5.5 μGy·s-1). Moreover, X-ray imaging with a high resolution of 16.6 lp·mm-1 is achieved with the as-explored Cs2Zr1-xPbxCl6 scintillator film. Herein, the Cs2Zr1-xPbxCl6 scintillator provides a feasible strategy for X-ray monitoring in the field of biomedicine, high-energy physics, national security, and other applications.

Highly Stable Lead‐Free Perovskite Single Crystals with NIR Emission Beyond 1100 nm

AbstractMaterials that emit in the near‐infrared (NIR) region are at the forefront of both research and industry, mainly due to their wide applications in national security, nondestructive bioimaging, long‐wave communications, and photothermal conversion for medical care. As a key member of the luminescent materials family, metal halide perovskites have been intensively demonstrated to emit light in ultraviolet and visible regions. However, NIR‐emitting perovskites suffer from severe limitations, such as low photoluminescence quantum yield and poor chemical/optical stability, thereby preventing them from practical applications. Herein, the synthesis and growth of Cs2MoCl6 and Cs2WCl6 perovskite single crystals with ultrahigh chemical and optical resistance to heat, moisture, polar solvents, and high‐energy radiation is reported. Upon ultraviolet or blue excitation, these lead‐free single crystals emit light beyond 1100 nm, the longest wavelength ever reported for perovskite hosts. Mechanistic studies indicate that self‐trapped excitons are responsible for the NIR emission. The authors fabricate optoelectronic devices using these single crystals and demonstrate their broad applications in noninvasive palm vein imaging, night vision, and nondestructive food analysis. These results may stimulate research in the development of high‐efficiency NIR perovskite phosphors for fast, accurate biometric identification and food inspection.