11115 research outputs found
Sort by
Ultraviolet and Blue Stimulated Emission from Cs Alkali Vapor Pumped using Two-photon Absorption and Four-wave Mixing
Get PDFStimulated emission on the ultraviolet and blue transitions in Cs has been achieved by pumping via two-photon absorption and four-wave mixing for the pump transition 62S½ → 82D3/2,5/2. The emission performance of the optically pumped cesium vapor laser operating in ultraviolet and blue has been extended to 650 nJ/pulse for 387 nm, 1 to 3 μ J / pulse for 388 nm, 200 nJ/pulse for 455 nm, and 500 nJ/pulse for 459 nm. Emission performance improves dramatically as the cesium vapor density is increased, and no scaling limitations associated with energy pooling or ionization kinetics have been observed
ScriptBlock Smuggling: Uncovering Stealthy Evasion Techniques in PowerShell and .NET Environments
Get PDFThe Antimalware Scan Interface (AMSI) plays a crucial role in detecting malware within Windows operating systems. This paper presents ScriptBlock Smuggling, a novel evasion and log spoofing technique exploiting PowerShell and .NET environments to circumvent the AMSI. By focusing on the manipulation of ScriptBlocks within the Abstract Syntax Tree (AST), this method creates dual AST representations, one for compiler execution and another for antivirus and log analysis, enabling the evasion of AMSI detection and challenging traditional memory patching bypass methods. This research provides a detailed analysis of PowerShell’s ScriptBlock creation and its inherent security features and pinpoints critical limitations in the AMSI’s capabilities to scrutinize ScriptBlocks and the implications of log spoofing as part of this evasion method. The findings highlight potential avenues for attackers to exploit these vulnerabilities, suggesting the possibility of a new class of AMSI bypasses and their use for log spoofing. In response, this paper proposes a synchronization strategy for ASTs, intended to unify the compilation and malware scanning processes to reduce the threat surfaces in PowerShell and .NET environments
Applying Instance Space Analysis for Metaheuristic Selection to the 0-1 Multidemand Multidimensional Knapsack Problem
No full textThe empirical testing of metaheuristic solution methods for optimization applications should consider the effect of the underlying structure of the optimization problem test instances employed. This paper presents a methodology for analyzing the performance of metaheuristics applied to the 0–1 multidemand multidimensional knapsack problem (MDMKP) specifically considering problem structure. This research leverages instance space analysis (ISA) to graphically depict both the multidimensional problem structure and metaheuristic performance. A new instance generation method augments the existing set of test instances; in doing so, it introduces correlation structure into the problem and helps ensure MDMKP instance feasibility. Testing compares four metaheuristics from the literature and trains an interpretable machine learning model to select a metaheuristic for a given instance based on that problem’s meta-features. The results show that the correlation structure meta-features are significant factors affecting metaheuristic performance and that a decision tree model can provide interpretable insights into the algorithm selection problem. This work demonstrates the usefulness of ISA for rigorous empirical testing to enhance understanding the performance of metaheuristics applied to the MDMKP
Recent Alcohol Intake Impacts Microbiota in Adult Burn Patients
No full textAlcohol use is associated with an increased incidence of negative health outcomes in burn patients due to biological mechanisms that include a dysregulated inflammatory response and increased intestinal permeability. This study used phosphatidylethanol (PEth) in blood, a direct biomarker of recent alcohol use, to investigate associations between a recent history of alcohol use and the fecal microbiota, short chain fatty acids, and inflammatory markers in the first week after a burn injury for nineteen participants. Burn patients were grouped according to PEth levels of low or high and differences in the overall fecal microbial community were observed between these cohorts. Two genera that contributed to the differences and had higher relative abundance in the low PEth burn patient group were Akkermansia, a mucin degrading bacteria that improves intestinal barrier function, and Bacteroides, a potentially anti-inflammatory bacteria. There was no statistically significant difference between levels of short chain fatty acids or intestinal permeability across the two groups. To our knowledge, this study represents the first report to evaluate the effects of burn injury and recent alcohol use on early post burn microbiota dysbiosis, inflammatory response, and levels of short chain fatty acids. Future studies in this field are warranted to better understand the factors associated with negative health outcomes and develop interventional trials
Spatiotemporal Network Vulnerability Identification for the Material Routing Problem: A Bilevel Programming Approach
No full textThe routing of material over a distribution network is subject to manmade and natural disruptions, and it is important to understand the network’s spatiotemporal vulnerabilities, i.e., when and where disruptions will notably affect outcomes. Knowledge of vulnerabilities informs mitigation efforts to ensure shipments are routed efficiently while meeting delivery deadlines. This research formulates and examines the bilevel material routing problem, wherein an upper-level problem identifies the time and location for a limited number of fixed-duration attacks on arcs, and a lower-level problem routes shipments over the network between respective origins and destinations. The defender minimizes a combination of the weighted distance traveled, transport time of shipments, and penalties for delivering shipments outside of desired time windows, while meeting required delivery deadlines. This research develops a customized genetic algorithm to search the attacker’s feasible region and develop high-quality solutions. For a representative scenario using a road network within the continental United States, testing examines the robustness of alternative assumptions a distance-maximizing attacker may make about defender priorities over the lower-level objective functions. For the most robust attacker assumption, testing examines for a range of attacker capabilities the spatiotemporal disruptions an effective attacker would make, i.e., the network vulnerabilities that merit mitigation by a defender
Characterization of an Omega Type Bi-Anisotropic Material
Get PDFA theory is presented for the extraction of the complete set of material parameters for a bi-anisotropic sample consisting of conductive omega-type particles governed by the mm21′ point-group symmetry. A rectangular-to-square wave-guide can be used to obtain the required measurements by allowing three distinct orientations of the material. The focus of this work is to develop the appropriate theory detailing the derivation of how the material parameters are extracted from the measurements in an analytical methodology that is similar in manner to the well-known Nicolson-Ross-Weir (NRW) algorithm
Measuring the Presentation of Supporting Content for a Set of Learning Objectives throughout the States of an Educational Game Tree
Get PDFIn an era of evolving warfare, the Department of Defense (DoD) recognizes the value of serious games as immersive tools for teaching critical concepts. This thesis introduces a pioneering framework tailored to enhance learning objectives through the presentation of educational game content. This addresses the unique needs of the DoD and other educators who use games by providing measurements to assess educational games. This researches investigates how instructors might assess games as potential teaching tools. It establishes a five-phase process, providing a framework to assess educational games against predefined learning objectives and informing future game development. This thesis demonstrates games strategically aligned to learning objectives, offering a contrast to games solely focused on winning. The framework leverages four types of AI agents with heuristics inspired by player profiles. Two agents represent a competitive agent and a random action agent. Another two agents guide players or other agents toward game states conducive to learning objectives. These agents avoid game termination unless all learning objective content has been presented. The framework is tested through an experiment of different two-player games and learning objectives where the first is identified as the learner and the second is identified as the opponent. The five-phase process was applied to playthroughs involving 80 combinations of game sizes and player AI agents. The results indicate that games with greater player autonomy and longer durations enhance the coverage of learning objective content. AI agents employing a competitive heuristic exhibit a higher win rate (averaging 61% across all playthrough types). However, depending on the specific game, they may not achieve a favorable coverage rate for the full set of learning objectives (observed in 32% of matches). AI agents with a heuristic to learn, show a higher coverage (40% of matches) and win more often as the learner player than as the opponent (56% as the learner vs 42% as the learner\u27s opponent). After thousands of two-player matches with various game inputs, no set of game size or AI agent produces a rate of full coverage greater than 65%. This non-linear game experiment can be contrasted with a linear lecture format, which yields 100% coverage of material but lacks some of the benefits games may provide. This experiment explores the trade space between learning objective content coverage and incorporating game elements in learning activities. This research not only addresses the critical challenge of evaluating educational content within DoD serious games but also delivers tangible contributions. These contributions include a novel framework, applicable metrics, and a demonstration of concept implementation. These advancements aim to measure the suitability of games and enhance educational outcomes in game-related learning activities
A Machine Learning Approach for Multipath Characterization and Mitigation Using Chipshape Observations
Get PDFMultipath continues to be a significant error source in satellite navigation. Recent solutions with Neural Networks (NN) model the effects of multipath on the autocorrelation function to predict errors in the Delay Lock Loop (DLL). Chipshape correlation provides a detailed look into the spreading code transitions in the time domain. It is useful in applications such as Signal Quality Monitoring (SQM) and is much more sensitive to multipath than autocorrelation. This research proposes NN models that each predict pseudorange or carrier range errors due to multipath by monitoring the chipshape correlation output. For a simulation with 50 MHz precorrelation bandwidth and an environment with one Line of Sight (LOS) source, one multipath ray with a Direct to Multipath ratio (D/M) of 3 dB, and noise with a Signal to Noise ratio (S/N) of -14 dB, the pseudorange model made predictions with an average of ±0.82 meters error, and the carrier range model made predictions with an average of ±5.63e-4 meters error. These models were accurate at predicting range errors for static multipath, however, the code range model is sensitive to the motion profile of the multipath ray relative to the LOS source
USMEPCOM Prescreens: A Value-Focused Thinking Approach
Get PDFAs the recruiting crisis continues to impact the United States Armed Forces, the United States Military Entrance Processing Command (USMEPCOM) continues to search for ways to increase its capability to efficiently determine which applicants are suited for military service. Unfortunately, USMEPCOM does not have a way to evaluate newly suggested alternatives. By leveraging Value-Focused Thinking (VFT), this research describes 28 fundamental objectives that can be applied to a variety of current and future decision problems. Further, this research applies these fundamental objectives to analyze a current decision problem: reengineering the prescreen process to decrease the time from prescreen submission to contract
Bayesian Augmentation of Object Detection Algorithms to Enhance Object Classification Stability
Get PDFNeural networks, despite their prowess in computer vision, often exhibit flickering . Flickering occurs when networks fail to maintain consistent object representation across frames, leading to inaccurate and inconsistent output. This problem is particularly critical in mission-surety applications where reliable object recognition is crucial. This research presents a novel approach that combines existing object detection and tracking algorithms like YOLO and SORT with a Bayesian backend model. This Bayesian backend incorporates probabilistic reasoning to analyze the network\u27s confidence in its predictions and infer the most likely object identity across multiple frames, effectively reducing flickering and enhancing robustness