An Improved Solution Methodology for the Arsenal Exchange Model (AEM)

The purpose of this research was to design a solution methodology for the Arsenal Exchange Model (AEM) that is faster and contains less precision error than the current one. The current solution methodology modifies some of the original constraints and uses a computationally slow matrix inverter. The improved methodology uses a revised simplex algorithm to first solve a subproblem having only the weapon constraints generated by the AEM. Given this optimal allocation, hedge constraints and target constraints that are violated by the current solution are added to the original subproblem. A dual simplex algorithm is used to find the optimal solution for this new subproblem. By only adding the violated constraints, redundant and identical constraints are not included in any of the subproblems. This eliminates the need to alter the problem as before, and also allows the use of a faster matrix inverter. Additionally, since fewer constraints are used to find the overall optimal solution, fewer computations are necessary. This new methodology was used to solve five test cases. In four of the five test cases, the improved solution methodology produced an optimal integer solution. In all five test cases, it maintained damage expectancy and target coverage, and was better at satisfying the user input goals

Constrained Optimization Based Adversarial Example Generation for Transfer Attacks in Network Intrusion Detection Systems

Deep learning has enabled network intrusion detection rates as high as 99.9% for malicious network packets without requiring feature engineering. Adversarial machine learning methods have been used to evade classifiers in the computer vision domain; however, existing methods do not translate well into the constrained cyber domain as they tend to produce non-functional network packets. This research views the payload of network packets as code with many functional units. A meta-heuristic based generative model is developed to maximize classification loss of packet payloads with respect to a surrogate model by repeatedly substituting units of code with functionally equivalent counterparts. The perturbed packets are then transferred and tested against three test network intrusion detection system classifiers with various evasion rates that depend on the classifier and malicious packet type. If the test classifier is of the same architecture as the surrogate model, near-optimal adversarial examples penetrate the test model for 69% of packets whereas the raw examples succeeds for only 5% of packets. This confirms hypotheses that NIDS classifiers are vulnerable to adversarial attacks, motivating research in robust learning for cyber

ENTRNA: A Framework to Predict RNA Foldability

RNA molecules play many crucial roles in living systems. The spatial complexity that exists in RNA structures determines their cellular functions. Therefore, understanding RNA folding conformations, in particular, RNA secondary structures, is critical for elucidating biological functions. Existing literature has focused on RNA design as either an RNA structure prediction problem or an RNA inverse folding problem where free energy has played a key role

The Influence of Operational Resources and Activities on Indirect Personnel Costs: A Multilevel Modeling Approach

Indirect activities often represent an underemphasized, yet significant, contributing source of costs for organizations. In order to manage indirect costs, organizations must understand how these costs behave relative to changes in operational resources and activities. This is of particular interest to the Air Force and its sister services, because recent and projected reductions in defense spending are forcing reductions in their operational variables, and insufficient research exists to help them understand how this may influence indirect costs. Furthermore, although academic research on indirect costs has advanced the knowledge behind the modeling and behavior of indirect costs, significant gaps in the literature remain. Our research provides important and timely advances to the indirect cost literature. First, our research disaggregates the indirect cost pool and focuses on indirect personnel costs, which represent 33% of all Air Force indirect costs and are a leading source of indirect costs in many organizations. Second, we employ a multilevel modeling approach to capture the hierarchical nature of an enterprise, allowing us to assess the influence that each level of an organization has on indirect cost behavior and relationships. Third, we identify the operational variables that influence indirect personnel costs in the Air Force enterprise, providing Air Force decision-makers with evidence-based knowledge to inform decisions regarding budget reduction strategies

'This is what democracy looks like' : New Labour's blind spot and peripheral vision

New Labour in government since 1997 has been roundly criticized for not possessing a clear, coherent and consistent democratic vision. The absence of such a grand vision has resulted, from this critical perspective, in an absence of 'joined-up' thinking about democracy in an evolving multi-level state. Tensions have been all too apparent between the government's desire to exert central direction - manifested in its most pathological form as 'control freakery' - and its democratising initiatives derived from 'third-way' obsessions with 'decentralising', 'empowering' and 'enabling'. The purpose of this article is to examine why New Labour displayed such apparently impaired democratic vision and why it appeared incapable of conceiving of democratic reform 'in the round'. This article seeks to explain these apparent paradoxes, however, through utilising the notion of 'macular degeneration'. In this analysis, the perceived democratic blind spot of New Labour at Westminster is connected to a democratic peripheral vision, which has envisaged innovative participatory and decentred initiatives in governance beyond Westminster

Short-term Building Energy Model Recommendation System: A Meta-learning Approach

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency

In-Vivo Expression Profiling of Pseudomonas aeruginosa Infections Reveals Niche-Specific and Strain-Independent Transcriptional Programs

Pseudomonas aeruginosa is a threatening, opportunistic pathogen causing disease in immunocompromised individuals. The hallmark of P. aeruginosa virulence is its multi-factorial and combinatorial nature. It renders such bacteria infectious for many organisms and it is often resistant to antibiotics. To gain insights into the physiology of P. aeruginosa during infection, we assessed the transcriptional programs of three different P. aeruginosa strains directly after isolation from burn wounds of humans. We compared the programs to those of the same strains using two infection models: a plant model, which consisted of the infection of the midrib of lettuce leaves, and a murine tumor model, which was obtained by infection of mice with an induced tumor in the abdomen. All control conditions of P. aeruginosa cells growing in suspension and as a biofilm were added to the analysis. We found that these different P. aeruginosa strains express a pool of distinct genetic traits that are activated under particular infection conditions regardless of their genetic variability. The knowledge herein generated will advance our understanding of P. aeruginosa virulence and provide valuable cues for the definition of prospective targets to develop novel intervention strategies

Toll-like receptor variation in the bottlenecked population of the endangered Seychelles warbler

In small populations, drift results in a loss of genetic variation, which reduces adaptive evolutionary potential. Furthermore, the probability of consanguineous mating increases which may result in inbreeding depression. Under certain circumstances, balancing selection can counteract drift and maintain variation at key loci. Identifying such loci is important from a conservation perspective and may provide insight into how different evolutionary forces interact in small populations. Toll-like receptor (TLR) genes play a pivotal role in vertebrate innate immune defence by recognizing invading pathogens. We characterize TLR variation in the Seychelles warbler (SW) Acrocephalus sechellensis, an endangered passerine that recently suffered a population bottleneck. Five of seven TLR loci were polymorphic, with one locus (TLR15) containing four functional variants and showing an excess of heterozygotes. Haplotype-level tests failed to detect selection at these loci, but site-specific tests detected signatures of positive selection within TLR3 and TLR15. After characterizing variation (excluding TLR15) in 5–6 other Acrocephalus species, we found that TLR variation was positively correlated with population size across species and followed the pattern observed at neutral microsatellite loci. The depauperate TLR variation observed suggests that even at important immunity-related loci, balancing selection may only attenuate the overriding effects of drift. However, in the SW, TLR15 appears to be an outlier and warrants further investigation. The low levels of TLR variation may be disadvantageous for the long-term viability of the SW and conservatio

Demonstration of Protein-Based Human Identification Using the Hair Shaft Proteome

YesHuman identification from biological material is largely dependent on the ability to characterize genetic polymorphisms in DNA. Unfortunately, DNA can degrade in the environment, sometimes below the level at which it can be amplified by PCR. Protein however is chemically more robust than DNA and can persist for longer periods. Protein also contains genetic variation in the form of single amino acid polymorphisms. These can be used to infer the status of non-synonymous single nucleotide polymorphism alleles. To demonstrate this, we used mass spectrometry-based shotgun proteomics to characterize hair shaft proteins in 66 European-American subjects. A total of 596 single nucleotide polymorphism alleles were correctly imputed in 32 loci from 22 genes of subjects’ DNA and directly validated using Sanger sequencing. Estimates of the probability of resulting individual non-synonymous single nucleotide polymorphism allelic profiles in the European population, using the product rule, resulted in a maximum power of discrimination of 1 in 12,500. Imputed non-synonymous single nucleotide polymorphism profiles from European–American subjects were considerably less frequent in the African population (maximum likelihood ratio = 11,000). The converse was true for hair shafts collected from an additional 10 subjects with African ancestry, where some profiles were more frequent in the African population. Genetically variant peptides were also identified in hair shaft datasets from six archaeological skeletal remains (up to 260 years old). This study demonstrates that quantifiable measures of identity discrimination and biogeographic background can be obtained from detecting genetically variant peptides in hair shaft protein, including hair from bioarchaeological contexts.The Technology Commercialization Innovation Program (Contracts #121668, #132043) of the Utah Governors Office of Commercial Development, the Scholarship Activitie