Improving Security in Software Acquisition and Runtime Integration With Data Retention Specifications

Naval Postgraduate School Acquisition Research Progra

A Fresh Catch of Massive Binaries in the Cygnus OB2 Association

Massive binary stars may constitute a substantial fraction of progenitors to supernovae and gamma-ray bursts, and the distribution of their orbital characteristics holds clues to the formation process of massive stars. As a contribution to securing statistics on OB-type binaries, we report the discovery and orbital parameters for five new systems as part of the Cygnus OB2 Radial Velocity Survey. Four of the new systems (MT070, MT174, MT267, and MT734 (a.k.a. VI Cygni #11) are single-lined spectroscopic binaries while one (MT103) is a double-lined system (B1V+B2V). MT070 is noteworthy as the longest period system yet measured in Cyg OB2, with P=6.2 yr. The other four systems have periods ranging between 4 and 73 days. MT174 is noteworthy for having a probable mass ratio q<0.1, making it a candidate progenitor to a low-mass X-ray binary. These measurements bring the total number of massive binaries in Cyg OB2 to 25, the most currently known in any single cluster or association.Comment: Accepted for publication in the Astrophysical Journa

Coping with climatic extremes: Dietary fat content decreased the thermal resilience of barramundi (Lates calcarifer)

Aquatic organisms, including important cultured species, are forced to contend with acute changes in water temperature as the frequency and intensity of extreme weather events worsen. Acute temperature spikes are likely to threaten aquaculture species, but dietary intervention may play an important protective role. Increasing the concentration of macronutrients, for example dietary fat content, may improve the thermal resilience of aquaculture species, however, this remains unexplored. To evaluate this hypothesis, we used two commercially available diets (20% versus 10% crude fat) to examine if dietary fat content improves the growth performance of juvenile barramundi (Lates calcarifer) while increasing their resilience to acute thermal stress. Fish were fed their assigned diets for 28-days before assessing the upper thermal tolerance (CTMAX) and the thermal sensitivity of swimming performance (UCRIT) and metabolism. We found that feeding fish a high fat diet resulted in heavier fish, but did not affect the thermal sensitivity of swimming performance or metabolism over an 18 °C temperature range (from 20 to 38 °C). Thermal tolerance was compromised in fish fed the high fat diet by 0.48 °C, showing significantly lower CTMAX. Together, these results suggest that while a high fat diet increases juvenile L. calcarifer growth, it does not benefit physiological performance across a range of relevant water temperatures and may even reduce fish tolerance of extreme water temperatures. These data may have implications for aquaculture production in a warming world, where episodic extremes of temperature are likely to become more frequent

A systematic review and analysis of long-term growth trials on the effect of diet on omega-3 fatty acid levels in the fillet tissue of post-smolt Atlantic salmon

Elucidating the specific effects of diet on the fatty acid composition in Atlantic salmon (Salmo salar), particularly health beneficial omega-3 long-chain polyunsaturated fatty acids (n-3 LC PUFA), remains an area of intense commercial interest given the increasing market restrictions placed on the supply of fishmeal and fish oil. The present study conducted a systematic review and subsequent analysis of published nutritional data from long-term growth trials using post-smolt Atlantic salmon to provide a summary of currently available information and to identify the most significant drivers of omega-3 levels in Atlantic salmon fillet tissue. Overall, there were relatively few studies which met the selection criteria and this had implications for further explanation of some results. Statistically significant regression models were generated for fillet DHA and fillet n-3 LC PUFA. Fish weight was a significant predictor in both models, and dietary 22:6n-3 (DHA) was an intuitive predictor of fillet DHA. Furthermore, dietary EPA and dietary 22:1 isomers were significant predictors of fillet n-3 LC PUFA

Improving Security in Software Acquisition and Runtime Integration With Data Retention Specifications

Naval Postgraduate School Acquisition Research Progra

Improving Security in Software Acquisition with Data Retention Specifications

The Department of Defense (DoD) Risk Management Framework (RMF) for IT systems is aligned with the National Institute for Standards and Technology (NIST) guidance for federal IT architectures, including emergent mobile and cloud-based platforms. This guidance serves as a prescriptive lifecycle for IT engineers to recognize, understand, and mitigate security risks. However, integrators are left with the challenge - during acquisition, and during runtime integration with external services - to reason about the actions on data inherent in their system designs that may have confidentiality risks. These risks may lead to data spills; loss of confidentiality for mission data, and/or revelations about private data related to service members and their families. Solutions are needed to assist acquisition professionals to align system data practices with the RMF and NIST guidance, as well as DoD IA directives - particularly with respect to the collection, usage, transfer, and retention of data. To provide support to this end, we extended our initial automation framework, to support reasoning over data retention actions using a formal language. We propose an evaluation method for these extensions, carried out through simulations of real-world IT systems using imitation but statistically accurate synthetic data. Our language aims to address dynamically composable, multi-party systems that preserve security properties and address incipient data privacy concerns. Software developers and certification authorities can use these profiles expressed in first-order logic with an inference engine to advance the RMF, express data retention actions that promote confidentiality, and re-evaluate risk mitigation and compliance as IT systems evolve over time.Naval Postgraduate School Acquisition Research Progra

The Best of Both Worlds: Mitigating Trade-offs Between Accuracy and User Burden in Capturing Mobile App Privacy Preferences

In today’s data-centric economy, data flows are increasingly diverse and complex. This is best exemplified by mobile apps, which are given access to an increasing number of sensitive APIs. Mobile operating systems have attempted to balance the introduction of sensitive APIs with a growing collection of permission settings, which users can grant or deny. The challenge is that the number of settings has become unmanageable. Yet research also shows that existing settings continue to fall short when it comes to accurately capturing people’s privacy preferences. An example is the inability to control mobile app permissions based on the purpose for which an app is requesting access to sensitive data. In short, while users are already overwhelmed, accurately capturing their privacy preferences would require the introduction of an even greater number of settings. A promising approach to mitigating this trade-off lies in using machine learning to generate setting recommendations or bundle some settings. This article is the first of its kind to offer a quantitative assessment of how machine learning can help mitigate this trade-off, focusing on mobile app permissions. Results suggest that it is indeed possible to more accurately capture people’s privacy preferences while also reducing user burden