Bald Eagles at the Savanna Army Depot

Eagle Valley Environmentalists Technical Report #SADE-81, Research Report conducted December 1980 - March 1981, under a contract with the United States Arm

Low-frequency QPO from the 11 Hz accreting pulsar in Terzan 5: not frame dragging

We report on 6 RXTE observations taken during the 2010 outburst of the 11 Hz accreting pulsar IGR J17480-2446 located in the globular cluster Terzan 5. During these observations we find power spectra which resemble those seen in Z-type high-luminosity neutron star low-mass X-ray binaries, with a quasi-periodic oscillation (QPO) in the 35-50 Hz range simultaneous with a kHz QPO and broad band noise. Using well known frequency-frequency correlations, we identify the 35-50 Hz QPOs as the horizontal branch oscillations (HBO), which were previously suggested to be due to Lense-Thirring precession. As IGR J17480-2446 spins more than an order of magnitude more slowly than any of the other neutron stars where these QPOs were found, this QPO can not be explained by frame dragging. By extension, this casts doubt on the Lense-Thirring precession model for other low-frequency QPOs in neutron-star and perhaps even black-hole systems.Comment: 6 pages, 5 figures, Accepted for publication in ApJ

Joining up health and bioinformatics: e-science meets e-health

CLEF (Co-operative Clinical e-Science Framework) is an MRC sponsored project in the e-Science programme that aims to establish methodologies and a technical infrastructure forthe next generation of integrated clinical and bioscience research. It is developing methodsfor managing and using pseudonymised repositories of the long-term patient histories whichcan be linked to genetic, genomic information or used to support patient care. CLEF concentrateson removing key barriers to managing such repositories ? ethical issues, informationcapture, integration of disparate sources into coherent ?chronicles? of events, userorientedmechanisms for querying and displaying the information, and compiling the requiredknowledge resources. This paper describes the overall information flow and technicalapproach designed to meet these aims within a Grid framework

Developing the framework for multi-criteria assessment of smart local energy systems

In response to the climate emergency, energy landscapes are rapidly shifting to cleaner, decentralised smart local energy systems (SLESs). SLES will facilitate connection of transport, heat and power through flexible energy supply, demand and storage options supported by digital technology. SLESs are expected to contribute to tackling the energy trilemma (cost, security and sustainability), but there is also scope for them to offer many co-benefits aligned with the United Nations (UN) Sustainable Development Goals (SDGs). These benefits may drive for ongoing political and financial investment in SLES; therefore, there’s a need to indicate how a SLES is performing over time relative to each of them. Currently, there is no standardised approach to evaluate SLES and most of the existing techno-socio-economic tools have limited scope to assess the complex multiple performance indices, scenarios and stakeholders. The Innovate UK-funded EnergyREV research consortium is developing a multi-criteria assessment tool (MCA) for SLES. This paper describes the first step in this process – developing a simplified and standardised framework for assessing the performance of the system and the realization of benefits. It explores existing protocols and stakeholder opinion to identify 50 potential factors that are important in monitoring the system performance. These are clustered into 10 key themes to create a taxonomy for SLES performance that are aligned with relevant UN SDGs to track wider co-benefits. The resulting MCA tool will be instrumental to project stakeholders in providing evidence to support performance claims and identifying potential benefits beyond targeted key performance indicators

A five-year review of quality of reporting of research using clinician surveys in high-ranked dermatology journals

Surveys of clinicians play a pivotal role in dermatology research, including to determine expert opinion, identify areas of uncertainty in clinical practice, define research priorities, investigate feasibility and explore areas of clinical equipoise. Despite the commonality of research involving surveys distributed to dermatologists, we previously identified the issue of poor‐quality survey design and lack of sufficient validation prior to distribution. Furthermore, a review of postal surveys of healthcare professionals from 1996 to 2005 has shown declining response rates, introducing potential non‐responder bias. To support stronger methodological quality and reporting of clinician survey, we developed a checklist for authors, based on our experience and published literature

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US