Operational status of TAMA300 with the seismic attenuation system (SAS)

TAMA300 has been upgraded to improve the sensitivity at low frequencies after the last observation run in 2004. To avoid the noise caused by seismic activities, we installed a new seismic isolation system —- the TAMA seismic attenuation system (SAS). Four SAS towers for the test-mass mirrors were sequentially installed from 2005 to 2006. The recycled Fabry–Perot Michelson interferometer was successfully locked with the SAS. We confirmed the reduction of both length and angular fluctuations at frequencies higher than 1 Hz owing to the SAS

State Space Modeling Using SAS

This article provides a brief introduction to the state space modeling capabilities in SAS, a well-known statistical software system. SAS provides state space modeling in a few different settings. SAS/ETS, the econometric and time series analysis module of the SAS system, contains many procedures that use state space models to analyze univariate and multivariate time series data. In addition, SAS/IML, an interactive matrix language in the SAS system, provides Kalman filtering and smoothing routines for stationary and nonstationary state space models. SAS/IML also provides support for linear algebra and nonlinear function optimization, which makes it a convenient environment for general-purpose state space modeling.

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

SAS Open Journals. Overlaying an Open Journals service onto an institutional repository

In 2011, the School of Advanced Study, University of London (SAS) and the University of London Computer Centre (ULCC) worked jointly to develop the SAS Open Journals Service (SAS OJS). The project was funded by the JISC as part of its Scholarly Communications Programme, and aimed to create an open journal system that interfaced with the existing Institutional Repository (SAS-Space). This poster was presented at the 2012 Open Repositories conference in Edinburgh

Reactivity and fate of secondary alkane sulfonates (SAS) in marine sediments

This research is focused on secondary alkane sulfonates (SAS), anionic surfactants widely used in household applications that access aquatic environments mainly via sewage discharges.We studied their sorption capacity and anaerobic degradation in marine sediments, providing the first data available on this topic. SAS partition coefficients increased towards those homologues having longer alkyl chains(from up to 141 L kg 1 for C14 to up to 1753 L kg 1 for C17), which were those less susceptible to undergo biodegradation. Overall, SAS removal percentages reached up to 98% after 166 days of incubation using anoxic sediments. The degradation pathway consisted on the formation of sulfocarboxylic acids after an initial fumarate attack of the alkyl chain and successive b-oxidations. This is the first study showing that SAS can be degraded in absence of oxygen, so this new information should be taken into account for future environmental risk assessments on these chemicals