This report summarizes the current statistical analysis capability of OVIS and how it works in conjunction with the OVIS data readers and interpolators. It also documents how to extend these capabilities. OVIS is a tool for parallel statistical analysis of sensor data to improve system reliability. Parallelism is achieved using a distributed data model: many sensors on similar components (metaphorically sheep) insert measurements into a series of databases on computers reserved for analyzing the measurements (metaphorically shepherds). Each shepherd node then processes the sheep data stored locally and the results are aggregated across all shepherds. OVIS uses the Visualization Tool Kit (VTK) statistics algorithm class hierarchy to perform analysis of each process's data but avoids VTK's model aggregation stage which uses the Message Passing Interface (MPI); this is because if a single process in an MPI job fails, the entire job will fail. Instead, OVIS uses asynchronous database replication to aggregate statistical models. OVIS has several additional features beyond those present in VTK that, first, accommodate its particular data format and, second, improve the memory and speed of the statistical analyses. First, because many statistical algorithms are multivariate in nature and sensor data is typically univariate, interpolation of data is required to provide simultaneous observations of metrics. Note that in this report, we will refer to a single value obtained from a sensor as a measurement while a collection of multiple sensor values simultaneously present in the system is an observation. A base class for interpolation is provided that abstracts the operation of converting multiple sensor measurements into simultaneous observations. A concrete implementation is provided that performs piecewise constant temporal interpolation of multiple metrics across a single component. Secondly, because calculations may summarize data too large to fit in memory OVIS analyses batches of observations at a time and aggregates these intermediate intra-process models as it goes before storing the final model for inter-process aggregation via database replication. This reduces the memory footprint of the analysis, interpolation, and the database client and server query processing. This also interleaves processing with the disk I/O required to fetch data from the database - also improving speed. This report documents how OVIS performs analyses and how to create additional analysis components that fetch measurements from the database, perform interpolation, or perform operations on streamed observations (such as model updates or assessments). The rest of this section outlines the OVIS analysis algorithm and is followed by sections specific to each subtask. Note that we are limiting our discussion for now to the creation of a model from a set of measurements, and not including the assessment of observations using a model. The same framework can be used for assessment but that use case is not detailed in this report.
The observation and characterization of a single atom system in silicon is a significant landmark in half a century of device miniaturization, and presents an important new laboratory for fundamental quantum and atomic physics. We compare with multi-million atom tight binding (TB) calculations the measurements of the spectrum of a single two-electron (2e) atom system in silicon - a negatively charged (D-) gated Arsenic donor in a FinFET. The TB method captures accurate single electron eigenstates of the device taking into account device geometry, donor potentials, applied fields, interfaces, and the full host bandstructure. In a previous work, the depths and fields of As donors in six device samples were established through excited state spectroscopy of the D0 electron and comparison with TB calculations. Using self-consistent field (SCF) TB, we computed the charging energies of the D- electron for the same six device samples, and found good agreement with the measurements. Although a bulk donor has only a bound singlet ground state and a charging energy of about 40 meV, calculations show that a gated donor near an interface can have a reduced charging energy and bound excited states in the D- spectrum. Measurements indeed reveal reduced charging energies and bound 2e excited states, at least one of which is a triplet. The calculations also show the influence of the host valley physics in the two-electron spectrum of the donor.
This paper proposes a definition of 'IA and IA-enabled products' based on threat, as opposed to 'security services' (i.e., 'confidentiality, authentication, integrity, access control or non-repudiation of data'), as provided by Department of Defense (DoD) Instruction 8500.2, 'Information Assurance (IA) Implementation.' The DoDI 8500.2 definition is too broad, making it difficult to distinguish products that need higher protection from those that do not. As a consequence the products that need higher protection do not receive it, increasing risk. The threat-based definition proposed in this paper solves those problems by focusing attention on threats, thereby moving beyond compliance to risk management. (DoDI 8500.2 provides the definitions and controls that form the basis for IA across the DoD.) Familiarity with 8500.2 is assumed.
This paper presents experimental results for two fuel-related topics in a diesel engine: (1) how fuel volatility affects the premixed burn and heat release rate, and (2) how ignition quality influences the soot formation. Fast evaporation of fuel may lead to more intense heat release if a higher percentage of the fuel is mixed with air to form a combustible mixture. However, if the evaporation of fuel is driven by mixing with high-temperature gases from the ambient, a high-volatility fuel will require less oxygen entrainment and mixing for complete vaporization and, consequently, may not have potential for significant heat release simply because it has vaporized. Fuel cetane number changes also cause uncertainty regarding soot formation because variable ignition delay will change levels of fuel-air mixing prior to combustion. To address these questions, experiments are performed using a constant-volume combustion chamber simulating typical low-temperature-combustion (LTC) diesel conditions. We use fuels that have the same ignition delay (and therefore similar time for premixing with air), but different fuel volatility, to assess the heat-release rate and spatial location of combustion. Under this condition, where fuel volatility is decoupled from the ignition delay, results show almost the same heat release rate and spatial location of the premixed burn. The effect of ignition quality on soot formation has also been studied while maintaining similar levels of fuel-ambient mixing prior to combustion. To achieve the same ignition delay, the high-cetane-number fuel is injected into an ambient gas at a lower temperature and vice versa. The total soot mass within the spray is measured and compared for fuels with different cetane numbers but with the same premixing level (e.g. the same ignition delay and lift-off length). Experimental results show that the combination of high cetane number and low ambient gas temperature produces lower soot than the other combination, because the ambient temperature predominantly affects soot formation.
The research described in this report developed the theoretical and conceptual framework for understanding, recognizing, and anticipating the origins, dynamic mechanisms, perceptions, and social structures of Islamic social reform movements in the Muslim homeland and in diaspora communities. This research has revealed valuable insights into the dynamic mechanisms associated with reform movements and, as such, offers the potential to provide indications and warnings of impending violence. This study produced the following significant findings: (1) A framework for understanding Islamic radicalization in the context of Social Movement Theory was developed and implemented. This framework provides a causal structure for the interrelationships among the myriad features of a social movement. (2) The degree to which movement-related activity shows early diffusion across multiple social contexts is a powerful distinguisher of successful and unsuccessful social movements. Indeed, this measurable appears to have significantly more predictive power than volume of such activity and also more power than various system intrinsics. (3) Significant social movements can occur only if both the intra-context 'infectivity' of the movement exceeds a certain threshold and the inter-context interactions associated with the movement occur with a frequency that is larger than another threshold. Note that this is reminiscent of, and significantly extends, well-known results for epidemic thresholds in disease propagation models. (4) More in-depth content analysis of blogs through the lens of Argumentation Theory has the potential to reveal new insights into radicalization in the context of Social Movement Theory. This connection has the potential to be of value from two important perspectives - first, this connection has the potential to provide more in depth insights into the forces underlying the emergence of radical behavior and second, this connection may provide insights into how to use the blogosphere to influence the emergent dialog to effectively impact the resulting actions taken by the potential radicals. The authors of this report recognize that Islamic communities are not the only source of radicalism; indeed many other groups, religious and otherwise, have used and continue to use, radicalism to achieve their ends. Further, the authors also recognize that not all Muslims use, or condone the use of, radical behavior. Indeed, only a very small segment of the Muslim communities throughout the world use and/or support such behavior. Nevertheless, the focus of this research is, indeed, on understanding, recognizing, and anticipating the origins, dynamic mechanisms, perceptions, and social structures of Islamic radicalism.