Believing Probabilistic Contents: On the Expressive Power and Coherence of Sets of Sets of Probabilities

Moss (2018) argues that rational agents are best thought of not as having degrees of belief in various propositions but as having beliefs in probabilistic contents, or probabilistic beliefs. Probabilistic contents are sets of probability functions. Probabilistic belief states, in turn, are modeled by sets of probabilistic contents, or sets of sets of probability functions. We argue that this Mossean framework is of considerable interest quite independently of its role in Moss’ account of probabilistic knowledge or her semantics for epistemic modals and probability operators. It is an extremely general model of uncertainty. Indeed, it is at least as general and expressively powerful as every other current imprecise probability framework, including lower probabilities, lower previsions, sets of probabilities, sets of desirable gambles, and choice functions. In addition, we partially answer an important question that Moss leaves open, viz., why should rational agents have consistent probabilistic beliefs? We show that an important subclass of Mossean believers avoid Dutch bookability iff they have consistent probabilistic beliefs

Self-tuning diagnosis of routine alarms in rotating plant items

Condition monitoring of rotating plant items in the energy generation industry is often achieved through examination of vibration signals. Engineers use this data to monitor the operation of turbine generators, gas circulators and other key plant assets. A common approach in such monitoring is to trigger an alarm when a vibration deviates from a predefined envelope of normal operation. This limit-based approach, however, generates a large volume of alarms not indicative of system damage or concern, such as operational transients that result in temporary increases in vibration. In the nuclear generation context, all alarms on rotating plant assets must be analysed and subjected to auditable review. The analysis of these alarms is often undertaken manually, on a case- by-case basis, but recent developments in monitoring research have brought forward the use of intelligent systems techniques to automate parts of this process. A knowledge- based system (KBS) has been developed to automatically analyse routine alarms, where the underlying cause can be attributed to observable operational changes. The initialisation and ongoing calibration of such systems, however, is a problem, as normal machine state is not uniform throughout asset life due to maintenance procedures and the wear of components. In addition, different machines will exhibit differing vibro- acoustic dynamics. This paper proposes a self-tuning knowledge-driven analysis system for routine alarm diagnosis across the key rotating plant items within the nuclear context common to the UK. Such a system has the ability to automatically infer the causes of routine alarms, and provide auditable reports to the engineering staff

Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques

The importance of size and shape in imprinted polymers

The existence of shape selectivity in non-covalent molecularly imprinted polymers has been proven using molecular probes. A series of amines varying with different structural motifs and secondary amines with different sized side chains were imprinted, and binding evaluated by HPLC for each amine on polymers imprinted with similar amines. Trends in the binding relationships revealed two major contributions of cavity structure on selectivity afforded by molecularly imprinted polymers (MIPs). First, sterics play a dominant role in cases where a molecules structure is too big too fit into an imprinted site formed from a smaller template molecule. Second, molecular structures that are equal to or smaller than those of the template molecule are selected by maximizing Van der Waals interactions within the MIP binding site

Investigation of gas circulator response to load transients in nuclear power plant operation

Gas circulator units are a critical component of the Advanced Gas-cooled Reactor (AGR), one of the nuclear power plant (NPP) designs in current use within the UK. The condition monitoring of these assets is central to the safe and economic operation of the AGRs and is achieved through analysis of vibration data. Due to the dynamic nature of reactor operation, each plant item is subject to a variety of system transients of which engineers are required to identify and reason about with regards to asset health. The AGR design enables low power refueling (LPR) which results in a change in operational state for the gas circulators, with the vibration profile of each unit reacting accordingly. The changing conditions subject to these items during LPR and other such events may impact on the assets. From these assumptions, it is proposed that useful information on gas circulator condition can be determined from the analysis of vibration response to the LPR event. This paper presents an investigation into asset vibration during an LPR. A machine learning classification approach is used in order to define each transient instance and its behavioral features statistically. Classification and reasoning about the regular transients such as the LPR represents the primary stage in modeling higher complexity events for advanced event driven diagnostics, which may provide an enhancement to the current methodology, which uses alarm boundary limits

Flap Endonuclease Disengages Dna2 Helicase/Nuclease from Okazaki Fragment Flaps

Okazaki fragments contain an initiator RNA/DNA primer that must be removed before the fragments are joined. In eukaryotes, the primer region is raised into a flap by the strand displacement activity of DNA polymerase {delta}. The Dna2 helicase/nuclease and then flap endonuclease 1 (FEN1) are proposed to act sequentially in flap removal. Dna2 and FEN1 both employ a tracking mechanism to enter the flap 5' end and move toward the base for cleavage. In the current model, Dna2 must enter first, but FEN1 makes the final cut at the flap base, raising the issue of how FEN1 passes the Dna2. To address this, nuclease-inactive Dna2 was incubated with a DNA flap substrate and found to bind with high affinity. FEN1 was then added, and surprisingly, there was little inhibition of FEN1 cleavage activity. FEN1 was later shown, by gel shift analysis, to remove the wild type Dna2 from the flap. RNA can be cleaved by FEN1 but not by Dna2. Pre-bound wild type Dna2 was shown to bind an RNA flap but not inhibit subsequent FEN1 cleavage. These results indicate that there is a novel interaction between the two proteins in which FEN1 disengages the Dna2 tracking mechanism. This interaction is consistent with the idea that the two proteins have evolved a special ability to cooperate in Okazaki fragment processing

Down the Rabbit Hole with Citizens United: Are Bans on Corporate Direct Campaign Contributions Still Constitutional?

Since the early twentieth century, the Tillman Act has barred corporations from contributing directly to candidates for federal office. In Citizens United v. FEC, the U.S. Supreme Court overturned a related ban that prevented corporations from making independent expenditures in candidate elections. The legal foundation of the independent expenditure ban was similar to that which still supports the corporate direct contributions ban, thus calling into question the continuing validity of the direct contributions ban. This Note argues that if the Court follows the logical path that it laid down in Citizens United, it should overturn the corporate direct contributions ban

A Comparison of Modeling Approaches in Simulating Chlorinated Ethene Removal in a Constructed Wetland by a Microbial Consortia

The purpose of this study is to compare different approaches to modeling the reductive dechlorination of chlorinated ethenes in the anaerobic region of an upward flow constructed wetland by microbial consortia. A controlled simulation experiment that compares three different approaches to modeling the degradation of chlorinated ethenes in wetland environments is conducted and investigates how each of the modeling approaches affect simulation results. Concepts like microbial growth in the form of a biofilm and spatially varying contaminant concentrations bring the validity of the CSTR assumption into question. These concepts are incorporated into the different modeling approaches to evaluate the CSTR assumption. Model simulations show that spatially varying contaminant concentrations have a significant effect on contaminant effluent concentrations. Additionally, the significance of the incorporation of a biofilm concept depends on the time characteristics of both diffusive mass transport and reaction kinetics

A Brief Study of Open Source Graph Databases

With the proliferation of large irregular sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query and compute on the topological structure of these relationships represented as set(s) of edges between set(s) of vertices. To store and process Facebook-scale datasets, they must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. These platforms must provide intuitive interfaces that enable graph experts and novice programmers to write implementations of common graph algorithms. In this paper, we explore a variety of graph analysis and storage platforms. We compare their capabil- ities, interfaces, and performance by implementing and computing a set of real-world graph algorithms on synthetic graphs with up to 256 million edges. In the spirit of full disclosure, several authors are affiliated with the development of STINGER.Comment: WSSSPE13, 4 Pages, 18 Pages with Appendix, 25 figure