Exploiting multi-agent system technology within an autonomous regional active network management system

This paper describes the proposed application of multi-agent system (MAS) technology within AuRA-NMS, an autonomous regional network management system currently being developed in the UK through a partnership between several UK universities, distribution network operators (DNO) and a major equipment manufacturer. The paper begins by describing the challenges facing utilities and why those challenges have led the utilities, a major manufacturer and the UK government to invest in the development of a flexible and extensible active network management system. The requirements the utilities have for a network automation system they wish to deploy on their distribution networks are discussed in detail. With those requirements in mind the rationale behind the use of multi-agent systems (MAS) within AuRA-NMS is presented and the inherent research and design challenges highlighted including: the issues associated with robustness of distributed MAS platforms; the arbitration of different control functions; and the relationship between the ontological requirements of Foundation for Intelligent Physical Agent (FIPA) compliant multi-agent systems, legacy protocols and standards such as IEC 61850 and the common information model (CIM)

Practical applications of data mining in plant monitoring and diagnostics

Using available expert knowledge in conjunction with a structured process of data mining, characteristics observed in captured condition monitoring data, representing characteristics of plant operation may be understood, explained and quantified. Knowledge and understanding of satisfactory and unsatisfactory plant condition can be gained and made explicit from the analysis of data observations and subsequently used to form the basis of condition assessment and diagnostic rules/models implemented in decision support systems supporting plant maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of in-service distribution circuit breakers and empirical UHF data captured from laboratory experiments simulating partial discharge defects typically found in HV transformers. This discovered knowledge then forms the basis of two separate decision support systems for the condition assessment/defect clasification of these respective plant items

The Cost of Producing Canning Peas in Utah 1945-1961

Since 1946, the cost of producing canning peas has been investigated on a continuing basis by the Utah Agricultural Experiment Station. The original comprehensive study was made in 1946. A second study was completed in 1951. The most recent study was made in 1961. In between the major studies annual costs of production have been calculated

Cost of Raising Dairy Heifers in Northern Utah

Dairymen have a continuing problem in maintaining herd size. Disease, injuries, and low production, make it necessary for them to cull animals from their herds. Replacements equal to a complete herd turnover are necessary every two to six years. Consequently, many are wondering as to the most profitable practice to supply the needed replacements in the herd. Three alternatives are available: Dairymen can produce herd replacements. They can purchase either heifers or cows. They can contract with growers specializing in raising heifers to provide replacements. Since all dairy enterprises differ to some degree, each dairyman should seek a specific solution to his herd replacement problem

Annexin A1 N-Terminal Derived Peptide Ac2-26 Exerts Chemokinetic Effects on Human Neutrophils

It is postulated that peptides derived from the N-terminal region of Annexin A1, a glucocorticoid-regulated 37-kDa protein, could act as biomimetics of the parent protein. However, recent evidence, amongst which the ability to interact with distinct receptors other then that described for Annexin A1, suggest that these peptides might fulfill other functions at variance to those reported for the parent protein. Here we tested the ability of peptide Ac2-26 to induce chemotaxis of human neutrophils, showing that this peptide can elicit responses comparable to those produced by the canonical activator formyl-Met-Leu-Phe (or FMLP). However, whilst disruption of the chemical gradient abolished the FMLP response, addition of peptide Ac2-26 in the top well of the chemotaxis chamber did not affect (10 μM) or augmented (at 30 μM) the neutrophil locomotion to the bottom well, as elicited by 10 μM peptide Ac2-26. Intriguingly, the sole addition of peptide Ac2-26 in the top wells produced a marked migration of neutrophils. A similar behavior was observed when human primary monocytes were used. Thus, peptide Ac2-26 is a genuine chemokinetic agent toward human blood leukocytes. Neutralization strategies indicated that engagement of either the GPCR termed FPR1 or its cognate receptor FPR2/ALX was sufficient to sustain peptide Ac2-26 induced neutrophil migration. Similarly, application of pharmacological inhibitors showed that cell locomotion to peptide Ac2-26 was mediated primarily by the ERK, but not the JNK and p38 pathways. In conclusion, we report here novel in vitro properties for peptide Ac2-26, promoting neutrophil and monocyte chemokinesis; a process that may contribute to accelerate the resolution phase of inflammation. We postulate that the generation of Annexin A1 N-terminal peptides at the site of inflammation may expedite the egress of migrated leukocytes thus promoting the return to homeostasis

Data management of on-line partial discharge monitoring using wireless sensor nodes integrated with a multi-agent system

On-line partial discharge monitoring has been the subject of significant research in previous years but little work has been carried out with regard to the management of on-site data. To date, on-line partial discharge monitoring within a substation has only been concerned with single plant items, so the data management problem has been minimal. As the age of plant equipment increases, so does the need for condition monitoring to ensure maximum lifespan. This paper presents an approach to the management of partial discharge data through the use of embedded monitoring techniques running on wireless sensor nodes. This method is illustrated by a case study on partial discharge monitoring data from an ageing HVDC reactor

Online conditional anomaly detection in multivariate data for transformer monitoring

Retrofitting condition monitoring systems to aging plant can be problematic, since the particular signature of normal behavior will vary from unit to unit. This paper describes a technique for anomaly detection within the context of the conditions experienced by an in-service transformer, such as loading, seasonal weather, and network configuration. The aim is to model the aged but normal behavior for a given transformer, while reducing the potential for anomalies to be erroneously detected. The paper describes how this technique has been applied to two transmission transformers in the U.K. A case study of 12 months of data is given, with detailed analysis of anomalies detected during that time

Machine learning model for event-based prognostics in gas circulator condition monitoring

Gas circulator (GC) units are an important rotating asset used in the Advanced Gas-cooled Reactor (AGR) design, facilitating the flow of CO2 gas through the reactor core. The ongoing maintenance and examination of these machines is important for operators in order to maintain safe and economic generation. GCs experience a dynamic duty cycle with periods of non-steady state behavior at regular refuelling intervals, posing a unique analysis problem for reliability engineers. In line with the increased data volumes and sophistication of available the technologies, the investigation of predictive and prognostic measurements has become a central interest in rotating asset condition monitoring. However, many of the state-of-the-art approaches finding success deal with the extrapolation of stationary time series feeds, with little to no consideration of more-complex but expected events in the data. In this paper we demonstrate a novel modelling approach for examining refuelling behaviors in GCs, with a focus on estimating their health state from vibration data. A machine learning model was constructed using the operational history of a unit experiencing an eventual inspection-based failure. This new approach to examining GC condition is shown to correspond well with explicit remaining useful life (RUL) measurements of the case study, improving on the existing rudimentary extrapolation methods often employed in rotating machinery health monitoring

Exploiting formyl peptide receptor 2 to promote microglial resolution: a new approach to Alzheimer's disease treatment.

Alzheimer’s disease and dementia are among the most significant current healthcare challenges given the rapidly growing elderly population, and the almost total lack of effective therapeutic interventions. Alzheimer’s disease pathology has long been considered in terms of accumulation of amyloid beta and hyperphosphorylated tau, but the importance of neuroinflammation in driving disease has taken greater precedence over the last 15–20 years. Inflammatory activation of the primary brain immune cells, the microglia, has been implicated in Alzheimer’s pathogenesis through genetic, preclinical, imaging and postmortem human studies, and strategies to regulate microglial activity may hold great promise for disease modification. Neuroinflammation is necessary for defence of the brain against pathogen invasion or damage but is normally self-limiting due to the engagement of endogenous pro-resolving circuitry that terminates inflammatory activity, a process that appears to fail in Alzheimer’s disease. Here, we discuss the potential for a major regulator and promoter of resolution, the receptor FPR2, to restrain pro-inflammatory microglial activity, and propose that it may serve as a valuable target for therapeutic investigation in Alzheimer’s disease

Ectoplasm & Superspace Integration Measure for 2D Supergravity with Four Spinorial Supercurrents

Building on a previous derivation of the local chiral projector for a two dimensional superspace with eight real supercharges, we provide the complete density projection formula required for locally supersymmetrical theories in this context. The derivation of this result is shown to be very efficient using techniques based on the Ectoplasmic construction of local measures in superspace.Comment: 18 pages, LaTeX; V2: minor changes, typos corrected, references added; V3: version to appear in J. Phys. A: Math. Theor., some comments and references added to address a referee reques