Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

Bid definition method for electricity markets based on an adaptive multiagent system

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. Each agent has the knowledge about a different method for defining a strategy for playing in the market, the main agent chooses the best among all those, and provides it to the market player that requests, to be used in the market. This paper also presents a methodology to manage the efficiency/effectiveness balance of this method, to guarantee that the degradation of the simulator processing times takes the correct measure.

Strategic bidding methodology for electricity markets using adaptive learning

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.

Detection of pathogens from periodontal lesions

OBJECTIVE: To comparatively detect A. actinomycetemcomitans and F. nucleatum from periodontal and healthy sites. METHODS: Subgingival clinical samples from 50 periodontitis adult patients and 50 healthy subjects were analyzed. Both organisms were isolated using a trypticase soy agar-bacitracin-vancomycin (TSBV) medium and detected by PCR. Conventional biochemical tests were used for bacteria identification. RESULTS: A. actinomycetemcomitans and F. nucleatum were isolated in 18% and 20% of the patients, respectively, and in 2% and 24% of healthy subjects. Among A. actinomycetemcomitans isolates, biotype II was the most prevalent. Primer pair AA was 100% sensitive in the detection of A. actinomycetemcomitans from both subject groups. Primers ASH and FU were also 100% sensitive to detect this organism in healthy subject samples. Primer pair FN5047 was more sensitive to detect F. nucleatum in patients or in healthy samples than primer 5059S. Primers ASH and 5059S were more specific in the detection of A. actinomycetemcomitans and F. nucleatum, respectively, in patients and in healthy subject samples. CONCLUSIONS: PCR is an effective tool for detecting periodontal pathogens in subgingival samples, providing a faster and safer diagnostic tool of periodontal diseases. The method's sensitivity and specificity is conditioned by the choice of the set of primers used.

Novel method using a laser scanning cytometer for detection of Mycobacteria in clinical samples

In order to evaluate the capacity of laser scanning cytometry (LSC) to detect acid-fast bacilli directly on clinical samples, a comparison between Kinyoun-stained smears analyzed under light microscopy and propidium iodide-auramine-stained smears analyzed by LSC was performed. The results were compared with those for culture on BACTEC MGIT 960. LSC is a new, reliable methodology to detect Mycobacteria.