Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Specific label-free and real-time detection of oxidized low density lipoprotein (oxLDL) using an immunosensor with three monoclonal antibodies

Increased levels of plasma oxLDL, which is the oxidized fraction of Low Density Lipoprotein (LDL), are associated with atherosclerosis, an inflammatory disease, and the subsequent development of severe cardiovascular diseases that are today a major cause of death in modern countries. It is therefore important to find a reliable and fast assay to determine oxLDL in serum. A new immunosensor employing three monoclonal antibodies (mAbs) against oxLDL is proposed in this work as a quick and effective way to monitor oxLDL. The oxLDL was first employed to produce anti-oxLDL monoclonal antibodies by hybridoma cells that were previously obtained. The immunosensor was set-up by selfassembling cysteamine (Cyst) on a gold (Au) layer (4 mm diameter) of a disposable screen-printed electrode. Three mAbs were allowed to react with N-hydroxysuccinimide (NHS) and ethyl(dimethylaminopropyl)carbodiimide (EDAC), and subsequently incubated in the Au/Cys. Albumin from bovine serum (BSA) was immobilized further to ensure that other molecules apart from oxLDL could not bind to the electrode surface. All steps were followed by various characterization techniques such as electrochemical impedance spectroscopy (EIS) and square wave voltammetry (SWV). The analytical operation of the immunosensor was obtained by incubating the sensing layer of the device in oxLDL for 15 minutes, prior to EIS and SWV. This was done by using standard oxLDL solutions prepared in foetal calf serum, in order to simulate patient's plasma with circulating oxLDL. A sensitive response was observed from 0.5 to 18.0 mg mL 1 . The device was successfully applied to determine the oxLDL fraction in real serum, without prior dilution or necessary chemical treatment. The use of multiple monoclonal antibodies on a biosensing platform seemed to be a successful approach to produce a specific response towards a complex multi-analyte target, correlating well with the level of oxLDL within atherosclerosis disease, in a simple, fast and cheap way.