Advanced Testing Chain Supporting the Validation of Smart Grid Systems and Technologies

New testing and development procedures and methods are needed to address topics like power system stability, operation and control in the context of grid integration of rapidly developing smart grid technologies. In this context, individual testing of units and components has to be reconsidered and appropriate testing procedures and methods need to be described and implemented. This paper addresses these needs by proposing a holistic and enhanced testing methodology that integrates simulation/software- and hardware-based testing infrastructure. This approach presents the advantage of a testing environment, which is very close to f i eld testing, includes the grid dynamic behavior feedback and is risks-free for the power system, for the equipment under test and for the personnel executing the tests. Furthermore, this paper gives an overview of successful implementation of the proposed testing approach within different testing infrastructure available at the premises of different research institutes in Europe.Comment: 2018 IEEE Workshop on Complexity in Engineering (COMPENG

STUDY: Socially Aware Temporally Causal Decoder Recommender Systems

Recommender systems are widely used to help people find items that are tailored to their interests. These interests are often influenced by social networks, making it important to use social network information effectively in recommender systems. This is especially true for demographic groups with interests that differ from the majority. This paper introduces STUDY, a Socially-aware Temporally caUsal Decoder recommender sYstem. STUDY introduces a new socially-aware recommender system architecture that is significantly more efficient to learn and train than existing methods. STUDY performs joint inference over socially connected groups in a single forward pass of a modified transformer decoder network. We demonstrate the benefits of STUDY in the recommendation of books for students who are dyslexic, or struggling readers. Dyslexic students often have difficulty engaging with reading material, making it critical to recommend books that are tailored to their interests. We worked with our non-profit partner Learning Ally to evaluate STUDY on a dataset of struggling readers. STUDY was able to generate recommendations that more accurately predicted student engagement, when compared with existing methods.Comment: 15 pages, 5 figure

Longitudinal Modeling of Multiple Sclerosis using Continuous Time Models

Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. The majority of prior work in machine learning for multiple sclerosis has been centered around using Magnetic Resonance Imaging scans or laboratory tests; these modalities are both expensive to acquire and can be unreliable. In a recent paper it was shown that disease progression can be predicted effectively using performance outcome measures (POMs) and demographic data. In our work we extend on this to focus on the modeling side, using continuous time models on POMs and demographic data to predict progression. We evaluate four continuous time models using a publicly available multiple sclerosis dataset. We find that continuous models are often able to outperform discrete time models. We also carry out an extensive ablation to discover the sources of performance gains, we find that standardizing existing features leads to a larger performance increase than interpolating missing features

What Is the Optimal Method for Cleaning Screen-Printed Electrodes?

Screen-printed electrodes-based sensors can be successfully used to determine all kinds of analytes with great precision and specificity. However, obtaining a high-quality sensor can be difficult due to factors such as lack of reproducibility, surface contamination or other manufacturing challenges. An important step in ensuring reproducible results is the cleaning step. The aim of the current work is to help researchers around the world who struggle with finding the most suitable method for cleaning screen-printed electrodes. We evaluated the cleaning efficiency of different chemical compounds and cleaning methods using cyclic voltammetry and electrochemical impedance spectroscopy. The percentage differences in polarization resistance (Rp) before and after cleaning were as follows: acetone—35.33% for gold and 49.94 for platinum; ethanol—44.50% for gold and 81.68% for platinum; H2O2—47.34% for gold and 92.78% for platinum; electrochemical method—3.70% for gold and 67.96% for platinum. Thus, we concluded that all the evaluated cleaning methods seem to improve the surface of both gold and platinum electrodes; however, the most important reduction in the polarization resistance (Rp) was obtained after treating them with a solution of H2O2 and multiple CV cycles with a low scanning speed (10 mV/s)