The electrocatalytic behavior of reduced nicotinamide adenine dinucleotide (NADH) was studied at the surface of a rutin biosensor, using various electrochemical methods. According to the results, the rutin biosensor had a strongly electrocatalytic effect on the oxidation of NADH with the overpotential being decreased by about 450 mV as compared to the process at a bare glassy carbon electrode, GCE. This value is significantly greater than the value of 220 mV that was reported for rutin embedded in a lipid-cast film. The kinetic parameters of the electron transfer coefficient, α, and the heterogeneous charge transfer rate constant, k h , for the electrocatalytic oxidation of NADH at the rutin biosensor were estimated. Furthermore, the linear dynamic range; sensitivity and limit of detection for NADH were evaluated using the differential pulse voltammetry method. The advantages of this biosensor for the determination of NADH are excellent catalytic activity and reproducibility, good detection limit and high exchange current density. The rutin biosensor could separate the oxidation peak potentials of NADH and acetaminophen present in the same solution while at a bare GCE, the peak potentials were indistinguishable.

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It is important in the energy management of a building that energy consumption forecasts made by neural networks (referred to as black boxes) are backed up by consistent explanations from the model itself. Although the existing interpretable methods provide helpful information, it is not practical enough for energy managers. Expressly, the managers are not provided with an explanation for a certain period in the forecasted time series of energy consumption. We cover this lack of explanation by proposing a novel interpretability use case: explaining the shapelet of a period's forecast based on similar patterns in the past energy consumption profile, which our forecasting model can verify. Another interpretability use case is presented to explain better the electricity consumption forecast: determining the importance of each exogenous variable in the prediction problem. Temporal Fusion Transformers (TFT), a state-of-the-art, interpretable, and accurate forecasting model is employed to address the interpretability use cases via analyzing the distribution of attention weights. The results of applying the use cases on our dataset are demonstrated.