Forecasting Energy Consumption Demand of Customers in Smart Grid Using Temporal Fusion Transformer (TFT)

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers' requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers' future demand. Using the proposed study, production companies can accurately have energy per their customers' needs by forecasting future energy consumption demands. Scientists and researchers are trying to minimize energy consumption by applying different optimization and prediction techniques;hence this study proposed a daily, weekly, and monthly energy consumption prediction model using Temporal Fusion Transformer (TFT). This study relies on a TFT model for energy forecasting, which considers both primary and valuable data sources and batch training techniques. The model's performance has been related to the Long Short-Term Memory (LSTM), LSTM interpretable, and Temporal Convolutional Network (TCN) models. The model's performance has remained better than the other algorithms, with mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) of 4.09, 2.02, and 1.50. Further, the overall symmetric mean absolute percentage error (sMAPE) of LSTM, LSTM interpretable, TCN, and proposed TFT remained at 29.78%, 31.10%, 36.42%, and 26.46%, respectively. The sMAPE of the TFT has proved that the model has performed better than the other deep learning models.

Aurasperone A Inhibits SARS CoV-2 In Vitro: An Integrated In Vitro and In Silico Study.

Several natural products recovered from a marine-derived Aspergillus niger were tested for their inhibitory activity against SARS CoV-2 in vitro. Aurasperone A (3) was found to inhibit SARS CoV-2 efficiently (IC50 = 12.25 µM) with comparable activity with the positive control remdesivir (IC50 = 10.11 µM). Aurasperone A exerted minimal cytotoxicity on Vero E6 cells (CC50 = 32.36 mM, SI = 2641.5) and it was found to be much safer than remdesivir (CC50 = 415.22 µM, SI = 41.07). To putatively highlight its molecular target, aurasperone A was subjected to molecular docking against several key-viral protein targets followed by a series of molecular dynamics-based in silico experiments that suggested Mpro to be its primary viral protein target. More potent anti-SARS CoV-2 Mpro inhibitors can be developed according to our findings presented in the present investigation.

Chemically modified mRNA beyond COVID-19: Potential preventive and therapeutic applications for targeting chronic diseases.

Chemically modified mRNA represents a unique, efficient, and straightforward approach to produce a class of biopharmaceutical agents. It has been already approved as a vaccination-based method for targeting SARS-CoV-2 virus. The COVID-19 pandemic has highlighted the prospect of synthetic modified mRNA to efficiently and safely combat various diseases. Recently, various optimization advances have been adopted to overcome the limitations associated with conventional gene therapeutics leading to wide-ranging applications in different disease conditions. This review sheds light on emerging directions of chemically modified mRNAs to prevent and treat widespread chronic diseases, including metabolic disorders, cancer vaccination and immunotherapy, musculoskeletal disorders, respiratory conditions, cardiovascular diseases, and liver diseases.

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images.

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support.