Comparative effects of Alpha Tocopherol and Ascorbic Acid on Chronic Stress Induced Neuropeptide Y Derangements

Background: Chronic stress decreases resilience of the body mainly due to hormonal imbalance. Neuropeptide Y-ergic system is abnormally regulated in chronic stress due to reduction-oxidation imbalance. The antioxidants such as alpha-tocopherol and ascorbic acid reduce this imbalance with positive effect on neuropeptide Y synthesis and release. This study was aimed to compare the protective effects of alpha-tocopherol and ascorbic acid on plasma neuropeptide Y levels in chronic stress.Material and Methods: This quasi-experimental study was done at Al-Nafees Medical College in collaboration with National Institute of Health Islamabad from January 2015 to January 2016 after taking institutional approval. Sixty male Sprague Dawley rats were obtained and divided equally into four groups; group I (control), group II (restraint stress group - chronic restraint stress six hours daily for 28 days), group III (restraint stress + alpha-tocopherol 50mg/kg body weight /day), and group IV (restraint stress + ascorbic acid 100mg /kg body weight /day). Cardiac puncture was done to obtain blood for biochemical analysis.Results: A significant decrease in plasma neuropeptide Y levels was seen in group II compared to group I, group III and group IV. However, alpha-tocopherol administration in group III showed positive effects on maintenance of plasma neuropeptide Y concentration with better p trend than that of ascorbic acid supplementation in group IV.Conclusions: Alpha-tocopherol supplementation has more potent effect than that of ascorbic acid on chronic restraint stress induced derangements in neuropeptide Y levels. It leads to less imbalance in neuropeptide Y levels during chronic stress.Key words: Ascorbic Acid, Alpha-Tocopherol, Chronic Stress, Neuropeptide

Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR)