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Smart Home Energy Management Systems (HEMS) constitute a vital necessity for optimizing electricity usage and saving energy in smart grids. However, these systems rely on dynamic factors that are stochastic and difficult to predict, such as the load consumption and electricity prices. Therefore, constructing an efficient control system for residential buildings requires an accurate prediction process of the associated parameters. This paper proposes an integrated predictive control system that consists of both predictive model and Demand Response (DR) scheme to predict and control the daily electricity usage in the residential sector. First, a Long Short-Term Memory-based (LSTM) optimized predictive model is implemented for predicting both the hourly load consumption and electricity price for a typical smart home. Then, the predicted data are transmitted to a DR fuzzy logic-based controller that can optimally schedule the home appliances usage. In comparison with the state-of-the-art prediction techniques for the residential load consumption and electricity price, the proposed LSTM predictive model outperforms Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Ensembled Boosted Trees (EBT). Moreover, the proposed DR-FIS controller has shown good results in terms of reducing the electricity cost by selecting the optimal time schedule. [ABSTRACT FROM AUTHOR]

• Assessment different structures of deep-stacked LSTM networks with performing six models that differ in the number and type of LSTM layers, in addition to different hyperparameter settings. • Implementing a hyperparameter optimization tool for each model. • Investigating the performance of the deep-stacked LSTM layer for both unidirectional LSTM and bidirectional LSTM. • Evaluating the best obtained unidirectional and bidirectional LSTM models in comparison with the traditional SVR model. Electricity load forecasting has been a substantial problem in the electric power system management process. An accurate forecasting model is essential to avoid imprecise predictions that can negatively affect system efficiency, economy, and sustainability. Among several prediction techniques, deep learning methods, especially the Long Short-Term Memory (LSTM), have been shown to have a superior performance in predicting the electricity load consumption. However, the consequences of using these methods have not fully been explored in terms of the various hidden layer structures, the depth of the model architecture, and the impact of tuning the model hyperparameters. In this paper, a systematic experimental methodology has been conducted to investigate the impact of using deep-stacked unidirectional (Uni-LSTM) and bidirectional (Bi-LSTM) networks on predicting electricity load consumption. In particular, two stacked configurations, which include two and three LSTM layers, are compared with the single-layered LSTM for both types to show the significant importance of adding the stacked layers. Moreover, for each proposed configuration, a hyperparameter optimization tool has been implemented to obtain the best model. The results indicate that the deep-stacked LSTM layers have no significant improvement in the prediction accuracy; nevertheless, they consume almost twice the time of the single-layered models. Also, the Bi-LSTM networks outperform the Uni-LSTM networks by 76.25%, 75.49%, and 75.35% in terms of Root Mean Square Error (RMSE), with respect to one, two, and three-layer model configurations, respectively. Furthermore, regarding the prediction accuracy comparison over the total tested period, the optimized Bi-LSTM model outperforms both the optimized Uni-LSTM model by 75.98%, 89.1%, and 89.37%, and the Support Vector Regression (SVR) model by 82.54%, 92.59%, and 92.89% in terms of (RMSE), the Mean Average Percentage Error (MAPE), and Mean Absolute Errors (MAE). [ABSTRACT FROM AUTHOR]

Background: Various populations of regulatory T cells play a central role in the development of peripheral tolerance to allergens. Culturing of CD4+ T cells isolated from peripheral blood of allergic patients with vitamin D induces the generation of stable IL-10 producing CD4+CD25+ Treg cells suppressing the proliferation of T helper cells obtained from the same patients. The immune regulatory role of vitamin D in allergic patients has been controversial and obviously needs a more clarifying research work. Aim of the work: to determine the percentage of induced T regulatory cells producing interleukin 10 after stimulation of T regulatory cells with cow milk allergen in the presence of vitamin D in culture. This aims to further in-vitro study the immune regulatory role of vitamin D in cow milk allergic patients. Results: there is association between decreased level of vitamin D and milk-allergy, as serum level of 25(OH) D3 was insufficient in 16 (80 %) patients (10-29.9 ng/ml) while 4 (20%) patients were sufficient (30-100 ng/ml). Addition of vitamin D, in culture, induces the production of CD4+ CD25hi Foxp3+ IL10+ . Treg cells within peripheral blood mononuclear cells (PMNCs) isolated from allergic children who had insufficient vitamin D, but not in allergic children who had normal level of vitamin D. Conclusion: this work provides further evidence for an important role of 1,25(OH)2D3 as an immune-modulatory molecule and suggests that supplementation of vitamin-D-deficient individuals, who are reported to have reduced numbers of circulating and Foxp3+ IL10+ Treg cells, may represent an attractive therapy for enhancing endogenous populations of Treg cells in allergy. [ABSTRACT FROM AUTHOR]

Cow's milk allergy (CMA) is known to be either IgE- or non-IgE mediated. Regulatory T (Treg) cell defect is involved in the pathogenesis of both types. Vitamin D has been suggested to improve the generation of allergen-specific Treg cell populations with the potential to provide safe and long-term alleviation of disease symptoms. This study aimed to assess Vitamin D status in children with physician-diagnosed CMA and to investigate the effect of in vitro cultivation with Vitamin D on the percentage of antigen-driven CD4+CD25highFoxp3+IL10+ Treg cells following in vitro stimulation of cells with cow's milk allergen in culture. This cross-sectional study included 20 children with CMA and 20 healthy age and sex-matched children as a control group. All patients were subjected to clinical evaluation, cow's milk skin prick test (SPT), cow's milk elimination and oral re-challenge test in patients with negative cow's milk SPT and in those with gastrointestinal presentation, measurement of serum Vitamin D level and assessment of the percentage of antigen-driven CD4+CD25highFoxp3+IL10+ Treg cells in response to stimulation with cow's milk allergen extract with and without Vitamin D in culture. Vitamin D deficiency was detected in 80% of children with CMA. Percentage of Foxp3+ and IL10+ co-expression on Treg cells was significantly increased after stimulation with cow's milk allergen extract in the presence of Vitamin D. A significant positive correlation was observed between serum Vitamin D level and percentage of antigen-driven CD4+CD25highFoxp3+IL10+ Treg cells as well as level of Foxp3+ and IL10+ co-expression on Treg cells at baseline (control cultures without stimulation) and after PBMCs stimulation with cow's milk allergen extract in the presence of Vitamin D. Re-stimulation with cow's milk allergen extract was performed in vitro in order to evaluate milk-induced immune stimulation and regulation. In conclusion, patients with CMA whether IgE- or non-IgE mediated had Vitamin D deficiency with a decreased number of CD4+CD25highFoxp3+IL10+ Treg cells which increased after in vitro addition of Vitamin D with increased Foxp3 and IL10 co-expression., (Copyright© by the Egyptian Association of Immunologists.)