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In this article, a novel design of a compact planar Ultra Wideband (UWB) monopole antenna with dual band notched characteristics has been presented. The antenna has a unique structure meeting UWB standards. The proposed antenna is fed through a 50 normalΩ micro strip feed line, whereas a better impedance is achieved by truncating the ground plane. The dual notches are achieved by incorporating a meandered slot on the radiator patch and U‐slot in the feed line. The antenna is fabricated on FR‐ 4 substrate having a compact size of  33 × 32 × 1.5 normalmnormalm3. A good agreement is observed between measured and simulated results. The radiation pattern is omnidirectional in H‐plane, whereas dipole like radiation pattern is observed in the E‐plane. The gain of antenna is stable across the whole UWB except at notched bands. It is shown that any UWB can be notched at any desired frequency band by incorporating appropriate slot length.

In this study, a novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between the electricity bill and user-discomfort in the smart grid. The proposed framework is an integrated framework of artificial neural network (ANN) based forecast engine and our proposed day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) based home energy management controller (HEMC). The forecast engine forecasts price-based demand response (DR) signal and energy consumption patterns and HEMC schedules smart home appliances under the forecasted pricing signal and energy consumption pattern for efficient energy management. The proposed DA-GmEDE based strategy is compared with two benchmark strategies: day-ahead genetic algorithm (DA-GA) based strategy, and dayahead game-theory (DA-game-theoretic) based strategy for performance validation. Moreover, extensive simulations are conducted to test the effectiveness and productiveness of the proposed DA-GmEDE based strategy for efficient energy management. The results and discussion illustrate that the proposed DA-GmEDE strategy outperforms the benchmark strategies by 33.3% in terms of efficient energy management. INDEX TERMS Advanced metering infrastructure, artificial neural networks, demand response, energy management, grey wolf modified enhanced differential evolution algorithm, smart grid. NOMENCLATURE SG Smart grid ANN Artificial neural network HEMS Home energy management system MILP Mixed integer linear programming BBSA Binary backtracking search algorithm The associate editor coordinating the review of this manuscript and approving it for publication was Behnam Mohammadi-Ivatloo .

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With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.

Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.

MgO is an attractive choice for carcinogenic cell destruction in photodynamic therapy, as confirmed by manifold analysis. The prime focus of the presented research is to investigate the toxicity caused by morphologically different MgO nanostructures obtained by annealing at various annealing temperatures. Smart (stimuli-responsive) MgO nanomaterials are a very promising class of nanomaterials, and their properties can be controlled by altering their size, morphology, or other relevant characteristics. The samples investigated here were grown by the co-precipitation technique. Toxicity-dependent parameters were assessed in a HeLa cell model after annealing the grown samples at 350 °C, 450 °C, and 550 °C. After the overall characterization, an analysis of toxicity caused by changes in the MgO nanostructure morphology was tested in a HeLa cell model using a neutral red assay and microscopy. The feasibility of using MgO for PDT was assessed. Empirical modelling was applied to corroborate the experimental results obtained from assessing cell viability losses and reactive oxygen species. The results indicate that MgO is an excellent candidate material for medical applications and could be utilized for its potential ability to upgrade conventionally used techniques.

Radio refractivity plays a significant role in the development and design of radio systems for attaining the best level of performance. Refractivity in the troposphere is one of the features affecting electromagnetic waves, and hence the communication system interrupts. In this work, a modified artificial neural network (ANN) based model is applied to predict the refractivity. The suggested ANN model comprises three modules: the data preparation module, the feature selection module, and the forecast module. The first module applies pre-processing to make the data compatible for the feature selection module. The second module discards irrelevant and redundant data from the input set. The third module uses ANN for prediction. The ANN model applies a sigmoid activation function and a multi-variate auto regressive model to update the weights during the training process. In this work, the refractivity is predicted and estimated based on ten years (2002–2011) of meteorological data, such as the temperature, pressure, and humidity, obtained from the Pakistan Meteorological Department (PMD), Islamabad. The refractivity is estimated using the method suggested by the International Telecommunication Union (ITU). The refractivity is predicted for the year 2012 using the database of the previous ten years, with the help of ANN. The ANN model is implemented in MATLAB. Next, the estimated and predicted refractivity levels are validated against each other. The predicted and actual values (PMD data) of the atmospheric parameters agree with each other well, and demonstrate the accuracy of the proposed ANN method. It was further found that all parameters have a strong relationship with refractivity, in particular the temperature and humidity. The refractivity values are higher during the rainy season owing to a strong association with the relative humidity. Therefore, it is important to properly cater the signal communication system during hot and humid weather. Based on the results, the proposed ANN method can be used to develop a refractivity database, which is highly important in a radio communication system.