Efficient Bifunctional Catalytic Electrodes with Uniformly Distributed NiN2 Active Sites and Channels for Long‐Lasting Rechargeable Zinc–Air Batteries

Freestanding bifunctional electrodes with outstanding oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) properties are of great significance for zinc–air batteries, attributed to the avoided use of organic binder and strong adhesion with substrates. Herein, a strategy is developed to fabricate freestanding bifunctional electrodes from the predeposited nickel nanoparticles (Ni‐NCNT) on carbon fiber paper. The steric effect of monodispersed SiO₂ nanospheres limits the configuration of carbon atoms forming 3D interconnected nanotubes with uniformly distributed NiN₂ active sites. The bifunctional electrodes (Ni‐NCNT) demonstrate ideal ORR and OER properties. The zinc–air batteries assembled with Ni‐NCNT directly exhibit extremely outstanding long term stability (2250 cycles with 10 mA cm^?2 charge/discharge current density) along with high power density of 120 mV cm^?2 and specific capacity of 834.1 mA h g^?1. This work provides a new view to optimize the distribution of active sites and the electrode structure.     

Optimal Implementation of Photovoltaic and Battery Energy Storage in Distribution Networks

Recently, implementation of Battery Energy Storage (BES) with photovoltaic (PV) array in distribution networks is becoming very popular in overall the world. Integrating PV alone in distribution networks generates variable output power during 24-hours as it depends on variable natural source. PV can be able to generate constant output power during 24-hours by installing BES with it. Therefore, this paper presents a new application of a recent metaheuristic algorithm, called Slime Mould Algorithm (SMA), to determine the best size, and location of photovoltaic alone or with battery energy storage in the radial distribution system (RDS). This algorithm is modeled from the behavior of SMA in nature. During the optimization process, the total active power loss during 24-hours is used as an objective function considering the equality and inequality constraints. In addition, the presented function is based on the probabilistic for PV output and different types of system load. The candidate buses for integrating PV and BES in the distribution network are determined by the real power loss sensitivity factor (PLSF). IEEE 69-bus RDS with different types of loads is used as a test system. The effectiveness of SMA is validated by comparing its results with those obtained by other well-known optimization algorithms.     

A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions, there is zero or no partial shading. But the changing irradiance causes a shading effect which is called Partial Shading. Many conventional and soft computing techniques have been in use to harvest solar energy. These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong. In this paper, a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning (OBL) to deal with partial shading conditions. Simulation studies on different cases of partial shading have proven this technique effective in attaining MPP.     

Photocatalytic activity and radiation-attenuation ability of copper ions surface-doped dysprosium oxide

Journal of Materials Science: Materials in Electronics - Herein, Cu ions surface-doped Dy2O3 photocatalyst (Cu–Dy2O3) is synthesized utilizing hydrothermal technique to degrade methyl violet...     

Cloud-based efficient scheme for handwritten digit recognition

Multimedia Tools and Applications - Handwritten character recognition has been acknowledged and achieved more prominent attention in pattern recognition research community due to enormous...     

Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling

Water Resources Management - The use of wavelet-coupled data-driven models is increasing in the field of hydrological modelling. However, wavelet-coupled artificial neural network (ANN) models...     

A new methodology for optimization and prediction of rate of penetration during drilling operations

Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations.     

A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

One of the undesirable phenomena in the surface mines, which results in various hazards for human and facilities, is flyrock. It seems that the careful study of the subject and its effects on the environment can affect the control of flyrock hazards in the studied area. Therefore, the use of intelligent models and methods which are capable of predicting and simulating the risk of flyrock can be considered as an appropriate solution in this regard. The current research was conducted using nonlinear models and Monte Carlo (MC) simulation. The data used in this study consist of 260 samples of rock thrown from a mine in Malaysia. The parameters used in these models include hole’s diameter (D), hole’s depth (HD), burden to spacing (BS), stemming (ST), maximum charge per delay (MC), and powder factor (PF). At first, multiple regression analysis (MRA) and artificial neural network (ANN) models were used in order to develop a non-linear relationship between dependent and independent parameters. The ANN model was an appropriate predictor of flyrock in the mine. Then using the best implemented model of ANN, the flyrock environmental phenomenon was simulated using MC technique. MC simulation showed a proper level of accuracy of flyrock ranges in the mine. Using this simulation, it can be concluded with 90% accuracy that the Flyrock phenomenon does not exceed 331 m. Under these conditions, this simulation can be used for various areas requiring risk assessment. Finally, a sensitive analysis was carried out on data. This analysis showed MC has the greatest effect on flyrock.     

Analysis of Protective Behavior and Security Incidents for Home Computers

This study analyzes the factors that affect security protective behavior and perceived security incidents. Protective behavior is found to have a positive impact on the perceived security incidents, especially for the more educated home computer user. Human factors such as “perceived barriers” (to use new security software tools), “self-efficacy” (confidence), and “cues to action” (awareness) are found to influence both the protective behavior and perceived security incidents.     

Tuning the interlaminar shear strength and thermo-mechanical properties of glass fiber composites by incorporation of (3-mercaptopropyl) trimethoxysilane-functionalized carbon black

The incorporation of functionalized nanoscale fillers into traditional glass fiber/unsaturated polyester (GF/UPE) composites provides a more robust mechanical attributes. The current study demonstrates the potential of 3-mercaptopropyl trimethoxysilane (MPTS)-functionalized carbon black (f-CB) for enhancing the thermo-mechanical properties of GF composites. The composites infused with 1, 3 and 5 wt% of pristine and MPTS-functionalized CB were fabricated by hand lay-up and hot press processing. Tensile testing, interlaminar shear strength (ILSS) testing and dynamic mechanical analysis were used to evaluate the performance of nanocomposites. Fourier transform infrared spectroscopy validated the MPTS functionalization of CB. Pristine CB-loaded nanocomposites exhibited marginal improvement in ultimate tensile strength (UTS), ILSS and thermo-mechanical properties. However, with the addition of f-CB, the improvement in all the studied properties was more substantial. The inclusion of 5 wt% f-CB increased the elastic modulus and UTS by 16 and 22%, respectively, whereas the ILSS was enhanced by 36%, in comparison to the neat GF composite. The scanning electron microscope analysis of fractured ILSS samples revealed better fiber-matrix adhesion and compatibility in f-CB-loaded nanocomposites. At the same filler weight percentage, the storage modulus at 25 °C was ~ 19% higher than that of neat composite. The f-CB inclusion resulted in increment of T _g by ~ 13 °C over the T _g of neat GF/UPE composite (~ 109 °C). These improvements were due to the chemical connection of f-CB to the UPE matrix and GF surface. With such improvements in thermal and mechanical properties, these nanocomposites can replace the conventional GF composites with prominent improvements in performance.