Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method

Thermal conductivity (TC) of a phase change material (PCM) may be enhanced by distributing nanostructured materials (NSMs) termed nano-PCM. It is critical to accurately estimate the TC of nano-PCM to assess heat transfer during phase transition processes, namely, solidification and melting. Here, we propose Gaussian process regression (GPR) strategies involving four various kernel functions (KFs) (including exponential (E), squared exponential (SE), rational quadratic (RQ), and matern (M)) to predict TC of n-octadecane as a PCM. The accessible computational techniques indicate the accuracy of our proposed GPR model compared to the previously proposed methods. In this research, the foremost forecasting strategy has been considered as a GPR method. This model consists of the matern KF whose R2 values of training and testing phases are 1 and 1, respectively. In the following, a sensitivity analysis (SA) is used to explore the effectiveness of variables in terms of outputs and shows that the temperature (T) of nanofluid (NF) is the most efficient input parameter. The work describes the physical properties of NFs and the parameters that should be determined to optimize their efficiency

Predicting Antecedents of Employee Smart Work Adoption Using SEM-Multilayer Perceptron Approach

The COVID-19 pandemic forced many organizations to move to telework and smart work (SW), and this practice is expected to continue even later in the postpandemic period. Hence, it is very important for managers and organizations to identify the motivating and deterrent factors in adopting smart work and plan to manage them. Therefore, the present study using an innovative methodology tried to identify and prioritize the factors influencing employee SW adoption. In the first stage, the conceptual model of the research was designed, inspired by the literature. In the next step, using structural equation modeling (SEM), antecedents whose effects on employee SW adoption were confirmed were identified. Finally, the output of the SEM model was considered as the input of the multilayer perceptron (MLP) model, which is an artificial neural network model, to determine the importance of each antecedent in the prediction of employee behavior. The present study provides quantitative empirical evidence that perceived value, institutional and technological support, perceived limited communication, and perceived cost are antecedents of employee SW adoption that are, respectively, important in predicting the behavioral intentions of employees in acceptance of SW. The findings of this study contribute to both the SW and the behavioral intention theory literature

Development of machine learning methods in hybrid energy storage systems in electric vehicles

The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies