ABX₃ perovskite-based materials have attracted research attention in various electronic and optoelectronic applications. The ability to tune the energy band gap through various dopants makes perovskites a potential candidate in many implementations. Among various perovskite materials, BaTiO₃ has shown great applicability as a robust UV absorber with an energy band gap of around 3.2 eV. Herein, we provide a new sonochemical-assisted solid-phase method for preparing BaTiO₃ thin films that optoelectronic devices can typically be used. BaTiO₃ nano-powder and the thin film deposited on a glass substrate were characterized using physicochemical and optical techniques. In addition, the work demonstrated a computational attempt to optically model the BaTiO₃ from the atomistic level using density functional theory to the thin film level using finite difference time domain Maxwell's equation solver. Seeking repeatability, the dispersion and the extinction behavior of the BaTiO₃ thin film have been modeled using Lorentz-Dude (LD) coefficients, where all fitting parameters are listed. A numerical model has been experimentally verified using the experimental UV–Vis spectrometer measurements, recording an average root-mean-square error of 1.44%.

Utilizing artificial intelligent based algorithms in solving engineering problems is widely spread nowadays. Herein, this study provides a comprehensive and insightful analysis of the application of machine learning (ML) models to complex datasets in the field of solar cell power conversion efficiency (PCE). Mainly, perovskite solar cells generate three datasets, varying dataset size and complexity. Various popular regression models and hyperparameter tuning techniques are studied to guide researchers and practitioners looking to leverage machine learning methods for their data-driven projects. Specifically, four ML models were investigated; random forest (RF), gradient boosting (GBR), K-nearest neighbors (KNN), and linear regression (LR), while monitoring the ML model accuracy, complexity, computational cost, and time as evaluating parameters. Inputs' importance and contribution were examined for the three datasets, recording a dominating effect for the electron transport layer's (ETL) doping as the main controlling parameter in tuning the cell's overall PCE. For the first dataset, ETL doping recorded 93.6%, as the main contributor to the cell PCE, reducing to 79.0% in the third dataset.

Tunability is considered one of the main advantages of dye-sensitized solar cells (DSSCs) over conventional Si-based solar cells. In DSSCs, the thickness of the active layer can tune the transparency of the cell. This, however, creates a tradeoff between the transparency and the cell's efficiency. DSSCs with tailored transparency would be capable of being utilized in photovoltaic window applications, where Si cells are barely suitable. In this article, a new figure of merit is introduced to evaluate the performance of the DSSCs, named “TED efficiency.” The proposed TED parameter (i.e., the transparency, conversion efficiency, and diffused light efficiency) not only is based on the cell conversion efficiency but also considers the optical transparency as well as the cell performance under diffused light. TED efficiency measurements were performed on three different types of DSSCs: standard (DSSC-A), simple semitransparent (DSSC-B), and scattering-enhanced DSSCs (DSSC-C). A 22.5%-transparent DSSC has been fabricated by reducing the thickness of the porous TiO $_2$ layer. To optimize the TED efficiency of the semitransparent DSSC, an opaline SiO $_2$ layer is used. This layer enhances the forward scattering, acts as an UV protecting layer, and colorizes the semitransparent cell in a decorative manner. The TED efficiency for the scattering-enhanced DSSC showed a significant improvement to the standard DSSC as well as to the semitransparent cell.

Computation offloading has proven to be an effective method for facilitating resource-intensive tasks on IoT mobile edge nodes with limited processing capabilities. Additionally, in context of Mobile Edge Computing (MEC) systems, edge nodes can offload its computation-intensive tasks to a suitable edge server. Hence, they can reduce energy cost and speed up processing. Despite the numerous accomplished efforts in task offloading problems on the Internet of Things (IoT), this problem remains a research gap mainly because of its NP-hardness in addition to the unrealistic assumptions in many proposed solutions. In order to accurately extract information from raw sensor data from IoT devices deployed in complicated contexts, deep learning (DL) is a potential method. Therefore, in this paper, an approach based on deep reinforcement learning (DRL) will just be presented for optimize the offloading process for IoT in MEC environments. This approach can achieve the optimal offloading decision. A Markov Decision Problem (MDP) is used to formulate the offloading problem. Delay time and consumed energy are the main optimization targets in this work. The proposed approach has been verified using extensive simulations. Simulation results demonstrate that the proposed model effectively improves the MEC system latency, energy consumption, and significantly outperforms the Deep Q Networks (DQNs) and Actor Critic (AC) approaches.