Cu2ZnSnSe4 (CZTSe) thin film solar cells are promising emergent photovoltaic technologies based on low-bandgap absorber layer with high absorption coefficient. To reduce optical losses in such devices and thus improve their efficiency, numerical simulations of CZTSe solar cells optical characteristics can be performed based on individual optical properties of each layer present in the cell structure. In this contribution, we have first determined the optical coefficients of individual thin films (i.e., (n, k) of the absorber, buffer, and window layers) to build a realistic model simulating the

Advanced oxidation processes (AOPs) using chemically prepared and green synthesized nano zero-valent iron (nZVI) has proved to be effective in removing organic contaminants. The green synthesized nano iron (GT-nZVI) was prepared by using extracted black tea reducing agent. The prepared nZVI particles were characterized using X-ray powder diffraction (XRD), scanning electron microscopy (SEM), and Energy Dispersive X-ray Analysis (EDAX) analysis. The main purpose of this study is to compare between chemically prepared nZVI and GT-nZVI in the biological oxygen demand (BOD) removal efficiency from

This study attempted to investigate the removal of biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), ammonia-nitrogen (NH4-N), and total phosphorus (TP) from secondary treated domestic wastewater using algal photo-bioreactor. A semi-pilot plant was constructed and operated for 112 days under continuous flow conditions at Zenin wastewater treatment plant, Giza, Egypt (WWTP) which consists of an algal photo-bioreactor with an effective volume of 188 litters and a lamella settler. The removal of the studied parameters was studied at different hydraulic

Wireless Sensor Network (WSN) has growth rapidly over the past years. As it is now applied in many fields as in health care systems, home automation, security surveillance, disaster management and more. Due to the high demand on WSN, it is important to find a solution for one of the major challenges in WSN which is the energy consumption of its battery operated sensor device. So in this paper we propose to use Software Defined technology into WSN to enhance network management and to prolong network lifetime. As the main feature of Software Defined Network (SDN) is the centralization of control

The present study focuses on the problem of automatic generation control (AGC) by employing the design of fractional-order proportional–integral– derivative controller (FOPID). A hydrothermal power system with governor dead band (GDB) and generation rate constraint (GRC) is considered for investigation. FOPID controller optimal values are obtained by using teacher learning-based optimization (TLBO) technique, and the employed objective function is integral time-multiplied absolute error (ITAE). The supremacy of the proposed controller is also shown by comparing with PID controller. Further

A technique for DPSK receiver-sensitivity improvement is demonstrated using numerical simulations. It is based on reshaping and reamplifying of received 80 Gbit/s DPSK using an SOA before a one bit delay interferometer. The SOA re-amplifies data without adding amplitude or differential phase noise due to its gain-compression. The system is tested using 231-1 PRBS RZ-DPSK (NRZ-DPSK) loaded with both phase and amplitude noise. It shows 2dB (1.7dB) quality-factor improvement. The estimated BER by error-counting shows receiver-sensitivity improvement of ≅3dB in case of single-ended detection and

Heart segmentation in Cardiac MRI images is a fundamental step to quantify myocardium global function. In this paper, we introduce a pipeline for heart localization and segmentation that is fast and robust even in the apical slices that have small myocardium. Also, we propose an enhancement to the popular U-Net architecture for segmentation. The proposed method utilizes the aggregation of different feature scales from the image by using the inception block along with the multi-gate block that propagates the multi-scale context of the supplied data where the heart is subject to changes in scale

Malignant Tumors are developed over several years due to unknown biological factors. These biological factors induce changes in the body and consequently, they lead to Malignant Tumors. Some habits and behaviors initiate these biological factors. In effect, the immune system cannot recognize a Malignant Tumor as foreign tissue. In order to discover a fascinating pattern of these habits, behaviors, and diseases and to make effective decisions, different machine learning techniques should be used. This research attempts to find the association between normal proteins (environmental factors) and

A multi-unit resource allocation system usually contains several processes and a number of resources with multiple units. Due to the competition for shared resources in these systems, deadlocks may occur. Recently, researchers have shown an increased awareness in deadlock control strategies for such a kind of systems without considering the dynamic changes such as processing failures and rework by using the Petri net paradigm. This article reports a new strategy for deadlock analysis and control in reconfigurable multi-unit resource systems (MRSs). We discuss a generalized class of Petri nets

One of the key challenges in applying deep learning to solve real-life problems is the lack of large annotated datasets. Furthermore, for a deep learning model to perform well on the test set, all samples in the training and test sets should be independent and identically distributed (i.i.d.), which means that test samples should be similar to the samples that were used to train the model. In many cases, however, the underlying training and test set distributions are different. In such cases, it is common to adapt the test samples by transforming them to their equivalent counterparts in the