Sol-gel technique is used to synthesize as-grown zinc oxide (ZnO) and iron-nickel (Fe-Ni) co-doped ZnO thin films deposited on glass substrates using dip coating technique. The structural properties and crystal imperfections of as-prepared thin films are investigated. We performed the structural analysis of films using X-ray diffraction (XRD). The XRD analysis reveal that the as-prepared films exhibit wurtzite structure. Furthermore, XRD-line profile analysis is performed to study the correlation between structural properties and imperfections of the nanocomposite thin films. The crystallite size and microstrains parameters are predicted using the Williamson–Hall method. We found that the crystallites size increases as the co-doped (Fe-Ni) concentration is increased. However, microstrains of the nanocomposite films decreases as (Fe-Ni) concentration is increased. The optical properties of the (Fe-Ni) co-doped nanocomposite films are investigated by performing UV-Vis (250 nm–700 nm) spectrophotometer measurements. We found that as the (Fe-Ni) concentration level is steadily increased, transmittance of the undoped ZnO thin films is decreased. Remarkably, refractive index of undoped ZnO thin films is found to exhibit values extending from 1.55 to1.88 that would increase as (Fe-Ni) concentration is increased.

Undoped ZnO and group III (B, Al, Ga, and In)-doped ZnO thin films at 3% doping concentration level are dip-coated on glass substrates using a sol-gel technique. The optical properties of the as-prepared thin films are investigated using UV–Vis spectrophotometer measurements. Transmittance of all investigated thin films is found to attain high values of ≥80% in the visible region. We found that the index of refraction of undoped ZnO films exhibits values ranging between 1.6 and 2.2 and approximately match that of bulk ZnO. Furthermore, we measure and interpret nonlinear optical parameters and the electrical and optical conductivities of the investigated thin films to obtain a deeper insight from fundamental and practical points of view. In addition, the structural properties of all studied thin film samples are investigated using the XRD technique. In particular, undoped ZnO thin film is found to exhibit a hexagonal structure. Due to the large difference in size of boron and indium compared with that of zinc, doping ZnO thin films with these two elements is expected to cause a phase transition. However, Al-doped ZnO and Ga-doped ZnO thin films preserve the hexagonal phase. Moreover, as boron and indium are introduced in ZnO thin films, the grain size increases. On the other hand, grain size is found to decrease upon doping ZnO with aluminum and gallium. The drastic enhancement of optical properties of annealed dip-synthesized undoped ZnO thin films upon doping with group III metals paves the way to tune these properties in a skillful manner, in order to be used as key candidate materials in the fabrication of modern optoelectronic devices.

As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.

In the Industrial Internet of Things (IIoT), non-orthogonal multiple access (NOMA) has emerged as a viable multiple access method due to its superior efficiency. In this paper, a new power allocation technique for NOMA-enabled IIoT devices is presented with trade-offs between increasing energy efficiency and decreasing power consumption. We present a joint optimization of transmission rate and energy harvesting in simultaneous wireless information and power transfer (SWIPT) NOMA-enabled IIoT devices. With the power splitting (PS) approach, we examine how to improve overall transmission rate and harvested energy, simultaneously, while fulfilling the minimum rate and harvested energy needs of each IIoT device in a SWIPT-enabled NOMA system. An objective function is established by adding transmission rates obtained from information decoding and the transformed throughput from energy harvesting. The combination of management approaches with Industry 4.0 technology provides a viable strategy to decrease industrial production’s energy use. Several performance metrics may be utilized to study manufacturing process optimization. The efficiency of production equipment may be measured by looking at the overall effectiveness (OE) of the equipment in use. We divide the non-convex optimization problem into two sub-problems, based on the Lagrangian duality method, and solve them to find the optimal solution for the non-convex problem. The approach is validated based on physical layer parameter settings that represent potential factory of the future scenarios. Simulation results confirm the effectiveness of the presented method in a SWIPT-enabled NOMA system, provide considerable performance gains over the classic rate maximization strategy, and demonstrate the energy efficiency of the presented method compared with the conventional system. The results show huge potential of our solutions to reduce the future huge energy demand related to factory automation.

In this paper, we address the problem of energy consumption associated with mixed signal components such as analog-to-digital components in millimeter-wave (mmWave) massive MIMO systems. We employ non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. The simultaneous wireless information and power transmission technology (SWIPT) will be used in mmWave massive Multiple-Input multiple-Output MIMO systems. The utilization of SWIPT contributes to prolonging the battery life of mobile users (MUs) and enhances the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). However, we initially designed a user grouping algorithm based on the affinity propagation clustering algorithm, which preferentially groups the user equipment (UE) based on their channel correlation and distance. Then, we design the analog RF precoder based on the selected user grouping for all beams, followed by a low-dimensional digital baseband precoder design to further mitigate inter-beam interference and maximize the achievable sum-rate for the considered system. Subsequently, we transform the original optimization problem into a joint power allocation and power-splitting maximization problem. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of coupled variables and inter-user interference. To cope with this problem, a decoupled approach is adopted, in which the power allocation and power splitting are separated, and the corresponding sub-problems are solved using the Lagrangian duality method. Simulation results confirm the effectiveness of the proposed method and demonstrate that the proposed method is near-optimal and enjoys higher spectrum and energy efficiency compared with state-of-the-art designs and the conventional SWIPT-enabled mmWave MIMO-NOMA system.

Problems with voltage and stability have arisen as a result of the dramatic development in renewable energy generating units, notably solar energy systems linked to low and medium voltage networks, and the influence of active loads that vary rapidly from time to time during the day. Because reactive power is directly proportional to voltage, its use to renewable energy producing units can only improve their efficiency. Better performance for these controllers is possible via the usage of several controller types. In this paper, we use a salp swarm optimization algorithm (SSA) to design fractional order proportional-integral (FO-PI) controllers, whose job it is to regulate the active and reactive power of solar inverters by compensating for the overvoltage and undervoltage presented by the inverters' ability to absorb and produce reactive power. After that, we compared the FO-PI controller's results to those of the standard PI controller. Grid-connected photovoltaic (PV) system modeling and simulation were performed using the MATLAB/ Simulink modeling and simulation tools. After that, the full PV system was simulated in the most likely situations across a range of grid and weather conditions. We may infer from the simulation results that this model is credible, reliable, and applicable to the analysis of grid-connected PV systems.