Highly Efficient,Solution‐Processed,Single‐Layer,Electrophosphorescent Diodes and the Effect of Molecular Dipole Moment

A new family of highly soluble electrophosphorescent dopants based on a series of tris‐cyclometalated iridium(III) complexes (1–4) of 2‐(carbazol‐3‐yl)‐4/5‐R‐pyridine ligands with varying molecular dipole strengths have been synthesized. Highly efficient, solution‐processed, single‐layer, electrophosphorescent diodes utilizing these complexes have been prepared and characterized. The high triplet energy poly(9‐vinylcarbazole) PVK is used as a host polymer doped with 2‐(4‐biphenylyl)‐5‐(4‐tert‐butyl‐phenyl)‐1,3,4‐oxadiazole (PBD) for electron transport. Devices with a current efficiency of 40 cd A^?1 corresponding to an EQE of 12% can thus be achieved. The effect of the type and position of the substituent (electron‐withdrawing group (CF₃) and electron‐donating group (OMe)) on the molecular dipole moment of the complexes has been investigated. A correlation between the absorption strength of the singlet metal‐to‐ligand charge‐transfer (¹MLCT) transition and the luminance spectral red shift as a function of solvent polarity is observed. The strength of the transition dipole moments for complexes 1–4 has also been obtained from TD‐DFT computations, and is found to be consistent with the observed molecular dipole moments of these complexes. The relatively long lifetime of the excitons of the phosphorescence (microseconds) compared to the charge‐carrier scattering time (less than nanoseconds), allows the transition dipole moment to be considered as a “quasi permanent dipole”. Therefore, the carrier mobility is sufficiently affected by the long‐lived transition dipole moments of the phosphorescent molecules, which are randomly oriented in the medium. The dopant dipoles cause positional and energetic disorder because of the locally modified polarization energy. Furthermore, the electron‐withdrawing group CF₃ induces strong carrier dispersion that enhances the electron mobility. Therefore, the strong transition dipole moment in complexes 3 and 4 perturbs both electron and hole mobilities, yielding a reduction in exciton formation and an increase in the device dark current, thereby decreasing the device efficiency.     

Production of biodiesel from palm oil (Elaeis guineensis) using heterogeneous catalyst: An optimized process

Response surface methodology (RSM) based on central composite design (CCD) was used to optimize the three important reaction variables — methanol/oil molar ratio (x₁), reaction time (x₂) and amount of catalyst (x₃) for production of biodiesel from palm oil using KF/ZnO catalyst. Based on the CCD, a quadratic model was developed to correlate the reaction variables to the biodiesel yield. From the analysis of variance (ANOVA), the most influential factor on the experimental design response was identified. The predicted yield after process optimization was found to agree satisfactory with the experimental value. The optimum conditions for biodiesel production were found as follows: methanol/oil ratio of 11.43, reaction time of 9.72 h and catalyst amount of 5.52 wt%. The optimum biodiesel yield was 89.23%.     

A self-tuning fuzzy PI controller for TCSC to improve power system stability

In this paper, a self-tuning fuzzy PI controller (STFPIC) is proposed for thyristor-controlled series capacitor (TCSC) to improve power system dynamic performance. In a STFPIC controller, the output-scaling factor is adjusted on-line by an updating factor (α). The value of α is determined from a fuzzy rule-base defined on error (e) and change of error (Δe) of the controlled variable. The proposed self-tuning controller is designed using a very simple control rule-base and the most natural and unbiased membership functions (MFs) (symmetric triangles with equal base and 50% overlap with neighboring MFs). The comparative performances of the proposed STFPIC and the standard fuzzy PI controller (FPIC) have been investigated on two multi-machine power systems (namely, 4 machine, 2 area system and 10 machine 39 bus system) through detailed non-linear simulation studies using MATLAB/SIMULINK. From the simulation studies it has been found out that for damping oscillations, the performance of the proposed STFPIC is better than that obtained by the standard FPIC. Moreover, the proposed STFPIC as well as the FPIC have been found to be quite effective in damping oscillations over a wide range of operating conditions and are quite effective in enhancing the power carrying capability of the power system significantly.     

Numerical study of mixed convection nanofluid in an annulus enclosure between outer rotating cylinder and inner corrugation cylinder

Numerical investigations are presented for mixed convection problems in a concentric inner sinusoidal cylinder and an outer rotating circular cylinder, which were kept at constant hot and cold temperatures, respectively. The free space between the cylinders and the enclosure walls was filled with a water‐Cu nanofluid. The governing equations are formulated for velocity, pressure, and temperature formulation and are modeled in COMSOL5.2a, a partial differential equation solver based on the Galerkin finite element method. The governing parameters considered are the solid volume fraction, [0, 0.02, 0.04, and 0.06], Re (1, 25, 100, 200, and 300), and Ra (less than 10⁴), and the inner cylinder corrugation frequencies varied from (N = 3, 6, and 9). According to the calculations, the Reynolds number, the Rayleigh number, the nanoparticle volume fraction, and the number of corrugations play an important role of forming the stream and isothermal lines, the local and the average Nusselt number inside the annulus enclosure. The average Nusselt number decreases with increasing Reynolds number and the number of corrugations, while it increases as the Rayleigh number and the volume fraction increase.     

Optimization of Preparation Conditions to Control Structural Characteristics of Silicon Dioxide Nanostructures Prepared by Magnetron Plasma Sputtering

In this work, highly-pure silicon oxide nanostructures were prepared by a closed-field unbalanced magnetron plasma sputtering technique. These nanostructures were characterized by Fourier-transform infrared spectroscopy, UV-visible spectroscopy, x-ray diffraction, scanning electron microscopy, energy-dispersive x-ray spectroscopy and atomic force microscopy in order to determine the optimum preparation conditions. Minimum particle size of 20 nm was determined for the samples prepared at an inter-electrode distance of 4 cm, Ar:O₂ gas mixing ratio of 70:30, total gas pressure of 0.08 torr, discharge voltage of 2.5 kV, discharge current of 35 mA, anode temperature of 27 ^°C (room temperature) and cathode temperature of about 40 ^°C. These conditions are optimized to control the structural characteristics of such nanostructures and hence to satisfy certain requirements and purposes in spectroscopic and photonic applications of SiO₂ nanostructures.     

Synthesis,structural, linear and nonlinear optical properties of chromium doped SnO2 thin films

The present work concentrates on some physical investigation of the undoped and Cr doped SnO₂ thin films deposited onto precleaned glass substrates by the spray pyrolysis system. The physical properties of the undoped and Cr doped SnO₂ thin films were investigated by the X-ray diffraction (XRD), atomic force electron microscope (AFM), field emission scanning electron microscope (FESEM), four-probe method and double beam spectrophotometer. The undoped and Cr doped SnO₂ films display a polycrystalline nature with orthorhombic crystal structure. The linear optical constants energy gap E_g, refractive index n, absorption coefficient α, static refractive index n_o, oscillation energy E_o, dispersion energy E_d and the Urbach energy of the undoped and Cr doped SnO₂ thin films were evaluated. The investigated films exhibit a direct energy gap and their values decrease with the increasing of Cr doping content while the Urbach energy follows a reverse behavior. On the other hand, the nonlinear optical constants (third-order nonlinear susceptibility ${\mathit{\chi }}^{(3)}$ and nonlinear refractive index n₂) have been increased with increasing the Cr doping content. Finally, it has been found that the sheet resistance and conductivity of the synthesized thin films were enhanced by increasing the Cr doping content. The 5?wt% Cr doped SnO₂ thin film has a high value of the figure of merit among other films.     

Fused and Modified Evolutionary Optimization of Multiple Intelligent Systems Using ANN,SVM Approaches

The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop yield and quality. Any delay or errors in the diagnosis of the disease can lead to delays in the management of plant disease spreading and damage and related material losses. Comparative new heuristic optimization of swarm intelligence, Grasshopper Optimization Algorithm was inspired by grasshopper movements for their feeding strategy. It simulates the attitude and social interaction of grasshopper swarm in terms of gravity and wind advection. In the decision on features extracted by an accelerated feature selection algorithm, popular approaches such as ANN and SVM classifiers had been used. For the evaluation of the proposed model, different data sets of plant leaves were used. The proposed model was successful in the diagnosis of the diseases of leaves the plant with an accuracy of 99.41 percent (average). The proposed biologically inspired model was sufficiently satisfied, and the best or most desirable characteristics were established. Finally, the results of the research for these data sets were estimated by the proposed Fused Modified Grasshopper Optimization Algorithm (FMGOA). The results of that experiment were demonstrated to allow classification models to reduce input features and thus to increase the precision with the presented Modified Grasshopper Optimization Algorithm. Measurement and analysis were performed to prove the model validity through model parameters such as precision, recall, f-measure, and precision.     

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.