Effect of Al Doping on Structural, Electrical, Optical and Photoluminescence Properties of Nano-Structural ZnO Thin Films

The nano-structural Al-doped ZnO thin films of different morphologies deposited on glass substrate were successfully fabricated at substrate temperature of 350 C by an inexpensive spray pyrolysis method. The structural, electrical, optical and photoluminescence properties were investigated. X-ray diffraction study revealed the crystalline wurtzite (hexagonal) structure of the films with nano-grains. Scanning electron mi- croscopy (SEM) micrographs indicated the formation of a large variety of nano-structures during film growth. The spectral absorption of the films occurred at the absorption edge of ～410 nm. In the present study, the optical band gap energy 3.28 eV of ZnO decreased gradually to 3.05 eV for 4 mol% of Al doping. The deep level activation energy decreased and carrier concentrations increased substantially with increasing doping. Exciting with the energy 3.543 eV (λ=350 nm), a narrow and a broad characteristic photoluminescence peaks that correspond to the near band edge (NBE) and deep level emissions (DLE), respectively emerged.     

Electrosynthesis of hierarchical Cu2O–Cu(OH)2 nanodendrites supported on carbon nanofibers/poly(para-phenylenediamine) nanocomposite as high-efficiency catalysts for methanol electrooxidation

The development of highly efficient catalysts using inexpensive and earth-abundant metals is a crucial factor in a large-scale commercialization of direct methanol fuel cells (DMFCs). In this study, we explored a new catalyst based on copper nanodendrites (CuNDs) supported on carbon nanofibers/poly (para-phenylenediamine) (CNF/PpPD) nanocomposite for methanol oxidation reaction (MOR). The catalyst support was prepared on a carbon paste electrode by electropolymerization of para-phenylenediamine monomer on a drop-cast carbon nanofibers network. Afterwards, CuNDs were electrodeposited on the nanocomposite through a potentiostatic method. The morphology and the structure of the prepared nanomaterials were characterized by transmission electron microscope, scanning electron microscope, energy dispersive X-ray, X-ray diffraction, and X-ray photoelectron spectroscope. The results suggested that a three-dimensional nanodendritic structure consisting of Cu₂O and Cu(OH)₂ formed on the hybrid CNF/PpPD nanocomposite. The catalytic performance of CuNDs supported on CNF, PpPD and CNF/PpPD was evaluated for MOR under alkaline conditions. The CNF/PpPD/CuNDs exhibits a highest activity (50 mA cm^?2) and stability toward MOR over 6 h, with respect to CNF/CuNDs (40 mA cm^?2) and PpPD/CuNDs (36 mA cm^?2). This inexpensive catalyst with high catalytic activity and stability is a promising anode catalyst for alkaline DMFC applications.     

Characteristics of lightning surges induced in telecommunication center in tropical area

The characteristics of lightning-induced surges in telecommunication equipment due to a direct lightning strike at a telecommunication center building or tower should be investigated to maintain the reliability of advanced telecommunication systems. In particular, investigations in tropical areas are important because these areas have many thunderstorm days. We observed lightning surges induced in a telecommunication building in Kuala Lumpur, Malaysia. The results show that the peak value occurrence frequency and waveforms of direct strike lightning surges were almost the same as those in a temperate area. The peak current relationships between the cables in the building and the tower legs indicate a strong correlation between the current at the tower legs, waveguide, power line, and outer and inner conductors of the coaxial cables. However, the peak value correlations between the tower leg currents and interface cable voltages were not strong. Based on the observation results, we obtained the correlation factors between the peak value at the observation point and the tower legs, and calculated the peak value at the interface cables as a function of the number of thunderstorm days.     

Empirical study on multiclass classification‐based network intrusion detection

Early and effective network intrusion detection is deemed to be a critical basis for cybersecurity domain. In the past decade, although a significant amount of work has focused on network intrusion detection, it is still a challenge to establish an intrusion detection system with a high detection rate and a relatively low false alarm rate. In this paper, we have performed a comprehensive empirical study on network intrusion detection as a multiclass classification task, not just to detect a suspicious connection but also to assign the correct type as well. To surpass the previous studies, we have utilized four deep learning models, namely, deep neural networks, long short‐term memory recurrent neural networks, gated recurrent unit recurrent neural networks, and deep belief networks. Our approach relies on the pretraining of the models by exploiting a particle swarm optimization–based algorithm for their hyperparameters selection. In order to investigate the performance differences, we also included two well‐known shallow learning methods, namely, decision forest and decision jungle. Furthermore, we used in our experiments four datasets, which are dedicated to intrusion detection systems to explore various environments. These datasets are KDD CUP 99, NSL‐KDD, CIDDS, and CICIDS2017. Moreover, 22 evaluation metrics are used to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. The results show a significant improvement in the detection of network attacks with our recommended approach.     

A hybrid list-based task scheduling scheme for heterogeneous computing

The Journal of Supercomputing - Efficient task scheduling is required to attain high performance in both homogeneous and heterogeneous computing systems. An application can be considered as a task...     

Characterization of soluble organic matter from Youssoufia rock phosphate

Extractable lipids represent 12% of the total organic matter contained in Youssoufla rock phosphate. This soluble organic matter corresponds mainly to free, directly extractable lipids. They are dominantly neutral lipids (neutral/acid fraction ratio: 1.5). Gas chromatographic-mass spectrometric studies showed for the alkanes a bimodal distribution C₁₅---C₂₁ and C₂₂---C₃₃ dominated, respectively, by C₁₈ and C₂₇. The dominant hopanes were identified under the thermodynamically stable αβ-form, αββ-steranes were also present with the non-diagenetic ααα- and βαα-steranes. Phytane and pristane deriving from the transformation of chlorophyll chains were also identified.Acids were saturated linear C₁₂---C₃₀ acids with a predominance of the C₁₆ and C₁₈ members, unsaturated C_16:1 and C_18:1 acids and even dicarboxylic acids. The results obtained suggest that the organic matter present in the phosphate has a marine origin with a significant continental contribution.     

The thermal effect on vibration of zigzag single walled carbon nanotubes using nonlocal Timoshenko beam theory

Based on nonlocal theory of thermal elasticity mechanics, a nonlocal elastic Timoshenko beam model is developed for free vibration analysis of zigzag single-walled carbon nanotube (SWCNT) considering thermal effect. The nonlocal constitutive equations of Eringen are used in the formulations. The equivalent Young’s modulus and shear modulus for zigzag SWCNT are derived using an energy-equivalent model. Results indicate significant dependence of natural frequencies on the temperature change as well as the chirality of zigzag carbon nanotube. These findings are important in mechanical design considerations of devices that use carbon nanotubes.     

A Numerical structural approach to surge detection and isolation in compression systems using fuzzy logic controller

Fuzzy control is a practical alternative for a variety of challenging control applications since it provides a convenient method for constructing nonlinear controllers via the use of heuristic information. This approach provides a formal methodology for representing, manipulating, and implementing a human’s heuristic knowledge about how to modeled and to control a system. This paper focuses on the application of fuzzy control approach to the surge phenomena in centrifugal compression system. Fuzzy controller is designed to consist of an active surge control and phase control without any explicit system models, but driven in the human thinking mechanism. This fuzzy control methodology suggested in this work reproduced well the main characteristics of the turbo compressor dynamic model developed by Moore and Gretzer and give place to a more precise and easy to handle representation. Application and simulation results are performed to demonstrate the effectiveness of this controller. Studies show that, the proposed controller provides good tracking control performance for the compression system at different operating conditions and moreover improves the dynamic performances of the system.     

Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks

Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input–output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export–import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption.