The role played by dopant ions for the production of broadband white light emission from metal oxide nano-powders under laser diode excitation

We report on the role played by dopant ions to create broadband white light emission from metal oxide nano-powders. Y₂O₃ was doped with different rare earth ions (Nd³⁺, Er³⁺, Yb³⁺) and Cr³⁺ ion to investigate their role on the production of white light emission. We have determined that the dopant ions have a facilitator but not a decisive role on the production of the white light emission by doing measurements such as reflectance, white light emission, excitation power dependency and color quality parameters. All measurements showed that the production of white light does not depend on the amount and the type of the dopant ions.     

A novel adaptive deadbeat- based control for load frequency control of low inertia system in interconnected zones north and south of Scotland

An adaptive deadbeat (ADB) controller was developed to investigate its capability in providing a fast frequency response to an electrical power system. This controller was developed to meet the requirements of the National Grid System Operability Framework (SOF), which requires frequency to be accelerated in line with a fast rate of change of frequency (RoCoF) when a high rate of nonsynchronous machines are presented. The controller’s parameters were optimized using particle swarm optimization (PSO) to ensure a robust operation and to maintain the proper operation of the power system. The design of the ADB controller was then integrated with the multiarea model of the north and south zones of Scotland. This model was developed in order to conform to the future energy requirements scenario stated by National Grid whereby regional control can be provided in both the north and south of Scotland. In comparison with the standard PI and Fuzzy-PI controllers used in the four highlighted case studies, it was shown that the ADB controller was able to significantly reduce the RoCoF and deviation of frequency when a sudden loss of generation occurred in a low inertia zone. The ADB also showed high robustness against a wide range of operating conditions.     

Adapting the Technology Acceptance Model to evaluate the innovative potential of e-learning systems

This paper describes an experience where the Technology Acceptance Model (TAM) has been adapted for use in the evaluation of methodological and technological innovations determined by the introduction of a new e-learning system in an Italian online university. While the original TAM allows one to assess acceptance and adoption of a new technology, in this case there was also a need to consider all the phases of use of the system (course design, running and evaluation), all the users of the system (students, teachers and e-learning management), and all the system’s components (the e-learning platform, the learning resources and mostly the underlying pedagogical approach). The resulting model, which is an extension of the original TAM, is a three-dimensional one, with three aspects to be considered on each axis (phases of use, users and components). For each of the 27 combinations of these aspects, indicators of usefulness and ease-of-use have been identified. When available, data concerning actual use (derived from the tracking functions of the platform) and effectiveness (based on teachers’ adoption of new tools and students’ learning outcomes) have also been used to complement the data.     

Optimization of millimeter wave system in ITER Equatorial EC H&CD Launcher

A high power (20 MW) and CW millimeter wave (mm-wave) injection is planned for Electron Cyclotron Heating and Current Drive (EC H&CD) in ITER. An optimization of the mm-wave system for the ITER EC H&CD Equatorial Launcher (EL) is performed. The optimization of the system is aimed to obtain the maximum transmission efficiency on the condition that 1.8 MW injection per waveguide, ∼20 cm in beam radius at the resonance layer and narrow opening of the Blanket Shielding Module (BSM). The transmission efficiency of 99.1% from the end of the waveguide inside the launcher to the output of the BSM is achieved.The mm-wave propagation with high order modes is also calculated by using an experimentally obtained high power mm-wave beam pattern that includes 95%HE₁₁, 0.6%LP₁₁, 0.2%LP₀₂ and 4.2% other higher order modes. The analysis predicts the 1–2% additional loss will be induced by the high order modes.     

Modelling the compaction curve of fine-grained soils

A theoretical model for the compaction curve of fine-grained soils at various compaction efforts for the entire range of water content is presented in this study. The prediction method is based on the assumption that the compaction curve represents the state surface at the yield state in an unsaturated condition. Thus, for each applied compaction effort, the compaction curve relates to one yielding point on the saturated normal consolidation line (NCL). For a given soil, the model requires the NCL, S_rc, and one point from any compaction curve to predict the compaction curves for different compaction efforts. Moreover, the lines of equal suctions on the compaction curves can be determined if the SWCC, the wetting path, is known. The model introduced here provides additional theoretical understanding of the soil׳s volume change behavior of the compaction curve. The model was verified in two ways: first it was verified quantitatively, by experimental results, and second it was verified qualitatively, by examining the relationships from other models in the literature. The model was further applied to experimental data reported in the literature on previous static and dynamic compaction tests. The results show that the model fits the experimental data very well. Finally, a simple chart, based on this model and using only liquid limits, is presented to estimate γ_dmax and OMC quickly.     

Controllable ZnO nanostructures evolution via synergistic pulsed laser ablation and hydrothermal methods

We report the morphology-controlled ZnO nanostructures (ZNSs) evolution synthesized via a novel and facile technique at different growth times, where the pulse laser ablation in liquid (PLAL) is creatively combined with hydrothermal (H) method (hereafter called PLAL-H technique). Four types of ZNSs with varying sizes and shapes such as tapers, multipods, flowers, and hollow flowers are produced on Si substrate via PLAL-H technique. Furthermore, multipod- and flower-like ZNSs are grown using direct hydrothermal method to compare them with the one obtained via synergistic effects of PLAL-H method. This catalyst-free fabrication method is not only cost-effective but greatly useful for the rapid production of different quality of ZNSs at low temperature. ZNSs synthesized under prolonged growth time (60 min) exhibited structural deformation. Growth technique and time dependent morphology, structure, composition, and optical properties of these as-grown ZNSs are characterized using FESEM, X-ray diffraction, FTIR, photoluminescence, and UV–vis measurements. Synthesized ZNSs revealed excellent crystallinity and growth process dependent variation in the physical and optical features. The ZNSs growth mechanism is understood. Excellent features of the results demonstrate that this synergized new growth technique may constitute a basis for modifying the morphology, sizes, and optical properties of ZNSs in a controllable manner useful for diverse applications.     

Feature extraction and classification for detecting the thermal faults in electrical installations

This paper proposed an effort to investigate the suitability of input features and classifier for identifying thermal faults within electrical installations. The features are extracted from the thermal images of electrical equipment and classified using a multilayered perceptron (MLP) artificial neural network and support vector machine (SVM). In the experiments, the classification performances from various input features are evaluated. The commonly used classification performance indices, including sensitivity, specificity, accuracy, area under curve (AUC), receiver operating characteristic (ROC) and F-score are employed to identify the most suitable input feature as well as the best configuration of classifiers. The experimental results demonstrate that the combination of features set T_max, T_delta and DT_bg produce the best input feature for thermal fault detection. In addition, the implementation of SVM using radial basis kernel function (RBF) produces slightly better performance than the MLP artificial neural network.     

Climate Change and Future Long-Term Trends of Rainfall at North-East of Iraq

Iraq is facing water shortage problem despite the presence of the Tigris and Euphrates Rivers. In this research, long rainfall trends up to the year 2099 were studied in Sulaimani city northeast Iraq to give an idea about future prospects. The medium high （A2） and medium low B2 scenarios have been used for purpose of this study as they are more likely than others scenarios, that beside the fact that no climate modeling canter has performed GCM （global climate model） simulations for more than a few emissions scenarios （HadCM3 has only these two scenarios） otherwise pattern scaling can be used for generating different scenarios which entail a huge uncertainty. The results indicate that the average annual rainfall shows a significant downward trend for both A2 and B2 scenarios. In addition, winter projects increase/decrease in the daily rainfall statistics of wet days, the spring season show very slight drop and no change for both scenarios. However, both summer and autumn shows a significant reduction in maximum rainfall value especially in 2080s while the other statistics remain nearly the same. The extremes events are to decrease slightly in 2080s with highest decrease associated with A2 scenario. This is due to the fact that rainfall under scenario A2 is more significant than under scenario B2. The return period of a certain rainfall will increase in the future when a present storm of 20 year could occur once every 43 year in the 2080s. An increase in the frequency of extreme rainfall depends on several factors such as the return period, season of the year, the period considered as well as the emission scenario used.     

A comparison between state-of-the-art and neural network modelling of solar collectors

The state-of-the-art modelling of solar collectors as described in the European Standard EN 12975-2 is based on equations describing the thermal behaviour of the collectors by characterising the physical phenomena, e.g. transmission of irradiance through transparent covers, absorption of irradiance by the absorber, temperature dependent heat losses and others. This approach leads to so called collector parameters that describe these phenomena, e.g. the zero-loss collector efficiency η₀ or the heat loss coefficients a₁ and a₂.Although the state-of-the-art approach in collector modelling and testing fits most of the collector types very well there are some collector designs (e.g. “Sydney” tubes using heat pipes and “water-in-glass” collectors) which cannot be modelled with the same accuracy than conventional collectors like flat plate or standard evacuated tubular collectors. The artificial neural network (ANN) approach could be an appropriate alternative to overcome this drawback.To compare the different approaches of modelling investigations for a conventional flat plate collector and an evacuated “Sydney” tubular collector have been carried out based on performance measurements according to the European Standard EN 12975-2. The investigations include the parameter identification (training), the comparisons between measured and modelled collector output and the simulated yearly collector yield for a solar domestic hot water system for both models.The obtained results show better agreement between measured and calculated collector output for the artificial neural network approach compared with the state-of-the-art modelling. The investigations also show that for the ANN approach special test sequences have to be designed and that the determination of the ANN that fits the thermal performance of the collector in the best way depends significantly on the expertise of the user.Nevertheless artificial neural networks have the potential to become an interesting alternative to the state-of-the-art collector models used today.     

Electrochemical energy storage applications of carbon nanotube supported heterogeneous metal sulfide electrodes

Electrochemical system centered on hierarchically carbon-based metal sulphide assemblies are of great fame for competent supercapacitors. Herein, the synthesis of a hierarchical CNT anchored MoS₂–Bi₂S₃ nanocomposite is reported. Attractively, a vertically grown Bi₂S₃ nanorods supported on MoS₂ nanosheets with carbon framework acts as a highly effective electrode in alkaline electrolyte. More interestingly, this hierarchical structure and synergetic upshot of CNT and composites provide excess coverage of active sites with improved conductivity and stability. Advancing from the physical and compositional properties of nanocomposites, the specific capacitance of MoS₂–Bi₂S₃@CNT composites is measured to be 1338 F/g at 10 mV/s, columbic efficiency of 99.5% over 10000 cycles and long-term stability (60% retention at 0.5 A g^?1 over 2000 cycles and 34.6% up to 10000 cycles). The success of this MoS₂–Bi₂S₃@CNT composite may be attributed to the structural advantages, admirable cyclic stability, and better capacitance retention for supercapacitor applications.