Basic Concepts for Alternating Current through Educational Videos

This article highlights the importance of complementing classes through educational videos, especially in disciplines exclusively with lectures. This proposition is exemplified through basic concepts in alternating current for both single-phase and three-phase circuits, which are critical in the formation of electrical engineers, mechanics, chemists, etc. The main objective of conducting educational videos is to make learning more attractive and stimulate interest in acquiring the knowledge of certain topics, given their importance in professional life in different engineering areas. The videos, filmed in laboratory and of short duration, aim to complement and consolidate the content taught in the classroom lectures.     

Regulation of delta opioid receptors by delta9-tetrahydrocannabinol in NG108-15 hybrid cells

In this study we employed the neuroblastoma x glioma NG 108-15 cell line as a model for investigating the effects of long-term activation of cannabinoid receptors on delta opioid receptor desensitization, down-regulation and gene expression. Exposure of NG 108-15 cells to (-)-delta9-tetrahydrocannabinol (delta9-THC) reduced opioid receptor binding, evaluated in intact cells, by approximately 40-45% in cells exposed for 24 h to 50 and 100 nM delta9-THC and by approximately 25% in cells exposed to 10 nM delta9-THC. Lower doses of delta9-THC (0.1 and 1 nM) or a shorter exposure time to the cannabinoid (6 h) were not effective. Down-regulation of 6 opioid receptors was not observed in cells exposed for 24 h to pertussis toxin (PTX) and then treated for 24 h with 100 nM delta9-THC. In cells that were exposed for 24 h to the cannabinoid, the ability of delta9-THC and of the delta opioid receptor agonist [D-Ser2, Leu5, Thr6]enkephalin to inhibit forskolin-stimulated cAMP accumulation was significantly attenuated. Prolonged exposure of NG 108-15 cells to 100 nM delta9-THC produced a significant elevation of steady-state levels of delta opioid receptor mRNA. This effect was not observed in cells pretreated with PTX. The selective cannabinoid receptor antagonist SR 141716A blocked the effects elicited by delta9-THC on delta opioid receptor desensitization, down-regulation and gene expression; thus indicating that these are mediated via activation of cannabinoid receptors. These data demonstrate the existence, in NG 108-15 cells, of a complex cross-talk between the cannabinoid and opioid receptors on prolonged exposure to delta9-THC triggered by changes in signaling through Gi and/or G0-coupled receptors.     

An efficient method for calculation of electric field in three-corepower cables

In this paper we applied the superposition principle with a new basic conductor voltage condition for the computed electric field in three-core cables and the results obtained have higher accuracy and lower computational time than the previous works. The maximum potential error is not more than 0.050% and the maximum field deviation angle is not more than 0.011°. The electric field problem under arbitrary conductor voltage condition is reduced to a field computation for a new basic conductor voltage condition (0,1,-1) which reduces the computing time because it doesn't require the repetitive solution of the algorithm for different ωt values. With this new configuration, it is possible to take advantages of a symmetrical conductor arrangement reducing the number of charges necessary for the field calculation and the superposition principle is applied only two times     

Cost analysis in two-unit warm standby models with a regular repairman and patience time

The present paper deals with two-unit warm standby models having one regular and one expert repairman. It is assumed that the expert is called only if the regular repairman is not able to complete the repairs within some tolerable (patience) time. In model 1, it is assumed that the regular repairman can always do the repairs of the unit, failed from standby state. In model 2, the regular repairman sometimes may not be able to do the repairs of the above nature within some patience time and the expert is called for in case of standby failure also.The various measures of system effectiveness are calculated using semi-Markov and regenerative processes. Based on these measures a rule is developed when the services of the expert man should be utilized profitably.     

Latest developments in image processing for the next generation of devices with a view on DEMO

In magnetic confinement fusion devices the use of cameras, both visible and infrared, has increased very significantly in the last years. The large amount of data (in the range of tens of Gbytes per shot) and the difficulty of the analysis tasks (ambiguity, ill posed problems, etc.), require new solutions. The technology of Cellular Nonlinear Networks (CNNs) has been successfully applied to various tasks, from the real time hot spot detection to the automatic identification of instabilities. The accuracy obtained is comparable to the one of more traditional serial algorithms but the CCNs guarantee deterministic computational times independently from the image contents. Moreover the latest developments have allowed obtaining these results also in the case of space variant image analysis, without compromising the computational speed (of the order of ten thousand frames per second). The method of the optical flow permits to derive information about the speed of the objects moving in the frames of a single camera. The results of previous applications have been so successful that the approach has been extended to videos in compressed format (MPEG) to reduce the computational time to less than 7 ms per frame, preserving the accuracy of the results. Since in the next generation of devices, also the edge will emit Soft X-rays (SXR), new technologies are being developed to perform imaging over this region of the spectrum for a global view of the entire plasma column.     

Recent developments in data mining and soft computing for JET with a view on ITER

In order to handle the vast amount of information collected by JET diagnostics, which can exceed 10 Gbytes of data per shot, a series of new soft computing methods are being developed. They cover various aspects of the data analysis process, ranging from information retrieval to statistical confidence and machine learning. In this paper some recent developments are described. History effects in the plasma evolution leading to disruptions have been investigated with the use of Artificial Neural Networks. New image processing algorithms, based on optical flow techniques, are being used to derive quantitative information about the movement of objects like filaments at the edge of JET plasmas. Adaptive filters, mainly of the Kalman type, have been successfully implemented for the online filtering of MSE data for real time purposes.     

Postfault Analysis of Operation of Distance Protective Relays of Power Transmission Lines

This paper deals with the postfault analysis of the operation of distance protective relays from the line terminals. The signals of the digital distance relays are processed in the offline regime with the aim of evaluating the particular relay operation case. First, the program enables imitating the fault-loop impedance measurements of the distance relays. Second, the two-end unsynchronized measurements of the relays are considered. The unknown synchronization angle is determined by using the developed noniterative procedure. Then, the distance to fault is calculated. It is applied for calculating the actual values of impedance for the line sections. Comparing the results of these two ways performs analysis of operation of the relays under resistive fault cases. The described postfault analysis algorithm has been tested with the fault data obtained from versatile Alternative Transients Program-Electromagnetic Transients Program simulations. The examples are reported and discussed     

Performance and emission comparison of a supercharged dual-fuel engine fueled by producer gases with varying hydrogen content

This study investigated the effect of hydrogen content in producer gas on the performance and exhaust emissions of a supercharged producer gas–diesel dual-fuel engine. Two types of producer gases were used in this study, one with low hydrogen content (H₂ = 13.7%) and the other with high hydrogen content (H₂ = 20%). The engine was tested for use as a co-generation engine, so power output while maintaining a reasonable thermal efficiency was important. Experiments were carried out at a constant injection pressure and injection quantity for different fuel–air equivalence ratios and at various injection timings. The experimental strategy was to optimize the injection timing to maximize engine power at different fuel–air equivalence ratios without knocking and within the limit of the maximum cylinder pressure. Two-stage combustion was obtained; this is an indicator of maximum power output conditions and a precursor of knocking combustion. Better combustion, engine performance, and exhaust emissions (except NO_x) were obtained with the high H₂-content producer gas than with the low H₂-content producer gas, especially under leaner conditions. Moreover, a broader window of fuel–air equivalence ratio was found with highest thermal efficiencies for the high H₂-content producer gas.     

Impact of parametric variation on combustion characteristics of hydrogen-fueled strut based scramjet combustor at supersonic speed

Supersonic combustion ramjet engine is more fascinating among all the air-breathing engines. Due to its higher thrust to weight ratio, researchers are more interested to get the superior combustion performance at the optimum boundary conditions. The flow field characteristics and combustion performance have been analysed with the help of Ansys 14.0 software. Generic scramjet combustor of German Aerospace Center (DLR) has been taken into consideration for comparison purpose and off design analysis has been conducted to investigate and analyse the changes. Two dimensional compressible Reynolds Averaged Navier-Stokes (RANS) turbulence model has been opted with the finite-rate/eddy-dissipation reaction model. K-ε two equation turbulence model has been selected to reach up to reasonable accuracy. Validation of the present work has been done with the help of both non-reacting and reacting type data from open literature. To choose the appropriate meshing of the computational model three different types of mesh elements, that is, coarse, medium and fine has been analysed and also grid independence analysis is performed. The present article objective is to get optimum boundary condition by changing the incoming air temperature and pressure at constant Mach number to connect the bridge between incoming air temperature and pressure to the change in velocity throughout the combustion chamber. The detailed understanding and explanation have been done by varying the temperature range of incoming air because of its major impact on combustion performance. Nonetheless, a small variation of air pressure will also discuss to observe the parameters which majorly influence while doing performance analysis. At the end the Optimum boundary condition for the present computation work is observed to be at 833 K temperature with 115 299 Pa pressure.     

A systemic approach to classification for knowledge discovery with applications to the identification of boundary equations in complex systems

Classification, which means discrimination between examples belonging to different classes, is a fundamental aspect of most scientific and engineering activities. Machine Learning (ML) tools have proved to be very performing in this task, in the sense that they can achieve very high success rates. However, both “realism” and interpretability of their models are low, leading to modest increases of knowledge and limited applicability, particularly in applications related to nonlinear and complex systems. In this paper, a methodology is described, which, by applying ML tools directly to the data, allows formulating new scientific models that describe the actual “physics” determining the boundary between the classes. The proposed technique consists of a stack of different ML tools, each one applied to a specific subtask of the scientific analysis; all together they form a system, which combines all the major strands of machine learning, from rule based classifiers and Bayesian statistics to genetic programming and symbolic manipulation. To take into account the error bars of the measurements generating the data, an essential aspect of scientific inference, the novel concept of the Geodesic Distance on Gaussian manifolds is adopted. The properties of the methodology have been investigated with a series of systematic numerical tests for different types of classification problems. The potential of the approach to handle real data has been tested with various experimental databases, built using measurements collected in the investigations of complex systems. The obtained results indicate that the proposed method permits to find physically meaningful mathematical equations, which reflect the actual phenomena under study. The developed techniques therefore constitute a very useful information processing system to bridge the gap between data, machine learning models and scientific theories.