This work presents a novel technique for the detection of oil aging in electrical transformers using a single mode optical fiber sensor based on surface plasmon resonance (SPR). The aging of insulating oil is a critical issue in the maintenance and performance of electrical transformers, as it can lead to reduce insulation properties, increase risk of electrical breakdown, and decrease operational lifespan. Many parameters are calculated in this study in order to examine the efficiency of this sensor like sensitivity (S), signal to noise ratio (SNR), resolution (refractive index unit) and figure of merit (FOM) and the values are for figure of merit is 11.05, the signal to noise ratio is 20.3, the sensitivity is 6.63, and the resolution is 3

Researchers employ behavior based malware detection models that depend on API tracking and analyzing features to identify suspected PE applications. Those malware behavior models become more efficient than the signature based malware detection systems for detecting unknown malwares. This is because a simple polymorphic or metamorphic malware can defeat signature based detection systems easily. The growing number of computer malwares and the detection of malware have been the concern for security researchers for a large period of time. The use of logic formulae to model the malware behaviors is one of the most encouraging recent developments in malware research, which provides alternatives to classic virus detection methods. To address the l

This paper present a simple and sensitive method for the determination of DL-Histidine using FIA-Chemiluminometric measurement resulted from oxidation of luminol molecule by hydrogen peroxide in alkaline medium in the presence of DL-Histidine. Using 70?l. sample linear plot with a coefficient of determination 95.79% for (5-60) mmol.L-1 while for a quadratic relation C.O.D = 96.44% for (5-80) mmol.L-1 and found that guadratic plot in more representative. Limit of detection was 31.93 ?g DL-Histidine (S/N = 3), repeatability of measurement was less that 5% (n=6). Positive and negative ion interferances was removed by using minicolume containing ion exchange resin located after injection valve position.

An automatic text summarization system mimics how humans summarize by picking the most ‎significant sentences in a source text. However, the complexities of the Arabic language have become ‎challenging to obtain information quickly and effectively. The main disadvantage of the ‎traditional approaches is that they are strictly constrained (especially for the Arabic language) by the ‎accuracy of sentence feature ‎functions, weighting schemes, ‎and similarity calculations. On the other hand, the meta-heuristic search approaches have a feature tha

The development of information systems in recent years has contributed to various methods of gathering information to evaluate IS performance. The most common approach used to collect information is called the survey system. This method, however, suffers one major drawback. The decision makers consume considerable time to transform data from survey sheets to analytical programs. As such, this paper proposes a method called ‘survey algorithm based on R programming language’ or SABR, for data transformation from the survey sheets inside R environments by treating the arrangement of data as a relational format. R and Relational data format provide excellent opportunity to manage and analyse the accumulated data. Moreover, a survey syste

This paper focuses on the optimization of drilling parameters by utilizing “Taguchi method” to obtain the minimum surface roughness. Nine drilling experiments were performed on Al 5050 alloy using high speed steel twist drills. Three drilling parameters (feed rates, cutting speeds, and cutting tools) were used as control factors, and L9 (3³) “orthogonal array” was specified for the experimental trials. Signal to Noise (S/N) Ratio and “Analysis of Variance” (ANOVA) were utilized to set the optimum control factors which minimized the surface roughness. The results were tested with the aid of statistical software package MINITAB-17. After the experimental trails, the tool diameter was found as the most important facto

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob