Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.
A new method for construction ion-selective electrode (ISE) by heating reaction of methyl orange with ammonium reineckate using PVC as plasticizer for determination methyl orange and determination Amitriptyline Hydrochloried drug by formation ion-pair on electrode surface . The characteristics of the electrode and it response as following : internal solution 10-4M , pH (2.5-5) ,temperature (20-30) and response time 2 sec. Calibration response for methyl orange over the concentrationrange 10-3 -10-9 M with R=0.9989 , RSD%=0.1052, D.O.L=0.315X10-9 MEre%=(-0.877- -2.76) , Rec%.=(97.230 -101.711) .
A study of the Torymid collection of Iraq. resulted in undescribed species of the genus
Liodontonierus Gah. L. longicorpus sp. n. with 2 figures.
Background: The purpose of the current study was to evaluate the efficacy of a new orthodontic bonding system (Beauty Ortho Bond) involving the shear bond strength in dry and wet environments, and adhesion remnant index (ARI) scores evaluation in regard to other bonding systems (Heliosit and Resilience Orthodontic Adhesives). Materials and methods: Sixty defect free extracted premolars were randomly divided into six groups of 10 teeth each, mounted in acrylic resin, three groups for a dry environment and three for a wet one. Shear bond strength test was performed with a cross head speed of 0.5 mm/min, while surfaces of enamel and bracket-adhesive-enamel surfaces were examined with stereomicroscope For ARI scores evaluation. Data were analyz
... Show MoreSome new 2,5-disubsituted-1,3,4-oxadiazole derivatives with azo group were synthesized by known reactions sequence . The structure of the synthesized compounds were confirmed by physical and spectral means .
This article investigates the decline of language loyalty in the age of audiovisual nearness. It is a socio-linguistic review of previous literature related to language disloyalty. It reviews the current theoretical efforts on the impact of audiovisual nearness created by social media and language loyalty. The descriptive design is used. The argument behind this review is that the audiovisual nearness provided by social media negatively affects language loyalty. This article concludes that the current theoretical efforts have paid much attention to the relationship between the audiovisual nearness and language loyalty. Such efforts have highlighted the fact that the social media platforms have provided unprecedented nearness that provoke in
... Show MoreThe convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog
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