Recently, wireless charging based RF harvesting has interfered our lives [1] significantly through the different applications including biomedical, military, IoT, RF energy harvesting, IT-care, and RFID technologies. Wirelessly powered low energy devices become significantly essential for a wide spectrum of sensing applications [1]. Such devices require for low energy resources from sunlight, mechanical vibration, thermal gradients, convection flows or other forms of harvestable energy [2]. One of the emerging power extraction resources based on passive devices is harvesting radio frequency (RF) signals powers [3]–[5]. Such applications need devices that can be organized in very large numbers, so, making separate node battery impractical. RF powered devices including sensor nods can be used potentially in ultra-low-power areas to extend the life battery span [4]. Moreover, modern biomedical implantable devices require power source channels for charging to prolong the lifetime of the implanted device and reduce the chances of battery replacements [5]. Furthermore, the ambient electromagnetic energy recycling possibility in dense urban zones population was significantly explored in [6]. Therefore, power conversion circuits to extract enough DC power from the incident electromagnetic waves for passive devices become urgent demand [7]. RF energy harvesters, generally, are consistent with an antenna, a power management circuit, and a rectifier [3]. The antenna part is the responsible element for collecting the RF energy from radiating sources. The appropriate antenna design is the one with a wide bandwidth of omnidirectional radiation patterns to collect the energy from a different direction at any frequency [8].
The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par
... Show MoreThe current research aims to prepare a proposed Programmebased sensory integration theory for remediating some developmental learning disabilities among children, researchers prepared a Programme based on sensory integration through reviewing studies related to the research topic that can be practicedby some active teaching strategies (cooperative learning, peer learning, Role-playing, and educational stories). The Finalformat consists of(39) training sessions.
Aim: This study aimed to assessing orthodontic knowledge and attitude among general dentists and non-orthodontic specialists. Background: Early detection of orthodontic disorders is essentialin motivating patients to intervene prior to long term complications when the disorders are not recongised. Methods: A questionnaire was distributed amongst dentistsother than orthodontists. This questionnaire consisted of three sections. The first one aimed to collect demographic, educational level and practice type information. Further two sections consisted of closed-end questions designed to evaluateknowledge and attitude of orthodontics. Results: A total of 313 responses to the survey were submitted. No significant correlation was observed, e
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