The growing use of tele

This paper presents a new secret diffusion scheme called Round Key Permutation (RKP) based on the nonlinear, dynamic and pseudorandom permutation for encrypting images by block, since images are considered particular data because of their size and their information, which are two-dimensional nature and characterized by high redundancy and strong correlation. Firstly, the permutation table is calculated according to the master key and sub-keys. Secondly, scrambling pixels for each block to be encrypted will be done according the permutation table. Thereafter the AES encryption algorithm is used in the proposed cryptosystem by replacing the linear permutation of ShiftRows step with the nonlinear and secret pe

Most researchers concentrate their studies on the design, stress and pressure distributions of the prosthetic socket. A little attention is considered for the stiffness of the various materials of the prosthetic sockets. Prosthetic laminated sockets in Iraq are costly to be manufactured while polypropylene socket is relatively cheap in comparing with the laminates.

Experimental study is conducted to compare the stiffness of five prosthetic sockets made of different materials. Compression, three point flexural and tensile tests are implemented by the Testometric machine. The laminate sockets give better results in compression than polypropylene. Polypropylene gives good results in bending compared with the laminate sockets. When t

HTH Ali Tarik Abdulwahid , Ahmed Dheyaa Al-Obaidi , Mustafa Najah Al-Obaidi, eNeurologicalSci, 2023

<p>Energy and memory limitations are considerable constraints of sensor nodes in wireless sensor networks (WSNs). The limited energy supplied to network nodes causes WSNs to face crucial functional limitations. Therefore, the problem of limited energy resource on sensor nodes can only be addressed by using them efficiently. In this research work, an energy-balancing routing scheme for in-network data aggregation is presented. This scheme is referred to as Energy-aware and load-Balancing Routing scheme for Data Aggregation (hereinafter referred to as EBR-DA). The EBRDA aims to provide an energy efficient multiple-hop routing to the destination on the basis of the quality of the links between the source and destination. In

Background: Loss of tooth structure may be due to tooth to tooth contact and presence of abrasive components in the work environment. The aim of study was planned to evaluate the occurrence of dental attrition among Cement factory workers. Material and Method: The Sample included all workers chronically exposed to cement dust in the EL-Kubaisa cement factory (95 workers). A comparative group of workers (97) were non-exposed to cement dust was selected. All workers were males in gender with age range (25-55) years. The assessment of tooth wear was based on the criteria of smith and knight, 1984. Results: The maximum tooth wear score for exposed workers was 84.2% while non exposed workers was 38.1%,with statistical differences between two g

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we

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