At the level of both individuals and companies, Wireless Sensor Networks (WSNs) get a wide range of applications and uses. Sensors are used in a wide range of industries, including agriculture, transportation, health, and many more. Many technologies, such as wireless communication protocols, the Internet of Things, cloud computing, mobile computing, and other emerging technologies, are connected to the usage of sensors. In many circumstances, this contact necessitates the transmission of crucial data, necessitating the need to protect that data from potential threats. However, as the WSN components often have constrained computation and power capabilities, protecting the communication in WSNs comes at a significant performance pena

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

Removable dental prostheses remain a reliable treatment for edentulous patients. Conventional fabrication requires multiple steps, whereas computer-aided design and manufacturing (CAD-CAM), particularly 3D printing, offers faster and more cost-effective alternatives. This in vitro study aimed to evaluate the effect of sandblasting on surface roughness and shear bond strength of silicone soft liner bonded to 3D-printed and heat-cured acrylic denture base materials. Eighty specimens were divided into four groups: heat-cured control, 3D-printed control, heat-cured sandblasted, and 3D-printed sandblasted. Surface roughness was measured using a profilometer, while shear bond strength was tested with a universal testing machine. D

Background: Regeneration dentistry demonstrates significant challenges due to the complexity of different dental structures. This study aimed to investigate osteogenic differentiation of human pulp stem cells (hDPSCs) cultured on a 3D-printed poly lactic acid (PLA) scaffold coated with nano-hydroxyapatite (nHA) and naringin (NAR) as a model for a dental regenerative. Methods: PLA scaffolds were 3D printed into circular discs (10 × 1 mm) and coated with nHA, NAR, or both. Scaffolds were cultured with hDPTCs to identify cellular morphological changes and adhesion over incubation periods of 3, 7, and 21 days using SEM. Then, the osteogenic potential of PLA, PLA/nHA/NAR, or PLA scaffolds coated with MTA elutes (PLA/MTA scaffolds) were evaluate

Traffic management at road intersections is a complex requirement that has been an important topic of research and discussion. Solutions have been primarily focused on using vehicular ad hoc networks (VANETs). Key issues in VANETs are high mobility, restriction of road setup, frequent topology variations, failed network links, and timely communication of data, which make the routing of packets to a particular destination problematic. To address these issues, a new dependable routing algorithm is proposed, which utilizes a wireless communication system between vehicles in urban vehicular networks. This routing is position-based, known as the maximum distance on-demand routing algorithm (MDORA). It aims to find an optimal route on a hop-by-ho

Precision is one of the main elements that control the quality of a geodetic network, which defines as the measure of the network efficiency in propagation of random errors. This research aims to solve ZOD and FOD problems for a geodetic network using Rosenbrock Method to optimize the geodetic networks by using MATLAB programming language, to find the optimal design of geodetic network with high precision. ZOD problem was applied to a case study network consists of 19 points and 58 designed distances with a priori deviation equal to 5mm, to determine the best points in the network to consider as control points. The results showed that P55 and P73 having the minimum ellipse of error and considered as control points. FOD problem was applie

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

Abstract: Facial defects resulting from neoplasms, congenital, acquired malformations or trauma can be restored with facial prosthesis using different materials and retention methods to achieve life-like look and function. A nasal prosthesis can re-establish aesthetic form and anatomic contours for mid-facial defects, often more effectively than by surgical reconstruction as the nose is relatively immobile structure. For successful results, lot of factors such as harmony, texture, color matching and blending of tissue interface with the prosthesis are important. The aim of this study is to describe the non-surgical rehabilitation with nasal prosthesis for an Iraqi patient who received rhinectomy as a result of squamous cell carcinoma of the