Background: Pregnancy is considered a major risk factor for development and progression of periodontal disease. There are hormonal changes in both estrogen and progesterone hormones in addition to bacterial effect and poor oral hygiene that will enhance development of periodontal disease in pregnant women. Materials and methods: Seventy subjects were enrolled in the study, the subjects with an age range (20-35) years old without any history of systemic disease. The subjects were divided into 20 non-pregnant women they represent the control group (G I), 30 pregnant women with gingivitis (GII) and 20 pregnant women with periodontitis (GIII).All periodontal parameters (plaque index, gingival index, bleeding on probing, probing pocket depth an

SAGD is a thermal recovery process in which steam oil ratio, SOR, is a key parameter that can affect the economic outcome of the process. Reservoirs with underlying or overlying lean bitumen present challenges for SAGD as they can act as a heat sink. Water has higher heat capacity than the bitumen and thus requires more steam to heat up the reservoir leading to higher SOR. The potential outcome of operating SAGD in these conditions may be lower bitumen rate and higher steam injection rate, both of which affect plant throughput and thus the economic matrix of SAGD. This paper looks at the performance of SAGD process in the presence of top lean bitumen. It will examine the theoretical CSOR that is needed to produce the bitumen with different

        In this study multi objective optimization is utilized to optimize a turning operation to reveal the appropriate level of process features. The goal of this work is to evaluate the optimal combination of cutting parameters like feed, spindle speed, inclination angle and workpiece material to have a best surface quality Taguchi technique L9 mixed orthogonal array, has been adopted to optimize the roughness of surface. Three rods of length around (200 mm) for the three metals are used for this work. Each rod is divided into three parts with 50 mm length. For brass the optimum parametric mix for minimum Ra is A1, B1 and C3, i.e., at tool inclination angle (5), feedrate of 0.01, spindle speed of 120

Abstract

 This study investigated the optimization of wear behavior of AISI 4340 steel based on the Taguchi method under various testing conditions. In this paper, a neural network and the Taguchi design method have been implemented for minimizing the wear rate in 4340 steel. A back-propagation neural network (BPNN) was developed to predict the wear rate. In the development of a predictive model, wear parameters like sliding speed, applying load and sliding distance were considered as the input model variables of the AISI 4340 steel. An analysis of variance (ANOVA) was used to determine the significant parameter affecting the wear rate. Finally, the Taguchi approach was applied to determine

Personalized Medicine represents a recent revolution in healthcare practice, focusing on tailoring different therapies to be precise for a specific individual; this is aided by exploring the number of genetic predispositions and lifestyle choices that fit each individual. In this article, the authors utilize and gather recent literature and opinions to discuss the impact of personalized medicine on chronic disease management and patient quality of life. Additional attention is paid to limits and possible ethical issues. Chronic diseases such as Hypertension, Diabetes, and chronic kidney diseases adversely affect multiple health indicators, including Quality of Life (QoL) and well-being. This will have additional impacts on physical

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

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.