Multiple sclerosis (MS) is a neurodegenerative disorder with various clinical types. Glial fibrillary acidic protein (GFAP) is significantly elevated in the cerebrospinal fluid (CSF) of MS patients compared with that of healthy controls. The aim of this study is to evaluate serum levels of GFAP in relation to disease activity in relapsing-remitting MS patients and to compare them with those of healthy controls. Method: This study involved 58 MS patients of relapsing-remitting MS (RRMS) type, 22 in an active stage of the disease and 36 in remission, and 50 healthy individuals as age- and sex-matched controls. Blood samples were taken from the patients at the MS Clinic of the Baghdad Teaching Hospital, and the serum levels of GFAP were determ

This work consists of a numerical simulation to predict the velocity and temperature distributions, and an experimental work to visualize the air flow in a room model. The numerical work is based on non-isothermal, incompressible, three dimensional, k turbulence model, and solved using a computational fluid dynamic (CFD) approach, involving finite volume technique to solve continuity, momentum and energy equations, that governs the room’s turbulent flow domain. The experimental study was performed using (1/5) scaled room model of the actual dimensions of the room to simulate room air flow and visualize the flow pattern using smoke generated  from burnt herbs and collected in a smoke generator to delivered through

The tunnel’s stability during construction is a very important matter. Some methods have been proposed for stability evaluation, but the hazard warning levels (HWLs) are more applicable among these methods. Despite monitoring and applying HWLs, several collapses in Shibli twin tunnels in Iran have cast doubts on the accuracy of this criterion in the presence of water. In this study, the critical strains under different water contents were measured through uniaxial compressive strength tests on 11 different shale and marl samples. A comparison of laboratory tests and numerical results shows that the influence of the moisture content on the critical strain is negligible. In addition, the results show that there is no dir

Various simple and complicated models have been utilized to simulate the stress-strain behavior of the soil. These models are used in Finite Element Modeling (FEM) for geotechnical engineering applications and analysis of dynamic soil-structure interaction problems. These models either can't adequately describe some features, such as the strain-softening of dense sand, or they require several parameters that are difficult to gather by conventional laboratory testing. Furthermore, soils are not completely linearly elastic and perfectly plastic for the whole range of loads. Soil behavior is quite difficult to comprehend and exhibits a variety of behaviors under various circumstances. As a result, a more realistic constitutive model is

This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT),(median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on re

The study objectives were to (1) describe the characteristics of the pharmacy professionals and (2) explore the association between job satisfaction and factors, such as work control, work stress, workload and organization and professional commitments.

This paper proposes a neuro-fuzzy system to model β-glucosidase activity based on the reaction’s pH level and temperature. The developed fuzzy inference system includes two input variables (pH level and temperature) and one output (enzyme activity). The multi-input fuzzy inference system was developed in two stages: first, developing a single input-single output fuzzy inference system for each input variable (pH, temperature) separately, using the robust adaptive network-based fuzzy inference system (ANFIS) approach. The neural network learning techniques were used to tune the membership functions based on previously published experimental data for β-glucosidase. Second, each input’s optimized membership functions from the ANF