A chemical optical fiber sensor based on surface plasmon resonance (SPR) was developed and implemented using multimode plastic optical fiber. The sensor is used to detect and measure the refractive index and concentration of various chemical materials (Urea, Ammonia, Formaldehyde and Sulfuric acid) as well as to evaluate the performance parameters such as sensitivity, signal to noise ratio, resolution and figure of merit. It  was noticed that the value of the sensitivity of the optical fiber-based SPR sensor, with 60nm and 10 mm long, Aluminum(Al) and Gold (Au) metals film exposed sensing region, was 4.4 μm, while the SNR was 0.20, figure of merit was 20 and resolution 0.00045. In this work a multimode

The brief description to the theory of propagation of electromagnetic waves in plasma was done. The cutoff and resonance regions have been showed. The principles of plasma heating at electron cyclotron resonance (ECRH) method have been mentioned. The numerical simulation to three different station: Tosca station in United Kingdom, ISX-B station in USA and T-10 station in Russia had been done. The optical depth and the friction of energy absorbed A have been calculated. The simulation results indicate that both and A are increase with size of the tokamak and it is possible to obtain full absorption in large tokamak.

Photonic Crystal Fiber (PCF) based on the Surface Plasmon Resonance (SPR) effect has been proposed to detect polluted water samples. The sensing characteristics are illustrated using the finite element method. The right hole of the right side of PCF core has been coated with chemically stable gold material to achieve the practical sensing approach. The performance parameter of the proposed sensor is investigated in terms of wavelength sensitivity, amplitude sensitivity, sensor resolution, and linearity of the resonant wavelength with the variation of refractive index of analyte. In the sensing range of 1.33 to 1.3624, maximum sensitivities of 1360.2 nm ∕ RIU and 184 RIU⁻¹ are achieved with the high sensor resolutions of 7

Experimental study on the effect of cylindrical hollow cathode, working pressure and magnetic field on spatial glow distribution and the characteristics of plasma produced by dc discharge in Argon gas, were investigated by image analyses for the plume within the plasma. It was found that the emission intensity appears as a periodic structure with many peaks appeared between the electrodes. Increasing the pressure leads to increase the number of intensity peaks finally converted to continuous form at high pressure, especially with applied of magnetic field, i.e. the plasma is more stable with the presence of magnetic field. The emission intensity study of plasma showed that the intensity has a maximum value at 1.07 mbar pressure and decre

Bioethanol produced from lignocellulose feedstock is a renewable substitute to declining fossil fuels. Pretreatment using ultrasound assisted alkaline was investigated to enhance the enzyme digestibility of waste paper. The pretreatment was conducted over a wide range of conditions including waste paper concentrations of 1-5%, reaction time of 10-30 min and temperatures of 30-70°C. The optimum conditions were 4 % substrate loading with 25 min treatment time at 60°C where maximum reducing sugar obtained was 1.89 g/L. Hydrolysis process was conducted with a crude cellulolytic enzymes produced by Cellulomonas uda (PTCC 1259).The maximum amount of sugar released and hydrolysis efficiency were 20.92 g/L and 78.4 %, respectively. Sugars

Ultrasound has been used as a diagnostic modality for many intraocular diseases, due its safety, low cost, real time and wide availability. Unfortunately, ultrasound images suffer from speckle artifact that are tissue dependent. In this work, we will offer a method to reduce speckle noise and improve ultrasound image to raise the human diagnostic performance. This method combined undecimated wavelet transform with a wavelet coefficient mapping function: where UDWT used to eliminate the noise and a wavelet coefficient mapping function used to enhance the contrast of denoised images obtained from the first component. This methods can be used not only as a means for improving visual quality of medical images but also as a preprocessing

Background: Aortic valve stenosis results from minor to severe degrees of aortic valve maldevelopment. This stenosis causes mild to severe obstruction of the left ventricular outflow .

Objectives : to study the immediate and intermediate results of percutaneous balloon aortic valvuloplasty in patients with congenital valvular aortic stenosis .

Type of the study: A prospective study.

Methods: The study was done on thirty five patients with congenital valvular aortic stenosis who had percutaneous balloon aortic valvuloplasty  in Ibn Al- Bitar Center for Cardiac Surgery from May 2009 to February 2011.

Results

Background: percutaneous balloon dilation of corotation of aorta is a less invasive and alternative to surgical repair for patients with discrete coaction of aorta and although the used of balloon angioplasty in patients with recurrent postoperative coarctation gained a wide consensus, the use this technique for native coarctation is still controversial in children less one years. 

Objective: to evaluate the immediate and late result of balloon dilation of native coarctation of aorta in infant and children.

Type of the study: A prospective study. 

Subjects & Methods: The study was done on forty-five patients who were referred for cardiac catheterization and balloon

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

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