In this thesis, we introduced some types of fibrewise topological spaces by using a near soft set, various related results also some fibrewise near separation axiom concepts and a fibrewise soft ideal topological spaces. We introduced preliminary concepts of topological spaces, fibrewise topology, soft set theory and soft ideal theory. We explain and discuss new notion of fibrewise topological spaces, namely fibrewise soft near topological spaces, Also, we show the notions of fibrewise soft near closed topological spaces, fibrewise soft near open topological spaces, fibrewise soft near compact spaces and fibrewise locally soft near compact spaces. On the other hand, we studied fibrewise soft near forms of the more essent

In this work, the linear properties of Vitamin D₃-5000IU soft gel were investigated by measuring its absorption and fluorescence spectra. It was observed that there was a shift towards longer wavelength within limits (75 nm), with quantitative efficiency equal to (33.58%). The values of absorbance were used to calculate the extinction coefficient, optical refractive index, optical conductivity and optical dielectric constant values.

The non-linear properties of Vitamin D₃-5000IU soft gel was also studied using the Z-Scan technique by using Neodymium-doped Yttrium Garnet (Nd: YAG) continuous laser (CW) emitting in       &n

Compressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry.The MCT bases functions are derived from GHM bases function using 2D linear convolution .The fast computation algorithm methods introduced here added desirable features to the current transform. We further assess the performance of the MCT in speech compression application. This paper discusses the effect of using DWT and MCT (one and two dimension) on speech compression. DWT and MCT performances in terms of comp

The demand for electronic -passport photo ( frontal facial) images has grown rapidly. It now extends to Electronic Government (E-Gov) applications such as social benefits driver's license, e-passport, and e-visa . With the COVID 19 (coronavirus disease ), facial (formal) images are becoming more widely used and spreading quickly, and are being used to verify an individual's identity, but unfortunately that comes with insignificant details of constant background which leads to huge byte consumption that affects storage space and transmission, where the optimal solution that aims to curtail data size using compression techniques that based on exploiting image redundancy(s) efficiently.

Background: Chronic cough is often the key
symptom not only of chronic pulmonary diseases
but for other important extrapulmonary
pathologies, in particular upper airway and
gastrointestinal diseases.
Objective: This study was designed to
determine the etiology of chronic cough and the
usefulness of the available diagnostic tests in
reaching its causes.
Methods: One hundred patients presenting with
chronic cough at Baghdad Teaching Hospital
Outpatient Clinic were enrolled in this study. The
patients underwent a full clinical interview,
physical examination with indicated diagnostic
test(s) (such as chest x ray, bronchoscope, PFT,
GIT study, sinus X ray or CT).
Results: An etiology of chronic

0

Spelling correction is considered a challenging task for resource-scarce languages. The Arabic language is one of these resource-scarce languages, which suffers from the absence of a large spelling correction dataset, thus datasets injected with artificial errors are used to overcome this problem. In this paper, we trained the Text-to-Text Transfer Transformer (T5) model using artificial errors to correct Arabic soft spelling mistakes. Our T5 model can correct 97.8% of the artificial errors that were injected into the test set. Additionally, our T5 model achieves a character error rate (CER) of 0.77% on a set that contains real soft spelling mistakes. We achieved these results using a 4-layer T5 model trained with a 90% error inject