Publications by authors named "M Sahar"

Neurodegeneration and neuroinflammation disorders are mainly the result of the deposition of various proteins, such as α-synuclein, amyloid-β and prions, which lead to the initiation and activation of inflammatory responses. Different chemokines are involved in the infiltration and movement of inflammatory leukocytes into the central nervous system (CNS) that express chemokine receptors. Dysregulation of several members of chemokines has been shown in the CNS, cerebrospinal fluid and peripheral blood of patients who have neurodegenerative disorders.

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The demand for increasingly fine detail in optical lithography for semiconductors necessitates the use of lower-wavelength lithographic light. This drives the need for lenses in optical lithography steppers made of vacuum ultraviolet-transparent (VUV-transparent) materials. In this work, the density functional theory (DFT) study of potassium magnesium fluoride KMgF is presented.

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To properly formulate diets, the ability to accurately estimate feed intake is critical as the amount of feed consumed will influence the amount of nutrients delivered to the animal. Inaccurate intake estimates may lead to under- or over-feeding of nutrients to the animal. Individual differences in equine forage intake are well-known, but predictive equations based on animal and nutritional factors are not comprehensive.

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Background: Cubic perovskite titanium stannous oxide (TiSnO3) is a promising material for various applications due to its functional properties. However, understanding how these properties change under external stress is crucial for its development and optimization.

Method: This study employed density functional theory calculations to investigate the structural, electronic, optical, thermal, and mechanical properties of TiSnO3 under varying degrees of external static isotropic stress (0-120 GPa).

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Nowadays, vast amounts of data representing feed intake, growth, and environmental impact of individual animals are being recorded in on-farm settings. Despite their apparent use, data collected in real-world applications often have missing values in one or several variables, due to reasons including human error, machine error, or sampling frequency misalignment across multiple variables. Since incomplete datasets are less valuable for downstream data analysis, it is important to address the missing value problem properly.

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