Publications by authors named "B Ali"

Background And Aim: Due to the global shortage in the surgical workforce, especially in low-resource settings, one solution to increase surgical volume is to delegate certain roles of surgeons to other trained non-surgeon health workers. However, quantifying the costs and benefits of surgical task-shifting has several challenges associated with it. The purpose of this study was to conduct a critical appraisal of studies on the cost-effectiveness of task shifting in surgical care.

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Using Density Functional Theory (DFT) calculations, we explored the electronic band structure and contact type (Schottky and Ohmic) at the interface of VS-BGaX (X = S, Se) metal-semiconductor (MS) van der Waals heterostructures (vdWHs). The thermal and dynamical stabilities of the investigated systems were systematically validated using energy-strain analysis, molecular dynamics (AIMD) simulations, as well as binding energy and phonon spectrum calculations. After analyzing the band structure, VS-BGaX (X = S, Se) MS vdWHs metallic behavior with type-III band alignment is revealed.

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The mechanical behavior of prosthetic liners significantly influences stress distribution, soft tissue protection, and the overall efficiency of the prosthetic. While extensive research has been conducted on liner materials, the impact of liner thickness (2 mm, 4 mm, and 6 mm) on biomechanical response remains underexplored. This study utilizes finite element analysis in Abaqus to investigate how liner material (Gel vs.

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Tuning the exciton fine structure of lead halide perovskites to brighten the dark excitonic ground state is crucial for enhancing their optoelectronic performance. While Rashba splitting is linked to dark-to-light exciton flipping, the specific nature of this phenomenon remains unclear. Here, we systematically studied 18 CsPbBr structures, representing 2D systems of CsPbBr with varying degrees of distortion, using density functional theory (DFT) and the Model-Bethe-Salpeter Equation (m-BSE).

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Backdoor attacks present a significant threat to the reliability of machine learning models, including Graph Neural Networks (GNNs), by embedding triggers that manipulate model behavior. While many existing defenses focus on identifying these vulnerabilities, few address restoring model accuracy after an attack. This paper introduces a method for restoring the original accuracy of GNNs affected by backdoor attacks, a task complicated by the complex structure of graph data.

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