The escalating demand for efficient particle separation in microfluidic systems necessitates innovative design solutions. This study presents a simulation-based topology optimization method to passively separate particles within a 2D microfluidic channel, eliminating the need for external forces. Leveraging a coupled Navier-Stokes solver and particle advection simulation, the framework iteratively refines the channel's geometry by minimizing an objective function quantifying particle mis-sorting. Our approach computationally generated optimal, manufacturable topologies, demonstrating a peak sorting efficiency of 0.6667 (66.67%) achieved by the second iteration, which then stabilized in subse... More >

The rapid global deployment of solar photovoltaic (PV) technology presents a significant and often overlooked challenge: the effective management of end-of-life (EoL) solar panels. This issue is particularly acute in developing and emerging economies, where established reverse logistics infrastructure is often lacking. A critical limitation in current academic literature is the oversimplified forecasting of EoL waste streams, which fails to account for the dynamic interplay of socio-economic, policy, and environmental variables. To bridge this gap, we propose a novel decision support system (DSS) for the design of a sustainable reverse logistics network. Our system uniquely integrates a hybr... More >

The escalating global prevalence of diabetes renders effective screening for Diabetic Retinopathy (DR) indispensable to prevent irreversible vision loss. Although deep learning models, particularly Convolutional Neural Networks (CNNs), attain diagnostic accuracy comparable to that of human experts, their black-box nature erodes clinical trust. To harmonize accuracy with interpretability, this paper proposes a novel CNN architecture that reformulates DR grading as a regression task. By substituting traditional dense layers with a Global Average Pooling (GAP) layer, our approach substantially reduces model complexity and training time while enabling the generation of Regression Activation Maps... More >