This study presents an advanced computational framework that integrates a finite-difference Navier--Stokes solver, a SIMP-based topology optimization engine, and a Lagrangian particle advection module into a unified, iteratively coupled pipeline for physics-driven geometric design. The framework autonomously evolves the internal material distribution of a 2D microchannel by minimizing an objective function that directly quantifies particle mis-sorting, eliminating reliance on manual heuristic design and external actuation forces. Applied to the problem of passive microfluidic particle separation, the computational approach generated manufacturable, binary-material topologies across five opti... 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 >