662 papers
This paper presents an efficient voxel quadrature scheme based on a modified moment-fitting approach that computes custom integration rules to absorb voxel information directly into weights, enabling exact stiffness matrix integration for locally refined finite element spaces while decoupling material resolution from the basis and outperforming traditional pre-integration and composed Gauss-Legendre methods.
This paper presents two compact, wideband (4–9 GHz) single-layer microstrip 180° hybrid couplers utilizing a novel topology that combines Wilkinson-based and five-port power dividers with phase-shifting networks to achieve excellent broadband performance, high isolation, and low amplitude and phase imbalances.
This study demonstrates that Gaussian Process Regression is a superior predictive model for optimizing the mechanical performance of sustainable ternary concrete blends using ground granulated blast furnace slag and limestone powder, identifying a 30% GGBFS and 15% LSP mix as the optimal balance of strength properties while reducing carbon emissions.
This study presents a physics-integrated machine learning framework that successfully predicts the lateral load capacity of single-row bolted steel-to-timber connections in Malaysian tropical hardwoods (Balau and Bitis) by training models to correct discrepancies between experimental results and existing design codes, thereby offering a reliable pathway for extending timber design standards to tropical species.
This study demonstrates that replacing up to 40% of natural fine aggregate with bottom ash in alkali-activated slag concrete yields a sustainable material with optimal workability and compressive strength at a 0.45 activator-to-binder ratio, while maintaining structural integrity and exceptional resistance to acid and sulfate attacks.
This paper demonstrates that liftered cepstral descriptors, which remove low-quefrency additive perturbations in the log-spectrum to mitigate FIR convolutional distortions, significantly outperform standard spectral profiles in robustness for bearing-fault classification while maintaining clean accuracy, a finding validated through controlled experiments on CWRU and DroneRF datasets.
This study presents and validates a low-cost, ESP32-based embedded system that utilizes a novel mathematical model to predict cognitive performance and occupational stress levels in real-time based on indoor air quality parameters, demonstrating high accuracy and practical utility for preventive occupational health management.
This paper proposes and validates a verifiable engineering framework that integrates 5D/6D BIM with IoT telemetry to automate and significantly reduce errors in the real-time financial settlement of civil construction projects in Peru, transforming a traditionally manual and dispute-prone process into a deterministic, auditable system.
This study compares the digital-twin-driven cost-scenario simulation frameworks of the Netherlands and Germany to propose a hybrid, context-sensitive adaptation pathway for reducing budget overruns in Peru's civil infrastructure sector by integrating open BIM standards with life-cycle costing under existing regulatory mandates.
This paper presents an adapted 3D Gaussian Splatting framework that enables rapid, high-quality 3D reconstruction of phase-dominant volumes from sparse-view X-ray projections by integrating absorption and phase-shift primitives with differentiable Fresnel propagation.