662 papers
This paper proposes and validates a functionally graded auxetic lattice sub-surface for polymer plain bearings that significantly mitigates edge-loading failure caused by shaft misalignment by enabling elastic bore conformity, thereby drastically reducing peak contact pressure and extending service life compared to traditional solid designs.
This study proposes a transformative Performance-Based Earthquake Engineering framework that integrates Digital Twins and Machine Learning to enhance the seismic resilience of Cross-Laminated Timber and masonry structures in Peru by benchmarking against US and Japanese practices, introducing novel analytical tools for uncertainty reduction, and bridging the gap between construction-phase modeling and real-time operational monitoring.
This systematic review analyzes the state-of-the-art of robotic modular prefabrication for roadway construction in industrialized nations and proposes a strategic three-phase roadmap for implementing this technology in Peru to reduce construction time, carbon emissions, and safety risks.
This paper demonstrates that a Simulink-tuned PID controller, optimized for a DC motor, maintains robust stability and effective noise rejection while preserving rapid transient performance under stochastic measurement perturbations.
This paper presents a systematic review of 47 studies (2020–2026) on UAV-assisted RIS networks, revealing significant throughput and energy efficiency gains—particularly when using Deep Reinforcement Learning and specific hardware configurations—while highlighting critical methodological gaps in current research that hinder quantitative meta-analysis.
This study demonstrates that while textile electrode size does not significantly impact ECG signal quality, placement location is critical, with positions under the left shoulder blade on the back and under the left nipple on the chest yielding optimal results that often surpass conventional Ag/AgCl electrodes during physical activity.
This study presents a quantum-enhanced Newton–Raphson framework that integrates the HHL algorithm into seismic time-stepping schemes to achieve a 1,000-fold speedup in nonlinear time-history analysis, thereby enabling real-time, city-scale seismic risk assessment for megathrust-prone urban environments like Lima, Peru.
This paper proposes a reliability-aware hybrid framework that integrates triplet-based metric learning with Bayesian weighted k-nearest neighbor inference to significantly enhance the robustness and failure resilience of multi-floor Wi-Fi RSSI indoor positioning systems, demonstrating through comprehensive evaluation that such an approach effectively reduces catastrophic localization errors and floor misclassifications compared to traditional accuracy-centric methods.
This paper proposes a unified framework for Architecturally-Induced Dynamic Irregularity (AIDI) that identifies four fundamental asymmetry types and introduces Dynamic Form Factors (DFFs) to systematically quantify how unconventional architectural geometries alter the dynamic behavior of mid-to-high-rise buildings, thereby enabling early-stage integration of these effects into design processes.
This paper proposes a hybrid deep reinforcement learning framework that jointly optimizes intelligent reflecting surface phase shifts, ground base station sleep modes, user association, power allocation, and aerial base station positioning to maximize energy efficiency in IRS-assisted aerial-terrestrial cellular networks.