1663 papers
This study proposes vehicular fuel consumption per kilometer as a scalable, low-cost spatial proxy to evaluate urban infrastructure performance in data-scarce Juba, South Sudan, revealing that intra-city routes with poor surface conditions and high junction density impose significantly higher economic and energy burdens compared to inter-city travel.
This study demonstrates that while building geometry is the primary driver of energy efficiency in Egypt's diverse climates, aligning structures with the Qibla direction incurs only a minimal energy penalty (under 7%), proving that cultural orientation requirements can be successfully integrated with sustainable design strategies.
This paper presents a memristor-based analogue iterative computing system (mAIC) that utilizes an analogue-compensated predictive one-step programming scheme to achieve energy-efficient, low-latency, and accurate dynamic matrix equation solving for real-time robotic autonomy, significantly outperforming traditional digital platforms like the NVIDIA Jetson.
This retrospective multi-dataset audit reveals that image-derived wound-healing proxies are highly sensitive to sparse visit schedules and image framing, demonstrating that current data supports only a failure-mode analysis rather than calibrated clinical prognosis due to unvalidated segmentation errors, lack of incremental value from CNNs over occupancy metrics, and instability under scale variations.
This paper introduces Time-Input State-Aware Calibration (TISAC), a novel framework utilizing co-evolving neural networks and physical constraints to calibrate engineering models by inferring uncertain parameters and discrepancy bias under temporally correlated, state-varying operational conditions.
This paper proposes a label-free training method for attention graph neural networks that minimizes finite-volume residuals of governing equations to efficiently generate accurate neural surrogates for coupled 3D thermo-fluid fields without incurring the high computational costs of conventional data generation.
This study presents an open-source Python implementation of a Hardening Soil material-point integrator validated against Ho Chi Minh City soil data, which successfully serves as a high-fidelity physics-based data generator for training a highly accurate neural-network surrogate model.
This paper presents VICP, a runtime for industrial edge clusters that ensures highly available and safe service failover by decoupling reversible state replication from irreversible physical output authority via majority-backed leases and fencing, achieving a 50.54% reduction in output interruption compared to active standby while maintaining precise control accuracy.
This study developed a dual-beam selective laser melting approach and its simulation model to optimize process parameters for porous stainless steel, demonstrating that the auxiliary laser's preheating effect reduces residual stress by approximately 3.75% compared to conventional single-beam SLM.
This study characterizes the spatial variability of a soft lacustrine clay deposit in southern Brazil using various penetrometer tests and random field theory, confirming the applicability of full-flow penetrometers for deriving statistical parameters essential for reliability-based geotechnical design.