Laboratory determination of uniaxial compressive strength (UCS) after in situ sampling is costly and slow. Coupling measurement while drilling (MWD) with machine learning can recover UCS in place. Most such models, however, return point estimates and quantify uncertainty only weakly.
Researchers have developed a continuous causal machine learning model that estimates individualized dose–response curves for physiotherapy intensity in older adults, outperforming binary-treatment baselines across factual, proxy counterfactual, and external validation. AI Maps Personalized Physiotherapy Dose–Response Curves for Older…
Reliable vibration prediction for gear systems requires calibration of uncertain mesh and bearing parameters. Previous gear physics-informed machine learning (PIML) studies mainly estimated time-varying meshing stiffness (TVMS) or calibrated gearbox dynamics for fault diagnosis or response reconstruction. Yet identifying multiple…
In response to the SAB recommendations, the EPA’s National Center for Environmental Economics (NCEE) Division was tasked with developing the Agency’s economy-wide modeling capabilities for regulatory analysis. One of the first capacity-building steps was to develop a new CGE model…
Hybrid approach based on deep learning Boost for applied research expected Apart from the developing partners HZDR and Sandia National Laboratories, MALA is already employed by institutions and companies such as the Georgia Institute of Technology, the North Carolina A…
Researchers have integrated all three electrodes and the circuits to build and read them on a single CMOS chip, achieving self-contained amperometric dopamine detection.
This retrospective study audits whether a two-campaign soybean-extrusion archive supports section-specific inference in a linear geometry-associated signal model. The archive contains 3966 records (2091 worn and 1875 new) with four temperature and three phase-current channels. Descriptive statistics and complete recorded trajectories characterize the…
A study of 148 built-up cold-formed steel column tests found that machine-learning corrections performed well within the database but transferred poorly to unseen experimental programmes, limiting their present design applicability. Article Title: Reliability calibration of knowledge informed machine learning for global…
The International Roughness Index (IRI) is a crucial indicator of rigid pavement performance and distress, and artificial neural networks (ANNs) have made pavement management system (PMS) forecasting more cost-effective. However, purely data-driven ANN models are not constrained to follow known…
A leakage-controlled study of 24 simulated cases found that machine learning could estimate prompt NOx in a hydrogen-enriched 207-megawatt boiler from field-measurable inputs, but not thermal NOx. Machine learning tracks prompt NOx in hydrogen-enriched boiler Subject of Research: Machine-learning prediction of…
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