Results 41 to 50 of about 992,502 (288)
Mixed modulus and anomaly mediation in light of the muon g − 2 anomaly
Journal of High Energy Physics, 2021 The new measurement of the anomalous magnetic moment of muon at the Fermilab Muon g − 2 experiment has strengthened the significance of the discrepancy between the standard model prediction and the experimental observation from the BNL measurement.
Kwang Sik Jeong +2 more
doaj +1 more source
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Purpose This study compares the dosimetric accuracy of deep‐learning‐based MR synthetic CT (sCT) in brain radiotherapy between the Analytical Anisotropic Algorithm (AAA) and AcurosXB (AXB). Additionally, it proposes a novel metric to predict the dosimetric accuracy of sCT for individual post‐surgical brain cases.
Jeffrey C. F. Lui +3 more
wiley +1 more source
Suppression of Supergravity Anomalies in Conformal Sequestering [PDF]
Phys.Lett.B663:247-249,2008, 2007 We show that the anomaly-mediated supersymmetry breaking via the Kahler and sigma-model anomalies is suppressed by conformal dynamics in the supersymmetry breaking sector.
arxiv +1 more source
Arthritis Care &Research, EarlyView.Objective Targeted synthetic disease‐modifying antirheumatic drugs (tsDMARDs) have expanded the management of autoimmune diseases, including rheumatic diseases. As the use of these drugs grows, it is important to understand their effects on pregnancy.
Vienna Cheng +7 more
wiley +1 more source
Advanced Materials Interfaces, EarlyView., 2023 This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi +4 more
wiley +1 more source
International Journal of Adaptive Control and Signal Processing, EarlyView.Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez +2 more
wiley +1 more source
GAN-AE: an anomaly detection algorithm for New Physics search in LHC data
European Physical Journal C: Particles and Fields, 2023 In recent years, interest has grown in alternative strategies for the search for New Physics beyond the Standard Model. One envisaged solution lies in the development of anomaly detection algorithms based on unsupervised machine learning techniques.
Louis Vaslin +2 more
doaj +1 more source
Hybrid Framework Materials: Next‐Generation Engineering Materials
Advanced Engineering Materials, EarlyView.Hybrid organic–inorganic materials merge the unique properties of organic and inorganic compounds, enabling applications in optoelectronics, gas storage, and catalysis. This review explores metal‐organic frameworks, hybrid organic–inorganic perovskites, and the emerging field of hybrid glasses, emphasizing their structures, functionalities, and ...
Jay McCarron +2 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.This article concerns room temperature tensile testing, with a range of (nominal) strain rates, of three titanium alloys. Stress–strain curves are analyzed to obtain creep characteristics. Profilometry‐based indentation plastometry testing is also covered, using different penetration velocities, and correlations established with tensile data.
Philip John McKeown +2 more
wiley +1 more source
Searches for new physics in collision events using a statistical technique for anomaly detection
SciPost Physics Proceedings, 2022 This paper discusses a statistical anomaly-detection method for model-independent searches for new physics in collision events produced at the Large Hadron Collider (LHC).
S. V. Chekanov
doaj +1 more source