Results 101 to 110 of about 7,728 (248)
ABSTRACT Mitochondria provide multiple functions for cellular physiology. Transplantation of mitochondria isolated from gastric epithelial cells GES‐1 reducing the malignancy of gastric cancer cells AGS was previously reported. To elucidate the underlying mechanisms, TMT‐based proteomic analysis coupling ingenuity pathway software prediction revealed ...
Ping‐Chen Chen +9 more
wiley +1 more source
Frontiers of Bright CEP‐Stable Broadband Infrared Sources
Bright CEP‐stable broadband infrared sources push the limits of ultrafast science by delivering exceptional brightness alongside few‐cycle durations and multi‐octave spectral coverage. This review highlights emerging architectures and nonlinear conversion strategies that scale power while preserving phase stability.
Ugaitz Elu, Jens Biegert
wiley +1 more source
Controlling the Center of Mass Motion of Levitated Particles Using Structured Wavefronts
Optically levitated particles have great potential for quantum‐enhanced sensing. Precise control of the trapping beam is crucial to improve the stability and confinement of these systems. Here, we study how structured wavefronts can be used to control the center‐of‐mass motion of levitated particles, enhancing trap stiffness and enabling stable ...
Shah Jee Rahman +7 more
wiley +1 more source
Plasmepsins as Antimalarial Drug Targets—Then, Now, and the Future
ABSTRACT Malaria is a devastating disease caused by Plasmodium parasites. Plasmodium parasites express ten cathepsin D‐like aspartyl proteases, called plasmepsins (PMs). These PMs have diverse roles fulfill diverse functions throughout the parasite's lifecycle, though several exhibit functional redundancies. Among them, PMV, PMIV, and PMX are essential
Brad E. Sleebs
wiley +1 more source
Model predictive control (MPC) with integral action; Reducing the control horizon and model free MPC
Model Predictive Control (MPC) is the most widely used strategy in process industries due to remarkable features. It has the capability to control the non-minimum phase, unstable processes and handle the constraints in a systematic way. MPC with integral action is an effective method to achieve the offset free control which can remove the unknown ...
openaire +1 more source
Overview of the holistic engineering lifecycle and core research pillars for wind turbine blades. ABSTRACT This paper reviews recent advancements across the lifecycle of wind turbine blades, focusing on three interconnected areas: advanced composites, structural optimization, and machine learning (ML) diagnostics. In materials, we highlight progress in
Kemal Hasirci +2 more
wiley +1 more source
Residual MPC: Blending Reinforcement Learning with GPU-Parallelized Model Predictive Control
Model Predictive Control (MPC) provides interpretable, tunable locomotion controllers grounded in physical models, but its robustness depends on frequent replanning and is limited by model mismatch and real-time computational constraints. Reinforcement Learning (RL), by contrast, can produce highly robust behaviors through stochastic training but often
Se Hwan Jeon +3 more
openaire +2 more sources
Gene turnover in the common ancestor of all C4 grasses
Understanding how plants evolve more efficient photosynthesis is important in a warming world where improving crop productivity and resilience is a global priority. By generating the first reference genomes for an early‐diverging group of grasses called the Aristidoideae, we were able to reconstruct the genetic makeup of the last common ancestor of all
Lara Pereira +6 more
wiley +1 more source
Path-Following Control of Unmanned Vehicles Based on Optimal Preview Time Model Predictive Control
In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC).
Xinyu Wang +3 more
doaj +1 more source
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control
Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline settings where the agent learns from a fixed dataset. We hypothesize that model-based RL agents struggle in these environments due to a lack of long-term planning capabilities ...
Rohan Chitnis +6 more
openaire +2 more sources

