Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite of the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem that meets all EVs’ energy demands before their deadlines while satisfying constraints in charging rate and total charging power. We propose an online algorithm, smoothed least-laxity-first (sLLF), that decides the current charging rates without the knowledge of future arrivals and demands. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has a significantly higher rate of feasible EV charging than several other existing EV charging algorithms. Resource augmentation framework is employed to assess the feasibility condition of the algorithm. The assessment shows that the sLLF algorithm achieves perfect feasibility with only a 7% increase in the maximal power supply of the charging station.

Aircraft skin surface defect detection is critical for aviation safety but is currently mostly reliant on manual or visual inspections. Recent advancements in computer vision offer opportunities for automation. This paper reviews the current state of computer vision algorithms and their application in aircraft defect detection, synthesizing insights from academic research (21 publications) and industry projects (18 initiatives). Beyond a detailed review, we experimentally evaluate the accuracy and feasibility of existing low-cost, easily deployable hardware (drone) and software solutions (computer vision algorithms). Specifically, real-world data were collected from an abandoned aircraft with visible defects using a drone to capture video footage, which was then processed with state-of-the-art computer vision models—YOLOv9 and RT-DETR. Both models achieved mAP50 scores of 0.70–0.75, with YOLOv9 demonstrating slightly better accuracy and inference speed, while RT-DETR exhibited faster training convergence. Additionally, a comparison between YOLOv5 and YOLOv9 revealed a 10% improvement in mAP50, highlighting the rapid advancements in computer vision in recent years. Lastly, we identify and discuss various alternative hardware solutions for data collection—in addition to drones, these include robotic platforms, climbing robots, and smart hangars—and discuss key challenges for their deployment, such as regulatory constraints, human–robot integration, and weather resilience. The fundamental contribution of this paper is to underscore the potential of computer vision for aircraft skin defect detection while emphasizing that further research is still required to address existing limitations.

Global optimization algorithms have been adopted to the simultaneously refinement of orientation and pattern center for electron backscatter diffraction patterns as well as deformation state extraction. The hyperparameter space and mutation schemes of differential evolution (DE) algorithm has been thoroughly investigated and showed to be a more efficient algorithm than the particle swarm optimization (PSO) algorithm. The optimal hyperparameters for DE generally depend on conditions such as the number of variables to be optimized and the size of bounded search space but reasonably close initial values for crossover probability is 0.9, mutation factor is 0.5, population size is ten times the number of variables, and number of iterations is at least 100. Validation on a set of simulated undeformed single crystal nickel patterns reveals a mean accuracy of ≈0.03° and ≈0.01% detector width across a large field of view. In addition, validation using noisy simulated deformed patterns with known deformation state and pattern center shows that the mean accuracy of shear strain and rotation components is ≈0.001 and for the normal strain ≈0.002.