Low-light image enhancement (LLIE) is essential for enabling reliable nighttime visual perception and improving the performance of downstream vision tasks, including object detection and image segmentation. Under complex illumination conditions, low-light images often suffer from insufficient luminance, loss of structural details, and unstable color reproduction. Existing methods struggle to simultaneously restore luminance, texture, and color in a coherent manner. This paper proposes a Hierarchical Adaptive Interaction Modulation Network (HAIMNet) designed for LLIE. The proposed method decouples luminance and chromaticity in the Horizontal/Vertical-Intensity (HVI) color space, and enhances luminance-texture consistency through an inter-branch attention-modulation block (IAMB). Furthermore, a cross-branch gated affine fusion module (CGAF) is introduced to calibrate features between luminance and chromatic-structural representations, reduce color deviations, and enhance perceptual consistency. Extensive experiments on 11 public datasets demonstrate the effectiveness, robustness, and generalization capability of HAIMNet. The enhanced results exhibit high naturalness and stability under extremely dark and complex illumination conditions. Our code is available at: https://github.com/ZekeWang13/HAIMNet.

Variational Quantum Algorithms (VQAs) are emerging as leading strategies with the potential to unlock practical applications and deliver significant advantages in the investigation of many-body quantum systems and quantum chemistry. A key challenge hindering the application of VQAs to large-scale problems is rooted in the no-cloning theorem in quantum mechanics, precluding standard backpropagation and leading to prohibitive quantum resource expenditure such as measurement cost. To address this challenge, we reformulate the training dynamics of VQAs as a non-linear partial differential equation and propose a novel protocol that leverages physics-informed neural networks (PINNs) to model this dynamical system efficiently. Given a small amount of training trajectory data collected from quantum devices, our protocol predicts the parameter updates of VQAs over multiple iterations on the classical side, dramatically reducing quantum resource costs. Through systematic numerical experiments, we demonstrate that our method achieves up to a 30x speedup compared to conventional methods and reduces quantum resource costs by as much as 90\% for tasks involving up to 40 qubits, including ground state preparation of different quantum systems, while maintaining competitive accuracy. Our approach complements existing techniques aimed at improving the efficiency of VQAs and further strengthens their potential for practical applications.

Infrastructure projects for harnessing renewable energy (e.g., wind farm projects) have recently gained popularity because of their low adverse impact on the environment. However, it is challenging to perform risk assessments for these projects because data are either scarce or of low quality. Therefore, risk assessments for renewable energy infrastructure projects must rely on expert knowledge and can be treated as multi-criteria group decision-making (MCGDM) problems. In group decision-making problems, consensus must be built between individual decision makers who each supply their own preference indices for decision alternatives. This paper introduces a novel technique for consensus building in MCGDM problems using the principle of justifiable granularity, thereby producing an interval-valued fuzzy set that represents the aggregated value of the preference indices assigned to decision alternatives by decision makers. The preference indices obtained from each expert are realized through the analytic hierarchy process (AHP). In this paper, the introduced MCGDM technique is used to assess risk for wind farm projects. First, a context-specific work breakdown structure for wind farm projects is developed. Second, construction work packages are ranked based on how much they contribute to the overall risk or uncertainty involved in achieving the project objectives of time, cost, quality, and safety.