Actuator-rate-sensitive applications require feedback laws that discourage abrupt changes in the control input. For unknown discrete-time linear systems, this paper develops an input-increment-aware off-policy Q-learning algorithm that learns the optimal rate-aware feedback from input-state data. The previous input is treated as part of an augmented state and the input increment as the new action. This restores Markovianity but yields a linear quadratic problem with a persistent state-action cross term, so the usual cross-term-free linear-quadratic-regulator Q-learning update is not directly applicable. We establish structural validity of the augmented problem, derive a cross-coupled off-policy regression and policy-improvement rule, and prove admissibility preservation, monotone value improvement, convergence to the optimal incremental gain, and joint convergence of the plant state and the control signal. The increment weight is further shown to provide an a-posteriori rate certificate and a certified structural lower bound on the conditioning of the policy-improvement step under regression error. Numerical tests on academic plants and a quarter-car active suspension benchmark confirm the predicted smoothing and robustness effects, and benchmark the learned closed loop against a rate-constrained model predictive control oracle that has full plant knowledge and road-disturbance preview.
Metal-organic frameworks (MOFs) and their derivatives have emerged as promising photocatalysts for CO2 reduction owing to their large surface area, tunable porosity, and adjustable coordination environment. However, a comprehensive understanding of how organic ligands govern the structure-function relationship in MOF-based photocatalysis remains limited. This review provides a ligand-centered perspective on the photocatalytic CO2 reduction (PCR) process, focusing on how ligand functionalization, electronic modulation, and coordination design influence catalytic activity, stability, and selectivity. We summarize recent progress in ligand-regulated MOF systems from both experimental and theoretical viewpoints and discuss the associated structure-activity mechanisms. Finally, current challenges and future opportunities—such as cost-effective synthesis, scalability, and sustainable catalyst design—are discussed to guide the rational development of high-performance MOF photocatalysts for efficient CO2 conversion.
Seismic exploration plays a pivotal role in subsurface characterization and has been greatly promoted by artificial intelligence techniques such as deep learning, yet its reliance on high-quality labeled data poses a significant challenge for conventional supervised learning methods. In recent years, label-efficient deep learning has emerged as a powerful solution to this bottleneck by leveraging unsupervised, self-supervised, semi-supervised, and weakly supervised paradigms. This survey provides a comprehensive overview of these learning paradigms and their growing impact on key stages of the seismic data processing workflow, including preprocessing, imaging and inversion, and geological interpretation. We first outline the theoretical foundations and representative architectures of each paradigm, highlighting their distinct supervision strategies and learning objectives. We then analyze recent advancements in applying label-efficient methods to tasks such as seismic denoising, interpolation, full waveform inversion, facies classification, fault detection, and salt body delineation. By systematically comparing methodologies across application scenarios, we identify their respective advantages, limitations, and domain-specific adaptations. Finally, we discuss the main challenges hindering large-scale deployment, including the lack of standardized benchmarks, difficulty in integrating geophysical constraints, and the interpretability gap, and we also suggest promising directions for future research. This review aims to serve as a comprehensive reference for geoscientists and machine learning practitioners seeking to harness label-efficient deep learning for intelligent and scalable seismic exploration.
As one of the most widely cultivated edible mushrooms in the world, Pleurotus mushrooms are popular among people for their delicious taste and rich nutritional value. Because of their great economic value, the research on the molecular biology of Pleurotus spp. has been deepening in recent years. The study first summarized the current situation of genomic resources available for this genus. The whole genome sequencing of 14 species, including Pleurotus tuoliensis and Pleurotus ostreatus, provides reference data for mining functional genes. Although the genomic data for Pleurotus mushrooms are continuously increasing, actual instances of successful genetic transformation remain restricted. Research on the regulatory mechanisms of key genes at different developmental stages and under various environmental stresses is insufficient. Then, the application of gene editing methods (CRISPR/Cas9, RNAi, and gene overexpression) in Pleurotus mushrooms was systematically described. RNAi and gene overexpression technologies have become well-established and are routinely used in most Pleurotus mushrooms. However, the application of CRISPR/Cas9 technology is still limited to P. eryngii and P. ostreatus. This limitation is attributed to the difficulties in establishing genetic transformation systems and the low efficiency of homologous recombination. Furthermore, this review explored the value of multi-omics technologies in elucidating the molecular mechanisms of morphogenesis and stress responses. To address the lack of specific antibodies for non-model organisms, we evaluated the application potential of DAP-seq technology in Pleurotus mushrooms and discussed its limitations, including the risk of false positives arising from the absence of a genuine environment in vivo. The purpose of this review is to evaluate the current molecular biology research on Pleurotus spp., and to provide systematic technical support and insights for functional genomics research and the analysis of molecular mechanisms of complex traits in Pleurotus mushrooms.
The anodic small-molecule electrooxidation reaction, which is both thermodynamically and kinetically more favorable than the oxygen evolution reaction, when coupled with the hydrogen evolution reaction, has garnered increasing attention and achieved significant progress. This method presents a promising avenue for hydrogen production at industrial current densities (≥ 200 mA/cm2) via water electrolysis while enabling the synthesis of value-added products or the removal of pollutants. However, the correlations among anode small-molecule types, catalyst design, reaction mechanisms, and electrolytic cell configuration remain unclear at industrial current densities. In this review, the characteristics and challenges of hydrogen production via coupling with various small-molecule oxidation reactions at industrial current densities are discussed for the first time, emphasizing key advances in catalyst design–substrate correlations, reaction mechanisms, and electrolytic cell configuration. Additionally, the challenges and future prospects of this field are explored.
