. In this paper, we present a novel and general framework for color image denoising by integrating a traditional variational approach and learningbased denoisers. Specifically, we incorporate the deep residual convolutional neural network (CNN) prior into the traditional saturation-value total variation (SVTV) based variational model to integrate the advantages of both approaches. Saturation regularization is applied to process the saturation channel of a color image, while CNN based denoiser handles the value channel of a color image. Then, the denoising process is performed in parallel on saturation channel and value channel by using saturation regularization and deep CNN prior respectively. Theoretically, we investigate the properties of the proposed model, and give a detailed discussion based on the mathematical foundation about the solution. Numerically, we formulate an efficient and effective algorithm to solve the proposed model based on the framework of alternating direction method of multipliers (ADMM). Numerical examples are presented to demonstrate that the performance of the proposed framework is competitive in terms of visual quality and some criteria such as peak signal-to-noise ratio (PSNR), structure similarity (SSIM), quaternion structural similarity (QSSIM), and feature similarity (FSIM).
In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, comprehensive comparative analyses of state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task, remain limited. This paper thoroughly reviews the SoTA deep learning-based algorithms, including (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) weakly-supervised methods developed for road crack detection. In addition, we establish UDTIRI-Crack, a curated and standardized benchmark suite comprising 2500 images from seven public annotated datasets, and develop an online benchmark platform to provide standardized, reproducible, and extensible evaluation of algorithms. Comprehensive experiments are conducted to compare the detection performance, computational efficiency, and generalizability of public SoTA deep learning-based algorithms for road crack detection. The feasibility of foundation models and vision–language foundation models for road crack detection is further explored. Finally, the existing challenges and future development trends of deep learning-based road crack detection algorithms are discussed. We believe this review, together with the released dataset and platform, can serve as practical guidance for developing intelligent road detection vehicles with the next-generation road condition assessment systems. The benchmark UDTIRI-Crack is available at https://udtiri.com/submission/.
Ground Delay Programs (GDPs) are widely used to mitigate demand-capacity imbalances in air traffic operations. However, existing studies typically address GDP planning from either the perspective of air traffic control or that of individual airlines, with limited attention to their coordination. This study develops a collaborative framework for the airline-driven GDP that integrates proactive slot reduction and airline precancellation. The problem is formulated as a two-stage stochastic program. The first determines slot reductions and flight precancellations in anticipation of various potential capacity scenarios, whereas the second adjusts slot allocations and executes airline recovery actions based on the realized slot capacity. To address the computational challenges of this large-scale problem, we propose an efficient Lagrangian dual decomposition algorithm, which reduces the computation time by 49.06% on average compared with the standard column generation approach. Numerical experiments based on real-world data show that the proposed framework consistently achieves lower expected total recovery costs than rule-based deterministic sequential strategies commonly used in current GDP practice. Moreover, when fairness constraints are imposed, allowing slot exchanges among airlines further reduces total costs by an average of 1.37%. These findings highlight the importance of collaborative decision-making between air traffic managers and airlines, providing a robust and practical tool to enhance system-wide efficiency under capacity disruptions.
Lignocellulosic materials are difficult to be utilized by microorganisms due to the matrix interactions between lignin and polysaccharides. Rumen microorganisms have been demonstrated to possess their abilities of effective degradation on lignocellulosic materials through the synergistic interactions among fungi, bacteria, and archaea. In this study, the total volatile fatty acids (VFAs) production from straw and algae by rumen microorganisms was found to be facilitated at alkaline pHs. On day 5, the VFAs production without pH regulation was 2024 mg COD/ L, whereas at pH 9.0, it increased to 2979 mg COD/L, representing a 47.2% enhancement. Mechanism investigation revealed that the activities of cellulase responsible for lignocellulose depolymerization, alpha-glucosidase and protease for hydrolysis, and phosphate transacetylase and acetate kinase for acidogenesis were promoted at pH 9.0. The relative abundance of certain fungi (Rhizopus, Wallemia, etc.) and bacteria (Brachymonas, Advenella, etc.) were also higher at pH 9.0. Functional genes, including hpaI and hpcH, exhibited growing abundance for fungi, and HEXA_B, amiABC, mepM, ACSL and fadD for bacteria. In addition, carbohydrate-active enzymes showed a notable enrichment in the abundance of glycosyltransferases and auxiliary activities. This study provided valuable insights into VFAs production from lignocellulosic waste in the complex anaerobic fermentation system fed with rumen microorganisms.
To address the critical challenges of constructing deep shafts in urban central areas, where water‑rich soft soils, strict disturbance control requirements, construction safety, and operational efficiency impose significant constraints, a novel Actively Controlled Prefabricated Press‑in Shaft (ACPP) method has been developed. The core of this method lies in an integrated system that unifies active sinking control, automated underwater excavation, and digital monitoring within a human‑in‑the‑loop control framework. The system is built upon a dedicated boring machine featuring four key hardware innovations: (1) a synchronized hydraulic jack system enabling active force and attitude control; (2) a multi‑degree‑of‑freedom subaqueous excavation robot with automated kinematic control for precise soil removal; (3) a specialized friction‑reducing grouting system to minimize sinking resistance; and (4) an auxiliary support system designed to maintain hydraulic equilibrium and eliminate dewatering risks. Serving as the central platform that orchestrates these hardware components, a real‑time data‑driven visualization and control system integrates multi‑source information to enable remote precision decision‑making, state transparency, and closed‑loop active regulation. The ACPP system performs active sinking regulation through force‑capacity modulation and attitude correction, achieved via differential jacking and zoned excavation management. The method’s effectiveness was demonstrated through a successful application in a 29.5‑m‑deep emergency shaft in Shanghai, where environmental disturbance was minimized (settlement ≤ 5 mm, compared to approximately 30 mm in conventional methods), construction efficiency doubled, and structural quality exceeded standards (verticality 0.62‰, ovality 1.2‰). The ACPP method shows strong potential for widespread application in deep shaft excavation within highly congested urban environments, and its applicability in hard or heterogeneous strata remains to be further validated.
