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The performance of the gimbal servo system in control moment gyro (CMG), which includes precision, lifespan etc., is one of the crucial factors of spacecraft attitude control. The various practical disturbances, however, will not only deteriorate the velocity‐tracking accuracy but will also cause the abnormal gimbal velocity problem (especially peak phenomenon). To this end, this paper proposes a refined anti‐disturbance control method to deal with velocity output constraints and multiple disturbances. Starting with fully understanding the prior information of multiple disturbances, a refined disturbance observer with a low conservativeness is designed to accurately estimate disturbances. The disturbance‐estimation error is analyzed in detail to ensure convergence to a bounded region. Subsequently, a novel barrier Lyapunov function‐based backstepping controller is proposed that considers the residuals of disturbance estimation to simultaneously achieve multiple disturbances attenuation and compensation, and handle velocity output constraints. Notably, the gimbal's maximum velocity is precisely limited to a pre‐specified low range, which benefits the CMG's lifespan and performance. Finally, both simulation and experimental results show that the proposed method performs better in disturbance estimation, velocity tracking, and robustness. This paper proposes a refined anti‐disturbance control method to deal with velocity output constraints and multiple disturbances. The gimbal's maximum velocity is precisely limited to a pre‐specified low range, which benefits the CMG's lifespan and performance. Both simulation and experimental results show that the proposed method performs better in disturbance estimation, velocity tracking, and robustness.

This paper studies the maximum reliability of multi-hop relay UAVs, in which UAVs provide wireless services for remote users as a coded cooperative relay without an end-to-end direct communication link. In this paper, the analytical expressions of the total power loss and total bit error rate are derived as reliability measures. First, based on the environmental statistical parameters, a LOS probability model is proposed. Then, the problem of minimizing the bit error rate of static and mobile UAVs is studied. The goal is to minimize the total bit error rate by jointly optimizing the height, elevation, power and path loss and introducing the maximum allowable path loss constraints, transmission power allocation constraints, and UAV height and elevation constraints. At the same time, the total path loss is minimized to achieve maximum ground communication coverage. However, the formulated joint optimization problem is nonconvex and generally difficult to solve. Therefore, we decomposed the problem into two subproblems and proposed an effective joint optimization iteration algorithm. Finally, the simulation results are given, and the analysis shows that the optimal height of different reliability measures is slightly different; thus, using the mobility of UAVs can improve the reliability of communication performance.

Background Tumor-associated macrophages (TAMs) are key components of colorectal cancer (CRC) microenvironment, but their role in CRC prognosis is not fully defined. Objective This study aimed to evaluate prognostic value of different types and distribution of TAMs in CRC. Methods Total 27 studies with 6115 patients were searched from PubMed and Embase and analyzed to determine the association between TAMs, including distinct TAM subsets and infiltration location, and CRC survival. The prognostic impact of TAMs on CRC was further stratified by tumor type and mismatch repair system (MMR) status. Results A pooled analysis indicated that high density of TAMs in CRC tissue was significantly associated with favorable 5-year overall survival (OS) but not with disease-free survival (DFS). CD 68 + TAM subset correlated with better 5-year OS, while neither CD68 + NOS2 + M1 subset nor CD163 + M2 subset was correlated with 5-year OS. Increased CD68 + TAM infiltration in tumor stroma but not in tumor islet predicted improved 5-year OS. Stratification by tumor type and MMR status showed that in colon cancer or MMR-proficient CRC, elevated TAM density was associated with better 5-year OS. Conclusions High infiltration of CD68 + TAMs could be a favorable prognostic marker in CRC. Future therapies stimulating CD68 + TAM infiltration may be promising in CRC treatment.

Accurate sensing and control are important for high-performance formation control of spacecraft systems. This paper presents a strategy of disturbance estimation and distributed predefined-time control for the formation of multi-spacecraft systems with uncertainties based on a disturbance observer. The process begins by formulating a kinematics model for the relative motion of spacecraft, with the formation’s communication topology represented by a directed graph for the formation system of the spacecraft. A disturbance observer is then developed to estimate the disturbances, and the estimation errors can be convergent in fixed time. Following this, a disturbance-estimation-based sliding mode control is proposed to guarantee the predefined-time convergence of the multi-spacecraft formation system, regardless of initial conditions. It allows each spacecraft to reach its desired position within a set time frame. The results of the analysis of the multi-spacecraft formation system are also provided. Finally, an example simulation of a five-spacecraft formation flying system is provided to demonstrate the presented formation control method.

Vibration signals are widely used in fault diagnosis of rotating machinery in real-world situations. However, it is very challenging to extract effective fault features from noisy signals and construct an accurate diagnosis model. In this paper, we propose a novel Gaussian–Bernoulli deep belief network (GDBN) model for intelligent fault diagnosis, where improved graph regularization and sparse features learning are embedded in the GDBN smoothly. In particular, the improved graph regularization is added to the hidden layer of original and reconstructed data. Therefore, our model can not only transform the original data into features with improved separability, but also generate discriminant features from vibration signal. An unsupervised pre-training learning process followed by a supervised fine-tuning is implemented in proposed model to contribute the classification capabilities. The effectiveness and superiority of the proposed model have been validated by gearbox and bearing cases studies. The results illustrate that our model can learn effective discriminative features and the extracted features are more separable. Furthermore, the proposed model achieves the better diagnosis accuracy in comparison with that of other models.

Soil aggregate formation and stabilization are pivotal for remediating saline-alkali soils, but the potential of cooling-phase organic fertilizer (CPOF)—a transitional stage in composting—has been overlooked. This study systematically investigated the regulatory effects of CPOF on saline soil aggregate systems, focusing on two key objectives: (1) comparing the efficacy of CPOF with organic fertilizers from other composting phases (initial, thermophilic, matured) in enhancing macroaggregate proportion and stability; and (2) clarifying how CPOF’s unique organic components influence carbon speciation across aggregate size fractions. Our results demonstrated that CPOF outperformed other composting phases in improving soil physical properties and aggregate dynamics. It reduced soil bulk density by 18.5% and increased porosity by 22.3%, driven by three mechanisms: (1) dominant humification microorganisms (e.g., Actinobacteria) secreting extracellular polymeric substances (EPS) that bind soil particles; (2) a balanced ratio of labile and stable organic components, providing both immediate binding agents and long-term humic substances; and (3) enhanced microbial-mineral interactions via ligand exchange and cation bridging. In aggregate dynamics, CPOF significantly promoted macroaggregate (>0.25 mm) formation and stability, with peak effects at 180 days. This was attributed to fungal communities (e.g., Peniophora, Mucorales) producing glomalin-related soil proteins (GRSP) and calcium-mediated particle bridging. CPOF also increased organic carbon content in macroaggregates by 18–22% through labile carbon (supporting microbial activity) and transitional humic substances (facilitating long-term sequestration), with sustained stability over 540 days. These findings highlight CPOF as a time-sensitive amendment that synchronizes microbial activity with physical carbon protection, offering a scalable solution for saline soil rehabilitation. A proposed two-phase strategy—initial CPOF application for rapid improvement, followed by mature compost for long-term maintenance—could enhance carbon sequestration efficiency by 15–20% in regions like the Yellow River Basin, supporting climate-smart agriculture and global saline soil management.

In order to improve the adaptability and robustness of gliding guidance under complex environments and multiple constraints, this study proposes an intelligent gliding guidance strategy based on the optimal guidance, predictor-corrector technique, and deep reinforcement learning (DRL). Longitudinal optimal guidance was introduced to satisfy the altitude and velocity inclination constraints, and lateral maneuvering was used to control the terminal velocity magnitude and position. The maneuvering amplitude was calculated by the analytical prediction of the terminal velocity, and the direction was learned and determined by the deep Q-learning network (DQN). In the direction decision model construction, the state and action spaces were designed based on the flight status and maneuvering direction, and a reward function was proposed using the terminal predicted state and terminal constraints. For DQN training, initial data samples were generated based on the heading-error corridor, and the experience replay pool was managed according to the terminal guidance error. The simulation results show that the intelligent gliding guidance strategy can satisfy various terminal constraints with high precision and ensure adaptability and robustness under large deviations.

The simulation of overland flow, wherein runoff yield and concentration are influenced by topography, is fundamental to hydrological forecasting. Therefore, critically evaluating the characteristics of overland flow under the influence of topographic depressions—which are one of the most common microtopographic structures—is vital for improving current hydrological models. In this study, we developed a solution for the real-world application of overland flow simulations under the influence of depressions in hydrological models. A relative depression storage–outflow curve (RDOC) was proposed to investigate surface outflow processes. Experiments were conducted based on the variable-controlling approach using three rainfall return periods, four slopes, and four depression rates while ensuring a consistent initial soil moisture content. A homogenized RDOC was achieved based on shape analysis; it was parameterized by the outflow threshold and the reciprocal of the curve index of two outflow stages (B and D). A relative depression storage–outflow function (RDOF) was generated and a complete calculation procedure was applied within a hydrological model. Furthermore, we analyzed the hydrological responses to parameters of different hydrological factors to improve our understanding of the parameter determination of the RDOF.

Efficient coordination and planning is essential for large-scale multi-agent systems that collaborate in a shared dynamic environment. Heuristic search methods or learning-based approaches often lack the guarantee on correctness and performance. Moreover, when the collaborative tasks contain both spatial and temporal requirements, e.g., as linear temporal logic (LTL) formulas, formal methods provide a verifiable framework for task planning. However, since the planning complexity grows exponentially with the number of agents and the length of the task formula, existing studies are mostly limited to small artificial cases. To address this issue, a new planning paradigm is proposed in this work for system-wide temporal task formulas that are released online and continually. It avoids two common bottlenecks in the traditional methods, i.e., (a) the direct translation of the complete task formula to the associated Büchi automaton and (b) the synchronized product between the Büchi automaton and the transition models of all agents. Instead, an adaptive planning algorithm is proposed, which computes the product of relaxed partially ordered sets (R-posets) on-the-fly and assigns these subtasks to the agents subject to the ordering constraints. It is shown that the first valid plan can be derived with a polynomial time and memory complexity with respect to the system size and the formula length. Our method can take into account task formulas with a length of more than 400 and a fleet with more than 400 agents, while most existing methods fail at the formula length of 25 within a reasonable duration. The proposed method is validated on large fleets of service robots in both simulation and hardware experiments.

Daily streamflow modeling is an important tool for water resources management and flood mitigation. This study compared the performance of the Xinanjiang (XAJ) model and random forests (RF) method in a daily streamflow simulation, and proposed several hybrid models based on the XAJ model, wavelet analysis, and RF method (including XAJ-RF model, WRF model, and XAJ-WRF model). The proposed methods were applied to Shiquan station, located in the Upper Han River basin in China. Five performance measures (NSE, RMSE, PBIAS, MAE, and R) were adopted to evaluate the modeling accuracy. Results showed that XAJ-RF model had a relatively higher level of accuracy than that of the XAJ model and the RF model. Compared to the RF and XAJ-RF models, the performance statistics of WRF and XAJ-WRF were better. The results indicated that the coupled XAJ-RF model can be effectively applied and provide a useful alternative for daily streamflow modeling and the application of wavelet analysis contributed to the increasing accuracy of streamflow modeling. Moreover, 14 wavelet functions from various families were tested to analyze the impact of various mother wavelets on the XAJ-WRF model.