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This paper describes the design of guaranteed transient performance based attitude control for the near space vehicle （NSV） with control input saturation using the backstepping method. To improve the robust controllability of the NSV, the parameter adaptive method is used to tackle the integrated effect of unknown time-varying disturbance and control input saturation. Based on the backstepping technique and parameter estimated outputs, a robust attitude control scheme is proposed for the NSV with input saturation. A novel robust attitude control scheme is then proposed based on a prescribed performance bound （PPB） which characterizes the convergence rate and maximum overshoot of the attitude tracking error. The closed-loop system stability under both the developed robust attitude control schemes is proved using Lyapunov＇s method and uniformly asymptotical convergence of all closed-loop signals is guaranteed. Finally, simulation results are given to show the effectiveness of both the proposed robust constrained attitude control schemes.

This paper aimed at assessing gravity variations as precursors for earthquake prediction in the Tibet (Xizang)-Qinghai-Xinjiang-Sichuan Region, western China. We here take a statistical approach to evaluate efficiency and possibility of earthquake prediction. We used the most recent spatiotemporal gravity field variation datasets of 2002–2008 for the region that were provided by the Crustal Movement Observation Network of China (CMONC). The datasets were space sparse and time discrete. In 2007–2010, 13 earthquakes (> M s 6.0) occurred in the region. The observed gravity variations have a statistical correlation with the occurrence of these earthquakes through the Molchan error diagram tests that lead to alarms over a good fraction of space–time. The results show that the prediction efficiency of amplitude of analytic signal of gravity variations is better than seismicity rate model and THD and absolute value of gravity variation, implying that gravity variations before earthquake may include precursory information of future large earthquakes.

This paper proposes an anti-disturbance control scheme for the near space vehicle （NSV） based on terminal sliding mode （TSM） technique and disturbance observer method. To tackle the system uncertainty and the time-varying unknown external disturbance of the NSV, a disturbance observer based on TSM technique is designed which can render the disturbance estimate error convergent in finite time. Furthermore, an auxiliary design system is introduced to analyze the input saturation effect. Based on the developed disturbance observer and the auxiliary design system, an anti-disturbance attitude control scheme is developed for the NSV using the TSM technique to speed up the convergence of all signals in closed-loop system. For the closed-loop system, the stability is rigorously proved by using the Lyapunov method and we guarantee the finite time convergence of all closed-loop system signals in the presence of the integrated affection of the system uncertainty, the input saturation, and the unknown external disturbance. Simulation study results are given to show the effectiveness of the developed TSM anti-disturbance attitude control scheme using the disturbance observer and the auxiliary system for the NSV.

We established a three-dimensional finite element model of the Anninghe-Zemuhe-Xiaojiang faults region using contact surfaces of different sizes to describe the spatial segmentation characteristics of the faults. Our model is based on con- straints from GPS observations, models of the crust and upper mantle, precise earthquake locations, the tectonic stress field, the slip rate of the faults, and the rheology of the lithosphere in the Sichuan-Yunnan area. Considering the influence of strong earthquakes since A.D. 1327, we analyzed the main controlling factors of the characteristics of the strong earthquakes and also studied by numerical simulation the possible areas of future earthquake risk and their relationship with tectonic stress. The numerical results showed that the gravitational potential energy of the Qinghai-Tibet Plateau and the interaction of adjacent blocks are the main kinetic factors affecting the characteristics of the tectonic stress distribution. There appears to be some correspondence between the distribution of tectonic stress and the b value; however, we also found that some low b value loca- tions correspond to regions of lower stress. This contradiction may be the result of some comprehensive factors, such as the release of strain energy caused by strong earthquakes.

Earthquake forecast and risk assessment are of key importance in reducing casualties and property losses. However, they have not been fully achieved due to the complexity of earthquakes. Numerous studies have explored the correspondence of the b-value with changes in effective stress, leveraging temporal and spatial variations to identify precursor characteristics of destructive events in both natural and induced seismic activities. However, robust interpretation of predictive b-values hinges on rigorous estimation, as biased results can mislead conclusions. This paper provides a comprehensive review of spatiotemporal b-value estimation methods alongside statistical significance tests. A pilot b-value analysis of natural earthquakes and induced seismicity manifested the valid impression. The expansion of monitoring datasets with the development of acquisition technology or dense array and advanced estimation methodology will augment the utility of b-value analysis in seismic research and hazard assessment.

The MS8.0 earthquake that occurred in Wenchuan, Sichuan in 2008 provides an important case for the study of operational earthquake forecasting and short-term aftershock forecasting of major disaster-inducing earthquakes in China. This paper focuses on the comparative study of the applicability of the epidemic-type aftershock sequence (ETAS) model, the Reasenberg–Jones (R–J) model and the Omi–R–J model, which are widely adopted internationally for short-term aftershock forecasting and seismic hazard mitigation strategy research. We compare the stability of model parameters and aftershock occurrence rate forecasting, and evaluate the effectiveness of forecasting using the N-test and T-test with multiple time windows. The results show that the sequence parameters of the ETAS model, the R–J model and the Omi–R–J model tend to stabilize after 15.50, 15.50 and 6.00 days following the earthquake respectively, and the attenuation of the Wenchuan MS8.0 earthquake is rather normal. Compared to the ETAS model and the R–J model, the Omi–R–J model obtain steadier model parameters in a shorter time with significantly smaller parameters pORJ, cORJ, bORJ and standard deviations. Among the three models, the overall aftershock occurrence rate forecasted by the R–J model is the highest, followed by the Omi–R–J model, while that of the ETAS model is the lowest. N-test results show overall forecasting effectiveness of 93.8, 80.7 and 97.7% for the ETAS, R–J and Omi–R–J models, respectively, with the ETAS and Omi–R–J models superior to the R–J model, and the Omi–R–J model slightly better than the ETAS model. The overall “information gain per earthquake” calculation results show that the ETAS model is superior to the Omi–R–J and R–J models, while the Omi–R–J model is better than the R–J model; thus the combined use of the ETAS and Omi–R–J models by focusing on their respective strengths might ensure optimal performance. These “maneuverable” forecasting approaches to short-term aftershock model forecasting will play a vital role in efficient post-disaster relief, emergency management decision-making and post-disaster reconstruction.

Seismic monitoring is an important verification technique under the Comprehensive Nuclear-Test-Ban Treaty. Phase association technology, which is an important component of seismic data processing, associates signals generated from the same event source recorded at multiple stations and determines event information based on signal features. Seismic event association based on the historical seismic data feature model is a research hot spot in the field of seismic monitoring. In this paper, an event association method called NET-VISA based on Bayesian theory is introduced; then, the application of the historical data feature model in NET-VISA is analyzed. The NET-VISA method is evaluated using the International Data Centre LEB bulletins published by the Comprehensive Nuclear-Test-Ban Treaty Organization, the ISC Reviewed Bulletins, and the China Earthquake Networks Center bulletin as reference sets. The results show that for the global sparse network, NET-VISA is generally superior to the GA method currently used by the IDC, which verifies NET-VISA's effectiveness. However, NET-VISA misses some events detected by the GA. The reasons might be that these events are located in regions with low seismic activity and that insufficient historical event data exists, resulting in unreasonable scoring results.Finally, the application method and research direction of NET-VISA in actual scenarios are discussed.

Crude protein (CP) in diets is essential for maintaining animal health and production performance. However, the protein requirements of Wanxi white geese during the laying period are not well understood. In this study, 120 one-year-old Wanxi white geese were selected and divided into three groups based on similar body weights, namely 14% CP, 15% CP, and 16% CP, with each group consisting of 40 animals. The feed was administered for 120 days. Compared with the 14% CP group, the 15% CP group showed a significant increase in the number of courtships and matings, a reduction in nesting frequency, an enhancement in the egg fertility, and an improvement in the nutritional components, and specific gravity of eggs. Additionally, the 16% CP group promoted the secretion of serum E2, LH, P4, and GnRH while inhibiting the secretion of LEP, compared with the 14% CP group. Taken together, it can be seen that a diet containing 15% CP can enhance the reproductive performance, egg fertility, and egg quality of Wanxi white geese. This study is the first to analyze the effects of different dietary CP levels on the reproductive performance and egg specific gravity of Wanxi white geese during the laying period, providing a theoretical basis for formulating feeding standards for this breed.

Actinobacillus pleuropneumoniae (APP) is a gram-negative pathogenic bacterium responsible for porcine contagious pleuropneumonia (PCP), which can cause porcine necrotizing and hemorrhagic pleuropneumonia. Actinobacillus pleuropneumoniae -RTX-toxin (Apx) is an APP virulence factor. APP secretes a total of four Apx toxins, among which, ApxI demonstrates strong hemolytic activity and cytotoxicity, causing lysis of porcine erythrocytes and apoptosis of porcine alveolar macrophages. However, the protein interaction network between this toxin and host cells is still poorly understood. TurboID mediates the biotinylation of endogenous proteins, thereby targeting specific proteins and local proteomes through gene fusion. We applied the TurboID enzyme-catalyzed proximity tagging method to identify and study host proteins in immortalized porcine alveolar macrophage (iPAM) cells that interact with the exotoxin ApxI of APP. His-tagged TurboID-ApxIA and TurboID recombinant proteins were expressed and purified. By mass spectrometry, 318 unique interacting proteins were identified in the TurboID ApxIA-treated group. Among them, only one membrane protein, caveolin-1 (CAV1), was identified. A co-immunoprecipitation assay confirmed that CAV1 can interact with ApxIA. In addition, overexpression and RNA interference experiments revealed that CAV1 was involved in ApxI toxin-induced apoptosis of iPAM cells. This study provided first-hand information about the proteome of iPAM cells interacting with the ApxI toxin of APP through the TurboID proximity labeling system, and identified a new host membrane protein involved in this interaction. These results lay a theoretical foundation for the clinical treatment of PCP.

Earthquake prediction is a long-standing problem in seismology that has garnered attention from the scientific community and the public. Despite ongoing efforts to understand the physical mechanisms of earthquake occurrence, there is no convincing physical or statistical model for predicting large earthquakes. Machine learning methods, such as random forest and long short-term memory (LSTM) neural networks, excel at identifying patterns in large-scale databases and offer a potential means to improve earthquake prediction performance. Differing from physical and statistical approaches to earthquake prediction, we explore whether small earthquakes can be used to predict large earthquakes within the framework of machine learning. Specifically, we attempt to answer two questions for a given region: (1) Is there a likelihood of a large earthquake (e.g., M ≥ 6.0) occurring within the next year? (2) What is the maximum magnitude of an earthquake expected to occur within the next year? Our results show that the random forest method performs best in classifying large earthquake occurrences, while the LSTM method provides a rough estimation of earthquake magnitude. We conclude that small earthquakes contain information relevant to predicting future large earthquakes and that machine learning provides a promising avenue for improving the prediction of earthquake occurrences.