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Respiratory diseases arising from co-infections involving Pasteurella multocida (P. multocida) and Mycoplasma ovipneumoniae (Mo) pose a substantial threat to the sheep industry. This study focuses on the isolation and identification of the P. multocida strain extracted from the lung tissue of an argali hybrid sheep infected with Mo. Kunming mice were used as a model to assess the pathogenicity of P. multocida. Subsequently, whole genome sequencing (WGS) of P. multocida was conducted using the Illumina NovaSeq PE150 platform. The whole genome sequencing analysis involved the construction of an evolutionary tree to depict conserved genes and the generation of a genome circle diagram. P. multocida, identified as serotype A, was named P. multocida SHZ01. Our findings reveal that P. multocida SHZ01 infection induces pathological manifestations, including hemorrhage and edema, in mice. The phylogenetic tree of conserved genes analyzing P. multocida from different countries and different host sources indicates close relatedness between the P. multocida SHZ01 strain and the P. multocida 40540 strain (A:12), originating from turkeys in Denmark. The genome of P. multocida SHZ01 comprises 2,378,508 base pairs (bp) with a GC content of 40.89%. Notably, this strain, designated P. multocida, exhibits two distinct gene islands and harbors a total of 80 effector proteins associated with the Type III Secretion System (T3SS). The P. multocida SHZ01 strain harbors 82 virulence genes and 54 resistance genes. In the P. multocida SHZ01 strain, the proteins, genes, and related GO and KEGG pathways have been annotated. Exploring the relationship between these annotations and the pathogenicity of the P. multocida SHZ01 strain would be valuable. This study holds great significance in further understanding the pathogenesis and genetic characteristics of the sheep-derived P. multocida SHZ01 strain. Additionally, it contributes to our understanding of respiratory diseases in the context of co-infection. [ABSTRACT FROM AUTHOR]

Characterizing the coseismic slip behaviors of earthquakes could offer a better understanding of regional crustal deformation and future seismic potential assessments. On 18 December 2023, an Mw 6.0 earthquake occurred on the Lajishan–Jishishan fault system (LJFS) in the northeastern Tibetan Plateau, causing serious damage and casualties. The seismogenic fault hosting this earthquake is not well constrained, as no surface rupture was identified in the field. To address this issue, in this study, we use Interferometric Synthetic Aperture Radar (InSAR) data to investigate the coseismic surface deformation of this earthquake and invert both ascending and descending line-of-sight observations to probe the seismogenic fault and its slip characteristics. The InSAR observations show up to ~6 cm surface uplift caused by the Jishishan earthquake, which is consistent with the thrust-dominated focal mechanism. A Bayesian-based dislocation modeling indicates that two fault models, with eastern and western dip orientations, could reasonably fit the InSAR observations. By calculating the coseismic Coulomb failure stress changes (∆CFS) induced by both fault models, we find that the east-dipping fault scenario could reasonably explain the aftershock distributions under the framework of stress triggering, while the west-dipping fault scenario produced a negative ∆CFS in the region of dense aftershocks. Integrating regional geological structures, we suggest that the seismogenic fault of the Jishishan earthquake, which strikes NNE with a dip of 56° to the east, may be either the Jishishan western margin fault or a secondary buried branch. The optimal finite-fault slip modeling shows that the coseismic slip was dominated by reverse slip and confined to a depth range between ~5 and 15 km. The released seismic moment is 1.61 × 1018 N·m, which is equivalent to an Mw 6.07 earthquake. While the Jishishan earthquake ruptured a fault segment of approximately 20 km, it only released a small part of the seismic moment that was accumulated along the 220 km long Lajishan–Jishishan fault system. The remaining segments of the Lajishan–Jishishan fault system still have the capability to generate moderate-to-large earthquakes in the future. [ABSTRACT FROM AUTHOR]

The fundamental component of the ecological compensation system, as well as the crucial basis for its efficient functioning, is calculating the ecological compensation amount and establishing the ecological compensation standard. This study integrates the ecological footprint with natural capital monetization and other methods by introducing a natural capital accounting system. From the standpoint of natural capital supply and demand, it also builds an accounting framework for ecological compensation standards that is standardized, dynamic, and regionally differentiated while taking local socioeconomic aspects into account. We determined the amount of ecological compensation by using Xinjiang as the research object and calculating and analyzing the features of regional and temporal changes in the monetary and physical quantities of natural capital in Xinjiang from 2010 to 2020. The findings show that from 2010 to 2020, Xinjiang's ecological footprint increased by 1.26 times in physical terms and 1.21 times in monetary terms and that its ecological carrying capacity increased by 4.13% in physical terms and 9.42% in monetary terms. The ecological deficit continues to grow in physical and monetary terms, with a per capita ecological deficit in 2020 of 19.92 s-nha/cap and 70,100 CNY/cap in physical and monetary terms, respectively. The amount of ecological compensation required to be paid in Xinjiang increased from CNY 5659 million to CNY 10,259 million, and the per capita ecological compensation payment standard increased from 259.42 CNY/cap/yr to 396.11 CNY/cap/yr. In summary, Xinjiang's natural capital supply is insufficient to meet the demand for consumption, and the ecological deficit is growing with time, necessitating the payment of ecological compensation. The study's results lay the foundation for formulating and implementing ecological compensation policies in Xinjiang and provide theoretical support for constructing ecological civilization in Xinjiang. In addition, the ecological compensation accounting framework constructed in this study organically integrates natural capital theory, ecosystem services, and socioeconomic influencing factors, which enriches the methodology of accounting for ecological compensation standards, and, at the same time, can be used as a paradigm of a dynamic and equitable ecological compensation accounting framework to further promote its use at different scales and regions. [ABSTRACT FROM AUTHOR]

The constitutive model refers to the mapping relationship between the stress and deformation conditions (such as strain, strain rate, and temperature) after being loaded. In this work, the hot deformation behavior of a Ni-Cr-Mo steel was investigated by conducting isothermal compression tests using a Gleeble-3800 thermal simulator with deformation temperatures ranging from 800 °C to 1200 °C, strain rates ranging from 0.01 s−1 to 10 s−1, and deformations of 55%. To analyze the constitutive relation of the Ni-Cr-Mo steel at high temperatures, five machine learning algorithms were employed to predict the flow stress, namely, back-propagation artificial neural network (BP-ANN), Random Committee, Bagging, k-nearest neighbor (k-NN), and a library for support vector machines (libSVM). A comparative study between the experimental and the predicted results was performed. The results show that correlation coefficient (R), root mean square error (RMSE), mean absolute value error (MAE), mean square error (MSE), and average absolute relative error (AARE) obtained from the Random Committee on the testing set are 0.98897, 8.00808 MPa, 5.54244 MPa, 64.12927 MPa2 and 5.67135%, respectively, whereas the metrics obtained via other algorithms are all inferior to the Random Committee. It suggests that the Random Committee can predict the flow stress of the steel more effectively. [ABSTRACT FROM AUTHOR]

In view of global warming, caused by the increase in the concentration of greenhouse gases, China has proposed a series of carbon emission reduction policies. It is necessary to obtain the spatiotemporal distribution of carbon emissions accurately. Nighttime light data is recognized as an important basis for carbon emission estimation. A large number of research results show that there is a positive correlation between nighttime light intensity and carbon emission. However, in the current context of China's industrial reforms, this positive relationship may not be entirely correct. First, we correct the nighttime light data from different satellites and established a long-term series data set. Then, we verify the positive correlation between nighttime light and carbon emission. However, the time scale of emission data often lags, and the carbon concentration data are released earlier and are more accurate than emission data. Therefore, we propose to investigate the relationship between nighttime light and carbon concentration. It is found that there may be different correlations between nighttime light and the carbon concentration, due to different urban industrial structure and development planning. Therefore, by exploring the relationship between nighttime light and the carbon concentration, the existing carbon emission estimation model can be modified to improve the accuracy of the emission model. [ABSTRACT FROM AUTHOR]

Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each "descriptor" corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global "feature", i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5–10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as O (n) for real-time person re-ID applications. [ABSTRACT FROM AUTHOR]

Peak ground displacement (PGD) and peak ground velocity (PGV) are critical parameters during earthquake early warning, as they can provide rapid magnitude estimation before rupture end. In this study, we used the high-rate Global Navigation Satellite System (GNSS) data from 55 continuous stations to estimate the magnitude of the 2021 Maduo earthquake in western China. We used the relative positioning method and variometric approach to acquire real-time GNSS displacement and velocity waveforms, respectively. The results showed the amplitude of displacement and velocity waveforms gradually decreased with increasing hypocentral distance. Our results showed that the fluctuation of PGD magnitudes over time is smaller than that of PGV magnitudes. Nonetheless, the earthquake magnitudes estimated from both methods were consistent with their counterparts (Mw 7.3) reported by the United States Geological Survey (USGS). The final magnitude estimated from the PGD and PGV methods were Mw 7.25 and Mw 7.31, respectively. In addition, our results highlighted how the number of high-rate GNSS stations could influence the stability and convergence time of magnitude estimation. [ABSTRACT FROM AUTHOR]