A Novel Systematic Error Compensation Algorithm Based on Least Squares Support Vector Regression for Star Sensor Image Centroid Estimation

The star centroid estimation is the most important operation, which directly affects the precision of attitude determination for star sensors. This paper presents a theoretical study of the systematic error introduced by the star centroid estimation algorithm. The systematic error is analyzed through a frequency domain approach and numerical simulations. It is shown that the systematic error consists of the approximation error and truncation error which resulted from the discretization approximation and sampling window limitations, respectively. A criterion for choosing the size of the sampling window to reduce the truncation error is given in this paper. The systematic error can be evaluated as a function of the actual star centroid positions under different Gaussian widths of star intensity distribution. In order to eliminate the systematic error, a novel compensation algorithm based on the least squares support vector regression (LSSVR) with Radial Basis Function (RBF) kernel is proposed. Simulation results show that when the compensation algorithm is applied to the 5-pixel star sampling window, the accuracy of star centroid estimation is improved from 0.06 to 6 × 10−5 pixels

A Stochastic Adjustment Strategy for Coordination Process in Distributed Networks

Cloud computing has become a popular basis that integrated into amount of large platforms to support applications (e.g., multimedia, vehicle traffic, and IoT). It is critical to focus on coordinating the part of these applications that execute in the cloud to provide reliable, scalable and available services. Nevertheless, the problem of optimally coordinating the applications is rarely addressed. In this paper, we develop a stochastic model to analyze the fundamental characteristics that occur in ZooKeeper during the coordination process. The model primarily addresses two aspects: demands of followers and the load of a leader. Then, we derive the optimal strategy for provision with deployment of coordinated servers to achieve load balancing based on various factors (e.g. server capacity and network load), so that the overall network performance is optimized. We evaluate our algorithm under realistic settings and reveal the trend of factors such as CPU, memory utilization and network bandwidth with the increasing number of requests. We propose the algorithm that considers how many servers should be deployed and when. Our results demonstrate that the strategy guarantees the performance by making suitable deployment adjustment

An efficient optimized independent component analysis method based on genetic algorithm

Three simulation experiments are designed to evaluate and compare the performance of three common independent component analysis implementation algorithms – FastICA, JADE, and extended-Infomax. Experiment results show that the above three algorithms can’t separate the mixtures of super-Gaussian and sub-Gaussian precisely, and FastICA fails in recovering weak source signals from mixed signals. In this case an independent component analysis algorithm, which applies genetic algorithm to minimize the difference between joint probability and product of marginal probabilities of separated signals, is proposed. The computation procedure, especially the fitness evaluation when signals are in discrete form, is discussed in detail. The validity of the proposed algorithm is proved by simulation tests. Moreover, the results indicate that the proposed algorithm outperforms the above three common algorithms significantly. Finally the proposed algorithm is applied to separate the mixture of rolling bearing sound signal and electromotor signal, and the results are satisfied

A Survey of the Evolution of Language Model-Based Dialogue Systems

Dialogue systems, including task-oriented_dialogue_system (TOD) and open-domain_dialogue_system (ODD), have undergone significant transformations, with language_models (LM) playing a central role. This survey delves into the historical trajectory of dialogue systems, elucidating their intricate relationship with advancements in language models by categorizing this evolution into four distinct stages, each marked by pivotal LM breakthroughs: 1) Early_Stage: characterized by statistical LMs, resulting in rule-based or machine-learning-driven dialogue_systems; 2) Independent development of TOD and ODD based on neural_language_models (NLM; e.g., LSTM and GRU), since NLMs lack intrinsic knowledge in their parameters; 3) fusion between different types of dialogue systems with the advert of pre-trained_language_models (PLMs), starting from the fusion between four_sub-tasks_within_TOD, and then TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be used to conduct TOD and ODD seamlessly. Thus, our survey provides a chronological perspective aligned with LM breakthroughs, offering a comprehensive review of state-of-the-art research outcomes. What's more, we focus on emerging topics and discuss open challenges, providing valuable insights into future directions for LLM-based_dialogue_systems. Through this exploration, we pave the way for a deeper_comprehension of the evolution, guiding future developments in LM-based dialogue_systems

Macrophage‐derived MMP‐9 enhances the progression of atherosclerotic lesions and vascular calcification in transgenic rabbits

Matrix metalloproteinase‐9 (MMP‐9), or gelatinase B, has been hypothesized to be involved in the progression of atherosclerosis. In the arterial wall, accumulated macrophages secrete considerable amounts of MMP‐9 but its pathophysiological functions in atherosclerosis have not been fully elucidated. To examine the hypothesis that macrophage‐derived MMP‐9 may affect atherosclerosis, we created MMP‐9 transgenic (Tg) rabbits to overexpress the rabbit MMP‐9 gene under the control of the scavenger receptor A enhancer/promoter and examined their susceptibility to cholesterol diet‐induced atherosclerosis. Tg rabbits along with non‐Tg rabbits were fed a cholesterol diet for 16 and 28 weeks, and their aortic and coronary atherosclerosis was compared. Gross aortic lesion areas were significantly increased in female Tg rabbits at 28 weeks; however, pathological examination revealed that all the lesions of Tg rabbits fed a cholesterol diet for either 16 or 28 weeks were characterized by increased monocyte/macrophage accumulation and prominent lipid core formation compared with those of non‐Tg rabbits. Macrophages isolated from Tg rabbits exhibited higher infiltrative activity towards a chemoattractant, MCP‐1 in vitro and augmented capability of hydrolysing extracellular matrix in granulomatous tissue. Surprisingly, the lesions of Tg rabbits showed more advanced lesions with remarkable calcification in both aortas and coronary arteries. In conclusion, macrophage‐derived MMP‐9 facilitates the infiltration of monocyte/macrophages into the lesions thereby enhancing the progression of atherosclerosis. Increased accumulation of lesional macrophages may promote vascular calcification.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154927/1/jcmm15087-sup-0001-FigS1-S13.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154927/2/jcmm15087.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154927/3/jcmm15087_am.pd

Prevalence characteristic of BVDV in some large scale dairy farms in Western China

The aim of this study was to analyze the prevalence characteristic of Bovine viral diarrhea virus (BVDV) in some large scale dairy farms in Western China. BVDV was detected in 30 samples of bulk tank milk (BTM) collected from 30 large dairy farms in 7 provinces of western China, 93.33% (28/30) of the farms were infected with BVDV, and S/P ratio was over 0.3 in 28 positive farms. The individual status was further estimated in the dairy farm (No. 10) with the highest positive rate (S/P ratio = 1.37) and the dairy farm (No. 17) with the lowest positive rate (S/P ratio = 0.39). Two hundred cows were, respectively, selected from calf, young cows and lactating cows in farm No. 10 and farm No. 17 and the serum sample of each enrolled cow was collected. The individual positive rate of serum antibody (Ab) was 87.17% (523/600) in farm No. 10 and 31.33% (188/600) in farm No. 17. The individual positive ratio of serum antibody in calves, young cows and lactating cows were 41.75 % (167/400), 58.75% (235/400) and 77.25% (309/400), respectively. BTM Ab of farm No. 10 has an S/P ratio more than 1.0, which indicated there were emergent or persistent infection (PI) cases, and further test showed that PI cases were 0.51% in farm No. 10. Pathogens were positive in 42.34% (163/385) of nasal mucus samples collected from cows with respiratory symptom, and BVDV cases were 57 in 163 positive samples. Three strains of NCP BVDV-1, one strain of CP BVDV-1, one strain of NCP BVDV-2 and one strain of CP BVDV-2 were successfully isolated. Phylogenetic analysis revealed that the subtypes of BVDV currently prevalent in western China were BVDV-1a, BVDV-1m, BVDV-1q and BVDV-2. The findings suggested that the BVDV infection is serious in some Large Scale Dairy Farms in Western China

Porphyromonas gingivalis induces an inflammatory response via the cGAS-STING signaling pathway in a periodontitis mouse model

Periodontitis is an inflammatory disease initiated by periodontopathogenic bacteria in the dental plaque biofilms. Understanding the role of Porphyromonas gingivalis (P. gingivalis), a keystone pathogen associated with chronic periodontitis, in the inflammatory response is crucial. Herein, we investigated whether P. gingivalis infection triggers the expression of the type I IFN gene and various cytokines and leads to activation of the cGAMP synthase–stimulator of IFN genes (cGAS-STING) pathway both in vitro and in a mouse model. Additionally, in an experimental model of periodontitis using P. gingivalis, StingGt mice showed lower levels of inflammatory cytokines and bone resorption than wild-type mice. Furthermore, we report that a STING inhibitor (SN-011) significantly decreased inflammatory cytokine production and osteoclast formation in a periodontitis mouse model with P. gingivalis. In addition, STING agonist (SR-717) -treated periodontitis mice displayed enhanced macrophage infiltration and M1 macrophage polarization in periodontal lesions compared with that in vehicle-treated periodontitis mice. In conclusion, our results demonstrate that the cGAS-STING signaling pathway may be one of the key mechanisms crucial for the P. gingivalis-induced inflammatory response that leads to chronic periodontitis

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve