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In the terminal guidance phase of radar seeker, low signal to noise ratio (SNR) leads to the deterioration of target detection and positioning performance. Therefore, a joint detection and DOA (Direction of Arrival) estimation algorithm based on random finite set is proposed. In the framework of single target Bernoulli filter, the algorithm constructs the target observation equation for the data after low threshold decision based on the point target spread function, establishes the state variable description in the antenna and line of sight hybrid coordinate system, solves the state vector differential equation and discretizes it to obtain the discrete-time difference equation. The state transition model is obtained through state error analysis, then the particle recursion is used to realize joint detection and state estimation. Simulation results show that the algorithm is effective. Compared with the traditional DBT (Detect before Track) method, the algorithm can improve the detection performance and DOA estimation accuracy at low SNR.

Precipitation changes in full response to CO2 increase are widely studied but confidence in future projections remains low. Mechanistic understanding of the direct radiative effect of CO2 on precipitation changes, independent from CO2‐induced SST changes, is therefore necessary. Utilizing global atmospheric models, we identify robust summer precipitation decreases across North America in response to direct CO2 forcing. We find that spatial distribution of CO2 forcing at land surface is likely shaped by climatological distribution of water vapor and clouds. This, coupled with local feedback processes, changes in convection, and moisture supply resulting from CO2‐induced circulation changes, could determine North American hydroclimate changes. In central North America, increasing CO2 may decrease summertime precipitation by warming the surface and inducing dry advection into the region to reduce moisture supply. Meanwhile, for the southwest and the east, CO2‐induced shift of subtropical highs generates wet advection, which might mitigate the drying effect from warming. Plain Language Summary: How precipitation changes in full response to increased CO2 remains unclear. Mechanistic understanding of how increasing CO2 alone changes precipitation can help us better predict future precipitation in full response to CO2 increase. We find that the precipitation responses are much stronger during summer than winter in North America. During summer, precipitation significantly decreases in central North America in response to the direct CO2 radiative effect, while in the southwest and the east, precipitation changes are small. There might be stronger longwave radiation forcing near the surface induced by CO2 increase in arid regions due to weaker masking effect of water vapor and clouds. This, along with local feedbacks, significantly warms the central region. Meanwhile, the direct forcing of CO2 induces northward shifts in subtropical highs, consequently generating anticyclonic anomalies. These wind anomalies result in drier air advected into central North America, leading to moisture divergence and hence reducing moisture supply. The reduced water supply, coupled with the warming, may decrease precipitation in the region. For the southwest and the east, the anticyclonic anomalies cause wetter air advected into the regions and generate moisture convergence, which increases moisture supply and might mitigate the drying effect caused by CO2‐induced warming. Key Points: Robust precipitation decreases during summer in response to CO2 direct radiative effect are identified in central North AmericaClimatology and changes of hydrological environment may collectively modulate the regional CO2 radiative effect on summer precipitationCO2‐induced circulation changes could alter the moisture supply to reshape the regional hydrological cycle [ABSTRACT FROM AUTHOR]

In order to investigate the effects of brassinolide (BR) on the ecological stoichiometric characteristics and internal stability of plants in slope ecological protection under different drought conditions, the biomass, proline content, nutrient content, and internal stability of tall fescue (Festuca arundinacea) in three stress periods were analyzed by weighing water control method, with three drought degrees (75% ± 5%), mild drought (55% ± 5%) and severe drought (35% ± 5%) and four BR concentrations (0 mg/L, 0.05 mg/L, 0.2 mg/L and 0.5 mg/L). The results showed that drought stress resulted in a decrease in plant biomass and nutrient content, and there were differences in carbon, nitrogen and phosphorus contents and their stoichiometric ratios in different organs. Spraying suitable concentration of BR could alleviate plant nutrient loss and promote nutrient accumulation of the tall fescue. Under normal water spraying conditions 0.2 mg/L, under mild and severe drought conditions with spraying of 0.5 mg/L BR, it is most suitable for the nutrient accumulation in tall fescue. The tall fescue showed high sensitivity to exogenous BR input, and the internal stability of the underground part of the tall fescue increased clearly. BR is propitious to the synthesis of proline and enhances the drought resistance of plants. According to stoichiometric characteristics, BR can improve the nitrogen and phosphorus utilization efficiency of tall fescue to a certain extent, and the results of the nitrogen and phosphorus ratio show that nitrogen is the main factor limiting plant growth in a vegetation concrete ecological restoration system, which can supplement nitrogen to accelerate the process of vegetation restoration. The application of BR can improve the biomass and stress resistance of tall fescue, adjust the nutrient distribution strategy and stoichiometric stability, and alleviate the adverse effects of drought on plants. This study provides new ideas and methods for ecological restoration and vegetation reconstruction in arid areas. [ABSTRACT FROM AUTHOR]

Oscillating neutron stars are sources of continuous gravitational waves. We study analytically the excitation of stellar oscillations by the mechanical impact on the stellar surface of 'clumps' of stochastically accreted matter. We calculate the waveform and spectrum of the gravitational wave signal emitted by the accretion-driven pulsations. Results are generated for an idealized model of a non-rotating, unmagnetized, one-component star with uniform polytropic index n poly assuming Newtonian gravity and the Cowling approximation. We find that the excited mode amplitudes grow with increasing n poly and mode order n. The gravitational wave signal forms a sequence of amplitude-modulated packets for n poly = 1, lasting ∼10−3 s after each impact. The gravitational wave strain increases with increasing n poly, but decreases with increasing n and increasing multipole order l for n poly = 1. In the observing band of current long-baseline interferometers, g modes emit higher, narrower peaks in the amplitude spectral density than f and p modes, with the highest peaks reaching ∼10−26 Hz−1/2 for modes with damping time τ nl ∼ 108 yr. The root-mean-square strain h rms, calculated by summing over modes with 2 ≤ l ≤ 4 and τ nl ≤ 108 yr, spans the range 10−33 ≤ h rms ≤ 10−32 for 1 ≤ n poly ≤ 2. [ABSTRACT FROM AUTHOR]

Background: To derive and validate a machine learning (ML) prediction model of acute kidney injury (AKI) that could be used for AKI surveillance and management to improve clinical outcomes.Methods: This retrospective cohort study was conducted in Fuwai Hospital, including patients aged 18 years and above undergoing cardiac surgery admitted between January 1, 2017, and December 31, 2018. Seventy percent of the observations were randomly selected for training and the remaining 30% for testing. The demographics, comorbidities, laboratory examination parameters, and operation details were used to construct a prediction model for AKI by logistic regression and eXtreme gradient boosting (Xgboost). The discrimination of each model was assessed on the test cohort by the area under the receiver operator characteristic (AUROC) curve, while calibration was performed by the calibration plot.Results: A total of 15,880 patients were enrolled in this study, and 4845 (30.5%) had developed AKI. Xgboost model had the higher discriminative ability compared with logistic regression (AUROC, 0.849 [95% CI, 0.837– 0.861] vs 0.803[95% CI 0.790– 0.817], P< 0.001) in the test dataset. The estimated glomerular filtration (eGFR) and creatine on intensive care unit (ICU) arrival are the two most important prediction parameters. A SHAP summary plot was used to illustrate the effects of the top 15 features attributed to the Xgboost model.Conclusion: ML models can provide clinical decision support to determine which patients should focus on perioperative preventive treatment to preemptively reduce acute kidney injury by predicting which patients are not at risk. [ABSTRACT FROM AUTHOR]

Effective selection of muon candidates is the cornerstone of the LHC physics programme. The ATLAS experiment uses a two-level trigger system for real-time selection of interesting collision events. The first-level hardware trigger system uses the Resistive Plate Chamber detector (RPC) for selecting muon candidates in the central (barrel) region of the detector. With the planned upgrades, the entirely new FPGA-based muon trigger system will be installed in 2025-2026. In this paper, neural network regression models are studied for potential applications in the new RPC trigger system. A simple simulation model of the current detector is developed for training and testing neural network regression models. Effects from additional cluster hits and noise hits are evaluated. Efficiency of selecting muon candidates is estimated as a function of the transverse muon momentum. Several models are evaluated and their performance is compared to that of the current detector, showing promising potential to improve on current algorithms for the ATLAS Phase-II barrel muon trigger upgrade.

Against the backdrop of China's growing environmental concerns, investing in green technology innovation is a crucial solution to achieve the goal of "carbon peak and carbon neutrality". Combining the perspectives of signaling theory and corporate governance theory, we use a sample of Chinese-listed companies from 2008 to 2020 to investigate the influence of analysts' green coverage (AGC) on corporate green innovation (CGI) and the moderating effect of corporate environmental information disclosure (CEID) based on a textual analysis approach. The results show that AGC can significantly promote the quantity and quality of CGI, and CEID has a positive moderating effect on the process. Moreover, the mechanism analysis reveals that enhancing investors' value recognition, improving corporate reputation capital, alleviating corporate financing constraints, reducing management agency costs, and curbing managerial myopia are the influence mechanisms of AGC on CGI. Additionally, the positive effect of AGC is more significant for firms located in regions with a favorable institutional environment, firms belonging to heavily polluting industries, and firms that have not adopted continuous innovation strategies. [ABSTRACT FROM AUTHOR]