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Based on the global high-resolution climate model CAMS-CSM developed by Chinese Academy of Meteorological Sciences, the seasonal prediction skill of downward short-wave radiation flux (DSWRF) in China and three key regions is evaluated during the period of 2011-2020. The results show that the high-resolution version of CAMS-CSM can well predict the seasonal and interannual variability of DSWRF, but the predicted intensity is relatively weaker in spring and summer, while slightly stronger in autumn and winter compared to the observation. The prediction of the climate mean state doesn't change much with the lead time, indicating the systematic bias of the DSWRF is formed steadily in the early stage of model integration. However, there are obvious diversities in the prediction skill of the DSWRF anomalies in different seasons and different regions. From the anomalous spatial and temporal correlation coefficients, it can be noted that the prediction skill is higher in Inner Mongolia and Northwest China in autumn and winter, while lower in some areas of Beijing-Tianjin-Hebei in summer and autumn. From the perspective of comprehensive assessment of trend anomalies (P index), the model can score more than 70 points for all seasons in China at 0-month lead time, and the best performance can be close to 80 points for summer and autumn in Northwest China. Overall, the high-resolution version of CAMS-CSM climate model has certain prediction capability for DSWRF at 0-1 month ahead in China, especially in northwest regions where the solar-radiation is rich all year, which can provide specific scientific guidance for the future DSWRF short-term prediction and the solar energy site selection. In addition to the systematic bias of the model, there is a significant negative correlation between the predicted DSWRF bias and the total cloud cover bias, indicating that the bias of DSWRF prediction mainly comes from the simulation bias of total cloud cover, especially in spring and summer, as well as in autumn and winter in South China. In order to improve the prediction accuracy of DSWRF, it is an effective way to reduce the uncertainty of the model cloud microphysical processes. However, it is difficult to meet the demand of practical application with only high-resolution climate model, and its results still need to be processed with methods such as dynamic downscaling and bias revision to further improve the prediction skills.

Summary: In this paper, the state estimation problem and structure identification problem are investigated for an array of complex networks with time‐varying delays and model uncertainty. Model uncertainty is generated by uncertain connections between nodes in complex networks. Combining probabilistic graph model and graph neural network, a probabilistic graph neural network is proposed to deal with the uncertainty, and the multiple linear terms of the errors between nodes are estimated. The purpose of the addressed state estimation problem is to design a state estimator such that, in the presence of the model uncertainty, the estimation error to converge to zero can be guaranteed and the explicit expression of the estimator parameters is given. Based on a Lyapunov function, the parameters of the estimator and the updating laws of the weights of probabilistic graph neural network can be obtained by the solutions to linear matrix inequalities. The purpose of the addressed structure identification problem is to infer a definite graph structure model based on Bayes' theorem in the presence of a stable probabilistic graph neural network and state estimator. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the developed state estimation and structure identification scheme. [ABSTRACT FROM AUTHOR]

Objective: To investigate the association between coronary heart disease (CHD) and the risk of perioperative ischemic stroke in patients undergoing noncardiac surgery. Methods: This retrospective study evaluated the incidence of ischemic stroke within 30 days after a noncardiac surgery. A cohort of 221,541 patients who underwent noncardiac surgery between January 2008 and August 2019 was segregated according to whether they were diagnosed with CHD. Primary, sensitivity, and subgroup logistic regression analyses were conducted to confirm that CHD is an independent risk factor for perioperative ischemic stroke. Propensity score matching analysis was used to account for the potential residual confounding effect of covariates. Results: Among the 221,541 included patients undergoing noncardiac surgery, 484 patients (0.22%) experienced perioperative ischemic stroke. The risk of perioperative ischemic stroke was higher in patients with CHD (0.7%) compared to patients without CHD (0.2%), and multivariate logistic regression analysis showed that CHD was associated with a significantly increased risk of perioperative ischemic stroke (odds ratio (OR), 3.7943; 95% confidence interval (CI) 2.865–4.934; p < 0.001). In a subset of patients selected by propensity score matching (PSM) in which all covariates between the two groups were well balanced, the association between CHD and increased risk of perioperative ischemic stroke remained significantly significant (OR 1.8150; 95% CI, 1.254–2.619; p = 0.001). In the subgroup analysis stratified by age, preoperative β‐blockers, and fibrinogen‐to‐albumin ratio (FAR), the association between CHD and perioperative ischemic stroke was stable (p for interaction >0.05). Subgroup analyses also showed that CHD was significantly increased the risk of perioperative ischemic stroke in the preoperative mean arterial pressure (MAP) ≥94.2 mmHg subgroups (p for interaction <0.001). Conclusion: CHD is significantly associated with an increased risk of perioperative ischemic stroke and is an independent risk factor for perioperative ischemic stroke after noncardiac surgery. Strict control of preoperative blood pressure may reduce the risk of perioperative ischemic stroke for patients with CHD undergoing noncardiac surgery. [ABSTRACT FROM AUTHOR]

Background: Body mass index (BMI) serves as a global metric for assessing obesity and overall health status. However, the impact of BMI, treated as a continuous variable, on the risk of perioperative stroke remains poorly understood. This retrospective cohort study aimed to elucidate the association between BMI and the risk of perioperative ischemic stroke in patients undergoing non‐cardiovascular surgery. Methods: A cohort of 223,415 patients undergoing noncardiac surgery at the First Medical Center of Chinese PLA General Hospital between January 1, 2008 and August 31, 2019 was screened. Preoperative high BMI, defined as BMI >22.64 kg/m2, was the primary exposure, and the outcome of interest was the new diagnosis of perioperative ischemic stroke within 30 days post‐surgery. Robust controls for patient and intraoperative factors were implemented to minimize residual confounding. Logistic regression and propensity score matching were employed, and patients were stratified into subgroups for further investigation. Results: The overall incidence of perioperative ischemic stroke was 0.23% (n = 525) in the cohort. After adjusting for patient‐related variables (OR 1.283; 95% CI, 1.04–1.594; p < 0.05), surgery‐related variables (OR 1.484; 95% CI, 1.2–1.849; p < 0.001), and all confounding variables (OR 1.279; 95% CI, 1.025–1.607; p < 0.05), patients with BMI >22.64 kg/m2 exhibited a significantly increased risk of perioperative ischemic stroke. This association persisted in the propensity score matched cohort (OR 1.577; 95% CI, 1.203–2.073; p < 0.01). Subgroup analyses indicated that preoperative BMI >22.64 kg/m2 correlated with an elevated risk of perioperative ischemic stroke in female patients, those with coronary heart disease, peripheral vascular diseases, and individuals undergoing neurosurgery. Conclusion: We first identified BMI >22.64 kg/m2 as a substantial and independent risk factor for perioperative ischemic stroke in Chinese noncardiac surgery patients. Normal BMI may not suffice as a universal preventive standard. Instead, a more stringent perioperative weight management approach is recommended, particularly for specific subgroups such as female patients, those with coronary heart disease and peripheral vascular disease, and individuals scheduled for neurosurgery. [ABSTRACT FROM AUTHOR]

This article discusses the escalating incidence of scabies in a tertiary hospital in Southern China from 2018 to 2022. Scabies is not a notifiable or recognized disease in China, leading to a lack of epidemic-based data. The study analyzed data from 1345 patients and found a sixfold increase in suspected and clinical scabies cases over the five-year period, with the most significant increase among patients aged 15-44 years. The study also identified a seasonal pattern, with the highest prevalence occurring during the winter season. The actual incidence of scabies may be even higher due to underreporting and misdiagnosis. The increase in cases may be attributed to factors such as shared dormitories and increased awareness of the disease. The study highlights the need to improve the diagnosis of scabies. [Extracted from the article]

The seasonal rainfall regime is a key factor control on local ecological and social processes and is commonly thought to be stable under long‐term climate changes. Here we present a unique high‐resolution rainfall record from the Thai‐Malay Peninsula, combined with a state‐of‐the‐art transient climate simulation, demonstrating a fundamental rainfall regime shift from summer to winter during the Holocene. Transient model simulation and new sensitivity experiments further reveal that westward migration of the boundary between summer and winter rainfall regimes results in a summer to winter rainfall regime shift forced by distinct changes in summer and winter monsoons. Our findings suggest that the seasonal rainfall regime could be unstable under climate change around the boundaries of rainfall regimes in the tropics and possibly worldwide, which might be more critical for shaping both past and future ecological environments. Plain Language Summary: The observed seasonal rainfall regime, which varies over time, challenges the assumption of stable seasonal patterns in reconstructions of paleorainfall, suggesting that a reappraisal of the understanding of past ecological‐social changes with respect to rainfall may be needed. In this study, we have used a high‐resolution rainfall record from the Thai–Malay Peninsula and a state‐of‐the‐art transient paleoclimate simulation to describe a novel summer to winter rainfall regime shift during the Holocene era in the tropics, which may apply to regime‐boundary regions worldwide and thus provide a new perspective on dramatic ecological and social changes recorded around regime boundary regions for both past and future studies. Key Points: A unique rainfall record in the Thai‐Malay and a transient simulation suggest a summer to winter rainfall regime shift during the HoloceneTransient paleoclimate simulation simulation reveals that this shift was caused by westward migration of the boundary between summer and winter rainfall regimesThis regime shift could occur in regions around the boundaries of rainfall regimes in the tropics and possibly worldwide [ABSTRACT FROM AUTHOR]

Control of nonlinear systems with time-varying delays is a common problem, particularly when the time-varying delays are unknown. This paper presents an adaptive compensation control method based on multiple neural networks. Firstly, considering the nonlinear systems with time-varying delays, a neural network method is adopted to approximate the unknown nonlinear structure with time-varying delays. On this basis, a backstepping controller is designed. Considering the unknown time-varying delays, the control effect of the neural network controller based on the backstepping method is not optimal. To deal with the problem, a double-layer adaptive compensator for the neural network is proposed. Furthermore, for the neural network adaptive compensation controller based on the backstepping method, the stability is also proved by a Lyapunov method. Finally, simulation results demonstrate the effectiveness of the scheme, especially when the nonlinear systems have an unknown time-varying delay. [ABSTRACT FROM AUTHOR]

The increasing demand for high-fidelity simulations of compressible turbulence on complex geometries poses a number of challenges for numerical schemes, and plenty of high-order methods have been developed. The high-order methods may encounter spurious oscillations or even blow up for strongly compressible flows, and a number of approaches have been developed, such as slope limiters and artificial viscosity models. In the family of artificial viscosity, which measures smoothness using the modal coefficients, the averaged modal decay (MDA) model employs all of the modes instead of only the highest mode as in the highest modal decay (MDH) model, which tends to underestimate the smoothness. However, the MDA approach requires high-order accuracy (usually P ≥ 4 ) to deliver a reliable estimation of smoothness. In this work, an approach used to extend the MDA model to lower orders, such as P 2 and P 3 , referred to as MDAEX, was proposed, where neighboring elements were incorporated to involve more information in the estimation process. A further controlling of the value of artificial viscosity was also introduced. The proposed model was applied to several typical benchmark cases and compared with other typical models. The results show that the MDAEX model recovers the expected accuracy better than the MDA model for P 2 and P 3 and captures flow structures well for shock-dominated flows. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of state estimation of memristor neural networks with model uncertainties. Considering the model uncertainties are composed of time-varying delays, floating parameters and unknown functions, an improved method based on long short term memory neural networks (LSTMs) is used to deal with the model uncertainties. It is proved that the improved LSTMs can approximate any nonlinear model with any error. On this basis, adaptive updating laws of the weights of improved LSTMs are proposed by using Lyapunov method. Furthermore, for the problem of state estimation of memristor neural networks, a new full-order state observer is proposed to achieve the reconstruction of states based on the measurement output of the system. The error of state estimation is proved to be asymptotically stable by using Lyapunov method and linear matrix inequalities. Finally, two numerical examples are given, and simulation results demonstrate the effectiveness of the scheme, especially when the memristor neural networks with model uncertainties. [ABSTRACT FROM AUTHOR]

In the simulation of compressible turbulent flows via a high-order flux reconstruction framework, the artificial viscosity model plays an important role to ensure robustness in the strongly compressible region. However, the impact of the artificial viscosity model in under-resolved regions on dissipation features or resolving ability remains unclear. In this work, the performance of a dilation-based (DB) artificial viscosity model to simulate under-resolved turbulent flows in a high-order flux reconstruction (FR) framework is investigated. Comparison is conducted with results via several typical explicit subgrid scale (SGS) models as well as implicit large eddy simulation (iLES) and their impact on important diagnostic quantities including turbulent kinetic energy, total dissipation rate of kinetic energy, and energy spectra are discussed. The dissipation rate of kinetic energy is decomposed into several components including those resulting from explicit SGS models or Laplacian artificial viscosity model; thus, an explicit evaluation of the dissipation rate led by those modeling terms is presented. The test cases consist of the Taylor-Green vortex (TGV) problem at R e = 1600 , the freely decaying homogeneous isotropic turbulence (HIT) at M a t 0 = 0.5 (the initial turbulent Mach number), the compressible TGV at Mach number 1.25 and the compressible channel flow at R e b = 15,334 (the bulk Reynolds number based on bulk density, bulk velocity and half-height of the channel), Mach number 1.5. The first two cases show that the DB model behaves similarly to the SGS models in terms of dissipation and has the potential to improve the insufficient dissipation of iLES with the fourth-order-accurate FR method. The last two cases further demonstrate the ability of the DB method on compresssible under-resolved turbulence and/or wall-bounded turbulence. The results of this work suggest the general suitability of the DB model to simulate under-resolved compressible turbulence in the high order flux reconstruction framework and also suggest some future work on controlling the potential excessive dissipation caused by the dilation term. [ABSTRACT FROM AUTHOR]