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This study aimed to explore how air pollution and green space influence ICE recurrence and whether they might interact with each other. A case-cross design was used in this study, which was carried out in Tianjin, China. A total of 8306 patients with recurrent ICE were collected from 2019 to 2020. The maximum effects of PM2.5, PM10, SO2, NO2, CO were 1.012 (95%CI: 1.004, 1.019), 1.010 (95%CI: 1.004, 1.016), 1.035 (95%CI: 0.982, 1.091), 1.067 (95%CI: 1.043, 1.091) and 1.012 (95%CI: 1.004, 1.021) , respectively, and the risk was higher in males and in the 50–60 age group. In the stratification of greening, it was found that air pollution except O3 had the highest risk of ICE recurrence for those with lower green space. Our study found that air pollution (except O3) can increase the risk of ICE recurrence, and this risk can be reduced by increasing green space. [ABSTRACT FROM AUTHOR]

Model order reduction usually consists of two stages: the offline stage and the online stage. The offline stage is the expensive part that sometimes takes hours till the final reduced‐order model is derived, especially when the original model is very large or complex. Once the reduced‐order model is obtained, the online stage of querying the reduced‐order model for simulation is very fast and often real‐time capable. This work concerns a strategy to speed up the offline stage of model order reduction for large and complex systems. In particular, it is successful in accelerating the greedy algorithm that is often used in the offline stage for reduced‐order model construction. We propose to replace the high‐fidelity error estimator in the greedy algorithm with multi‐fidelity error estimation. Consequently, the computational complexity of the greedy algorithm is reduced and the algorithm converges more than two times faster without incurring noticeable accuracy loss. [ABSTRACT FROM AUTHOR]

The Poisson–Boltzmann equation (PBE) is a fundamental implicit solvent continuum model for calculating the electrostatic potential of large ionic solvated biomolecules. However, its numerical solution encounters severe challenges arising from its strong singularity and nonlinearity. In (P. Benner, V. Khoromskaia, B. Khoromskij, C. Kweyu, and M. Stein, "Regularization of Poisson-Boltzmann type equations with singular source terms using the range-separated tensor format," SIAM J. Sci. Comput., vol. 43, no. 1, pp. A415–A445, 2021; C. Kweyu, V. Khoromskaia, B. Khoromskij, M. Stein, and P. Benner, "Solution decomposition for the nonlinear Poisson-Boltzmann equation using the range-separated tensor format," arXiv:2109.14073, 2021), the effect of strong singularities was eliminated by applying the range-separated (RS) canonical tensor format (P. Benner, V. Khoromskaia, and B. N. Khoromskij, "Range-separated tensor format for many-particle modeling," SIAM J. Sci. Comput., vol. 40, no. 2, pp. A1034–A1062, 2018; B. N. Khoromskij, "Range-separated tensor representation of the discretized multidimensional Dirac delta and elliptic operator inverse," J. Comput. Phys., vol. 401, p. 108998, 2020) to construct a solution decomposition scheme for the PBE. The RS tensor format allows deriving a smooth approximation to the Dirac delta distribution in order to obtain a regularized PBE (RPBE) model. However, solving the RPBE is still computationally demanding due to its high dimension N , where N is always in the millions. In this study, we propose to apply the reduced basis method (RBM) and the (discrete) empirical interpolation method ((D)EIM) to the RPBE in order to construct a reduced order model (ROM) of low dimension N ≪ N , whose solution accurately approximates the nonlinear RPBE. The long-range potential can be obtained by lifting the ROM solution back to the N -space while the short-range potential is directly precomputed analytically, thanks to the RS tensor format. The sum of both provides the total electrostatic potential. The main computational benefit is the avoidance of computing the numerical approximation of the singular electrostatic potential. We demonstrate in the numerical experiments, the accuracy and efficacy of the reduced basis (RB) approximation to the nonlinear RPBE (NRPBE) solution and the corresponding computational savings over the classical nonlinear PBE (NPBE) as well as over the RBM being applied to the classical NPBE. [ABSTRACT FROM AUTHOR]

In the context of energy transformation and power market system reform, it is crucial to address the network risks associated with enhancing the integration of the "Energy–Information–Market" paradigm. This necessitates research on multi-energy market trading modes and the corresponding offensive and defensive technologies. This paper proposes a novel approach centered around a node-local Energy Hub (EH) that represents large industrial users with diverse energy demands. To facilitate multi-energy two-way trading, a price-oriented Transactive Energy (TE) market clearing strategy is developed. Building upon this transaction network framework, a data-driven attack strategy targeting the state estimator of the Transmission System Operator (TSO) is introduced and implemented in two stages, encompassing real-time topology estimation and False Data Injection attacks. By leveraging Matrix Transfer Entropy (MTE), the optimal attack target is identified to disrupt the economic stability of the system and the profit of the attacker increases significantly. The proposed attack strategy is validated through simulations conducted on a 30-node system, yielding conclusive evidence of its effectiveness while offering vital insights for system defense. [ABSTRACT FROM AUTHOR]

The prevalence of hypertension in children has increased significantly in recent years in China. The aim of this study was to provide scientific support to control ambient heavy metals (HMs) pollution and prevent childhood hypertension. In this study, ambient HMs in PM2.5 were collected, and 1339 students from Tianjin were randomly selected. Positive matrix factorization (PMF) was used to identify and determine the sources of HMs pollution. The generalized linear model, Bayesian kernel machine regression (BKMR) and the quantile g-computation method were used to analyze the relationships between exposure to HMs and the risk of childhood hypertension. The results showed that HMs in PM2.5 mainly came from four sources: soil dust, coal combustion, incineration of municipal waste and the metallurgical industry. The positive relationships between As, Se and Pb exposures and childhood hypertension risk were found. Coal combustion and incineration of municipal waste were important sources of HMs in the occurrence of childhood hypertension. Based on these accomplishments, this study could provide guidelines for the government and individuals to alleviate the damaging effects of HMs in PM2.5. The government must implement policies to control prime sources of HMs pollution. [ABSTRACT FROM AUTHOR]

Mathematical models of the human heart increasingly play a vital role in understanding the working mechanisms of the heart, both under healthy functioning and during disease. The ultimate aim is to aid medical practitioners diagnose and treat the many ailments affecting the heart. Towards this, modeling cardiac electrophysiology is crucial as the heart's electrical activity underlies the contraction mechanism and the resulting pumping action. Apart from modeling attempts, the pursuit of efficient, reliable, and fast solution algorithms has been of great importance in this context. The governing equations and the constitutive laws describing the electrical activity in the heart are coupled, nonlinear, and involve a fast moving wave front, which is generally solved by the finite element method. The numerical treatment of this complex system as part of a virtual heart model is challenging due to the necessity of fine spatial and temporal resolution of the domain. Therefore, efficient surrogate models are needed to predict the electrical activity in the heart under varying parameters and inputs much faster than the finely resolved models. In this work, we develop an adaptive, projection‐based reduced‐order surrogate model for cardiac electrophysiology. We introduce an a posteriori error estimator that can accurately and efficiently quantify the accuracy of the surrogate model. Using the error estimator, we systematically update our surrogate model through a greedy search of the parameter space. Furthermore, using the error estimator, the parameter search space is dynamically updated such that the most relevant samples get chosen at every iteration. The proposed adaptive surrogate model is tested on three benchmark models to illustrate its efficiency, accuracy, and ability of generalization. [ABSTRACT FROM AUTHOR]

This article immerses in the event‐triggered containment control problem for a Lipschitz‐like nonlinear multi‐agent system with multiple active leaders. First, two event‐triggered mechanisms with internal dynamic variables are designed in both sensor‐observer (S‐O) and controller‐actuator (C‐A) channels to monitor data transmission and reduce communication burden. Second, an observer is constructed with the aid of the output information to estimate the states that cannot be acquired directly. Then, a control protocol considering time‐varying communications delays is proposed based on the observer. Third, the boundedness analysis of the containment error system is constructed by Lyapunov stability theory, the sufficient conditions are given in light of strict feasible LMIs. Finally, the validity of the proposed controller is verified by two simulations. [ABSTRACT FROM AUTHOR]

We develop a compact, reliable model order reduction approach for fast frequency sweeps in microwave circuits by means of the reduced-basis method. Contrary to what has been previously done, special emphasis is placed on certifying the accuracy of the reduced-order model (ROM) with respect to the original full-order model (FOM) in an effective and efficient way. Previous works on model order reduction (MOR) accuracy certification rely on costly a posteriori error estimators, which typically require expensive inf-sup constant evaluations of the underlying FOM. This scenario is often too time-consuming and unaffordable in electromagnetic applications. As a result, less expensive and heuristic error estimators are commonly used instead. Very often, one is interested in knowing about the full state vector, instead of just some output quantities derived from the full state. Therefore, error estimators for the full state vector become relevant. In this work, we detail the frequency behavior of both the electric field and the state error when an approximation to the electric field solution is carried out. Both the field quantities share the same frequency behavior. Based on this observation, we focus on the efficient estimation of the electric field state error and propose a fast evaluation of the ROM state error in the frequency band of analysis, minimizing the number of FOM evaluations. This methodology is of paramount importance to carry out a reliable fast frequency sweep in microwave circuits. Finally, real-life applications will illustrate the capabilities and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

To investigate the health effects of fine particulate matter (≤ 2.5 μm in aerodynamic diameter; PM2.5)-bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) before and after the implementation of the Urban Natural Gas Heating Project (UNGHP), the lifetime cancer risks, hazard quotients (HQs) of heavy metals and PAHs were calculated. Seven kinds of heavy metals (Al, As, Cd, Cr, Mn, Ni and Se) and 12 kinds of PAHs including acenaphthylene (ANY), acenaphthene (ANA), fluoranthene (FLT), pyrene (PYR), chrysene (CHR), benz[a]anthracene (BaA), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), dibenz[a,h]anthracene (DBA), benzo[ghi]perylene (BPE) and indeno[1,2,3-cd]pyrene (IPY) were analyzed and used for the health risk assessments. It was found that HQ of Mn fell from 1.09 in the coal-burning period to 0.72 in the gas-burning period in the suburban area. And lifetime cancer risks of PAHs fell from 35.7 × 10–6 in the coal-burning period to 17.22 × 10–6 in the gas-burning period in the urban area. It could be concluded that, during the gas-burning period, downward trends were observed for the lifetime cancer risks and HQs of most kinds of heavy metals and PAHs in all regions of Tianjin compared to those during the coal-burning period. The UNGHP was effective, and we should also take other measures to control the pollution. [ABSTRACT FROM AUTHOR]

Motivated by a recently proposed error estimator for the transfer function of the reduced-order model of a given linear dynamical system, we further develop more theoretical results in this work. Moreover, we propose several variants of the error estimator, and compare those variants with the existing ones both theoretically and numerically. It is shown that some of the proposed error estimators perform better than or equally well as the existing ones. All the error estimators considered can be easily extended to estimate the output error of reduced-order modeling for steady linear parametric systems. [ABSTRACT FROM AUTHOR]

Adaptive algorithms for computing the reduced‐order model of time‐delay systems (TDSs) are proposed in this work. The algorithms are based on interpolating the transfer function at multiple expansion points and greedy iterations for selecting the expansion points. The ℒ∞‐error of the reduced transfer function is used as the criterion for choosing the next new expansion point. One heuristic greedy algorithm and one algorithm based on the error system and adaptive sub‐interval selection are developed. Results on four TDSs with tens of delays from electromagnetic applications are presented and show the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This study focuses on effects of fine particulate matter (PM2.5) on chronic disease under different levels of temperature. We obtained type 2 diabetes, cerebral stroke and coronary heart disease hospital admissions (HAs) from five hospitals in urban Tianjin as well as the concentrations of PM2.5, nitrogen dioxide (NO2) and sulphur dioxide (SO2). We used distributed lag nonlinear models to explore nonlinear and lag effects of PM2.5. In single-pollutant models, PM2.5 was positively associated with type 2 diabetes, cerebral stroke and coronary heart disease HAs, with strongest effects at lag1, lag0 and lag06, respectively. The corresponding relative risk rates (RR%) were 1.836%, 2.083% and 6.428%. In co-pollutant models, the correlation between PM2.5 and HAs on high-temperature days was generally stronger than that on low-temperature days. This study indicated that PM2.5 can increase HA rates for these chronic diseases, and effects of PM2.5 on high-temperature days were stronger than that on low-temperature days. [ABSTRACT FROM AUTHOR]