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Lung cancer is the cancer with the highest mortality, and non‐small cell lung cancer (NSCLC) accounts for more than 80%. Tumor cells often have high reactive oxygen species (ROS) and antioxidant capacity. Redox balance is very important for tumor. The decline of antioxidant capacity and excessive ROS will induce the death of tumor cells. Destroying the redox balance of tumor cells is a promising tumor treatment strategy. Xanthatin is an active sesquiterpene lactone isolated from Xanthium strumarium L. We observed that xanthatin induced the up regulation of mitochondrial ROS and mitochondrial damage. Meanwhile, our results showed that xanthatin could inhibit system xc− and reduce glutathione (GSH) synthesis. Antioxidant GSH and N‐acetyl‐ l‐cysteine (NAC) significantly reversed cell proliferation inhibition and apoptosis induced by xanthatin. β‐Mercaptoethanol (β‐ME) which can avoid inhibition of system xc− can also reverse the inhibition of cell proliferation induced by xanthatin, si‐SLC7A11 was the opposite. Based on these results, we believe that the inhibition of xanthatin on the proliferation of NSCLC cells may be related to breaking the intracellular redox balance. Our data suggest that xanthatin is a promising antitumor candidate for the treatment of NSCLC. [ABSTRACT FROM AUTHOR]

Bayesian Physics Informed Neural Networks (B-PINNs) have gained significant attention for inferring physical parameters and learning the forward solutions for problems based on partial differential equations. However, the overparameterized nature of neural networks poses a computational challenge for high-dimensional posterior inference. Existing inference approaches, such as particle-based or variance inference methods, are either computationally expensive for high-dimensional posterior inference or provide unsatisfactory uncertainty estimates. In this paper, we present a new efficient inference algorithm for B-PINNs that uses Ensemble Kalman Inversion (EKI) for high-dimensional inference tasks. By reframing the setup of B-PINNs as a traditional Bayesian inverse problem, we can take advantage of EKI's key features: (1) gradient-free, (2) computational complexity scales linearly with the dimension of the parameter spaces, and (3) rapid convergence with typically O (100) iterations. We demonstrate the applicability and performance of the proposed method through various types of numerical examples. We find that our proposed method can achieve inference results with informative uncertainty estimates comparable to Hamiltonian Monte Carlo (HMC)-based B-PINNs with a much reduced computational cost. These findings suggest that our proposed approach has great potential for uncertainty quantification in physics-informed machine learning for practical applications. • Proposed a gradient-free inference algorithm for Bayesian physics informed neural networks (BPINNs). • Reframed BPINNs as a classic inverse problem to utilize Ensemble Kalman Inversion (EKI). • Demonstrated our method achieves informative uncertainty estimates at lower cost. [ABSTRACT FROM AUTHOR]

Scientific and engineering problems often involve parametric partial differential equations (PDEs), such as uncertainty quantification, optimizations, and inverse problems. However, solving these PDEs repeatedly can be prohibitively expensive, especially for large-scale complex applications. To address this issue, reduced order modeling (ROM) has emerged as an effective method to reduce computational costs. However, ROM often requires significant modifications to the existing code, which can be time-consuming and complex, particularly for large-scale legacy codes. Non-intrusive methods have gained attention as an alternative approach. However, most existing non-intrusive approaches are purely data-driven and may not respect the underlying physics laws during the online stage, resulting in less accurate approximations of the reduced solution. In this study, we propose a new non-intrusive bi-fidelity reduced basis method for time-independent parametric PDEs. Our algorithm utilizes the discrete operator, solutions, and right-hand sides obtained from the high-fidelity legacy solver. By leveraging a low-fidelity model, we efficiently construct the reduced operator and right-hand side for new parameter values during the online stage. Unlike other non-intrusive ROM methods, we enforce the reduced equation during the online stage. In addition, the non-intrusive nature of our algorithm makes it straightforward and applicable to general nonlinear time-independent problems. We demonstrate its performance through several benchmark examples, including nonlinear and multiscale PDEs. • We proposed a novel bi-fidelity non-intrusive reduced order model for parametric time-independent PDE problems. • Unlike other non-intrusive reduced order models, we enforce reduced equation during online stage to improve accuracy. • We demonstrated the effectiveness of our method over various benchmarks, including nontrivial multiscale problems. [ABSTRACT FROM AUTHOR]

Acrylonitrile (AN) is a known animal carcinogen and suspected human carcinogen. Recently, occupational exposure to AN has considerably increased. Previously, we demonstrated that streptozotocin-induced diabetes potentiates AN-induced acute toxicity in rats and that the induced cytochrome P450 2E1 (CYP2E1) is responsible for this effect. In the present study, we examined whether induction of CYP2E1 is also the underlying mechanism for the potentiation of AN-induced acute toxicity in type 2 diabetes in db/db mice. The effect of phenethyl isothiocyanate (PEITC) in reducing potentiation was also investigated. The mice were randomly divided into the normal control, diabetic control, AN, diabetes + AN, PEITC + AN, and diabetes + PEITC + AN groups. PEITC (40 mg/kg) was orally administered to rats for 3 days, and 1 h after the last PEITC gavage, 45 mg/kg AN was intraperitoneally injected. Time to death was observed. The CYP2E1 level and enzymatic activity, cytochrome c oxidase (CCO) activity, and reactive oxygen species (ROS) levels were measured. The survival rate was decreased in AN-treated db/db mice compared with that in AN-treated wild-type mice. The hepatic CYP2E1 level and enzymatic activity remained unaltered in db/db mice. Phenethyl isothiocyanate alleviated AN-induced acute toxicity in db/db mice as evident in the increased survival rate, restored CCO activity, and decreased ROS level in both the liver and brain. The study results suggested that CYP2E1 may not be responsible for the sensitivity to AN-induced acute toxicity in db/db mice and that PEITC reduced the potentiation of AN-induced acute toxicity in db/db mice. [ABSTRACT FROM AUTHOR]

Dibromoacetonitrile (DBAN) is a high-risk haloacetonitrile (HAN) generated as a byproduct of chloramine disinfection in drinking water. DBAN-induced neurotoxicity in mouse hippocampal neuronal cells (HT22) and mammals was observed to be related to reactive oxygen species (ROS). ROS, endoplasmic reticulum stress (ERS) and autophagy play crucial roles in regulating a variety of cellular processes. However, whether ERS and autophagy are associated with HAN-responsive apoptosis remains unclear. This study indicated that DBAN (10 μM, 24 h) activated the ERS protein kinase like endoplasmic reticulum kinase (PERK) signaling pathway. The ERS inhibitor 4-phenylbutyric acid (4-PBA) reversed DBAN-inhibited cell viability and alleviated DBAN-induced apoptosis in HT22 cell, indicating that activation of the ERS PERK pathway mediates DBAN induced cytotoxicity. Moreover, DBAN activated autophagy. The autophagy inhibitor 3-methyladenine(3-MA) reversed DBAN-inhibited cell viability and alleviated DBAN-induced apoptosis in HT22 cell, suggesting that autophagy activation mediates DBAN-induced cell toxicity. Notably, the results showed that 4-PBA inhibited DBAN-activated autophagy, demonstrating that ERS–PERK promotes DBAN-induced cellular autophagy. Pretreatment with antioxidant N-acetylcysteine (NAC) inhibited the increase in ROS production and the activation of ERS, and protected cells from toxicity. Furthermore, 4-PBA pretreatment reduced the increase in ROS production, indicating that the ROS and PERK promote each other and form a positive feedback loop. ROS also promoted DBAN-induced autophagy. In summary, our findings indicate that DBAN induced autophagy by mediating the PERK signalling pathway and ROS interaction, leading to HT22 cell damage. Accordingly, targeting these pathogenic mechanisms may provide a potential target and theoretical basis for preventing and improving HAN-induced neurotoxicity. [Display omitted] • Dibromoacetonitrile (DBAN) exhibited a significant toxicity in HT22 cells. • DBAN induced endoplasmic reticulum stress (ERS)-mediated autophagy in HT22 cell. • DBAN induced autophagy by mediating ERS and reactive oxygen species (ROS) interaction in HT22 cell. • DBAN induced ERS through activating pathway of protein kinase-like endoplasmic reticulum kinase (PERK). • ROS and PERK pathway form a positive feedback loop in DBAN-treated HT22 cell. [ABSTRACT FROM AUTHOR]

In this article, we present one numerical approach to infer the model parameters and state variables of acoustic wave equations. The method we consider is based on the recently proposed method-the so-called hidden physics model. With placing Gaussian process (GP) prior on the state variables, the structure and model parameters of acoustic wave equations are encoded into the kernel function of a multioutput GP. The purpose of this article includes: 1) testing the applicability of hidden physics model to infer the velocity, density, and state variables of the acoustic wave equation, which is important for many applications in geophysics; 2) adapting the method to handle for both homogeneous and heterogeneous media that are of practical interest; and 3) exploring efficient sequential sampling methods to improve the sampling efficiency. We suggest that the expected-improvement-based sequential sampling method would be effective for most practical problems related to acoustic wave propagation. Besides, we demonstrate the performance of the proposed scheme via several benchmark problems. [ABSTRACT FROM AUTHOR]

We present an efficient numerical algorithm to approximate the statistical moments of stochastic problems, in the presence of models with different fidelities. The method extends the multi-fidelity approximation method developed in [18,26] . By combining the efficiency of low-fidelity models and the accuracy of high-fidelity models, our method exhibits fast convergence with a limited number of high-fidelity simulations. We establish an error bound of the method and present several numerical examples to demonstrate the efficiency and applicability of the multi-fidelity algorithm. [ABSTRACT FROM AUTHOR]

Multiscale elliptic equations with scale separation are often approximated by the corresponding homogenized equations with slowly varying homogenized coefficients (the G-limit). The traditional homogenization techniques typically rely on the periodicity of the multiscale coefficients, thus finding the G-limits often requires sophisticated techniques in more general settings even when multiscale coefficient is known, if possible. Alternatively, we propose a simple approach to estimate the G-limits from (noise-free or noisy) multiscale solution data, either from the existing forward multiscale solvers or sensor measurements. By casting this problem into an inverse problem, our approach adopts physics-informed neural networks (PINNs) algorithm to estimate the G-limits from the multiscale solution data by leveraging a priori knowledge of the underlying homogenized equations. Unlike the existing approaches, our approach does not rely on the periodicity assumption or the known multiscale coefficient during the learning stage, allowing us to estimate homogenized coefficients in more general settings beyond the periodic setting. We demonstrate that the proposed approach can deliver reasonable and accurate approximations to the G-limits as well as homogenized solutions through several benchmark problems. [ABSTRACT FROM AUTHOR]

A numerical approach based on the hidden physics model to estimate the model parameters of elastic wave equations with the sparse and noisy data is presented in this paper. Through discretizing the time derivatives of elastic wave equations and placing the priors of the state variables as Gaussian process, the model parameters and structure of elastic wave equations are encoded in the kernel function of a multi-output Gaussian process. In the learning stage, a parameter bound constraint condition is incorporated to enforce the physical bound of the model parameters. The numerical results from several benchmark problems, including homogeneous media, layer media, anisotropic media, and homogeneous model with an inclusion, demonstrate the feasibility and performance of the hidden physics model. • A hidden physics model is developed for parameter estimation of elastic wave equations. • A Physics-informed kernel is derived to encode the structure and model parameters of elastic wave equations. • An active learning strategy is suggested to improve the efficiency. • The performance of the hidden physics model is demonstrated by several benchmark problems. [ABSTRACT FROM AUTHOR]

Dibromoacetonitrile (DBAN) and dichloroacetonitrile (DCAN) are haloacetonitriles (HANs) produced as by-products of chloramine disinfection of drinking water and can cause neurotoxicity. The molecular pathways leading to HAN-induced neuronal cell death remain unclear. The nuclear factor erythroid 2–related factor 2 (Nrf2) is an important regulator of oxidation reactions. We explored the role of the sequestosome 1 (p62)–Kelch-like ECH-associated protein 1 (Keap1)–Nrf2 pathway in DBAN- and DCAN-induced mouse hippocampal neuronal (HT22) cell injury. DBAN and DCAN reduced cell viability, increased lactate dehydrogenase release rate, and promoted apoptosis. Over the same treatment time, DBAN at lower concentrations caused cell injury, suggesting that DBAN is more cytotoxic than DCAN. DBAN and DCAN triggered oxidative stress by reducing intracellular glutathione and increasing reactive oxygen species concentrations. DBAN and DCAN activated the Nrf2 pathway. Furthermore, Nrf2 inhibitors (all-trans retinoic acid) attenuated DBAN- and DCAN-induced toxicity, whereas Nrf2 activators (tert-Butylhydroquinone) achieved the opposite effect. This indicates that activation of the Nrf2 pathway mediates DBAN- and DCAN-induced cell injury. Notably, the expression of p62, a noncanonical pathway that mediates Nrf2 activation, increased, whereas the expression of Keap1, another regulator of Nrf2, decreased. We noted that high p62 expression activated the Nrf2 pathway, and p62 was regulated through Nrf2, forming a positive feedback loop. N-acetyl- L -cysteine, a mercaptan substance, protected against DBAN- and DCAN-induced toxicity and inhibited the Nrf2 pathway. In summary, Nrf2 pathway inhibition and mercaptan supplementation prevent DBAN- and DCAN-induced HT22 cell injury, accordingly, targeting them is a potential approach to preventing HAN-induced neurotoxicity. [Display omitted] • DBAN and DCAN induce apoptosis and oxidative injury in HT22 cells. • DBAN is more cytotoxic than is DCAN. • DBAN and DCAN activate the p62–Keap1–Nrf2 pathway. • Activation of the Nrf2 mediates DBAN- and DCAN-induced cell injury. • AC prevents DBAN- or DCAN-induced cell injury and inhibits the Nrf2 pathway. [ABSTRACT FROM AUTHOR]

Image-based computational fluid dynamics (CFD) modeling enables derivation of hemodynamic information (e.g., flow field, wall shear stress, and pressure distribution), which has become a paradigm in cardiovascular research and healthcare. Nonetheless, the predictive accuracy largely depends on precisely specified boundary conditions and model parameters, which, however, are usually uncertain (or unknown) in most patient-specific cases. Quantifying the uncertainties in model predictions due to input randomness can provide predictive confidence and is critical to promote the transition of CFD modeling in clinical applications. In the meantime, forward propagation of input uncertainties often involves numerous expensive CFD simulations, which is computationally prohibitive in most practical scenarios. This paper presents an efficient bi-fidelity surrogate modeling framework for uncertainty quantification (UQ) in cardiovascular simulations, by leveraging the accuracy of high-fidelity models and efficiency of low-fidelity models. Contrary to most data-fit surrogate models with several scalar quantities of interest, this work aims to provide high-resolution, full-field predictions (e.g., velocity and pressure fields). Moreover, a novel empirical error bound estimation approach is introduced to evaluate the performance of the surrogate a priori. The proposed framework is tested on a number of vascular flows with both standardized and patient-specific vessel geometries, and different combinations of high- and low-fidelity models are investigated. The results show that the bi-fidelity approach can achieve high predictive accuracy with a significant reduction of computational cost, exhibiting its merit and effectiveness. Particularly, the uncertainties from a high-dimensional input space can be accurately propagated to clinically relevant quantities of interest (e.g., wall shear stress) in the patient-specific case using only a limited number of high-fidelity simulations, suggesting a good potential in practical clinical applications. • Presented a bi-fidelity surrogate modeling framework for hemodynamic simulations. • Proposed a novel error estimation method to assess bi-fidelity surrogate a priori. • Demonstrated effectiveness of the approach on idealized/patient-specific cases. [ABSTRACT FROM AUTHOR]

In this paper, we study the multiscale Boltzmann equation with multi-dimensional random parameters by a bi-fidelity stochastic collocation (SC) method developed in [52,70,71]. By choosing the compressible Euler system as the low-fidelity model, we adapt the bi-fidelity SC method to combine computational efficiency of the low-fidelity model with high accuracy of the high-fidelity (Boltzmann) model. With only a small number of high-fidelity asymptotic-preserving solver runs for the Boltzmann equation, the bi-fidelity approximation can capture well the macroscopic quantities of the solution to the Boltzmann equation in the random space. A priori estimate on the accuracy between the high- and bi-fidelity solutions together with a convergence analysis is established. Finally, we present extensive numerical experiments to verify the efficiency and accuracy of our proposed method. • Multiscale Boltzmann equation with multi-dimensional random parameters. • An efficient bi-fidelity stochastic collocation method. • Compressible Euler system as the low-fidelity model. • Accuracy and convergence analysis. [ABSTRACT FROM AUTHOR]

Deep learning models combining spectral and spatial features have been proven to be effective for hyperspectral image (HSI) classification. However, most spatial feature integration methods only consider a single input spatial scale regardless of various shapes and sizes of objects over the image plane, leading to missing scale-dependent information. In this paper, we propose a hierarchical multi-scale convolutional neural networks (CNNs) with auxiliary classifiers (HMCNN-AC) to learn hierarchical multi-scale spectral–spatial features for HSI classification. First, to better exploit the spatial information, multi-scale image patches for each pixel are generated at different spatial scales. These multi-scale patches are all centered at the same central spectrum but with shrunken spatial scales. Then, we apply multi-scale CNNs to extract spectral–spatial features from each scale patch. The obtained multi-scale convolutional features are considered as structured sequential data with spectral–spatial dependency, and a bidirectional LSTM is proposed to capture the correlation and extract a hierarchical representation for each pixel. To better train the whole network, weighted auxiliary classifiers are employed for the multi-scale CNNs and optimized together with the main loss function. Experimental results on three public HSI datasets demonstrate the superiority of our proposed framework over some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Develop a bi-fidelity method for linear transport equations with random parameters. • Employ the Goldstein-Taylor as low-fidelity model to accelerate convergence of the scheme. • The first work to apply multi-fidelity approach to kinetic equations under the diffusive scaling. In this paper, we consider the development of efficient numerical methods for linear transport equations with random parameters and under the diffusive scaling. We extend to the present case the bi-fidelity stochastic collocation method introduced in [33,50,51]. For the high-fidelity transport model, the asymptotic-preserving scheme [29] is used for each stochastic sample. We employ the simple two-velocity Goldstein-Taylor equation as low-fidelity model to accelerate the convergence of the uncertainty quantification process. The choice is motivated by the fact that both models, high fidelity and low fidelity, share the same diffusion limit. Speed-up is achieved by proper selection of the collocation points and reasonable approximation of the high-fidelity solution. Extensive numerical experiments are conducted to show the efficiency and accuracy of the proposed method, even in non diffusive regimes, with empirical error bound estimations as studied in [16]. [ABSTRACT FROM AUTHOR]

When investigating epidemic dynamics through differential models, the parameters needed to understand the phenomenon and to simulate forecast scenarios require a delicate calibration phase, often made even more challenging by the scarcity and uncertainty of the observed data reported by official sources. In this context, Physics-Informed Neural Networks (PINNs), by embedding the knowledge of the differential model that governs the physical phenomenon in the learning process, can effectively address the inverse and forward problem of data-driven learning and solving the corresponding epidemic problem. In many circumstances, however, the spatial propagation of an infectious disease is characterized by movements of individuals at different scales governed by multiscale partial differential equations. This reflects the heterogeneity of a region or territory in relation to the dynamics within cities and in neighboring zones. In presence of multiple scales, a direct application of PINNs generally leads to poor results due to the multiscale nature of the differential model in the loss function of the neural network. To allow the neural network to operate uniformly with respect to the small scales, it is desirable that the neural network satisfies an Asymptotic-Preservation (AP) property in the learning process. To this end, we consider a new class of AP neural networks for multiscale hyperbolic transport models of epidemic spread that, thanks to an appropriate AP formulation of the loss function, is capable of working uniformly at the different scales of the system. A series of numerical tests for different epidemic scenarios confirms the validity of the proposed approach, highlighting the importance of the AP property in the neural network when dealing with multiscale problems especially in presence of sparse and partially observed systems. [ABSTRACT FROM AUTHOR]

In this paper we develop a set of stochastic numerical schemes for hyperbolic and transport equations with diffusive scalings and subject to random inputs. The schemes are asymptotic preserving (AP), in the sense that they preserve the diffusive limits of the equations in discrete setting, without requiring excessive refinement of the discretization. Our stochastic AP schemes are extensions of the well-developed deterministic AP schemes. To handle the random inputs, we employ generalized polynomial chaos (gPC) expansion and combine it with stochastic Galerkin procedure. We apply the gPC Galerkin scheme to a set of representative hyperbolic and transport equations and establish the AP property in the stochastic setting. We then provide several numerical examples to illustrate the accuracy and effectiveness of the stochastic AP schemes. [ABSTRACT FROM AUTHOR]

Abstract: We introduce a multi-dimensional point-wise multi-domain hybrid Fourier-Continuation/WENO technique (FC–WENO) that enables high-order and non-oscillatory solution of systems of nonlinear conservation laws, and essentially dispersionless, spectral, solution away from discontinuities, as well as mild CFL constraints for explicit time stepping schemes. The hybrid scheme conjugates the expensive, shock-capturing WENO method in small regions containing discontinuities with the efficient FC method in the rest of the computational domain, yielding a highly effective overall scheme for applications with a mix of discontinuities and complex smooth structures. The smooth and discontinuous solution regions are distinguished using the multi-resolution procedure of Harten [A. Harten, Adaptive multiresolution schemes for shock computations, J. Comput. Phys. 115 (1994) 319–338]. We consider a WENO scheme of formal order nine and a FC method of order five. The accuracy, stability and efficiency of the new hybrid method for conservation laws are investigated for problems with both smooth and non-smooth solutions. The Euler equations for gas dynamics are solved for the Mach 3 and Mach 1.25 shock wave interaction with a small, plain, oblique entropy wave using the hybrid FC–WENO, the pure WENO and the hybrid central difference–WENO (CD–WENO) schemes. We demonstrate considerable computational advantages of the new FC-based method over the two alternatives. Moreover, in solving a challenging two-dimensional Richtmyer–Meshkov instability (RMI), the hybrid solver results in seven-fold speedup over the pure WENO scheme. Thanks to the multi-domain formulation of the solver, the scheme is straightforwardly implemented on parallel processors using message passing interface as well as on Graphics Processing Units (GPUs) using CUDA programming language. The performance of the solver on parallel CPUs yields almost perfect scaling, illustrating the minimal communication requirements of the multi-domain strategy. For the same RMI test, the hybrid computations on a single GPU, in double precision arithmetics, displays five- to six-fold speedup over the hybrid computations on a single CPU. The relative speedup of the hybrid computation over the WENO computations on GPUs is similar to that on CPUs, demonstrating the advantage of hybrid schemes technique on both CPUs and GPUs. [Copyright &y& Elsevier]

Abstract: This paper reports on a high-repetition-rate dual signal-wave (DSW) optical parametric oscillator (OPO) operating at the 1.5μm band with tunable wavelength intervals from 2.5nm to 69.1nm. Two periodically poled crystals, a periodically poled lithium niobate (PPLN) with multiple gratings and a single grating MgO-doped PPLN (PPMgOLN), are cascaded in the same OPO cavity to generate dual signal-waves by using quasi-phase-matched (QPM) technique. The pump source was a Q-switched diode-pumped Nd:YVO4 laser operating at 50kHz. At an incident pump power of 3W, an average output power of 169.6mW at 1489.2nm and 1558.3nm has been achieved. [Copyright &y& Elsevier]

Data assimilation (DA) techniques such as the Ensemble Kalman filter (EnKF) and its extensions allow for real-time corrections of state-space models and model parameters based on an assumption of Gaussian error. The hydrological DA literature primarily documents applications of the EnKF to solve sequential state estimation problems. Recent advances in the DA literature demonstrate the potential of applying EnKF-based methods as efficient, derivative-free algorithms to solve various general Bayesian inverse problems, such as parameter estimation, while simultaneously providing Uncertainty Quantification (UQ). In this paper, the authors employ the Ensemble Kalman Inversion (EKI) algorithm to infer the distribution of a set of routing parameters. Through this correction, we improve streamflow at locations upstream of the gauged site in a virtual catchment setting. The algorithm enables learning spatially distributed routing parameters with observations available only at the outlet. The study reveals that this method sufficiently improves model performance throughout the basin. The performance of this method is demonstrated in a virtual catchment for three different model/data configurations. Favorable results, even with model misspecification, indicate that this method holds promise for operational application and more general hydrologic parameter estimation problems. • Ensemble Kalman Inversion (EKI) to infer spatially distributed routing parameters. • Learned parameters well approximate the reference parameters for virtual catchments. • Improves streamflow upstream of gauged site for several metrics via indirect updates. [ABSTRACT FROM AUTHOR]

Abstract We develop a numerical framework to implement the cumulative density function (CDF) method for obtaining the probability distribution of the system state described by a stochastic kinematic wave model. The approach relies on the computation of the fine-grained CDF equation of system state, as derived by the CDF method, via the method of characteristics. Due to its linearity, the fine-grained CDF equation is solved far more efficiently than the direct simulation of the kinematic wave model. Since the ensemble mean of the fine-grained CDF solutions is the probability distribution of the original system state, the proposed scheme requires less realizations than direct Monte Carlo simulations (MCS) of the kinematic model and thus converges relatively quickly. We verify the accuracy and effectiveness of our procedure via comparisons with direct MCS of several examples, including a particular kinematic wave system, the Saint-Venant equation. Highlights • Numerical framework of the cumulative density function (CDF) method for the stochastic kinematic wave model. • Computation of the fine-grained CDF equation from the CDF formulation via method of characteristics. • Numerically more efficient than direct simulation of the nonlinear kinematic wave model at each realization. • Less realizations are needed than direct simulation of the stochastic kinematic wave model for a desired accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a new flexible capacitive force sensor for cardiopulmonary resuscitation (CPR) to evaluate the quality of chest compression (CC). Compared with other feedback devices, this sensing element is flexible, which helps avoid the risk of tissue injury to the patient. We describe the relationship between the compressive force and the capacitance of the sensor element, and design a circuit to transduce the capacitance into a voltage. Finally, we tested the system during CPR training with a manikin. The experiment shows that the sensing element can help rescuers distinguish between effective and ineffective CC due to inadequate compression rate and/or depth. [ABSTRACT FROM AUTHOR]

Abstract: We study nontrivial applications of the reduced basis method (RBM) for electromagnetic applications with an emphasis on scattering and the estimation of radar cross section (RCS). The method and several extensions are explained with two examples with different characteristics. Parameters that are allowed to vary within the model include frequency, incident angle and measurement angle as well as the geometry of the scatterers. With appropriate applications of the empirical interpolation method (EIM), transformation of the domain, configuration of perfectly matched layer, exponential convergence of the reduced basis solution over the entire parameter domain is achieved. Moreover, we demonstrate that this approach allows for the effective capture of the critical behavior, in this case through shapes that minimize scattering. This further highlights the robustness and quality of the greedy approximation and the reduced basis method approach. [Copyright &y& Elsevier]