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Between the Boxer Movement and the 1911 Revolution, missionaries in China confronted distinct social challenges compared to their counterparts in home countries. In response, American missionary Young John Allen and his Chinese assistant, Van Yi, co-translated James Wideman Lee's The Making of a Man into Chinese as Ren Xue. This translation aimed to counter Spencer's social evolutionism, introduced by native intellectuals like Yan Fu, offering a reinterpretation of the relationships among individuals, nations, and God. Additionally, it sought to remap philosophy, evolutionism, ethics, and religion tailored for late Qing China. In contrast to clergymen in America and China, Chinese native intellectuals developed their unique reinterpretations and reshaping of philosophy, science, evolutionism, ethics, and religion for China. Through a cross-comparison of works by Yan Fu, Huxley, Spencer, Lee and Allen, this study explored the diverse responses to Spencer's evolutionary theory and related issues among the advocates of evolutionism, Chinese intellectuals, and clergies in America and China. [ABSTRACT FROM AUTHOR]

The path tracking controller often cannot maintain the lateral stability of vehicles on large curvy roads with low adhesion road, which is easy to cause vehicle instability. In this paper, an adaptive active disturbance rejection control (ADRC) is designed to generate a front wheel angle and an additional yaw moment at the same time to guarantee the vehicle tracking accuracy and vehicle stability at the same time. The vehicle two-degree-of-freedom reference model and the path tracking error model are built to derive the error dynamics state equation. The multiinput multioutput ADRC decoupling control is studied. The BP neural network is designed to online adjust the four output parameters that are difficult to be determined in the nonlinear state feedback to form an adaptive ADRC. The yaw moment distribution scheme is studied next. By the joint simulation and hardware-in-the-loop experiment, the results show that the proposed adaptive ADRC can obtain good vehicle tracking accuracy and vehicle stability at the same time not only on both high and low adhesion roads but on the road with large curvatures. [ABSTRACT FROM AUTHOR]

Ultrafast laser processing has recently emerged as a new tool for processing fiber-reinforced polymer (FRP) composites. In the astronautic industry, the modified epoxy resin (named 4211) and the modified cyanate ester resin (known as BS-4) are two of the most widely used polymers for polymer-based composites. To study the removal mechanism and ablation process of different material components during the ultrafast laser processing of FRPs, we isolated the role of the two important polymers from their composites by studying their femtosecond UV laser (260 fs, 343 nm) ablation characteristics for controllable machining and understanding the related mechanisms. Intrinsic properties for the materials' transmission spectrum, the absorption coefficient and the optical bandgap (Eg), were measured, derived, and compared. Key parameters for controllable laser processing, including the ablation threshold (Fth), energy penetration depth (δeff), and absorbed energy density (Eabs) at the ablation threshold, as well as their respective "incubation" effect under multiple pulse excitations, were deduced analytically. The ablation thresholds for the two resins, derived from both the diameter-regression and depth-regression techniques, were compared between resins and between techniques. An optical bandgap of 3.1 eV and 2.8 eV for the 4211 and BS-4 resins, respectively, were obtained. A detectable but insignificant-to-ablation difference in intrinsic properties and ablation characteristics between the two resins was found. A systematic discrepancy, by a factor of 30~50%, between the two techniques for deriving ablation thresholds was shown and discussed. For the 4211 resin ablated by a single UV laser pulse, a Fth of 0.42 J/cm2, a δeff of 219 nm, and an Eabs of 18.4 kJ/cm3 was suggested, and they are 0.45 J/cm2, 183 nm, and 23.2 kJ/cm3, respectively, for the BS-4 resin. The study may shed light on the materials' UV laser processing, further the theoretical modeling of ultrafast laser ablation, and provide a reference for the femtosecond UV laser processing characteristics of FRPs for the future. [ABSTRACT FROM AUTHOR]

Although the guided image filter (GIF) is an excellent edge-preserving filter, it generally suffers from halo artifacts due to the local property and the fixed regularization parameter. To address the problem, a weighted guided image filter (WGIF) was proposed by incorporating an edge-aware weighting into the GIF. In the filtering process, WGIF employs an averaging strategy for edge-aware weighting. Although the averaging strategy is a highly efficient method, it is susceptible to extreme values and tends to obscure critical factors, so it often leads to inaccurate results. Consequently, the output results quality of the WGIF is often degraded. To remedy the deficiency, a weighted guided image filter with entropy evaluation weighting (EEW-WGIF) is proposed in this paper. EEW-WGIF employs an edge-aware weighting strategy based on entropy evaluation method to detect edges more accurately, and incorporates an explicit constraint based on the gradient variation to better preserve edges. To verify the filtering effectiveness of the EEW-WGIF, it was applied to edge-preserving smoothing filtering, exposure images fusion, single image detail enhancement, structure-transferring filtering and image denoising. Experimental results show that the proposed filter can achieve excellent performance in both visual quality and objective evaluation. [Display omitted] • An edge-aware weighting strategy based on entropy evaluation method is proposed, which is more reliable in calculating the importance of each edge-aware factor. • The proposed EEW-WGIF incorporates an explicit constraint based on gradient variation and hyperbolic function to handle the edges so that the edges can be better preserved. • The EEW-WGIF was applied to edge-preserving smoothing filtering, exposure images fusion, single image detail enhancement, structure-transferring filtering and image denoising. [ABSTRACT FROM AUTHOR]

A methodology to control the flexural wave propagation via modulating the beam profile is presented in this paper. Firstly, the power series method is utilized to theoretically solve the flexural waves in a non-uniform Timoshenko beam with arbitrarily contoured profiles. It is demonstrated that this method is effective for most of inhomogeneous beams as long as the convergence criterion about the beam thickness is satisfied in advance. After validation by comparing the theoretical results with those from the finite element analysis for different thickness variations, lenses are designed by using the quadratic thickness variation pattern with the aim of focusing the wave emitted from a point source on one or two particular positions. Additionally, according to the generalized Snell's law, the thickness-induced phase modulation of the flexural wave is proved from the views of theoretical analysis and numerical simulations, based on which lenses for focusing a plane flexural wave are also realized. It is illustrated through systematic analysis in the frequency domain that all of the lenses designed can exhibit good performances with the evidently increased energy at actual focal positions and the focusing sizes smaller than one working wavelength, which has great potentials in versatile engineering applications. • Flexural wave in a beam with arbitrary profiles is solved by the power series method. • A flexural-wave-based lens for energy focusing is designed via beam thickness modulation. • Energy focusing is realized with the aid of phase control. [ABSTRACT FROM AUTHOR]

Efficient visual tracking is a challenging task in the computer vision community due to its large motion uncertainty induced by occlusion, abrupt motion or appearance changes. In this paper, we propose a Hamiltonian Markov Chain Monte Carlo (MCMC) based tracking scheme for efficient tracking within the Bayesian filtering framework, aiming at handling full or partial occlusions, abrupt motion and appearance changes. In this tracking scheme, no complex models are built for motion uncertainties. The object states are augmented by introducing a momentum item and the Hamiltonian dynamics (HD) is integrated into the traditional MCMC-based tracking method. A new object state is proposed by computing a trajectory according to HD, implemented with the Leapfrog method. The new state can be distant from the current object state but, nevertheless, has a high acceptance probability, which consequently bypasses the slow exploration of the state space suffered by traditional random-walk proposal distribution. In addition, the proposed tracking algorithm can avoid being trapped in local maxima, which is suffered by conventional MCMC-based tracking algorithms. Experimental results reveal that our approach is efficient and effective in dealing with various types of tracking scenarios compared with several alternatives. [ABSTRACT FROM PUBLISHER]

A novel marching on in time and Poggio-Miller- Chang-Harrington-Wu-Tsai (PMCHWT)-based time domain integral equation solver for analyzing transient scattering from composite multiregion structures comprising homogeneous penetrable volumes and perfectly conducting surfaces is presented. To render the marching on in time procedure stable, observer and source integrals are approximated via multipoint collocation and evaluated in closed form. The proposed scheme avoids the costly evaluation of five dimensional integrals required in full space-time Galerkin schemes, and proves to be stable for all targets investigated, including coated perfect electrically conducting surfaces and multiregion penetrable volumes. [ABSTRACT FROM AUTHOR]

A marching-on-in-time (MOT)-based scheme for the analysis of transient scattering from closed surfaces characterized by an impedance boundary condition (IBC) is described. The time-domain integral equations being solved involve no analytical approximation and are free of spurious solutions. The proposed scheme is validated by a host of numerical examples. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 42: 213–220, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20256 [ABSTRACT FROM AUTHOR]

The primary impediment to analyzing two-dimensional transient scattering phenomena using classical marching-on-in-time-based integral equation solvers is these schemes' high computational complexity that scales as O (N s 2 N t 2 , where N s and N t denote the number of spatial and temporal degrees of freedom of the current on the scatterer. Here, three schemes that reduce this cost by permitting the rapid evaluation of the temporal convolution of a bandlimited transient source signature with the two-dimensional wave equation Green function are studied; these three methods rely on (i) blocked fast Fourier transforms, (ii) truncated singular value decompositions, and (iii) multiresolution concepts. The computational cost of all three proposed algorithms scales as O(N s 2 N t log α N t ) with α ≤ 2. The three schemes are compared on their respective multiplicative constants inherent in this cost estimate, their memory requirements, and their ease of implementation. [ABSTRACT FROM AUTHOR]

A new algorithm is presented for analyzing scattering from two-dimensional (2-D) nonlinear penetrable objects that are illuminated by transient transverse magnetic (TM) polarized fields. The proposed scheme solves a time-domain volume-integral inequation (TDVIE) using a marching-on-in-time (MOT) technique. Numerical results obtained using this technique and the finite-difference-time-domain (FDTD) method are in excellent agreement. © 2000 John Wiley & Sons, Inc. Microwave Opt Technol Lett 26: 419–423, 2000. [ABSTRACT FROM AUTHOR]

The adaptive block size processing method in different image areas makes block-matching and 3D-filtering (BM3D) have a very good image denoising effect. Based on these observation, in this paper, we improve BM3D in three aspects: adaptive noise variance estimation, domain transformation filtering and nonlinear filtering. First, we improve the noise-variance estimation method of principle component analysis using multilayer wavelet decomposition. Second, we propose compressive sensing based Gaussian sequence Hartley domain transform filtering to reduce noise. Finally, we perform edge-preserving smoothing on the preprocessed image using the guided filtering based on total variation. Experimental results show that the proposed denoising method can be competitive with many representative denoising methods on the evaluation criteria of PSNR. However, it is worth further research on the visual quality of denoised images. [ABSTRACT FROM AUTHOR]

We analyzed a three-dimensional thermal-electrical coupled model based on COMSOL to characterize the thermal damage response of a woven carbon fiber reinforced polymer (CFRP) composite with and without a lightning strike protection (LSP) system. Fuzzy fiber (FF) fabric serving as a carbon-based protection layer was attached to the outermost ply of the CFRP composite to fabricate a fuzzy fiber reinforced polymer (FFRP) composite. CFRP and FFRP composites with temperature-dependent properties were inspected to predict lightning-induced damage resulting from a 20- and 40-kA peak current for 100 μs. The predicted area of thermal damage and the appearance of the composite surface agreed fairly well with post-lightning damage observed from experiments, thus demonstrating the credibility of the numerical model. LSP characteristics were evaluated for a range of CFRP composite properties in the outermost layer by enhancing the functional conductivity of the top layer in the in-plane and out-of-plane directions. The irreversible thermal damage region in the in-plane and thickness directions was dramatically mitigated by enhanced electrical conductivity, whereas a slight matrix decomposition damage change was observed by varying the thermal conductivity. Due to the increased electrical conductivity and integration of the lightweight FF carbon-based protection layer into the uppermost composite layer, the depth and area of damage can be limited by decreasing the thermal damage penetration through the underlying composites. These results reveal that a highly conductive FF layer may serve as a lightweight and effective anti-lightning strike layer for protecting the underlying composite. [ABSTRACT FROM AUTHOR]

In order to make the weighted guided image filter (WGIF) more fully utilize the edge direction of the image, a steering kernel weighted guided image filter (SKWGIF) is proposed by employing the steering kernel to learn the edge direction and incorporating the learning results into the WGIF. However, SKWGIF does not provide a good compromise between the two possibly contradictory objectives of edge-preserving and smoothing, where the image edges are inevitably smoothed. To overcome the drawback, a SKWGIF with gradient constraint (GC-SKWGIF) is proposed by introducing the gradient constraints into the SKWGIF. The gradient constraints allow the filter to take into account the gradient variation of the edges in the filtering process, and therefore the image edges can be better preserved. To verify the effectiveness of the proposed filter, the GC-SKWGIF is applied to edge-aware smoothing, tone mapping of high range dynamic images, image denoising and haze removal. Both theoretical analysis and experimental results show that the proposed filter can produce good resultant images. [Display omitted] • A new edge-aware weighting is defined by using local variance and local gradient magnitude of 3 × 3 windows of all pixels. • A steering kernel weighted guided image filter with gradient constraint (GC-SKWGIF) is proposed by considering the gradient variation at the edges and introducing the gradient constraint. • Experiments such as edge-aware smoothing, tone mapping of high dynamic images, image denoising and haze removal show that GC-SKWGIF has very outstanding performance. [ABSTRACT FROM AUTHOR]

Fractional flow reserve (FFR) has been established as a gold standard for functional coronary ischemia. At present, the FFR can be calculated from coronary computed tomography angiography (CCTA) images (CT-FFR). Previous studies have suggested that CT-FFR outperforms CCTA and invasive coronary angiography (ICA) in determining hemodynamic significance of stenoses. Recently, a novel automatical algorithm of CT-FFR called RuiXin-FFR has been developed. The present study is designed to investigate the predictive value of this algorithm and its value in therapeutic decision making. The present study retrospectively included 166 patients with stable coronary artery disease (CAD) who underwent CCTA screening and diagnostic ICA examination at Peking University People's Hospital, in 73 of whom wire-derived FFR was also measured. CT-FFR analyses were performed with a dedicated software. All patients were followed up for at least 1 year. We validated the accuracy of RuiXin-FFR with invasive FFR as the standard of reference, and investigated the role of RuiXin-FFR in predicting treatment strategy and long-term prognosis. The mean age of the patients was 63.3 years with 63.9% male. The CT-FFR showed a moderate correlation with wire-derived FFR (r = 0.542, p < 0.0001) and diagnostic accuracy of 87.6% to predict myocardial ischemia (AUC: 0.839, 95% CI 0.728–0.950), which was significantly higher than CCTA and ICA. In the multivariate logistic regression analysis, CT-FFR ≤ 0.80 was an independent predictor of undergoing coronary revascularization (OR: 45.54, 95% CI 12.03–172.38, p < 0.0001), whereas CT-FFR > 0.80 was an independent predictor of non-obstructive CAD (OR: 14.67, 95% CI 5.42–39.72, p < 0.0001). Reserving ICA and revascularization for vessels with positive CT-FFR could have reduced the rate of ICA by 29.6%, lowered the rate of ICA in vessels without stenosis > 50% by 11.7%, and increased the rate of revascularization in patients receiving ICA by 21.2%. The average follow-up was 23.7 months, and major adverse cardiovascular events (MACE) occurred in 11 patients. The rate of MACE was significantly lower in patients with CT-FFR > 0.80. The new algorithm of CT-FFR can be used to predict the invasive FFR. The RuiXin-FFR can also provide useful information for the screening of patients in whom further ICA is indeed needed and prognosis evaluation. [ABSTRACT FROM AUTHOR]

The research of object-oriented concept is one of the basic contents of formal concept analysis. To overcome the complexity of computing object-oriented concept, this paper proposes an Object-oriented Concept Acquisition model (OCA) based on attribute topology. The object-oriented attribute topology is first proposed to visualize the coupling relationship between attributes. Second, inspired by rough set theory, object-oriented attribute topology is transformed into rough object-oriented attribute topology. Furthermore, based on the weights of the edges in the rough object-oriented attribute topology, object-oriented concepts are obtained by finding reachable paths. Finally, examples and experiments are used to demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. [ABSTRACT FROM AUTHOR]

The development of time-domain integral equation solvers with robust late-time stability properties has been a long-standing quest. Among the many methods that have been investigated, those leveraging quasi-exact integration techniques appear to be most successful. This article presents stable and accurate marching-on-in-time (MOT) solvers for time-domain electric, magnetic, and combined field integral equations (EFIE, MFIE, and CFIE) based on quasi-exact integration techniques. The novel MOT solvers exhibit excellent stability while yielding highly accurate results, as demonstrated by various numerical examples. In addition, the solvers’ excellent stability and accuracy properties are used to examine spurious modes encountered when time-domain integral equations are applied to closed surfaces. It is demonstrated that MOT solutions to the time domain EFIE and MFIE often are polluted by spurious modes at the cavity’s resonant frequencies whereas those of the CFIE solver are devoid of such contamination. [ABSTRACT FROM AUTHOR]

In many application domains, there is an urgent need for data owners to mine attribute associations hidden in linguistic conceptual knowledge. Numerous linguistically valued facts from the actual world have been modeled using the fuzzy linguistic approach. To solve the problem of association rule mining with fuzzy linguistic information, this paper proposes an association rule mining approach based on fuzzy linguistic attribute partial ordered structure diagram (FL-APOSD). First, complex relationships between linguistic values in association rule mining are represented by fuzzy linguistic association nodes and association paths via FL-APOSD. On this basis, the maximum frequent attribute set is mined from the FL-APOSD, and then the non-redundancy association rules are extracted. Second, to show the information hidden in the rules and help users to deeply understand the mining results, a fuzzy linguistic association rule visualization approach is proposed to convert the association rules into the FL-APOSD-based knowledge representation. Finally, experimental results on real-world datasets show the proposed approach's high efficiency, outperforming two relevant state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Thin-walled structure deformation detection technology is one of the key technologies for structural health monitoring and fault diagnosis of high-end mechanical equipment. Aiming at the problem that the existing Fiber Bragg Grating (FBG) strain sensor is difficult to effectively measure the deformation of thin-walled structures, an FBG strain sensor based on a symmetrical lever structure is proposed. The sensitivity of the sensor is analyzed theoretically, and the sensor is simulated and analyzed by the SOLIDWORKS and Abaqus software, and then, the structural parameters are optimized. According to the simulation results, the sensor is developed and a strain testing system is set up to test the performance of the sensor. The results indicate that the sensor sensitivity is ∼6.6 pm/με, which is about 5.5 times that of bare FBG. Its strain measurement sensitivity and stability are much higher than those of bare FBG, thus meeting the strain detection requirements of thin-walled structural parts during deformation. Moreover, the linearity is more than 99%, which enables the accurate measurement of tiny strains caused by the deformation and reconstruction of the thin-walled structure by the strain sensor. The results of this study provide a reference for the development of like sensors and a further improvement in the sensitivity of the optic-fiber strain sensor. [ABSTRACT FROM AUTHOR]

Deep neural networks have recently achieved impressive performance on multilabel text classification. However, the uncertainty in multilabel text classification tasks and their application in the model are often overlooked. To better understand and evaluate the uncertainty in multilabel text classification tasks, we propose a general framework called Uncertainty Quantification for Multilabel Text Classification framework. Based on the prediction results produced by traditional neural networks, the aleatory uncertainty of each classification label and the epistemic uncertainty of the prediction result can further be obtained by this framework. We design experiments to characterize the properties of aleatory uncertainty and epistemic uncertainty from the data characteristics and model features. The experimental results show that this framework is reasonable. Furthermore, we demonstrate how this framework allows us to define the model optimization criterion to identify policies that balance the expected training cost, model performance, and uncertainty sensitivity. This article is categorized under:Algorithmic Development > Bayesian Models [ABSTRACT FROM AUTHOR]

Owing to their ability to detect small defects and flaws at very low frequencies, ultrasonic guided waves, as well as their artificial manipulation in structural health monitoring and non-destructive evaluation systems, are generating growing interest both in scientific research and engineering applications. In this paper, a methodology to artificially steer guided waves, i.e. attaching multiple graded cylindrical stubs on the plate surface, is proposed, and two kinds of devices, an S0 wave-based low-pass wave filter and an A0 wave-based Luneburg lens, are designed. Firstly, the band structure of a periodic crystal consisting of a cylindrical stub, hexagonally distributed on the surface of an aluminum plate, is calculated numerically. After validation via the transmission power, band gap evolution with the stub height is achieved, on which basis an S0 wave-based low-pass wave filter is realized and exemplified in both the time and frequency domains. It is revealed that the incident S0 wave with a lower frequency in the range of 110 kHz to 170 kHz can travel over longer distances when it enters the low-pass wave filter, and vice versa. Subsequently, the relationship between the frequency of the A0 branch and the stub height are quantified, which is used to design an omnidirectional A0 wave-based Luneburg lens. This lens exhibits good focusing ability, demonstrated by a focusing size smaller than the working wavelength. Not restricted to a fixed frequency, the Luneburg lens designed is shown to be wideband, and also presents high focusing performance in an effective frequency range centered on the designed frequency. The methodology presented is straightforward, and the two devices fulfilled are easy to manufacture with natural materials, which provides much convenience for experimental verification and final engineering applications. [ABSTRACT FROM AUTHOR]

Clinical notes are comprehensive files containing explicit information about a patient's visit. However, accurately assigning medical codes from clinical documents can be a persistent challenge due to the complexity of clinical data and the vast range of medical codes. Moreover, the large volume of medical records, the noisy medical records, and the uneven quality of coders all negatively impact the quality of the final codes. Deep learning technology has recently been integrated into automatic International Classification of Diseases (ICD) coding tasks to improve accuracy. Nevertheless, the imbalanced class problem, the complexness of code associations, and the noise in lengthy records still restrict the advancement of ICD coding tasks in deep learning. Thus, we present the Note-code Interaction Denoising Network (NIDN) that employs the self-attention mechanism to pull critical semantic features in electronic medical records (EMRs). Our model utilizes the label attention mechanism for retaining code-specific text expression. We introduce Clinical Classifications Software coding for multitask learning, capturing the functional relationships of medical coding to oblige in model prediction. To minimize the impact of noise on model prediction and improve the label distribution imbalance, a denoising module is introduced to filter noise. Our practical consequences indicate that the model NIDN exceeds competitive models on a third version of Medical Information Mart for Intensive Care data set. [ABSTRACT FROM AUTHOR]

Despite significant progress made in image semantic segmentation, most research has primarily focused on daytime scenes. Semantic segmentation of nighttime images is equally critical for autonomous driving; however, this task poses more challenges due to inadequate lighting and difficulties associated with obtaining accurate manual annotations. In this paper, we propose a novel method called Latent Domain Knowledge Distillation (LDKD) for accurate nighttime semantic segmentation. Within a teacher-student framework, our LDKD combines domain alignment and knowledge distillation in a mutually reinforcing manner. First, we introduce a bidirectional photometric alignment module capable of generating daytime-like and nighttime-like latent images in real-time. This module effectively bridges the appearance gap between the source domain (daytime) and the target domain (nighttime). Second, we enhance collaboration between the teacher and student networks. The student network unifies image-level and feature-level domain alignment, while the teacher network generates reliable pseudo-labels by distilling knowledge from the latent domain. Furthermore, to account for potential noise in pseudo-labels, we propose a noise-tolerant learning method aimed at mitigating the risks associated with overreliance on pseudo-labels during model training. Extensive experiments on Dark Zurich and ACDC datasets show that our LDKD achieves state-of-the-art performance, demonstrating the effectiveness of our method for nighttime semantic segmentation. [ABSTRACT FROM AUTHOR]

Recent research in the analysis of human images, such as human parsing and pose estimation, usually requires input images to have a sufficiently high resolution. However, small images of people are commonly encountered in our daily lives, particularly in surveillance applications. This paper aims to ultra-resolve a tiny person image to its high-resolution counterpart by learning effective feature representations and exploiting useful human body prior knowledge. First, we propose the Residual Clique Block (RCB) to fully exploit compact feature representations for image Super-Resolution (SR). Second, a series of RCBs are cascaded in a coarse-to-fine manner to construct the Pyramid Residual Clique Network (PRCN), which simultaneously reconstructs multiple SR results (e.g. 2 × , 4 × , and 8 ×) in one feed-forward pass. Third, we utilize the human parsing map as the shape prior, and the high-frequency sub-bands of Uniform Discrete Curvelet Transform (UDCT) as the texture prior to enhance the details of reconstructed human body image. Experimental results demonstrate that our proposed method achieves state-of-the-art performance with superior visual quality and PSNR/SSIM scores. Moreover, we show that our results can considerably enhance the performance of human parsing and pose estimation tasks. • We propose a new feature extraction block to fully utilize the hierarchical features from LR images. • We develop a framework to progressively reconstruct HR images and estimate human body prior in a coarse-to-fine manner. • Two kinds of human body priors are estimated in our framework and then used to improve SR performance. • Our method outperforms other methods in image super-resolution, pose estimation and human parsing. [ABSTRACT FROM AUTHOR]

Objectives: The present study is designed to investigate the impact of coronary angiography-derived index of microcirculatory resistance (caIMR) on left ventricular performance recovery.Background: IMR has been established as a gold standard for coronary microvascular assessment and a predictor of left ventricular recovery after ST-segment elevation myocardial infarction (STEMI). CaIMR is a novel and accurate alternative of IMR.Methods: The present study retrospectively included 80 patients with STEMI who underwent primary percutaneous coronary intervention (PCI). We offline performed the post-PCI caIMR analysis of the culprit vessel. Echocardiography was performed within the first 24 hours and at 3 months after the index procedure. Left ventricular recovery was defined as the change in left ventricular ejection fraction (LVEF) more than zero.Results: The mean age of the patients was 58.0 years with 80.0% male. The average post-PCI caIMR was 43.2. Overall left ventricular recovery was seen in 41 patients. Post-PCI caIMR (OR: 0.948, 95% CI: 0.916-0.981, p = 0.002), left anterior descending as the culprit vessel (OR: 3.605, 95% CI: 1.23-10.567, p = 0.019), and male (OR: 0.254, 95% CI: 0.066-0.979, p = 0.047) were independent predictors of left ventricular recovery at 3 months follow-up. A predictive model was established with the best cutoff value for the prediction of left ventricular recovery 2.33 (sensitivity 0.610, specificity 0.897, and area under the curve 0.765). In patients with a predictive model score less than 2.33, the LVEF increased significantly at 3 months.Conclusions: The post-PCI caIMR can accurately predict left ventricular functional recovery at 3 months follow-up in patients with STEMI treated by primary PCI, supporting its use in clinical practice. [ABSTRACT FROM AUTHOR]

In order to improve the intelligent vehicle lane-changing performance, an active lane-changing control algorithm is proposed considering the changes of road curvature and vehicle speed. Firstly, the vehicle dynamics model considering vehicle speed variation and lane-changing safety distance is established, and the expected lane-changing trajectory model under the curved road is designed simultaneously. Then, taking the yaw rate and longitudinal speed as the control objectives of lateral and longitudinal motions, respectively, the sliding-mode variable structure control method based on Lyapunov stability condition is adopted, and the trajectory tracking controller is designed by combining the inverted method to track the desired lane-changing trajectory. Finally, the lane-changing trajectory model and trajectory tracking controller are verified in simulation platform of CarSim/Simulink and hardware-in-the-loop (HIL) test bench. The results show that the proposed trajectory tracking control method can perform the lane-changing behavior well under different road curvatures and vehicle speeds while maintaining high trajectory tracking control accuracy. [ABSTRACT FROM AUTHOR]

In this article, a high-input power (>30 dBm) rectifier circuit for 2.45-GHz microwave wireless power transmission is presented. To overcome the shortcomings of the traditional Schottky rectifier circuit, the novel rectifier circuit is designed based on a GaAs field-effect transistor (FET) and a Schottky diode together as the rectification part. A system-level verification demonstrates that the novel rectifier circuit has a potential application value for microwave wireless power transmission system. Measurement results show that higher input power range and MW-dc power conversion efficiency are realized at the same time by this design scheme. The maximum MW-dc conversion efficiency of the novel rectifier circuit can reach 74.42% when the input power is 30 dBm, and it is 20% higher than the peak conversion efficiency of the traditional rectifier without FET. In addition, the proposed rectifier circuit can operate with over 50% efficiency when the input power varies from 16 to 31 dBm, and has a good performance with the value of load ranging from 70 to 730 Ω, it is mean that the novel rectifier circuit is able to provide power for some electronic devices with different impedance. [ABSTRACT FROM AUTHOR]

The interior resonance problem of time domain integral equations (TDIEs) formulated to analyze acoustic field interactions on penetrable objects is investigated. Two types of TDIEs are considered: The first equation, which is termed the time domain potential integral equation (TDPIE), suffers from the interior resonance problem, i.e., its solution is replete with spurious modes that are excited at the resonance frequencies of the acoustic cavity in the shape of the scatterer. Numerical experiments demonstrate that, unlike the frequency-domain integral equations, the amplitude of these modes in the time domain could be suppressed to a level that does not significantly affect the solution. This is achieved by increasing the numerical solution accuracy through the use of a higher-order discretization in space and the band limited approximate prolate spheroidal wave function with high interpolation accuracy as basis function in time. The second equation is obtained by linearly combining TDPIE with its normal derivative. The solution of this equation, which is termed the time domain combined potential integral equation (TDCPIE), does not involve any spurious interior resonance modes but it is not as accurate as the TDPIE solution at non-resonance frequencies. In addition, TDCPIE's discretization calls for treatment of hypersingular integrals. [ABSTRACT FROM AUTHOR]

The combination of deep learning and the medical field has recently achieved great success, particularly in recommending medicine for patients. However, patients' clinical records often contain repeated medical information that can significantly impact their health condition. Most existing methods for modeling longitudinal patient information overlook the impact of individual diagnoses and procedures on the patient's health, resulting in insufficient patient representation and limited accuracy of medicine recommendations. Therefore, we propose a medicine recommendation model called KEAN, which is based on an attention aggregation network and enhanced graph convolution. Specifically, KEAN can aggregate individual diagnoses and procedures in patient visits to capture significant features that affect patients' diseases. We further incorporate medicine knowledge from complex medicine combinations, reduce drug–drug interactions (DDIs), and recommend medicines that are beneficial to patients' health. The experimental results on the MIMIC-III dataset demonstrate that our model outperforms existing advanced methods, which highlights the effectiveness of the proposed method. [Display omitted] • An attention aggregation model KEAN is proposed for medicine recommendation. • The enhanced graph convolution module is employed to capture domain knowledge from medicine combinations. • The proposed KEAN achieves the state-of-the-art performance on MIMIC-III datasets. [ABSTRACT FROM AUTHOR]

Internal resonant modes are always observed in the marching-on-in-time (MOT) solution of the time domain electric field integral equation (EFIE), although “relaxed initial conditions,” which are enforced at the beginning of time marching, should in theory prevent these spurious modes from appearing. It has been conjectured that, numerical errors built up during time marching establish the necessary initial conditions and induce the internal resonant modes. However, this conjecture has never been proved by systematic numerical experiments. Our numerical results in this communication demonstrate that, the internal resonant modes' amplitudes are indeed dictated by the numerical errors. Additionally, it is shown that in a few cases, the internal resonant modes can be made “invisible” by significantly suppressing the numerical errors. These tests prove the conjecture that the internal resonant modes are induced by numerical errors when the time domain EFIE is solved by the MOT method. [ABSTRACT FROM PUBLISHER]

[Display omitted] • The addition of CaO and SO 3 can increase the strength of geopolymer cured at ordinary temperature; • The CaO and SO 3 increase the water immersion expansion rate of geopolymer; • Ettringite and calcium silicate hydrate gel are formed in fly ash based geopolymer; • The slight expansion of ettringite effectively reduces the drying shrinkage of geopolymer. Fly ash based geopolymer is a green cementitious material with the potential to replace portland cement. Due to the changes of raw coal and coal combustion technology in recent years, the fly ash often has high CaO and SO 3 contents. The action mechanism of CaO and SO 3 on the mechanical properties and dimensional stability of fly ash-based geopolymers is not clear, which restricts its application. The influence of CaO and SO 3 content on the flexural strength, compressive strength, water immersion expansion rate and drying shrinkage rate of fly ash based geopolymer were studied, and X-ray diffractography (XRD), scanning electron microscope (SEM), energy dispersive spectrometer (EDS) and 29Si nuclear magnetic resonance (NMR) were used to study the influence mechanism of calcium and sulfur on the geopolymer. The results show that the mechanical properties of fly ash based geopolymers increase initially and then decrease with the increase of CaO and SO 3 content. When the contents of CaO and SO 3 are 11% and 4% respectively, the properties of geopolymer prepared at ordinary temperature (20 ℃) are the optimum. The 28d flexural strength and compressive strength can be increased by 17.91% and 15.56% respectively, and compared with the unmodified fly ash based geopolymer, the drying shrinkage is reduced by 60.65%. Microscopic test results show that the addition of CaO and SO 3 promoted the geopolymer reaction, increased the gel products and generated ettringite, which enhanced the strength of the matrix and compensated the shrinkage of geopolymer. [ABSTRACT FROM AUTHOR]

During the multi-attribute group decision-making (MAGDM) processing, the individuals often hold different opinions about the alternatives. It is necessary to aggregate the different individual opinions into a unified group opinion. In the real world, experts sometimes use linguistic expressions to evaluate attributes in uncertain environments. To address the problem of reducing the information loss of expert opinion aggregation in MAGDM, this paper proposes a MAGDM approach based on linguistic concept lattices in the context of uncertain linguistic expression. A linguistic concept lattice for multi-expert linguistic formal context is first constructed based on linguistic truth-valued lattice implication algebra, which can express both comparable and incomparable linguistic information in the decision-making process. Different expert opinions are aggregated via the extent of fuzzy linguistic concepts, which can reduce information loss in the aggregation process. Second, meet-irreducible elements in the linguistic concept lattice are introduced to reduce the computational complexity of obtaining all fuzzy linguistic concepts in the decision-making process. the distance between the intents of different fuzzy linguistic concepts is considered to enhance the rationality of linguistic decision results. In addition, the expert's decision-making process for each alternative is visualized via linguistic concept lattices. Finally, the case study and comparative analysis illustrate the validity and rationality of the proposed approach in MAGDM with linguistic information. • The fuzzy linguistic concepts are used to aggregate expert opinions. • The comparable and incomparable linguistic information is handled in MAGDM. • Visualize the decision-making process of experts by constructing concept lattices. • A novel linguistic concept lattice-based MAGDM approach is proposed. [ABSTRACT FROM AUTHOR]

Background: Clinical notes are documents that contain detailed information about the health status of patients. Medical codes generally accompany them. However, the manual diagnosis is costly and error-prone. Moreover, large datasets in clinical diagnosis are susceptible to noise labels because of erroneous manual annotation. Therefore, machine learning has been utilized to perform automatic diagnoses. Previous state-of-the-art (SOTA) models used convolutional neural networks to build document representations for predicting medical codes. However, the clinical notes are usually long-tailed. Moreover, most models fail to deal with the noise during code allocation. Therefore, denoising mechanism and long-tailed classification are the keys to automated coding at scale. Results: In this paper, a new joint learning model is proposed to extend our attention model for predicting medical codes from clinical notes. On the MIMIC-III-50 dataset, our model outperforms all the baselines and SOTA models in all quantitative metrics. On the MIMIC-III-full dataset, our model outperforms in the macro-F1, micro-F1, macro-AUC, and precision at eight compared to the most advanced models. In addition, after introducing the denoising mechanism, the convergence speed of the model becomes faster, and the loss of the model is reduced overall. Conclusions: The innovations of our model are threefold: firstly, the code-specific representation can be identified by adopted the self-attention mechanism and the label attention mechanism. Secondly, the performance of the long-tailed distributions can be boosted by introducing the joint learning mechanism. Thirdly, the denoising mechanism is suitable for reducing the noise effects in medical code prediction. Finally, we evaluate the effectiveness of our model on the widely-used MIMIC-III datasets and achieve new SOTA results. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to conduct a comprehensive experimental study to achieve optimal surface roughness of aluminum rolled with freshly ground rollers of two high rolling mills. Design/methodology/approach – Various rolling oils and processing conditions are applied in the rolling process. Resultant surface roughness is measured with a profilometer; and surface topography of aluminum after rolling is observed under scanning electron microscope. In order to examine the oil film thickness (the most critical factor in rolling process), a planimeter is used to measure the area of oil patch resulting from a precisely controlled oil droplet, which in turn allows calculation of outlet oil film thickness as an approximation to inlet oil film thickness in the deformation region. Findings – The experimental results indicate that two major factors have dominant impacts on aluminum's surface roughness in the rolling lubrication process: reduction of roller; and viscosity of lubrication oil. Based upon analysis of the experimental data, optimal roller reduction is found to be within range (23%, 30%) and optimal oil viscosity should be chosen, such that the ratio between inlet oil film thickness in the deformation region and the combined surface roughness is around 1. Originality/value – According to the authors' observations, the above parameter choices generally assure excellent surface quality of aluminum after rolling. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to conduct a comprehensive experimental study to achieve optimal surface roughness of aluminum rolled with freshly ground rollers of two high rolling mills. Design/methodology/approach – Various rolling oils and processing conditions are applied in the rolling process. Resultant surface roughness is measured with a profilometer; and surface topography of aluminum after rolling is observed under scanning electron microscope. In order to examine the oil film thickness (the most critical factor in rolling process), a planimeter is used to measure the area of oil patch resulting from a precisely controlled oil droplet, which in turn allows calculation of outlet oil film thickness as an approximation to inlet oil film thickness in the deformation region. Findings – The experimental results indicate that two major factors have dominant impacts on aluminum's surface roughness in the rolling lubrication process: reduction of roller; and viscosity of lubrication oil. Based upon analysis of the experimental data, optimal roller reduction is found to be within range (23%, 30%) and optimal oil viscosity should be chosen, such that the ratio between inlet oil film thickness in the deformation region and the combined surface roughness is around 1. Originality/value – According to the authors' observations, the above parameter choices generally assure excellent surface quality of aluminum after rolling. [ABSTRACT FROM AUTHOR]

This paper designs and experimentally demonstrates an electronic hardware architecture to measure impulse signals without resorting to high-speed analog-to-digital converters (ADCs). The proposed architecture is based on a frequency-domain approach. It consists of multiple channels to down-convert the input signal, each of which obtains a small portion of the input's spectrum. Overlapping bands are designated in between every two adjacent channels, so that multiple narrow-band outcomes can be accurately merged to construct the entire spectrum through postprocessing. The resultant complex-valued spectrum is equivalent to that where the impulses are directly measured in the time domain. The architecture in this paper resembles traditional superheterodyne receivers, and hence, its circuit implementation is simple and robust and has low cost. In our circuit implementation, all the components involved (like oscillators and mixers) are commercially available and have low costs. The circuit is tested by two types of input impulses, both having spectra centered at 2.1 GHz. The input signals' spectra (including both magnitude and phase) obtained by our circuit show excellent agreement with the data measured by a 40-GHz oscilloscope. When the input signal-to-noise ratio is as low as 2 dB, the output of our frequency-domain approach is equivalent to that of a 4-b high-speed ADC. [ABSTRACT FROM AUTHOR]

A method for modeling singular electric currents near conducting edges within marching-on-in-time (MOT) simulators is presented. The use of singular basis functions in MOT simulators presents unique challenges not encountered in frequency-domain implementations. Numerical results demonstrate that the proposed expansion results in accelerated convergence for scatterers with sharp features. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 34: 327–332, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10452 [ABSTRACT FROM AUTHOR]

This article presents a low-cost circuit that generates narrow pulses with programmable waveform, amplitude, and position based on the spectrum stitching method. Specifically in this article, narrow pulses are generated through stitching the spectra of five subsignals, and the subsignals are generated by three regular arbitrary waveform generators with a bandwidth of 200 MHz. The experimental results of pulse generation with various waveforms, amplitude profiles, and position profiles are demonstrated. The pulses demonstrated in this article have a pulse width between 2 and 4 ns, with spectral coverage of about 500 MHz, which is well beyond the capacity of each of the three arbitrary waveform generators used in this work. The experimental results of this article indicate that the spectrum stitching technique has excellent potential to increase the effective bandwidth of arbitrary waveform generators using low-cost circuits. [ABSTRACT FROM AUTHOR]

[Display omitted] • Modified dynamic time warping method is proposed for non-uniform temperature compensation of Lamb waves. • Warping adjusting mechanism is introduced to resolve the temperature over-compensation problem. • A modified dynamic time warping-based imaging is developed to realize a triple-suppression for the complicated effects of non-uniform temperature variations on Lamb wave damage identification. In practical applications of Lamb wave monitoring, the environmental temperature fields usually vary non-uniformly over the monitored structures, which can inconsistently affect the wavepackets of every diagnostic signal and reduce the reliability of final detection results. A number of temperature compensation methods have been reported. However, under the complicated non-uniform temperature variations, many methods are deteriorated in their performance or require large amount of Lamb wave data. To effectively improve the reliability of Lamb wave damage detection under non-uniform temperature variations, modified dynamic time warping (MDTW) is presented in this paper. In MDTW, with the flexible time alignment ability of dynamic time warping (DTW), the non-uniform temperature variation effects on Lamb wave signals can be compensated just requiring one set of baselines. Furthermore, to preserve the damage information in the compensated signals, the warping adjusting mechanism is introduced to resolve the temperature over-compensation problem of traditional DTW. This makes MDTW a robust non-uniform temperature compensation method for high reliability Lamb wave detection. After the basic principle of DTW is analyzed, MDTW is proposed and numerically validated. Then, a MDTW-based Lamb wave imaging method is developed for high reliability damage imaging, in which a triple-suppression of non-uniform temperature variations can be accomplished. Finally, the efficiency of MDTW and MDTW-based imaging is demonstrated by the temperature experiment of A 0 mode Lamb wave damage imaging on an aluminum plate with its temperature field non-uniformly varied by 13℃∼43℃. [ABSTRACT FROM AUTHOR]

Large-scale model sizes and expensive computing costs cause the challenge of deploying and applying large pre-trained models. Hence, this paper presents a novel Triple Concepts Attention Mechanism and a lightweight TCAMixer model for edge devices to classify texts. Furthermore, the TCAMixer abstracts textual concepts in a human way, which is unmatched by other counterparts such as pNLP-Mixer (a projection-based MLP-Mixer model for Nature Language Processing) and HyperMixer (a hyper network using dynamic token-mixing layers). Experimental results on several public datasets demonstrate that the TCAMixer outperforms the counterparts by a significant margin, for example, achieving 3% higher accuracy with a smaller model size of 0.177M. Additionally, the TCAMixer achieves a performance of 85% to 98.7% compared to that of large pre-trained models but only occupies 1/3000 to 1/2000 of their size on most test datasets. [ABSTRACT FROM AUTHOR]

Electromagnetic scattering from electrically large objects with multiscale features is an increasingly important problem in computational electromagnetics. A conventional approach is to use an integral equation-based solver that is then augmented with an accelerator, a popular choice being a parallel multilevel fast multipole algorithm (MLFMA). One consequence of multiscale features is locally dense discretization, which leads to low-frequency breakdown and requires nonuniform trees. To the authors’ knowledge, the literature on parallel MLFMA for such multiscale distributions capable of arbitrary accuracy is sparse; this paper aims to fill this niche. We prescribe an algorithm that overcomes this bottleneck. We demonstrate the accuracy (with respect to analytical data) and performance of the algorithm for both PEC scatterers and point clouds as large as $755{\lambda }$ with several hundred million unknowns and nonuniform trees as deep as 16 levels. [ABSTRACT FROM AUTHOR]

A prompt gamma neutron activation analysis logging method for formation water salinity determination is proposed. The response of formation water salinity for capture gamma ray spectrum and thermal neutron counts ratio are studied by Monte Carlo simulation method. Formation porosity and borehole fluid salinity influence on formation water salinity is corrected. A synthetical logging case is simulated to indicate the effectiveness of this logging method. The new logging method is also applied in oil field, and the formation water salinity of different depth is calculated. [ABSTRACT FROM AUTHOR]

Visual tracking is a fundamental research topic in computer vision community, which is of great importance in many application areas including augmented reality, traffic control, medical imaging and video editing. This paper presents an ordered over-relaxation Langevin Monte Carlo sampling (ORLMC) based tracking method within the Bayesian filtering framework, in which the traditional object state variable is augmented with an auxiliary momentum variable. At the proposal step, the proposal distribution is designed by simulation of the Hamiltonian dynamics. We first use the ordered over-relaxation method to draw the momentum variable which could suppress the random walk behavior in Gibbs sampling stage. Then, we leverage the gradient of the energy function of the posterior distribution to draw new samples with high acceptance ratio. The proposed tracking method could ensure that the tracker will not be trapped in local optimum of the state space. Experimental results show that the proposed tracking method successfully tracks the objects in different video sequences and outperforms several conventional methods. [ABSTRACT FROM AUTHOR]

ABSTRACT A full-wave simulation tool based on the method of moments is developed and validated by experiments to study the feasibility of wireless power transmission in rectangular cavity that emulates satellite interior. Our results demonstrate power transmission efficiency higher than 80% in a cubic cavity with dimension of 1 cubic-meter. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2518-2522, 2016 [ABSTRACT FROM AUTHOR]

Coronary calcified lesions can exert serious effects on stent expansion. A calcium scoring system, based on optical coherence tomography (OCT), has been previously developed to identify relatively mild calcified lesions that would benefit from plaque modification procedures. Therefore, the present study aimed to establish a novel OCT-based scoring system to predict the stent expansion of moderate and severe calcified lesions. A total of 33 patients who underwent percutaneous coronary intervention (PCI; 34 calcified lesions were observed using coronary angiography) were retrospectively included in the present study. Coronary angiography and OCT images were subsequently reviewed and analyzed. Furthermore, a calcium scoring system was developed based on the results of multivariate analysis before the optimal threshold for the prediction of stent underexpansion in patients with moderate and severe calcified lesions was determined. The mean age of the patients was 67±10 years. The present analysis demonstrated that the final post-PCI median stent expansion was 70.74%, where stent underexpansion (defined as stent expansion <80%) was observed in 23 lesions. The mean maximum calcium arc, length and thickness, which were assessed using OCT, were found to be 230˚, 25.10 mm and 1.18 mm, respectively. A multivariate logistic regression model demonstrated that age and the maximum calcium arc were independent predictors of stent underexpansion. A novel calcium scoring system was thereafter established using the following formula: (0.16 x age) + (0.03 x maximum calcium arc) according to the β-coefficients in the multivariate analysis, with the optimal cut-off value for the prediction of stent underexpansion being 16.87. Receiver operating characteristic curve analysis demonstrated that this novel scoring system yielded a larger area under the curve value compared with that from a previous study's scoring system. Therefore, in conclusion, since the calcium scoring system of the present study based on age and the maximum calcium arc obtained from OCT was specifically developed in the subjects with moderate and severe calcified lesions, it may be more accurate in predicting the risk of stent underexpansion in these patients. [ABSTRACT FROM AUTHOR]

ABSTRACT This letter demonstrates a wireless power transmission scheme based on distributed retro-reflective beamforming experimentally. The wireless power transmitter, which includes eight antennas distributed in space, receives pilot signals from a mobile device and then focuses radio frequency power onto the device's location through analyzing the pilot signals. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:1873-1876, 2015 [ABSTRACT FROM AUTHOR]