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X-section cast-in-place concrete pile (referred to as XCC pile) has a different velocity response compared with circular section pile in the low strain testing due to the special cross section. Full-scale model tests of XCC pile were conducted to reveal the velocity response characteristics. The time-domain velocity responses on the pile top were obtained, which showed obvious three-dimensional effects because of the different high-frequency interferences. The test results were compared with the numerical results to validate the numerical model. Furthermore, numerical simulations were conducted to investigate the propagation characteristic of velocity waves along the longitudinal direction in the pile. The results indicated that the wave propagation was complicated as a result of the superposition of the incident wave and the reflected wave. The effects of the geometrical parameters of cross section on the three-dimensional effects of velocity responses were also studied. Three-dimensional effects would be more significant with a larger arc distance. However, the effects of arc angle were not obvious.

Research has been conducted on Mycobacterium avium. The effect of natural exposure to low M. avium doses in immunocompetent BALB/c or athymic BALB/c mice has been investigated in studying the acquired resistance rate and immune response and the details are reported.

BackgroundSepsis-associated encephalopathy (SAE) is one of the most ubiquitous complications of sepsis and is characterized by cognitive impairment, poor prognosis, and a lack of uniform clinical diagnostic criteria. Therefore, this study investigated the early diagnostic and prognostic value of serum neuron-specific enolase (NSE) in SAE.MethodsThis systematic review and meta-analysis systematically searched for clinical trials with serum NSE information in patients with sepsis in the PubMed, Web of Science, Embase, and Cochrane databases from their inception to April 10, 2023. Included studies were assessed for quality and risk of bias using The Quality Assessment of Diagnostic Accuracy-2 tool. The meta-analysis of the included studies was performed using Stata 17.0 and Review Manager version 5.4.FindingsEleven studies were included in this meta-analysis involving 1259 serum samples from 947 patients with sepsis. Our results showed that the serum NSE levels of patients with SAE were higher than those of the non-encephalopathy sepsis group (mean deviation, MD,12.39[95% CI 8.27–16.50, Z = 5.9, p

Secret image sharing (SIS) technology has received wide attention from researchers due to its multi-party management of secret information. Traditional SIS algorithms usually convert secret information into the shares of noise-like images, which may attract the attention of hackers and cannot be effectively managed. Moreover, for communication scenario with multiple user participation, participants hope to embed additional data in their shares, while the secret image and the embedded additional information to be reversibly recovered. However, the embedding capacity, security, and authentication capability are limited in the existing SIS algorithms. Therefore, this paper proposes a dual reversible secret image sharing mechanism, called DR-SISM, which includes reversible hiding stage and reversible sharing stage. This proposed DR-SISM can simultaneously achieve the reversible recovery of secret image and additional data embedded. In the first stage, a reversible data hiding method is constructed that leverages the predictive technique of median edge detection to obtain the layered label map for capturing a higher embedding load and utilizes the bit-plane replacement technique to embed additional information. In the second stage, a novel (k,n) threshold reversible SIS strategy is presented by employing the Chinese remainder theorem (CRT), which can generate visually meaningful shares. The authentication bits produced by employing the hash function are embedded into the share, which are utilized to compare with the authentication bits owned by users for verifying the identities of the participants before the secret image reconstruction. Through the comparison of advanced SIS algorithms, our proposed DR-SISM in this article is preferred over the recent algorithms in respect of embedding capacity, security, and authentication capability.

The electrostatic pull-in effect is a common phenomenon and a key parameter in the design of microscale and nanoscale devices. Flexible electronic devices based on the pull-in effect have attracted increasing attention due to their unique ductility. This review summarizes nanoelectromechanical switches made by flexible materials and classifies and discusses their applications in, among others, radio frequency systems, microfluidic systems, and electrostatic discharge protection. It is supposed to give researchers a more comprehensive understanding of the pull-in phenomenon and the development of its applications. Also, the review is meant to provide a reference for engineers to design and optimize devices.

Abstract Anomaly detection has attracted considerable search attention. However, existing anomaly detection databases encounter two major problems. Firstly, they are limited in scale. Secondly, training sets contain only video‐level labels indicating the existence of an abnormal event during the full video while lacking annotations of precise time durations. To tackle these problems, we contribute a new Large‐scale Anomaly Detection (LAD) database as the benchmark for anomaly detection in video sequences, which is featured in two aspects. 1) It contains 2000 video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence etc. with large scene varieties, making it the largest anomaly analysis database to date. 2) It provides the annotation data, including video‐level labels (abnormal/normal video, anomaly type) and frame‐level labels (abnormal/normal video frame) to facilitate anomaly detection. Leveraging the above benefits from the LAD database, we further formulate anomaly detection as a fully supervised learning problem and propose a multi‐task deep neural network to solve it. We firstly obtain the local spatiotemporal contextual feature by using an Inflated 3D convolutional (I3D) network. Then we construct a recurrent convolutional neural network fed the local spatiotemporal contextual feature to extract the spatiotemporal contextual feature. With the global spatiotemporal contextual feature, the anomaly type and score can be computed simultaneously by a multi‐task neural network. Experimental results show that the proposed method outperforms the state‐of‐the‐art anomaly detection methods on our database and other public databases of anomaly detection. Supplementary materials are available at http://sim.jxufe.cn/JDMKL/ymfang/anomaly‐detection.html.

Plasmonic nanostructures emerged as an appealing approach for black metal absorbers (BMAs) to provide significant light absorption. We present the simple and rapid fabrication of BMAs using laser-induced forward transfer of silver nanoparticles (AgNPs). Randomly distributed plasmon silver nanostructures at a multiscale were produced simultaneously during laser transfer, providing significant light absorption and antireflection features. The laser-transferred BMAs exhibit excellent absorption capacity of a value over 96% and a minimum reflection of 0.38%. A high scanning speed of 10 m/s was used that can complete the laser-induced forward transfer (LIFT) process within 2 s for a 1 cm2 area. The nearfield light confinement is discussed in terms of the morphology of the obtained nanostructures. Photothermal conversion with macroscale temperature rising was demonstrated for the LIFT process is an effective method for the industrial production of BMAs with excellent absorption capacity.

A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well. [ABSTRACT FROM AUTHOR]

Acute kidney injury (AKI) is one of the most severe consequences of kidney injury, and it will also cause or aggravate the complications by the fast decline of kidney excretory function. Accurate AKI prediction, including the AKI case, AKI stage, and AKI onset time interval, can provide adequate support for effective interventions. Besides, discovering how the medical features affect the AKI result may also provide supporting information for disease treatment. An attention-based temporal neural network approach was employed in this study for AKI prediction and for the analysis of the impact of medical features from temporal electronic health record (EHR) data of patients before AKI diagnosis. We used the publicly available dataset provided by the Medical Information Mart for Intensive Care (MIMIC) for model training, validation, and testing, and then the model was applied in clinical practice. The improvement of AKI case prediction is around 5% AUC (area under the receiver operating characteristic curve), and the AUC value of AKI stage prediction on AKI stage 3 is over 82%. We also analyzed the data by two steps: the associations between the medical features and the AKI case (positive or inverse) and the extent of the impact of medical features on AKI prediction result. It shows that features, such as lactate, glucose, creatinine, blood urea nitrogen (BUN), prothrombin time (PT), and partial thromboplastin time (PTT), are positively associated with the AKI case, while there are inverse associations between the AKI case and features such as platelet, hemoglobin, hematocrit, urine, and international normalized ratio (INR). The laboratory test features such as urine, glucose, creatinine, sodium, and blood urea nitrogen and the medication features such as nonsteroidal anti-inflammatory drugs, agents acting on the renin–angiotensin system, and lipid-lowering medication were detected to have higher weights than other features in the proposed model, which may imply that these features have a great impact on the AKI case.

In this work, we develop a novel no-reference (NR) quality assessment metric for stereoscopic images based on monocular and binocular features, motivated by visual perception properties of the human visual system (HVS) named binocular rivalry and binocular integration. To be more specific, we first calculate the normalized intensity feature maps of right- and left-view images through local contrast normalization, where statistical intensity features are extracted by the histogram of the normalized intensity feature map to represent monocular features. Then, we compute the disparity map of stereoscopic image, with which we extract structure feature map of stereoscopic image based on local binary pattern (LBP). We further extract statistical structure features and statistical depth features from structure feature map and disparity map by histogram to represent binocular features. Finally, we adopt support vector regression (SVR) to train the mapping function from the extracted monocular and binocular features to subjective quality scores. Comparison experiments are conducted on four large-scale stereoscopic image databases and the results demonstrate the promising performance of the proposed method in stereoscopic image quality assessment.

To obtain effective pedestrian detection results in surveillance video, there have been many methods proposed to handle the problems from severe occlusion, pose variation, clutter background, and so on. Besides detection accuracy, a robust surveillance video system should be stable to video quality degradation by network transmission, environment variation, and so on. In this paper, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation. The main contribution of this paper includes the following three aspects. First, a large-scale distorted surveillance video data set (DSurVD) is constructed from high-quality video sequences and their corresponding distorted versions. Second, we design a method to evaluate detection stability and a robustness measure called robustness quadrangle, which can be adopted to the visualize detection accuracy of pedestrian detection algorithms on high-quality video sequences and stability with video quality degradation. Third, the robustness of seven existing pedestrian detection algorithms is evaluated by the built DSurVD. Experimental results show that the robustness can be further improved for existing pedestrian detection algorithms. In addition, we provide much in-depth discussion on how different distortion types influence the performance of pedestrian detection algorithms, which is important to design effective pedestrian detection algorithms for surveillance.

Laser-direct writing has become an alternative method to fabricate microwave devices. We present a laser-scribed wideband open-end termination that relies on conductor loss of the microstrip line structure to obtain effective absorption. The proposed design consists of a resistive film overlapped on a strip conductor, providing an enlarged sheet-resistance range (20 Ω/□~1.2 kΩ/□) of the resistive film to reduce the fabrication difficulties. The resistive film is in tapered shape to enable small gradual changes in impedance, yielding minimized reflections (|S11|). The prototype is demonstrated utilizing the laser-direct writing technique, with a measured |S11| over −15 dB from 6 GHz to at least 30 GHz. The termination can also be used for attenuation over a −10 dB attenuation level (>8.5 GHz) with a low reflection level better than −15 dB (>2.0 GHz). This study can be employed for the applications where cheap wideband planar terminations are needed and promote fast, flexible, and low-cost prototyping or modification of the existing microwave circuits.

Recently, there are lots of visual tracking algorithms proposed to improve the performance of object tracking in video sequences with various real conditions, such as severe occlusion, complicated background, fast motion, and so on. In real visual tracking systems, there are various quality degradation occurring during video acquisition, transmission, and processing. However, most existing studies focus on improving the accuracy of visual tracking while ignoring the performance of tracking algorithms on video sequences with certain quality degradation. In this paper, we investigate the performance evaluation of existing visual tracking algorithms on video sequences with quality degradation. A quality-degraded video database for visual tracking (QDVD-VT), including the reference video sequences and their corresponding distorted versions, is constructed as the benchmarking for robustness analysis of visual tracking algorithms. Based on the constructed QDVD-VT, we propose a method for robustness measurement of visual tracking (RMVT) algorithms by accuracy rate and performance stability. The performance of ten existing visual tracking algorithms is evaluated by the proposed RMVT based on the built QDVD-VT. We provide the detailed analysis and discussion on the robustness analysis of different visual tracking algorithms on video sequences with quality degradation from different distortion types. To visualize the robustness of visual tracking algorithms well, we design a robustness pentagon to show the accuracy rate and performance stability of visual tracking algorithms. Our initial investigation shows that it is still challenging for effective object tracking for existing visual tracking algorithms on video sequences with quality degradation. There is much room for the performance improvement of existing tracking algorithms on video sequences with quality degradation in real applications.

This paper presents a fast discrete Curvelet transform (FDCT)-based technique for multi-focus image fusion to address two problems: texture selection in FDCT domain and block effect in spatial-based fusion. First, we present a frequency-based model by performing FDCT on the input images. Considering the human visual system characteristics, a union of pulse coupled neural network and sum-modified-Laplacian algorithms are proposed to extract the detailed information of frequencies. Then, we construct a hybrid spatial-based model. Unlike other spatial-based methods, we combine the image difference and the detailed information extracted from input images to detect the focused region. Finally, to evaluate the robustness of proposed method, we design a completed evaluation process considering the misregistration, noise error, and conditional focus situations. Experimental results indicate that the proposed method improves the fusion performance and has less computational complexity compared with various exiting frequency-based and spatial-based fusion methods.

In this paper, we propose a novel multi-frame super-resolution (SR) method, which is developed by considering image enhancement and denoising into the SR processing. For image enhancement, a gradient vector flow hybrid field (GVFHF) algorithm, which is robust to noise is first designed to capture the image edges more accurately. Then, through replacing the gradient of anisotropic diffusion shock filter (ADSF) by GVFHF, a GVFHF-based ADSF (GVFHF-ADSF) model is proposed, which can effectively achieve image denoising and enhancement. In addition, a difference curvature-based spatial weight factor is defined in the GVFHF-ADSF model to obtain an adaptive weight between denoising and enhancement in the flat and edge regions. Finally, a GVFHF-ADSF-based multi-frame SR method is presented by employing the GVFHF-ADSF model as a regularization term and the steepest descent algorithm is adopted to solve the inverse SR problem. Experimental results and comparisons with existing methods demonstrate that the proposed GVFHF-ADSF-based SR algorithm can effectively suppress both Gaussian and salt-and-pepper noise, meanwhile enhance edges of the reconstructed image.

Microcrack is commonly seen as a defect in materials that affects the performance of flexible radio frequency (RF) devices. Here, we investigate the influence of microcracks on the RF characteristics of flexible microstrip by stretching flexible microstrip that is based on polydimethylsiloxane (PDMS) substrate and an Ag microparticles/PDMS (AgMP/PDMS) composite conductor. The RF characteristics of the microstrip were monitored with a variety of tensile displacements. An equivalent circuit model of the microstrip with microcracks was proposed to reveal the mechanisms. The fitting results matched the actual measurement well. In addition, the morphology of the microcracks was characterized by SEM and the direct-current (DC) resistance was monitored. The results show that the changes in equivalent circuit element parameters (R, L, C) are due to the change in the conductive pathways, which affect the transmission and reflection of the RF signals.

Squeeze-film damping plays a significant role in the performance of micro-resonators because it determines their quality factors. Perforations in microstructures are often used to control the squeeze-film damping in micro-resonators. To model the perforation effects on the squeeze-film damping, many analytical models have been proposed, however, most of the previous models have been concerned with the squeeze-film damping due to the normal motion between the perforated vibrating plate and a fixed substrate, while there is a lack of works that model the squeeze-film damping of perforated torsion microplates, which are also widely used in MEMS devices. This paper presents an analytical model for the squeeze-film damping of perforated torsion microplates. The derivation in this paper is based on a modified Reynolds equation that includes compressibility and rarefaction effects. The pressure distribution under the vibrating plate is obtained using the double sine series. Closed-form expressions for the stiffness and the damping coefficients of the squeeze-film are derived. The accuracy of the model is verified by comparing its results with the finite element method (FEM) results and the experimental results available in the literature. The regime of validity and limitations of the present model are assessed.

Laser-direct writing has become an alternative method to fabricate flexible electronics, whereas the resistive nature of laser-scribed conductors may distort the radio-frequency characteristics of circuits for high-frequency applications. We demonstrate that the transmission characteristics of microstrip lines are insensitive to the resistance of laser-scripted conductors when the sheet resistance is not above 0.32 Ω/□. On the other hand, the transmission and reflection characteristics of the MS lines can be simply modified through the accommodation of the resistance of the conductors, because a laser can trigger the sintering and melting of laser produced silver nanostructures. This could provide an alternative way to fabricate radio frequency (RF) resistors and promote their applications to flexible radio-frequency devices and systems.

To evaluate the significance of reverse osmosis (RO) and nanofiltration (NF) surface morphology on membrane performance, productivity experiments were conducted using flat-sheet membranes and three different nanoparticles, which included SiO2, TiO2 and CeO2. In this study, the productivity rate was markedly influenced by membrane surface morphology. Atomic force microscopy (AFM) analysis of membrane surfaces revealed that the higher productivity decline rates associated with polyamide RO membranes as compared to that of a cellulose acetate NF membrane was due to the inherent ridge-and-valley morphology of the active layer. The unique polyamide active layer morphology was directly related to the surface roughness, and was found to contribute to particle accumulation in the valleys causing a higher flux decline than in smoother membranes. Extended RO productivity experiments using laboratory grade water and diluted pretreated seawater were conducted to compare the effect that different nanoparticles had on membrane active layers. Membrane flux decline was not affected by particle type when the feed water was laboratory grade water. On the other hand, membrane productivity was affected by particle type when pretreated diluted seawater served as feed water. It was found that CeO2 addition resulted in the least observable flux decline, followed by SiO2 and TiO2. A productivity simulation was conducted by fitting the monitored flux data into a cake growth rate model, where the model was modified using a finite difference method to incorporate surface thickness variation into the analysis. The ratio of cake growth term (k1) and particle back diffusion term (k2) was compared in between different RO and NF membranes. Results indicated that k2 was less significant for surfaces that exhibited a higher roughness. It was concluded that the valley areas of thin-film membrane surfaces have the ability to capture particles, limiting particle back diffusion.

This paper considers the longitudinal coupled vibration of an elastic bearing concrete pipe pile with the saturated soil. The outer and inner saturated soil are governed by the dynamic consolidation theory originally presented by Biot. The governing equations of soil are transferred to ordinary differential equations by Laplace transform. The volumetric strain and pore pressure of soil are obtained by directly solving the coupling equations of soil without introducing potential functions. The analytical expressions of the displacements and shear stresses of the soil are then obtained. The pile response is derived on the basis of 1D elastic theory and the perfect contacts between the pile and soils. The displacement and velocity of the pile in time domain are obtained by using numerical inverse transformation. Selected numerical results are presented to portray the influence of the existences of soils, pile geometry and dynamic permeability coefficients of soils on the vibration characteristics of the pipe pile. At last, the displacement response between a pipe pile and solid pile are compared. [ABSTRACT FROM AUTHOR]