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Pulse pile-up presents a significant challenge in nuclear radiation measurements, particularly in neutron-gamma pulse shape discrimination, as it causes pulse distortion and diminishes identification accuracy. To address this, we propose an optimized Support Vector Regression (SVR) algorithm for correcting pulse pile-up. Initially, the Dung Beetle Optimizer (DBO) and Whale Optimization Algorithm (WOA) are integrated to refine the correction process, with performance evaluated using charge comparison methods (CCM) for pulse shape discrimination. Leveraging prior knowledge from simulated data, we further analyze the relationships between various types of pulse pile-ups, including their combinations, inter-peak distances, and the accuracy of corrections. Extensive experiments conducted in a mixed neutron-gamma radiation field using plastic scintillators demonstrate that the proposed method effectively corrects pulse pile-up and accurately discriminates between neutron and gamma. Moreover, our approach significantly improves the fidelity of pulse shape discrimination and enhances the overall reliability of radiation detection systems in high-interference environments. [ABSTRACT FROM AUTHOR]

Remote sensing object detection (RSOD) plays a crucial role in resource utilization, geological disaster risk assessment and urban planning. Deep learning-based object-detection algorithms have proven effective in remote sensing image studies. However, accurate detection of objects with small size, dense distribution and complex object arrangement remains a significant challenge in the remote sensing field. To address this, a refined and efficient object-detection algorithm (RE-YOLO) has been proposed in this paper for remote sensing images. Initially, a refined and efficient module (REM) was designed to balance computational complexity and feature-extraction capabilities, which serves as a key component of the RE_CSP block. RE_CSP block efficiently extracts multi-scale information, overcoming challenges posed by complex backgrounds. Moreover, the spatial extracted attention module (SEAM) has been proposed in the bottleneck of backbone to promote representative feature learning and enhance the semantic information capture. In addition, a three-branch path aggregation network (TBPAN) has been constructed as the neck network, which facilitates comprehensive fusion of shallow positional information and deep semantic information across different channels, enabling the network with a robust ability to capture contextual information. Extensive experiments conducted on two large-scale remote sensing datasets, DOTA-v1.0 and SCERL, demonstrate that the proposed RE-YOLO outperforms state-of-the-art other object-detection approaches and exhibits a significant improvement in generalization ability. [ABSTRACT FROM AUTHOR]

In the process of Cr8 roller production, the phenomenon of coarse grain size and uneven grain size often appears, which makes the mechanical properties of the material decrease sharply. Accurate dynamic recrystallization model is the basis for predicting the change of grain size during thermal processing, and is an important basis for refining grain and improving material properties. In this study, the isothermal compression experiment was carried out on Cr8 alloy steel at 900–1200 °C and 0.005–0.1 s−1 by Gleeble –1500D thermal simulation compressor, and the stress dates of Cr8 alloy steel were obtained. According to experimental data, the Kopp dynamic recrystallization model of Cr8 alloy steel was established. The dynamic recrystallization volume fraction obtained by Kopp model was compared with that obtained by experiment at the same temperature and strain rate. The correlation value was 0.988, and the root mean square error (RMSE) was 0.053, which proved that the DRX model established was reliable. Through the secondary development of the program, the DRX model of Cr8 alloy steel was written into the software Forge® to verify the microstructure evolution model. The compression process of a cylindrical specimen of Cr8 alloy steel at 0.1 s−1 and 1050 °C was simulated, and the DRX microstructure evolution of the alloy was calculated. The comparison between the final grain size calculation results and the test metallographic photos of samples in different deformation zones shows the relative error of the grain size was less than 10.6%, indicating that the DRX model of Cr8 alloy steel can better predict the dynamic recrystallization of Cr8 alloy steel. [ABSTRACT FROM AUTHOR]

This letter introduces an efficient shift operator discontinuous Galerkin time domain (SO‐DGTD) method with both nonconforming and local time stepping techniques for the simulation of nanophotonic problems. In this method, the constitutive relation of metallic properties is described by introducing the shift operator method (SO) and the relation of electric field and electric displacement can be obtained in the time domain. To make the nanophotonic simulation more efficient, we introduce the non‐conforming technique based on upwind numerical flux to SO‐DGTD method. In addition, we further introduce the local time stepping technique to SO‐DGTD method to improve the computational efficiency for nanophotonic problems with complex structures. Finally, some numerical examples are given to show the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a high efficiency three‐dimensional electromagnetic radiation simulation tool based on finite element method (FEM) is presented. This simulation tool has the functions of human‐computer interaction, constructing model, generating mesh, setting simulation parameters and making FEM analysis. In this simulation tool, the FEM analysis is the most critical part, so we mainly focus on the FEM electromagnetic radiation analysis technique in this article. In order to improve the efficiency of FEM analysis of electromagnetic radiation problems, we introduce the high order hierarchical vector basis function and the p‐type multiplicative Schwarz (p‐MUS) preconditioner to accelerate the iterative solution. What is more, we further improve the direct factorization process of p‐MUS preconditioner by multifrontal block incomplete Cholesky preconditioner. Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed tool. [ABSTRACT FROM AUTHOR]

In recent years, an increasing number of circular RNAs (circRNAs) have been discovered in hepatocellular carcinoma (HCC). However, the functions of most circRNAs require further investigation. Here, we found that circBACH1 was significantly upregulated in HCC tissues and that high circBACH1 levels were closely associated with poor prognosis. In addition, circBACH1 could promote HCC growth by accelerating cell cycle progression in vitro and in vivo. We next investigated the cellular and molecular mechanisms and discovered that circBACH1 inhibited p27 translation, which influenced cell cycle progression. Moreover, we revealed that circBACH1 could combine directly with HuR using RNA immunoprecipitation assays, pull‐down assays, and electrophoretic mobility shift assays. The combination of these molecules facilitated HuR translocation from the nucleus to the cytoplasm according to the fluorescence in situ hybridization and immunofluorescence results. Finally, silencing HuR abrogated circBACH1's inhibition of p27 translation and abolished the circBACH1‐induced effect on HCC proliferation. In sum, circBACH1 plays a significant role as an oncogene through the circBACH1/HuR/p27 axis in HCC development. [ABSTRACT FROM AUTHOR]

Initial alignment technology is a key technology for inertial navigation systems (INSs) because the speed and accuracy of the initial alignment determine the working accuracy and rapid response capability of the INS. Thus, the initial alignment technique of an INS must be investigated. In a swaying base environment, traditional alignment methods cannot meet the requirements of alignment accuracy due to the occurrence of interference acceleration and angular velocity, and filtering methods do not perform well in practical and universal applications. This paper studies the self-alignment problem for the swaying base of a rotary strapdown inertial navigation system and uses the latest hybrid inertial navigation system (HINS) as the research platform. Using the double-position rotation strategy, a navigation algorithm based on an updated attitude transformation matrix and a dynamic error compensation algorithm are established, and they effectively resolve the fast and high-precision self-alignment problem of the rotary inertial navigation system (RINS) under the oscillating state. The accuracy and feasibility of the method are demonstrated by mathematical simulations. Experiments show that compared with traditional single-position and continuous-rotation alignment methods, the proposed dynamic self-alignment technology of HINS has the advantages of short alignment time, high alignment accuracy, and dynamic environment adaptability. [ABSTRACT FROM AUTHOR]

In this paper, a stereo calibration method for binocular ultra-wide angle long-wave infrared camera is proposed on the basis of an equivalent small field of view camera. Extrinsic parameters are calibrated through the corrected images from the left and right cameras. They can be viewed as images taken by a small field of view camera. The calibration procedure consists of three steps: monocular calibration, distortion correction and extrinsic parameters calibration. In order to evaluate the accuracy of the method, stereo vision of the camera is modelled and a 3D reconstruction approach is presented. A series of experiments, including intrinsic parameters, extrinsic parameters and 3D reconstruction, are conducted to validate the proposed method. The results show that the baseline length error decreases to 0.67%, and the relative error for the 3D reconstruction of corners is smaller than 8.11%. In contrast to the common stereo calibration method, it improves calibration accuracy. [ABSTRACT FROM AUTHOR]

The plastid acetyl coenzyme carboxylase (ACCase) Trp 1999 Leu mutation was identified in a Beckmannia syzigachne population resistant to fenoxaprop-p-ethyl. The pattern of cross-resistance for the Trp 1999 Leu mutation is still ambiguous. In this paper, mutant homozygote (1999Leu/Leu, RR) and wild type (1999Trp/Trp, SS) B. syzigachne plants with the same genetic background were purified from the JS-26 population using the dCAPS method. The activity of ACCase in RR and SS was determined. Then, the cross-resistance pattern to ACCase inhibiting herbicides of the Trp 1999 Leu mutation was determined using the whole-plant method. ACCase activity showed that the Trp 1999 Leu mutation decreased ACCase sensitivity to fenoxaprop-p-ethyl by 2.73-fold. A dose-response experiment indicated that the Trp 1999 Leu mutation conferred high resistance to quizalofop-p-ethyl (20.29-fold), metamifop (12.22-fold) and pinoxaden (18.60-fold), moderate resistance to fenoxaprop-p-ethyl (8.20-fold) and sethoxydim (6.38-fold), low resistance to cyhalofop-butyl (2.73-fold) and no resistance to clodinafop-propargyl (1.42 fold) and clethodim (1.59-fold). This is the first report of the role of Trp 1999 Leu in fenoxaprop-p-ethyl resistance and of the patterns of cross-resistance to ACCase-inhibiting herbicides in B. syzigachne. Unlabelled Image • A novel W 1999 L mutation in ACCase was identified in fenoxaprop resistant Beckmannia syzigachne. • W 1999 L mutation resulted in 2.73-folds decrease on ACCase sensitivity to fenoxaprop-p-ethyl. • W 1999 L mutation mutation conferred moderate to high or no to low resistance to APPs with two-rings or one-ring aromatic moiety. [ABSTRACT FROM AUTHOR]

Highlights • Three band pass filters are used to quickly estimate the oxygen band-average transmission near 762 nm. • Three-channel structure capturing bandpass signals simultaneously eliminates variation effect of intensity and turbulence. • A transmission model is established based on line-by-line algorithm to derive path length from band-average transmission. Abstract A three-channel filter-based non-imaging passive ranging system based on oxygen absorption is proposed and demonstrated. This system utilizes three band pass filters to quickly estimate the oxygen band-average transmission near 762 nm, and uses three simultaneous spectral measurement channels which are defined by the different band pass filters to capture the three band pass signals of the target. A short range experiment from 100 m to 300 m and a long range experiment at 1945 m are conducted with a 1000 W halogen lamp, and the measured band-average oxygen transmission of the both experiments are compared to the predicted curve derived from the line-by-line transmission model to estimate the range. Averaged range error of 2.14% is achieved in the short range experiment and range error of 6.72% is obtained in the long range experiment. [ABSTRACT FROM AUTHOR]

A Kalman-filter-based method is proposed to address challenges in nuclear signal processing, including pileup events and noise cancelation. The state-transition equation is established on the basis of suitable pulse shape models. By regarding the output signal of particle detectors as a measurable variable, the measurement equation is also introduced to correct dynamic processes of the state transition. A linear Kalman filter with $n$ tunnels is constructed to separate pileup events and maintain the original waveforms of pulses. In addition, two terms are defined and addressed for separation operations, namely, Availability, which is used to prepare for upcoming events, and Clearance Mechanism, which is given to remove thumbnail information from the previous events and make occupied tunnels available to subsequent nuclear events. Simulation and field experiments demonstrate that the proposed method can handle critical situations of pulse pileup and minimize count loss and dead time in the X-ray energy spectrum while maintaining the original pulse shapes. [ABSTRACT FROM AUTHOR]

Abstract: The passive ranging technology can be used to accurately estimate range from sensor to target. It can simultaneously carry out detecting, tracking, locating and alarming of boost phase missiles and rocket planes. The Oxygen absorption band, located in visible light and near infrared band, has unique spectral lineshape, independent absorption spectrum, large dynamic range and relatively smaller absorption characteristic, and then could carry out the high precision and stability distance measuring for auto-power aerial vehicle. In order to discuss the effect of different meteorologic factors on this technique, this paper researched the Oxygen absorption spectral characteristics and introduced the basic principle of the passive ranging in detail, and then theoretically analyzed and numerically simulated the effect of the meteorologic factors to passive ranging technology. Research result shows that the passive ranging based on the Oxygen absorption spectrum has good adaptability and stability, and the worsening of meteorologic factors only reduces the efficient measurement range of the system, and then does not have any effect for ranging accuracy in the validity range. The result will provide a certain amount of theoretical and experimental basis for the research and application of the passive ranging based on Oxygen absorption spectrum. [Copyright &y& Elsevier]

Abstract: With the development of airborne and spaceborne remote sensing from the 1980s, as a new and growing technology, hyperspectral imaging is widely used in different fields, such as military investigation, battlefield information acquisition, environmental monitoring, mineral exploration and public security. Because of the unique characteristic of acquiring spectral and spatial information simultaneously, it brings the hyperspectral detection advantages when dealing with target detection problem under complex conditions. Target detecting models of hyperspectral image are established, including the target subspace model and the probability statistical model. And several algorithms are introduced, which are based on original spectral features, sub-space projection and probability statistical model separately. Comparison shows that if the background includes fault objectives, GLRT is the best algorithm, and its SINR is the largest; on condition of anomaly target detection, LPTD is the best, and have a quite high SINR. [Copyright &y& Elsevier]

This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models' features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signal covariance matrix as the input of the dilated convolutional neural network to directly perform 2D DOA estimation in the absence of a low-elevation target. The algorithm employs 3D convolution to fully retain and extract features. The simulation experiments and the analysis of their results revealed that for both L-shaped uniform and L-shaped sparse arrays, the dilated convolutional autoencoder could effectively suppress the multipath signals without affecting the direct wave and non-low-elevation targets, whereas the dilated convolutional neural network could effectively achieve 2D DOA estimation with a matching rate and an effective ratio of pitch and azimuth angles close to 100% without the need for additional parameter matching. Under the condition of a low signal-to-noise ratio, the estimation accuracy of the proposed algorithm was significantly higher than that of the traditional DOA estimation. [ABSTRACT FROM AUTHOR]

As one of the renewable and clean energy sources, wave energy has high density and all-day availability characteristics. However, the development of wave energy converters has been hampered, mainly because of the low efficiency induced high levelized energy costs. The present study introduces an improved bistable mechanism composed of three linear springs, aiming to enhance the performance of a point absorber employed as a wave energy converter. The improved bistable mechanism features an asymmetric configuration of three linear stiffness springs: one horizontal and two obliques on a vertical plane. Numerical simulations based on Cummins equations in the time domain describe the point absorber dynamic behavior in regular and irregular waves. The improved bistable wave energy converter featured a wider low equivalent stiffness range than linear and conventional bistable counterparts, improving its ability to frequency shifting. Its lower potential barrier greatly improves the capture width ratio and frequency bandwidth at low excitations. Such characteristics facilitate the improved bistable wave energy converter to achieve higher efficiency than its linear and conventional bistable counterparts at low frequencies, and enhance its robustness against power-take-off damping detuning and sea state changes. These features altogether make the improved bistable mechanism an efficient alternative to explore the benefits of the bistable dynamics applied to wave energy converters. • Introduced a simple but effective improved bistable mechanism to a point absorber wave energy converter. • The IB-WEC has a strong frequency shifting ability. • The IB-WEC has a high power capture width ratio and wide frequency bandwidth under low wave excitations. • The IB-WEC has strong robustness against PTO damping detuning and sea state changes. [ABSTRACT FROM AUTHOR]

• Fleet-based vehicle emission factors were calculated using low-cost sensors. • Measuring in parking garages enabled built-in validation with vehicle counting. • Emission factors were within range of those measured by reference-grade instruments. • Cold start emissions and heavy emitters contributed disproportionately to the fleet. • Low-cost sensors are a promising alternative for measuring real-world emissions. Traditionally, vehicle emissions measurements rely on reference-grade instruments whose high cost and complexity have limited their deployment in real-world environments. New simple-to-operate, low-cost sensing technologies are a potential solution to this problem. To assess their suitability, we deployed six Sensit Real-time, Affordable, Multi-Pollutant (RAMP) monitors measuring PM 2.5 , NO, NO 2 , CO 2 , O 3 and CO in three parking garages on the UBC Vancouver campus from April–August 2019. UBC Parking Services provided real-time vehicle counts to help validate our method. After sensor calibration, integrated pollutant and CO 2 signals were converted to fuel-based emission factors (EFs). Our calculated EFs fell within the range of previous studies. Evening EFs when vehicles were cold were 10–50% higher than in the morning. We also observed a disproportional contribution of high emitters; the top 25% of plumes contributed 45–65% of total emissions. Our findings indicate that low-cost sensors are a promising technology for real-world vehicle emissions measurement. [ABSTRACT FROM AUTHOR]

In current fusion methods based on sparse representation (SR) and different frequency, the SR is usually applied to fusion of the low-frequency components. In contrast, the direct fusion is usually adopted for high-frequency components due to their significant diversity. However, the effect of the direct fusion is degraded by the redundant information resulting from the correlation between original signals. A multimodel fusion framework is proposed by applying the SR to low-frequency fusion at pixel-level and high-frequency fusion at feature-level, respectively. First, the multimodal images are decomposed into high-frequency and low-frequency components by nonsubsampled contourlet transform (NSCT). Second, the universal high-frequency dictionary is constructed by using the fast independent component analysis (ICA) of the source high-frequency and its subband images. They represent the general feature part and unique feature part for the high-frequency signals, respectively. The universal low-frequency dictionary is constructed by using the original low-frequency signals. Third, the direct fusion of the high-frequency is converted into sparse coefficients fusion in fast ICA domain. Moreover, the multiple directive contrasts by modifying sum-modified Laplacian are taken as the fusion rule. The low-frequency signals are fused by using an activity measure based on weights. Finally, the fused image is obtained by inverse NSCT on the merged components. The experiments are conducted on three types of image pairs, and the results demonstrate that the proposed method outperforms seven state-of-the-art methods, in terms of four subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

In this paper a approach of corners positioning for calibrating binocular ultra-wide angle long-wave infrared camera is presented. Firstly, the generalized camera model which suits for its imaging relationship is introduced. Secondly, a method for subpixel corners positioning is proposed, containing edge extraction, gray centroid and curve fitting. Thirdly, aiming at calibrating extrinsic parameters of the camera, we raise a method with least squares optimization. The calibration experiments are conducted on the basis of an active infrared radiation calibration checkerboard. The performance of the approach is discussed by using other data sets. The results reveal that: MRE (mean reprojection error) and RMS (root mean square) respectively reduce to 0.23pixels and 0.33pixels, and the error of the baseline length declines from 1.00 % to 0.23 % in comparison with common methods. [ABSTRACT FROM AUTHOR]

It is crucial to precisely calculate temperature utilizing thermal models, which require the determination of thermal parameters that optimally align model outcomes with experimental data. In many instances, the refinement of these models is undertaken within space instruments. This paper introduces an optimization methodology for thermal network models, with the objective of enhancing the accuracy of temperature predictions for aerial cameras. The investigation of internal convective heat transfer coefficients for both cylindrical and planar structures provides an estimation of convective thermal parameters. Based on the identification of thermally sensitive parameters and the reliability evaluation of transient temperature data through the Monte-Carlo simulation, the genetic algorithm is employed to search for global optimal parameter values that minimize the root mean square error (RMSE) between calculated and measured node temperatures. As a result, the optimized model shows significantly improved accuracy in temperature prediction, attaining an RMSE of 1.07 ℃ and reducing the maximum relative error between predicted and experimental results from 33.8 to 3.1%. Furthermore, the flight simulation and thermal control experiments validate the robustness of the optimized model, demonstrating that discrepancies between the observed and predicted temperatures are within 2 °C after re-correcting the external convection heat transfer coefficient value. [ABSTRACT FROM AUTHOR]

It is crucial to precisely calculate temperature utilizing thermal models, which require the determination of thermal parameters that optimally align model outcomes with experimental data. In many instances, the refinement of these models is undertaken within space instruments. This paper introduces an optimization methodology for thermal network models, with the objective of enhancing the accuracy of temperature predictions for aerial cameras. The investigation of internal convective heat transfer coefficients for both cylindrical and planar structures provides an estimation of convective thermal parameters. Based on the identification of thermally sensitive parameters and the reliability evaluation of transient temperature data through the Monte-Carlo simulation, the genetic algorithm is employed to search for global optimal parameter values that minimize the root mean square error (RMSE) between calculated and measured node temperatures. As a result, the optimized model shows significantly improved accuracy in temperature prediction, attaining an RMSE of 1.07 ℃ and reducing the maximum relative error between predicted and experimental results from 33.8 to 3.1%. Furthermore, the flight simulation and thermal control experiments validate the robustness of the optimized model, demonstrating that discrepancies between the observed and predicted temperatures are within 2 °C after re-correcting the external convection heat transfer coefficient value. [ABSTRACT FROM AUTHOR]

In current fusion methods based on sparse representation (SR) and different frequency, the SR is usually applied to fusion of the low-frequency components. In contrast, the direct fusion is usually adopted for high-frequency components due to their significant diversity. However, the effect of the direct fusion is degraded by the redundant information resulting from the correlation between original signals. A multimodel fusion framework is proposed by applying the SR to low-frequency fusion at pixel-level and high-frequency fusion at feature-level, respectively. First, the multimodal images are decomposed into high-frequency and low-frequency components by nonsubsampled contourlet transform (NSCT). Second, the universal high-frequency dictionary is constructed by using the fast independent component analysis (ICA) of the source high-frequency and its subband images. They represent the general feature part and unique feature part for the high-frequency signals, respectively. The universal low-frequency dictionary is constructed by using the original low-frequency signals. Third, the direct fusion of the high-frequency is converted into sparse coefficients fusion in fast ICA domain. Moreover, the multiple directive contrasts by modifying sum-modified Laplacian are taken as the fusion rule. The low-frequency signals are fused by using an activity measure based on weights. Finally, the fused image is obtained by inverse NSCT on the merged components. The experiments are conducted on three types of image pairs, and the results demonstrate that the proposed method outperforms seven state-of-the-art methods, in terms of four subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

The quality of aerial remote sensing imaging is heavily impacted by the thermal distortions in optical cameras caused by temperature fluctuations. This paper introduces a lumped parameter thermal network (LPTN) model for the optical system of aerial cameras, aiming to serve as a guideline for their thermal design. By optimizing the thermal resistances associated with convection and radiation while considering the camera's unique internal architecture, this model endeavors to improve the accuracy of temperature predictions. Additionally, the proposed LPTN framework enables the establishment of a heat leakage network, which offers a detailed examination of heat leakage paths and rates. This analysis offers valuable insights into the thermal performance of the camera, thereby guiding the refinement of heating zones and the development of effective active control strategies. Operating at a total power consumption of 26 W, the thermal system adheres to the low-power limit. Experimental data from thermal tests indicate that the temperatures within the optical system are maintained consistently between 19 °C and 22 °C throughout the flight, with temperature gradients remaining below 3 °C, satisfying the temperature requirements. The proposed LPTN model exhibits swiftness and efficacy in determining thermal characteristics, significantly facilitating the thermal design process and ensuring optimal power allocation for aerial cameras. [ABSTRACT FROM AUTHOR]

The navigation accuracy of the inertial navigation system (INS) can be greatly improved when the inertial measurement unit (IMU) is effectively calibrated and compensated, such as gyro drifts and accelerometer biases. To reduce the requirement for turntable precision in the classical calibration method, a continuous dynamic self-calibration method based on a three-axis rotating frame for the hybrid inertial navigation system is presented. First, by selecting a suitable IMU frame, the error models of accelerometers and gyros are established. Then, by taking the navigation errors during rolling as the observations, the overall twenty-one error parameters of hybrid inertial navigation system (HINS) are identified based on the calculation of the intermediate parameter. The actual experiment verifies that the method can identify all error parameters of HINS and this method has equivalent accuracy to the classical calibration on a high-precision turntable. In addition, this method is rapid, simple and feasible. [ABSTRACT FROM AUTHOR]

Electromagnetic metamaterials possess characteristics such as having large-scale, periodic, multi-media, and complex geometric structures, making them highly suitable for simulation using the finite element domain decomposition method (DDM). This work proposes a non-conformal DDM for simulating finite-period metamaterials, achieving simulation with a minimal number of domains while significantly reducing computational resource consumption. First, the computational domain of the periodic metamaterial is partitioned into several non-conformal hexahedral subdomains. Subsequently, a limited number of non-repetitive subdomain models with non-matching grids are constructed, leveraging the three-dimensional rotational and translational properties of these subdomains to facilitate simulation. By eliminating the necessity to construct models for every subdomain, a substantial reduction in memory consumption is achieved. Second, an iteration method for solving the matrix equations of subdomains is enhanced by introducing a multifrontal block incomplete Cholesky decomposition preconditioner, thereby enhancing the computational efficiency of matrix equations with a large number of unknowns. Meanwhile, parallel computing techniques are employed to accelerate the proposed method. Finally, we integrate the aforementioned method into a solver and leverage it to develop an electromagnetic simulation tool tailored for metamaterials. The tool is employed to simulate metamaterial structures of varying scales, resulting in notable reductions in both memory and time consumption while maintaining accuracy comparable to commercial software. [ABSTRACT FROM AUTHOR]

Abstract Motivated by the human visual system, we propose an infrared dim target detection method that is based on a fuzzy accurate updating symmetric adaptive resonance theory network. From the bottom-up perspective, the regions of interest (ROIs) are extracted using a difference of Gaussians at multiple scales and our designed ROI model in a saliency map. From the top-down perspective, five feature categories are extracted using the ROI model, which are used to train the proposed Fuzzy AUSART network. The well-trained network realizes the true identification of all ROI candidates. The results of the receiver operating characteristic (ROC) curves verify that the proposed method can better adapt to different circumstances and targets in our experiment. The average detection accuracy of the Fuzzy AUSART is improved by 15.4%, and the average F 1 index of the proposed method is higher than six typical comparison methods by more than a factor of 2.48. [ABSTRACT FROM AUTHOR]

Warm deformation is a plastic-forming process that differs from traditional cold and hot forming techniques. At the macro level, it can effectively reduce the problem of high deformation resistance in cold deformation and improve the surface decarburization issues during the hot deformation process. Microscopically, it has significant advantages in controlling product structure, refining grain size, and enhancing product mechanical properties. The Gleeble-1500D thermal–mechanical physical simulation system was used to conduct isothermal compression tests on GCr15 bearing steel. The tests were conducted at temperatures of 600–1050 °C and strain rates of 0.01–5 s−1. Based on the experimental data, the critical strain model and dynamic recrystallization model for the warm–hot forming of GCr15 bearing steel were established in this paper. The model accuracy is evaluated using statistical indicators such as the correlation coefficient (R). The dynamic recrystallization model exhibits high predictive accuracy, as indicated by an R-value of 0.986. The established dynamic recrystallization model for GCr15 bearing steel was integrated into the Forge® 3.2 numerical simulation software through secondary program development to simulate the compression process of GCr15 warm–hot forming. The dynamic recrystallization fraction was analyzed in various deformation regions. The grain size of the severe deformation zone, small deformation zone, and difficult deformation zone was compared based on simulated compression specimens under the conditions of 1050 °C and 0.1 s−1 with the corresponding grain size obtained with measurement based on metallographic photos; the relative error between the two is 5.75%. This verifies the accuracy of the established dynamic recrystallization and critical strain models for warm–hot deformation of GCr15 bearing steel. These models provide a theoretical basis for the finite element method analysis and microstructure control of the warm–hot forming process in bearing races. [ABSTRACT FROM AUTHOR]

The dynamic behaviours of subsea pipe-valve structures—the principal components of subsea equipment—are of critical concern for the control of the lowering through the splash zone during installation. Nonlinear interactions between waves and pipe-valve combinations are challenging the investigation of the wave forces acting on large-dimension structures. This paper proposes a numerical model to study the nonlinear hydrodynamic performance of partially submerged pipe-valve structures. A two-dimensional (2D) numerical water tank (NWT) was established to efficiently generate high-accuracy incident waves, and a semi-immersed horizontal cylinder was applied to the numerical simulations of wave-structure interaction (WSI) to study the mesh and time convergence. The numerical model was validated with a comparative analysis of the modelling results with experimental data, other numerical results, and theoretical results. Numerical simulations of the interaction between Stokes-II waves and subsea pipe-valve structures were performed, and the effects of the valve height ratio, valve length ratio, and valve spacing were investigated. The subsea pipe-valve structure is a kind of multi-scale structure, the wave forces exhibited stronger nonlinearity when acting on the partially submerged valves than on the completely submerged pipe. Changes in valve structure and distribution significantly increased the overall change in the wave forces acting on the valves. The stronger nonlinearity was primarily derived from phenomena such as wave reflection, wave blockage, and wave run-up. The numerical results provide references for the structure and strength designs and offshore installations of large subsea equipment like subsea manifolds. (1) This paper presents the systematic study on the hydrodynamic performance of subsea pipe-valve multi-scale structures under the nonlinear incident waves. (2) We use the static-boundary method to establish a two-dimensional numerical water tank, which can be applied to accurately and efficiently generate nonlinear incident waves. (3) A numerical simulation scheme with high accuracy and efficiency is proposed to investigate the interaction between Stokes-II waves and partially submerged structures. (4) Numerical simulations of the interaction between Stokes-II waves and subsea pipe-valve structures are performed, and the effects of the valve height ratio, valve length ratio, and valve spacing are investigated. (5) The numerical results provide references for the structure and strength designs and offshore installations for large subsea equipment. [ABSTRACT FROM AUTHOR]

• An overcomplete dictionary of cartoon components is constructed and it gives a rise to the robust and universal representation of the cartoon components. A modified OMP is proposed to enhance the efficiency of sparse coding. • The iterative visual saliency map is proposed to compute the weight map. The multi-scale guided filter is then designed to optimize the weight map to extract the scale-aware structures and improve the spatial consistency. • A multi-model image fusion framework is proposed based on cartoon-texture decomposition. On the basis of it, the structures are better integrated into the fused image. To completely integrate the structures of images, a cartoon-texture decomposition based image fusion framework is proposed in this paper. The cartoon components represent the background and main structures information. Due to its high similarity, the sparse representation is utilized based on an overcomplete cartoon dictionary. Moreover, a modified orthogonal matching pursuit is proposed to improve the fusion image continuity and eliminate artifacts interferences. The texture components describe more distinct structures, which have diverse scale characteristics. To preserve more relevant infrared details in fused texture, an iterative visual saliency map is proposed. Furthermore, the weight maps are computed on the basis of a designed multi-scale guided filter and saliency map; then, the weight maps are utilized to guide the texture fusion, aiming to improve the visual and scale-aware salience. The final fused image is obtained by combining the cartoon fusion and texture fusion results. The comprehensive and objection evaluations demonstrate the outperformance on accuracy, robustness and versatility of the proposed fusion framework. [ABSTRACT FROM AUTHOR]

As an element content analysis technology, X-ray fluorescence spectrometry can be used for quantitative or semiquantitative analysis of the element content in the sample, which is of great significance for mineral census and spent fuel reprocessing. Due to the limitation of the inherent energy resolution of the detector itself, the accuracy of X-ray fluorescence analysis is difficult to be greatly improved. In some applications, even if the semiconductor detector with the best energy resolution is used, the characteristic peaks of different elements cannot be completely separated. Therefore, greatly improving the energy resolution of the detection system is a hot issue in the existing research field. To solve these problems, this paper analyzes the advantages and disadvantages of the traditional MCA (multichannel analyzer) and SLA (seeds local averaging) algorithm and proposes an ISLA (improved seeds local averaging) algorithm based on mathematical statistics. In the section of theoretical derivation, the principle of ISLA algorithm is described, whose theoretical characteristics and spectral results with different parameters are derived and simulated. In the application effect evaluation, the spectrum obtained by each method is analyzed in detail. Simulation and experimental results show that the spectrum obtained by SLA algorithm has a smaller full width at half maximum than that obtained by MCA, but the seed average process in SLA algorithm also reduces its counting rate. The optimized ISLA algorithm can not only effectively reduce the full width at half maximum of the spectral line and sharpen the spectrum peak but also compensate for the loss of the count rate of SLA algorithm. [ABSTRACT FROM AUTHOR]

• An automatic calibration method is proposed for the ultra-field infrared and visible cameras. • A thermal radiation checkerboard is designed, which can be used in infrared and visible bands at the same time. • The corners and center can be automatically located at subpixel level. • RBF based nonlinear refinement is proposed to improve the calibration accuracy. Camera calibration is a vital and important work for machine vision. In the paper, an optimized calibration method for ultra-field dual bands cameras is proposed, which is based on the designed thermal radiation checkerboard. The optimizations are achieved in subpixel location, center location and mapping relationship, respectively. Specifically, the subpixel corner is located by performing the designed variable quadrilateral template on edge gradients and calculating their minimum orthogonal value. Then, the center location is accomplished through Gaussian fitting, in which the bottom point is taken as the potential center in iterations. Moreover, the RBF network is employed to replace the polynomial fitting and fit the mapping relationship between the camera coordinates and sensor coordinates. Thus, the accuracy of the mapping is increased, which contributes to the optimization of the intrinsic and extrinsic parameters. The experiments results show that the average root mean square error of the reprojection is reduced by nearly 50%. The calibrated parameters are accurate and have physical significance in the proposed calibration scheme. [ABSTRACT FROM AUTHOR]

• An exponential and logarithmic form builds the SOC-OCV characteristic curve. • Aggregating cubature and particle filters achieves more stable estimation of SOC. • Noise self-adjustment makes estimator more applicable to practical engineering. In this study, a more precise numerical method to discretize the equation of State-of-Charge is proposed. Unscented particle filter and cubature Kalman filter are performed to estimate State-of-Charge. A hybrid cubature particle filter is presented by aggregating the cubature filter and particle filter to achieve a more stable estimation of State-of-Charge under harsh charging & discharging schedules. Furthermore, the noise self-adjustment strategy is applied to make the proposed estimator more applicable to practical engineering environment. Extensive experiments are conducted on the real data from the Federal Urban Driving Schedule and Dynamic Stress Test, and the results verify that the proposed hybrid method is more robust than the existing models. [ABSTRACT FROM AUTHOR]

With the rapid development of inertial technology, the rotational inertial system has been widely used for high-accuracy navigation. In addition, the single-axis rotational inertial navigation system is one of the most popular navigation systems due to its low cost. However, the fact which cannot be ignored is that although single axis rotational inertial navigation system can restrain divergence of attitude error, it cannot eliminate velocity error, which is mainly caused by the gyro misalignment and scale factor error related to rotational axis. A novel calibration method established on filter has been proposed. Position, velocity, and attitude are chosen to be the state variables. In order to estimate the gyro errors related to rotational axis, these errors must be included in the state equation. Correspondingly, zero velocity and rate of turntable are chosen to be measured. Simulation has been carried out to verify the correctness of the theory, and the real test has been performed to further demonstrate the validity of the method. By compensating the main error estimated by filter, it can be found that the accumulated errors in velocity and attitude are decreased. So, the precision of navigation is greatly improved with the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This work considers an input and output constraint control problem for pure‐feedback systems with nonaffine functions possibly being in‐differentiable. A locally semibounded and continuous condition for nonaffine functions is presented to guarantee the controllability, and the nonaffine system is transformed to an equivalent pseudoaffine one based on the mild condition. Combined with backstepping technique, a novel prescribed performance controller with new performance functions is constructed to circumvent high frequency chattering in control input. An auxiliary system with bounded compensation term is utilized in this paper, successfully avoiding the overrun of control input. The methodology achieves the desired transient and steady‐state performance and presents excellent robustness against the system uncertainty. Finally, two numerical simulations are performed to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A novel 2D heterojunction composite photocatalyst Co-g-C3N4/MoS2 obtained by coupling Co-doped g-C3N4 and MoS2 nanosheets has been successfully fabricated via a facile impregnation and calcination method. The results of photocatalytic experiments showed that the as-prepared catalyst exhibited markedly enhanced photocatalytic activity. The rates for the degradation of rhodamine B (RhB) and hydrogen evolution under simulated sunlight irradiation using a catalyst were about 6.4 and 4.7 times higher than those of pristine g-C3N4. Moreover, the photocatalytic activity of the as-prepared product was compared with that of the 2D heterojunction g-C3N4/MoS2 or Co-doped g-C3N4 only. It was revealed that the photocatalytic activity of Co-g-C3N4/MoS2 was better than those of g-C3N4/MoS2 and Co-g-C3N4. The great improvement in photocatalytic activity performance is mainly ascribed to the Co-doping and formation of a 2D heterojunction, resulting in a fast charge carrier transfer, effective suppression of recombination of charge carriers, and an increase of light absorption. The doping amount of cobalt and the loading amount of MoS2 have a great influence on the photocatalytic activity of g-C3N4, and they were studied in detail. The present work reveals that the combination of the formed-2D heterojunction with Co-doping should be a good strategy to boost the solar-light-driven photocatalytic activity of semiconductor catalysts. [ABSTRACT FROM AUTHOR]

Lanthanide-doped upconversion nanoparticles (UCNPs) have shown potential applications in diverse fields. However, their upconversion luminescence (UCL) intensity and excitation wavelength range are limited by the weak and narrowband absorption of lanthanide ions. Herein, we introduce and validate a strategy to largely increase the absorptivity and upconversion luminescence intensity under 808 nm excitation by broadband dye-sensitized quenching-shield sandwich structured upconversion nanocrystals NaLuF4:Gd,Yb,Tm@NaLuF4:Gd,Yb@NaNdF4:Yb. The dye molecules anchored on the surface of the UCNPs serve as an antenna which can broadly and strongly harvest NIR light. The Nd3+ facilitates the energy transfer and photon upconversion of the lanthanide activator at a biocompatible excitation wavelength (around 800 nm) with a significant increase in penetration ability and minimizes the overheating problem associated with conventional 980 nm excitation. The quenching-shield sandwich structure can greatly eliminate the deleterious cross-relaxation pathway between the activator and sensitizer. This approach combines the merits of the use of Nd3+ as a sensitizer, a quenching-shield sandwich structure and the “antenna” effect, leading to a tremendous enhancement of UCL under excitation at 808 nm. These well-designed UCNPs excited at 808 nm with improved optical performances will outperform conventional UCNPs excited at 980 nm and play an important role in the development of luminescent probes for future biological and medical applications. [ABSTRACT FROM AUTHOR]

Titanium alloy is widely applied in aerospace, medical, shipping and other fields due to its high specific strength and low density. The purpose of this study was to analyze the formability of Ti6Al4V alloys at elevated temperatures. An accurate constitutive model is the basic condition for accurately simulating the plastic forming of materials, and it is an important basis for optimizing the parameters of the hot forging forming process. In this study, the optimization algorithm was used to accurately identify the high-temperature constitutive model parameters of Ti6Al4V titanium alloy, and the hot working diagram was established to optimize the hot forming process parameters. The optimal forming conditions of Ti6Al4V titanium alloy are given. Ti6Al4V alloy was subjected to high-temperature compression tests at 800–1000 °C and at strain rates of 0.01–5 s−1 on a Gleeble-1500D thermal/mechanical simulation machine. Each parameter of the Hansel–Spittel constitutive model was taken as an independent variable, and the accumulated error between the stress calculated by the constitutive model and the stress obtained by experimentation was used as an objective function. Based on response surface methodology, an inverse optimization method for identifying the parameters of the high-temperature constitutive model of Ti6Al4V alloy is proposed in this paper. An orthogonal test design was adopted to obtain sample point data, and a third-order response surface approximate model was established. The genetic algorithm (GA) was applied to reversely optimize the parameters of the constitutive model. To verify the accuracy of the optimized constitutive model, the average absolute relative error (AARE) and correlation coefficient (R) were used to evaluate the reliability of optimized constitutive model. The R value of the model was 0.999, and the AARE value was 0.048, respectively, indicating that the established high-temperature constitutive model for Ti6Al4V alloy has good calculation accuracy. The flow stress behavior of the material could be accurately delineated. Meanwhile, in order to study the formability of Ti6Al4V alloy, the hot processing map of the alloy, based on a dynamic material model, was established in this paper. The optimum hot working domains of the Ti6Al4V alloy were determined within 840–920 °C/0.01–0.049 s−1 and 940–980 °C/0.11–1.65 s−1; the hot processing map was verified in combination with the microstructure, and the fine and equiaxed grains and a large amount of β phase could be found at 850 °C/0.01 s−1. [ABSTRACT FROM AUTHOR]

A new neutron–gamma pulse pileup correction algorithm based on mathematical morphology and optimized grey model is proposed. The pulse characteristic information is extracted through basic mathematical morphology operations to perform pileup discrimination and correction. The hyperbolic tangent function transformation is used to optimize the grey model, and the class ratio dispersion is reduced without reducing the initial sequence smooth ratio, resulting in improved model prediction accuracy. Through experiments, the feasibility of the proposed algorithm is verified and a good correction effect is achieved. [ABSTRACT FROM AUTHOR]

With the development of neutron detection technology, the demand for real-time and fast neutron gamma discrimination has become of increasing importance. In this study, neutron and gamma pulse signals measured by an EJ299-33A plastic scintillator detector were analyzed via Matlab off-line. This study compares the effects of seven fast neutron and gamma (n/ γ) discrimination methods; (i) charge comparison method, (ii) time-domain oriented method, (iii) amplitude oriented method, (iv) pulse time–amplitude two types of width method, (v) angle difference method, (vi) falling-edge numerical integral method, and, lastly, (vii) falling-edge percentage slope method proposed in this study. We verified that filtering and amplitude normalization improve the discrimination quality but consume much time that accounts for 99.6 % of the total discrimination time. The proposed method was compared with other six methods in two aspects: (i) the figure of merit (F o M), (ii) the discrimination time. The F o M value and time consumption of the falling-edge percentage slope method with amplitude normalization are 1.26 and 0.0621s, respectively, while an unnormalized F o M value is 1.02. Experimental results showed that compared with all aforementioned methods, the F O M value of our proposed method is the second high followed the charge comparison method, retaining a short discrimination time which is only half of that of the charge comparison method. This relatively short time consuming is on account of its low volume of data storage and calculation. Generally, the falling-edge percentage slope method has a good discrimination performance while consumes little time, which is capable to be implemented on real-time discrimination applications. [ABSTRACT FROM AUTHOR]

The solutions provided through natural evolution of living creatures serve as an ingenious source of inspiration for many technological and applicative fields. Along these lines, bone-inspired concepts lead to fascinating advances in product design, architecture and garments, thanks to the bone's exceptional combination of strength, toughness and lightness. Structural applications are inspired by the bone's ability to resist fracture under a large spectrum of forces, while the high surface area and pore connectivity of bone architecture present exciting opportunities from an aesthetic point of view. Behind these inspirations, a disruptive common belief emerges: "down to the bone", a journey in search of equality, universality and substantiality. Herein, we explore the current state of the art in bone-inspired applications in these fields, considering the two major categories of structural and aesthetic inspirations and discussing further technological developments. [ABSTRACT FROM AUTHOR]

As a novel kind of cold roller steel, Cr8 alloy steel has the characteristics of high hardness, high wear resistance and good toughness, which can effectively prolong the service life of the roller that is an important part of the steel rolling mill. How to accurately define the constitutive model parameters of metal materials is the major problem, because it seriously affects the accuracy of numerical simulation results of the roller hot forming process. In the study of Cr8 alloy steel's thermal deformation behavior of the present paper, the high temperature compression test was done on a Gleebel-1500D thermal/force simulation testing machine. A novel method of parameter identification was proposed based on inverse optimization. The Hansel–Spittel constitutive model was established by using the inverse optimization method. To carry out the verification on the accuracy of the established constitutive model, the predicted flow-stress of constitutive model was made a contrast to the experimental flow-stress, and the standard statistical parameters were also applied to further evaluation. The results showed a relatively high prediction accuracy of the Hansel–Spittel constitutive model based on the inverse optimization algorithm. Meanwhile, to obtain optimal parameters of Cr8 alloy steel in the thermal processing, 3D thermal processing maps concerning strain-rate, strain and temperature were built based on the dynamic material model. According to the 3D processing map, the most adequate thermal processing parameters of Cr8 alloy steel were obtianed as follows: strain 0.2–0.4, strain-rate 0.05–0.005 s−1, temperature 1100–1150 °C. [ABSTRACT FROM AUTHOR]

X12 (X12CrMoWVNbN10-1-1) ferritic heat resistant steel is an important material for the production of new-generation ultra-supercritical generator rotors. Hot compression tests of X12 ferritic heat-resistant steel were performed via a Gleeble-1500D testing machine under temperatures of 1050–1250 °C and strain rates of 0.05–5 s−1. In order to provide material model data for finite element simulations and accurately predict the hot deformation behavior, a reverse optimization method was proposed to construct elevated temperature constitutive models of X12 ferritic heat-resistant steel in this paper, according to the Hansel–Spittel constitutive model. To verify the accuracy of the model, the predicted and experimental values of the constitutive model were compared. The results indicated that the model had a high prediction accuracy. Meanwhile, the correlation coefficient between the experimental value and the predicted value of constitutive model was 0.97833. For further verification of the accuracy of the model, it was implemented in finite element FORGE@ software to simulate the compression tests of different samples under different conditions. Comparing actual displacement–load curves with displacement–load curves acquired through finite element simulations, the results indicated that displacement–load curves predicted by the model were very consistent with actual displacement–load curves, which verified the accuracy of the model. Moreover, to research the optimal processing parameters of the material, hot processing maps were drawn according to the dynamic material model. In terms of microstructure evolution, a characteristic area distribution map of the hot processing map was established. Therefore, the optimal hot forming parameters regions were in the range of 1150–1200 °C/0.05–0.62 s−1 for X12 ferritic heat-resistant steel. [ABSTRACT FROM AUTHOR]

RNA-binding proteins (RBPs) closely regulate the whole lifecycle of most RNA molecules, from the very early stage of transcription to RNA decay. Dysregulation of RBPs significantly affects the fate of cancer-related transcripts. Therefore, it is imperative to fully understand the complicated RBP-RNA regulatory networks in malignant diseases and to explore novel therapeutic targets. The RBP DAZAP1 (deleted in azoospermia-associated protein 1), originally identified as an important protein in spermatogenesis, had rarely been studied in the context of carcinogenesis. The role of DAZAP1 in hepatocellular carcinoma (HCC) was unveiled in this study. The relative expression of DAZAP1 was significantly upregulated in HCC and was positively associated with several key malignant characteristics and poor postoperative survival in patients. DAZAP1 knockdown by small interfering RNA markedly inhibited HCC cell proliferation, migration and invasion. Furthermore, DAZAP1 significantly reduced cellular sensitivity to sorafenib (SF), which had been proven to be an inducer of ferroptosis by targeting the system Xc− (composed of a light chain, xCT/SLC7A11, and a heavy chain, 4F2 heavy chain). At the mechanistic level, DAZAP1 was identified as a potent inhibitor of ferroptosis and an efficient binding partner of SLC7A1 1 mRNA. Further study revealed that DAZAP1 interacted with the 3′UTR (untranslated region) of SLC7A1 1 mRNA and positively regulated its stability. In our work, we clarified novel functions of DAZAP1 and preliminarily revealed its underlying mechanism in ferroptosis, which may be conducive to the exploration of biomarkers and therapeutic targets in HCC patients. • DAZAP1 effectively promotes HCC progression. • DAZAP1 negatively regulates ferroptosis in HCC cells. • DAZAP1 interacts with SLC7A1 1 mRNA, and positively regulates its stability. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a highly malignant disease with a poor prognosis and high mortality due to a low early diagnosis rate, resistance to systemic treatments and progression to late-stage liver disease. Owing to limitations in the detection of HCC and the lack of awareness of healthcare systems, fewer than 40% of HCC patients are eligible for surgery due to advanced stages of the disease at the time of diagnosis and the occurrence of multiple lesions in the cirrhotic or fibrotic liver. At present, the updated American Association for the Study of Liver Disease (AASLD) guidelines no longer recommend alpha-fetoprotein (AFP) testing as a part of diagnostic evaluation. Thus, it is imperative to establish a novel diagnostic strategy with high sensitivity and reliability to monitor risk factors to detect HCC at an early stage. In recent years, “liquid biopsy,” (including circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA)), has emerged as a technique for the characterization of circulating cells, providing a strong basis for the individualized treatment of patients. As a noninvasive detection method, liquid biopsy is expected to play an important role in the early diagnosis, dynamic monitoring of cancer patients and drug screening. In this review, we will focus on the clinical applications, recent studies and future prospects of liquid biopsy, particularly focusing on HCC. [ABSTRACT FROM AUTHOR]