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In rural areas, landslides can bury houses and result in major disasters in affected areas, which can greatly hinder local economic development and construction. When landslides occur, sliding debris can result in large impact forces, causing varying degrees of damage to building structures. Given the great harm caused by landslides, this paper studies the prediction of dynamic response characteristics of building structures under the impact of the Zhengjiamo landslide. The engineering geological background of the Zhengjiamo landslide is analyzed in detail via field investigations. Then, a numerical method is presented to simulate the runout process of the sliding debris, and the runout process and final deposition area are studied. A node-to-surface contact algorithm is adopted to transfer the displacements and contact forces between the sliding debris and the building. The building structure is simulated via the finite element method (FEM). The sliding debris is simulated via the smoothed particle hydrodynamics (SPH) method. An element erosion algorithm is adopted to simulate the destruction process. The SPH-FEM fluid–structure coupling method is implemented to simulate the landslide dynamic disaster process (the impact behavior for the building). The destruction process is considered in the interactions between the sliding debris and the building on the three-dimensional terrain. The landslide motion during each stage is analyzed, including the sliding, energy dissipation, impact, and damage to the building structure. The maximum runout of the landslide is about 280 m. The maximum stress of the impact on the building is 2 × 106 Pa. The impact speed of the sliding body is generally 16.5–24.07 m/s. On the basis of this, the disaster background, disaster status, and prediction of the landslide disaster effect are studied to provide new insights into landslide disaster prevention and reduction. [ABSTRACT FROM AUTHOR]

With a significant number of mechanical products approaching the retirement phase, the batch recycling of discarded mechanical parts necessitates a preliminary assessment of their surface condition. However, the presence of surface rust poses a challenge to defect identification. Therefore, this paper proposes a method for detecting heavily rusted surface defects based on an improved YOLOv8n network. In the Backbone, the C2f-DBB module of re-parameterized deep feature extraction was introduced, and the attention module was designed to improve the accuracy of information extraction. In the Neck part, a Bi-Afpn multiscale feature fusion strategy is designed to facilitate information exchange between features at different scales. Finally, Focal-CIoU is employed as the bounding box loss function to enhance the network's localization performance and accuracy for defects. Experimentally, it is proved that the improved network in this paper improves the Recall, Precision, and mAP0.5 by 1.2%, 2.1%, and 1.9%, respectively, on the original basis, which is better than other network models. [ABSTRACT FROM AUTHOR]

Background: Urease subunit B (UreB), a conserved and key virulence factor of Helicobacter pylori (H. pylori), can induce the host CD4+ T cell immune responses to provide protection, but less is known regarding CD8+ T cell responses. The characteristics of H. pylori‐specific CD8+ T cell responses and the mechanism underlying antigen processing and presentation pathways remain unclear. This study was focus on protective antigen recombinant UreB (rUreb) to detect specific CD8+ T cell responses in vitro and elucidate the mechanism of UreB antigen processing and presentation. Methods: The peripheral blood mononuclear cells (PBMCs) collected from H. pylori‐infected individuals were stimulated with rUreB in vitro to detect specific CD8+ T cell responses after co‐culture with rUreB‐pulsed autologous hMDCs. Through blocking assay, we investigated the potential pathway of UreB antigen processing and presentation via the cytosolic pathway or vacuolar pathway. The cytokines production of UreB specific CD8+ T cell were evaluated as well. Results: We demonstrated UreB can induce specific CD8+ T cell immune responses in H. pylori infected individuals. Importantly, we characterized that UreB were mainly processed by proteasome instead of lysosomal proteases and presented through cytosolic pathway of cross‐presentation, which requires endoplasmic reticulum–Golgi transport and newly synthesized MHC‐I molecules, to induce functional‐specific CD8+ T cell (IFN‐γ + TNF‐α + Grz A+ Grz B+) responses. Conclusions: These results suggest that H. pylori UreB induces specific CD8+ T cell responses through cytosolic pathway of cross‐presentation in infected individuals. [ABSTRACT FROM AUTHOR]

Timeseries representation underpin our ability to understand and predict the change of natural system. Series are often predicated on our choice of highly redundant factors, and in fact, the system is driven by a much smaller set of latent intrinsic keys. It means that a better representation of data makes points in phase space clearly for researchers. Specially, a 2D structure of timeseries could combine the trend and correlation characters of different periods in timeseries together, which provides more clear information for top tasks. In this work, the effectiveness of 2D structure of timeseries is investigated in clustering tasks. There are 4 kinds of methods that the Recurrent Plot (RP), the Gramian Angular Summation Field (GASF), the Gramian Angular Differential Field (GADF) and the Markov Transition Field (MTF) have been adopted in the analysis. By classifying the CSI300 and S&P500 indexes, we found that the RP imaging series are valid in recognizing abnormal fluctuations of financial timeseries, as the silhouette values of clusters are over 0.6 to 1. Compared with segment methods, the 2D models have the lowest instability value of 0. It verifies that the SIFT features of RP images take advantage of the volatility of financial series for clustering tasks. [ABSTRACT FROM AUTHOR]

Nicotine has shown the toxic effects on male reproductive system, and testicular damage is associated with ferroptosis, which is a non-apoptotic regulated cell death driven by iron-dependent lipid peroxidation. However, the role of nicotine on ferroptosis of testicular cells is largely elusive. In the present study, we showed that nicotine destroyed blood-testis barrier (BTB) by interfering with the circadian rhythm of BTB-related factors (ZO-1, N-Cad, Occludin and CX-43) and induced ferroptosis, as reflected via increased clock-control levels of lipid peroxide and decreased ferritin and GPX4, which involved in the circadian. Inhibition of ferroptosis with Fer-1 alleviated nicotine-induced injury of BTB and impaired sperm in vivo. Mechanically, we uncover that the core molecular clock protein, Bmal1, regulates the expression of Nrf2 via direct E-box binding to its promoter to regulate its activity, and nicotine decreases the transcription of Nrf2 through Bmal1 and inactivates Nrf2 pathway and its downstream antioxidant gene, which leads to the imbalance of redox state and ROS accumulation. Intriguingly, nicotine induced lipid peroxidation and subsequent ferroptosis by Bmal1-mediated Nrf2. In conclusion, our study reveals a clear role for the molecular clock in controlling Nrf2 in testis to mediate the ferroptosis induced by nicotine. These findings provide a potential mechanism to prevent smoking and/or cigarette smoke-induced male reproductive injury. [Display omitted] • Nicotine interfered with the rhythmic expression of BTB-related proteins, leading to BTB injury. • Nicotine induced BTB damage by causing the ferroptosis in the testis. • Nicotine decreases the transcription of Nrf2 through Bmal1 and inactivates Nrf2 pathway and its downstream antioxidant gene. • Nicotine contributed to ferroptosis via inactivation of the Bmal1/Nrf2 pathway. [ABSTRACT FROM AUTHOR]

The shear mechanical behavior of weak interlayer is of great significance for the stability analysis of landslides. Based on the shear deformation-failure characteristics of weak interlayer soil and the clay damage theory, a shear failure mode is proposed to describe this kind of phenomenon. Triaxial compression tests and numerical simulation are conducted on weak-interlayer samples at conditions of different confining pressures and inclination angles. The numerical and experimental results show basically good agreement for both the peak strength and failure patterns. With increases in the thickness and dip angle (in the range of 0°–60°) of the weak interlayer, the shear strength of the samples decreased gradually. The weak-interlayer samples having a low dip angle were crushed and inflated; when the dip angle exceeded 15°, there was shear slip along the weak interlayer; when the weak interlayer was thin, the sample sheared along the structural plane; when the weak interlayer was thick, there was extrusion inside the weak interlayer or shear slip along the contact surface of the weak interlayer and mudstone. These results can improve our understanding of the mechanical behavior of mudstone rock mass with weak interlayers and can be used to analyze the stability of rock slopes or other rock engineering cases such as tunneling construction in rock mass with weak interlayers. [ABSTRACT FROM AUTHOR]

As a measure of complexity, information entropy is frequently used to categorize time series, such as machinery failure diagnostics, biological signal identification, etc., and is thought of as a characteristic of dynamic systems. Many entropies, however, are ineffective for multivariate scenarios due to correlations. In this paper, we propose a local structure entropy (LSE) based on the idea of a recurrence network. Given certain tolerance and scales, LSE values can distinguish multivariate chaotic sequences between stochastic signals. Three financial market indices are used to evaluate the proposed LSE. The results show that the L S E F S T E 100 and L S E S & P 500 are higher than L S E S Z I , which indicates that the European and American stock markets are more sophisticated than the Chinese stock market. Additionally, using decision trees as the classifiers, LSE is employed to detect bearing faults. LSE performs higher on recognition accuracy when compared to permutation entropy. [ABSTRACT FROM AUTHOR]

The defects within the surface of the insulating material considerably influence the performance of the dielectric barrier. The trenches are one of the foremost common defects usually caused by the impact of hit and chemical corrosion. The trenches within the surface can change the propagation path of the streamers and the volumetric force produced. In this work, a two-dimensional self-consistent fluid model was built to study surface dielectric barrier discharge with trenches within the surface of the dielectric barrier, including electron density distribution, surface charge evolution, and electric parameters. The results indicate that with the increase in trenches' depth, the streamer will diffuse outward into the ambient air, which is closely related to the ionization and electric field distortion in the trenches and at the edge of the trenches. With different sequences of the trenches in the dielectric, the deeper trench is the dominate role in changing the streamer propagation path. The distorted orientation and value of the electric field in and on the trenches account for the differences. Compared with the discharge on the flat dielectric, the diffusion of plasma and propagation distance is enhanced with the increase in the trenches' depth, bringing larger dealing area and targeted species distributions. [ABSTRACT FROM AUTHOR]

We have studied the image of Bonnor black dihole surrounded by a thin accretion disk where the electromagnetic emission is assumed to be dominated respectively by black body radiation and synchrotron radiation. Our results show that the intensity of Bonnor black dihole image increases with the magnetic parameter and the inclination angle in both radiation models. The image of Bonnor black dihole in the synchrotron radiation model is one order of magnitude brighter than that in the black body radiation model, but its intensity in the former decreases more rapidly with the radial coordinate. Especially, for the synchrotron radiation model, the intensity of the secondary image is stronger than that of the direct image at certain an inclination angle. We also present the polarization patterns for the images of Bonnor black dihole arising from the synchrotron radiation, which depend sharply on the magnetic parameter and inclination angle. Finally, we make a comparison between the polarimetric images of Bonnor black dihole and M87*. Our result further confirms that the image of black hole depends on the black hole's properties itself, the matter around black hole and the corresponding radiation occurred in the accretion disk. [ABSTRACT FROM AUTHOR]

The growing demands for multiple-level optical information encryption require the development of novel room temperature phosphorescence (RTP) materials with long afterglow performance and multi-color phosphorescence. Carbon dots (CDs), as a new generation of carbon-based luminescent nanomaterials, have received widespread attention. Here, a series of sulfur-doped carbon dots (S-CDs) with different sulfur doping regulation are proposed as emitters to fabricate RTP materials by embedding boric acid (BA) matrix. With different sulfur doping amount, S-CDs exhibit green, yellow-green and orange RTP, respectively. The RTP spectrum can be split into three emission centers with different intensities. Thus, the multi-color RTP emission is ascribed to varied surface state of S-CDs involving the phosphorescence center intensity changes, which results from sulfur doping. The RTP lifetimes of these S-CDs are calculated to be 579.28, 540.87, and 288.89 ms, which is measured for S-CDs-G@BA, S-CDs-Y@BA and S-CDs-O@BA, respectively. In addition, the phosphorescence quantum yield (PQY) is 27.93 %, 10.62 % and 13.58 %, respectively. The encryption test is performed utilizing these multi-color RTP S-CDs, demonstrating that these S-CDs have good RTP performance and are suitable for anti-counterfeiting and information encryption. • The nitrated pyrene as the sole precursor to prepare multicolor RTP materials. • It proves sulfur doping and oxidation degree of CDs on adjusting RTP emission. • The multicolor RTP has enormous application potential in information encryption. [ABSTRACT FROM AUTHOR]

Particles will aggregate in the presence of dense mist in the wet electrostatic precipitator (WESP), and larger size particles with changed distribution make discharge more significant. This article puts forward the simulation of corona discharge with the existence of particle aggregation effect, which shows the characteristics of electron density distribution during the propagation. Besides, we also provide propagation patterns of branching and quenching phenomenon of plasma channels in the presence of particle aggregation. The results indicate that particle aggregation will considerably influence the plasma patterns. When the electron avalanche reaches the particle distributing areas, there will be several plasma channels formed and a narrow diffusion radius around the aggregated particles. When particles gather around the electrode, which means dense particle distribution, corona quenching happens with dense particle distribution. [ABSTRACT FROM AUTHOR]

The SF6 gap switch has obvious advantages in the flexible control very fast switch of the controllable arrester used in hybrid UHVDC system. However, the plasma jet ability in SF6 atmosphere at high pressure is greatly restricted, which is easy to cause trigger failure. The triggering characteristics and induced breakdown mechanism are still unclear during the plasma jet-triggered process of the SF6 gap switch at extremely low working voltage. In this study, a plasma jet-induced breakdown research platform based on two-stage continuous surface-triggered gas gap switch (GGS) was established to investigate time-space distribution evolution characteristics, the critical triggering conditions parameters, and working voltage functioning rules of the SF6 gap. The jet plasma has a high-brightness-like conical shape, and its initial jet velocity can reach more than 2 km/s. Increasing capacitance value and charging voltage of the secondary chamber in SF6 gap switch trigger cavity, its creeping arc current is increased, and the time delay and dispersion will be reduced. Moreover, the geometric characteristics (including jet area, jet radial and axial length, and jet velocity) and electrical parameters of the jet plasma increase gradually. Increasing the working voltage of main gap in SF6 gap switch, the plasma jet velocity lift phenomenon appears at induced breakdown end of SF6 gap switch. With the continuous increase of the working voltage, the lift effect is more significant. The purpose of this study is to solve the key problem of plasma-induced breakdown failure of the SF6 gap switch, which will guide the design of fast self-recovery energy discharge protection switches for hybrid UHVDC transmission systems. [ABSTRACT FROM AUTHOR]

Due to the complexity of biological sample matrix, the automated and high-throughput pretreatment technology is urgently needed for monitoring the antipsychotic drugs for mental patients. In this study, functionalized magnetic zirconium-based organic framework nanocomposites (Fe 3 O 4 @SiO 2 @Zr-MOFs) were successfully designed and synthesized by the layer-by-layer growth. Among them, Fe 3 O 4 @SiO 2 @UiO-67-COOH showed the best adsorption performance, and at the same time it exhibited excellent water dispersibility, high thermal stability, chemical stability and high hydrophobicity. Results of adsorption kinetics, isotherm and FT-IR showed that the adsorption process was dominated by chemical adsorption (hydrogen bond, electrostatic interaction, π-π interaction) and monolayer adsorption. Moreover, the smaller pore size improved the protein exclusion rate which reached 98.9–99.8%. Based on the above result, the synthesized magnetic nanoparticles were introduced to 96-well automatic extractor, antipsychotic drugs in 96 serum samples were automatically extracted within 9 min, which most greatly saved the time and labor costs and avoided artificial errors. By further integrating with ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), antipsychotic drugs can be detected in the range of 0.2–3.0 ng mL−1 with a quantitative limit of 0.06–0.9 ng mL−1. The recoveries of antipsychotic drugs and their metabolites in serum ranged from 95.7% to 112.3% within 1.4–6.5% of RSD. These features indicate that the proposed method is promising for high throughput and sensitively monitoring of drugs and other hazardous substances. [Display omitted] • A novel functional magnetic MOF composite with strong adsorption capacity against antipsychotic drugs was designed and synthesized. • Combining 96-well extraction plate with M-µSPE, high throughput and automatic pretreatment of serum samples were realized. • Protein is removed by the size exclusion effect of MOF, and the protein precipitation step is omitted. • 96W-M-µSPE-UPLC-MS/MS was simple, rapid and saves manpower and human error. [ABSTRACT FROM AUTHOR]

We have studied polarized image of a Schwarzschild black hole with an equatorial thin accretion disk as photon couples to Weyl tensor. The birefringence of photon originating from the coupling affect the black hole shadow, the thin disk pattern and its luminosity distribution. We also analyze the observed polarized intensity in the sky plane. The observed polarized intensity in the bright region is stronger than that in the darker region. The stronger effect of the coupling on the observed polarized vector appears only in the bright region close to black hole. These features in the polarized image could help us to understand black hole shadow, the thin accretion disk and the coupling between photon and Weyl tensor. [ABSTRACT FROM AUTHOR]

[Display omitted] • Propose a deep learning-based image segmentation method for ore sorting. • Analyze the performance of backbone selection and module setting. • Propose a loss function for ore image segmentation model training. • The proposed method solves the particle adhesion problem. Mineral image segmentation plays a vital role in the realization of machine vision based intelligent ore sorting equipment. However, the existing image segmentation methods still cannot effectively solve the problem of adhesion and overlap between mineral particles, and the segmentation performance of small and irregular particles still needs to be improved. To overcome these bottlenecks, we propose a deep learning based image segmentation method to segment the key areas in mineral images using morphological transformation to process mineral image masks. This investigation explores four aspects of the deep learning-based mineral image segmentation model, including backbone selection, module configuration, loss function construction, and its application in mineral image classification. Specifically, referring to the designs of U-Net, FCN, Seg Net, PSP Net, and DeepLab Net, this experiment uses different backbones as Encoder to building ten mineral image segmentation models with different layers, structures, and sampling methods. Simultaneously, we propose a new loss function suitable for mineral image segmentation and compare CNNs-based segmentation models' training performance under different loss functions. The experiment results show that the proposed mineral image segmentation has excellent segmentation performance, effectively solves adhesion and overlap between adjacent particles without affecting the classification accuracy. By using the Mobile Net as backbone, the PSP Net and DeepLab can achieve a high segmentation performance in mineral image segmentation tasks, and the 15 × 15 is the most suitable size for erosion element structure to process the mask images of the segmentation models. [ABSTRACT FROM AUTHOR]

Distinguishing between chaotic and stochastic dynamics given an input series is a widely studied topic within the time series analysis due to high demand from the practitioners in various fields. Due to one of the fundamental properties of chaotic systems, namely, being sensitive to parameters and initial conditions, chaotic time series exhibit features also observed in randomly generated signals. In this paper, we introduce distance as a measure of similarity between segments based on the ordinal structure. Furthermore, we introduce a new fuzzy entropy, Fuzzy Permutation Entropy (FPE), which can be used to detect determinism in time series. FPE immunes from repeated equal values in signals to some extent, especially for chaotic series. With specific embedding dimensions, it can be employed to distinguish chaotic signals from noise. We show an example for white Gaussian noise, autoregressive moving-average, continuous or discrete chaotic time series, and test FPE's performance with additive observational noise. We show an application of FPE on rolling bearings' fault diagnosis. [ABSTRACT FROM AUTHOR]

• Presented a novel end-to-end segmentation architecture AMLcGAN based on conditional generative adversarial net (cGAN) to segment acute myeloid leukemia (AML) in Wright-Giemsa-stained bone marrow aspirate smear images. • Compared with other three popular deep learning-based segmentation models, AMLcGAN can achieve the best segmentation performance. • We validated AMLcGAN on a specific diagnosis task, the comparative clinical indicators show that presented model can provide accurate assistance for pathologists in the diagnosis of AML. The diagnosis of acute myeloid leukemia (AML) requires reliable detection and counting of immature white blood cells called myeloblasts on bone marrow aspirate and biopsy slides. Experienced hematologists/oncologists typically spend many hours manually reviewing blood cells and performing a microscopic morphological examination of myeloblasts on glass slides to determine this diagnosis. While manual counting of myeloblasts is considered the gold standard for an accurate diagnosis of AML, the process is time-consuming and difficult to standardize due to high intra- and inter-observer variability. Machine learning algorithms coupled with deep learning techniques represent potentially promising approaches for automated counting. Since myeloblasts possess very similar texture patterns to other hematopoietic cells (OHCs), accurately differentiating myeloblasts from other blood cells is a challenge for most segmentation models. We present a new conditional generative adversarial network (cGAN)-based segmentation model (AMLcGAN) to efficiently segment myoblasts from slides of patients with AML. Validation of the approach on 204 cytological images reveals a mean dice coefficient (mDICE) of 82.51 % with respect to manual segmentations. We also compared the new AMLcGAN method with three deep learning models: Unet, Segnet and Pix2pix, the corresponding mDICE values were 77.4 %, 78.2 %, and 79.53 %, respectively. We also employed the segmentation results from 4 different models to extract blast statistics feature, Haralick texture feature, Fractal dimension feature and myeloblast shape features to create 4 different prognostic models to predict risk of recurrence in AML patients. TheConcordance Index values for AMLcGAN and the three other models respectively were 0.63, 0.53, 0.58, and 0.56. The comprehensive evaluation results presented suggest that the AMLcGAN approach can enable accurate blast segmentation and for construction of accurate prognostic models for predicting risk of recurrence in AML patients. [ABSTRACT FROM AUTHOR]

The sustainable applications of plastics are extremely challenging in the field of environment. Here, a facile synthesis of temperature-triggered fluorescence platform composed of boron-doped carbon nanoparticles (BCNPs) and polyethylene terephthalate (PET) was proposed as a convenient way to reuse PET plastic products. Notably, the fluorescence of this platform was tuned by incubation temperature and suitable for double-level anti-counterfeiting by utilizing significant difference of fluorescence emission upon 365 and 254 nm excitations, respectively. With a long-term heating regulation, multi-fluorescence emissions could be obtained as fluorescent codes. The fluorescence was assigned that blue emission originated from PET state and red emission derived from surface state of BCNPs. The red emission was enhanced with the increase of temperature, which was the formation mechanism of varied fluorescence. The multi-color emission performance was also validated using slices cut from PET bottle to demonstrate practicability of the platform. The adjustable fluorescence performance made the platform had good anti-counterfeiting ability, which provided a new way for the reuse of PET. • A fluorescence platform composed of BCNPs and PET is proposed as a new example to expand applications of PET plastics. • The different incubation temperature and UV excitation stimulate the platform to exhibit various fluorescence emission. • A double level anti-counterfeiting is performed using PET bottle. [ABSTRACT FROM AUTHOR]

Burning rate modulation of solid fuel can improve the performance of solid fuel ramjet over a wider altitude or Mach number envelopes. To better control the burning rate of a solid fuel, the effect of magnetic fields on the regression rate of solid fuel was experimentally and numerically investigated. In this experiment, the regression rates of polymethyl methacrylate (PMMA) rods under different types of magnetic field gradients were measured. Meanwhile, the morphological changes in the flame during combustion were recorded by a complementary metal-oxide semiconductor (CMOS) camera. The experimental results indicated that the regression rate for PMMA varies regularly with various magnetic field gradients, with rates in the range from -32.5% to 10.8%. Simultaneously, the flame structure also changes. The numerical simulation that considered both gas-phase combustion and gas-solid heat transfer produced results that were in good agreement with the experiment. The results show that the change in the regression rate is a result of the magnetic force acting on the paramagnetic and diamagnetic materials. By changing the flow field, the magnetic force can change the flame shape and temperature distribution. This alters the heat exchanged between the gas and the fuel surface and thus, changes the burning rate. It was found that the burning rate of solid fuel can be adjusted regularly by applying different types of non-uniform magnetic fields, and it may be used in the solid fuel ramjet burning rate modulation. [ABSTRACT FROM AUTHOR]

Background: Medical image data, like most patient information, have a strong requirement for privacy and confidentiality. This makes transmitting medical image data, within an open network, problematic, due to the aforementioned issues, along with the dangers of data/information leakage. Possible solutions in the past have included the utilization of information-hiding and image-encryption technologies; however, these methods can cause difficulties when attempting to recover the original images.Methods: In this work, we developed an algorithm for protecting medical image key regions. Coefficient of variation is first employed to identify key regions, a.k.a. image lesion areas; then additional areas are processed as blocks and texture complexity is analyzed. Next, our novel reversible data-hiding algorithm embeds lesion area contents into a high-texture area, after which an Arnold transformation is utilized to protect the original lesion information. After this, we use image basic information ciphertext and decryption parameters to generate a quick response (QR) code used in place of original key regions.Results: The approach presented here allows for the storage (and sending) of medical image data within open network environments, while ensuring only authorized personnel are able to recover sensitive patient information (both image and meta-data) without information loss.Discussion: Peak signal to noise ratio and the Structural Similarity Index measures show that the algorithm presented in this work can encrypt and restore original images without information loss. Moreover, by adjusting the threshold and the Mean Squared Error, we can control the overall quality of the image: the higher the threshold, the better the quality and vice versa. This allows the encryptor to control the amount of degradation as, at appropriate amounts, degradation aids in the protection of the image.Conclusions: As shown in the experimental results, the proposed method allows for (a) the safe transmission and storage of medical image data, (b) the full recovery (no information loss) of sensitive regions within the medical image following encryption, and (c) meta-data about the patient and image to be stored within and recovered from the public image. [ABSTRACT FROM AUTHOR]

Summary: High‐capacity LiFePO4 batteries are widely used in public transportation in China. However, overcharge causes serious safety issues, and the nature of the process requires further research. This study investigates an overcharge‐induced thermal runaway of 20 and 24 Ah LiFePO4 batteries under different initial states of charge (SOC) and charging rates. Chemical reactions inside the battery are influenced by the capacity of the battery, that is, a higher capacity induces faster heating and a higher maximal surface temperature than the lower capacity under the same conditions. The temperature curve of low initial SOC battery at low chargingratedoes not change notably. Under other conditions, the thermal runaway exhibits two stages, an initial slow temperature increase (stage I) followed by a rapid temperature increase (stage II). The initial SOC and charging rate are relevant only for the rate of temperature increase in stage I, with little effect in stage II. The study on the temperature characteristics of overcharge‐induced thermal runaway can promotethe safety research of LiFePO4 power batteries. [ABSTRACT FROM AUTHOR]

Summary: High‐capacity LiFePO4 batteries are widely used in public transportation in China. However, overcharge causes serious safety issues, and the nature of the process requires further research. This study investigates an overcharge‐induced thermal runaway of 20 and 24 Ah LiFePO4 batteries under different initial states of charge (SOC) and charging rates. Chemical reactions inside the battery are influenced by the capacity of the battery, that is, a higher capacity induces faster heating and a higher maximal surface temperature than the lower capacity under the same conditions. The temperature curve of low initial SOC battery at low chargingratedoes not change notably. Under other conditions, the thermal runaway exhibits two stages, an initial slow temperature increase (stage I) followed by a rapid temperature increase (stage II). The initial SOC and charging rate are relevant only for the rate of temperature increase in stage I, with little effect in stage II. The study on the temperature characteristics of overcharge‐induced thermal runaway can promotethe safety research of LiFePO4 power batteries. [ABSTRACT FROM AUTHOR]

Online multi-information detection of mineral properties and composition plays a vital role in the realization of digital mining and digital concentrating mill, and the way of machine vision technology is put forward as a cost-effective and safe approach at present. This paper presents an exploratory study employing a bench-scale approach to detect the multi-information of coal quality online by machine vision simultaneously, including particle size distribution, density distribution, the ash content of each density fraction, and the total ash content. Firstly, we adopt a Finite-Erosion-and-Exact-Dilation (FEED) algorithm and a particle-on-edge region segmentation algorithm to segment overlapped particles and ensure the full analysis of target regions. Moreover, twenty-nine features are extracted and optimized to enable the particle mass estimation model, particle size characterization, classification model of density fraction, and prediction model of ash content to be implemented. Finally, an experimental study shows the merits of the proposed approach, and the average prediction errors of size distribution, density distribution, and ash content of each density fraction are 1.85%, 2.57%, 3.36%, respectively. The total ash content error is 2.54%. Results derived using the proposed approach reveal that it has the potential to be applied to the coal processing industry. Technical route of multi-information online detection of coal quality based on machine vision. Unlabelled Image • We proposed innovative FEED and PERS segmentation algorithms for online coal images. • An online multi-information detection approach of coal quality was proposed systematically. • The average errors of multi-information online detection were less than 3.5%. [ABSTRACT FROM AUTHOR]

In this study, we investigated a recent landslide that occurred in Zhouqu County, China. At 18:00 pm Beijing time on July 19, 2019, the Yahuokou landslide was reactivated. About 3.92 × 106 m3 of debris slumped from the slope, blocking the Minjiang River channel, causing the water level to rise, and disrupting road. Fortunately, there were no injuries. A field survey, remote sensing imaging, borehole core analysis, landslide monitoring, and rainfall data analysis were conducted to examine the geomorphologic and stratigraphic characteristics of the landslide, the reactivation causes, and the dynamic mechanism of the landslide. A model for the reactivation and evolution of the landslide is presented. The field survey of the sliding zone indicates that the landslide has a flow-slide deformation pattern. The landslide mechanism is summarized as follows. Initially, slow plastic flow sliding occurred in the upper sliding body. Then the platform in the middle gentle slope was preloaded by deposits from the upper part failure, resulting in a "cutting and filling" effect. The failure occurred in the form of creep–tension cracks–debris flow–slide. Finally, the block on the lower part slumped. Thus, the upper, middle, and lower parts of the landslide all transferred stress in the same manner, being activated step by step, and slowly slumped into the river. [ABSTRACT FROM AUTHOR]

As effective representations of complex systems, complex networks have attracted scholarly attention for their many practical applications. They also represent a new tool for time series analysis. In order to characterize the underlying dynamic features, the structure of transformed networks should be encoded with the systematic evolution information that always hides behind the time series data. Thus, the way of mapping segments of the time series into nodes of the network is particularly crucial, but it is liable to be unstable under noise and missing values. In this paper, we propose a coarse-graining based on statistics of segments (CBS) founded complex network method, which can make it immune to interference to a certain degree. The time series is divided into many segments by a slide window, of which the width is determined by the multi-scale entropy of the data. We use a multi-dimensional symbol to represent the motion state of every segment. Due to the utilization of the distribution information of the fragments' numerical characteristics, the coarse-graining process is self-adaptive to some extent. The complex network is then established based on the adjacent relations of the symbolic sequence. With our method, the differences in the network measurements between the periodic and chaotic motion is easily observable. Furthermore, we investigated the robustness of CBS by adding noise and missing values. We found that CBS is still valid, even with strong noise and 15% missing values, and simulation shows that it is more robust than the VG and LS approaches. By mapping a time series into a complex network, we provide a new tool for understanding the dynamic evolution mechanism of a complex system. This method has been applied in various fields, such as physics, engineering, medicine and economics. However, the interference of noise may greatly affects the reliability of judgment, which is based on the structures of transformed networks. An insufficient robustness is mostly to blame for the transformation from a time series to a symbolic sequence. In this paper, we suggest a new approach to the coarse-graining process which is self-adaptive for threshold choosing. Simulations show that even with strong disturbances, our network structure is easily distinguishable under different dynamic mechanisms. [ABSTRACT FROM AUTHOR]

The red mudstone sediments are widely encountered in earthquake region northwestern China which is vulnerable to the occurrence of landslides. Previous studies focused on rain-induced shallow landslides. Some deep-seated landslides in red clay with a gentle inclination are still not well understood. A fatal landslide named as Zhengjiamo landslide, located in Tianshui, China, was investigated. The aim of this study was to study mechanism of shear surface development under earthquake using a series of cyclic triaxial tests. The test results of the red clay from the Zhengjiamo landslide revealed that the undrained shear strength and excess pore pressure increase with increasing confining stresses σ 3 and OCR. It is also observed that those stress–strain behaviors on the deviatoric stress, axial strain and pore water pressure, subsequent to the initial contractive response, the saturated clay exhibited dilation upon continued shearing. The dilated tendency of the clay becomes more prevalent with increasing confining stresses σ 3 and OCR during the dynamic response. The reduction of shear strength of the red clay is substantially caused by the increasing number of cycles N. Under earthquakes, the sliding zone may behave in a brittle- ductile pattern. [ABSTRACT FROM AUTHOR]

Little is known about ghrelin’s effects on cell proliferation in pregnant mammary epithelial cells (MECs) even though it is known to be a mitogen for a variety of other cell types. The objectives of this study were to evaluate the expression and localization of ghrelin and its functional receptor, GHSR-1a, in the mammary glands of dairy goats during pregnancy and to investigate the direct role of ghrelin in cell proliferation of primary cultured MECs. Compared to the early stage (days 30) of pregnancy, the abundance of transcripts and protein of ghrelin and GHSR-1a were significantly greater in mid- and late-phases (between days 90 and days 120) of pregnancy (p < .05). Immunohistochemistry analysis showed that ghrelin and GHSR-1a were predominantly localized in the alveolar and ductal mammary epithelial cells at various stages of pregnancy. In our in vitro experiments, ghrelin induced a dose- and time-dependent promotory effect on cell proliferation of MECs. At the dose of 10 3  pg/mL treatment 24 h, ghrelin augmented the expression of proliferation-related peptides (PCNA and cyclin B1). Furthermore, ghrelin promoted the expression of prolactin (PRL) and GHSR-1a in cultured MECs. Additionally, the stimulatory effects of ghrelin were blocked by d -Lys3-GHRP6, a selective antagonist of GHSR-1a. As the temporal changes in ghrelin and GHSR-1a expression in pregnant goat mammary glands coincided with the mammary growth and development during the pregnancy, activation of GHSR-1a signal transduction pathways by ghrelin may play a direct role in the regulation of mammary growth in dairy goats. [ABSTRACT FROM AUTHOR]

Deep-seated mudstone landslides are widely distributed in northwest China. The Zhengjiamo landslide occurred in 734 CE near Taijing, Tianshui region. It was triggered by the main shock of the historical Ms = 7.0734 CE earthquake. The earthquake also induced a wide range of landslides in this area, with hundreds of fatalities and many houses destroyed by landslide debris. Based on a detailed field survey, borehole data, and mapping, a detailed geological reconstruction of the slope was achieved and the geo-mechanical features of the claystone involved in the landslide were obtained. Using the Particle Flow Code to simulate the mechanism of the Zhengjiamo landslide, the features of the landslide dynamics were obtained using a 5-stage model. During the landslide process, the slip mass at the front and upper edges exhibited higher mobility than at the rear and bottom edges. Furthermore, the contact bond strength of the sliding body gradually weakened. The model indicates that the particle bond strength has a larger influence on the maximum runout distance and landslide debris morphology than the type of weak intercalated layers. The slip mass presented a rotational slide pattern and remained on the slip plane throughout the sliding process. Finally, the earthquake uplifted the slope toe, which prevented the slip mass from gliding. The modelled scenario provided a reasonably good fit to the actual topography, verifying the modelling results. After the historical earthquake, with the clay minerals swelling and shrinking under rainfall and efflorescence conditions, the structure of the soft rock changed, contributing to the weakening of the sliding rock mass. The landslide gradually crept and evolved to its current topography. [ABSTRACT FROM AUTHOR]

Multi-wavelength wavelength convertor (MWC) has many important applications in the fields of optical communications and optical fiber sensing. In this paper, a brand-new MWC driven by forward Brillouin scattering (FBS) in a large effective area fiber (LEAF) is proposed and demonstrated. In the MWC, the incident multi-wavelength light waves excite acoustic vibration in fiber by optomechanical interactions, and causes a change in the refractive index of the fiber through electrostriction effect. The changed refractive index causes phase modulation which applied on a single-wavelength light wave with target wavelength, and the single-wavelength light wave is modulated into multi-wavelength light waves while keeping the target wavelength as the central frequency. In a proof-of-concept experiment, a 2-km long LEAF is used as wavelength conversion medium, and the obtained MWC features very low deteriorations in side-mode suppression ratio (SMSR), multi-wavelength output power flatness and signal to noise ratio (SNR). The demonstrated MWC also has a wide tunable target wavelength ranges up to 23.6 nm, discretely tunable frequency interval up to over 1 GHz, low total output power fluctuation of 4.68%, and a conversion efficiency improvement of 2.55 dB. Its good performance may provide a promising way to design high-performance multi-wavelength convertor. • Novel multi-wavelength convertor based on forward Brillouin scattering. • Multi-wavelength convertor performs perfect in proof-of-concept experiment. • Wide tunable wavelength ranges up to 23.6 nm. • Conversion efficiency higher than that of convertors based on four-wave mixing. • Provide a new idea for designing wavelength convertor. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to evaluate the effect of achievable td on the accuracy of microstructural mapping based on simulation and patient experiments, and investigate the feasibility of td-dMRI in distinguishing prognostic factors in breast cancer patients. Methods: Simulation was performed using different td settings. Patients with breast cancer were enrolled prospectively between November 2020 and January 2021, who underwent oscillating and pulsed gradient encoded dMRI on a 3-T scanner using short-/long-td protocol with oscillating frequency up to 50/33 Hz. Data were fitted with a two-compartment model to estimate cell diameter (d), intracellular fraction (fin), and diffusivities. Estimated microstructural markers were used to differentiate immunohistochemical receptor status and the presence of lymph node (LN), which were correlated with histopathological measurements. Results: Simulation results showed that d fitted from the short-td protocol significantly reduced estimation error than those from long-td (2.07 ± 1.51% versus 3.05 ± 1.92%, p < 0.0001) while the estimation error of fin was robust to different protocols. Among a total of 37 breast cancer patients, the estimated d was significantly higher in HER2-positive and LN-positive (p < 0.05) groups compared to their negative counterparts only using the short-td protocol. Histopathological validation in a subset of 6 patients with whole slide images showed the estimated d was highly correlated with measurements from H&E staining (r = 0.84, p = 0.03) only using the short-td protocol. Conclusions: The results indicated the necessity of short-td for accurate microstructural mapping in breast cancer. The current td-dMRI with a total acquisition time of 4.5 min showed its potential in the diagnosis of breast cancer. Key Points: • Short td is important for accurate microstructural mapping in breast cancer using the td-dMRI technique, based on simulation and histological validation. • The 4.5-min td-dMRI protocol showed potential clinical value for breast cancer, given the difference in cell diameter between HER2/LN positive and negative groups. [ABSTRACT FROM AUTHOR]

The response of a loess-mudstone landslide model to seismic activity is examined using centrifuge shaking table tests and 2D seismic input waves. The physical model of a dormant landslide is subjected to simulated seismic signals of different input amplitudes, and the dynamic response is analyzed in terms of relative peak ground acceleration (PGA), which is used to assess deformation and failure mechanisms of the slope. Relative PGA increases with increasing landslide height, reaching maximum values at the crest, while values at the toe are slightly larger than those in the middle of the slope. Relative PGA is the highest along the outer surface and weakest along the sliding surface. Horizontal values are generally larger than vertical values, and the amplitude of the seismic input strongly affects the landslide dynamic response. The PGA amplification factors increase considerably with increasing input amplitude, reaching maximum values at 0.3-0.4 g, and then decrease. The largest displacement is observed at the crest, while displacements in the middle part and at the toe increase steadily with higher input amplitudes, and displacement at the crest increases at a higher, irregular rate. Deformation is most apparent at the crest, developing from cracks into collapsed soil deposited at the toe. Local shallow sliding is observed along the landslide surface, and the dormant sliding body may generate displacement along the sliding surface caused by earthquake reactivation. A numerical model of the landslide provides results consistent with centrifuge model testing; both indicate similarity in seismically induced dynamic responses and failure characteristics. [ABSTRACT FROM AUTHOR]

Dynamic behavior and failure mechanism of slope during earthquake are hot topics in geotechnical engineering community. In Tianshui area, Northwest of China, the deformation features of loess-mudstone slopes induced by earthquakes are still not well understood. The current study aims to discuss the dynamic behavior of the slope under earthquake, by performing a series of centrifuge shaking table tests and numerical simulations. The results are processed for PGA amplificatory effect study and to evaluate the earthquake induced deformation mechanism. Better agreement is obtained between the results of numerical simulations and centrifuge tests. The results indicated that the acceleration response of the loess-mudstone slope showed several amplifying characteristic: the height effect, lithologic effect and surface effect. It also proves that the displacements in the loess layer are much larger than mudstone. As the input amplitude increased, the shear plastic zone developed along a further slide surface. Tension plastic zone mainly occurred at the crest. Roughly, the regions of earthquake induced deformation state increased with the increase in amplitude of earthquake wave. Deformations occurred at the upper part of the slope. Tensile cracks evolved on the crest and the upper part of slope in loess layer, moreover, failures occurred at slope surface, and only slight deformation at slope toe. Deformations with continuous shear cracks and slope surface upheavals developed at the middle part of slope. [ABSTRACT FROM AUTHOR]

In this paper, we study the effect of the amplitude of input shaking on the dynamic response of a loess-mudstone slope through a series of staged centrifuge tests, including different amplitude earthquake excitations, in which the seismic performance of the slope models was analyzed and investigated. The derived outputs were processed to study the earthquake acceleration amplification effect and to assess the induced deformation mechanism in terms of the resulting displacements and deformation mode. The test results indicated that the amplification factors of peak ground acceleration (PGA) increased with increasing slope height, reaching maximum values at the crest. The amplification effect was also demonstrated on the slope surface. In addition, the predominant amplitude of the input seismic excitation was critical to the response of the slope. The results proved that the displacements in the loess layer were much larger than those in the weathered mudstone. Tensile cracks were formed mainly on the crest and the upper part of the slope, and failure modes were dominant at the slope surface. The location of the slide plane in the model was consistent with the location of maximum acceleration. A continuous shallow slide plane was formed in the loess layer. The analysis provides good information for identifying both surface movement and mass movement. [ABSTRACT FROM AUTHOR]

An online machine vision system to predict the ash content of a fine cleaned-coal product on a moving conveyor belt was established and tested. Observing the changes of different coal images and their corresponding ash content, six statistical features from a gray histogram and one feature reflecting the light-spot ratio were extracted. Three methods of different types— radial basis function (RBF) neural network, support vector machine (LIB-SVM), and multiple linear regressions (MLR)—were carried out to predict the ash content and to compare their prediction accuracies. RBF and LIB-SVM also considered the performance of original values and normalized values. Through comparing the fitting effect and generalization ability by RMSE and R2, RBF had the best fitting effect, but its generalization ability was the worst. The fitting effect and generalization ability of models by LIB-SVM and MLR were all good, while the normalized LIB-SVM model is the best one of the above three methods. [ABSTRACT FROM AUTHOR]

Tianshui City is located in a seismically active region of China. Historically, many earthquakes with magnitudes of 6.0-8.0 have been reported in this area, and most of loess-red mudstone landslides in this area are known to have been triggered by earthquakes. However, the soil dynamics under earthquakes in the Tianshui area have been little studied. Therefore, to understand the dynamic stress-strain behavior of undisturbed soils during the earthquake, cyclic and shear strength tests were conducted on soil samples (loess and weathered red mudstone) obtained from Tianshui in a cyclic triaxial test system. Two types of laboratory tests were carried out on two kinds of soils. The first type involved the tests with different cyclic stress ratios applied and the number of cycles N more than 20, while the second type are series of stress-controlled undrained reverse-shear tests. By doing so, the differences on stress condition on the behavior of Young's modulus E , stress-strain and shear strength were measured. In addition, tests modeling natural seismic loading were also carried out for comparative purposes. Some residual shear behaviors were also recognized. The results of a series of tests designed to study the stress-strain behavior of undisturbed soils under repeated loading are presented. These tests will provide useful parameters (e.g. λ and E ) for analysis of slope stability during earthquakes and earthquake-resistant design. [ABSTRACT FROM AUTHOR]

The local regions of complex gyration class mechanical parts are largely different, which results in the problem that their surface images are partially clear. This problem directly affects the effectiveness of vision methods on detecting surface defects. To tackle this problem, a large depth-of-field (DoF) surface image fusion method is proposed to obtain a full-focus image of complex mechanical parts, such as gyration class mechanical parts. We design an image acquisition platform based on the characteristics of gyration class parts to acquire their surface images accurately and clearly. We then propose a feature detection-based image registration method, by which the registered image can represent the surface information of the part completely and accurately. Additionally, we adopt a convolutional neural network-based image fusion method to achieve a fused large DoF surface image. Experimental studies were conducted to evaluate the performance of the deep-learning-based methods. The experimental results show that the proposed method can completely and clearly fuse the large DoF surface images of complex gyration class mechanical parts. The quality of the fused images has a significant improvement, and the proposed method is significantly more efficient than traditional fusion methods and other deep-learning methods. [ABSTRACT FROM AUTHOR]

A method based on coarse-graining to construct a directed weighted complex network which models the transformation of the trading data of an individual stock is introduced. The degree (strength) distribution of derived network follows a power-law. A moderated regression equation with interaction effects of average return and out-degree (in-degree) on out-strength (in-strength) is established. Moreover, we found that the differences of nodes affect the network’s structure and average return level impacts nodes’ eigenvector centrality and pagerank, significantly. [ABSTRACT FROM AUTHOR]

Earthquake waves influence rock toppling stability, and the effects of local earthquake propagation and topographic also affect slope deformation. Detailed engineering geology conditions of slopes are obtained and a geomechanical characterization of rock topplings is performed. To study the input earthquake waves and slope interactions, a dynamic analysis is performed using the UDEC 4.0 discrete element method (DEM) numerical code under viscoplastic conditions. The earthquake signals are representative of different peak ground acceleration, Arias intensities, and frequencies, and are used in the study of different rock topplings with different height, slope angle, and strata dip angle. The derived outputs are processed for the earthquake propagation study and to assess the induced deformation mechanisms in terms of resulting displacements, plastic zone features, and deformation mode. The results prove that an interaction exists between stratigraphic and topographic effects on earthquake wave propagation, and that these effects cannot be assessed independently. The obtained results describe the effect of topography and geological settings in rock topplings, amplifying or de-amplifying earthquake ground motion, and demonstrate that the tensile state dominates at the slope surface but evolves with depth. A shear state dominates at the toe or in the deep part of rock topplings. The rock toppling deformation mode may develop into a composite of tension fractures at the crest and sliding at depth. Compared with the static scenario, under earthquake load, tensile deformation evolves over a larger area at the crest and develops a shear zone at the toe and in the substrata. The necessary earthquake-induced toppling conditions are discussed, and the UDEC 4.0 DEM method and conventional pseudostatic approach are compared. This study shows there are a broader range of deformations inside the slope. [ABSTRACT FROM AUTHOR]

Particle overlapping is a serious but largely ignored problem in size distribution estimation using image processing technology. A new correction method for particle overlapping error in coal size distribution estimation is proposed in this paper. Particles of four different size fractions were each sprayed with different colors to easily identify the size fractions. A semi-automatic local-segmentation algorithm was proposed to segment coal particle regions in this investigation. Moreover, an interval statistical method was used to calculate the probability of overlapping particles that belong to the four size fractions, and the probability distribution curves of the four size fractions were fitted by the Least Square Method. Similar function models were selected according to the characteristics and shapes of the probability distribution curves. Root Mean Square Error (RMSE) and R-squared parameters were used to measure the fitting effects. It was found that using overlap correction models reduced the maximum and minimum absolute error of particle size estimation from 7.5% and 4.3% with no overlap correction to 4.5% and 0.5%, respectively, when the developed overlap models were used. [ABSTRACT FROM AUTHOR]

We obtain a range of new exact solutions for the Schrödinger–Hirota equation, including a triangular function solution, a rational function solution and Jacobian elliptic function solutions by the elliptic function method. • We obtain a range of new exact solutions for the Schrödinger–Hirota equation. • We give the graphics of typical exact solutions, which reveals the affection. • From the graphics of above typical exact solutions, it can be seen that the image changes periodically. [ABSTRACT FROM AUTHOR]

Complex block-flexure slope toppling is observed in interbedded sandstone and slate in the upper Yellow River, China. Block toppling is observed in the relatively hard sandstone and flexural toppling is observed in the relatively soft slate. The evolution of toppling slope deformation is characterized by long-term progress and spatial variability. In order to study these characteristics, field investigations, adit prospecting, borehole drilling, sonic tests and numerical simulation were carried out. In addition, the effect of the structure, the composite mode of the rock mass, the unloading fissures and geomorphology are discussed. Furthermore, we explored the toppling mechanisms and simulated it numerically; on this basis, the slope evolution is divided into four stages. Results obtained from the numerical simulation compared well with field investigation data (investigation data, rock sonic survey and strata dip statistics). Comparative studies have demonstrated that the analytical methods presented in this paper are appropriate for back analysis of the toppling evolution process. The result showed that this toppling slope has not yet entered the later progressivity failure stage and is currently limited to collapse at shallow levels, whereas the deep-seated rock mass will remain stable for a long time. This study might provide reference for a stability evaluation and hazard prevention analysis for rock toppling. [ABSTRACT FROM AUTHOR]

• Image-based monitoring of the seafloor is expanding into new depths. • Annotation of images is often the bottleneck in obtaining data. • Annotation approaches impact the ability to detect changes. • Count-based approaches may often be preferable to percent cover calculation. • Other metrics such as condition and size structure can be useful for monitoring changes. Image-based surveys of the marine benthos are being increasingly adopted as a monitoring tool for habitats and biota, particularly in mesophotic depths (∼30–150 m) which are technically difficult to survey. Many modern tools for these surveys, such as remotely operated vehicles and autonomous underwater vehicles, can capture thousands of images in a single deployment. Turning this into quantitative data typically involves human annotation which is often time-consuming and costly. Percent cover of organisms is one of the most common metrics for monitoring changes in abundance, which may be attained through visual estimation, digitization, or point-count approaches. However, alternative metrics of abundance such as density (direct counts) or presence-absence, as well as metrics that quantify condition (e.g., bleaching) and size-structure can also provide quantitative information for tracking change. Understanding the statistical power of different approaches is critical to designing effective image-based monitoring programs. Given the differing statistical power and time taken using different approaches, program managers need to decide where to allocate resources. Here we use benthic imagery from two long-term monitoring sites in south-eastern Australia to annotate three example morphospecies (morphologically distinct organisms) using three approaches: point-count (percent cover), full count and presence-absence within imagery. Also, we compare the performance of the point count and full count approaches for monitoring bleaching in one of our morphospecies. We use spatio-temporal models to quantify trends in the empirical data and simulations to quantify the power of these approaches to detect different levels of temporal change using different sampling efforts (either 100 or 200 images). Additionally, we examine the additional insights that size-structure information can provide for two morphospecies. We find that the full count approach provides a higher statistical power to detect change than the other approaches for our example morphospecies, including tracking bleaching status. Size-structure information can provide additional insights such as the occurrence of recruitment or mortality events or growth of individuals. We recommend that monitoring programs using benthic imagery should consider the choice of annotation approach as this is likely to impact observed temporal patterns, particularly when the focus is specific indicator species rather than total biodiversity. [ABSTRACT FROM AUTHOR]

Ash content is one of the most important indexes of coal quality, and fast prediction of ash content is urgent and important for coal processing industry. The aim of this paper is to propose a method of ash content prediction of coarse coal by the use of image analysis and GA-SVM. Coal particles on the surface were randomly selected to measure the ash content, and a semi-automatic local-segmentation algorithm was proposed to identify the corresponding coal particle regions. Thirty-eight features were extracted, and selected by GA. Ash content prediction model was established by SVM, and K-CV method is used to determine the hyper-parameters (c, g) of SVM. RMSE and R-square were used to measure the prediction effects of ash content. Results indicated that the prediction effects of narrow size fractions are better than wide size fraction, and larger size fraction is more accurate than smaller size fraction in ash content prediction. [ABSTRACT FROM AUTHOR]

We have studied the polarized image of an equatorial emitting ring around a rotating black hole surrounded by a cold dark matter (CDM) halo. Results show that the CDM halo density has the similar effects of the halo’s characteristic radius on the polarized image for the black hole. The effects of the CDM halo on the polarized image depend on the magnetic field configuration, the fluid velocity and the observed inclination. With the increase of the CDM halo parameters, the observed polarization intensity decreases when the magnetic field lies in equatorial plane, but in the case where the magnetic field is perpendicular to the equatorial plane, the change of the observed polarization intensity with CDM halo also depends on the position of the emitting point in the ring. The change of the electric vector position angle (EVPA) with the CDM halo becomes more complicated. Our results also show that the influence of the CDM halo on the polarized image is generally small, which are consistent with the effects of dark matter halo on black hole shadows. These results could help to further understand dark matter from black hole images. [ABSTRACT FROM AUTHOR]

Deep learning is an effective way to improve the classification accuracy of coal images for the machine vision-based coal sorting. However, the related research on deep learning-based mineral image classification has not systematically considered the models for multi-coal and multi-class sorting. Additionally, the universal CNNs model for multi-coal image classification has not been proposed. Given the above problems, combined with deep learning and transfer learning and based on VGG Net, Inception Net, and Res Net, this study builds four CNNs models with different depth and structure for multi-coal and multi-class image classification. Finally, we take anthracite, gas coal, coking coal as the research objects and propose a universal CNNs model suitable for multi-coal and multi-class sorting. Moreover, with the Channel Visualization map, Heatmap, Gard-CAM map, and Guided Backpropagation map, the operational processes of CNNs model in coal image recognition and classification are revealed, and the features that affect the classification weights are analyzed. ● Establishing multi-class image classification models of multi-coal by deep learning ● Developing a universal CNNs classification model for multi-coal images ● Exploring the operational process of CNNs model in coal image classification [ABSTRACT FROM AUTHOR]

Abstract: An improved mass model with only two parameters for coarse coal particles using image analysis was established. Five variables—density, projected area, perimeter, length and breadth of the best-fit-rectangle, were used for the initial mass model. Due to the discovery of some relationships between the variables, the improved mass model only consisted of projected area and density and was established with a satisfied accuracy. Simultaneously, the computation speed of improved mass model is 5 times more than that of initial mass model. When tested 50 times against a different batch of modeling sample, the total relative errors of the improved mass model were less than 6%, however, the relative errors of 25–50mm were less than 7% and that of other size fractions were less than 5%. Experimental results revealed that only projected area and density were required to develop such model with satisfactory results. A promising application of this mass model will improve the on-line control of coal industry. [Copyright &y& Elsevier]