Average Gray Value Research Articles

Three-dimensional Stereoscopic Evaluation and Quantitative Gray Scale Assessment of the Nasolabial Fold and Its Application in Injection Therapy

With the growing demand for aesthetic enhancements, particularly filler injections for nasolabial folds (NLFs), there is a need for more objective and quantitative assessment methods beyond the traditional subjective scales like the Wrinkle Severity Rating Scale (WSRS). This study introduces a novel evaluation method combining 3-dimensional (3D) imaging and grayscale analysis to objectively assess NLF severity and treatment outcomes. A total of 100 healthy participants (86 females, 14 males; mean age 41.85±11.53 years) were assessed using WSRS scores and quantitative 3D measurements. The results demonstrated a significant positive correlation between WSRS scores and gray values (r=0.985, P < 0.05). Significant differences in gray values and 3D linear measurements were observed across WSRS score groups, with high intra-rater reliability confirmed by intraclass correlation coefficients. In addition, 54 patients (mean age 42.9±8.9 y) with moderate to severe NLFs received hyaluronic acid injections. One month post-treatment, WSRS scores significantly decreased (from 3.74±0.45 to 1.63±0.59, P < 0.05), and significant improvements were observed in several 3D measurements. The average gray value also decreased significantly (P < 0.05), indicating a reduction in wrinkle depth. These findings were supported by representative photos showing patient satisfaction and no severe complications. This study validates the use of 3D imaging and grayscale analysis as reliable tools for objectively assessing NLF severity and monitoring treatment efficacy, potentially improving the precision and reliability of aesthetic evaluations in clinical practice.

Effect of light source wavelength on surface defect imaging in deep-water concrete dams

This study explores the relationship between surface defect imaging in deep-water concrete dams and the wavelength of the light source, aiming to improve concrete defect image quality by varying the wavelength. The underwater optical imaging model of concrete dam surfaces was developed by applying the principle of object reflection and the underwater optical propagation model. This model reflects the effect of different light wavelengths on underwater concrete defect imaging. Through a self-designed underwater optical darkroom test platform, defect image acquisition tests were conducted under various color light conditions. The results reveal the significant influence of light source wavelength on images captured in both air and water. Furthermore, under the same initial illuminance, blue light significantly enhanced the average gray value, sharpness, contrast, and the number of feature points in underwater defect images, exhibiting increments of 2.97 times, 0.48 times, 2.70 times, and 2.95 times, respectively, compared to white light. These findings can serve as a reference for the selection of color-light for the non-destructive testing of surface defects in actual underwater concrete dams.

Adaptive adjustment of brightness and blur of the camera for high precision internal parameter calibration

Camera calibration is a prerequisite for many computer vision tasks, such as visual measurement and visual localization. This paper proposes a new method for high-precision calibration of camera intrinsic parameters. By dynamically adjusting image brightness and blur to cope with changes in external lighting conditions and camera working distance, high-precision internal parameter measurement is achieved. This method solves the problems of the traditional camera intrinsic parameter calibration process being cumbersome and susceptible to changes in the placement of calibration boards and fluctuations in external lighting conditions. Firstly, an adaptive exposure adjustment algorithm is proposed to adjust the camera exposure value by statistically analyzing the average gray value of randomly selected rectangular regions on the calibration board, thereby solving the problem of the camera calibration accuracy being affected by overly bright or dark environments. Subsequently, an evaluation criterion and adaptive adjustment strategy for adjusting image blur are proposed. By extracting edge points of feature circles and obtaining the slope of the fitted curve, the relationship between edge sharpness and slope is utilized to adjust the image blur. Finally, the proposed method achieves high-precision semi-automatic calibration. Experimental results demonstrate that the proposed method has significant advantages in terms of correctness, robustness, and flexibility, with an average reprojection error of 0.0167.

Metal Laser-Based Powder Bed Fusion Process Development Using Optical Tomography.

In this study, a set of 316 L stainless steel test specimens was additively manufactured by laser-based Powder Bed Fusion. The process parameters were varied for each specimen in terms of laser scan speed and laser power. The objective was to use a narrow band of parameters well inside the process window, demonstrating detailed parameter engineering for specialized additive manufacturing cases. The process variation was monitored using Optical Tomography to capture light emissions from the layer surfaces. Process emission values were stored in a statistical form. Micrographs were prepared and analyzed for defects using optical microscopy and image manipulation. The results of two data sources were compared to find correlations between lack of fusion, porosity, and layer-based energy emissions. A data comparison of Optical Tomography data and micrograph analyses shows that Optical Tomography can partially be used independently to develop new process parameters. The data show that the number of critical defects increases when the average Optical Tomography grey value passes a certain threshold. This finding can contribute to accelerating manufacturing parameter development and help meet the industrial need for agile component-specific parameter development.

Quantitative evaluation of endobronchial ultrasound elastography in the diagnosis of benign and malignant mediastinal and hilar lymph nodes

ObjectiveTo compare the diagnostic value of different quantitative methods of endobronchial ultrasound elastography in benign and malignant mediastinal and hilar lymph nodes. MethodsThis retrospective study included patients who underwent endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) for mediastinal and hilar lymph node enlargement in our hospital between January 2019 and August 2022. We compared different quantitative elastography parameters [red area ratio (RAR, lymph node red area/lymph node area), green area ratio (GAR, lymph node green area/lymph node area), blue area ratio (SAR, lymph node blue area/lymph node area), mixed area ratio (MAR, lymph node green area/lymph node area), blue-green lymph node area/lymph node area), strain rate ratio (SR), strain rate in the target lymph node (LPA), ratio of blue area to total lymph node area outside the center of the target lymph node (PAR), and average grey value (MGV)], in order to find the best quantitative evaluation method. ResultsA total of 244 patients (346 lymph nodes) were included in this study. All quantitative elastography parameters were statistically significant for the differentiation of benign and malignant lesions except the average grey value of the target lymph nodes. The area under the receiver operating characteristic curve of SAR was 0.872 (95% confidence interval: 0.83−0.91), the cutoff value was 0.409, and the sensitivity, specificity, positive and negative predictive values were 85.4%, 78.0%, 80.4%, and 83.4%, respectively. ConclusionCompared with other types of quantitative analysis, SAR has a higher predictive significance for benign and malignant lymph nodes.

EXPERIMENTAL STUDY ON FLASH BOILING SPRAY OF HIGH-PRESSURE LIQUID AMMONIA JET WITH ROUND AND ELLIPTICAL HOLE NOZZLES

Ammonia is an ideal alternative fuel for mitigating carbon emissions. High-pressure direct injection of liquid ammonia (LNH<sub>3</sub>) offers significant advantages in enhancing energy efficiency and minimizing emissions. Due to the high saturation vapor pressure, the injection of LNH<sub>3</sub> is susceptible to flash boiling. In this study, we used high-speed micro-imaging technology with backlight lighting to establish a high-pressure common-rail LNH<sub>3</sub> jet experimental platform and investigate the flash boiling spray characteristics of nozzles with round and elliptical holes. The results demonstrated that under non-flash boiling conditions, the residual LNH<sub>3</sub> in the sac chamber and nozzle can rapidly corrode the acrylic material of the nozzle, leading to deformation and failure of the nozzle structure. Under flash boiling conditions, LNH<sub>3</sub> ejected from the hole will produce spherical macroscopic spray morphology. Then, the spray gradually transitions from an elliptical profile to a conical profile as the back pressure increases. Compared to nozzles with round holes, nozzles with elliptical holes exhibit higher flow velocity, which enhances oil-gas mixing and promotes more pronounced flash boiling phenomena. Flash boiling occurs at an earlier stage with an increase in the spray cone angle, thereby improving the atomization characteristics under both flash and non-flash boiling conditions. The tail jet of nozzles with elliptical holes terminates earlier while exhibiting a higher decrease rate in the average gray value, which improves the atomization quality in the tail spray stage and meets the requirements of timing, quantification, and precise control of the fuel injection system.

Microcosmic characteristics of particulate matter emitted by GDI gasoline engine in plateau environment

The particulate matter samples of gasoline direction injection (GDI) gasoline engine obtained from road experiments are photographed to obtain the emission particulate matter images based on the transmission electron microscope under high altitude environment. The results show that the micro-morphology characteristics of the particulate matter emitted by GDI gasoline engine in plateau area are similar to those in low altitude area, and the main morphology is cloud-like, filamentous, flocculent, chain-like, cluster-like, like altitude contour line, etc. The images are processed by MATLAB and Python software, and the fractal dimension, average gray value and gray histogram are obtained. The fractal dimensions of the emission particulate data obtained are between 1.81 and 1.89, which has some deviation compared with the fractal dimension of the emission particulate data at low altitude of 1.58 and 1.80. With the increase of altitude, its fractal dimension increases more obviously. It can be seem from the characteristics of gray value and gray histogram of particle images that the average gray value of GDI gasoline engine particles is higher. The gray histogram distribution of 5–50 nm images is uniform, and the individual particles in the images are clear. The gray histogram of 100–500 nm images is scattered, so it is easy to extract and separate the image edge contour. 5–50 nm images are used to study the details and characteristics of individual particles, and 100–500 nm transmission electron microscope images are used to study the overall morphology of particles.

Smartphone-based low-cost and rapid quantitative detection of urinary creatinine with the Tyndall effect

This research aims to develop a robust and quantitative method for measuring creatinine levels by harnessing the enhanced Tyndall effect (TE) phenomenon. The envisioned sensing assay is designed for practical deployment in resource-limited settings or homes, where access to advanced laboratory facilities is limited. Its primary objective is to enable regular and convenient monitoring of renal healthcare, particularly in cases involving elevated creatinine levels. The creatinine sensing strategy is achieved based on the aggregation of gold nanoparticles (AuNPs) triggered via the direct crosslinking reaction between creatinine and AuNPs, where an inexpensive laser pointer was used as a handheld light source and a smartphone as a portable device to record the TE phenomenon enhanced by the creatinine-induced aggregation of AuNPs. After evaluation and optimization of parameters such as AuNP concentrations and TE measurement time, the subsequent proof-of-concept experiments demonstrated that the average gray value change of TE images was linearly related to the logarithm of creatinine concentrations in the range of 1–50 μM, with a limit of detection of 0.084 μM. Meanwhile, our proposed creatinine sensing platform exhibited highly selective detection in complex matrix environments. Our approach offers a straightforward, cost-effective, and portable means of creatinine detection, presenting an encouraging signal readout mechanism suitable for point-of-care (POC) applications. The utilization of this assay as a POC solution exhibits potential for expediting timely interventions and enhancing healthcare outcomes among individuals with renal health issues.

Multifractal analysis on CT soil images: Fluctuation analysis versus mass distribution

The combined action of physical, chemical and biological processes at different scales creates a highly complex soil architecture. At the same time, this soil pores' complexity is crucial in maintaining soil biogeochemical and biophysical processes. The development of digital image processing and a multiscaling analysis allow a better study to quantify pore complexity and evaluate pore spatial variability. This study took saline soils from coastal reclamation areas as an example and aimed to compare the multifractality of porosity series using different methods on sample cubes of 512 pixels in length extracted at different soil depths. Two measures were selected to represent the porosity series: the average grey values (AVG) of each slice and the CT-porosity (CTP) of binarized slices. The mass distribution (MD), detrended fluctuation analysis (DFA), and the moments function (K) were the methods applied in the multiscaling study. Shuffled and surrogated series were used to analyze the two possible multifractality sources, the probability distribution density function (PDF) and long-range correlations, respectively.The results showed that MD gave the minimum multifractal spectrum, which appeared as a point in AGV and a narrow concave parabola in CTP. On the other hand, both AGV and CTP showed a higher complexity, a clear and significant multifractal behaviour when DFA and K were used. The shuffled and surrogated series pointed out that the long-range correlations were the main factor of the multifractal sources of the porosity series. Both long-range correlations and PDF influenced more heavily on the multifractality of porosity in topsoil (0– 20 cm) than in the deeper soil (20– 60 cm), implying that the porosity series in topsoil had stronger multifractality. In addition, the DFA and K, combined with shuffled and surrogated series analysis, can distinguish between different factors in the multiscaling behaviour of different soil textures and time reclamation.

Objective quantification of posterior capsule opacification after cataract surgery with swept-source optical coherence tomography

PurposeTo evaluate the application of swept-source optical coherence tomography (SS-OCT) and pentacam scheimpflug tomography in posterior capsule opacification (PCO) severity assessment.MethodsThe posterior capsule image region segmentation and adaptive threshold algorithm are used to process the SS-OCT scanned image to obtain the posterior capsule thickness (PCT). Scheimpflug tomography reconstructed and analysized by image J software can obtain the average gray value and evaluate the effectiveness with the two methods.ResultOne hundred sixty-two IOL eyes of 101 patients were divided into two groups, laser group (65 eyes) with the mean PCT was 8.0 ± 2.7 pixel unit and the mean gray value of the eyes was 66 ± 33 pixel unit. However, these figures in the control group (97 eyes) were 5.0 ± 0.9 and 11 ± 17. The sensitivity, specificity and area under curve(AUC) of SS-OCT PCT were 85%, 74% and 0.942,the sensitivity, specificity and AUC of Pentacam gray value were 91%, 76% and 0.947, respectively. After using the multivariable model of generalized estimation equation to corrected the dependence of subjects' eyes, it was found that SS-OCT PCT, Pentacam gray value, low vision quality of life questionnaire (LVQ questionnaire) for distance vision, and mobility and lighting dimension were significantly correlated with the PCO score (P = 0.012, P = 0.001, P = 0.005, respectively).ConclusionThe region segmentation and adaptive threshold algorithm of posterior capsule image will accurately quantify the posterior capsule. Computer aided quantifications of posterior capsule are of great significance in the early surgical decision-making of PCO. The average occurrence time of most PCO was around 34 months, and the severity of PCO worsened with increasing postoperative time.

Study on Temperature Measurement Model of Rare Earth Metal Electrolysis Based on Infrared Image

The temperature of rare earth electrolysis plays an important role in the process of electrolysis control. It is the main parameter to control the efficiency of electrolysis. In production practice, most of the process parameters are controlled by the artificial thermocouple temperature measurement method. This extensive technical management method greatly affects the accuracy of production control. And the traditional thermocouple temperature measurement is artificial and slow, not timely, short life, and has other issues. Therefore, it is extremely urgent to explore a continuous and safe surface temperature measurement method to achieve precise control of process parameters. Therefore, based on the principle of infrared temperature measurement, this paper introduces the theory of infrared temperature measurement, uses Matlab software to extract the average gray value of the infrared image, and obtains the infrared temperature measurement model by least square method and bp neural network for temperature and gray fitting curve. At the same time, the experimental data are measured, and the model is verified. This method provides a reference for online temperature measurement of electrolytic molten metal.

Quantitative colorimetric sensing of heavy metal ions via analyte-promoted growth of Au nanoparticles with timer or smartphone readout.

This work describes two new colorimetric nanosensors for label-free, equipment-free quantitative detection of nanomolar copper (II) (Cu2+) and mercury (II) (Hg2+) ions. Both are based on the analyte-promoted growth of Au nanoparticles (AuNPs) from the reduction of chloroauric acid by 4-morpholineethanesulfonic acid. For the Cu2+ nanosensor, the analyte can accelerate such aredox system to rapidly form a red solution containing dispersed, uniform, spherical AuNPs that is related to these particles' surface plasmon resonance property. For the Hg2+ nanosensor, on the other hand, a blue mixture consisting of aggregated, ill-defined AuNPs with various sizes can be created, showing a significantly enhanced Tyndall effect (TE) signal (in comparison with that produced in the red solution of AuNPs). By using a timer and a smartphone to quantitatively measure the time of producing the red solution and the TE intensity (i.e., the average gray value of the corresponding image) of the blue mixture, respectively, the developed nanosensors are well demonstrated to achieve linear ranges of 6.4nM to 100μM and 6.1nM to 1.56μM for Cu2+ and Hg2+, respectively, with detection limits down to 3.5 and 0.1nM, respectively. The acceptable recovery results obtained from the analysis of the two analytes in the complex real water samples including drinking water, tap water, and pond water ranged from 90.43 to 111.56%.

An attention-based long short-term memory prediction model for working conditions of copper electrolytic plates

Copper is an important source of non-ferrous metals, with electrolytic refining being one of the main methods to produce fine copper. In the electrolytic process, plate states seriously affect the output and quality of the copper. Therefore, timely and accurate prediction of the working states of the plates is of great significance to the copper electrolytic refining process. Aiming at the issues associated with traditional plate state detection algorithms of large lag, poor anti-interference ability and low accuracy, a plate state prediction model based on a long short-term memory (LSTM) neural network with an attention mechanism is here proposed in this paper. The average gray values of the plates in infrared imagery are used to characterize the plates’ working states. To address the problems of large fluctuation and the large amount of time series data required in such a study, a double-layer LSTM neural network structure is used to improve the efficiency and accuracy of model training. Meanwhile, in view of the periodicity of the time series data and the possible correlation between adjacent data, a unique attention mechanism is proposed to enable the model to learn this correlation between the adjacent data so as to improve the accuracy of the model prediction. The experimental results show that the accuracy of the proposed model for plate state prediction reaches 95.11%. Compared with commonly used prediction algorithms, the plate state prediction model proposed in this paper demonstrates stronger prediction ability.

High-Performance Polarization Microscopy Reveals Structural Remodeling in Rat Calcaneal Tendons Cultivated In Vitro.

Collagenous tissues exhibit anisotropic optical properties such as birefringence and linear dichroism (LD) as a result of their structurally oriented supraorganization from the nanometer level to the collagen bundle scale. Changes in macromolecular order and in aggregational states can be evaluated in tendon collagen bundles using polarization microscopy. Because there are no reports on the status of the macromolecular organization in tendon explants, the objective of this work was to evaluate the birefringence and LD characteristics of collagen bundles in rat calcaneal tendons cultivated in vitro on substrates that differ in their mechanical stiffness (plastic vs. glass) while accompanying the expected occurrence of cell migration from these structures. Tendon explants from adult male Wistar rats were cultivated for 8 and 12 days on borosilicate glass coverslips (n = 3) and on nonpyrogenic polystyrene plastic dishes (n = 4) and were compared with tendons not cultivated in vitro (n = 3). Birefringence was investigated in unstained tendon sections using high-performance polarization microscopy and image analysis. LD was studied under polarized light in tendon sections stained with the dichroic dyes Ponceau SS and toluidine blue at pH 4.0 to evaluate the orientation of proteins and acid glycosaminoglycans (GAG) macromolecules, respectively. Structural remodeling characterized by the reduction in the macromolecular orientation, aggregation and alignment of collagen bundles, based on decreased average gray values concerned with birefringence intensity, LD and morphological changes, was detected especially in the tendon explants cultivated on the plastic substrate. These changes may have facilitated cell migration from the lateral regions of the explants to the substrates, an event that was observed earlier and more intensely upon tissue cultivation on the plastic substrate. The axial alignment of the migrating cells relative to the explant, which occurred with increased cultivation times, may be due to the mechanosensitive nature of the tenocytes. Collagen fibers possibly played a role as a signal source to cells, a hypothesis that requires further investigation, including studies on the dynamics of cell membrane receptors and cytoskeletal organization, and collagen shearing electrical properties.

Brain structure variability study in pilots based on VBM.

The impact of occupations on brain structures has attracted considerable research interests in the last decade. The aim of this research is to find the effect of flight training on brain gray matter volume of pilots. The whole-brain structural magnetic resonance imaging (sMRI) data collected from 26 pilots and 24 controls was analyzed using Voxel-based morphological analysis method (VBM) combined with T1 data to quantitatively detect the local gray matter of brain tissue and calculate the gray matter volume. The result shows that the pilot group has larger gray matter volume in the lingual gyrus and fusiform gyrus compared to the control group (P<0.05). Furthermore, there is a positive correlation between the gray matter volume and the number of flight hours (r = 0.426, P = 0.048) after studying the average gray matter volume value of the agglomerate of participants whose flight hours are between 0 and 1000 hours. The lingual gyrus and fusiform gyrus are involved in high-level visual processing, memory, multisensory integration and perception. The study has indicated the flight training could enlarge gray matter volume in the lingual gyrus and fusiform gyrus. During flying, pilots need to observe the instrumentation in the cockpit and fully interpret the readings, which may lead to the results.

Flame stability measurement through image moments and texture analysis

In this article, the first two moments of the image, mean and standard deviation, uniform local binary pattern (LBP) texture analysis methods were experimentally investigated in coal-fired boilers to measure flame stability. The first two moments of the flame image were used to evaluate the flame stability in terms of color and brightness (average gray value). Although the radiation signal of the flame is widely obtained by the spectral analysis method, the radiation signal of the flame was obtained by the LBP texture analysis method in this study. The flame stability measurement technique proposed in this study does not require prior knowledge about charged coupling devices camera features. Therefore, it can be easily applied to measure flame stability without expensive and complicated adaptation processes. Flame stability was measured with R = 0.9868 accuracy with the proposed method. The experimental results show that the proposed texture analysis method is more effective than current spectral analysis methods. The results obtained within the scope of this study also show that it can be easily applied to existing closed-loop control systems to monitor flame stability.

Opportunities and Methods for Using Fluorescent Gel as a Proxy for Pathogen Transfer in Biosecurity Research

Highlights While fluorescing gel may evaporate from a surface, luminance of the surface does not change. Fluorescing gel exhibits thresholds beyond which additional gel density does not increase luminance. Fluorescing gel only transfers between surfaces when it is wet. There are limits to relating luminance and mass transfer. Fluorescent material is a useful proxy for contamination transfer demonstration and research. Abstract. Glo Germ fluorescing material is a popular tool for teaching and researching contaminant transfer in and out of agriculture. The objectives of this paper were to: (1) quantify relationships between gel area density (mass per unit area) on a surface and its luminance, and (2) identify factors important in measuring Glo Germ gel transfer from one surface to another. Varying densities of Glo Germ gel were applied to paper, plastic, and rubber surfaces; each combination was replicated three times. Digital images collected over one hour were analyzed for luminance (the average gray value per unit area) under ultraviolet light. Changes in mass were also measured. For the gel transfer objective, a fixed weight was placed over varying wet and dried fluorescent material densities on paper and plastic surfaces. Gel masses were weighed, and images of the surface and receptor were taken before and after transfer. Evaporation was significantly faster (p = 0.0019) on the paper surface compared to the plastic surface. The luminance did not change as the gel evaporated from either surface. For each material, luminance initially increased with increasing density until a threshold, after which additional fluorescing gel density did not change luminance. The thresholds for paper, plastic, and rubber surfaces were 0.018, 0.014, and 0.041 g cm-2, respectively. Wet gel transfer test results suggest that transfer is easier to quantify on the receptor than the source. The dried gel did not exhibit measurable transfer. This research found limitations in equating mass transfer and luminance, but luminance threshold values can inform maximum Glo Germ application for imaging purposes. These research results support continued research and outreach with fluorescent material to reduce and prevent the spread of disease or other harmful contaminants in food and animal production. Keywords: Biosecurity, Fluorescence, Luminance, Mass transfer.

Ink Drop Displacement Model-Based Direct Binary Search.

A novel statistical ink drop displacement (IDD) printer model for the direct binary search (DBS) halftoning algorithm is proposed. It is intended primarily for pagewide inkjet printers that exhibit dot displacement errors. The tabular approach in the literature predicts the gray value of a printed pixel based on the halftone pattern in some neighborhood of that pixel. However, memory retrieval time and the complexity of memory requirements hamper its feasibility in printers that have a very large number of nozzles and produce ink drops that affect a large neighborhood. To avoid this problem, our IDD model embodies dot displacements by moving each perceived ink drop in the image from its nominal location to its actual location, rather than manipulating the average gray values. This enables DBS to directly compute the appearance of the final printout without retrieving values from a table. In so doing, the memory issue is eliminated and the computation efficiency is enhanced. The deterministic cost function of DBS is replaced by the expectation over the ensemble of the displacements for the proposed model such that the statistical behavior of the ink drops is accounted for. Experimental results show significant improvement in the quality of the printed image over the original DBS. Besides, the image quality obtained by the proposed approach appears to be slightly better than that obtained by the tabular approach.

Development of facile image processing techniques regarding crevice corrosion of U75V high-speed rail steel

In-situ crevice corrosion images of U75V high-speed rail steel samples before and after heat treatment for microstructure refinement were obtained by a self-developed monitoring device, and facile image processing techniques have been developed. Gray value and entropy have been demonstrated to be the significant parameters able to describe the process of crevice corrosion. Gray value has been found to have good correlation with corrosion depth, and distribution of column average gray value can be applied to obtain corrosion depth profiles along crevice depth. Gray histogram is a good indicator to distinguish local preferential dissolution and uniform dissolution via deviation from original peak position and valley height. Entropy means uncertainty of how corrosion patterns develop. Its increase indicates high activity for change of corrosion patterns, while low activity leads to cease of increase. When metal surface has been corroded extensively, entropy will decrease, indicating less potential routes for development of corrosion patterns. This work has provided new ideas on the application of image processing on corrosion study.

Investigation of the strength of concrete-like material with waste rock and aeolian sand as aggregate by machine learning

Abstract Solid waste filling is an important development direction for filling mining technology. This paper proposes to use waste rock and aeolian sand as aggregates to fill the underground extraction area in order to reduce the environmental damage of related waste solid. The experiments are optimized by response surface methodology and multivariate nonlinear response models are constructed to investigate the effects of different factors on uniaxial compressive strength (UCS) of concrete-like material (CLM). The performance of different swarm intelligence optimization algorithms is analyzed and combined with support vector regression model (SVR) to construct an intelligent prediction model for UCS. The results show that the packing density has a maximum value of 0.74 when the proportion of waste rock is around 0.6. The response model constructed in this paper has a P-value &amp;lt; 0.01 and R2 &amp;gt; 0.8, which indicates its high significance and goodness of fit. The UCS of CLM increases with the increase of cement content and slurry mass fraction, while it also increases and then decreases with the increase of proportion of waste rock. The ratio of waste rock and aeolian sand will affect the compactness of cemented structure. The better the ratio, the higher the average grey value of the cemented structure, the more compactness the cemented structure. The whale optimization algorithm-SVR model constructed in this paper has a prediction accuracy of more than 99% for UCS of CLM, which achieves high accuracy and fast prediction of UCS under multifactor conditions.