Digital Image Research Articles

A facile cellulosic paper-based colorimetric sensor for detection of perchlorate using albumin-conjugated gold nanoparticles

Perchlorate, a prevalent and dangerous contaminant, is being increasingly found in untreated wastewater from urban areas and industrial discharges, as well as in unmonitored surface and groundwater sources. The urgent need for the creation of straightforward, efficient, and environmentally friendly sensing techniques to detect perchlorate in water. Cellulosic paper-based sensors show great promise as sustainable tools for detecting chemical pollutants. The study aimed to develop and evaluate the effectiveness of a paper-based sensor for detecting perchlorate levels in water samples. The approach showcased a reliable perchlorate colorimetric detection method, integrating digital smartphone imagery with paper-based sensors. The sensor employs a colorimetric assay that relies on the modification of gold nanoparticles with egg albumin protein, a material derived from biological sources. A digital smartphone facilitates on-site colorimetric data collection, and image processing methods are used to determine the presence of perchlorate in water samples based on these digital pictures. The grayscale color intensity derived from digital images is utilized for quantitative analysis. Comprehensive parametric evaluations were conducted to examine the stability and selectivity of the color intensities obtained from the sensor. The limits of detection and quantification are established at 2.86 µg/L and 9.55 µg/L, respectively. This study delivers a facile, highly sensitive, and cost-effective technique for the detection of perchlorate, particularly advantageous in industrial and rural settings where advanced detection devices are scarce.

CO-19 PDB 2.0: A Comprehensive COVID-19 Database with Global Auto-Alerts, Statistical Analysis, and Cancer Correlations.

Biological databases serve as critical basics for modern research, and amid the dynamic landscape of biology, the COVID-19 database has emerged as an indispensable resource. The global outbreak of Covid-19, commencing in December 2019, necessitates comprehensive databases to unravel the intricate connections between this novel virus and cancer. Despite existing databases, a crucial need persists for a centralized and accessible method to acquire precise information within the research community. The main aim of the work is to develop a database which has all the COVID-19-related data available in just one click with auto global notifications. This gap is addressed by the meticulously designed COVID-19 Pandemic Database (CO-19 PDB 2.0), positioned as a comprehensive resource for researchers navigating the complexities of COVID-19 and cancer. Between December 2019 and June 2024, the CO-19 PDB 2.0 systematically collected and organized 120 datasets into six distinct categories, each catering to specific functionalities. These categories encompass a chemical structure database, a digital image database, a visualization tool database, a genomic database, a social science database, and a literature database. Functionalities range from image analysis and gene sequence information to data visualization and updates on environmental events. CO-19 PDB 2.0 has the option to choose either the search page for the database or the autonotification page, providing a seamless retrieval of information. The dedicated page introduces six predefined charts, providing insights into crucial criteria such as the number of cases and deaths', country-wise distribution, 'new cases and recovery', and rates of death and recovery. The global impact of COVID-19 on cancer patients has led to extensive collaboration among research institutions, producing numerous articles and computational studies published in international journals. A key feature of this initiative is auto daily notifications for standardized information updates. Users can easily navigate based on different categories or use a direct search option. The study offers up-to-date COVID-19 datasets and global statistics on COVID-19 and cancer, highlighting the top 10 cancers diagnosed in the USA in 2022. Breast and prostate cancers are the most common, representing 30% and 26% of new cases, respectively. The initiative also ensures the removal or replacement of dead links, providing a valuable resource for researchers, healthcare professionals, and individuals. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://www.co-19pdb.habdsk.org/. Database URL: https://www.co-19pdb.habdsk.org/.

Deformation field evolution characteristics of layered cemented paste backfill and strength prediction based on ultrasonic pulse velocity

ABSTRACT This study focused on investigating the damage development mode and strength prediction model of layered cemented paste backfill (LCPB) using digital image correlation (DIC) and ultrasonic pulse velocity (UPV). The results indicated that the damage mode was associated with the cement-to-tailings (c/t) ratio and the height (h/H) ratio of the middle layer of LCPB. The damage mode of LCPB was mainly tensile damage for a small h/H ratio while increasing the h/H ratio to 0.8 resulted in LCPB exhibiting macroscopic shear damage. With the reduction of the c/t ratio, the damage area of LCPB gradually shifted from the upper layer to the middle layer. The UPV tended to decrease exponentially with both the decrease of the c/t ratio and the increase of the h/H ratio. Comparing the predicted and experimental values, the maximum error of the predicted values was only 0.299, and the value of the a20-index was 0.9792, indicating that the predictive model was reliable. The findings could provide guidance for the design of LCPB.

Lightweight digital image correlation network for stratospheric airship skin deformation measurement (Erratum)

Lightweight digital image correlation network for stratospheric airship skin deformation measurement (Erratum)

Age-dependent changes in skin features and perceived facial appearance in women of five ethnic groups.

Research documents effects of skin features on assessments of age, health and attractiveness of female faces. Ethnic variation also has been reported for the impact of age-related changes in skin features on face assessments. Here, we investigate women's self-ratings across age cohorts and ethnic groups and discrepancies with (non-expert) assessor ratings of facial appearance together with age-dependent changes in skin features. Faces of women 20-65 years from five ethnic groups (each n = 36) were imaged. Participants provided self-ratings of age, health and attractiveness, and were judged on these attributes by members of the same ethnic group (each n = 120). Digital image analysis was used to quantify skin gloss, tone evenness, wrinkling and sagging. Age-dependent changes in ratings and skin features within and between ethnic groups were assessed by comparing information from 10-year cohorts. We also tested whether menopausal status could be predicted by self-ratings, assessor ratings and image-based skin features. Women of all ethnic groups judged themselves younger and higher in attractiveness and health compared to third-party assessors, with the largest discrepancies for age in French women and for attractiveness and health in South African women. In Indian and South African women, specular gloss and skin tone evenness were lower compared to other participants, and sagging was higher in Indian, Japanese and South African women compared to Chinese and French women. Women's menopausal status could be predicted from assessor ratings and image-based skin features but not from self-ratings. There are differences between women's self-ratings and assessor ratings of facial appearance. These discrepancies vary with female age and ethnicity. Age and ethnicity effects also are evident in age-dependent changes in skin features within and across ethnic groups, which together with assessor (but not self-) ratings of facial appearance predict menopausal status.

Robustness assessment of an automated AI-based white blood cell morphometric analysis system using different smear preparation methods.

Numerous AI-based systems are being developed to evaluate peripheral blood (PB) smears, but the feasibility of these systems on different smear preparation methods has not been fully understood. In this study, we assessed the impact of different smear preparation methods on the robustness of the deep learning system (DLS). We collected 193 PB samples from patients, preparing a pair of smears for each sample using two systems: (1) SP50 smears, prepared by the DLS recommended fully automated slide preparation with double fan drying and staining (May-Grunwald Giemsa, M-G) system using SP50 (Sysmex) and (2) SP1000i smears, prepared by automated smear preparation with single fan drying by SP1000i (Sysmex) and manually stained with M-G. Digital images of PB cells were captured using DI-60 (Sysmex), and the DLS performed cell classification. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the performance of the DLS. The specificity and NPV for all cell types were 97.4%-100% in both smear sets. The average sensitivity and PPV were 88.9% and 90.1% on SP50 smears, and 87.0% and 83.2% on SP1000i smears, respectively. The lower performance on SP1000i smears was attributed to the intra-lineage misclassification of neutrophil precursors and inter-lineage misclassification of lymphocytes. The DLS demonstrated consistent performance in specificity and NPV for smears prepared by a system different from the recommended method. Our results suggest that applying an automated smear preparation system optimized for the DLS system may be important.

Estimation of the absorbed dose in simultaneous digital breast tomosynthesis and mechanical imaging.

Use of mechanical imaging (MI) as complementary to digital mammography (DM), or in simultaneous digital breast tomosynthesis (DBT) and MI - DBTMI, has demonstrated the potential to increase the specificity of breast cancer screening and reduce unnecessary biopsies compared with DM. The aim of this study is to investigate the increase in the radiation dose due to the presence of an MI sensor during simultaneous image acquisition when automatic exposure control is used. A radiation dose study was conducted on clinically available breast imaging systems with and without an MI sensor present. Our estimations were based on three approaches. In the first approach, exposure values were compared in paired clinical DBT and DBTMI acquisitions in 97 women. In the second approach polymethyl methacrylate (PMMA) phantoms of various thicknesses were used, and the average glandular dose (AGD) values were compared. Finally, a rectangular PMMA phantom with a 45mm thickness was used, and the AGD values were estimated based on air kerma measurements with an electronic dosemeter. The relative increase in exposure estimated from digital imaging and communications in medicine headers when using an MI sensor in clinical DBTMI was . For the phantom measurements of various thicknesses of PMMA, the relative increases in the AGD for DM and DBT measurements were, on average, and , respectively. The relative increase in the AGD using the electronic dosemeter was in DM and in DBT. The average difference in dose between the methods was . Our measurements suggest that the use of simultaneous breast radiography and MI increases the AGD by an average of . The increase in dose is within the acceptable values for mammography screening recommended by European guidelines.

Study of tensile‐tensile fatigue behavior of patch repaired carbon‐glass hybrid composite

AbstractAside from the numerous benefits of fiber‐reinforced composites, these materials are prone to impact damage, reducing structure's strength and stiffness. In this case, composite repair offers a unique and cost‐effective alternative for restoring structural integrity. In this work, post‐repair performance of the carbon‐glass hybrid composite is investigated in tensile‐tensile fatigue loading environments. To analyze repair performance, two distinct parent configurations (H1:G2C4G2 and H2: C2G4C2) and four patch configurations (Repaired1: CG3, Repaired2: C2G2, and Repaired3: C3G, Repaired4: G4) are considered. The digital image correlation approach and acoustic emission sensors are used to examine the specimens' damage behavior. First, fatigue tests are performed for drilled specimens, the H2 specimen dissipate more energy during fatigue cycles than the drilled H1 specimen due to the early damage initiation in the H2 specimen. The repaired specimens with the parent H1 laminate performed better than the parent H2 laminate. The Repaired4 configuration with H1 parent laminate sustained 300% higher cycles than the drilled H1 without final failure, which showed the highest fatigue life among all the repaired configurations. Patches with fewer glass plies outperformed all hybrid patches. The damage events recorded by acoustic emission sensors indicate that delamination damage considerably impacts fatigue life.Highlights Post‐repair fatigue behavior of carbon‐glass hybrid composite was examined. Effect of parent laminate stacking sequence and patch configuration was studied. Fatigue performance was analyzed using the damage mode and energy loss factor. Digital image correlation technique and acoustic emission sensors were utilized. Delamination damage showed a higher impact on the fatigue life of the sample.

Robust image tamper detection and recovery with self-embedding watermarking using SPIHT and LDPC

In today’s digital landscape, the pervasive use of digital images across diverse domains has led to growing concerns regarding their authenticity and reliability. The potential for malicious manipulation of these images underscores the critical need to develop robust methods for detecting tampering and ensuring their integrity. Fragile watermarking has been found to have extensive applications for tamper detection and recovery. An image watermarking technique for tamper detection, correction, and recovery is presented in this study. The proposed method employs a self-embedding method to generate the reference watermark from the original image, which has the advantage of superior tamper detection, localization, recovery capabilities, and robustness against attacks. Set Partitioning in the Hierarchical Tree (SPIHT) algorithm is applied to generate a reference watermark of the original image. Low-Density Parity Check (LDPC) is employed for error correction, providing higher-quality reconstruction to recover the original image. Schur decomposition processed the watermarked image blocks to generate authentication bits for each block to enhance tampering detection. The proposed watermarking method was evaluated using PSNR, SSIM, BER, and NC metrics for grayscale and colored images. The technique demonstrated high robustness in tamper detection and recovery against various malicious attacks. Comparative analysis with existing methods shows the efficacy of the proposed method.

Advancing Microplastic Detection Technology through Digital Image Processing, Fractal Analysis, and Polynomial Approximation Methods

Advancing Microplastic Detection Technology through Digital Image Processing, Fractal Analysis, and Polynomial Approximation Methods

Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.

Currently, precise patient body weight (BW) at the time of diagnostic imaging cannot always be used for radiation dose management. Various methods have been explored to address this issue, including the application of deep learning to medical imaging and BW estimation using scan parameters. This study develops and evaluates machine learning-based BW prediction models using 11 features related to radiation dose obtained from computed tomography (CT) scans. A dataset was obtained from 3996 patients who underwent positron emission tomography CT scans, and training and test sets were established. Dose metrics and descriptive data were automatically calculated from the CT images or obtained from Digital Imaging and Communications in Medicine metadata. Seven machine-learning models and three simple regression models were employed to predict BW using features such as effective diameter (ED), water equivalent diameter (WED), and mean milliampere-seconds. The mean absolute error (MAE) and correlation coefficient between the estimated BW and the actual BW obtained from each BW prediction model were calculated. Our results found that the highest accuracy was obtained using a light gradient-boosting machine model, which had an MAE of 1.99kg and a strong positive correlation between estimated and actual BW (ρ=0.972). The model demonstrated significant predictive power, with 73% of patients falling within a±5% error range. WED emerged as the most relevant dose metric for BW estimation, followed by ED and sex. The proposed machine-learning approach is superior to existing methods, with high accuracy and applicability to radiation dose management. The model's reliance on universal dose metrics that are accessible through radiation dose management software enhances its practicality. In conclusion, this study presents a robust approach for BW estimation based on CT imaging that can potentially improve radiation dose management practices in clinical settings.

Retinal Image Dataset of Infants and Retinopathy of Prematurity

Retinopathy of prematurity (ROP) represents a vasoproliferative disease, especially in newborns and infants, which can potentially affect and damage the vision. Despite recent advances in neonatal care and medical guidelines, ROP still remains one of the leading causes of worldwide childhood blindness. The paper presents a unique dataset of 6,004 retinal images of 188 newborns, most of whom are premature infants. The dataset is accompanied by the anonymized patients’ information from the ROP screening acquired at the University Hospital Ostrava, Czech Republic. Three digital retinal imaging camera systems are used in the study: Clarity RetCam 3, Natus RetCam Envision, and Phoenix ICON. The study is enriched by the software tool ReLeSeT which is aimed at automatic retinal lesion segmentation and extraction from retinal images. Consequently, this tool enables computing geometric and intensity features of retinal lesions. Also, we publish a set of pre-processing tools for feature boosting of retinal lesions and retinal blood vessels for building classification and segmentation models in ROP analysis.

The Precision of All-on-Four Implant Position Recorded from Three Different CBCT Machines.

To investigate the dimensional discrepancy and degree of deviation of All-on-Four implant position between different cone-beam computed tomography (CBCT) machines. Four implants (4.5 × 10 mm Superline II, Dentium, South Korea) were placed in an All-on-Four style in an artificial mandible. The jaw was radiated 30 times using three different CBCT machines (Rainbow CT, Dentium; Veraview X800, Morita, Japan; Planmeca Viso G3, Planmeca OY, Finland). A total of 30 Digital Imaging and Communications in Medicine (DICOM) files were exported, n = 10. All-on-Four implants from each DICOM file were segmented and exported as an STL file (three-dimensional image) using Blue Sky Plan software (version 4.12.13/Blue Sky Bio, United States). All-on-Four implant zone dimensions (X, Y, and Z axes) and the total degree of deviation between All-on-Four implants per CBCT machine were measured using Autodesk Meshmixer software (version 3.5.474/California, United States). The data distribution's normality and variances' equality were tested with Shapiro-Wilk's and Levene's tests, respectively (p-value < 0.05). Data were analyzed using Brown-Forsythe one-way analysis of variance and Tamhane's post hoc tests to compare the differences between the groups (p-value <0.05). The respective X, Y, and Z mean dimensions of the All-on-Four implant zone were: Dentium (34.95, 14.71, and 9.97); Morita (34.88, 14.74, and 10.56); and Planmeca (34.73, 15.15, and 12.33). Significant differences between CBCT machines were found in all axes (p-value < 0.05); however, the Z-axis had the most differences. Notably, Planmeca exhibited the highest standard deviation (SD) in all axes (0.16-0.35), exhibiting the lowest consistency in the CBCT machines' readings. The Dentium exhibited the lowest deviation in the implant position, with the lowest SD (0.61). A significant difference in the total degree of deviation was spotted when only Morita was included in the comparison (p-value < 0.05). This study's findings are of significant importance as they reveal that the implant position recorded from the CBCT machines was most discrepant in the buccolingual dimension (Z-axis). Planmeca exhibited the least implant-dimensional accuracy of the CBCT machines, while Dentium exhibited the highest implant position accuracy. These results could significantly impact the choice of CBCT machine for implant placement, especially since an accurate CBCT image is crucial for digital implant planning.

Damage evolution and ductile fracture of commercially-pure titanium sheets subjected to simple tension and cyclic bending under tension

This paper describes a study into the evolution of damage in commercial-purity titanium (CP-Ti) sheets subjected to cyclic bending under tension (CBT) and uniaxial tension (simple tension, ST). Sections were taken from sheets that were strained to various levels under both methods and were imaged using X-ray computed tomography (XCT) to reveal insights into the size, density, and distribution of microvoids in the sheets. Digital image correlation (DIC) was also used to observe the surface strain of CBT and ST sheets during testing. These results showed that elongation to failure (ETF) for CBT deformation is about 2.5x higher than for ST, local longitudinal strains in the bulk of the CBT sample are around 1.3x higher than the peak strain in the ST necked region, and 1.7x higher in the localized region of CBT failure. It was found that the volume-density of voids in both sheets followed a similar exponential increase with strain, reaching nearly 45x higher in the CBT than the ST sheets before the onset of failure, mainly due to the higher strain levels achieved. A higher volume density of voids developed in the center of sheets at high levels of strain, for both processes, with the density in the sheet center becoming approximately double that found near the edges. Scanning electron microscopy (SEM) was used to examine the fracture surface of CBT and ST sheets after failure. The observations are presented and discussed highlighting the CBT process as effective to delay ductile fracture of the CP-Ti sheets.

Identification, uncertainty quantification and validation of orthotropic material properties for additively manufactured polymers

The mechanical behavior of polymeric parts manufactured by fused filament fabrication is often characterized as orthotropic because of the layer-wise extrusion of the material. To perform reliable simulations of the mechanical response of these parts subjected to certain loads, the material parameters have to be known. This leads to the task of material parameter identification from suitable experimental data. In this work, the identification of the material parameters for orthotropic material behavior is studied in detail with a focus on the specific characteristics of additively manufactured parts. This comprises working out the differences that arise compared to the parameter identification for classical orthotropic composite materials. Moreover, the material parameters are identified from tensile and shear test information together with full-field displacement data from digital image correlation measurements. Then, the obtained parameters are compared for analytical and numerical approaches, where a non-linear least-squares method with finite elements is drawn on. The parameters are not only identified, but also an uncertainty analysis is carried out and, subsequently, the different methods are validated. Further, a clear derivation of commonly applied analytical equations is provided with a special focus on the underlying assumptions. It turns out that the analytical parameter identification procedures can not be easily transferred to numerical procedures, especially when using full-field data, because of correlations between the parameters. Moreover, the identified parameters are employed to prove the reasonability of orthotropic material behavior for specimens manufactured by fused filament fabrication. Finally, during validation it turns out that all parameter identification methods provide a good agreement with experimental data, showing only small differences between the results of the different calibration strategies.

Residual stress determination in a C-C composite consisting of a carbonized elastomer matrix filled with graphite, carbon black and short carbon fibers

In this study, composites obtained through low-temperature carbonization of elastomeric matrix highly filled with graphite, carbon black and short carbon fibers were studied for the purpose of determining residual stresses at different scales using a combination of several complementary methods. The state-of-the-art techniques included X-ray stress analysis using the sin2⁡ψ method, the micro-ring-core technique via Focused Ion Beam milling and Digital Image Correlation (FIB-DIC), the contour method, the strain gauge method, and the hole drilling technique with digital laser speckle pattern interferometry (DLSPI). It was found that the contour method could not be used implemented for residual stress evaluation due to the low electrical conductivity of composite. Moreover, the DLSPI hole drilling method did not reveal any fringes indicating significant residual stress level exceeding a few MPa. The strain gauge method also revealed a narrow residual stress distribution with an average value of approximately zero. In contrast, the X-ray sin2⁡ψ method as well as FIB-DIC technique both returned values of about 150–250 MPa. A hierarchical model of the composite is proposed based on the Davidenkov Type I–II–III stress classification that provides an explanation of these observations.

Effects of landscape changes on urban climate change: A case study in the city of São Paulo

Given the vulnerability of Brazilian cities to climate change, it is imperative to monitor urban areas’ susceptibility to temperature fluctuations. In this article, we employ thermal remote sensing and digital image processing techniques to illustrate a substantial rise in surface temperatures across the Northwest Region of São Paulo City over the past three decades. This surge in surface temperature is closely linked to alterations in the urban landscape. Our findings emphasize that one significant environmental consequence of São Paulo City’s rapid urbanization is the pronounced increase in surface temperatures. These results also underscore the significance of assessing landscape features, such as vegetation cover, to inform the prudent, sustainable, and resilient management of urban centers, thereby mitigating climate change effects in metropolitan areas.

Dynamic bending analysis of metallized silicon nitride substrate during thermal cycling via digital image correlation

AbstractDigital image correlation (DIC) method was employed to analyze the dynamic bending of a metallized Si3N4 substrate in thermal cycles ranging from −40°C to 250°C. The substrate was monitored by DIC over several consecutive cycles, spanning from 0 to 2501. Three DIC measurement intervals, specifically during 999–1001, 1999–2001, and 2499–2501 cycles, were selected to analyze the bending behavior in response to temperature fluctuations. By the scanning acoustic microscope (SAM), the substrate revealed negligible delamination after 1001 cycles, light delamination after 2001 cycles, and serious delamination post‐2501 cycles. The dynamic bending remained unchanged during 999–1001 cycles. A curve with a negative bending was observed during 1999–2001 cycles, with a peak curvature of .024 mm−1 at −40°C; the negative bending intensified with a curvature reaching .050 mm−1 at −40°C during 2499–2501 cycles. The bending curve then decreased during the heating phase and returned to a non‐bent state at elevated temperatures. The bending curve reversed to positive at 250°C during thermal cycles from 2499 to 2501. The bending behavior throughout thermal cycles was discussed in relation to the asymmetric delamination observed on both sides of the substrate, as well as the distribution of delamination.

Evaluation method of tunnel cracking disease under biased pressure based on enhanced image fractal features

This paper focuses on the tunnel cracking disease under biased pressure. Through model tests on three lining conditions, the study investigates the expansion process of surface cracks on the lining under biased pressure. Proposing an improved method for calculating fractal characteristics of lining crack disease based on box counting dimension. Quantifying crack disease through digital image processing to obtain two parameters: total width and fractal dimension. Discussed the relationship between quantification parameters of surface crack disease in linings and the service performance of the lining. 29 groups of crack disease quantization parameters obtained by model test were selected to establish the disease sample space, and the spatial data were automatically learned and grouped based on clustering method. Finally, a new diagnostic Index of Tunnel Lining Defect Index-Crack & Dimension (TLDI-C&D) and its disease classification criteria for the whole lining ring are proposed. The results indicate that under the biased pressure, regardless of the presence of pre-existing defects, the biased pressure region is the most severely diseased area in terms of cracking. Compared to the intact lining, the initial cracking loads for arch crown defects and arch waist defects are advanced by 33.1 % and 73.0 %, respectively. After the apparent cracks in the lining are fully formed, pre-existing defects no longer affect the speed and distribution of crack propagation.The larger the fractal dimension, the more complex the morphology of the corresponding crack disease. Under the same image area, the ratio of crack quantity to width has a greater impact on the fractal dimension. There is a corresponding relationship between quantification parameters of crack disease and the bearing performance of the lining structure, making them indicators for measuring the load-bearing capacity of the lining. The most reasonable grading level for lining crack disease under the biased pressure is four levels. The diagnostic index for tunnel lining cracks, TLDI-C&D is 1.0～2.2 (level I), 2.2～3.2 (level II), 3.2～4.3 (level III), and 4.3～5.5 (level IV). On-site measurements have proven the accuracy of this crack disease diagnostic evaluation method.

Digital image correlation approach for low-cycle fatigue life monitoring of 13HMF power engineering steel

In this paper, strain evolution of 13HMF power engineering steel was analysed during force-controlled low cycle fatigue (LCF). The material performance under cyclic loading was monitored by using Digital Image Correlation (DIC) technique for different values of stress amplitude exceeding the yield strength of the material significantly. Data collected from DIC was compared to the extensometer ones to confirm the effectiveness of the proposed approach. Finally, the cloud data reflecting the fatigue performance of 13HMF was generated in the form of strain-stress amplitude - service life distribution map.