High-precision Method Research Articles

Fragment-pair based drug molecule solubility prediction through attention mechanism.

The purpose of drug discovery is to identify new drugs, and the solubility of drug molecules is an important physicochemical property in medicinal chemistry, that plays a crucial role in drug discovery. In solubility prediction, high-precision computational methods can significantly reduce the experimental costs and time associated with drug development. Therefore, artificial intelligence technologies have been widely used for solubility prediction. This study utilized the attention layer in mechanism in the deep learning model to consider the atomic-level features of the molecules, and used gated recurrent neural networks to aggregate vectors between layers. It also utilized molecular fragment technology to divide the complete molecule into pairs of fragments, extracted characteristics from each fragment pair, and finally fused the characteristics to predict the solubility of drug molecules. We compared and evaluated our method with five existing models using two performance evaluation indicators, demonstrating that our method has better performance and greater robustness.

Development of auxiliary early predicting model for human brucellosis using machine learning algorithm

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.

Synchrotron radiation of a single electron application for optical spectroradiometry

Objectives. The investigations of optical radiation sources and metrological detector characteristics in the infrared (IR), visible, and air ultraviolet (UV) spectral regions are partially based on the unique metrological properties of synchrotron radiation. The aim of this work is to develop a high-precision method for determining the storage ring accelerated electron number with synchrotron radiation of a single electron to establish spectroradiometry and photometry units.Methods. By determining the number of accelerated electrons, any storage ring can be used to calculate the synchrotron radiation characteristics at wavelengths of many large then the critical wavelength in the visible, air UV, and IR regions of the spectrum. This makes it possible to determine the main metrological characteristics normalized to the number of electrons, such as luminous intensity, luminance, illuminance, radiant power, radiance, irradiance, etc., regardless of the energy of the electrons.Results. When applying the method for determining the number of accelerated electrons at low currents of the electronic storage ring, a total standard deviation of the number of accelerated electrons is less than 0.01% for an exposure range of the CCD matrix from 10−2 to 3 · 103 s in a wide dynamic range of 1−1010 electrons per orbit.Conclusions. The use of a CCD-based radiometer-comparator calibrated by responsivity on a synchrotron radiation source is particularly relevant in monitoring luminance contrast thresholds and spatial distribution of object and background brightness, as well as determining metrological characteristics of optoelectronic measuring instruments, including CCD cameras, radiometers, spectroradiometers and photometers.

A high-precision online compensation method for random errors of optical gyroscope

The traditional Allan variance cannot reflect the time-varying characteristics of random error signals, and the dynamic Allan variance cannot simultaneously guarantee both the signal dynamic tracking performance and confidence of variance estimation due to adopting the sliding window with fixed window length to intercept and segment random error signals. To address these issues, a dynamic Allan variance analysis method with adaptive window length is proposed. Based on the idea of piecewise stability of time-varying random error signals, a sliding window is introduced to intercept and segment the signal to be analyzed along the time axis, and the Allan variance analysis results of all signal segments are synthesized to track and describe the time-varying characteristics of the signal. Then according to the changes in the statistical characteristics of the random error signal, an adaptive factor is constructed to adaptively adjust the length of the sliding window, thereby guaranteeing both the signal dynamic tracking performance and confidence of variance estimation at the same time. On this basis, this method is applied to the online compensation of optical gyroscope random errors, and then a high-precision online compensation method for random errors of optical gyroscope is proposed. The experimental results show that the proposed method can achieve high-precision online compensation for the time-varying random error signals of optical gyroscope measured in complex environment such as uncertainty and noise interference.

High-precision laser beam lateral displacement measurement based on differential wavefront sensing.

Accurately lateral displacement measurement is essential for a vast of non-contact sensing technologies. Here, we introduce a high-precision lateral displacement measurement method based on differential wavefront sensing (DWS). Compared to the conventional differential power sensing (DPS) method, the DWS method based on phase readout has the potential to achieve a higher resolution. The beam lateral displacement can be obtained by the curvature distribution of the wavefront on the surface of the detector. According to the theoretical model of the DWS method, the sensitivity of the lateral displacement can be greatly improved by increasing the wavefront curvature of the measured laser beam by means of lenses. An optical system for measuring the lateral displacement of the laser beam is built and calibrated by a high-precision hexapod. The experimental results show that the DWS-based lateral displacement measurement achieves a resolution of 40 pm/Hz1/2 (at 1-10 Hz) with a linear range of about 40 µm, which is consistent with the theoretical model. This technique can be applied to high-precision multi-degree-of-freedom interferometers.

Dosimetric comparison between stereotactic body radiotherapy and carbon-ion radiation therapy for prostate cancer.

Prostate cancer rates have been steadily increasing in recent years. As high-precision radiation therapy methods, stereotactic body radiation therapy (SBRT) and carbon-ion radiation therapy (CIRT) have unique advantages. Analyzing the dosimetric differences between SBRT and CIRT in the treatment of localized prostate cancer can help provide patients with more accurate, individualized treatment plans. We selected computed tomography positioning images and the contours of target volumes of 16 patients with localized prostate cancer who received radiotherapy. We delineated the organs at risk (OARs) on the CyberKnife (CK) treatment planning system (TPS) MultiPlan4.0, which were imported into the CIRT uniform scanning TPS HIMM-1 ci-Plan. Two treatment plans, SBRT and CIRT, were designed for the same patient, and we used SPSS 22.0 for the statistical analysis of data. Both SBRT and CIRT plans met the prescribed dose requirements. In terms of target volume exposure dose, D2 (P<0.001), D5 (P<0.001), D50 (P<0.001), D90 (P=0.029), D95 (P<0.001), D98 (P<0.001), and Dmean (P<0.001) under SBRT were significantly higher than those under CIRT; the conformity index (CI) under SBRT was significantly better than that under CIRT (P<0.001); the target volume coverage rate (V95%) and dose homogeneity index (HI) under CIRT were significantly better than those under SBRT (P<0.001). In terms of OAR exposure dosage, the Dmax of the bladder and rectum under SBRT was significantly lower than that under CIRT (P<0.001), but Dmean was in the other direction; the exposure dose of the intestinal tract under CIRT was significantly lower than that under SBRT (P<0.05); Dmax of the femoral head under CIRT was significantly lower than that under SBRT (P<0.05), and there was no statistical difference between them at other doses. In this study, we found that when CIRT was used for treating localized prostate cancer, the dose distribution in target volume was more homogeneous and the coverage rate was higher; the average dose of OARs was lower. SBRT had a better CI and higher dose in target volume; the dose hotspot was lower in OARs. It is important to comprehensively consider the dose relationship between local tumor and surrounding tissues when selecting treatment plans.

High precision triple oxygen isotope composition of small size urban micrometeorites indicating constant influx composition in the early geologic past

AbstractIn this study, we present a method for high precision Δ′17O (Δ′17ORL = ln(δ17O + 1) – λRL ln(δ18O + 1)) analysis of small mass silicate and oxide materials. The analyses were conducted by laser fluorination in combination with gas chromatography and continuous flow isotope ratio monitoring gas spectrometry. We could analyze the oxygen isotope composition of samples down to 1 μg, which corresponded to about 13 nmol O2. The analytical error (we report the 1σ external reproducibility of a single analysis) in δ18O increases with decreasing sample sizes from ~0.2‰ for ~20 μg samples to ~0.9‰ for 1 μg samples. For Δ′17O, we achieved an external reproducibility of 0.04‰ for a sample mass range between 1 and 27 μg. The uncertainty in Δ′17O is smaller than the uncertainty in δ18O due to the correlated errors in δ17O and δ18O. We applied the method to urban micrometeorites, that is, small meteorites (&lt;2 mm) that were sampled from a rooftop in Berlin, Germany. A total of 10 melted micrometeorites (S‐type cosmic spherules, masses between 11 and 22 μg) were analyzed. The oxygen isotope compositions are comparable to that of modern Antarctic collections, indicating that the urban micrometeorites sample the same population. No indication for terrestrial weathering had been identified in the studied set of urban micrometeorites making them suitable materials for the study of micrometeorite origins.

100 Picosecond/Sub-10−17 Level GPS Differential Precise Time and Frequency Transfer

A Global Navigation Satellite System (GNSS) is a high-precision method for comparing clocks and time transfer. The GNSS carrier phase can provide more precise observable information than pseudorange. However, the carrier phase is ambiguous, and only pseudorange can provide the absolute time difference between two clocks. In our study, by taking full advantage of GNSS pseudorange and carrier-phase observables, a differential precise time transfer (DPT) method with a clustering constraint was employed to estimate the time difference between two clocks, aiming to achieve accurate and precise time and frequency transfer. Using this method, several time transfer results were analyzed for different baselines. For the common clock experiment, the time transfer results showed good consistency across different days, with an intra-day accuracy of within 20 ps. Furthermore, we evaluated the self-consistency of DPT using closure among three stations. DPT closure of the three stations had a peak-to-peak value of closure of about 25 ps. The closure did not change over time, indicating the self-consistency of the DPT processing in time transfer. Moreover, our results were compared to station clock solutions provided by the International GNSS Service (IGS), and the standard deviations (STDs) of the four baselines were all less than 100 ps within one month, confirming the time and frequency stability of the DPT method. In addition, we found that the time stability of DPT was less than 20 ps within one week. As for frequency stability, DPT achieved a 10−16 level of modified Allan deviation (MDEV) at an averaging time of about 1 day and a sub-10−17 level at an averaging time of one week.

The history of the development of the capillary method of measuring kinematic viscosity: from the Lomonosov viscometer to the information measuring system

The history of the development of the capillary method of viscosity measurements is described and the tasks of automation and improvement of this method are considered. A comparative analysis of the currently relevant vibrational, rotational, capillary methods of measuring the viscosity of a liquid and the measuring instruments implementing them is given. The State verifi cation scheme for viscosity measuring instruments to represent the role of the capillary method in general and glass capillary viscometers in particular in this structure is briefl y described. The reasons why the capillary method is used in many countries as the main high-precision method for measuring the viscosity of liquids are considered. A detailed derivation of the formula for calculating the kinematic viscosity of the liquid under study is given. As one of the possible ways to further develop the capillary method of measuring kinematic viscosity, it is proposed to create an information-measuring system for measuring the kinematic viscosity of the liquid under study. The principle of operation of this system is described and plans for its implementation are formulated. It is shown that it is possible to increase the accuracy of measurements of the viscosity of liquid media by the capillary method by automating measurements and installing detectors of a certain type. The results of the performed analysis are relevant to ensure the uniformity of viscosity measurements in the Russian Federation.

High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning.

Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.

An Interpretable High-Accuracy Method for Rice Disease Detection Based on Multisource Data and Transfer Learning.

With the evolution of modern agriculture and precision farming, the efficient and accurate detection of crop diseases has emerged as a pivotal research focus. In this study, an interpretative high-precision rice disease detection method, integrating multisource data and transfer learning, is introduced. This approach harnesses diverse data types, including imagery, climatic conditions, and soil attributes, facilitating enriched information extraction and enhanced detection accuracy. The incorporation of transfer learning bestows the model with robust generalization capabilities, enabling rapid adaptation to varying agricultural environments. Moreover, the interpretability of the model ensures transparency in its decision-making processes, garnering trust for real-world applications. Experimental outcomes demonstrate superior performance of the proposed method on multiple datasets when juxtaposed against advanced deep learning models and traditional machine learning techniques. Collectively, this research offers a novel perspective and toolkit for agricultural disease detection, laying a solid foundation for the future advancement of agriculture.

High-precision rotation angle measurement method based on polarization self-mixing interference.

In this study, a high-precision rotation angle measurement method based on polarization self-mixing interference (SMI) is proposed. The higher signal-to-noise ratio SMI signal can be obtained by the differential processing of two polarized SMI signals with opposite phases. In order to reduce the influence of the speckle effect, the envelope signal is used to normalize the SMI signal. The fringe subdivision method is used to improve the accuracy of the rotation angle measurement. The experimental results show that the error of the rotation angle measurement is within ±0.5%, and the measurement range can reach up to 20°.

High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios

High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios

Calculation method of HJC constitutive model parameters of natural joint angle slate

In the course of underground engineering, layered slate is often encountered. Understanding the mechanical characteristics of layered slate is a prerequisite for engineering construction and disaster prevention and mitigation. As a result, at the beginning of a project, a large number of indoor tests are required, which are time-consuming and laborious. In addition, the natural joint angle of the layered slate is random, so it is extremely difficult to establish a database of the mechanical characteristics of layered slate. Hence, it is necessary to find a simple, feasible, and high-precision method to determine the Holmquist–Johnson–Cook (HJC) constitutive model parameters for naturally jointed layered slate with different dip angles. This study first determines the HJC constitutive model parameters of layered slate with five specific joint dip angles (0°, 30°, 45°, 60°, and 90°) through static tests and the split Hopkinson pressure bar (SHPB) test. Furthermore, by employing sensitivity analysis methods, the influence of key parameters of the HJC constitutive model on the dynamic peak stress of slate is determined. Among them, parameters A and B have the most significant impact on stress, exceeding 50%. Thirdly, a nonlinear fitting regression method is used to determine the HJC constitutive model parameters of naturally jointed angular slate. The relationship between the HJC model parameters and the inclination angle of slate joints is derived, and the accuracy of these parameters is verified through numerical simulation methods. The error between the numerical simulation and indoor experiments is within 10%, indicating a high level of simulation accuracy. The research findings provide a highly precise numerical simulation method for similar projects.

High-Precision Control of Industrial Robot Manipulator Based on Extended Flexible Joint Model

High-precision industrial manipulators are essential components in advanced manufacturing. Model-based feedforward is the key to realizing the high-precision control of industrial robot manipulators. However, traditional feedforward control approaches are based on rigid models or flexible joint models which neglect the elasticities out of the rotational directions and degrade the setpoint precision significantly. To eliminate the effects of elasticities in all directions, a high-precision setpoint feedforward control method is proposed based on the output redefinition of the extended flexible joint model (EFJM). First, the flexible industrial robots are modeled by the EFJM to describe the elasticities in joint rotational directions and out of the rotational directions. Second, the nonminimum-phase EFJM is transformed into a minimum-phase system by using output redefinition. Third, the setpoint control task is transformed from Cartesian space into joint space by trajectory planning based on the EFJM. Third, a universal recursive algorithm is designed to compute the feedforward torque based on the EFJM. Moreover, the computational performance is improved. By compensating the pose errors caused by elasticities in all directions, the proposed method can effectively improve the setpoint control precision. The effectiveness of the proposed method is illustrated by simulation and experimental studies. The experimental results show that the proposed method reduces position errors by more than 65% and the orientation errors by more than 62%.

Automated estimation of stomatal number and aperture in haskap (Lonicera caerulea L.).

This study developed the reliable Mask R-CNN model to detect stomata in Lonicera caerulea. The obtained data could be utilized for evaluating some characters such as stomatal number and aperture area. The native distribution of haskap (Lonicera caerulea L.), a small-shrub species, extends through Northern Eurasia, Japan, and North America. Stomatal observation is important for plant research to evaluate the physiological status and to investigate the effect of ploidy levels on phenotypes. However, manual annotation of stomata using microscope software or ImageJ is time consuming. Therefore, an efficient method to phenotype stomata is needed. In this study, we used the Mask Regional Convolutional Neural Network (Mask R-CNN), a deep learning model, to analyze the stomata of haskap efficiently and accurately. We analyzed haskap plants (dwarf and giant phenotypes) with the same ploidy but different phenotypes, including leaf area, stomatal aperture area, stomatal density, and total number of stomata. The R-square value of the estimated stomatal aperture area was 0.92 and 0.93 for the dwarf and giant plants, respectively. The R-square value of the estimated stomatal number was 0.99 and 0.98 for the two phenotypes. The results showed that the measurements obtained using the models were as accurate as the manual measurements. Statistical analysis revealed that the stomatal density of the dwarf plants was higher than that of the giant plants, but the maximum stomatal aperture area, average stomatal aperture area, total number of stomata, and average leaf area were lower than those of the giant plants. A high-precision, rapid, and large-scale detection method was developed by training the Mask R-CNN model. This model can help save time and increase the volume of data.

Analysis of the ammunition explosion seismic wave propagation law

The seismic wave generated in the ammunition explosion process is one of the important technical indicators to evaluate the ammunition damage power. It is of great significance to evaluate the ammunition damage power and study the targets damage characteristics. In this study, experimental studies were carried out with TNT explosive mass of 1.25 kg, 5.25 kg, 10.5 kg and 30.75 kg, and the ground vibration velocity time-history curves at different measuring points were obtained. The functional relationship between ground particle vibration acceleration and TNT explosive mass was established by dimensional analysis method, and the functional relationship calculation accuracy was verified by the measured test data. The results show that the particle vibration acceleration peak value is positively correlated with the TNT explosive mass, and negatively correlated with the measuring point distance; The particle vibration acceleration calculation function established by the dimensional analysis method has high fitting accuracy for the existing test data, and can be used to calculate the ammunition explosion ground vibration acceleration peak value. The research results provide a high-precision calculation method for the peak value of explosion seismic wave ground vibration acceleration.

The Method of High Precision Self localization of Transmission Line Bolts by Lightweight Maintenance Robot

The conventional high-precision autonomous positioning method of transmission line bolts mainly focuses on image detection and positioning. Affected by the weather environment, it is difficult to take real-time images of transmission lines, which affects the accuracy of bolt positioning. Therefore, a method of high precision and autonomous positioning of transmission line bolts by the lightweight maintenance robot is designed. Extracting the contour features of the connected area of the bolt cross arm of the transmission line, and marking the connected area of the bolt, it is easier to locate the target bolt. Build a high-precision self positioning model for the transmission line bolts of the lightweight maintenance robot, compensate for the self positioning joint error of the lightweight maintenance robot, and maximize the bolt positioning accuracy. The simulation experiment verifies that the positioning method has higher accuracy and can be applied in real life.

Genome-wide Association Studies of Retinal Vessel Tortuosity Identify Numerous Novel Loci Revealing Genes and Pathways Associated With Ocular and Cardiometabolic Diseases.

To identify novel susceptibility loci for retinal vascular tortuosity, to better understand the molecular mechanisms modulating this trait, and reveal causal relationships with diseases and their risk factors. Genome-wide Association Studies (GWAS) of vascular tortuosity of retinal arteries and veins followed by replication meta-analysis and Mendelian randomization (MR). We analyzed 116 639 fundus images of suitable quality from 63 662 participants from 3 cohorts, namely the UK Biobank (n=62 751), the Swiss Kidney Project on Genes in Hypertension (n=397), and OphtalmoLaus (n=512). Using a fully automated retina image processing pipeline to annotate vessels and a deep learning algorithm to determine the vessel type, we computed the median arterial, venous and combined vessel tortuosity measured by the distance factor (the length of a vessel segment over its chord length), as well as by 6 alternative measures that integrate over vessel curvature. We then performed the largest GWAS of these traits to date and assessed gene set enrichment using the novel high-precision statistical method PascalX. We evaluated the genetic association of retinal tortuosity, measured by the distance factor. Higher retinal tortuosity was significantly associated with higher incidence of angina, myocardial infarction, stroke, deep vein thrombosis, and hypertension. We identified 175 significantly associated genetic loci in the UK Biobank; 173 of these were novel and 4 replicated in our second, much smaller, metacohort. We estimated heritability at ∼25% using linkage disequilibrium score regression. Vessel type specific GWAS revealed 116 loci for arteries and 63 for veins. Genes with significant association signals included COL4A2, ACTN4, LGALS4, LGALS7, LGALS7B, TNS1, MAP4K1, EIF3K, CAPN12, ECH1, and SYNPO2. These tortuosity genes were overexpressed in arteries and heart muscle and linked to pathways related to the structural properties of the vasculature. We demonstrated that retinal tortuosity loci served pleiotropic functions as cardiometabolic disease variants and risk factors. Concordantly, MR revealed causal effects between tortuosity, body mass index, and low-density lipoprotein. Several alleles associated with retinal vessel tortuosity suggest a common genetic architecture of this trait with ocular diseases (glaucoma, myopia), cardiovascular diseases, and metabolic syndrome. Our results shed new light on the genetics of vascular diseases and their pathomechanisms and highlight how GWASs and heritability can be used to improve phenotype extraction from high-dimensional data, such as images. The author(s) have no proprietary or commercial interest in any materials discussed in this article.

A high-precision automatic recognition method based on target detection for nanometer scaled precipitates or carbides in different alloys

The recognition and statistical analysis of nanometer scaled second phase (NSP), such as precipitates or carbides in alloys is considered to be very important for optimization of mechanical properties. Statistical analysis of the size, number, and distribution of NSPs is required, and manual statistics are difficult and inefficient. In order to solve the above problems, this paper provides an object detection technology for the automatic identification of the NSPs in different alloys. A dataset is constructed by labeling the NSPs in typical transmission electron micrographs (TEM) photographs using a labelImg software. First of all, based on the YOLOv3 object detection network in deep learning, changing the network input size is compatible with the TEM image size. Secondly, using two improved schemes, a priori box K-means clustering and CBAM attention mechanism, a variety of class NSPs detection network is proposed. The statistical results show that the network developed herein has strong applicability. This network can be used for identification for the NSP in several types of alloys, i.e., Cu–Sc alloy, 4Cr5MoSiV1 and Cu–Ni–Si alloy. The accuracy of this network for the recognition of these NSP in Cu–Sc alloy (Cu4Sc precipitates), 4Cr5MoSiV1 (carbides) and Cu–Ni–Si alloy (Ni2Si precipitates) is 93.55%, 81.82% and 93.13%, respectively.