Fitting Precision Research Articles

Calibration of GRACE on-board accelerometers for thermosphere density derivation

ABSTRACT Low Earth Orbit satellite on-board accelerometers play an important role in improving our understanding of thermosphere density; however, the accelerometer-derived densities are subject to accelerometer calibration errors. In this study, two different dynamic calibration schemes, the accelerometer parameter-incorporated orbit fitting and precise orbit determination (POD), are investigated with the Gravity Recovery And Climate Experiment (GRACE) satellite accelerometers for thermosphere density derivation during years 2004–2007 (inclusive). We show that the GRACE accelerometer parametrization can be optimized by fixing scale coefficients and estimating biases every 60 min so that the orbit fitting and POD precision can be improved from 10 cm to 2 cm in the absence of empirical acceleration compensations and as a result the integrity of calibration parameters may be reserved. The orbit-fitting scheme demonstrates similar calibration precision with respect to POD. Their bias estimates in the along-track and cross-track components exhibit an offset within 0.1% and a standard deviation (STD) less than 0.3%. Correspondingly, a bias of 2.20% and a STD of 5.75% exists between their thermosphere density estimates. The orbit-fitting and POD-derived thermosphere densities are validated through the comparison against the results published by other institution. The comparison shows that either of them can achieve a precision level at 6%. To derive thermosphere density from the rapid-increasing amount of on-board accelerometer data sets, it is suggested to take full advantage of the orbit-fitting scheme due to its high efficiency as well as high precision.

A technique to verify implant abutment position intraorally by using a vacuum thermomolded guide

The precision of fit at the implant-abutment interface is an essential criterion for the long-term success of dental implants.1,2 Implant abutment systems are designed to accommodate definitive restorations in a single position.3 Therefore, accurate transfer and positioning of implant abutments intraorally from the definitive cast is crucial to avoid technical complications caused by a misfit between the prosthesis and the abutment.4 Although several methods have been reported in the literature for accurate positioning of the abutment,5-13 this article presents a straightforward technique that can assist the clinician in positioning and verifying the implant abutment orientation intraorally by using a vacuum thermomolded guide.

Spatial downscaling method and application of CMIP5 global climate models based on improved U-Net in Lake Poyang Basin

利用降尺度方法对CMIP5全球气候模式进行空间降尺度并以此研究鄱阳湖流域未来气候时空变化趋势，能够为流域生态环境保护提供数据、技术和理论上的支持.通过简化原始网络结构，在网络首部添加插值层，采用反卷积算法作为上采样算法对传统U-Net网络进行改进，建立基于深度学习的气候模式空间降尺度模型（DLDM）.以1965-2005年鄱阳湖流域共18个气象站点的实测数据为基准，基于IPSL-CM5A-LR和BCC-CSM1.1两个气候模式，在拟合精度和流域极端气候事件模拟能力两方面对比验证了降尺度方法和降尺度后气候模式的模拟性能，结果表明基于DLDM的方法优于基于线性回归的传统统计降尺度方法，IPSL-CM5A-LR模式模拟效果优于BCC-CSM1.1模式.利用DLDM对RCP2.6和RCP8.5两个排放情景下的IPSL-CM5A-LR模式数据进行空间降尺度，基于2006-2100年流域高空间分辨率气候数据分析两情景下流域未来气候的时空变化特征，结果表明流域未来气温在两情景下均持续升高，表现出位于流域中北部、西部、东部和南部4个高温中心，RCP8.5情景下流域气温更高，升温趋势和局部周期变化更加明显；流域未来降水在两情景下先增加后减少，变化趋势较为平缓，表现出北部、中东部和南部3个降水中心，在未来呈现五次枯-丰交替，RCP8.5情景下流域降水更少且变化更为剧烈，在2075年左右出现突变并存在周期性振荡.;Predicting spatiotemporal trend of the future climate in Lake Poyang Basin using CMIP5 global climate models downscaled by downscaling methods can provide data, technology, and theoretical support for the ecological and environmental protection of the basin. In current research area downscaling methods mainly include dynamic downscaling method and statistical downscaling method, among which statistical downscaling method based on linear regression is widely used thus defective in modeling non-linear relationships. In order to build up a better downscaling model, the deep learning ased spatial downscaling method (DLDM) which is more efficient in learning climate data features is established by simplifing the original network structure of U-Net, adding an interpolation layer at the head of the network, and using the deconvolution algorithm as upsampling algorithm. Then based on climate models as IPSL-CM5A-LR and BCC-CSM1.1, simulating ability of downscaled climate model is verified by making contrastive analysis to observed data from 18 meteorological stations in Lake Poyang Basin from 1965 to 2005 on fitting precision and ability to simulate extreme weather events. The results show that DLDM has higher fitting accuracy over the linear regression based statistical downscaling method and IPSL-CM5A-LR has higher fitting accuracy over BCC-CSM1.1. Based on high spatial resolution data of IPSL-CM5A-LR downscaled by DLDM from 2006 to 2100, the spatiotemporal trend of future climate in Lake Poyang Basin under RCP2.6 and RCP8.5 emissions scenarios is studied through M-K analysis and wavelet transform. The results show that the future temperature of the basin will continue to increase under the two scenarios, showing four high temperature centers located in the central north, west, east, and south of the basin. Under the RCP8.5 scenario, the temperature of the basin will be higher, and the warming trend and local cycle change will be more obvious. The future precipitation in the basin will increase first and then decrease under the two scenarios, and the changing trend is relatively gentle, showing three precipitation centers in the north, the central east, and the south of the basin. In the future, there will be drought-wet alternation for five times. Under the RCP8.5 scenario, the precipitation in the basin will be less and the change will be more intense, and there will be a sudden change and periodic oscillation at around 2075.

Glucose fluxes in glycolytic and oxidative pathways detected in vivo by deuterium magnetic resonance spectroscopy reflect proliferation in mouse glioblastoma

ObjectivesGlioblastoma multiforme (GBM), the most aggressive glial brain tumors, can metabolize glucose through glycolysis and mitochondrial oxidation pathways. While specific dependencies on those pathways are increasingly associated with treatment response, detecting such GBM subtypes in vivo remains elusive. Here, we develop a dynamic glucose-enhanced deuterium spectroscopy (DGE 2H-MRS) approach for differentially assessing glucose turnover rates through glycolysis and mitochondrial oxidation in mouse GBM and explore their association with histologic features of the tumor and its microenvironment. Materials and methodsGL261 and CT2A glioma allografts were induced in immunocompetent mice and scanned in vivo at 9.4 Tesla, harnessing DGE 2H-MRS with volume selection and Marchenko-Pastur PCA (MP-PCA) denoising to achieve high temporal resolution. Each tumor was also classified by histopathologic analysis and assessed for cell proliferation (Ki67 immunostaining), while the respective cell lines underwent in situ extracellular flux analysis to assess mitochondrial function. ResultsMP-PCA denoising of in vivo DGE 2H-MRS data significantly improved the time-course detection (~2-fold increased Signal-to-Noise Ratio) and fitting precision (−19 ± 1 % Cramér-Rao Lower Bounds) of 2H-labelled glucose, and glucose-derived glutamate-glutamine (Glx) and lactate pools in GL261 and CT2A orthotopic tumors. Kinetic modeling further indicated inter-tumor heterogeneity of glucose consumption rate for glycolysis and oxidation during a defined epoch of active proliferation in both cohorts (19 ± 1 days post-induction), with consistent volumes (38.3 ± 3.4 mm3) and perfusion properties prior to marked necrosis. Histopathologic analysis of these tumors revealed clear differences in tumor heterogeneity between the two GBM models, aligned with metabolic differences of the respective cell lines monitored in situ. Importantly, glucose oxidation (i.e. Glx synthesis and elimination rates: 0.40 ± 0.08 and 0.12 ± 0.03 mM min−1, respectively) strongly correlated with cell proliferation across the pooled cohorts (R = 0.82, p = 0.001; and R = 0.80, p = 0.002, respectively), regardless of tumor morphologic features or in situ metabolic characteristics of each GBM model. ConclusionsOur fast DGE 2H-MRS enables the quantification of glucose consumption rates through glycolysis and mitochondrial oxidation in mouse GBM, which is relevant for assessing their modulation in vivo according to tumor microenvironment features such as cell proliferation. This novel application augurs well for non-invasive metabolic characterization of glioma or other cancers with mitochondrial oxidation dependencies.

Outlier Detection and Forecasting for Bridge Health Monitoring Based on Time Series Intervention Analysis

The method of time series analysis, applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior, stands out as a novel and viable research direction for bridge state assessment. However, outliers inevitably exist in the monitoring data due to various interventions, which reduce the precision of model fitting and affect the forecasting results. Therefore, the identification of outliers is crucial for the accurate interpretation of the monitoring data. In this study, a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory, and the forecasting of the structural responses is carried out. There are three techniques that we focus on: (1) the modeling of seasonal autoregressive integrated moving average (SARIMA) model; (2) the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown; (3) forecasting of the model with outlier effects. The method was tested with a case study using monitoring data on a real bridge. The establishment of the original SARIMA model without considering outliers is first discussed, including the stationarity, order determination, parameter estimation and diagnostic checking of the model. Then the time-by-time iterative procedure for outlier detection, which is implemented by appropriate test statistics of the residuals, is performed. The SARIMA-outlier model is subsequently built. Finally, a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out. The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data. When the influence of outliers is taken into account, the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.

Effect of Finish Line Design on the Fit Accuracy of CAD/CAM Monolithic Polymer-Infiltrated Ceramic-Network Fixed Dental Prostheses: An In Vitro Study.

A polymer-infiltrated ceramic network (PICN) material has recently been introduced for dental use and evidence is developing regarding the fit accuracy of such crowns with different preparation designs. The aim of this in vitro study was to evaluate the precision of fit of machined monolithic PICN single crowns in comparison to lithium disilicate crowns in terms of marginal gap, internal gap, and absolute marginal discrepancies. A secondary aim was to assess the effect of finish line configuration on the fit accuracy of crowns made from the two materials. Two master metal dies were used to create forty stone dies, with twenty each for the two finish lines, shoulder and chamfer. The stone dies were scanned to produce virtual models, on which ceramic crowns were designed and milled, with ten each for the four material–finish line combinations (n = 10). Marginal gaps and absolute marginal discrepancies were evaluated at six pre-determined margin locations, and the internal gap was measured at 60 designated points using a stereomicroscope-based digital image analysis system. The influence of the material and finish line on the marginal and internal adaptation of crowns was assessed by analyzing the data using two-way analysis of variance (ANOVA), non-parametric, and Bonferroni multiple comparison post-hoc tests (α = 0.05). ANOVA revealed that the differences in the marginal gaps and the absolute marginal discrepancies between the two materials were significant (p < 0.05), but that those the finish line effect and the interaction were not significant (p > 0.05). Using the Mann–Whitney U test, the differences in IG for ‘material’ and ‘finish line’ were not found to be significant (p > 0.05). In conclusion, the finish line configuration did not seem to affect the marginal and internal adaptation of PICN and lithium disilicate crowns. The marginal gap of PICN crowns was below the clinically acceptable threshold of 120 µm.

Kinetic analysis of oxidation characteristics in synthetic motor oil

Kinetic approaches are commonly used to evaluate the thermal stability of motor oils. Hence it is crucial to identify the optimal kinetic method to gain the acceptable accuracy of the result. Thermal stability of motor oil was studied under a thermal degradation time of 12 months with thermogravimetric analysis (TGA) and differential scanning calorimeter (DSC). Stability was determined by model-fitting (Coats-Redfern) and model-free (Friedman, Kissinger-Akahira-Sunose (KAS), Flynn-Wall-Ozawa (FWO) and Vyazovkin) methods. According to model-fitting, motor oil oxidation could be described by the first-order reaction while model-free methods revealed the existence of multiple reactions at the initial of the reaction process. All model-free methods had a similar tendency in stability throughout the conversion extent. A sudden decrease in the activation energy was apparent in the conversion range between 0.1 and 0.2 while a relative plateau in the stability was found in the conversion extent from 0.2 to 0.9. During the thermal degradation period, KAS was considered as the optimal kinetic approach for motor oil oxidation study, which proposed the highest fitting precision and a closer result to the average activation energy between 90.54-92.60 kJ/mol. The optimal kinetic model promotes well understanding of oxidation behavior by providing accurate stability prediction.

Implant Impression Making: Take-Off Guide for Beginners

AbstractDental implants are fixtures that constitute for the replacements of the root of a missing natural tooth. Dental implant therapy has been widely used for the restoration of partially and fully edentulous patients. The implant literature emphasizes the importance of a passively fitting prosthesis to prevent prosthodontic complications or even loss of fixture integration. Failure to achieve a passively fitting prosthesis and force tightening of superstructure may result in complications such as abutment, framework, and gold screw loosening or fracture. Various materials that can be used for making an implant impression are polyether, polyvinylsiloxane, condensation silicone, polysulfide, irreversible hydrocolloid material, and various others. There are various studies in relation to the accuracy of these impression materials out of which various scientists concluded different results with most studies stating polyether with the maximum amount of dimensional accuracy in comparison to other materials. An accurate implant impression plays a significant role and serves as a starting point in the process of producing good working casts. Thus, the accuracy of impression techniques becomes a significant issue in consideration of passive fit. Reproduction of intraoral relationship of implants through impression procedures is the first step in achieving accurate fit prosthesis. This transference is still complicated by the number, angulation, depth, and position of implants. The advent of computer-aided design/computer-assisted manufacturing technology improved the framework fabrication procedures and has increased the precision of fit of implant prosthesis.

A daytime sky analytical model of the degree of polarization for JHKs bands

The navigation technology of the near-earth space star sensors based on short-wave-infrared (SWIR, especially for JHKs bands) is a focused frontier research area. The research group has built an analytical model of daytime zenith radiance, which has a practical guiding value for optical system design, physical process simulation, and software integration development. However, it is not inadequate to consider only the spectral energy distribution of the sky background. In order to expand the approach of star identification, improve the contrast of target-to-background, and enhance the sensitivity of star detection, it is necessary to consider the polarization pattern of the daytime sky. Therefore, a sky analytical model of the degree of polarization for JHKs bands is constructed based on the Rayleigh sky model, which comprehensively takes into account the depolarization effect caused by the reflection of the earth-surface, the anisotropy of the air molecules, and the Mie scattering of the aerosol particles. The model's fitting precision is evaluated based on the error between raw data and analytical values, and the model's validity is confirmed based on the deviation between measurement data and simulation data. The results indicate that the analytical model of daytime sky degree of polarization is constructed exactly and can provide a more robust reference for the star sensor's project demonstration, performance assessment, and optimization design based on SWIR technology.

Establishment of NOx Concentration Model at the Outlet of SCR Denitrification System for Alkali Recovery Furnace Flue Gas Based on Improved LSSVM

In the case of alkali recovery, it is difficult to measure with precision the NOx concentration at the output of the denitrification system. In order to solve this problem,variable adjustment experiments was carried out on the work site. According to the obtained modeling data, the least squares support vector machine (LSSVM) optimized by adaptive inertia weight particle swarm optimization (AIWPSO) was used to establish the NOx concentration model at the outlet of the SCR flue gas denitrification system in the alkali recovery section. Finally, comparing the model with the traditional mechanism model, the verification results show that the outlet NOx concentration model based on AIWPSO optimized LSSVM had the better fitting ability and error precision.

Evaluation of trueness and precision of two intraoral scanners and a conventional impression: an in vivo clinical study.

To evaluate, in vivo, trueness and precision of two intraoral scanners, CEREC Omnicam (OMNI) and CEREC Primescan (PRIM), compared to a conventional impression serving as a master model. Impressions were performed for seven participants. For each participant, conventional polyvinylsiloxane impression and digital impressions using two intraoral scanners, OMNI (software 4.6; CEREC ORTHO Protocol) and PRIM (10 digital impressions per participant, per scanner), were made. Conventional impression was digitized with a laboratory scanner (INEOS X5), and used as reference model. .STL files were superimposed with software (Geomagic Control X) using the tools Initial Alignment and Best Fit Alignment, and trueness and precision were evaluated. Statistical evaluation was performed with Shapiro-Wilk and Mann-Whitney tests (P < .05). Total mean trueness for the OMNI system was 56.45 ± 7.80 µm, and 47.29 ± 5.47 µm for the PRIM system. Regarding precision, values from the OMNI system were 42.47 ± 6.91 µm and from the PRIM system 21.86 ± 4.40 µm. PRIM presented better results for both trueness (P = .000) and precision (P = .000) when compared to OMNI. PRIM provided a better combination of trueness and precision than its predecessor OMNI. However, both PRIM and OMNI performed acceptably when performing indirect restorations, according to the current acceptable thresholds, considering both trueness and precision. Full-arch impressions with Primescan presented more precision and trueness than Omnicam; however, compared to previous reported values of conventional impressions, they still presented lower accuracy.

A novel method for optimizing air temperature estimation and quantifying canopy layer heat island intensity in eastern and central China

Many predictors are considered in estimating the spatial distribution of air temperature. However, a key problem remains the optimal selection of predictors. In this study, genetic algorithm (GA) was used to select optimal regression variables among 17 predictors, and GA-selected predictors were applied to random forest (RF) regression models for mapping air temperature in eastern and central China. By comparison with observed air temperature, results indicate that estimation errors of RF regression models constructed with GA-selected predictors first decrease (up to 5 predictors) and then remain stable. It is found that optimal combination of regression variables has the lowest estimation error (RMSE = 1.21 °C) and the best fitting precision (R2 = 0.9662), including 5 predictors that are latitude, relative humidity, elevation, distance to prefecture city and distance to third-order stream. Next, we compared the quantification of canopy layer heat island intensity (CLHII) based on the analysis of observed and estimated air temperature. We infer that quantification of CLHII using ground monitoring network results in obvious deviation due to the sparse weather stations only providing limited information over wide areas. Finally, we evaluated the robustness in quantifying the CLHII based on estimated air temperature derived from GA-1, -2, -3, -4, -5 and -17 models. Results show that the estimation of air temperature with low estimation error facilitates quantifying accurate CLHII, emphasizing the importance of selecting optimal regression variables. In summary, results reveal the effectiveness of GA in selecting optimal regression variables and provide insight into quantifying CLHII.

Fundamental parameters for 30 faint open clusters with Gaia EDR3 based on the more reliable members

Abstract An open cluster is an ideal region to study the evolution of stars. In this work, we use Gaia Early Data Release 3 (Gaia EDR3) to derive the fundamental parameters of 30 faint open clusters listed in the catalogue given by Cantat-Gaudin et al. (2018, A&amp;A, 618, A93), but the G magnitude of all of the member stars of that catalogue is brighter than ∼18 mag. This catalogue does not provide isochrone fitting parameters and spatial structure parameters. We acquired the member stars of 30 open clusters using the Density-Based Spatial Clustering of Applications with Noise algorithm in Gaia EDR3. The G magnitude of the member stars using our method can be found down to ∼21 mag. The G-band, GBP-band, and GRP-band data of the member stars construct a good color–magnitude diagram, which can further ensure the precision of isochrone fitting. We also calculated the spatial structure parameters, which are the core radius and the limiting radius, using Markov chain Monte Carlo algorithm.

Optimisation of ultrasonic-electrocoagulation process efficiency in the landfill leachate treatment: a novel advanced oxidation process

ABSTRACT Landfilling in many countries is the most embraced method for disposal of municipal, agricultural and industrial waste. However, the main issue imposed for landfilling is the landfill leachate (LL) leakage. Leachate with high pollution is threating the soil and water resources. Leachate treatment is known as a challenging and issues due to inherent liable and refractory composition. A hybrid processes sono-electrocoagulation is an efficient and applicable method for LL treatment. In the present study, response surface methodology (RSM) and central composite design (CCD) approach were employed to optimise the important influencing factors (pH, reaction time, direct electrical current) in Sono-electrocoagulation for LL treatment. The results obtained from the present study indicated that the optimum condition is pH = 6 for efficient COD removal. An important quadratic regression model was taken to fit the laboratory data; the influencing factors were statistically significant. The purposed model was considered for the response in permitted error. The fit precision ratio was achieved as 54.36. The COD removal in optimised conditions (pH: 5.7, reaction time: 80 min, direct current: 1.9 A) was found to be 86%. The kinetics of reaction (R2 = 0.98) followed from first-order kinetic. Electrocoagulation process (45%) and sonolysis mechanism (12%) have a lower performance in COD removal individually. However, the combined Sono-electrocoagulation showed a synergist effect and increased the COD removal in leachate (SF = 1.5). In the optimum condition, the operational cost of the sono-electrocoagulation process is 6.28 US $/m3. The changes in the sludge the during process indicate the changes in the microscopic structure and elements in the sludge produced after the process.

Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

It is difficult to determine the main control factors owing to the complex geological conditions of heavy oil reservoirs, including high viscosity, a wide range of variation of crude oil, and the great difference in production between different recovery methods. In this context, main control factors of heavy oil production in different recovery methods are analyzed and obtained based on the Apriori algorithm. The prediction of heavy oil production is faced with problems such as low prediction precision and insufficient data usage. Therefore, a novel intelligent simulation and prediction model of data-driven heavy oil production with time-varying characteristics is established based on differential simulation, machine learning, and intelligent optimization theory, which overcomes the defects of nonlinear, multifactor, and low fitting precision of dynamic data of heavy oil development. The parameters of the heavy oil production time-varying simulation model are identified by the least square support vector machine (LSSVM) to realize the intelligent prediction of the production. Numerical experiments show that the prediction result of the novel intelligent simulation and prediction model is better than the BP neural network model and the GM (1, N) model. This study provides a novel feasible method for data-driven heavy oil production prediction, and it can be helpful in further study of data-driven heavy oil production.

Learning Cuboid Abstraction of 3D Shapes via Iterative Error Feedback

Abstracting human-made 3D models by a set of primitives, such as cuboid abstraction, is a fundamental task in 3D shape modelling and analysis. Traditionally, different forms of representations, such as edges, volumes, or curves, were used as primitives. Although methods that apply local operations to compute such primitives can produce satisfactory results with their own merits, the computations can be very slow with complex models. Learning-based abstraction methods are much faster but cannot guarantee the fitting precision between the primitives and the original shape. To solve this problem, we propose an unsupervised learning approach for shape abstraction. Our method’s key idea is to use an iterative error feedback (IEF)-based network to improve primitive precision. Our method contains two main steps. First, we use a regression network to predict the initial primitives. Second, we increase the accuracy of the initial primitives by using an IEF-based network, which iteratively outputs the primitive updates. We demonstrate the advantages of our method by comparing it to existing state-of-the-art methods. We also thoroughly evaluate our method by ablation studies.

TSSM: Three-State Switchable Memristor Model Based on Ag/TiOx Nanobelt/Ti Configuration

Memristive technologies are attractive due to their nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance. For memristive devices, a model corresponding with practical behavioral characteristics is highly favorable for the realization of its neuromorphic system and applications. In this paper, we propose a novel memristor model based on the Ag/TiOx nanobelt/Ti configuration, which can reflect three different states (i.e. original stage, transition stage, and resistive switching state) of the physical memristor with a satisfactory fitting precision (greater than 99.88%). Meanwhile, this work gives (1) an insight onto the electrical characteristics of the memristor model under different humidity conditions; (2) the influence of the water molecular concentration on the memristor behavior, which is of importance for the memristor fabrication and subsequent applications. For verification purposes, the proposed three-state switchable memristor is applied into the memristor-based logic implementation. The experimental results demonstrate that the constructed circuit is able to realize basic Boolean logic operations with fast response speed and high efficiency.

Spectral envelope control for a flat frequency comb spectrum

Laser frequency combs have properties that make them promising spectrograph calibration light sources. One drawback for this application is the high dynamic range in the supercontinuum spectra of some frequency combs. We aim to flatten the spectrum of a Ti:sapphire laser frequency comb to improve the calibration performance for a Fourier transform spectrograph. For this, we develop a compact Fourier transform optical pulse shaping setup, which enables control of the spectral envelope via dispersion of the light onto a spatial light modulator (SLM). We demonstrate that this setup allows us to flatten the comb spectrum in the wavelength range 780 to 980 nm. For 90% of the wavelength range, the dynamic range is below 3 dB. In the flattened spectra 3% more comb lines can be detected. The line center fit precision, however, is reduced due to noise caused by the SLM.

Statistical analysis of defects within concrete under elevated temperatures based on SEM image

Concrete is a multiphase composite material consisting of aggregates, mortar, and initial defects. This work aims to quantitatively study the distribution and evolution of initial defects under elevated temperatures. To minimize the effect of hydration process and thermal and endogenous shrinkage, concrete samples cured in water for 12 months is tested. In order to study the effect of external load on the initial distribution of defects within concrete, concrete samples without pre-loading and pre-applied with 30% of the failure load are selected as research objects. The evolution of defects within the concrete samples are characterized using scanning electron microscopy (SEM). By employing an image processing software, the two-dimensional configurations of defects are extracted from the backscattered images obtained by SEM. Each defect is identified and approximated using an optimal ellipse. Thereafter, the defects are automatically recognized and classified into microcracks and pores by utilizing the image processing software and MATLAB programming. Consequently, the distribution and propagation of microcracks and pores in concrete could be quantitatively and separately analyzed. Statistical analysis of area, perimeter, and fitting ellipse parameters (e.g., major axis, minor axis, coordinates of the center, axis orientation, aspect ratio, and solidity) can provide the distribution of initial defects within concrete. It is demonstrated that the area of microcracks consistently increases with temperature. Log-normal and normal distributions may be suitable for the distribution of aspect ratio and solidity of microcracks, respectively, for both concrete samples with and without pre-applied loading under all elevated temperatures (40, 105, 150, 200, and 250 ℃). However, the distributions of area, coordinates of the center, and axis orientation of microcracks may vary with the initial concrete loading condition and temperature. For the pre-loaded concrete sample, the distribution of parameters does not conform to any type of distributions. Moreover, for the sample without pre-loading, the distributions of area, coordinates of the center, and axis orientation of microcracks exhibit good fitting precisions with log-normal, normal, and normal distributions, respectively at lower temperatures (40–150 ℃); however, such relationships do not apply at higher temperatures (200 and 250 ℃). As reference for identifying the variations in microstructures, 150 ℃ is selected as the threshold temperature. The statistical approach employed in this study can lay the theoretical and experimental bases for determining the microstructure evolution in concrete. It can also be used for identifying the meso-defect and macro-defect and establishing their relationship with concrete properties.

A research on the spectral Bidirectional Reflectance Distribution Function measurement of tensile polyimide film rough surfaces

The reflection distribution function is important for calculating the scatter distribution on material surface. In order to calculate the light scatter distribution of the tensile polyimide, it is necessary to investigate the relationship between the Bidirectional Reflectance Distribution Function (BRDF) of tensile polyimide film with its surface roughness and the incident light, and test the fitting function to evaluate the BRDF. The measurement spectrum is from 300nm to 750nm. The measurement results show that the BRDF of polyimide film are different with the tensile stress, and the reflectance intensity in the reflective direction decreases with the increase of the tensile stress, increases with the wavelength of the incident light. The reflectance intensity is associated with the roughness changing of polyimide surfaces for the tensile stress, as the roughness increases with the more stress, and the intensity increases in the none reflective direction. The fitting precision is up to 95% applying with the T-S model compared with the classical Phong model. The research data can be used to evaluate the reflective intensity of pharmaceutical packaging film, medical light barrier film, thermal insulation film and other film materials.