Variation Of Matrix Research Articles

Polydopamine-Mediated Protein Adsorption Alters the Epigenetic Status and Differentiation of Primary Human Adipose-Derived Stem Cells (hASCs).

Polydopamine (PDA) is a biocompatible cell-adhesive polymer with versatile applications in biomedical devices. Previous studies have shown that PDA coating could improve cell adhesion and differentiation of human mesenchymal stem cells (hMSCs). However, there is still a knowledge gap in the effect of PDA-mediated protein adsorption on the epigenetic status of MSCs. This work used gelatin-coated cell culture surfaces with and without PDA underlayer (Gel and PDA-Gel) to culture and differentiate primary human adipose-derived stem cells (hASCs). The properties of these two substrates were significantly different, which, in combination with a variation in extracellular matrix (ECM) protein bioactivity, regulated cell adhesion and migration. hASCs reduced focal adhesions by downregulating the expression of integrins such as αV, α1, α2, and β1 on the PDA-Gel compared to the Gel substrate. Interestingly, the ratio of H3K27me3 to H3K27me3+H3K4me3 was decreased, but this only occurred for upregulation of AGG and BMP4 genes during chondrogenic differentiation. This result implies that the PDA-Gel surface positively affects the chondrogenic, but not adipogenic and osteogenic, differentiation. In conclusion, for the first time, this study demonstrates the sequential effects of PDA coating on the biophysical property of adsorbed protein and then focal adhesions and differentiation of hMSCs through epigenetic regulation. This study sheds light on PDA-mediated mechanotransduction.

Model-Centric Data Manifold: The Data Through the Eyes of the Model

We discover that deep ReLU neural network classifiers can see a low-dimensional Riemannian manifold structure on data. Such structure comes via the local data matrix, a variation of the Fisher information matrix, where the role of the model parameters is taken by the data variables. We obtain a foliation of the data domain and we show that the dataset on which the model is trained lies on a leaf, the data leaf, whose dimension is bounded by the number of classification labels. We validate our results with some experiments with the MNIST dataset: paths on the data leaf connect valid images, while other leaves cover noisy images.

Online graph learning for time‐varying graphs

In this paper, the is focus on the online graph learning problems in time-varying environment. Traditional graph learning methods always assume that the underlying graph is static and that enough training data are available. However, in many real world applications, the underlying graph always changes slowly and only a small batch of data can be accessed at each time step. Under the assumption that the variation of the adjacency matrix is smooth, an online graph learning method is proposed, which improves the performance by leveraging the prior from the previous estimations. Experiments show that the proposed method performs best in both dynamic and static settings.

A mathematical perspective on edge-centric brain functional connectivity

Edge time series are increasingly used in brain imaging to study the node functional connectivity (nFC) dynamics at the finest temporal resolution while avoiding sliding windows. Here, we lay the mathematical foundations for the edge-centric analysis of neuroimaging time series, explaining why a few high-amplitude cofluctuations drive the nFC across datasets. Our exposition also constitutes a critique of the existing edge-centric studies, showing that their main findings can be derived from the nFC under a static null hypothesis that disregards temporal correlations. Testing the analytic predictions on functional MRI data from the Human Connectome Project confirms that the nFC can explain most variation in the edge FC matrix, the edge communities, the large cofluctuations, and the corresponding spatial patterns. We encourage the use of dynamic measures in future research, which exploit the temporal structure of the edge time series and cannot be replicated by static null models.

An observer-based adaptive fault-tolerant control for hypersonic vehicle with unexpected centroid shift and input saturation

This paper makes an investigation on the fault-tolerant control (FTC) problem for a hypersonic reentry vehicle (HSV) in the coexistence of unknown movement of center-of-mass, system input constraint and failure of actuator. Firstly, the dynamics of HSV’s attitude system with the unknown factors mentioned above are developed to illustrate the particularity of the researched topic. The influences of unexpected centroid shift on an FTC design can be summarized into the following three parts: unknown system uncertainties, eccentric torque as well as changing system moment of inertia matrix, which are coupled and unknown. Secondly, due to the difficulty in decoupling and estimating these influences (embodied in the output states of the system) one by one, it is the attitude system states observer that is proposed to estimate those detrimental unknown effects. The designed observer is consisted of an adaptive fault observer and an adaptive sliding-mode observer, supporting an innovative adaptive FTC scheme free from the variation of inverse matrix that might be singular due to an unexpected centroid shift. This fault-tolerant controller established in the estimated system states is derived by utilizing the above mentioned observer and adaptive backstepping control in conjunction with adaptive auxiliary compensation systems to handle the system input saturation. Moreover, the convergence of attitude tracking error and the boundedness of all closed-loop signals are achieved in the light of Lyapunov stability theory and boundedness analysis. Ultimately, simulation results are delivered to demonstrate the effectiveness of the proposed FTC scheme.

Differential proteomics between unhatched male and female egg yolks reveal the molecular mechanisms of sex-allocation and sex-determination in chicken

There is a huge demand to identify the sex of unhatched fertilized eggs for laying industry and to understand the differences between male and female eggs as early as possible. Then the molecular mechanisms of sex determination and sex allocation in chicken were revealed. Therefore, TMT proteomic was applied to characterize the variation of molecular matrix between unhatched male and female egg yolks. A total of 411 proteins were identified and 35 differentially expressed proteins (DEPs), including 375332005, 015809562, 763550308 (upregulated, UPs) and 1337178851, 89000557, 89000581 (downregulated, DPs), etc. were confirmed between them. Gene ontology analyses showed that DEPs were mainly involved in response to stimulus, distributed in the extracellular region and participated in binding; KEGG analyses showed that few DPs were participated in cell growth and death, transport and catabolism, signaling molecules, interaction and were enriched in ubiquitin mediated proteolysis, endocytosis, ferroptosis, etc. metabolic pathways. Moreover, most of the DEPs and related metabolic pathways were associated with sex hormones. More importantly, this study supports maternal sex-allocation theory and extends our understanding of the molecular mechanism of sex determination and differentiation in avian. Which also provides a powerful evidence for ovo sexing of unhatched fertilized domestic chicken eggs by nondestructive approach and will be of great significance to eggs processing and production.

Simultaneous Detection of Methane, Ethane, and Propane by QEPAS Sensors for On-Site Hydrocarbon Characterization and Production Monitoring.

Natural gas is sampled and produced throughout the lifespan of a petroleum field. Gas composition and isotope data are critical inputs in the exploration and field development, such as gas show identification, petroleum system analysis, fluid characterization, and production monitoring. On-site gas analysis is usually conducted within a mud gas unit, which is operationally unavailable after drilling. Gas samples need to be taken from the field and shipped back to the laboratory for gas chromatography and isotope-ratio mass spectrometry analyses. Results are usually without sufficient resolution to fully characterize the heterogeneity and dynamics of fluids within the reservoir and the production system. In addition, it often takes a considerable time to obtain the results using the traditional method. A novel QEPAS (quartz-enhanced photoacoustic spectroscopy) sensor system was developed to move gas composition analyses to field for quasi-real-time characterization and monitoring. With respect to previously reported QEPAS prototypes for trace gas detection, the new system realized measuring concentrations of methane (C1), ethane (C2), and propane (C3) in gas phase within the percentage range that is typically encountered in natural gas samples from oil and gas fields. A gas mixing enclosure was used to dilute the natural gas-like mixtures in nitrogen gas (N2) to avoid the saturation of QEPAS signals. An iterative analysis based on multilinear regression of QEPAS spectra was developed to filter out the influence of gas matrix variation from multiple hydrocarbon components. The advance in simultaneous measuring hydrocarbon gases and expanded linearity range of QEPAS, with previously reported detection of H2S, CO2, and gas isotopes (12CO2/13CO2, 13CH4/12CH4), opens a way to use the advanced sensing technology for in situ and real-time gas detection and chemical analysis in the oil industry.

Microstructural dependence of mechanical properties and their relationship in modern resin-based composite materials

ObjectiveThe aim of the study was to determine the influence of the microstructure on the mechanical behavior of resin-based composites (RBC) with little variation in chemical composition, and to verify a possible mutual dependency. MethodsFour RBCs with similar chemical composition of fillers (Ba-Al-B-F-Si-glass, SiO2), but with different mean (0.7 µm to 1.8 µm) and maximum particle size values (5 µm to 20 µm) were selected. Fracture toughness/KIC was evaluated in an NTP (Notchless Triangular Prism) test. Flexural strength/FS and modulus/E were measured in a 3-point-bending test. A fractographic analysis determined the fracture mode and origin. Quasi-static and viscoelastic parameters were assessed by a depth-sensing indentation test equipped with a DMA-module. One and multiple-way analysis of variance (ANOVA), Tukey honestly significant difference (HSD) post-hoc tests (α = 0.05), and Weibull statistics were applied. ResultsThe influence of the microstructure and the chemical composition of the organic matrix is weighted differently in the measured properties. The inorganic filler amount influences highest KIC (ηP2=0.813), followed by FS (ηP2=0.768) and E (ηP2=0.611). An optimized filler distribution with a high proportion of larger fillers up to 20 µm contributes to high FS and E data. The reliability of the material (Weibull parameter m) did not depend on the origin of fracture. Smaller inorganic fillers and the addition of 1% larger pre-polymer fillers improve the fracture toughness. Analysed RBCs were well adapted to chewing frequencies. ConclusionsThe properties of RBCs can be tuned by little variation in microstructure and monomer matrix. Clinical significanceReverse engineering of clinically successful materials can be used to better design future RBCs.

Spectropolarimeter on board the Aditya-L1: polarization modulation and demodulation

One of the major science goals of the Visible Emission Line Coronagraph (VELC) payload aboard the Aditya-L1 mission is to map the coronal magnetic field topology and quantitative estimation of longitudinal magnetic field on a routine basis. The infrared channel of VELC is equipped with a polarimeter to carry out full Stokes spectropolarimetric observations in the Fe xiii line at 1074.7 nm. The polarimeter is in a dual-beam setup with a continuously rotating wave plate as the polarization modulator. Detection of circular polarization due to the Zeeman effect and depolarization of linear polarization in the presence of a magnetic field due to the saturated Hanle effect in the Fe xiii line require a high signal-to-noise ratio (SNR). Due to the limited number of photons, long integration times are expected to build the required SNR. In other words, signals from a large number of modulation cycles are to be averaged to achieve the required SNR. This poses several difficulties. One is the increase in data volume and the other is the change in the modulation matrix in successive modulation cycles. The latter effect arises due to a mismatch between the retarder's rotation period and the length of the signal detection time in the case of the VELC spectropolarimeter. It is shown in this paper that by appropriately choosing the number of samples per half rotation, the data volume can be optimized. A potential solution is suggested to account for modulation matrix variation from one cycle to another.

Simulation studies to optimize genomic selection in honey bees

BackgroundWith the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees.MethodsStochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ.ResultsIncluding the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required.ConclusionsThis study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

Thermo-mechanical forging of 708M40 steel ring samples: experiments and modelling

A series of ring compression tests using BS970:708M40 alloy steel samples were studied. These tests were conducted using a 2-factor soak-temperature variable, namely 1030 °C and 1300 °C, and a 4-factor lubricant variable consisting of unlubricated samples, synthetic water-based, graphite water-based, and graphite and molybdenum disulphide viscous grease. The lubricant agents were all applied to the tool/billet interface. Process variables such as blow force and heating were controlled with the use of a gravitationally operated drop hammer and an automated programmable induction-heating unit. This matrix of the experimental parameters offered a sound base for exploring dominant factors impacting upon bulk deformation. This deformation was measured using fully calibrated equipment and then systematically recorded. A finite element modelling framework was developed to further improve the thermo-mechanical deformation process understanding, with finite element (FE) predictions validated through experimental measurement. Through the combined experimental and FE work, it was shown that temperature variation in the experimental parameter matrix played a larger role in determining deformation than the lubrication agent. Additionally, the use of synthetic and graphite water-based lubricants does not necessarily produce greater deformation when used in high-temperature forgings due to the lubricants breaking down, evaporating, or inducing rapid billet cooling as a result of the carrier used (water). Graphite-molybdenum disulphate grease far outperforms the other lubricants used in this trial in reducing friction and allowing deformation to occur across a die-face.

Quantum Confined Stark Effect on the Linear and Nonlinear Optical Properties of SiGe/Si Semi Oblate and Prolate Quantum Dots Grown in Si Wetting Layer.

We have studied the parallel and perpendicular electric field effects on the system of SiGe prolate and oblate quantum dots numerically, taking into account the wetting layer and quantum dot size effects. Using the effective-mass approximation in the two bands model, we computationally calculated the extensive variation of dipole matrix (DM) elements, bandgap and non-linear optical properties, including absorption coefficients, refractive index changes, second harmonic generation and third harmonic generation as a function of the electric field, wetting layer size and the size of the quantum dot. The redshift is observed for the non-linear optical properties with the increasing electric field and an increase in wetting layer thickness. The sensitivity to the electric field toward the shape of the quantum dot is also observed. This study is resourceful for all the researchers as it provides a pragmatic model by considering oblate and prolate shaped quantum dots by explaining the optical and electronic properties precisely, as a consequence of the confined stark shift and wetting layer.

Modeling and modal analysis of suspension bridge based on continual formula method

A continnum model is developed for the dynamic analysis of a suspension bridge with inclined main cables and hangers, based on incremental Lagrangian formulation and kinematics theories. The static equilibrium equations are derived from a shape-finding method followed by the equations of motion. Comparison of modal analysis results of the proposed model and the finite element method demonstrates the accuracy of the proposed modelling technique.Modal analysis is further conducted with variations of the following system parameters, i.e. inclination angle of main cable, the ratio of flexural stiffness of deck in the vertical and lateral directions, the ratio of vertical flexural stiffness of deck and tensile stiffness of cable, and torsional moment of inertia of cross-section of deck. Results show that the difference of eigenfrequencies within the eigenfrequency cluster is smaller for a suspension bridge with vertical cables than that with inclined cables. Phenomena of crossover and veering of eigenfrequency loci of the bridge system are observed. The mechanism of such phenomena is further studied with the help of a sectional bridge model. The effect of variation of stiffness matrix of bridge system on the eigenfunction is studied. The condition for crossover and veering of the eigenfrequency loci of this bridge model is identified. It is noted that veering can affect the modal property of the system by coupling two involved modes. This effect should be carefully examined during the design of the suspension bridge.

Monitoring of the Looseness in Cargo Bolts under Random Excitation Based on Vibration Transmissibility

Bolted joints are widely used in industrial applications and joint failure can cause a disastrous accident if loosening happens. Bolt loosening detection can be made by regular manual inspection or by using sensors based on different physical principles, such as acoustoelastic effect, piezoelectric active sensing, and electromechanical impedance. Compared with the above methods, vibration based bolt looseness monitoring using accelerometers is appealing for its economy and convenience for measurement. In this paper, cargo bolts looseness monitoring under random excitation is studied based on vibration transmissibility, which overcomes the drawback of commonly used vibration methods in finding local bolt looseness. Vibration transmissibility analysis only uses two vibration transducers to monitor bolt group looseness, where the vibration signal below the cargo bolts is used as the “input” and the other one above the cargo bolts is used as the “output.” There are 12 bolts in the cargo bolts studied in this paper, providing an essential clamping force to fix cargo during transportation. Six kinds of bolt group looseness with an increasing degree are simulated in the experiment. The experimental analysis shows that variation of the spectral moment can be used to monitor the global variation of the torque wrench exerted on the cargo bolts. The early stage of the bolt group looseness is that some one or two bolts begin to loose; however, the spectrum moment factor is insensitive to the local bolt looseness in the bolt group. To address this issue, the eigensystem realization algorithm (ERA) based on random input and output is utilized to find the subtle eigenvalue variation of the system matrix, which is neglected by the frequency transmissibility function. The experimental results show the effectiveness of the proposed method for detecting local bolt looseness.

Plant-microbe interactions regulate the aboveground community nitrogen accumulation rate in different environmental conditions on the Tibetan Plateau

Experimental studies have well illustrated that plant and microbe interactions play an important role in the absorption of nitrogen (N) under different environmental conditions. However, it remains to be seen what role plant–microbe interactions play in regulating plant N accumulation rates (NAR) over broad climatic and geographic scales. Here, a transect survey was conducted in alpine grassland on the Tibetan Plateau to measure aboveground net primary production and plant N concentration corresponding with phenology indexes, the NAR patterns were measured by “changepoint” analysis. Meanwhile, matrix correlation analysis and variation partitioning analysis were performed to explore the dynamic of NAR in response to environment factors. Finally, structural equation model was employed to express the path coefficients of environmental factors on NAR. A significantly (P＜0.05) lower NAR (8.9 mg/m2/day) in cold-dry climates than that in warm-wet climates (31.1 mg/m2/day) were detected. Our results further demonstrated a cooperative interaction between microbes and plants stimulated NAR in weakly resource-limited environments but a competitive effect of microbes on plants-constrained NAR in strongly resource-limited environments. The NAR was strongly governed by coupled water-heat availability and microbial biomass via their effects on plant diversity. Our findings highlighted that plant–microbe interactions offered an important framework to better understand the generalizable mechanisms of plant N accumulation in different environmental conditions. The knowledge of ecological N accumulation mechanisms obtained here not only emphasized the importance of incorporating plant phenology and dynamics of plant N into the ecological N cycle model but also promoted our understanding of resource utilization between plants and soil microbes under ongoing global climate change.

Response and yield stability of canola (Brassica napus L.) genotypes to multi-environments using GGE biplot analysis

he GxE interaction (GEI) provides essential information for selecting and recommending cultivars in multi-environment trials. This study aimed to evaluate genotype (G) and environment (E) main effects and GxE interaction of 15 canola genotypes (10 canola lines and 5 check varieties) over 8 environments and to examine the existence of different mega environments. Canola yield performances were evaluated during 2015/16 and 2016/17 production season in three different locations (Southern Marmara, Thrace side of Marmara, and Black Sea regions) of Turkey. The trial in each location was arranged in a randomized complete block design with four replications. The seed yield data were analyzed using GGE biplot and the yield components data were analyzed using ANOVA. The agronomical traits revealed that environments, genotypes, and GEI were significant at 1 % probability for all of the characters. The variance analysis exhibited that genotypes, environments, and GEI explained 21.6, 21.7, and 25.7 % of the total sum of squares for seed yield, respectively. The GGE biplot analysis showed that the first and second principal components explained 57.3 and 18.3 % of the total variation in the data matrix, respectively. GGE biplot analysis showed that the polygon view of a biplot is an excellent way to visualize the interactions between genotypes and environments.

Behaviour and performance of a dynamic cobble berm revetment during a spring tidal cycle in North Cove, Washington State, USA

In many places, sandy coastlines and their associated assets are at high risk of erosion and flooding, with this risk increasing under climate change and sea level rise. In this context, dynamic cobble berm revetments represent a potentially sustainable protection technique to armour sandy beaches, reduce wave runup and protect the hinterland against wave attack. However, the behaviour and performance of such structures is not well understood. The dynamic cobble berm revetment located in North Cove, WA, USA, was monitored over a spring tidal cycle in January 2019. A representative 60 m alongshore section was monitored over 10 days using 2D laser scanner (lidar) measurements, GPS ground elevation surveys, Radio Frequency Identification of individual cobbles and revetment thickness measurements. These data were used together to assess the dynamic behaviour and functionality of the revetment throughout the experiment. Over the course of the experiment, the surface elevation changed by up to ±0.5 m, and the revetment volume reduced by an average 0.67 m3/m. These changes were found to be caused by relatively large significant wave height and high water levels. The revetment demonstrated a dynamic stability and the capacity to quickly reshape under changing hydrodynamic conditions. The instrumented cobbles were transported along and cross-shore and accumulated at the toe of the revetment, but were never transported seaward of the toe. The revetment also managed to recover some of the lost volume under moderate wave conditions. The revetment behaviour was found to be influenced by variation in the cobble-sand matrix. The underlying sand dynamics – i.e., accumulation or removal of sand within the cobbles – were found to govern the overall volume changes and were important to the overall stability of the revetment. Seven possible transport regimes were identified, and a model of the internal sand dynamics was developed. During the spring tidal cycle measured here, the revetment protected the sand scarp immediately landward and prevented flooding of the hinterland, while armouring the underlying sand. Over time, renourishment will likely be required due to longshore sediment transport, and preliminary guidelines for this and other aspects of design are suggested.

Can We Compare Effect Size of Spatial Genetic Structure Between Studies and Species Using Moran Eigenvector Maps?

As the field of landscape genetics is progressing toward comparative empirical studies and meta-analysis, it is important to know how best to compare the strength of spatial genetic structure between studies and species. Moran’s Eigenvector Maps are a promising method that does not make an assumption of isolation-by-distance in a homogeneous environment but can discern cryptic structure that may result from multiple processes operating in heterogeneous landscapes. MEMgene uses spatial filters from Moran’s Eigenvector Maps as predictor variables to explain variation in a genetic distance matrix, and it returns adjusted R2 as a measure of the amount of genetic variation that is spatially structured. However, it is unclear whether, and under which conditions, this value can be used to compare the degree of spatial genetic structure (effect size) between studies. This study addresses the fundamental question of comparability at two levels: between independent studies (meta-analysis mode) and between species sampled at the same locations (comparative mode). We used published datasets containing 9,900 haploid, biallelic, neutral loci simulated on a quasi-continuous, square landscape under four demographic scenarios (island model, isolation-by-distance, expansion from one or two refugia). We varied the genetic resolution (number of individuals and loci) and the number of random sampling locations. We considered two measures of effect size, the MEMgene adjusted R2 and multivariate Moran’s I, which is related to Moran’s Eigenvector Maps. Both metrics were highly sensitive to the number of locations, even when using standardized effect sizes, SES, and the number of individuals sampled per location, but not to the number of loci. In comparative mode, using the same Moran Eigenvector Maps for all species, even those with missing values at some sampling locations, reduced bias due to the number of locations under isolation-by-distance (stationary process) but increased it under expansion from one or two refugia (non-stationary process). More robust measures of effect size need to be developed before the strength of spatial genetic structure can be accurately compared, either in a meta-analysis of independent empirical studies or within a comparative, multispecies landscape genetic study.

Theoretical analysis of the multi-GNSS contribution to partial ambiguity estimation and R-ratio test-based ambiguity validation

The fast and high-precision positioning with multiple Global Navigation Satellite Systems (multi-GNSS) has been challenging for decades. Although the single-frequency single system (SF-SS), satellite selection for multi-GNSS, and multi-GNSS-based partial ambiguity resolution (PAR) can achieve rapid positioning, the varying theoretical bases of them result in different fixed reliability of ambiguities. Hence, we provide the theory analyzing the ambiguity resolution capabilities of the named systems. By adding satellite observations, the equations giving the variance–covariance matrix variation of the original float parameters are derived. Then, the relationship between the ambiguity dilution of precision (ADOP) values of the original ambiguity vector (OAV) before and after adding observations is obtained. This is followed by the analyses of the changing trends in the OAV’s probability density function, integer least-squares pull-in region, and the R-ratio test-based integer aperture pull-in region. In terms of precision, ADOP, and R-ratio test-based fixed reliability of ambiguities, the analyses indicate that the multi-GNSS can improve the partial ambiguity estimation and validation. Besides, compared to satellite selection and SF-SS, the PAR is optimal. The BeiDou Navigation Satellite System (BDS) and the Global Positioning System (GPS)-based single-epoch positioning experiments showed that both BDS B1 and B1-based PAR outperform GPS L1 and L1-based PAR in terms of ADOP and R-ratio test-based fixed reliability. The ADOP of the former is smaller than 0.14, and both the R-ratio test-based acceptance and success rates are up to 99.64%. Finally, the false alarm, failure, and detection rates are reduced to 0.34%, 0.0%, and 0.02%, respectively.

Effect of combination variation of particle and matrix on the damage evolution and mechanical properties of particle reinforced metal matrix composites

Abstract In this article, SiCp/Al composites with matrices of different strengths reinforced by three sizes particles were manufactured by powder metallurgy process. The yield strength and ultimate strength of these composites ranged from 95 MPa to 379 MPa and 131 MPa–561 MPa respectively. In particular, in addition to the expected strengthening behavior, a weakening of the composite's yield strength and ultimate strength was found. This abnormal phenomenon was correlated with the effects of the combination variation of the particle and matrix. To study the mechanisms driving these effects, images and statistical data describing the evolution of the whole damage process were analyzed through in situ tensile testing. Two major types of damage, particle cracking and matrix slippage, were identified by the particle cracking rate f c and the slip band density ρ s . Based on the growth characteristics of these two major damage types, three stages of damage evolution were defined, and the effect of these stages on the strengthening limit of the composites' mechanical properties was discussed. Furthermore, through the analysis of the microstructures and fracture morphologies of the composites, a damage factor Q was defined to describe the different failure mechanism and the strengthening limits of the composites. This work provides a better understanding of the relation between the composite components and the mechanical properties.