Quantitative Characterization Research Articles

Quantitative microstructure characterization for a red-bed soft rock in the Three Gorges Reservoir area

Quantitative microstructure characterization for a red-bed soft rock in the Three Gorges Reservoir area

Recognition and quantification of apparent damage to concrete structure based on computer vision

To solve the problems of poor recognition of apparent concrete damage and the difficulty of efficient quantitative characterization of the damage parameters, this study proposes a damage detection method that integrates the target localization algorithm and the quantitative characterization algorithm of the damage based on computer vision. This paper proposes a YOLO v5-RS network combining Res2Net and SimAM to solve the problem of indistinctive apparent damage feature. The damage is segmented by image processing, and the crack and crush damage quantification method are proposed. The results show that compared with YOLO v5, the mAP and F1 of YOLO V5-RS are improved by 8.4% and 0.07, respectively, which indicates that the recognition of apparent structural damage has high robustness. To verify the recognition method’s generalization ability and the characterization method’s accuracy, four cases of different scale are introduced in this paper.

Spatiotemporal weighted neural network reveals surface seawater pCO2 distributions and underlying environmental mechanisms in the North Pacific Ocean

The North Pacific Ocean plays a pivotal role as a carbon sink within the global carbon cycle. However, a comprehensive understanding of the spatiotemporal dynamics of carbon dioxide concentration and its determinants in this domain remains elusive due to its vast dimensions and the intricacies of influencing factors, with previous research on carbon dioxide partial pressure in the North Pacific Ocean also being relatively scarce. While prevalent machine learning methodologies have been extensively applied to predict the partial pressure of ocean carbon dioxide (pCO2), their limited interpretability has impeded substantial progress in elucidating the underlying mechanisms. This study introduces a gridded spatiotemporal neural network weighted regression (GSTNNWR) model to illuminate temporal and spatial relationships among relevant environmental variables and pCO2. The GSTNNWR model achieves high-precision and high-resolution forecasts of surface pCO2 in the North Pacific Ocean, demonstrating commendable performance (R2 = 0.863 and RMSE=15.123 µatm). Simultaneously, we obtain a quantitative characterization of how various environmental factors influence pCO2 across different temporal and spatial scales. Results show a dominant positive effect of temperature on the pCO2, with an averaged normalized coefficient of 0.28, and variability in the effects of chlorophyll and salinity on the pCO2 at different spatial and temporal locations and temperatures, whose average normalized coefficients are −0.10 and −0.04.The findings of our study will provide insights into the mechanisms and interactions within the North Pacific carbon cycle, contributing to a better understanding of ocean carbon sink formation and the dynamic regulation of the North Pacific carbon cycle.

Experimental study on the mass transfer and microscopic distribution characteristics of remaining oil and CO2 during water-miscible CO2 flooding

Accurate understanding and quantitative characterization of the microscopic distribution and mobilization mechanisms of remaining oil are crucial for further enhancing oil recovery during CO2 flooding. In this study, miscible CO2 flooding experiments after water flooding were performed to investigate the mass transfer and microscopic distribution characteristics of remaining oil and CO2 using micro-computed tomography technique. Additionally, the distribution characteristics of multiphase fluids (oil, water and CO2) in the pores were comprehensively investigated. Furthermore, the mass transfer effects during the miscible CO2 flooding process, as well as the CO2 sequestration ratio (SR) and sequestration factor (SF) were systematically examined. The experimental results show that the ultimate oil recovery factor after miscible CO2 flooding were 80.9 %, 70.7 %, and 64.7 %, respectively. During the water flooding stage, the produced oil mainly consisted of light hydrocarbons (C5–C16), followed by medium hydrocarbons (C17–C27). The remaining oil was mainly in cluster and network pattern, followed by multiple and oil film pattern. After miscible CO2 flooding, the produced gas mainly consisted of CO2 and CH4, with a significant increase in the mass fraction of light hydrocarbons (C5–C16) in the produced oil. The remaining oil was mainly in multiple and singlet pattern, followed by network and film pattern, with a small portion in cluster pattern. The higher the displacement rate, the smaller the SR, but most of the injected CO2 (SR > 70 %) was retained in the porous media, demonstrating the feasibility of CO2 geological sequestration during the miscible flooding process.

Molecular dynamics of quantitative evaluation of confined fluid behavior in nanopores media and the influencing mechanism: Pore size and pore geometry

Understanding the potential mechanisms of reservoir fluid storage, transport, and oil recovery in shale matrices requires an accurate and quantitative evaluation of the fluid behavior and phase state characteristics of the confined fluid in nanopores as well as the elucidation of the mechanisms within complex pore structures. The research to date has preliminary focused on the fluid behavior and its influencing factors within a single nanopore morphology, with limited attention of the role of pore structures in controlling fluid behavior and a lack of quantitative methods for characterizing the phase state of fluids. To address this gap, we utilize molecular dynamics simulations to examine the phase state characteristics of confined fluids across various pore sizes and geometries, revealing the mechanisms by which wall boundary conditions influence fluid behavior. We use the simulation results to validate the accuracy and applicability of the quantitative characterization model for fluid phase state properties. Our findings show that the phase state features of fluids differ significantly between slit-like and cylindrical pores, with lower absorption limits in pore sizes of 2.8 and 7 nm, respectively. Based on pore sizes, we identified three regions of confined fluid phases and determined that the influence of the adsorbed state fraction on fluid phase state cannot be ignored for pores smaller than approximately 85 nm. Additionally, cylindrical pores interact with the internal fluids about 1.8 times stronger than slit-like pores.

Correlation between Comprehensive Evaluation Method for Fracture Complexity in Tight Reservoirs Post-Fracturing and Production Capacity

The complexity of post-fracturing fractures in tight reservoirs is critical for evaluating fracturing effectiveness and is also a significant factor influencing well productivity. Currently, some scholars have utilized an improved G function to study the complexity of hydraulic fractures after fracturing. However, in certain specific construction curves, the characteristics and values of the G function alone are insufficient for comprehensive interpretation. To conduct a more thorough analysis of fracture complexity, this study introduces net pressure and liquid efficiency—parameters related to fracture complexity—based on the G function, and performs statistical analysis. Since the G function curve morphology is image data, it is not suitable for quantitative analysis. Therefore, digital calibration was conducted based on its corresponding morphological features to render it amenable to data analysis. Using Principal Component Analysis (PCA), a comprehensive fracture complexity coefficient was proposed, which considers four parameters: the digital calibration of the G function curve, G function characteristic values, net pressure, and liquid efficiency. The relationship between this coefficient and productivity was examined through Pearson and Spearman correlation analyses, utilizing post-fracturing production data from 26 wells in the Dabei block for validation. The results indicate a significant positive correlation between the comprehensive fracture complexity coefficient and productivity, with a high degree of correlation. This method not only addresses the shortcomings in interpreting certain specific construction curves but also provides a more comprehensive quantitative characterization of fracture complexity. Preliminary validation confirms the applicability of the method, offering valuable insights for evaluating post-fracturing effectiveness.

Imaging the intramuscular pressure of living muscles with shear waves

Shear wave elastography (SWE) is an innovative method that allows for the nondestructive and quantitative characterization of muscular mechanical properties. This method finds extensive utility in fields such as sports medicine, sports rehabilitation, and the diagnosis of muscle-related ailments. Existing studies have demonstrated the promise of SWE in probing intramuscular pressure (IMP), a factor intimately tied to muscular physiological functions and the onset of certain diseases. Nonetheless, there remains a lack of a SWE method grounded in an appropriate biomechanical model capable of effectively imaging IMP in vivo. Addressing this issue, we propose a shear wave imaging method relaying on a porohyperelastic model encompassing well-defined parameters for both muscular active behavior and intramuscular pressure. Drawing upon wave motion analysis, we establish a correlation between shear wave velocities and IMP in analytical form. This theoretical solution on one hand help understand the interplay between IMP and muscle active stress and the impact of muscle contraction on IMP. On the other hand, it enables us to develop an elastography method to assess IMP in vivo. We conducted a series of experiments to underscore the applicability of our theory and elastography method. Ex vivo experiments were performed on porcine muscles, while in vivo tests were carried out on human skeletal muscles. The results from the ex vivo tests validate the efficacy of our method. Meanwhile, the in vivo outcomes suggest that our approach holds potential to assess the variation of IMP with muscle fatigue and injuries, inspect intramuscular injections, and diagnose acute and chronic compartment syndrome.

Research and Analysis on Enhancement of Surface Flashover Performance of Epoxy Resin Based on Dielectric Barrier Discharge Plasma Fluorination Modification.

To conquer the challenges of charge accumulation and surface flashover in epoxy resin under direct current (DC) electric fields, numerous efforts have been made to research dielectric barrier discharge (DBD) plasma treatments using CF4/Ar as the medium gas, which has proven effective in improving surface flashover voltage. However, despite being an efficient plasma etching medium, SF6/Ar has remained largely unexplored. In this work, we constructed a DBD plasma device with an SF6/Ar gas medium and explored the influence of processing times and gas flow rates on the morphology and surface flashover voltage of epoxy resin. The surface morphology observed by SEM indicates that the degree of plasma etching intensifies with processing time and gas flow rate, and the quantitative characterization of AFM indicates a maximum roughness of 144 nm after 3 min of treatment. Flashover test results show that at 2 min of processing time, the surface flashover voltage reached a maximum of 19.02 kV/mm, which is 25.49% higher than that of the untreated sample and previously reported works. In addition to the effect of surface roughness, charge trap distribution shows that fluorinated groups help to deepen the trap energy levels and density. The optimal modification was achieved at a gas flow rate of 3.5 slm coupled with 2 min of processing time. Furthermore, density functional theory (DFT) calculations reveal that fluorination introduces additional electron traps (0.29 eV) and hole traps (0.38 eV), enhancing the capture of charge carriers and suppressing surface flashover.

Thermal Reading of Texts Buried in Historical Bookbindings.

In the manufacture of ancient books, it was quite common to insert written scraps belonging to earlier library material into bookbindings. For scholars like codicologists and paleographers, it is extremely important to have the possibility of reading the text lying on such scraps without dismantling the book. In this regard, in this paper, we report on the detection of these texts by means of infrared (IR) pulsed thermography (PT), which, in recent years, has been specifically proven to be an effective tool for the investigation of Cultural Heritage. In particular, we present a quantitative analysis based, for the first time, on PT images obtained from books of historical relevance preserved at the Biblioteca Angelica in Rome. The analysis has been carried out by means of a theoretical model for the PT signal, which makes use of two image parameters, namely, the distortion and the contrast, related to the IR readability of the buried texts. As shown in this paper, the good agreement between the experimental data obtained in the historical books and the theoretical analysis proved that the capability of the adopted PT method could be fruitfully applied, in real case studies, to the detection of buried texts and to the quantitative characterization of the parameters affecting their thermal readability.

Quantitative Characterization of the Impact of Protein-Protein Interactions on Ligand-Protein Binding: A Multi-Chain Dynamics Perturbation Analysis Method.

Many protein-protein interactions (PPIs) affect the ways in which small molecules bind to their constituent proteins, which can impact drug efficacy and regulatory mechanisms. While recent advances have improved our ability to independently predict both PPIs and ligand-protein interactions (LPIs), a comprehensive understanding of how PPIs affect LPIs is still lacking. Here, we examined 63 pairs of ligand-protein complexes in a benchmark dataset for protein-protein docking studies and quantified six typical effects of PPIs on LPIs. A multi-chain dynamics perturbation analysis method, called mcDPA, was developed to model these effects and used to predict small-molecule binding regions in protein-protein complexes. Our results illustrated that the mcDPA can capture the impact of PPI on LPI to varying degrees, with six similar changes in its predicted ligand-binding region. The calculations showed that 52% of the examined complexes had prediction accuracy at or above 50%, and 55% of the predictions had a recall of not less than 50%. When applied to 33 FDA-approved protein-protein-complex-targeting drugs, these numbers improved to 60% and 57% for the same accuracy and recall rates, respectively. The method developed in this study may help to design drug-target interactions in complex environments, such as in the case of protein-protein interactions.

A comprehensive analysis of the tubarial glands.

The tubarial glands (TGs) are a collection of salivary glands (SGs) located within the nasopharynx, proximal to the eustachian tube. Currently, there is no quantitative characterization of the TGs. We investigated the histological architecture of the TGs and compared it with the major and minor SGs for categorization. Tubarial, parotid, submandibular, sublingual, buccal, labial, and lingual glands were excised from human donors (8 male and 3 female). Hematoxylin and eosin-stained tissue sections were analyzed to measure the area of the largest lobule, number of ducts, number of mucinous acini, and mean mucinous acini area. Based on our observation, the TGs' histology resembles the minor SGs, while having some unique characteristics that distinguish them from both major and minor SGs. The area of the largest lobule in the TGs and minor SGs was smaller than the major SGs. TGs have a lower number of ducts than the major and minor SGs. TGs contain densely packed clusters of predominantly mucinous acini surrounded by loose connective tissue resembling minor SGs. This density may explain their previously observed high prostate-specific membrane antigen uptake. In our cohort of donors, sex-based differences were observed in the mean mucinous acini area between male and female TGs, submandibular and sublingual glands. Taken together, our findings suggest the histological characteristics of all SGs are better organized on a spectrum rather than discrete groups (major vs. minor) and provide information to open new avenues for research into the TGs' role in head and neck pathologies and sexual dimorphism of the SGs.

Non-invasive estimation of the powder size distribution from a single speckle image

Non-invasive characterization of powders may take one of two approaches: imaging and counting individual particles; or relying on scattered light to estimate the particle size distribution (PSD) of the ensemble. The former approach runs into practical difficulties, as the system must conform to the working distance and other restrictions of the imaging optics. The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. The principle relies on the pupil function determining the basic sidelobe shape, whereas the particle size spread modulates the sidelobe intensity. We recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network, trained efficiently through a physics-informed semi-generative approach. In this work, we eliminate one of the most time-consuming steps of our previous method by engineering the pupil function. By judiciously blocking portions of the pupil, we sacrifice some photons but in return we achieve much enhanced sidelobes and, hence, higher sensitivity to the change of the size distribution. The result is a 60 × reduction in total acquisition and processing time, or 0.25 seconds per frame in our implementation. Almost real-time operation in our system is not only more appealing toward rapid industrial adoption, it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying, blending, and other chemical and pharmaceutical manufacturing processes.

Electrical characterization of freeze-thaw damage in saturated concrete and its interfacial transition zones

Determining the freeze-thaw (F-T) damage of the interfacial transition zone (ITZ) is crucial to understanding the mechanism of F-T in concrete and improving its frost resistance. In this paper, the F-T damage of saturated concrete/mortar and ITZ is characterized using AC impedance spectroscopy, and the micro-mechanisms of F-T damage in cement-based materials (CBMs) are summarized. Firstly, the damage factor was defined by the effective resistivity of the CBMs, and a correlation between the damage factor and the compressive strength loss was established. The F-T damage process was divided into a damage development stage and a damage stabilization stage based on the damage factor and fractal dimension. Then, the effective resistivity separation formula was proposed to calculate the ITZ effective resistivity, and the ITZ damage factor was calculated for non-destructive quantitative characterization of the F-T damage in the ITZ. Finally, the microscopic mechanism of F-T damage in CBMs and their ITZs was summarized based on damage factor, fractal dimension, super depth of field microscopy, and nanoindentation. The results showed a linear relationship between the damage factor and compressive strength loss (Lc = 0.176Er+0.030); the ITZ had damage factors of 80 % and 70 %, which were 1.6 and 1.4 times higher than those of the matrix, respectively; F-T cycles produce micro-damage, part of which connects with the disconnected pores to form connected pores, the micro-damage extends and accumulates along the surface of the aggregates, leading to ITZ debonding from aggregate and deterioration of the mechanical properties.

In vivo optimization of the experimental conditions for the non-invasive optical assessment of breast density

In this study, time domain diffuse optical spectroscopy is performed in the range 600–1100 nm on 11 healthy volunteers with a portable system for the quantitative characterization of breast tissue in terms of optical properties and optically-derived blood parameters, tissue constituent concentrations, and scattering parameters. A measurement protocol involving different geometries (reflectance and transmittance), subject’s positions (sitting and lying down), probing locations (outer, lower, and inner breast quadrants), and source-detector distances (2 and 3 cm) allowed us to investigate the effect of tissue heterogeneity and different measurement configurations on the results with the aim of identifying the best experimental conditions for the estimate of breast density (i.e., amount of fibro-glandular tissue in the breast) as a strong independent risk factor for breast cancer. Transmittance results, that in previous studies correlated strongly with mammographic density, are used as a reference for the initial test of the simpler and more comfortable reflectance measurement configuration. The higher source-detector distance, which probes deeper tissue, retrieves optical outcomes in agreement with higher average density tissue. Similarly, results on the outer quadrants indicate higher density than internal quadrants. These findings are coherent with breast anatomy since the concentration of dense fibro-glandular stroma is higher in deep tissue and towards the external portion of the breast, where the mammary gland is located. The dataset generated with this laboratory campaign is used to device an optimal measurement protocol for a future clinical trial, where optical results will be correlated with conventional mammographic density, allowing us to identify a subset of wavelengths and measurement configurations for an effective estimate of breast density. The final objective is the design of a simplified, compact and cost-effective optical device for a non-invasive, routine assessment of density-associated breast cancer risk.

Full Quantitative Near-Field Characterization of Strongly Coupled Exciton–Plasmon Polaritons in Thin-Layered WSe2 on a Monocrystalline Gold Platelet

Full Quantitative Near-Field Characterization of Strongly Coupled Exciton–Plasmon Polaritons in Thin-Layered WSe₂ on a Monocrystalline Gold Platelet

Microalloying effects on the mechanical and electrical properties of CuCrSnZnSi alloys

The dissolution of CrxSi compounds at the grain boundaries of Cu-0.2 wt%Cr-0.252 wt%Sn-0.166 wt%Zn-0.014 wt%Si alloy into the matrix is more complete after solid solution treatment, but a small amount of CrxSi compounds remains inside the grains. After annealing, Cr is precipitated in the matrix as a nanophase, whereas Sn, Zn, and Si are dispersed in the matrix as solid solutions. Quantitative analysis and characterization of the strengthening mechanism and electrical conductivity of the alloy were conducted herein, and the calculated results were consistent with the experimental results. Grain boundary scattering, Cr nanophase scattering, and solid solution scattering of elemental Si had a relatively small impact on the electrical conductivity of the alloy. Solid solution scattering of the Sn and Zn elements affected the electrical conductivity of the alloy more significantly. In addition, grain boundary strengthening contributes the most to the increase in yield strength, followed by nanophase precipitation strengthening, solid solution strengthening, and dislocation strengthening. The quantitative calculation of microalloying can provide a basis for the composition ratio of alloys with different performance requirements, reduce the number of experiments, improve research and development efficiency, and reduce costs.

A novel fine-grained assembly sequence planning method based on knowledge graph and deep reinforcement learning

In the assembly sequence planning (ASP) of aviation products, recalibration of components or sufficient space to assemble subsequent components are critical factors for ensuring product quality. To address this need, a fine-grained ASP (FASP) is defined to take assembly operations as units to plan sequences. Lots of operations have complex sequence constraints that are attended unequally in the FASP. A method based on knowledge graph (KG) and deep reinforcement learning is proposed to plan assembly operations. Firstly, continuous and discrete procedures are defined, and a quantitative characterization method is presented to deduce complex constraints objectively. Then, a dynamic KG is designed to establish and update the information model mainly composed of constraints. Finally, a labeled degree centrality algorithm (LDCA) considers edge labels to minimize the number of assembly tool changes and assembly direction changes for sequences. An improved deep Q-network (IDQN) introduces a convolutional layer to extract local features of technical requirements for planning procedures more efficiently. A helicopter structure assembly is used to verify the effectiveness of the proposed method. The improved algorithms have better performance in solving speed, sequence quality, and convergence speed than ordinary ASP methods, respectively. The fine-grained assembly sequence is more reasonable and feasible by comparing it with the ordinary sequence.

Characterizing the dynamics of multi-scale global high impact weather events

The quantitative characterization and prediction of localized severe weather events that emerge as coherences generated by the highly non-linear interacting multivariate dynamics of global weather systems poses a significant challenge whose solution is increasingly important in the face of climate change where weather extremes are on the rise. As weather measurement systems (multiband satellite, radar, etc) continue to dramatically improve, increasingly complex time-dependent multivariate 3D datasets offer the potential to inform such problems but pose an increasingly daunting computational challenge. Here we describe the application to global weather systems of a novel computational method called the Entropy Field Decomposition (EFD) capable of efficiently characterizing coherent spatiotemporal structures in non-linear multivariate interacting physical systems. Using the EFD derived system configurations, we demonstrate the application of a second novel computational method called Space-Time Information Trajectories (STITs) that reveal how spatiotemporal coherences are dynamically connected. The method is demonstrated on the specific phenomenon known as atmospheric rivers (ARs) which are a prime example of a highly coherent, in both space and time, severe weather phenomenon whose generation and persistence are influenced by weather dynamics on a wide range of spatial and temporal scales. The EFD reveals how the interacting wind vector field and humidity scalar field couple to produce ARs, while the resulting STITS reveal the linkage between ARs and large-scale planetary circulations. The focus on ARs is also motivated by their devastating social and economic effects that have made them the subject of increasing scientific investigation to which the EFD may offer new insights. The application of EFD and STITs to the broader range of severe weather events is discussed.

Scanning electron microscopy-based quantification of keratin and hyaluronic acid microstructure in electrospun scaffolds

BackgroundThe extracellular matrix (ECM) structural deficiencies in chronic wounds prevent the wounds from healing through natural physiological processes. Electrospun biocompatible polymers offer a platform to produce microstructure wound dressing materials that mimic the ECM containing various bioactives to address the deficiencies in the chronic wound healing process. Quantitative characterization of the electrospun fiber microstructure could provide valuable information on using fiber constructs to facilitate wound healing. This work employed a validated image analysis tool to quantitatively explain various parameters for the microstructure of six electrospun fiber constructs, D1{Polycaprolactone (PCL), Polyvinyl alcohol (PVA), Keratin}, D2{PCL, PVA, keratin, Co-enzyme Q10 (CoQ10)}, D3 (PCL, PVA, keratin, mupirocin), D4 (PCL, PVA, keratin, CoQ10, mupirocin, valsartan), D5 {PVA, Hyaluronic acid (HA)}, and D6 (PVA), using scanning electron microscopy imaging modality.ResultsThe fiber intersection density (FID) parameter was quantified in the formulations, e.g., 0.272% for D5 and 0.416% for D4. Orientation histograms for D1 and D6 are characteristic of isotropic materials, while orientations for D2 and D3 indicate anisotropy with 2 preferred orientations in each formulation. D4 and D5 present orientations characteristic of transversely isotropic materials. The tortuosity for D2 and D4 indicates almost straight fiber segments, in contrast with undulated fiber segments in all other formulations. Furthermore, the mean fiber diameter was quantified, e.g., 1.414 and 1.630 mm for D3 and D4, respectively.ConclusionCo-electrospun PVA/PCL microfibers offer great potential for controlled delivery of bioactives needed to accelerate the healing of chronic wounds. This image-based analysis technology quantitatively characterized different formulations of electrospun fiber scaffolds. This analysis sets the stage for future study that utilizes microstructural information in finite element biomechanical modeling, to investigate possible influence of structure-based mechanical factors on the ECM restorative potential of wound dressings. Adjustment of electrospinning conditions could produce fabricated constructs like the native ECM structural components with a functional role in wound healing.

Quantitative characterisation of the influence of different environmental factors on coal spontaneous combustion

Quantitative characterisation of the influence of different environmental factors on coal spontaneous combustion