Quantitative Structural Analysis Research Articles

Multi-layers hexagonal hole MXene trap constructed by carbon vacancy defect regulation strategy enables high energy density potassium-ions storage

Due to the slow reaction kinetics of potassium-ions with large size radius in layered materials, the energy density of aqueous potassium-ion hybrid supercapacitors (PIHCs) is severely limited. At the same time, the treatment of defects is a "double-edged sword" in energy storage materials, which requires targeted and precise regulation. In order to solve this problem, multi-layers hexagonal hole MXene trap was constructed by using the carbon vacancy defect regulation strategy, and high specific capacitance and energy density potassium-ion storage was realized in PIHCs. The structure-activity relationship at the atomic level was elucidated in combination with the pseudo-capacitance effect caused by the change of valency of newly exposed titanium atoms in the inner wall of the hexagonal hole MXene trap. By density functional theory, the adsorption energy and kinetic analysis of multi-layers hexagonal hole MXene for potassium-ions were calculated, and the optimal position and quantity of potassium-ions were determined. By quantitative analysis of electron band structure and differential charge density, the internal mechanism of high conductivity and energy density of multi-layers hexagonal hole MXene PIHCs was revealed.

Shock compression experiments using the DiPOLE 100-X laser on the high energy density instrument at the European x-ray free electron laser: Quantitative structural analysis of liquid Sn

X-ray free electron laser (XFEL) sources coupled to high-power laser systems offer an avenue to study the structural dynamics of materials at extreme pressures and temperatures. The recent commissioning of the DiPOLE 100-X laser on the high energy density (HED) instrument at the European XFEL represents the state-of-the-art in combining x-ray diffraction with laser compression, allowing for compressed materials to be probed in unprecedented detail. Here, we report quantitative structural measurements of molten Sn compressed to 85(5) GPa and ∼3500 K. The capabilities of the HED instrument enable liquid density measurements with an uncertainty of ∼1% at conditions which are extremely challenging to reach via static compression methods. We discuss best practices for conducting liquid diffraction dynamic compression experiments and the necessary intensity corrections which allow for accurate quantitative analysis. We also provide a polyimide ablation pressure vs input laser energy for the DiPOLE 100-X drive laser which will serve future users of the HED instrument.

Value of quantitative microsurface structure analysis for evaluating the invasion depth of type 0-II early gastric cancer.

The microsurface structure reflects the degree of damage to the glands, which is related to the invasion depth of early gastric cancer. To evaluate the diagnostic value of quantitative microsurface structure analysis for estimating the invasion depth of early gastric cancer. White-light imaging and narrow-band imaging (NBI) endoscopy were used to visualize the lesions of the included patients. The area ratio and depth-predicting score (DPS) of each patient were calculated; meanwhile, each lesion was examined by endoscopic ultrasonography (EUS). Ninety-three patients were included between 2016 and 2019. Microsurface structure is related to the histological differentiation and progression of early gastric cancer. The receiver operating characteristic curve showed that when an area ratio of 80.3% was used as a cut-off value for distinguishing mucosal (M) and submucosal (SM) type 0-II gastric cancers, the sensitivity, specificity, and accuracy were 82.9%, 80.2%, and 91.6%, respectively. The accuracies for distinguishing M/SM differentiated and undifferentiated early gastric cancers were 87.4% and 84.8%, respectively. The accuracy of EUS for distinguishing M/SM early gastric cancer was 74.9%. DPS can only distinguish M-SM1 (SM infiltration <500 μm)/SM (SM infiltration ≥500 μm) with an accuracy of 83.8%. The accuracy of using area ratio for distinguishing 0-II early gastric cancers was better than those of using DPS and EUS (P < 0.05). Quantitative analysis of microsurface structure can be performed to assess M/SM type 0-II gastric cancer and is expected to be effective for judging the invasion depth of gastric cancer.

Genre-Based Instruction and Iranian EFL Learners’ Argumentative Essay writing: Performance, Structure, Perceptions, and Authorial Identity in Focus

Genre-Based Instruction and Iranian EFL Learners’ Argumentative Essay writing: Performance, Structure, Perceptions, and Authorial Identity in Focus

Pore structure analysis of glacial till under freeze-thaw cycles based on calibration CT data

ABSTRACT Acquiring the interior pore characteristics is essential to the utilisation of glacial till which is the material of permeability barrier to many engineering projects in cold regions. In this work, the pore space of glacial till samples with different cementation degrees and freeze-thaw cycles was investigated by CT technique. Representative elementary volume method (REV) was utilised to ensure a suitable scale of CT volume data, and a novel calibration method was proposed to detect the accuracy of CT segmentation. Subsequently, the effect of cyclic freeze-thaw action on the pore structure of glacial till samples was investigated. Quantitative pore structure analysis shows that cyclic freeze-thaw action resulted in a decrease in porosity of both well cemented and poorly cemented glacial till. The accessible porosity, fractal dimension, and coordination number of glacial till pores also decrease with the increment of freeze-thaw cycles. It can be observed that blocking of the channels of macropores is the main morphological evolution characteristic of glacial till with cyclic freezing and thawing, which can provide reference in designing permeability barrier in glacial till layers.

Algorithm for Point Cloud Dust Filtering of LiDAR for Autonomous Vehicles in Mining Area

With the continuous development of the transformation of the “smart mine” in the mineral industry, the use of sensors in autonomous trucks has become very common. However, the driving of trucks causes the point cloud collected by through Light Detection and Ranging (LiDAR) to contain dust points, leading to a significant decline in its detection performance, which makes it easy for vehicles to have failures at the perceptual level. In order to solve this problem, this study proposes a LiDAR point cloud denoising method for the quantitative analysis of laser reflection intensity and spatial structure. This method uses laser reflectivity as the benchmark template, constructs the initial confidence level template and initially screens out the sparse dust point cloud. The results are analyzed through the Euclidean distance of adjacent points, and the confidence level in the corresponding template is reduced for rescreening. The experimental results show that our method can significantly filter dust point cloud particles while retaining the rich environmental information of data. The computational load caused by filtering is far lower than that of other methods, and the overall operation efficiency of the system has no significant delay.

Rapid quantitative analysis of soybean protein isolates secondary structure by two-dimensional correlation infrared spectroscopy through pH perturbation

The infrared spectroscopy (IR) signal of protein is prone to being covered by impurity signals, and the accuracy of the secondary structure content calculated using spectral data is poor. To tackle this challenge, a rapid high-precision quantitative model for protein secondary structure was proposed. Firstly, a two-dimensional correlation calculation was performed based on 60 groups of soybean protein isolates (SPI) infrared spectroscopy data, resulting in a two-dimensional correlation infrared spectroscopy (2DCOS-IR). Subsequently, the optimal characteristic bands of the four secondary structures were extracted from the 2DCOS-IR. Ultimately, partial least squares (PLS), long short-term memory (LSTM), and bidirectional long short-term memory (BILSTM) algorithms were used to model the extracted characteristic bands and predict the content of SPI secondary structure. The findings suggested that BILSTM combined with 2DCOS-IR model (2DCOS-BILSTM) exhibited superior predictive performance. The prediction sets for α-helix, β-sheet, β-turn, and random coil were designated as 0.9257, 0.9077, 0.9476, and 0.8443, respectively, and their corresponding RMSEP values were 0.26, 0.48, 0.20, and 0.15. This strategy enhances the precision of IR and facilitates the rapid identification of secondary structure components within SPI, which is vital for the advancement of protein industrial production.

A comparative study of two correlation functions applying to the quantitative characterization of the pore structure within hardened concrete

The paper presents a comparative study between the autocorrelation function (ACF) and the 2-point correlation function (2PCF) for the quantitative analysis of the pore structure within hardened concrete via the image analysis technique. This work was carried out on five cross-sectional images of concrete specimens with varying area porosities ranging from 2.6% to 21.2%. This research indicates that: (i) the ACF provides a comprehensive and effective tool that is capable of capturing the contribution of the area porosity, size, and orientation of the pores in quantifying the pore structure within hardened concrete, while the 2PCF only enables quantifying the size and spacing between the pores; (ii) the ACF requires the cross-sectional images to be square, whereas the 2PCF can analyze images of any size and shape; (iii) for an area porosity below 20%, the characteristic size of the pore structure within hardened concrete, determined by both the ACF and 2PCF approaches, is not significantly different; (iv) on average, the correlation length estimated by 2PCF is more than 1.6 times greater than the corresponding value determined by ACF; and (v) the characteristic size provides a more accurate and comprehensive quantification of the pore structure within hardened concrete specimens than the correlation length.

Quantitative analysis of the spatial distance between autophagy-related membrane structures and the endoplasmic reticulum in Saccharomyces cerevisiae

ABSTRACT Macroautophagy/autophagy is the process by which cells degrade their cytoplasmic proteins or organelles in vacuoles to maintain cellular homeostasis under severe environmental conditions. In the yeast Saccharomyces cerevisiae, autophagy-related (Atg) proteins essential for autophagosome formation accumulate near the vacuole to form the dot-shaped phagophore assembly site/pre-autophagosomal structure (PAS). The PAS then generates the phagophore/isolation membrane (PG), which expands to become a closed double-membrane autophagosome. Hereinafter, we refer to the PAS, PG, and autophagosome as autophagy-related structures (ARSs). During autophagosome formation, Atg2 is responsible for tethering the ARS to the endoplasmic reticulum (ER) via ER exit sites (ERESs), and for transferring phospholipids from the ER to ARSs. Therefore, ARS and the ER are spatially close in the presence of Atg2 but are separated in its absence. Because the contact of an ARS with the ER must be established at the earliest stage of autophagosome formation, it is important to know whether the ARS is tethered to the ER. In this study, we developed a rapid and objective method to estimate tethering of the ARS to the ER by measuring the distance between the ARS and ERES under fluorescence microscopy, and found that tethering of the ARS to the ER was lost without Atg1. This method might be useful to predict the tethering activity of Atg2. Abbreviation: ARS, autophagy-related structure; Dautas, automated measurement of the distance between autophagy-related structures and ER exit sites analysis system; ERES, endoplasmic reticulum exit site; PAS, phagophore assembly site/pre-autophagosomal structure; PCR, polymerase chain reaction; PG, phagophore/isolation membrane; prApe1, precursor of vacuolar aminopeptidase I; Qautas, quantitative autophagy-related structure analysis system; SD/CA; synthetic dextrose plus casamino acid medium; WT, wild-type

Mechanism of enhanced thermal conductivity of hybrid nanofluids by adjusting mixing ratio of nanoparticles

At certain mixing ratios, hybrid nanofluids exhibit superior thermal conductivity performance compared with mono nanofluids, without significant enhancements in viscosity. Studying the microstructures formed by nanoparticles in the base fluid is key to revealing the mechanism of enhanced heat transfer in nanofluidic systems. Here, effects of varying the nanoparticle volume fraction (0.5–2.0 vol%) and Cu:Al volume mixing ratio (20:80, 40:60, 50:50, 60:40, and 80:20) of Cu-Al/Ar hybrid nanofluids were investigated using non-equilibrium molecular dynamics (NEMD) simulations at a temperature of 85 K. The mechanism of thermal conductivity enhancement was elucidated through quantitative analysis of the nanolayer structure and atom trajectory. The results showed that the thermal conductivities of Cu-Al/Ar hybrid nanofluids with a mixing ratio of 20:80 were lower than those of the corresponding Cu/Ar mono nanofluids with volume fraction below 1.3 vol% due to the low quantity of Cu nanoparticles. However, when the quantity of Cu nanoparticles was increased by using mixing ratios from 40:60 to 80:20, the thermal conductivities were higher at all volume fractions. Compared to Cu/Ar nanofluids, the thermal conductivity of Cu-Al/Ar hybrid nanofluids improved from 0.149 to 0.166 W/(m·K) which was higher 2.76 % to 14.48 % as the mixing ratio increased from 20:80 to 80:20 at 2.0 vol%. Radial distribution function (g(r)), the number density and diffusion rate calculations indicated that the density and velocity of Ar atoms around Cu atoms were higher than those of Al atoms due to stronger interactions between Cu-Ar atoms. The main reason for the enhanced thermal conductivity of hybrid nanofluids with increasing mixing ratio was the higher unit nanolayer density and Brownian motion velocity.

Combining traditional and quantitative multiscale structural analysis to reconstruct the tectono-metamorphic evolution of migmatitic basements: the case of the Valpelline Series, Dent-Blanche Tectonic System, Western Alps

Due to the ongoing development of new technologies, many instruments are available to assist geological investigations at different scales. These techniques, including 3D outcrop modelling from aerial photogrammetry and quantitative microstructural analysis are useful in crystalline basements studies. This contribution combines traditional and quantitative multiscale structural analysis techniques to the migmatitic rocks of the Valpelline Series (Dent-Blanche Tectonic System, Western Alps). Conventional structural analysis is integrated with the extraction of structural data from 3D models of representative smooth outcrops. Quantitative microstructural and mineral-chemical analyses are combined to link structural and metamorphic evolution. This approach allows identifying and correlating foliations that developed during three tectono-metamorphic stages. The first (D1) includes solid-state deformation associated with an early foliation (S1) preserved within metabasite boudins enclosed in migmatite gneiss. The second (D2) is related to the dominant foliation in migmatite gneiss (S2), coeval with the regional scale anatexis and growth of garnet and cordierite. The third (D3) is related to the late folding of S2 and the development of a sillimanite-rich axial plane foliation (S3) which wraps around garnet and cordierite. Finally, this work discusses pros and cons of each innovative methodology, still emphasising the importance of using manually acquired field data as ground control.

Comprehensive evaluation of the organic-rich saline lacustrine shale in the Lucaogou Formation, Jimusar sag, Junggar Basin, NW China

To explore a comprehensive evaluation method for saline lacustrine shale, samples were collected from the shale strata of the Lucaogou Formation in the Jimusar Sag, Junggar Basin. Organic geochemical and mineralogical parameters were assessed through TOC evaluation, rock pyrolysis, and XRD. Quantitative analysis of pore structure was achieved using a combined low-pressure N2 adsorption and mercury intrusion. Optical microscopy and FE-SEM analyzed macerals and pore morphology, respectively, while 2D NMR identified varied hydrogen-containing components in the shale. The findings revealed predominance of inorganic pores in saline lacustrine shale strata, with a lower occurrence of organic pores. The saline lacustrine sediments were in the late stage of oil generation. The siltstone and type III3 carbonate felsic shale displayed an abundance of type II1 organic matter, low vitrinite and inertinite contents, high macroporous volumes and surface areas, and they possessed a high content and fluidity of free oil, despite being lower maturity (0.85%<Ro<1.1%), making them attractive for shale oil exploration. Finally, we propose a comprehensive evaluation method for saline lacustrine shale strata considering free oil content (26% contribution), pore structure (23% contribution), maturity (16% contribution), macerals (13% contribution), free oil mobility (12% contribution), and organic matter type (10% contribution), encompassing various lithofacies.

Intelligently Quantifying the Entire Irregular Dental Structure

Quantitative analysis of irregular anatomical structures is crucial in oral medicine, but clinicians often typically measure only several representative indicators within the structure as references. Deep learning semantic segmentation offers the potential for entire quantitative analysis. However, challenges persist, including segmentation difficulties due to unclear boundaries and acquiring measurement landmarks for clinical needs in entire quantitative analysis. Taking the palatal alveolar bone as an example, we proposed an artificial intelligence measurement tool for the entire quantitative analysis of irregular dental structures. To expand the applicability, we have included lightweight networks with fewer parameters and lower computational demands. Our approach finally used the lightweight model LU-Net, addressing segmentation challenges caused by unclear boundaries through a compensation module. Additional enamel segmentation was conducted to establish a measurement coordinate system. Ultimately, we presented the entire quantitative information within the structure in a manner that meets clinical needs. The tool achieved excellent segmentation results, manifested by high Dice coefficients (0.934 and 0.949), intersection over union (0.888 and 0.907), and area under the curve (0.943 and 0.949) for palatal alveolar bone and enamel in the test set. In subsequent measurements, the tool visualizes the quantitative information within the target structure by scatter plots. When comparing the measurements against representative indicators, the tool’s measurement results show no statistically significant difference from the ground truth, with small mean absolute error, root mean squared error, and errors interval. Bland-Altman plots and intraclass correlation coefficients indicate the satisfactory agreement compared with manual measurements. We proposed a novel intelligent approach to address the entire quantitative analysis of irregular image structures in the clinical setting. This contributes to enabling clinicians to swiftly and comprehensively grasp structural features, facilitating the design of more personalized treatment plans for different patients, enhancing clinical efficiency and treatment success rates in turn.

Quantitative photothermal analysis and multispectral imaging of dental structures: insights into optical and thermal properties of carious and healthy teeth.

In the analysis of two-layered turbid dental tissues, the outer finite-thickness layer is modeled by an optical transport coefficient distinct from its underlying semi-infinite substrate layer. The optical and thermophysical parameters of healthy and carious teeth across the various wavelengths were measured leading to the determination of the degree of reliability of each of the fitted parameters, with most reliable being thermal diffusivity and conductivity, enamel thickness, and optical transport coefficient of the enamel layer. Quantitative pixel-by-pixel images of the key reliable optical and thermophysical parameters were constructed. We introduced a theoretical model of pulsed photothermal radiometry based on conduction-radiation theory and applied to quantitative photothermal detection and imaging of biomaterials. The theoretical model integrates a combination of inverse Fourier transformation techniques, avoiding the conventional cumbersome analytical Laplace transform method. Two dental samples were selected for analysis: the first sample featured controlled, artificially induced early caries on a healthy tooth surface, while the second sample exhibited natural defects along with an internal filling. Using an Nd:YAG laser and specific optical parametric oscillator (OPO) wavelengths (675, 700, 750, and 808nm), photothermal transient signals were captured from different points on these teeth and analyzed as a function of OPO wavelength. Measurements were also performed with an 808-nm laser diode for comparison with the same OPO wavelength excitation, particularly for the second sample with natural defects. The findings demonstrated that the photothermal transient signals exhibit a fast-decaying pattern at shorter wavelengths due to their higher scattering nature, while increased scattering and absorption in the carious regions masked conductive and radiative contributions from the underlayer. These observations were cross-validated using micro-computed tomography, which also enabled the examination of signal patterns at different tooth locations. The results of our study showed the impact of optical and thermal characteristics of two-layered turbid dental tissues via an inverse Fourier technique, as well as the interactions between these layers, on the patterns observed in depth profiles.

THE EVOLUTION OF TEMPORAL AND SPATIAL LANGUAGE: A DIACHRONIC ANALYSIS ACROSS CULTURES AND EPOCHS

The perception and expression of time and space in human language has long captivated researchers across multiple disciplines. This study delves into the diachronic evolution of temporal and spatial language, examining the shifts and development patterns in various languages throughout different time periods and cultural contexts. By analyzing linguistic corpora representing diverse languages, epochs, and genres, the research investigates the impact of historical, cultural, and environmental factors on the evolution of temporal and spatial expressions. Through quantitative and qualitative analysis of grammatical structures, lexicon, and metaphors, this study highlights significant patterns and changes in human understanding and communication of time and space. The findings offer insights into the dynamic nature of linguistic evolution and suggest implications for theories of language development, cognitive processes, and the intricate relationship between language and human experience. Through an examination of linguistic artifacts, texts, and historical records, this study uncovers the intricate trajectory of how societies have linguistically represented temporal and spatial dimensions. By identifying patterns of change and continuity, the research highlights the interplay between linguistic structures, cognitive capacities, and cultural contexts. The findings illuminate the dynamic processes that have contributed to the shaping of temporal and spatial language, ultimately deepening our understanding of the complex relationship between language, cognition, and cultural evolution. This work offers valuable insights into the mechanisms that underlie linguistic change and provides a broader perspective on the ways in which humans have grappled with conceptualizing time and space throughout history.

Stability of the Fe3O4 (√2 x √2)R 45° Surface in 0.1 M NaOH Investigated by High-Energy Surface X-Ray Diffraction

Transition metal oxides, especially Fe, Co, and Ni oxides, are of great interest as electrocatalysts for the oxygen evolution reaction (OER) in alkaline solution. However, detailed data on the atomic-scale oxide-electrolyte interface structure of these systems are rare. We present studies of a model oxide electrocatalyst, -reconstructed Fe3O4(001). This oxide surface has been extensively investigated under ultrahigh vacuum (UHV) conditions, leading to a detailed understanding of its atomic structure [2]. In previous in situ surface X-ray diffraction studies, we demonstrated that the reconstructed surface can survive transfer into electrochemical environment and determined the stability of the reconstruction as a function of potential [2]. Specifically, we showed that the ()R45° reconstructed Fe3O4(001) surface can be transferred into 0.1 M NaOH at 0.2 V vs. Ag/AgCl while maintaining surface structure and that the reconstruction is irreversibly lifted at sufficiently negative potentials.Here, we present more detailed in situ studies of this system, using the novel technique of High-Energy Surface X-Ray Diffraction (HESXRD) [3,4]. This technique enabled us to measure rapidly a huge reciprocal space volume, which allows to obtain very large sets of crystal truncation rods (CTRs) that can be analysed by surface crystallographic methods. These data provide detailed insights into the temporal stability of the surface at fixed potentials as well as potential-dependent changes in surface structure.In the experiments the Fe3O4(001) single crystal samples were prepared by sputtering and annealing in UHV, followed by sample transfer into the hanging-meniscus cell employed for the HESXRD studies. A new experimental procedure allowed to reduce the time of sample transfer and establishment of potential control to below one minute.The in situ HESXRD measurements were performed at beamline P21.2 of PETRA III at DESY. 7 symmetrically independent CTRs and 2 ()R45° fractional order rods could be measured. These data enabled detailed comparison of the oxide-electrolyte interface with the surface structure under UHV conditions [1]. The potential-dependent data indicate clear structural changes when applying more positive or more negative potentials than 0.2 V vs. Ag/AgCl, as illustrated in part A and B of Fig. 1. However, all surface structures were stable at constant potential over several hours, as indicated in part C of Fig. 1. The quality of the obtained results allows to determine quantitative analysis of the interface structure. First results of this analysis will be presented.We gratefully acknowledge financial support by the German ministry of Science and Education (BMBF) via projects 05K19FK3 and 05K22FK1.

Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.

The thickness of the buccal bone of the anterior maxilla is an important aesthetic-determining factor for dental implant, which is divided into the thick (≥1 mm) and thin type (<1 mm). However, as a micro-scale structure that is evaluated through low-resolution cone-beam computed tomography (CBCT), its thickness measurement is error-prone under the circumstance of enormous patients and relatively inexperienced primary dentists. Further, the challenges of deep learning-based analysis of the binary thickness of buccal bone include the substantial real-world variance caused by pixel error, the extraction of fine-grained features, and burdensome annotations. This study built bilinear convolutional neural network (BCNN) with 2 convolutional neural network (CNN) backbones and a bilinear pooling module to predict the binary thickness of buccal bone (thick or thin) of the anterior maxilla in an end-to-end manner. The methods of 5-fold cross-validation and model ensemble were adopted at the training and testing stages. The visualization methods of Gradient Weighted Class Activation Mapping (Grad-CAM), Guided Grad-CAM, and layer-wise relevance propagation (LRP) were used for revealing the important features on which the model focused. The performance metrics and efficacy were compared between BCNN, dentists of different clinical experience (i.e., dental student, junior dentist, and senior dentist), and the fusion of BCNN and dentists to investigate the clinical feasibility of BCNN. Based on the dataset of 4,000 CBCT images from 1,000 patients (aged 36.15±13.09 years), the BCNN with visual geometry group (VGG)16 backbone achieved an accuracy of 0.870 [95% confidence interval (CI): 0.838-0.902] and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.924 (95% CI: 0.896-0.948). Compared with the conventional CNNs, BCNN precisely located the buccal bone wall over irrelevant regions. The BCNN generally outperformed the expert-level dentists. The clinical diagnostic performance of the dentists was improved with the assistance of BCNN. The application of BCNN to the quantitative analysis of binary buccal bone thickness validated the model's excellent ability of subtle feature extraction and achieved expert-level performance. This work signals the potential of fine-grained image recognition networks to the precise quantitative analysis of micro-scale structures.

Considerations for quantitative in situ X-ray powder diffraction studies of solid-state reactions

The importance of sample preparation in collecting high-fidelity powder diffraction data suitable for quantitative structure and phase analysis is well established. Such powder diffraction experiments are increasingly being applied in situ, during reactions, to explore solid-state reactivity. When applied in situ, X-ray diffraction is widely used to gain insight into the mechanism and kinetics, and to identify dynamic intermediate states. Here, using a model ion-exchange reaction (NaFeO2 + LiCl → LiFeO2 + NaCl), we show that sample preparation not only influences the fidelity of powder diffraction analysis but also impacts the observed reaction progress. Specifically, we found that the observed reaction progress can differ by ∼50% depending on the capillary sample preparation. Thus, for in situ diffraction studies of solid-state reactions, packing fraction is an important and previously unrecognized consideration that impacts reproducibility and fidelity of the reaction study.

Deep matched filtering for retinal vessel segmentation

The structure of the retinal vascular tree can reflect many indicators of ophthalmic health status. Retinal vessel segmentation, an important basis for the quantitative analysis of vascular structure, has been developed over decades. However, in the areas of imaging quality and contrast, there are still vascular ruptures and missed detection by existing methods. Moreover, the problem of false positives caused by existing lesions has not been properly addressed. Inspired by the primary visual cortex of the biological visual system, this paper proposes Anisotropic Perceptive Convolution (APC) and Anisotropic Enhancement Module (AEM) to model visual cortex cells and their orientation selection mechanism, respectively, as well as a novel network named W-shaped Deep Matched Filtering (WS-DMF) having a W-shaped overall framework. In the feature extraction backbone, a DMF based on a multilayer aggregation of APC is designed to enhance vascular features and inhibit the expression of pathological features. In addition, AEMs are embedded into DMF to enhance the orientation and position information of high-dimensional features. Furthermore, in order to enhance the ability of APC for perceiving linear textures (such as blood vessels), an Orientation Anisotropic Loss (OAL) is introduced. Based on experiments on several widely used datasets and several professional vessel segmentation metrics, the proposed method exhibits strong abilities of vessel extraction and lesion inhibition, surpassing many state-of-the-art algorithms. The results indicate the potential value of WS-DMF in clinical practice. The source code is available at https://www.github.com/tyb311/WS-DMF.

Quantitative structural uncertainty analysis of composite honeycomb sandwich using a feedback neural network

The quantification of margins and uncertainty (QMU) method was applied using a neural network to inversely determine the strength boundary of a composite honeycomb sandwich structure considering the random uncertainty of its elastic modulus, shear modulus, tensile strength, compressive strength, and shear strength in the warp and fill directions, and panel thickness. An application framework for the deterministic quantitative analysis of strength was proposed based on margin design theory, combining the strength problem with the quantitative analysis of uncertainty. First, the failure criterion of the structure was modeled based on the progressive damage principle using ABAQUS. Next, Latin hypercube sampling was applied to simulate the stochastic uncertainty of the structural parameters, and the resulting sample sets were input into ABAQUS to calculate the corresponding structure lateral bearing capacities. These results were considered to collectively represent the actual strength boundary of the composite structure. A feedback neural network was subsequently trained on these ABAQUS data to obtain the functional mapping relationship between the parameters and capacity. The capacity obtained using this relationship was multiplied by an empirical safety factor to serve as the theoretical strength boundary. Finally, the QMU method was applied using cumulative distribution functions of the theoretical and actual strength boundaries to calculate the confidence factor and thereby evaluate the reliability of the composite honeycomb sandwich structure.