Detailed Structure Research Articles

Imprints of dark matter on the structural properties of minimally deformed compact stars

In this manuscript, we investigate the possibility of constructing anisotropic dark matter compact stars motivated by the Einasto density profile. This work develops analytical solutions for an anisotropic fluid sphere within the framework of the well-known Adler–Finch–Skea metric. This toy model incorporates an anisotropic fluid distribution that includes a dark matter component. We use the minimal geometric deformation scheme within the framework of gravitational decoupling to incorporate anisotropy into the pressure profile of the stellar system. In this context, we model the temporal constituent of the Θ-field sector to characterize the contribution of dark matter within the gravitational matter source. We present an alternative approach to studying anisotropic self-gravitating structures. This approach incorporates additional field sources arising from gravitational decoupling, which act as the dark component. We explicitly verify whether the proposed model satisfies all the requirements for describing realistic compact structures in detail. We conclude that the modeling of the Einasto density model with the Adler–Finch–Skea metric gives rise to the formation of well-behaved and viable astrophysical results that can be employed to model the dark matter stellar configurations.

Multi-view multi-label network traffic classification based on MLP-Mixer neural network

Network traffic classification is the basis of many network security applications and has received significant attention in the field of cyberspace security. Existing research on deep traffic analysis typically involves converting traffic data into images to extract spatial traffic features using Convolutional Neural Networks (CNNs). However, this approach ignores the semantic differences and details in the various packet structures. In this paper, we propose an MLP-Mixer based multi-view multi-label neural network for network traffic classification. Compared with the existing CNN-based methods, our method adopts the MLP-Mixer structure, which is more in line with the structure of the packet than the conventional convolution operation. In our method, one packet is divided into the packet header and the packet payload, together with the flow statistics of the packet as input from different views. We utilize a multi-label setting to learn different scenarios simultaneously to improve the classification performance by exploiting the correlations between different scenarios. We conduct experiments on three public datasets, and the experimental results show that our method can achieve superior performance. Code is available at https://github.com/ZxuanDang/MV-ML-traffic-classification.

Fatigue performance of innovative open-rib orthotropic steel deck for super-long suspension bridge

This paper presents experimental and numerical studies on the fatigue performance of an innovative orthotropic steel deck (OSD) with open-ribs, apple-shaped cut-outs, and additional transverse ribs, which is employed by the Zhangjinggao Yangtze River Bridge. This bridge is set to become the world's longest suspension bridge upon completion. A full-scale section of the OSD was fabricated and subjected to fatigue loading with a total cycle of 12.3 million times. The interested cracking-prone fatigue details include rib-to-deck and rib-to-crossbeam welded joints, along with the apple-shaped cut-outs of the crossbeam. Experimental results indicated that fatigue cracks initiated separately beneath the deck plate, on the flange of the rib, and at the rib-to-crossbeam welded joint, with equivalent stress amplitudes of 159 MPa, 148 MPa, and 99 MPa at 2 million cycles, respectively. In addition, detailed finite element model of the OSD was developed using ABAQUS and validated against the experimental results. The effects of the geometry dimensions and structural details of the bridge deck, longitudinal ribs, crossbeams and transverse ribs that influence the fatigue performance of this OSD were analyzed and discussed. Parametric analyses revealed that increasing the thickness of the deck plate, the thickness or the height of the longitudinal rib, and the number of transverse ribs can effectively reduce the stress amplitudes at corresponding fatigue details, which therefore improves their fatigue performance. This study provides useful references for the fatigue evaluation and design of OSDs with open-ribs and apple-shaped cut-outs.

Melittin can permeabilize membranes via large transient pores

Membrane active peptides are known to porate lipid bilayers, but their exact permeabilization mechanism and the structure of the nanoaggregates they form in membranes have often been difficult to determine experimentally. For many sequences at lower peptide concentrations, transient leakage is observed in experiments, suggesting the existence of transient pores. For two well-know peptides, alamethicin and melittin, we show here that molecular mechanics simulations i) can directly distinguish equilibrium poration and non-equilibrium transient leakage processes, and ii) can be used to observe the detailed pore structures and mechanism of permeabilization in both cases. Our results are in very high agreement with numerous experimental evidence for these two peptides. This suggests that molecular simulations can capture key membrane poration phenomena directly and in the future may develop to be a useful tool that can assist experimental peptide design.

Conversion of soybean oil under subcritical water conditions into liquid biofuels with low oxygen contents

Although the hydrotreatment of natural triglycerides is a well-established route for producing sustainable aviation fuel and renewable diesel, its high hydrogen consumption is a concern. The hydrothermolysis of natural triglycerides is considered a highly promising alternative route; however, the correlation between process parameters and fuel properties and detailed chemical structures of the produced fuel have not been established. Herein, we investigated the conversion of soybean oil to gasoline-, jet fuel- and diesel-fraction hydrocarbons with low oxygen contents. The properties of fuels produced under various reaction conditions were analyzed comprehensively using simulated distillation, total acid number (TAN), and elemental analysis. Under the optimized reaction conditions of 420 °C, 4.5 MPa, an oil-to-water ratio of 6:1, and reaction time of 10 min, 26 % gasoline-, 62 % jet fuel-, and 90 % diesel-range hydrocarbons with a low oxygen content of 5.1 %, low TAN of 0.04 mg KOH g−1, and high calorific value of 42.7 MJ kg−1 resulted. To elucidate the reaction mechanism, the liquid products produced from soybean oil were analyzed using comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry. In addition, based on the oleic acid conversion under varying hydrothermal conditions, we proposed deoxygenation, cracking, cyclization, and aromatization pathways. The findings of this study present possibilities for producing sustainable biofuels with low oxygen contents for the aviation industry.

Potential of graphene as an adsorbent for HSSS· radical – A DFT investigation

The sulfane sulfur molecules are known to regulate many biological processes for the normal functioning of cells. Herein, the interactions of pristine, B-doped, N-doped, BN-codoped, monovacancy defected, and Stone-Wales (SW) defected graphene with HSSS· radical have been investigated theoretically in the framework of density functional theory (DFT) with an aim to understand if pristine or modified graphene can be used as an adsorbent for HSSS· radical. The analysis based on the structural details, Wiberg bond, ZPE-corrected adsorption energies, reaction enthalpies, reaction free energies and density of states (DOS) reveal that the pristine, BN-codoped, monovacancy defected and SW defected graphene would serve as potential adsorbents for adsorption of HSSS· radicals; the monovacancy defected graphene could be the best choice for this job. It is found that the B-doped graphene would act as a weak adsorbent but the N-doped graphene would not at all be suitable for adsorption of HSSS· radicals.

Development of the Guide to Disseminating Research (GuiDiR): A consolidated framework

BackgroundLess than one third of research evidence is translated into policy or practice. Knowledge translation requires effective dissemination, adoption and finally implementation. These three stages are equally important, however, existing knowledge translation models and frameworks provide little and disparate information about the steps and activities required for effective dissemination. ObjectiveThis study aimed to empirically develop a consolidated framework of evidence-based steps and activities for disseminating research evidence. MethodsWe identified models and frameworks from a scoping review and dissemination and implementation webtool. We synthesised them into a prototype dissemination framework. Models and frameworks were eligible to inform steps in our framework if they fulfilled at least one of three elements of dissemination: intending to generate awareness of a message, incorporates targeting an audience: tailoring communication. An initial coding framework was created to organise data into dissemination steps.Drawing on ‘co-approach’ principles, authors of the included models and frameworks (dissemination experts) and health service researchers (end users) were invited to test and refine the prototype framework at a workshop. ResultsFrom 48 models and frameworks reviewed, only 32 fulfilled one or more of the three dissemination elements. The initial coding framework, upon refinement, yielded the Guide to Disseminating Research (GuiDiR) comprising five steps.1) Identify target audiences and dissemination partners.2) Engage with dissemination partners.3) Identify barriers and enablers to dissemination.4) Create dissemination messages.5) Disseminate and evaluate.Multiple activities were identified for each step and no single model or framework represents all steps and activities in GuiDiR. ConclusionsGuiDiR unifies dissemination components from knowledge translation models and frameworks and harmonises language into a format accessible to non-experts. It outlines for researchers, funders and practitioners the expected structure of dissemination and details the activities for executing an evidence-based dissemination strategy.

Droplet size, spray structure and droplet velocity mapping in hollow cone sprays using SLIPI-based techniques

The Structured Laser Illumination Planar Imaging (SLIPI) technique received significant attention in recent years due to notable improvements in the measurement of droplet size and the visualization of internal structure in both dilute and dense sprays. However, despite the improvements, the technique remains limited to these microscopic characteristics of the spray, primarily due to the complexity of the optical setup and image acquisition process. In this article, the capabilities of the SLIPI application are exploited, specifically employing the 3p-SLIPI-LIF/Mie (SLSD), and combined application of 1p-SLIPI-LIF & PIV (1p-SLIPI-PIV) for mapping of both Sauter Mean Diameter (SMD) and mean droplet velocity in combination with detailed spray structure analysis. The measurements were carried out in hollow cone sprays operated at the range of injection pressures from 25 to 107 bar. The results obtained using the SLIPI techniques were compared with the Phase Doppler Anemometry (PDA) measurements showing good agreement for both the SMD and mean droplet velocity. The significant improvements in the important microscopic characteristics (droplet size & velocity) and visualization of the internal structure in a wider flow field of hollow cone sprays allowed detailed analysis and understanding of the dispersed phase flow owing to the suppression of diffused light resulting from the multiple scattering effects.

Abdominal MR Multitasking for radiotherapy treatment planning: A motion-resolved and multicontrast 3D imaging approach.

Radiotherapy treatment planning (RTP) using MR has been used increasingly for the abdominal site. Multiple contrast weightings and motion-resolved imaging are desired for accurate delineation of the target and various organs-at-risk and patient-tailored planning. Current MR protocols achieve these through multiple scans with distinct contrast and variable respiratory motion management strategies and acquisition parameters, leading to a complex and inaccurate planning process. This study presents a standalone MR Multitasking (MT)-based technique to produce volumetric, motion-resolved, multicontrast images for abdominal radiotherapy treatment planning. The MT technique resolves motion and provides a wide range of contrast weightings by repeating a magnetization-prepared (saturation recovery and T2 preparations) spoiled gradient-echo readout series and adopting the MT image reconstruction framework. The performance of the technique was assessed through digital phantom simulations and in vivo studies of both healthy volunteers and patients with liver tumors. In the digital phantom study, the MT technique presented structural details and motion in excellent agreement with the digital ground truth. The in vivo studies showed that the motion range was highly correlated (R2 = 0.82) between MT and 2D cine imaging. MT allowed for a flexible contrast-weighting selection for better visualization. Initial clinical testing with interobserver analysis demonstrated acceptable target delineation quality (Dice coefficient = 0.85 ± 0.05, Hausdorff distance = 3.3 ± 0.72 mm). The developed MT-based, abdomen-dedicated technique is capable of providing motion-resolved, multicontrast volumetric images in a single scan, which may facilitate abdominal radiotherapy treatment planning.

Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD

Hematoxylin and Eosin (H&E) color variation among histological images from different laboratories can significantly degrade the performance of Computer-Aided Diagnosis systems. The staining procedure is the primary factor responsible for color variation, and consequently, the methods designed to reduce such variations are designed in concordance with this procedure.In particular, Blind Color Deconvolution (BCD) methods aim to identify the true underlying colors in the image and to separate the tissue structure from the color information. Unfortunately, BCD methods often assume that images are stained solely with pure staining colors (e.g., blue and pink for H&E). This assumption does not hold true when common artifacts such as blood are present, requiring an additional color component to represent them. This is a challenge for color standardization algorithms, which are unable to correctly identify the stains in the image, leading to unexpected results.In this work, we propose a Blood-Robust Bayesian K-Singular Value Decomposition model designed to simultaneously detect blood and extract color from histological images while preserving structural details. We evaluate our method using both synthetic and real images, which contain varying amounts of blood pixels.

A New Spin on Material Properties: Local Magnetic Structure in Functional and Quantum Materials.

The past few decades have made clear that the properties and performances of emerging functional and quantum materials can depend strongly on their local atomic and/or magnetic structure, particularly when details of the local structure deviate from the long-range structure averaged over space and time. Traditional methods of structural refinement (e.g., Rietveld) are typically sensitive only to the average structure, creating a need for more advanced structural probes suitable for extracting information about structural correlations on short length- and time-scales. In this Perspective, we describe the importance of local magnetic structure in several classes of emerging materials and present the magnetic pair distribution function (mPDF) technique as a powerful tool for studying short-range magnetism from neutron total-scattering data. We then provide a selection of examples of mPDF analysis applied to magnetic materials of recent technological and fundamental interest, including the antiferromagnetic semiconductor MnTe, geometrically frustrated magnets, and iron-oxide magnetic nanoparticles. The rapid development of mPDF analysis since its formalization a decade ago puts this technique in a strong position for making continued impact in the study of local magnetism in emerging materials.

Thermal vibrations in the inversion of dynamical electron scattering

Relativistic forward scattering of electrons at finite temperature involves the incoherent superposition of diffraction patterns formed by different snapshots of thermal atomic displacements. In experiments, thermal vibrations lead to thermal diffuse scattering (TDS), partly dominating diffraction patterns of thick specimens. This study sheds light on the effects of TDS on solutions to the inverse scattering problem using combined real- and diffraction-space information acquired in a scanning transmission electron microscope (STEM) to retrieve the object's phase. Using frozen phonon multislice within the Einstein approximation, realistic ground truth data of 20-nm-thick SrTiO3 is generated and subjected to contemporary inverse multislice schemes to retrieve the projected Coulomb potential slicewise. We first classify phase retrieval algorithms as to their assumptions on periodicity along the incident beam direction, as well as pixelwise and parametrized reconstruction methods. It is found that pixelwise object reconstructions are capable of retrieving structural details qualitatively while being prone to contain TDS-related artifacts which can result in unphysical potentials. For pixelwise reconstructions of multiple independent specimen slices, we observe that the origin of TDS, i.e., thermal atomic displacements, starts to emerge naturally. However, the quantitative assessment tends to too small mean squared thermal displacements, also when reconstructing multiple object modes. Using an atomistically parametrized inversion strategy which exploits the explicit separation of thermal vibrations and potentials, temperature and chemistry of the specimen can be retrieved quantitatively with high accuracy. Published by the American Physical Society 2024

A Comparative Analysis of Illustris TNG and THESAN Cosmological Simulations

Cosmological simulations play a crucial role in understanding the evolution of the universe, offering valuable insights when compared with observational data. With advancements in computational capabilities and data-gathering techniques, simulations have evolved to model larger volumes and finer details of cosmic structures. This review compares two prominent simulations, Illustris TNG and THESAN, focusing on their algorithms and factors considered. Illustris TNG primarily employs smoothed particle hydrodynamics (SPH), while THESAN utilizes moving mesh hydrodynamics (MMH), each offering unique advantages in simulating different aspects of the universe. Furthermore, THESAN incorporates additional factors such as cosmological dust and sophisticated radiation transport algorithms, enhancing its accuracy in predicting early galaxies and stars. The comparison underscores the impact of algorithmic differences and the consideration of various factors on simulation accuracy. This analysis highlights the importance of understanding these specifications in designing simulations that effectively capture the complexities of the cosmos.

A human brain atlas of χ-separation for normative iron and myelin distributions.

Iron and myelin are primary susceptibility sources in the human brain. These substances are essential for a healthy brain, and their abnormalities are often related to various neurological disorders. Recently, an advanced susceptibility mapping technique, which is referred to as χ-separation (pronounced as "chi"-separation), has been proposed, successfully disentangling paramagnetic iron from diamagnetic myelin. This method provided a new opportunity for generating high-resolution iron and myelin maps of the brain. Utilizing this technique, this study constructs a normative χ-separation atlas from 106 healthy human brains. The resulting atlas provides detailed anatomical structures associated with the distributions of iron and myelin, clearly delineating subcortical nuclei, thalamic nuclei, and white matter fiber bundles. Additionally, susceptibility values in a number of regions of interest are reported along with age-dependent changes. This atlas may have direct applications such as localization of subcortical structures for deep brain stimulation or high-intensity focused ultrasound and also serve as a valuable resource for future research.

Intra-Pulse Modulation Recognition of Radar Signals Based on Efficient Cross-Scale Aware Network.

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time-frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time-frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios.

Face-degree-based topological descriptors of germanium phosphide

In graph theory, topological indices are numerical metrics that give information about a graph’s structural traits. The face index is one such topological index that describes planar networks. Since the discovery of graphene, the genealogy of two-dimensional 2D crystals has expanded and currently contains a large variety that has all logical electrical properties required for nano electronics. Nanotechnology benefits from the use of materials that resemble Dirac, such as silicon, graphite, semiconductors, and germanene, as well as TMDC (phosporene), a transition metal dichalcogenide. In contrast with standard topological descriptors, which are numerical values utilised to characterise molecular structures, the face index presents a potentially more comprehensive method for obtaining structural details. In investigations involving quantitative structure-property relationships (QSPR), this may result in more precise predictions. We calculated the recently created face index of Germanium Phosphide (GeP) and its many shapes, including triangle, rhombus, hourglass, and concentric circles.

Optical Coherence Tomography-Guided Stenting for Common Carotid Free-Floating Thrombus.

Digital subtraction angiography (DSA) has traditionally been considered an effective method for visualizing carotid free-floating thrombus (CFFT), but it falls short in providing detailed structures of the lumen and the composition of thrombi, making it challenging to determine the etiology. Intravascular optical coherence tomography (OCT) is a valuable adjunct to DSA that can precisely evaluate the characteristics of the intrinsic vessel wall and accurately distinguish between red and white thrombus, providing clues to the etiology of CFFTs. Moreover, OCT not only precisely determined the scope of a floating thrombus but also provided guidance for decision-making in endovascular treatment.

Direct Determination of POSS Cage Structure in a Self-Assembled Ionic Bridged Silsesquioxane by Using the Total Pair Distribution Function G(r) and Density Functional Theory.

In the present study, both short-range and long-range structural features of an ionic bridged silsesquioxane, specifically one containing the 1,4-diazoniabicyclo[2.2.2]octane chloride group (ISSQ), were elucidated. This ionic silsesquioxane was synthesized via direct polycondensation of a bridged organosilane precursor, without any additional functionalization step. Si-O-Si cage structures typical of Polyhedral Oligomeric Silsesquioxanes (POSS) were identified. The average interatomic distances of the POSS cages, including the open T8 cage and the T12 cage for the ISSQ, as well as the T8 cage for a commercially available pendant POSS were determined. It is the first report of the interatomic distance determination of POSS cage; achieved by using total pair distribution function G(r) values obtained through high-resolution synchrotron X-ray diffraction combined with density functional theory (DFT) calculations. The application of DFT was crucial for accurately assigning X-ray peaks and verifying structural details. Furthermore, the analysis of X-ray diffraction peaks and the examination of crystalline domains via transmission electron microscopy enabled the proposal of a hexagonal arrangement of Si-O-Si cages over long ranges within the ionic bridged silsesquioxane. This proposed arrangement highlights a distinctive structural organization that could impact the material's properties and applications.

Construction of negative electrostatic sugared gourd pore within nickel-based metal-organic framework for one-step purification acetylene from ethylene and carbon dioxide mixture

The development of highly efficient adsorbents for purifying acetylene from multi-component mixtures is crucial in the chemical industry, yet the tradeoff between renewability and selectivity severely constrains practical industrial applications. This study introduces sugar gourd pore framework (SGPF-1), an innovative ultra-microporous framework synthesized using 1,2-di(4-pyridyl)ethylene (BPE), 4,4′,4′’-nitrilotribenzoic acid (NTA), and hexanitronickel under solvothermal conditions. The X-ray diffraction analysis has been used to prove the detailed structure and phase purity of SGPF-1. The structure of SGPF-1 was tailored for selectively adsorbing and separating the target compound, C2H2. The negative electrostatic effect, inspired by the natural sugared gourd structure, enhances the selectivity and adsorption capacity for C2H2, while minimizing the interaction with impurities like C2H4 and CO2. Experimental data show that SGPF-1 exhibits significantly higher adsorption of C2H2 compared to C2H4 and CO2 at 298 K and 100 kPa, demonstrating exceptional selectivity towards 50/50 C2H2/CO2 and 1/99 C2H2/C2H4 to be 7.33 and 15.46. Breakthrough experiments confirm its effectiveness in separating C2H2 from C2H4/CO2 mixtures, with C2H4 saturation reached within 5 to 15 min, while C2H2 retention time at 298 K was as long as 26 min. Theoretical calculations have revealed that SGPF-1 exhibits electrostatic compatibility with C2H2 while repelling C2H4 and CO2 through an electrostatic exclusion mechanism. This highlights the importance of rational pore engineering in optimizing the electrostatic properties of adsorbents for high-efficiency multi-component separations. The research aims to advance gas separation technologies and improve the purity of acetylene, a valuable industrial raw material.

Cell-to-cell interactions revealed by cryo-tomography of a DPANN co-culture system

DPANN is a widespread and diverse group of archaea characterized by their small size, reduced genome, limited metabolic pathways, and symbiotic existence. Known DPANN species are predominantly obligate ectosymbionts that depend on their host for proliferation. The structural and molecular details of host recognition, host-DPANN intercellular communication, and host adaptation in response to DPANN attachment remain unknown. Here, we use electron cryotomography (cryo-ET) to show that the Microcaldus variisymbioticus ARM-1 may interact with its host, Metallosphaera javensis AS-7 through intercellular proteinaceous nanotubes. Combining cryo-ET and sub-tomogram averaging, we show the in situ architectures of host and DPANN S-layers and the structures of the nanotubes in their primed and extended states. In addition, comparative proteomics and genomic analyses identified host proteomic changes in response to DPANN attachment. These results provide insights into the structural basis of host-DPANN communication and deepen our understanding of the host ectosymbiotic relationships.