Detailed Structure Research Articles

Machine learning based software effort estimation using development-centric features for crowdsourcing platform

Multi-label text classification is a method for categorizing textual data based on features extracted from the original textual information. When it comes to modelling text structural properties, Graph Convolutional Network (GCN) has demonstrated outstanding performance. However, most existing graph-based models do not model the structure of a single text unit and do not consider the sequence information in each document (e.g., word order). To resolve these issues and fully utilize the text’s structural and sequential details, a text classification model called Sequential GCN with Multi-Head Attention (SGCN-MHA) is proposed in this paper. For each text, a separate text graph is constructed in which nodes are the words of the text, and the edges between nodes corresponding to the word relations. Then the GCN is used to extract the structural feature. To enable the word nodes in the document graph to hold contextual information, the BiLSTM is also applied to learn the sequential feature for each graph. Finally, the Multi-Head Attention mechanism is adopted to interact with these two features and then aggregate them to get access to critical information in the text. The efficiency of our approach has been tested on two standard datasets, including comparative and ablation experiments.

Impact of Anisotropic Cosmic-Ray Transport on the Gamma-Ray Signatures in the Galactic Center

The very high energy (VHE) emission of the central molecular zone (CMZ) is rarely modeled in 3D. Most approaches describe the morphology in 1D or simplify the diffusion to the isotropic case. In this work, we show the impact of a realistic 3D magnetic field configuration and gas distribution on the VHE γ-ray distribution of the CMZ. We solve the 3D cosmic-ray transport equation with an anisotropic diffusion tensor using the approach of stochastic differential equations as implemented in the CRPropa framework. We test two different source distributions for five different anisotropies of the diffusion tensor, covering the range of effectively fieldline-parallel diffusion to isotropic diffusion. Within the tested magnetic field configuration, the anisotropy of the diffusion tensor is close to the isotropic case, and three point sources within the CMZ are favored. Future missions such as the upcoming CTA will reveal more small-scale structures that are not yet included in the model. Therefore, a more detailed 3D gas distribution and magnetic field structure will be needed.

Linguistic Models: Optimization With the Use of Conditional Fuzzy C-Means

Most fuzzy models are just numeric. In this study, we revisit, explore and augment a concept of linguistic models, viz., fuzzy models producing results that are information granules, and, specifically, intervals or fuzzy sets. The proposed architecture is formed by constructing a network of linked fuzzy sets (information granules) ininput and output spaces with the aid of a context-based Fuzzy C-Means clustering method. The user centricity of such clustering method is implied by the explicit formulation of fuzzy sets in the output space. The resulting information granules constructed in the input space are conditioned by the corresponding fuzzy sets in the output space. This arrangement can increase the interpretability of the model and represent the model as a collection of logically arranged associations among information granules. The model's overall design process is discussed along with a detailed algorithmic structure. Its experimental evaluations are provided by using both synthetic and publicly datasets. For the former, the model brings the performance improvement ranging from 91% to 250% over the models with information granules uniformly distributed in output space. For the latter, such improvement ranges from 6% to 94%. Finally, a thorough discussion is provided together with guidelines on how to develop such a linguistic model in different contexts.

Numerical simulation of the interaction between a planar shock wave and a backward-facing triangular bubble containing gases with different Atwood numbers

The interaction between a shock wave and an interface delineating two gases engenders intricate flow physics, with particular attention drawn to the hydrodynamic instability due to its practical significance. Previous studies have primarily focused on elucidating different wave patterns and instabilities evolution at the interface during the initial phase of shock interaction with cylindrical or spherical bubbles. However, scant literature has shifted its focus toward exploring the long-term morphology of bubbles, especially those characterized by polygonal interfaces. Notably, the detailed examination of shock interaction with a polygonal interface, such as a triangular one with a constant incident angle, remains largely unexplored in existing literature. Recently, the longtime evolution of detailed flow structures across the interface of shock-forward-facing triangular bubbles was captured by Kundu et al. [“A study on dynamics of shock-accelerated forward-facing triangular bubbles at different Atwood numbers,” Phys. Fluids 36, 016110 (2024)] through numerical simulation. In this study, the dynamics of a shock-accelerated backward-facing triangular interface containing various gases, namely, Sulfur Hexafluoride, Refrigerant-22, Argon, Neon, and Helium, is studied for a shock Mach number of 1.21. Simulations were performed by solving the two-dimensional Euler equation using low-dissipative advection upwind splitting methods (AUSMD), in conjunction with a derived ninth-order upwind scheme and a four-stage third-order Runge–Kutta scheme for temporal integration. The development of Richtmyer–Meshkov (RM) and Kelvin–Helmholtz (K–H) instabilities at the interface, mixing, and normalized movements of backward-facing triangular bubbles is captured at different Atwood (At) numbers.

Evaluation of Shoulder Injuries: A Comparative Study of Imaging by Magnetic Resonance Imaging (MRI) and Magnetic Resonance Arthrography (MRA).

In contrast to the standard shoulder arthroscopy, current radio-diagnostic techniques like magnetic resonance arthrography (MRA), and magnetic resonance imaging (MRI) provide less invasive intricate structural detail of shoulder anatomy. Comparison of efficacy of MRA and MRI for diagnosing suspected rotator cuff injury. Over the course of 4 years (from June 2017 to June 2021), a comparative study, including 100 individuals with suspected rotator cuff pathology, was conducted. For the evaluation of shoulder injuries, the assessment and comparison of MRA and MRI were done in terms of sensitivity (Sn), positive predictive value (PPV), and diagnostic accuracy (DA). MRI and MRA were positive in 76 (76%) and 98 (98%) patients, respectively. The Sn and PPV of MRI for diagnosing the shoulder injury were 76% and 100%, respectively, whereas the Sn and PPV of MRA were 98% and 100%, respectively. MRA was better than MRI in terms of diagnostic accuracy (98% vs. 76%, P = 0.03). MRA is a nonsurgical effective method in evaluating and diagnosing rotator cuff injuries in comparison to MRI.

A Reconstruction Algorithm for Compression Perception Based on Local Learning Dictionary and Sparsity Iterative Update

The global sparse representation method based on compressed sensing fails to capture the local texture and detail structure of an image. To address this, a local dictionary learning method based on the wavelet domain is proposed. The wavelet high-frequency subband sub-block classification and local dictionary learning are implemented using the FCM clustering method and K-L method, respectively. At the reconstruction end, a compressed sampling Matching pursuit algorithm based on iterative update of sparsity is proposed. This adaptive iteration can effectively reconstruct the original image under unknown sparsity conditions. Simulation experiments show that compared to existing reconstruction methods, this approach has the advantages of simple computation, strong adaptive sparse representation ability for the original image, and superior reconstructed image performance.

Split Bregman quantum noise removal algorithm for 3D reconstruction of neutron computed tomography image

The low intensity of the neutron source for neutron computed tomography (CT) results in a long acquisition time for a single projection, which causes the neutron projection data to contain a large amount of quantum noise. Quantum noise will degrade the quality of neutron CT reconstruction images. Therefore, an efficient quantum noise removal algorithm must be used in CT reconstruction. In this paper, an efficient quantum noise removal algorithm for neutron CT 3D image reconstruction is proposed by analysing classical image processing algorithms and quantum image processing algorithms, which employs the maximum likelihood expectation maximization to reconstruct the image and split Bregman algorithm to solve for the total variation (MLEM-SBTV). Experimental results show that MLEM-SBTV performs well in removing quantum noise and reconstructing the detailed structure of images.

New records of the subgenus Soarella Koenike, 1907 from China (Acari, Hydrachnidia, Unionicolidae, Neumania), with the description of five new species

Five new species of the subgenus Soarella are described from P. R. China, i.e. Neumania endophora sp. nov., Neumania paranodophora sp. nov., Neumania curosa sp. nov., Neumania parahirsuta sp. nov. and Neumania acanthophora sp. nov. Detailed structures are illustrated through observation with an optical microscope, and new identifying features of Soarella are discussed.

ADVANCED ELECTRON MICROSCOPY IMAGE PROCESSING FOR ANALYZING AMORPHOUS ALLOYS: ELECTRON MICROSCOPY IMAGE CLUSTER ANALYZER (EMICA). TOOL AND RESULTS

This article unveils EMICA, a Python-based software tool revolutionizing electron microscopy image processing for amorphous alloys. EMICA addresses the unique challenges posed by these materials, which lack long-range order, by providing specialized capabilities for cluster analysis and spatial pattern recognition. This research explored software tool development and application through illustrative examples, answering the key question of how they enhance amorphous alloy analysis. By integrating advanced image processing techniques and algorithms, EMICA uncovers hidden patterns, offering quantitative insights into cluster distributions. The key message emphasizes the application's transformative impact on material science research, providing a specialized solution for electron microscopy image analysis in the amorphous alloy domain. Our key findings, presented through real-world examples and case studies, attest to the efficacy of the software in revealing nuanced details of amorphous alloy structures. From identifying subtle variations in atomic configurations to quantifying cluster distributions, EMICA represents a significant leap forward in the field of advanced electron microscopy image processing, contributing significantly to the advancement of this domain.

Biological Properties of Cyclohex-3-Encarbonyl Acid Pyridin-2-Yl Amide Compound

The synthesized carboxyamide compound was obtained by the reaction of cyclohex-3-ene carbonic acid chloroanhydride with 2-aminopyridine. The biologically active property of this compound has been studied. Based on the experience in the pharmacology department, it was determined that the carboxyamide compound can be used as a drug. The experiment was conducted on animals in the laboratory and how those substances affect the cell was studied. Substances can cause certain changes in the body only when they react with the cells of the body and combine with them; for this, the structure of their molecules must be specific. Not only the composition of the molecules, but also all the structural details, including the distribution of their charges and the location of their functional groups, are of great importance. The general structure of molecules of physiologically active substances should correspond to the structure of specific molecules of cells. As a result of biological research, it was determined that this substance has a calming effect on the nervous system, eliminates muscle spasms. It has myorelaxation and antihypoxia properties. Myorelaxation in animals strengthens skeletal muscles. And the antihypoxia effect doubles the duration of life in animals.

Chemical bonding dictates drastic critical temperature difference in two seemingly identical superconductors

Though YB6 and LaB6 share the same crystal structure, atomic valence electron configuration, and phonon modes, they exhibit drastically different phonon-mediated superconductivity. YB6 superconducts below 8.4 K, giving it the second-highest critical temperature of known borides, second only to MgB2. LaB6 does not superconduct until near-absolute zero temperatures (below 0.45 K), however. Though previous studies have quantified the canonical superconductivity descriptors of YB6's greater Fermi-level (Ef) density of states and higher electron-phonon coupling (EPC), the root of this difference has not been assessed with full detail of the electronic structure. Through chemical bonding, we determine low-lying, unoccupied 4f atomic orbitals in lanthanum to be the key difference between these superconductors. These orbitals, which are not accessible in YB6, hybridize with π B-B bonds and bring this π-system lower in energy than the σ B-B bonds otherwise at Ef. This inversion of bands is crucial: the optical phonon modes we show responsible for superconductivity cause the σ-orbitals of YB6 to change drastically in overlap, but couple weakly to the π-orbitals of LaB6. These phonons in YB6 even access a crossing of electronic states, indicating strong EPC. No such crossing in LaB6 is observed. Finally, a supercell (the M k-point) is shown to undergo Peierls-like effects in YB6, introducing additional EPC from both softened acoustic phonons and the same electron-coupled optical modes as in the unit cell. Overall, we find that LaB6 and YB6 have fundamentally different mechanisms of superconductivity, despite their otherwise near-identity.

Analysis of the microstructure of high viscosity components in Tahe ultra heavy oil based on the combination of ESI (±) FT-ICR/MS

In addressing the challenge of high viscosity in Xinjiang Tahe heavy oil, crucial for optimizing extraction processes, this study focuses on identifying and analyzing the microstructure of key components influencing oil viscosity. The aim is to uncover the underlying reasons for its high viscosity and guide research towards effective viscosity reduction techniques. Electrospray ionization (ESI) combined with fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS) characterizes the molecular-level constituents contributing to oil viscosity. The investigation covers the composition and distribution of oxygen-containing, nitrogen-containing, and organometallic compounds, developing a molecular structure model for these critical viscosity-inducing components. Findings reveal Xinjiang Tahe extra heavy oil as a solid mass at room temperature, with significant viscosity reduction observed post-deasphalting, highlighting asphaltenes' pivotal role in viscosity. Analysis indicates a higher content of metal elements (such as V and Ni) in asphaltenes compared to the crude oil itself, with an abundance of porphyrin structures in asphaltenes markedly influencing oil viscosity. Cyclic, aromatic, and unsaturated compound structures are predominantly found in the asphaltenes of heavy oil. Through combined ESI (±) FT-ICR/MS analysis, the crude oil is determined to mainly comprise acidic compounds, non-basic nitrogen compounds, and basic nitrogen compounds, offering significant insight into heavy oil's complex chemical composition. Specifically, non-basic nitrogen compounds in Tahe crude oil, including alkylcarbazoles, alkylbenzocarbazoles, and alkylpolybenzocarbazoles, are identified as key components, with molecular carbon numbers ranging from C12 to C56 and a predominance near C27. Additionally, asphaltenes in Tahe heavy oil exhibit a higher double bond equivalence (DBE) than the crude oil, indicating a greater degree of condensation with an increased number of double bonds and heterocyclic structures. The significant difference in DBE values between asphaltenes and crude oil underscores their impact on viscosity. A detailed molecular structure model of the asphaltenes in Tahe extra heavy oil has been constructed based on FT-ICR/MS analysis, providing a foundational understanding for developing viscosity reduction strategies.

Electrophysiological properties and structural prediction of the SARS-CoV-2 viroprotein E.

COVID-19, the infectious disease caused by the most recently discovered coronavirus SARS- CoV-2, has caused millions of sick people and thousands of deaths all over the world. The viral positive-sense single-stranded RNA encodes 31 proteins among which the spike (S) is undoubtedly the best known. Recently, protein E has been reputed as a potential pharmacological target as well. It is essential for the assembly and release of the virions in the cell. Literature describes protein E as a voltage-dependent channel with preference towards monovalent cations whose intracellular expression, though, alters Ca2+ homeostasis and promotes the activation of the proinflammatory cascades. Due to the extremely high sequence identity of SARS-CoV-2 protein E (E-2) with the previously characterized E-1 (i.e., protein E from SARS-CoV) many data obtained for E-1 were simply adapted to the other. Recent solid state NMR structure revealed that the transmembrane domain (TMD) of E-2 self-assembles into a homo-pentamer, albeit the oligomeric status has not been validated with the full-length protein. Prompted by the lack of a common agreement on the proper structural and functional features of E-2, we investigated the specific mechanism/s of pore-gating and the detailed molecular structure of the most cryptic protein of SARS-CoV-2 by means of MD simulations of the E-2 structure and by expressing, refolding and analyzing the electrophysiological activity of the transmembrane moiety of the protein E-2, in its full length. Our results show a clear agreement between experimental and predictive studies and foresee a mechanism of activity based on Ca2+ affinity.

Photonically Projected Atoms: A Paradigm Shift in Atomic Physics

This paper presents the discovery of photonically projected atoms, first observed serendipitously during the development of a smartphone-based technique for material identification through reflected light measurements. Upon illuminating samples with ≥75,000 lumen from a light-emitting diode, we observed exact atomic projections emanating from charged particles within the sample. Analysis determined that protons within each atom bond with and re-emit contacted photons. After proton emission, a spatial lensing effect enlarges the projections of the photons to a diameter of 0.5 mm when measured 1 mm from the surface. The smaller 1⁄3” sensors and pixel size of smartphone cameras provide a better resolution of the fine structural details compared with larger digital camera alternatives. Through seven years of imaging analysis under various conditions, fundamental insights emerged on atomic architectures, particle interactions, and the role of space, empirically revising mainstream quantum theory. This research aims to disseminate recent advancements in the direct visualization and updated modeling of atomic projections to enable ongoing physics discoveries through this accessible technique.

Theoretical relationships between axoneme distortion and internal forces and torques in ciliary beating.

The axoneme is an intricate nanomachine responsible for generating the propulsive oscillations of cilia and flagella in an astonishing variety of organisms. New imaging techniques based on cryoelectron-tomography (cryo-ET) and subtomogram averaging have revealed the detailed structures of the axoneme and its components with sub-nm resolution, but the mechanical function of each component and how the assembly generates oscillations remains stubbornly unclear. Most explanations of oscillatory behavior rely on the dynamic regulation of dynein by some signal, but this may not be necessary if the system of dynein-driven slender filaments is dynamically unstable. Understanding the possibility of instability-driven oscillations requires a multifilament model of the axoneme that accounts for distortions of the axoneme as it bends. Active bending requires forces and bending moments that will tend to change the spacing and alignment of doublets. We hypothesize that components of the axoneme resist and respond to these loads in ways that are critical to beating. Specifically, we propose (i) that radial spokes provide torsional stiffness by resisting misalignment (as well as spacing) between the central pair and outer doublets, and (ii) that the kinematics of dynein arms affect the relationships between active forces and bending moments on deforming doublets. These proposed relationships enhance the ability of theoretical, multifilament models of axonemal beating to generate propulsive oscillatory waveforms via dynamic mechanical instability.

Optical and structural properties of chlorophyll a aggregates in PVA film

The aggregated properties of Chlorophyll a (Chla) in polymer play a pivotal role in light capture and transfer as photostable pigment sensitizers of solar power systems. Here, we embedded the natural Chla molecules (2 × 10−3 M) extracted from spinach leaves into polyvinyl alcohol (PVA) films by casting method under different temperature. UV–VIS, fluorescence, Circular Dichroism (CD) spectroscopy, and Transmission Electron Microscopy (SEM) have been applied to investigate the optical properties and aggregated structures in detail. The excited state dynamics of these aggregates in PVA were explored using Time-Correlated Single Photon Counting (TCSPC). The de-composited components from absorption, emission, TCSPC and the CD shapes in the study show the analogous characteristics to the photosystem of cyanobacteria in some papers that features a coexisting of several aggregates (dimer, oligomer, bulk oligomer), in which the proportion of bulk oligomer can be greatly promoted under lower temperature (0 °C) due to the involvement of a large amount of water. The analysis of spectral and dynamical components reveals the bulk oligomer model of Chla in PVA, which is constructed from several oligomers by PVA chain and water. Besides, a specific red trapping center within bulk oligomer is proposed to perform capturing energy in 18 ps, which is located on the lowest end of energy flow and devotes large than 99% florescence in TCSPC.

PROFESSIONAL GRAPHIC JUSTIFICATION OF TEST PROGRAMS FOR PROFESSIONALLY SIGNIFICANT QUALITIES ASSESSMENT OF FUTURE SPECIALISTS (ON THE EXAMPLE OF KH. DOSMUKHAMEDOV ATYRAU UNIVERSITY)

Professional education and development involve not only learning extensive theoretical knowledge but also applying it through practical skills, especially those involving motor abilities that are crucial to the profession. The current vocational education system integrates professional diagnostics most effectively when it incorporates methods from professionally oriented physical culture. This approach is key to developing the physical competence and readiness required for specialists to excel in their fields. This study aims to establish comprehensive testing programs to evaluate the conditioning qualities that are important for the professional development of students as they train to become specialists. The methodology used for this study involves a professional analysis of the university's specialty training programs. This includes categorizing professions into standard groups based on the core subject matter and defining broad professional categories with common features. A detailed professional structure was outlined for each faculty, and specialties were categorized by profession type: “H-H”; “H-S”; “H-T”; “H-N”; “H-AR”. On the basis of analysis of special features and conditions of learned types professional activity were created their generalized profiles (professiograms) with highlighting a block of professionally important qualities, properties and functions. Subsequently, for each typical group of professions were selected motor test tasks to assess the level of professionally important formation for these quality professions.

Heparan sulfate selectively inhibits the collagenase activity of cathepsin K

Cathepsin K (CtsK) is a cysteine protease with potent collagenase activity. CtsK is highly expressed by bone-resorbing osteoclasts and plays an essential role in resorption of bone matrix. Although CtsK is known to bind heparan sulfate (HS), the structural details of the interaction, and how HS regulates the biological functions of CtsK, remains largely unknown. In this report, we discovered that HS is a multifaceted regulator of the structure and function of CtsK. Structurally, HS forms a highly stable complex with CtsK and induces its dimerization. Co-crystal structures of CtsK with bound HS oligosaccharides reveal the location of the HS binding site and suggest how HS may support dimerization. Functionally, HS plays a dual role in regulating the enzymatic activity of CtsK. While it preserves the peptidase activity of CtsK by stabilizing its active conformation, it inhibits the collagenase activity of CtsK in a sulfation level-dependent manner. These opposing effects can be explained by our finding that the HS binding site is remote from the active site, which allows HS to specifically inhibit the collagenase activity without affecting the peptidase activity. At last, we show that structurally defined HS oligosaccharides effectively block osteoclast resorption of bone in vitro without inhibiting osteoclast differentiation, which suggests that HS-based oligosaccharide might be explored as a new class of selective CtsK inhibitor for many diseases involving exaggerated bone resorption.

XFEM based study on fatigue performance of rib-to-diaphragm welded joints considering initial defects

Orthotropic steel bridge decks (OSBDs) have been widely used in the field of bridges. However, their service life is significantly limited by fatigue issues. This study presents an analysis of the fatigue performance at the structural details of the rib-to-diaphragm joint, taking into account the impact of initial defects. A numerical segment model employing shell-solid coupling is established to determine the influence surface range and the most unfavorable working condition of the fatigue details, using fatigue standard vehicle loading. The accuracy of the local full-scale model is validated. A local model test of the rib-to-deck welded joint is carried out to deduce the parameter selection for the numerical simulation, based on the test results. Additionally, the crack propagation process in rib-to-diaphragm joints is simulated using XFEM. The simulation results of crack propagation demonstrate good agreement with the actual bridge crack path and shape. The crack propagation model can effectively evaluate the fatigue life, crack development stage, and shape of fatigue details. The study determines the effects of crack depth, crack length, and crack angle on fatigue performance. This study provides a basis for studying the residual fatigue life and crack propagation behavior of rib-to-diaphragm welded joints with initial defects. It is of great significance for understanding the crack propagation mechanism, formulating repair measures, and determining repair time.

Super-resolution reconstruction of 3D digital rocks by deep neural networks

Digital rock technology provides valuable insights into the pore structure and fluid flow properties of geoenergy resources. Artificial intelligence technology in vision and image processing, especially the image super-resolution, has great potential for digital rock reconstruction and resolution enhancement. However, the analyzed core samples are typically sandstones/carbonates in micro-scale resolutions and in two-dimensional (2D) space, whereas the shale rocks in nano-scale resolutions for unconventional resources or three-dimensional (3D) digital cores are rarely investigated. Additionally, previous studies primarily emphasized image quality from a computer vision perspective, with little consideration given to estimating physical properties of digital rocks using super-resolution techniques.This study presents a very deep super-resolution (VDSR) algorithm, specifically designed to generate high-resolution 3D digital rock images, for nano-scale shale matrix and micro-scale hydraulic fractures. We compare both image quality and permeability accuracy between the original high-resolution images and the super-resolution images reconstructed by the proposed method. The results reveal that the reconstructed images using the proposed method closely resemble the actual images, and effectively reduce errors in permeability computations. This study highlights the applicability of the proposed VDSR algorithm in establishing the detailed structures of 3D nano-scale shale matrix and hydraulic fractured rocks, thus advancing super-resolution techniques in digital core analysis for geoenergy resources development.