Large Complex Structure Research Articles

3D-printed brachytherapy in patients with cervical cancer: improving efficacy and safety outcomes

ObjectiveThis study aims to evaluate the efficacy and safety of 3D printing technology in brachytherapy for cervical cancer, comparing its outcomes with conventional free hand implantation brachytherapy.MethodsA total of 50 cervical cancer patients treated at the First Affiliated Hospital of Gannan Medical College from January 2019 to July 2023 were included in this study. Patients were divided into two groups: 25 patients received intensity-modulated radiotherapy (IMRT) combined with 3D-printed brachytherapy, and 25 patients underwent IMRT combined with free hand brachytherapy implantation. Key indicators analyzed included short-term therapeutic effects, survival outcomes, operation times, the number of CT scans, the number of needles inserted, dosimetric parameters, and complications.ResultsThe use of 3D-printed brachytherapy significantly improved the safety of radiation therapy operations, especially for large tumors (≥ 30 mm), by providing more precise dose distribution and reducing the radiation doses received by critical organs such as the bladder and rectum. Compared to the artificial implant group (88% prevalence), the 3D-printed brachytherapy group showed a significantly lower incidence of radiation enteritis (29.2% prevalence, p < 0.001). There were no significant differences in other complications between the two groups. For instance, the incidence of radiation cystitis was relatively high in the 3D-printed brachytherapy group (79.2% prevalence) compared to the artificial implant group (64% prevalence, p = 0.240). The median follow-up period in this study was 22.5 months [IQR 18–29]. Among the 49 patients included, 43 had cervical squamous carcinoma and 6 had cervical adenocarcinoma. Short-term therapeutic response rates were comparable, with no significant difference in overall survival observed between the two groups.Conclusion3D-printed brachytherapy offers a more effective and safer therapeutic option for patients with cervical cancer, particularly for those with large tumors or complex anatomical structures.

Multi-source information fused loose particle localization and material identification method for sealed electronic equipment

Sealed electronic equipment are1 an important component of aerospace defense systems, and loose particles pose a significant threat to their reliable operation. Loose particle detection is crucial. For sealed electronic equipment with large scale and complex structure, loose particle detection should not only include the judgment of existence, but also obtain location and material information to facilitate the cleaning and control work. In this paper, the authors proposed a multi-source information fused loose particle localization and material identification method. Firstly, the equipment model was designed, the loose particle samples were made, and loose particle signals were collected. Secondly, the two-stage adaptive energy threshold pulse extraction algorithm was newly proposed to extract effective pulses, and the threshold-judgement-search pulse matching algorithm was improved to match the effective pulse groups. Next, spectrograms were transformed from effective pulses to create the localization and material image set. The time-domain, frequency-domain and gray-level co-occurrence matrix features were used to construct the joint feature library. Then, the channel-weighting feature selection method was used to create the localization and material data set. Finally, PReLU-VGG19-Plus was trained on the localization and material image set to obtain the optimal localization and material neural network, while parameter-optimized XGBoost was trained on the localization and material data set to obtain the optimal localization and material classifier. On this basis, combined with the triple majority voting process, the combined localization and material framework were constructed. Extensive test results show that, the location-identification achieved by combined localization framework and the material-identification accuracy achieved by combined material framework are all 100%. The feasibility, stability, and superiority of the method proposed in this paper have been fully verified. It is an important supplement to the existing loose particle detection research, providing important reference for signal detection and classification research in similar fields, and effectively improving the reliability of sealed electronic equipment.

DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model.

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution decreases. Here we present DiffModeler, a fully automated method for modeling large protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. DiffModeler showed an average template modeling score of 0.88 and 0.91 for two datasets of cryo-EM maps of 0-5 Å resolution and 0.92 for intermediate resolution maps (5-10 Å), substantially outperforming existing methodologies. Further benchmarking at low resolutions (10-20 Å) confirms its versatility, demonstrating plausible performance.

Learned liquid crystal microlens array for joint optimized deep optical architecture in identifying metameric materials.

Multispectral imaging holds great promise for the detection of metameric materials. However, traditional multispectral imaging systems are characterized by their large volume, complex structure, and high computational requirements, limiting their practical application. We propose a jointly optimized deep optical architecture that combines the liquid crystal (LC) microlens array (MLA) characteristics and a multi-level perceptual spectral reconstruction network (MLP-SRN). The core of the architecture is to integrate the physical properties of the LC-MLA into the MLP-SRN using point spread function (PSF) optical convolution kernels, decoupling the light-field characteristic information collected by the LC-MLA at different voltages. Experimental results demonstrate that the incorporation of the physical properties of the LC-MLA not only reduces the system size and computational complexity but demonstrates excellent performance in identifying a metameric material.

An advanced fast multipole dual boundary element method for analyzing multiple cracks propagation

A new fast multipole dual boundary element method (FMDBEM) is developed to analyze multiple crack propagations. To evaluate accurately the stress fields around the crack tip, a variable-order asymptotic element (VAE) is first proposed to express the singular behavior. This VAE is also suitable for the V-notches with different opening angles, requiring only minor adjustments of stress exponents. Then the VAE is introduced into the FMDBEM framework, where several singularity problems of integrals are solved. Finally, the FMDBEM with VAEs, combined with an adaptive scheme, is used to determine the crack propagation paths. Numerical examples show that the present method is accurate and easy to implement making it an appealing tool for analyzing large complex structures that include randomly distributed cracks and notches.

Yukawa couplings at infinite distance and swampland towers in chiral theories

We study limits of vanishing Yukawa couplings of 4d chiral matter fields in Quantum Gravity, using as a laboratory type IIA orientifolds with D6-branes. In these theories chiral fermions arise at brane intersections, where an infinite tower of charged particles dubbed gonions are localised. We show that in the limit Y → 0 some of these towers become asymptotically massless, while at the same time the kinetic term of some chiral fields becomes singular and at least two extra dimensions decompactify. For limits parametrised by a large complex structure saxion u, Yukawa couplings have a behaviour of the form Y ~ 1/ur, with r some positive rational number. Moreover, in this limit some of the gauge couplings associated to the Yukawa vanish. The lightest gonion scales are of order mgon ~ gsMP with s > 1, verifying the magnetic WGC with room to spare and with no need of its tower/sublattice versions. We also show how this behaviour can be understood in the context of the emergence of kinetic terms in Quantum Gravity. All these results may be very relevant for phenomenology, given the fact that some of the Yukawa couplings in the Standard Model are very small.

Towards a concurrency platform for scalable multi-axial real-time hybrid simulation

Multi-axial real-time hybrid simulation (maRTHS) uses multiple hydraulic actuators to apply loads and deform experimental substructures, enacting both translational and rotational motion. This allows for an increased level of realism in seismic testing. However, this also demands the implementation of multiple-input, multiple-output control strategies with complex nonlinear behaviors. To realize true real-time hybrid simulation at the necessary sub-millisecond timescales, computational platforms will need to support these complexities at scale, while still providing deadline assurance. This paper presents initial work towards supporting (and is influenced by the need for) envisioned larger-scale future experiments based on the current maRTHS benchmark: it discusses aspects of hardware, operating system kernels, runtime middleware, and scheduling theory that may be leveraged or developed to meet those goals. This work aims to create new concurrency platforms capable of managing task scheduling and adaptive event handling for computationally intensive numerical simulation and control models like those for the maRTHS benchmark problem. These should support real-time behavior at millisecond timescales, even for large complex structures with thousands of degrees of freedom. Temporal guarantees should be maintained across behavioral and computational mode changes, e.g., linear to nonlinear control. Pursuant to this goal, preliminary scalability analysis is conducted towards designing future maRTHS experiments. The results demonstrate that the increased capabilities of modern hardware architectures are able to handle larger finite element models compared to prior work, while imposing the same latency constraints. However, the results also illustrate a subtle challenge: with larger numbers of CPU cores, thread coordination incurs more overhead. These results provide insight into the computational requirements to support envisioned future experiments that will take the maRTHS benchmark problem to nine stories and beyond in scale. In particular, this paper (1) re-evaluates scalability of prior work on current platform hardware, and (2) assesses the resource demands of a basic smaller scale model from which to gauge the projected scalability of the new maRTHS benchmark as ever larger and more complex models are integrated within it.

Framework of Physically Consistent Homogenization and Multiscale Modeling of Shell Structures**

A framework of physically consistent homogenization and multiscale modeling (PCHMM) for reduced-order analysis of plate/shell structures is developed in this paper. To address the inapplicability of conventional periodic boundary conditions and Hill’s condition involved in homogenization of shear-deformable shell structures, the paper proposes physically consistent boundary conditions and modified Hill’s condition for plate/shells. Unlike the PCHMM method for beams, considering the contradiction between high-order displacement fields induced by shear forces and low-order kinematic assumptions, additional constraints are applied to the plate/shell structure sectional strains during the solution of perturbation fields. The correctness and effectiveness of the proposed plate/shell PCHMM framework and method are verified by typical numerical examples. The proposed theory can also be conveniently embedded into commercial finite element software for homogenization and multiscale analysis of structures such as microscale metamaterials like lattice plates and large complex structures like aircraft fuselage sections.

Using Python to Analyze Financial Big Data

As technology and the internet develop, more data are generated every day. These data are in large sizes, high dimensions, and complex structures. The combination of these three features is the “Big Data” [1]. Big data is revolutionizing all industries, bringing colossal impacts to them [2]. Many researchers have pointed out the huge impact that big data can have on our daily lives [3]. We can utilize the information we obtain and help us make decisions. Also, the conclusions we drew from the big data we analyzed can be used as a prediction for the future, helping us to make more accurate and benign decisions earlier than others. If we apply these technics in finance, for example, in stock, we can get detailed information for stocks. Moreover, we can use the analyzed data to predict certain stocks. This can help people decide whether to buy a stock or not by providing predicted data for people at a certain convincing level, helping to protect them from potential losses.

Changes in the Aggregation Behaviour of Zinc Oxide Nanoparticles Influenced by Perfluorooctanoic Acid, Salts, and Humic Acid in Simulated Waters.

The increasing utilization of zinc oxide nanoparticles (ZnO-NPs) in many consumer products is of concern due to their eventual release into the natural environment and induction of potentially adverse impacts. The behaviour and environmental impacts of ZnO-NPs could be altered through their interactions with environmentally coexisting substances. This study investigated the changes in the behaviour of ZnO-NPs in the presence of coexisting organic pollutants (such as perfluorooctanoic acid [PFOA]), natural organic substances (i.e., humic acid [HA]), and electrolytes (i.e., NaCl and CaCl2) in simulated waters. The size, shape, purity, crystallinity, and surface charge of the ZnO-NPs in simulated water after different interaction intervals (such as 1 day, 1 week, 2 weeks, and 3 weeks) at a controlled pH of 7 were examined using various characterization techniques. The results indicated alterations in the size (such as 162.4 nm, 1 day interaction to >10 µm, 3 weeks interaction) and zeta potential (such as -47.2 mV, 1 day interaction to -0.2 mV, 3 weeks interaction) of the ZnO-NPs alone and when PFOA, electrolytes, and HA were present in the suspension. Different influences on the size and surface charge of the nanoparticles were observed for fixed concentrations (5 mM) of the different electrolytes. The presence of HA-dispersed ZnO-NPs affected the zeta potential. Such dispersal effects were also observed in the presence of both PFOA and salts due to their large aliphatic carbon content and complex structure. Cation bridging effects, hydrophobic interactions, hydrogen bonding, electrostatic interactions, and van der Waals forces could be potential interaction forces responsible for the adsorption of PFOA. The presence of organic pollutants (PFOA) and natural organic substances (HA) can transform the surface characteristics and fate of ZnO-NPs in natural and sea waters.

Piezoresistive nanocomposite sensing using electrical impedance tomography and machine learning

Strain measurements have traditionally been accomplished by connecting many gauges to crucial spots on structures. Installing strain gauges can be costly and limited in providing ‎comprehensive data for large surface areas or complex structures, posing significant challenges. This work explores the use of piezoresistive nanocomposite sensors as a cost-effective and informative solution for wide surface area strain detection. Piezoresistive sensors are created using two methodologies: film-coated and pellet-fed 3D printing technique (PF3DP), with variable gauge factors achieved by using 6 and 10 wt% multi-wall carbon nanotubes (MWCNTs), respectively. An electrical impedance tomography (EIT) setup is utilized to analyze electrical changes with respect to strain and predict the conductivity distribution across the sensor’s surface. This study employs two efficient machine learning (ML) techniques, artificial neural networks (ANN) and convolutional neural networks (CNN), to analyze and compare image reconstruction resolution at different loads. The machine learning models are designed to reconstruct images quickly and reliably. The study demonstrates that the hybrid-ML model, which combines ANN and CNN models, achieves high-resolution image construction with minimal errors compared to outcomes obtained through EIT for the experimental measurements. Sensors created using 3D printing provide higher image resolution and data stability than film-coated sensors. This technology has the potential for accurate strain sensing and measuring the structural health of complex surfaces.

High-temperature mechanical performances of SiC whisker in-situ toughened 3DN C/SiC countersunk bolts prepared by chemical vapor infiltration

High performance bolts, made of continuous fiber reinforced silicon carbide composites (C/SiC), are crucial to the design and preparation of the hot-end C/SiC components with large complex structures both in aerospace and aeronautical fields. In this work, in-situ grown SiC whiskers were uniformly introduced into the porous laminated 3DN C/SiC countersunk bolt, in order to improve the brittle nature of the 3DN C/SiC threads. Results showed that 7 % increase of tensile strength of the 3DN C/SiC countersunk bolts was achieved via in-situ grown SiC whiskers, and a fatigue life of 106 cycles was also passed. The stud fracture morphologies indicated that the SiC whiskers grew within the woven pores and toughened the layered SiC matrix, so that the load transfer between the layered SiC matrix and carbon fibers was improved under tension. Further high temperature tests showed that the tensile and the shear strengths of the in-situ toughened 3DN C/SiC countersunk bolts were degraded to 64.74 % and 28.83 % at 1000 °C in air respectively. During the thermal exposure, the carbon fibers were largely consumed by oxidation, while the SiC whiskers grew slender and formed a fluffy layer to partially reinforce the SiC matrix. The synergy effect of SiC whiskers and carbon fibers to hinder crack propagation and to toughen the SiC matrix contributes to the above mechanical properties of the bolts.

Preparation of Dendrobium officinale Polysaccharide by Lactic Acid Bacterium Fermentation and Its Protective Mechanism against Alcoholic Liver Damage in Mice.

Dendrobium officinale polysaccharide (DP) was prepared with lactic acid bacterium fermentation to overcome the large molecular weight and complex structure of traditional DP for improving its functional activity and application range in this work. The structure was analyzed, and then the functional activity was evaluated using a mouse model of alcoholic liver damage. The monosaccharide compositions were composed of four monosaccharides: arabinose (0.13%), galactose (0.50%), glucose (24.38%), and mannose (74.98%) with a molecular weight of 2.13 kDa. The connection types of glycosidic bonds in fermented D. officinale (KFDP) were →4)-β-D-Manp(1→, →4)-β-Glcp(1→, β-D-Manp(1→, and β-D-Glcp(1→. KFDP exhibited an excellent protective effect on alcoholic-induced liver damage at a dose of 80 mg/kg compared with polysaccharide separated and purified from D. officinale without fermentation (KDP), which increased the activity of GSH, GSH-Px, and GR and decreased the content of MDA, AST, T-AOC, and ALT, as well as regulated the level of IL-6, TNF-α, and IL-1β to maintain the normal functional structure of hepatocytes and retard the apoptosis rate of hepatocytes. The results proved that fermentation degradation is beneficial to improving the biological activity of polysaccharides. The potential mechanism of KFDP in protecting alcoholic liver damage was inhibiting the expression of miRNA-150-5p and targeting to promote the expression of Pik3r1. This study provides an important basis for the development of functional foods.

Beyond large complex structure: quantized periods and boundary data for one-modulus singularities

We study periods in an integral basis near all possible singularities in one-dimensional complex structure moduli spaces of Calabi-Yau threefolds. Near large complex structure points these asymptotic periods are well understood in terms of the topological data of the mirror Calabi-Yau manifold. The aim of this work is to characterize the period data near other boundaries in moduli space such as conifold and K-points. Using results from Hodge theory, we provide the general form of these periods in a quantized three-cycle basis. Based on these periods we compute the prepotential and related physical couplings of the underlying supergravity theory. Moreover, we elucidate the meaning of the model-dependent coefficients that appear in these expressions: these can be identified with certain topological and arithmetic numbers associated to the singular geometry at the moduli space boundary. We illustrate our findings by studying a wide set of examples.

Ultraviolet color image sensor based on CsPbBr3 inorganic perovskite nanocrystal film.

Ultraviolet and color imaging require different image sensors and optical channels, which results in large size, complex structure, and high cost of imaging systems. Here, we report a novel, to the best of our knowledge, image sensor that combines ultraviolet and color imaging functions. The fabrication of this image sensor is achieved by coating high-transparency CsPbBr3 perovskite nanocrystals in a polymer film on the color filter layer of a silicon-based detector. The film, serving as an ultraviolet photoluminescent layer, exhibits high transparency, exceeding 91.5% at wavelengths beyond the photoluminescence peak of 513 nm. During ultraviolet imaging, the film converts ultraviolet light into visible light, which passes through the green filter layer to reach the detector for imaging. During visible light imaging, red light, green light, and most of the blue light pass through the CsPbBr3 perovskite nanocrystal film and color filter layer to reach the detector for imaging. As a result, the image sensor can capture both 257 nm solar-blind ultraviolet images and color photos in the visible light.

IDRWalker: A Random Walk Based Tool for Generating Intrinsically Disordered Regions in Large Protein Complexes.

Intrinsically disordered regions (IDRs), which may be functionally important, are common in proteins. However, the structures of IDRs are often missing due to their highly dynamic nature. In the study of IDRs, integrative modeling combining computational simulations and experimental data is a common approach, for which initial structures of the IDRs need to be built. However, applying this method to large protein complexes is challenging because existing structure generation tools are sometimes unsuitable for IDRs in large systems. To facilitate convenient and rapid structure generation of IDRs in large protein complexes, we developed a computational tool named IDRWalker based on self-avoiding random walks. Three protein complexes were used to illustrate the efficiency of the tool, and it was found that IDRs in more than 800 chains of the nuclear pore complex could be generated in minutes. These structures of large protein complexes with added IDRs can be further used to run computational simulations for integrative modeling.

Experimental Studies on the Anisotropic Fatigue Behaviour of IN718 Fabricated via Wire Arc Additive Manufacturing

Wire and arc additive manufacturing (WAAM) offers promise in creating large complex structures due to its flexibility and high material deposition rates. The nickel-based alloy IN718 is favoured for WAAM due to its weldability and compatibility. However, WAAM can introduce issues like anisotropic grain structure, porosity, and residual stresses which can lead to directional variations in tensile, fatigue, and fracture behaviour. This paper studied the WAAM process of IN718, utilising cold metal transfer (CMT). The optimised CMT-WAAM parameters for IN718 were identified to as a wire feed speed of 8–10 m/min and a torch travel speed of 0.5–0.7 m/min, resulting in stable deposition and minimal defects. Nevertheless, columnar grain structures were observed in the build direction (BD), with coarse grains in the wall-length direction (WD). This anisotropic microstructure coupled with stress concentrators, contributes to the directional dependence observed in tensile properties, fatigue endurance, and crack growth. The investigation revealed superior ductility in the BD compared to the WD. Interestingly, the fatigue endurance testing showed a longer life in the WD compared with the BD, attributed to stronger stress concentrators in the BD specimens. However, when examining a cracked specimen, the fatigue crack propagated faster in the WD rather than the BD.

Design on thermal-mechanism coordination for binding reinforcing mesh based on shape memory polymer

The traditional rebar binding devices require complex drive and transmission mechanisms, which leads to large volume and complex structure. In this paper, a cylindrical thermoplastic shape memory polymer (SMP) fixture is proposed to verify the rebar binding method of thermal-mechanism coordination. The SMP fixture is manufactured by the injection molding technology through selecting suitable-ratio Polylactic acid and Polycaprolactone (PCL) blend materials. Besides, an additional auxiliary device is presented to overcome the incomplete recovery disadvantage existing in the thermoplastic SMP and completely achieve binding the rebar. On this base, two different binding methods are proposed to compare the mechanical performance after fixing the rebar, and the external force/thermal contributions are tested and discussed in detail. The tested results show that the binding contribution of heat could reach 70% while the binding contribution of external force could reach 30% above the transition temperature (Tg ). The maximum tensile force that the binding rebar can withstand under the thermal-mechanism coordination action could reach up to 657.7 N, which is higher than the maximum tensile force of the wire binding. In addition, the maximum friction force between rebar and notches of fixture could reach up to 94.1 N, which further verifies the feasibility of thermal-mechanism coordination for binding reinforcing mesh based on SMP fixture.

MoLPC2: improved prediction of large protein complex structures and stoichiometry using Monte Carlo Tree Search and AlphaFold2.

Today, the prediction of structures of large protein complexes solely from their sequence information requires prior knowledge of the stoichiometry of the complex. To address this challenge, we have enhanced the Monte Carlo Tree Search algorithms in MoLPC to enable the assembly of protein complexes while simultaneously predicting their stoichiometry. In MoLPC2, we have improved the predictions by allowing sampling alternative AlphaFold predictions. Using MoLPC2, we accurately predicted the structures of 50 out of 175 nonredundant protein complexes (TM-score ≥ 0.8) without knowing the stoichiometry. MoLPC2 provides new opportunities for predicting protein complex structures without stoichiometry information. MoLPC2 is freely available at https://github.com/hychim/molpc2. A notebook is also available from the repository for easy use.

Bioinformatic prediction of proteins relevant to functions of the bacterial OLE ribonucleoprotein complex.

OLE (ornate, large, extremophilic) RNAs are members of a noncoding RNA class present in many Gram-positive, extremophilic bacteria. The large size, complex structure, and extensive sequence conservation of OLE RNAs are characteristics consistent with the hypothesis that they likely function as ribozymes. The OLE RNA representative from Halalkalibacterium halodurans is known to localize to the phospholipid membrane and requires at least three essential protein partners: OapA, OapB, and OapC. However, the precise biochemical functions of this unusual ribonucleoprotein (RNP) complex remain unknown. Genetic disruption of OLE RNA or its partners revealed that the complex is beneficial under diverse stress conditions. To search for additional links between OLE RNA and other cellular components, we used phylogenetic profiling to identify proteins that are either correlated or anticorrelated with the presence of OLE RNA in various bacterial species. This analysis revealed strong correlations between the essential protein-binding partners of OLE RNA and organisms that carry the ole gene. Similarly, proteins involved in sporulation are correlated, suggesting a potential role for the OLE RNP complex in spore formation. Intriguingly, the Mg2+ transporter MpfA is strongly anticorrelated with OLE RNA. Evidence indicates that MpfA is structurally related to OapA and therefore MpfA may serve as a functional replacement for some contributions otherwise performed by the OLE RNP complex in species that lack this device. Indeed, OLE RNAs might represent an ancient RNA class that enabled primitive organisms to sense and respond to major cellular stresses.IMPORTANCEOLE (ornate, large, extremophilic) RNAs were first reported nearly 20 years ago, and they represent one of the largest and most intricately folded noncoding RNA classes whose biochemical function remains to be established. Other RNAs with similar size, structural complexity, and extent of sequence conservation have proven to catalyze chemical transformations. Therefore, we speculate that OLE RNAs likewise operate as ribozymes and that they might catalyze a fundamental reaction that has persisted since the RNA World era-a time before the emergence of proteins in evolution. To seek additional clues regarding the function of OLE RNA, we undertook a computational effort to identify potential protein components of the OLE ribonucleoprotein (RNP) complex or other proteins that have functional links to this device. This analysis revealed known protein partners and several additional proteins that might be physically or functionally linked to the OLE RNP complex. Finally, we identified a Mg2+ transporter protein, MpfA, that strongly anticorrelates with the OLE RNP complex. This latter result suggests that MpfA might perform at least some functions that are like those carried out by the OLE RNP complex.