Large Complex Structure Research Articles

Novel approach to managing two enormous bezoars with successive snare-tip electrocautery: A case report

BACKGROUND Gastric bezoars are indigestible masses that can lead to gastrointestinal obstruction and ulceration. Standard treatments include endoscopic mechanical lithotripsy with a polypectomy snare and Coca-Cola dissolution therapy or a combination of both approaches. However, giant bezoars frequently require multiple treatment sessions and extended hospital stays. Additionally, snare-based mechanical fragmentation may be limited by factors such as bezoar size, shape, density, slipperiness, and restricted working space. In cases where refractory giant bezoars are unresponsive to traditional methods, surgical intervention is often necessary. CASE SUMMARY A 57-year-old male with a history of type 2 diabetes presented with severe epigastric pain and vomiting. Endoscopy revealed two large phytobezoars and a gastric ulcer. Initial attempts at mechanical fragmentation with a polypectomy snare and Coca-Cola ingestion for dissolution were unsuccessful due to the large size and complex structure of the bezoars. An innovative approach using snare-tip electrocautery was then employed. It successfully penetrated the slippery, hard surface of the bezoars and fragmented them into smaller pieces. The patient was subsequently treated with Coca-Cola ingestion, enzyme supplements, and proton pump inhibitors. He was discharged without complications following the endoscopic sessions. CONCLUSION Snare-tip electrocautery is a safe, cost-effective, and minimally invasive alternative for managing large, refractory gastric bezoars. This is a valuable option in resource-limited settings.

Atomic-scale insights into topotactic transformations in an extra-large-pore zeolite using time-resolved 3D electron diffraction

AbstractUnderstanding the atomic-scale structural dynamics of phase transformations is crucial for developing materials and tailoring their properties. However, many materials are obtained as polycrystalline powders with large unit cells and/or complex structures, making it challenging to investigate detailed structural changes using conventional X-ray diffraction techniques. Here we employ time-resolved three-dimensional electron diffraction to reveal the topotactic reactions and transformations that convert the extra-large-pore silicate zeolite ECNU-45 into ECNU-46. ECNU-45 features three-dimensional interconnecting 24 × 10 × 10-ring channels, while ECNU-46 consists of one-dimensional 24-ring channels connected to 10-ring pockets. ECNU-45 and ECNU-46 are both examples of pure silicate zeolites with pore openings larger than 22-ring. Our findings indicate changes at six distinct tetrahedral silicon sites, involving atom displacement, addition and removal of framework atoms through bond breakage and formation. This work presents the synthesis of zeolites and also provides atomic-level insights into the dynamic processes of topotactic reactions. Our results have implications for advancing materials engineering and understanding complex solid-state reactions at an atomic scale.

A Time-Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen.

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process. This study developed a time-frequency-based data-driven approach aiming to improve the effectiveness of traditional data-driven structural damage identification approaches for large complex structures. Firstly, the structural acceleration signals in the time domain were converted into two-dimensional images via the Gram angle difference field (GADF). Subsequently, the characteristic feature in the image data was studied by convolutional neural networks (CNNs) to predict the structural damage conditions. An experimental study on a scale model of a cable-stayed bridge was conducted to identify the damage of stay cables under the moving vehicle load on the main girders. The CNN was employed to extract the characteristic features from the time-varying monitoring data of vehicle-bridge interactions. The CNN parameters were optimized to conduct the structural damage classification task. The performance of the proposed method was evaluated by comparing it with various traditional pre-trained networks. The effect of environmental noise on the prediction accuracy was also investigated. The numerical results show that the ResNet model has the best performance in terms of damage identification accuracy and convergence speed, achieving higher accuracy and faster convergence compared to the other four traditional networks. The method can accurately identify damage on bridges using insufficient sensors on the bridge deck, which has valuable potential for application to real-world bridges with monitoring data. As the Signal-to-Noise Ratio (SNR) decreases from 20 dB to 2.5 dB, the prediction accuracy of ResNet decreases from 86.63% to 62.5%, which demonstrates the robustness and reliability in identifying structural damage.

Innovative Strategies for Exploring the Potential of China’s Market

As the world's second-largest economy, China plays an important role in the development of world trade. With nearly one-fifth of the world's population, China's market has huge trade potential for each country. In the process of expanding exports and investment to China, many countries face a lot of trade barriers due to China's large market span, complex structure, and different consumer preferences, so exploring China’s market has become a difficult research topic for many economists and entrepreneurs. It is also a complicated strategy for different companies choose different cities and regions when entering China’s market or expanding their market share in China. The research subject of this paper is exploring the potential of China’s market, and the research object is three dimensions (geographical, economic, and import potential) analysis of market entry approaches in China. According to the depth, breadth and market potential of China’s market, this paper makes a comprehensive comparison of different methods to enter China’s market. The research results will help a company improve the efficiency of penetrating China’s market. In the conclusion part, this paper summarizes new scientific methods and steps for international companies to explore the potential of China’s market.

The Next Chapter of Big Data in Finance

Abstract The second special issue on big data in finance showcases advancements in research related to data of large size, high dimension, and complex structure since the first NBER/RFS big data conference. The papers published in this next chapter address some questions that were proposed in the initial special issue in 2021. Other papers are more directly connected to recent developments in the market. We discuss some new research directions, following on the papers published here. They include evaluating market microstructure reforms, understanding medium-frequency trading, improving missing data imputations, and deepening data valuation. We look forward to more developments to follow.

Ultra-low cycle fatigue of ship hull structure – an alternately-cyclically loaded four-point bending test of a large box girder

Ultra-low cycle fatigue (ULCF) refers to material failure at small number of loading cycles. For large complex structures like ships, the damage from ULCF can bring hazardous consequences. In this study, an alternately-cyclically loaded four-point bending test of a large box girder is introduced as the specimen to represent the ULCF of ship hull structure. In every load during the test, large deformation is applied to the specimen even after reaching its ultimate hull girder strength (UHGS), thus extensive plastic deformation and obvious fracture can occur in the specimen. The severely damaged specimen is further tested until 1.5 cycles of bending are finished, thus the test of post-damage box girder is realized. Moreover, the box girder is divided into 3 sub-sections, which show different but still interacting structural behavior. The result of the test shows the structural behavior of a large complex structure suffering severe damage during alternate hogging and sagging after reaching its UHGS, which corresponds to the consequence of ULCF. The presented ULCF test also provides experiences for investigations of large complex structures with existing damages or after accidental loads. Considering the number of cycles in the test, this study can bridge the gap between monotonic overload and ultra-low cycle fatigue.

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.

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.

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.

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.

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.

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.

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.