Large Complex Research Articles

A Survey on Progressive Visualization.

Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges.

A novel tensor-based modal decomposition method for reduced order modeling and optimal sparse sensor placement

Data-driven modal decomposition methods have become fundamental algorithms in constructing reduced order models (ROMs), which serve as a critical tool in the digital twin modeling of large complex systems. Classical modal decomposition methods are particularly adept at handling matrix-form input data that contains columnized snapshots without retaining spatial structure and neighborhood information. Focusing on addressing this problem, this paper proposed a Tensor-Based Modal Decomposition (TBMD) method to deal with the high-order tensor-form input data. TBMD method is effective in finding the potential low-order manifolds of a given high-order dynamical system with low energy loss within fewer decomposited modes. Moreover, TBMD is extended to be used in the field of optimal sensor placement via the development of a tensor-based QR factorization method and a tensor-based compressive sensing algorithm. The tensor-based QR factorization method is able to consider the spatial structure and neighborhood information of the basis matrices data in tensor form. This is the first time that a compressive sensing model has been discussed and solved for the frontal slice-wise product of a tensor and a vector. Subsequently, comprehensive case studies are conducted based on the cylinder wake dataset, airfoil vortex dataset, sea surface temperature dataset, eigenfaces dataset and turbulent channel flow dataset. The experiment results show that TBMD can accurately capture lower-order manifolds, and the tensor-based QR factorization is effective in optimizing sensor placement with fewer sensors while maintaining high reconstruction accuracy. The proposed methods in this paper is promising to be utilized in the construction of large-scale complex system digital twins, in which TBMD can serve as a fundamental algorithm in constructing ROM.

Deep reinforcement learning assisted memetic scheduling of drones for railway catenary deicing

Icy rainfall and snowfall in 2024 Spring Festival struck the high-speed railway catenary systems and caused serious traffic disruptions in central and eastern China. Deicing drones are an effective method in response to these freezing events due to their fast speed and high environmental tolerance. However, the large disaster-affected area and the large scale and complexity of catenary networks make deicing drone scheduling a very difficult problem. In this paper, we formulate two versions of deicing drone scheduling problem, one for single drone scheduling and the other for multiple drone scheduling. Unlike most existing vehicle/drone routing problems, our problem aims to minimize the total negative effect caused by the freezing events on train operations, which reflects the prime concern of the decision-maker and is highly complex. To efficiently solve the problem, we propose a reinforcement learning assisted memetic optimization algorithm, which integrates global mutation and a set of neighborhood search operators adaptively selected by deep reinforcement learning. Computational results on real-world problem instances demonstrate its significant performance advantages over selected popular optimization algorithms in the literature.

To 200,000 m/z and Beyond: Native Electron Capture Charge Reduction Mass Spectrometry Deconvolves Heterogeneous Signals in Large Biopharmaceutical Analytes.

Great progress has been made in the detection of large biomolecular analytes by native mass spectrometry; however, characterizing highly heterogeneous samples remains challenging due to the presence of many overlapping signals from complex ion distributions. Electron-capture charge reduction (ECCR), in which a protein cation captures free electrons without apparent dissociation, can separate overlapping signals by shifting the ions to lower charge states. The concomitant shift to higher m/z also facilitates the exploration of instrument upper m/z limits if large complexes are used. Here we perform native ECCR on the bacterial chaperonin GroEL and megadalton scale adeno-associated virus (AAV) capsid assemblies on a Q Exactive UHMR mass spectrometer. Charge reduction of AAV8 capsids by up to 90% pushes signals well above 100,000 m/z and enables charge state resolution and mean mass determination of these highly heterogeneous samples, even for capsids loaded with genetic cargo. With minor instrument modifications, the UHMR instrument can detect charge-reduced ion signals beyond 200,000 m/z. This work demonstrates the utility of ECCR for deconvolving heterogeneous signals in native mass spectrometry and presents the highest m/z signals ever recorded on an Orbitrap instrument, opening up the use of Orbitrap native mass spectrometry for heavier analytes than ever before.

Native Proteomics by Capillary Zone Electrophoresis-Mass Spectrometry.

Native proteomics measures endogenous proteoforms and protein complexes under a near physiological condition using native mass spectrometry (nMS) coupled with liquid-phase separations. Native proteomics should provide the most accurate bird's-eye view of proteome dynamics within cells, which is fundamental for understanding almost all biological processes. nMS has been widely employed to characterize well-purified protein complexes. However, there are only very few trials of utilizing nMS to measure proteoforms and protein complexes in a complex sample (i.e., a whole cell lysate). Here, we pioneer the native proteomics measurement of large proteoforms or protein complexes up to 400 kDa from a complex proteome via online coupling of native capillary zone electrophoresis (nCZE) to an ultra-high mass range (UHMR) Orbitrap mass spectrometer. The nCZE-MS technique enabled the measurement of a 115-kDa standard protein complex while consuming only about 0.1 ng of protein material. nCZE-MS analysis of an E.coli cell lysate detected 72 proteoforms or protein complexes in a mass range of 30-400 kDa in a single run while consuming only 50-ng protein material. The mass distribution of detected proteoforms or protein complexes agreed well with that from mass photometry measurement. This work represents a technical breakthrough in native proteomics for measuring complex proteomes.

Native Proteomics by Capillary Zone Electrophoresis‐Mass Spectrometry

Native proteomics measures endogenous proteoforms and protein complexes under a near physiological condition using native mass spectrometry (nMS) coupled with liquid‐phase separations. Native proteomics should provide the most accurate bird’s‐eye view of proteome dynamics within cells, which is fundamental for understanding almost all biological processes. nMS has been widely employed to characterize well‐purified protein complexes. However, there are only very few trials of utilizing nMS to measure proteoforms and protein complexes in a complex sample (i.e., a whole cell lysate). Here, we pioneer the native proteomics measurement of large proteoforms or protein complexes up to 400 kDa from a complex proteome via online coupling of native capillary zone electrophoresis (nCZE) to an ultra‐high mass range (UHMR) Orbitrap mass spectrometer. The nCZE‐MS technique enabled the measurement of a 115‐kDa standard protein complex while consuming only about 0.1 ng of protein material. nCZE‐MS analysis of an E.coli cell lysate detected 72 proteoforms or protein complexes in a mass range of 30‐400 kDa in a single run while consuming only 50‐ng protein material. The mass distribution of detected proteoforms or protein complexes agreed well with that from mass photometry measurement. This work represents a technical breakthrough in native proteomics for measuring complex proteomes.

Damage detection and classification of carbon fiber-reinforced polymer composite materials based on acoustic emission and convolutional recurrent neural network

Carbon fiber-reinforced polymer (CFRP) composite laminates generate acoustic emission signals during the tensile process, which can be used to identify the type of acoustic emission (AE) signal at a given moment and determine the current damage condition. However, due to the large number and complexity of AE signals generated during the tensile process of carbon fiber composite laminates, it is challenging to manually identify and annotate them. To address this issue, we propose a low-complexity classification and detection network model based on the convolutional recurrent neural network (CRNN) architecture. First, we construct a dataset of composite damage signals for a single damage category and use log-mel spectrogram features to train the model for that specific category of damage signals. Then, we utilize the trained model to recognize the AE signals of CFRP composite laminates. The proposed method achieves an accuracy of 99.02% in classifying single damage modes in the test set and effectively distinguishes the types of AE signals generated during the damage process of CFRP composite laminates. Compared with the classic classification model using AE signals, the number of parameters in our proposed low-complexity CRNN model is reduced by 94.42%. It also demonstrates that 19.4% of the AE signals during the damage process are in a superimposed state, indicating the simultaneous occurrence of multiple damage modes in CFRP composite laminates.

Self-distillation enhanced adaptive pruning of convolutional neural networks

Convolutional neural networks (CNNs) suffer from issues of large parameter size and high computational complexity. To address this, we propose an adaptive pruning algorithm based on self-distillation. The algorithm introduces a trainable parameter for each channel to control channel pruning and integrates the pruning process into network training, enabling pruning and fine-tuning in a single training iteration to derive the final pruned model. Moreover, this framework requires only a single overall pruning rate to achieve adaptive pruning for each layer, avoiding tedious hyperparameter tuning for a less iterative, simple and efficient pruning process. Additionally, self-distillation is utilized in the pruning algorithm, leveraging the knowledge from the pretrained CNN to guide its own pruning process, facilitating the recovery from performance degradation caused by pruning and achieving higher accuracy. Extensive pruning experiments on various CNN models over different datasets demonstrate that at least 75% of redundant parameters can be reduced without sacrificing model accuracy.

Advanced fire emergency management based on potential fire risk assessment with informative digital twins

Modern large complex buildings, with their vast expanses and multipurpose usability, present diverse fire-spread scenarios, necessitating precise awareness of fire situations. Despite numerous studies integrating building information models with IoT and AI technologies to provide contextual information, there may be limitations in recognizing potential risks under dynamic fire behaviors during urgent situations. This paper presents a fire emergency management system based on an informative digital twin (IDT) to provide instantaneous fire situational information. Utilizing IDT, the system gathers building-specific fire scenario knowledge for immediate fire recognition. Leveraging this knowledge, the IDT assesses building fire risks and shares safety information on egress routes and zones with fire-related applications. A simulation case study revealed a fire recognition processing time of 0.88 s, acceptable for fire emergencies. Additionally, the IDT’s risk assessment reduced the total evacuation time by 65 s and potential injuries by 406, demonstrating the system’s effectiveness in fire emergency management.

NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

AbstractAs software grows in size and complexity, software vulnerabilities are increasing, leading to a range of serious insecurity issues. Open‐source software vulnerability reports and documentation can provide researchers with great convenience for analysis and detection. However, the quality of different data sources varies, the data are duplicated and lack of correlation, which often requires a lot of manual management and analysis. In order to solve the problems of scattered and heterogeneous data and lack of correlation in traditional vulnerability repositories, this paper proposes a software vulnerability feature knowledge extraction method that combines the N‐gram model and mask similarity. The method generates mask text data based on the extraction of N‐gram candidate keywords and extracts vulnerability feature knowledge by calculating the similarity of mask text. This method analyzes the samples efficiently and stably in the environment of large sample size and complex samples and can obtain high‐value semi‐structured data. Then, the final node, relationship, and attribute information are obtained by secondary knowledge cleaning and extraction of the extracted semi‐structured data results. And based on the extraction results, the corresponding software vulnerability domain knowledge graph is constructed to deeply explore the semantic information features and entity relationships of vulnerabilities, which can help to efficiently study software security problems and solve vulnerability problems. The effectiveness and superiority of the proposed method is verified by comparing it with several traditional keyword extraction algorithms on Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) vulnerability data.

Martini 3 OliGo̅mers: A Scalable Approach for Multimers and Fibrils in GROMACS.

Martini 3 is a widely used coarse-grained simulation method for large-scale biomolecular simulations. It can be combined with a Go̅ model to realistically describe higher-order protein structures while allowing the folding and unfolding events. However, as of today, this method has largely been used only for individual monomers. In this article, we describe how the Go̅ model can be implemented within the framework of Martini 3 for a multimer system, taking into account both intramolecular and intermolecular interactions in an oligomeric protein system. We demonstrate the method by showing how it can be applied to both structural stability maintenance and assembly/disassembly of protein oligomers, using aquaporin tetramer, insulin dimer, and amyloid-β fibril as examples. We find that addition of intermolecular Go̅ potentials stabilizes the quaternary structure of proteins. The strength of the Go̅ potentials can be tuned so that the internal fluctuations of proteins match the behavior of atomistic simulation models, however, the results also show that the use of too strong intermolecular Go̅ potentials weakens the chemical specificity of oligomerization. The Martini-Go̅ model presented here enables the use of Go̅ potentials in oligomeric molecular systems in a computationally efficient and parallelizable manner, especially in the case of homopolymers, where the number of identical protein monomers is high. This paves the way for coarse-grained simulations of large protein complexes, such as viral protein capsids and prion fibrils, in complex biological environments.

Research on 3D Geological and Numerical Unified Model of in Mining Slope Based on Multi-Source Data

As mining engineering progresses into the deep excavation phase, the intensification of high pressure, high temperature, strong disturbances, and complex geological conditions becomes increasingly prominent. Researchers perform stability analysis on the excavation area to reduce potential safety hazards during the extraction process. Developing a detailed numerical calculation model that accurately reflects the true geological structure is essential for numerical simulation analysis in mining engineering. Based on the excellent 3D geological modeling capabilities of 3D Mine software, this paper introduces a new 3D geological and numerical unified modeling method (3DMine-Rhino-HyperMesh) involving multi-software coupling and details the specific steps and concepts of this modeling approach. Subsequently, using a certain open-pit mine in Panzhihua as a backdrop, a detailed geological and numerical unified model is established, reflecting the true geological structure of the mining area, and the potential failure mechanisms of the mine slope are analyzed. The results indicate that the modeling method aligns well with the actual geological conditions, enhancing the grid quality of the numerical model and offering a new modeling approach for simulating and analyzing large complex geological entities in mining operations.

Dendritically localized RNAs are packaged as diversely composed ribonucleoprotein particles with heterogeneous copy number states.

Localization of mRNAs to dendrites is a fundamental mechanism by which neurons achieve spatiotemporal control of gene expression. Translationally repressed neuronal mRNA transport granules, also referred to as ribonucleoprotein particles (RNPs), have been shown to be trafficked as single or low copy number RNPs and as larger complexes with multiple copies and/or species of mRNAs. However, there is little evidence of either population in intact neuronal circuits. Using single molecule fluorescence in situ hybridization studies in the dendrites of adult rat and mouse hippocampus, we provide evidence that supports the existence of multi-transcript RNPs with the constituents varying in amounts for each RNA species. By competing-off fluorescently labeled probe with serial increases of unlabeled probe, we detected stepwise decreases in Arc RNP number and fluorescence intensity, suggesting Arc RNAs localize to dendrites in both low- and multiple-copy number RNPs. When probing for multiple mRNAs, we find that localized RNPs are heterogeneous in size and colocalization patterns that vary per RNA. Further, localized RNAs that are targeted by the same trans-acting element (FMRP) display greater levels of colocalization compared to an RNA not targeted by FMRP. Simultaneous visualization of a dozen FMRP-targeted mRNA species using highly multiplexed imaging demonstrates that dendritic RNAs are mostly trafficked as heteromeric cargoes of multiple types of RNAs (at least one or more RNAs). Moreover, the composition of these RNA cargoes, as assessed by colocalization, correlates with the abundance of the transcripts even after accounting for the expected differences in colocalization based on expression. Collectively, these results suggest that dendritic RNPs are packaged as heterogeneous co-assemblies of different mRNAs and that RNP contents may be driven, at least partially, by highly abundant dendritic RNAs; a model that favors efficiency over fine-tuned control for sustaining long-distance trafficking of thousands of messenger molecules.

1H, 15N and 13C resonance assignments of eggcase silk protein 3.

Spider silk is a high-performance biomaterial known for its outstanding combination of strength and flexibility. Among the six distinct types of spider silk, eggcase silk stands out as it is exclusively produced from the tubuliform gland, playing a specialized role in offspring protection. In the spider species Latrodectus hesperus, eggcase silk is spun from a large spidroin complex, including the major silk component tubuliform spidroin 1 (TuSp1) and at least six different minor silk components. One of these minor components is eggcase protein 3 (ECP3), a small silk protein of 11.8kDa that lacks the typical spidroin architecture. ECP3 shows very limited homology to all known spidroins. In this study, we report nearly complete backbone and side-chain resonance assignments of ECP3 as a basis for studying the structural mechanisms involved in eggcase silk formation.

An efficient eigenvalue bounding method: CFL condition revisited

Direct and large-eddy simulations of turbulence are often solved using explicit temporal schemes. However, this imposes very small time-steps because the eigenvalues of the (linearized) dynamical system, re-scaled by the time-step, must lie inside the stability region. In practice, fast and accurate estimations of the spectral radii of both the discrete convective and diffusive terms are therefore needed. This is virtually always done using the so-called CFL condition. On the other hand, the large heterogeneity and complexity of modern supercomputing systems are nowadays hindering the efficient cross-platform portability of CFD codes. In this regard, our leitmotiv reads: relying on a minimal set of (algebraic) kernels is crucial for code portability and maintenance! In this context, this work focuses on the computation of eigenbounds for the above-mentioned convective and diffusive matrices which are needed to determine the time-step à la CFL. To do so, a new inexpensive method, that does not require to re-construct these time-dependent matrices, is proposed and tested. It just relies on a sparse-matrix vector product where only vectors change on time. Hence, both implementation in existing codes and cross-platform portability are straightforward. The effectiveness and robustness of the method are demonstrated for different test cases on both structured Cartesian and unstructured meshes. Finally, the method is combined with a self-adaptive temporal scheme, leading to significantly larger time-steps compared with other more conventional CFL-based approaches.

Visualizing chaperonin function in situ by cryo-electron tomography

Chaperonins are large barrel-shaped complexes that mediate ATP-dependent protein folding1–3. The bacterial chaperonin GroEL forms juxtaposed rings that bind unfolded protein and the lid-shaped cofactor GroES at their apertures. In vitro analyses of the chaperonin reaction have shown that substrate protein folds, unimpaired by aggregation, while transiently encapsulated in the GroEL central cavity by GroES4–6. To determine the functional stoichiometry of GroEL, GroES and client protein in situ, here we visualized chaperonin complexes in their natural cellular environment using cryo-electron tomography. We find that, under various growth conditions, around 55–70% of GroEL binds GroES asymmetrically on one ring, with the remainder populating symmetrical complexes. Bound substrate protein is detected on the free ring of the asymmetrical complex, defining the substrate acceptor state. In situ analysis of GroEL–GroES chambers, validated by high-resolution structures obtained in vitro, showed the presence of encapsulated substrate protein in a folded state before release into the cytosol. Based on a comprehensive quantification and conformational analysis of chaperonin complexes, we propose a GroEL–GroES reaction cycle that consists of linked asymmetrical and symmetrical subreactions mediating protein folding. Our findings illuminate the native conformational and functional chaperonin cycle directly within cells.

A wearable, rapidly manufacturable, stability-enhancing microneedle patch for closed-loop diabetes management

The development of a wearable, easy-to-fabricate, and stable intelligent minisystem is highly desired for the closed-loop management of diabetes. Conventional systems always suffer from large size, high cost, low stability, or complex fabrication. Here, we show for the first time a wearable, rapidly manufacturable, stability-enhancing microneedle patch for diabetes management. The patch consists of a graphene composite ink-printed sensor on hollow microneedles, a polyethylene glycol (PEG)-functionalized electroosmotic micropump integrated with the microneedles, and a printed circuit board for precise and intelligent control of the sensor and pump to detect interstitial glucose and deliver insulin through the hollow channels. Via synthesizing and printing the graphene composite ink, the sensor fabrication process is fast and the sensing electrodes are stable. The PEG functionalization enables the micropump a significantly higher stability in delivering insulin, extending its lifetime from days to weeks. The patch successfully demonstrated excellent blood glucose control in diabetic rats. This work may introduce a new paradigm for building new closed-loop systems and shows great promise for widespread use in patients with diabetes.

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.

Super-shear ruptures steered by pre-stress heterogeneities during the 2023 Kahramanmaraş earthquake doublet

The 2023 M7.8 and M7.5 earthquake doublet near Kahramanmaraş, Turkey, provides insight regarding how large earthquakes rupture complex faults. Here we determine the faults geometry using surface ruptures and Synthetic Aperture Radar measurements, and the rupture kinematics from the joint inversion of high-rate Global Navigation Satellite System (GNSS), strong-motion waveforms, and GNSS static displacement. The M7.8 event initiated on a splay fault and subsequently propagated along the main East Anatolian Fault with an average rupture velocity between 3.0 and 4.0 km/s. In contrast, the M7.5 event demonstrated a bilateral supershear rupture of about 5.0–6.0 km/s over an 80 km length. Despite varying strike and dip angles, the sub-faults involved in the mainshock are nearly optimally oriented relative to the local stress tensor. The second event ruptured a fault misaligned with respect to the regional stress, also hinting at the effect of local stress heterogeneity in addition to a possible free surface effect.

Quality and Efficiency of Coupled Iterative Coverage Path Planning for the Inspection of Large Complex 3D Structures

To enable unmanned aerial vehicles to generate coverage paths that balance inspection quality and efficiency when performing three-dimensional inspection tasks, we propose a quality and efficiency coupled iterative coverage path planning (QECI-CPP) method. First, starting from a cleaned and refined mesh model, this was segmented into narrow and normal spaces, each with distinct constraint settings. During the initialization phase of viewpoint generation, factors such as image resolution and orthogonality degree were considered to enhance the inspection quality along the path. Then, the optimization objective was designed to simultaneously consider inspection quality and efficiency, with the relative importance of these factors adjustable according to specific task requirements. Through iterative adjustments and optimizations, the coverage path was continuously refined. In numerical simulations, the proposed method was compared with three other classic methods, evaluated across five aspects: image resolution, orthogonality degree, path distance, computation time, and total path cost. The comparative simulation results show that the QECI-CPP achieves maximum image resolution and orthogonality degree while maintaining inspection efficiency within a moderate computation time, demonstrating the effectiveness of the proposed method. Additionally, the flexibility of the planned path is validated by adjusting the weight coefficient in the optimized objective function.