Multiple Capture Research Articles

High-Order Local Clustering on Hypergraphs

Graphs are a commonly used model in data mining to represent complex relationships, with nodes representing entities and edges representing relationships. However, graphs have limitations in modeling high-order relationships. In contrast, hypergraphs offer a more versatile representation, allowing edges to join any number of nodes. This capability empowers hypergraphs to model multiple relationships and capture high-order information present in real-world applications. We focus on the problem of local clustering in hypergraphs, which computes a cluster near a given seed node. Although extensively explored in the context of graphs, this problem has received less attention for hypergraphs. Current methods often directly extend graph-based local clustering to hypergraphs, overlooking their inherent high-order features and resulting in low-quality local clusters. To address this, we propose an effective hypergraph local clustering model. This model introduces a novel conductance measurement that leverages the high-order properties of hypergraphs to assess cluster quality. Based on this new definition of hypergraph conductance, we propose a greedy algorithm to find local clusters in real time. Experimental evaluations and case studies on real-world datasets demonstrate the effectiveness of the proposed methods.

Analysis of phylogenetic relationships in Macadamia shows evidence of extensive reticulate evolution.

The genus Macadamia in the Proteaceae family includes four species native to Australia. Two of the four species, M. integrifolia and M. tetraphylla, have recently been utilized to generate domesticated macadamia varieties, grown for their edible nuts. To explore diversity in macadamia genetic resources, a total of 166 wild genotypes, representing all four species, were sequenced. The four species were clearly distinguished as four separate clades in a phylogenetic analysis of the nuclear genome (based upon concatenated nuclear gene CDS and SNPs). The two larger species (M. integrifolia and M. tetraphylla) formed a clade, that had diverged from a clade including the smaller species (M. ternifolia and M. jansenii). The greatest diversity in nuclear and chloroplast genomes was found in the more widely distributed M. integrifolia while the rare M. jansenii showed little diversity. The chloroplast phylogeny revealed a much more complex evolutionary history. Multiple chloroplast capture events have resulted in chloroplast genome clades, including genotypes from different species. This suggests extensive reticulate evolution in Macadamia despite the emergence of the four distinct species that are supported by the analysis of their nuclear genomes. The chloroplast genomes showed strong associations with geographical distribution reflecting limited maternal gene movement in these species that have large seeds. The nuclear genomes showed lesser geographical differences, probably reflecting the longer distance pollen movement. This improved understanding of the distribution of diversity in Macadamia will aid in the conservation of these rare species now found in highly fragmented rainforest remnants.

The Chemistry of Metal–Organic Frameworks for Multicomponent Gas Separation

AbstractAdsorbents‐based gas separation technologies are regarded as the potential energy‐efficient alternatives towards current thermal‐driven methods, and the study on multi‐component gas separation is essential to deepen our understanding of the adsorbents for practical use. Relative to the ideal two‐component mixtures, both the adsorption behavior and separation mechanisms are obviously more complex in multiple gas mixtures due to their close or even overlapped sizes and properties. The emergence of metal–organic frameworks with controllable pore size and pore chemistry provides the platform for the tailor‐made pore structure to satisfy the harsh requirements of multi‐component gas separation. This minireview highlights the recent advance of multi‐component gas separation using metal–organic frameworks, including multiple impurities removal and selective molecular capture. Combining with the typical cases of hydrocarbon separations (C2, C4, and C8), the detailed discussion about the developed strategies (e.g. self‐adaptive binding sites, multiple binding spaces, synergistic binding sites, synergistic sorbent separation technology, gate‐opening effect, size and thermodynamic combine effect) that are adaptive to different scenarios would be provided. The review will conclude with our perspective on the existing barriers and the future direction of this topic.

ID: 325816 A novel approach to peripheral nerve stimulation: Axillary placement with multiple nerve capture

ID: 325816 A novel approach to peripheral nerve stimulation: Axillary placement with multiple nerve capture

Data-Driven Method for Nonlinear Modal Interaction Identification in Fighter Aircraft

The purpose of this paper is to report a new data-driven analysis methodology for identifying nonlinear modal interactions and its application to aeroelastic wind-tunnel data from a generic fighter configuration, which reveals global modal interactions in this system caused by multiple resonance capture events initiated by local nonlinearities. It is shown that during supersonic testing at the Transonic Dynamics Tunnel, vibro-impacts at the embedded external fuel tank shakers on the inboard underwing stations of the generic fighter introduce strong nonlinearity that gives rise to nonlinear modal interactions. Specifically, time–frequency analysis of the acceleration data demonstrates that 6∶5 and 7∶6 transient resonance captures occur across the aircraft exclusively during this vibro-impact forcing window. Further analysis suggests that these resonance captures are dependent on Mach number. These results show experimentally that local vibro-impacts can cause global nonlinear effects in an aeroelastic system and that these effects can be identified using the new methodology herein. The data-driven analysis scheme employed shows applicability to full-aircraft flight test data for identifying nonlinear modal interactions without knowledge of the precise nonlinear contributors.

Estimation of Denudation Parameters and River Capture Events From Neural Network Inverse Modeling of River Profiles and Thermo‐ and Geochronology Data

AbstractEarth's topography represents the cumulative effects of tectonics and surface processes modulated by climate and lithology. These factors shape landscapes through time. River profiles can be inverted to estimate the rock uplift histories or lithology‐specific erodibilities. However, river systems are dynamic and evolve in response to spatial and temporal internal dynamics, such as river capture events. Here, we present a modeling framework to infer denudation rates from the inversion of river profiles and thermo‐ and geochronology data. We achieve this by coupling a landscape evolution model and an efficient inverse modeling scheme to infer poorly resolved erosional and tectonic parameters. An application of the approach is presented for the Neckar catchment, southwest Germany, characterized by stark lateral variation in bedrock erodibility and rock uplift, and that have demonstrably undergone multiple river capture events. Different end‐member scenarios are explored in the simulations. First, we test uniform and spatial variability in rock uplift rate and bedrock erodibility, and second, temporal variations in rock uplift rate and base level. Finally, we simulate river capture events by adding upstream sections (drainage area) at specific times and locations within the fluvial network. We find that spatial variation in rock uplift rate is necessary to reproduce the Neckar's river profile while honoring analytical observations. Simulations integrating river captures allow improved river profile predictions of specific tributaries of the Neckar catchment, leading to potentially more realistic erodibility and rock uplift history estimates. The time and location of the capture events determined from the modeling agree with previous estimations from geological evidence.

Generation controllable optical chain using an optical pen.

An aperiodic snake-like optical chain has been proposed and generated by using an optical pen technique, whose numbers and positions of focal points are controllable. Moreover, by introducing a fan phase together with a twisted phase into the optical pen technique, a self-rotation optical chain can be obtained; meanwhile, it transforms the bright optical chain into a twisted optical chain with a rotating hollow region in a three-dimensional (3D) space. The properties of the rotatable focal points and the variable diameters of cross-sectional intensities during the propagation of the optical chain are demonstrated in the experimental results. Consequently, this research framework can be applied in the techniques such as deep multiplexing and rotation angle multiplexing while also enabling the realization of multiple capture sites and more intricate manipulations.

A Study on the Mechanisms of Coal Fly Ash to Improve the CO2 Capture Efficiency of Calcium-Based Adsorbents

Utilizing calcium-based adsorbents for CO2 adsorption through cyclic calcination/carbonization is one of the most cost-effective methods for carbon emission reduction. In order to improve the cycle stability of the adsorbents and the capture efficiency of CO2, this study used industrial solid waste coal fly ash for the hydration treatment of calcium-based adsorbent to explore the variations in the cyclic adsorption performance of the adsorbent under different doping ratios and hydration conditions. By means of various characterization techniques, the microscopic mechanism for improving the performance of the modified adsorbent was analyzed from the perspectives of chemical composition, physical structure, and surface functional groups of the adsorbents. The results demonstrated that the modification of coal fly ash could significantly enhance the carbonation performance and cycle stability of the adsorbent in multiple CO2 capture processes. The modified material doped with 5% coal fly ash had the highest total CO2 adsorption capacity, which increased by 13.7% compared to before modification. Additionally, the modified material doped with 10% coal fly ash exhibited the strongest cyclic adsorption capacity, which was 14.0% higher than that before modification, and the adsorption attenuation rate decreased by 32.2%. The characterization results showed that the reaction between calcium oxide and coal fly ash formed CaSiO3 and Ca12Al14O33 during the modification process, which was the primary reason for the improvement in the CO2 capture performance of the modified materials. This study provided a new perspective on the resource utilization of solid waste fly ash and efficient CO2 capture.

Constructing multiple capture domains in molecularly imprinted nanocomposite membranes by flower-like covalent organic framework for separation of acteoside

The selective separation of bioactive components by molecularly imprinted nanocomposite membranes (MINMs) has attracted widespread attention, but the traditional MINMs often were confronted with low rebinding capacity. Herein, the flower-like covalent organic framework (FL-COF) based-molecularly imprinted nanocomposite membranes (FMINMs) with multiple capture domains were developed for separation of acteoside (ACT). The multiple capture domains of FMINMs were constructed by the following three different domains: Firstly, the imprinted layer of FMINMs constructed the imprinted capture domain for specific capture of ACT, improving separation performance of FMINMs for ACT. Secondly, the amino groups on the surface of petals of FL-COF formed affinity capture domain to enrich ACT during the adsorption process, enhancing rebinding capacity and permselectivity of FMINMs for ACT. Thirdly, the hollow structure of FL-COF provided a large spatial capture domain for accommodating ACT, thus enhancing the rebinding capacity of FMINMs. Consequently, the FMINMs with multiple capture domains exhibited high separation performance with rebinding capacity (141.47 mg/g) and permselectivity (βECH/ACT=8.25) of ACT. It demonstrated that designed MINMs with multiple capture domains has great potential for efficient separation of bioactive components.

The Chemistry of Metal-Organic Frameworks for Multicomponent Gas Separation.

Adsorbents-based gas separation technologies are regarded as the potential energy-efficient alternatives towards current thermal-driven methods, and the study on multi-component gas separation is essential to deepen our understanding of the adsorbents for practical use. Relative to the ideal two-component mixtures, both the adsorption behavior and separation mechanisms are obviously more complex in multiple gas mixtures due to their close or even overlapped sizes and properties. The emergence of metal-organic frameworks with controllable pore size and pore chemistry provides the platform for the tailor-made pore structure to satisfy the harsh requirements of multi-component gas separation. This minireview highlights the recent advance of multi-component gas separation using metal-organic frameworks, including multiple impurities removal and selective molecular capture. Combining with the typical cases of hydrocarbon separations (C2, C4, and C8), the detailed discussion about the developed strategies (e.g. self-adaptive binding sites, multiple binding spaces, synergistic binding sites, synergistic sorbent separation technology, gate-opening effect, size and thermodynamic combine effect) that are adaptive to different scenarios would be provided. The review will conclude with our perspective on the existing barriers and the future direction of this topic.

Automatic Electrochemiluminescence Method for the Detection of Cancerous Exosomes Incorporating Specific Aptamer-Magnetic Beads and Signal Nanoprobes.

Exosomes, as an emerging biomarker, have exhibited remarkable promise in early cancer diagnosis. Here, a highly sensitive, selective, and automatic electrochemiluminescence (ECL) method for the detection of cancerous exosomes was developed. Specific aptamer-(EK)4 peptide-tagged magnetic beads (MBs-(EK)4-aptamer) were designed as a magnetic capture probe in which the (EK)4 peptide was used to reduce the steric binding hindrance of cancerous exosomes with a specific aptamer. One new universal ECL signal nanoprobe (CD9 Ab-PEG@SiO2ϵRu(bpy)32+) was designed and synthesized by using microporous SiO2 nanoparticles as the carrier for loading ECL reagent Ru(bpy)32+, polyethylene glycol (PEG) layer, and anticluster of differentiation 9 antibody (CD9 Ab). A "sandwich" biocomplex was formed on the surface of the magnetic capture probe after mixing the capture probe, target exosomes, and ECL signal nanoprobe, and then it was introduced into an automated ECL analyzer for rapid and automatic ECL measurement. It was found that the designed signal nanoprobe shows a 270-fold improvement in the signal-to-noise ratio than that of the ruthenium complex-labeled CD9 antibody signal probe. The relative ECL intensity was proportional to MCF-7 exosomes as a model in the range of 102 to 104 particle/μL, with a detection limit of 11 particle/μL. Furthermore, the ECL method was employed to discriminate cancerous exosomes based on fingerprint responses using the designed multiple magnetic capture probes and the universal ECL signal nanoprobe. This work demonstrates that the utilization of a designed automated ECL tactic using the MBs-(EK)4-aptamer capture probe and the CD9 Ab-PEG@SiO2ϵRu(bpy)32+ signal nanoprobe will provide a unique and robust method for the detection and discrimination of cancerous exosomes.

MvMRL: a multi-view molecular representation learning method for molecular property prediction.

Effective molecular representation learning is very important for Artificial Intelligence-driven Drug Design because it affects the accuracy and efficiency of molecular property prediction and other molecular modeling relevant tasks. However, previous molecular representation learning studies often suffer from limitations, such as over-reliance on a single molecular representation, failure to fully capture both local and global information in molecular structure, and ineffective integration of multiscale features from different molecular representations. These limitations restrict the complete and accurate representation of molecular structure and properties, ultimately impacting the accuracy of predicting molecular properties. To this end, we propose a novel multi-view molecular representation learning method called MvMRL, which can incorporate feature information from multiple molecular representations and capture both local and global information from different views well, thus improving molecular property prediction. Specifically, MvMRL consists of four parts: a multiscale CNN-SE Simplified Molecular Input Line Entry System (SMILES) learning component and a multiscale Graph Neural Network encoder to extract local feature information and global feature information from the SMILES view and the molecular graph view, respectively; a Multi-Layer Perceptron network to capture complex non-linear relationship features from the molecular fingerprint view; and a dual cross-attention component to fuse feature information on the multi-views deeply for predicting molecular properties. We evaluate the performance of MvMRL on 11 benchmark datasets, and experimental results show that MvMRL outperforms state-of-the-art methods, indicating its rationality and effectiveness in molecular property prediction. The source code of MvMRL was released in https://github.com/jedison-github/MvMRL.

FMA-Net: Fusion of Multi-Scale Attention for Grading Cervical Precancerous Lesions

Cervical cancer, as the fourth most common cancer in women, poses a significant threat to women’s health. Vaginal colposcopy examination, as the most cost-effective step in cervical cancer screening, can effectively detect precancerous lesions and prevent their progression into cancer. The size of the lesion areas in the colposcopic images varies, and the characteristics of the lesions are complex and difficult to discern, thus heavily relying on the expertise of the medical professionals. To address these issues, this paper constructs a vaginal colposcopy image dataset, ACIN-3, and proposes a Fusion Multi-scale Attention Network for the detection of cervical precancerous lesions. First, we propose a heterogeneous receptive field convolution module to construct the backbone network, which utilizes combinations of convolutions with different structures to extract multi-scale features from multiple receptive fields and capture features from different-sized regions of the cervix at different levels. Second, we propose an attention fusion module to construct a branch network, which integrates multi-scale features and establishes connections in both the spatial and channel dimensions. Finally, we design a dual-threshold loss function and introduce positive and negative thresholds to improve sample weights and address the issue of data imbalance in the dataset. Multiple experiments are conducted on the ACIN-3 dataset to demonstrate the superior performance of our approach compared to some classical and recent advanced methods. Our method achieves an accuracy of 92.2% in grading and 94.7% in detection, with average AUCs of 0.9862 and 0.9878. Our heatmap illustrates the accuracy of our approach in focusing on the locations of lesions.

Photoelectricity Theory-Based Concrete Crack Image Segmentation and Optimal Exposure Interval Research

To solve the problem of low accuracy in automatic concrete crack image segmentation and the non-standardization of concrete crack image datasets, an exposure-based concrete crack image capture scene characterization method was proposed, and the optimal exposure interval for crack segmentation was presented by multiple scene image capture experiments. First, current public crack datasets were collected and analyzed, and it was shown that improper spatial resolution, mislabeling, overexposure, and defocus are frequent non-standardization problems in crack dataset production. Through the analysis of the photoelectric principle in concrete crack imaging, an equivalent exposure was set as a core indicator for scene characterization. Twenty-one indoor scenes were designed by varying the illumination intensity and exposure time, and the experimental results showed that an equivalent exposure can be a core control index for scene characterization. The grayscale distribution law of concrete crack images was analyzed with four specimens’ images captured indoors in 50 exposure scenes, and the segmentation accuracy of an image from each scene was calculated through comparison with corresponding manually labeled binary files. The experiment’s results revealed that 5~50 lx·s was the optimal equivalent exposure interval for concrete crack image segmentation, in which better segmentation accuracy was achieved with an F1 score of up to 96.3%.

All-silicon metasurface-enabled non-coaxial superposition of vector vortex beams

Vector vortex beams (VVBs) possess great applications in fundamental science, from particle trapping to quantum communication. Recently, the complex system for evaluating VVBs has been simplified by implementing metasurfaces and demonstrating the evolutionary trends of its coaxial polarization states. Nevertheless, it is quite challenging to obtain the polarization singularities generated in the terahertz (THz) band when non-coaxial superposition is performed by two multiplexed VVBs. Herein, a minimalist all-silicon metasurface platform is proposed, capable of being implemented to evaluate the non-coaxial superposition states of two identical VVBs. The employment of spin-polarization multiplexing techniques gives higher encoding degrees of freedom within the orthogonal circular polarization channel, enabling the superposition behavior of inhomogeneous polarization states to be performed at predefined focal planes. A systematic analysis of the newly generated polarization singularities in the interaction region is performed by applying the full Stokes polarization vector. With the ultra-compact configuration, the experimental results obtained by the proposed strategy under THz wave illumination exhibit good agreement with the numerical simulations. Thus, such mechanisms offer potential applications for multiple particle capture based on non-coaxial superimposition of VVB and provide efficient meta-platform in data encryption, high-resolution imaging, and quantum communications.

Sensor location influences the associations between IMU and motion capture measurements of impact landing in healthy male and female runners at multiple running speeds

ABSTRACT This study investigated the relationships between inertial measurement unit (IMU) acceleration at multiple body locations and 3D motion capture impact landing measures in runners. Thirty healthy runners ran on an instrumented treadmill at five running speeds (9–17 km/h) during 3D motion capture. Axial and resultant acceleration were collected from IMUs at the distal and proximal tibia, distal femur and sacrum. Relationships between peak acceleration from each IMU location and patellofemoral joint (PFJ) peak force and loading rate, impact peak and instantaneous vertical loading rate (IVLR) were investigated using linear mixed models. Acceleration was positively related to IVLR at all lower limb locations (p < 0.01). Models predicted a 1.9–3.2 g peak acceleration change at the tibia and distal femur, corresponding with a 10% IVLR change. Impact peak was positively related to acceleration at the distal femur only (p < 0.01). PFJ peak force was positively related to acceleration at the distal (p = 0.03) and proximal tibia (p = 0.03). PFJ loading rate was positively related to the tibia and femur acceleration in males only (p < 0.01). These findings suggest multiple IMU lower limb locations are viable for measuring peak acceleration during running as a meaningful indicator of IVLR.

Porous materials with suitable pore size and dual‐functional sites for benchmark one‐step ethylene purification

AbstractMultiple impurities removal represents one of the most daunting challenges in gas purification, but the attainment of efficient adsorptive separation is hindered by the difficulty in designing adsorbents that could simultaneously capture different impurities. Herein, we revealed a molecular trap within Zn‐trz‐ox (trz = 1,2,4‐triazole; ox = oxalic acid) that featured positive H and negative O sites, and the suitable pore size, which exhibited remarkable one‐step C2H4 purification performance directly from quaternary C2H4/C2H2/C2H6/CO2 mixtures. The separation selectivities of C2H4 with respect to CO2, C2H2, and C2H6 are 9.8, 2.6, and 2.5, higher than the sole adsorbent yet reported. Meanwhile, polymer grade C2H4 (≥99.95%) could be directly obtained with record C2H4 productivity of 1.5 mol kg−1, over 10 times higher than that of the previous benchmark material. The deep insight into the binding behavior revealed by simulation studies offers important clues for the design of advanced adsorbent for multiple impurities capture.

Multiplex fluorescence detection of single-cell droplet microfluidics and its application in quantifying protein expression levels.

This study presented a platform of multiplex fluorescence detection of single-cell droplet microfluidics with demonstrative applications in quantifying protein expression levels. The platform of multiplex fluorescence detection mainly included optical paths adopted from conventional microscopy enabling the generation of three optical spots from three laser sources for multiple fluorescence excitation and capture of multiple fluorescence signals by four photomultiplier tubes. As to platform characterization, microscopic images of three optical spots were obtained where clear Gaussian distributions of intensities without skewness confirmed the functionality of the scanning lens, while the controllable distances among three optical spots validated the functionality of fiber collimators and the reflector lens. As to demonstration, this platform was used to quantify single-cell protein expression within droplets where four-type protein expression of α-tubulin, Ras, c-Myc, and β-tubulin of CAL 27 (Ncell = 1921) vs WSU-HN6 (Ncell = 1881) were quantitatively estimated, which were (2.85 ± 0.72) × 105 vs (4.83 ± 1.58) × 105, (3.69 ± 1.41) × 104 vs (5.07 ± 2.13) × 104, (5.90 ± 1.45) × 104 vs (9.57 ± 2.85) × 104, and (3.84 ± 1.28) × 105 vs (3.30 ± 1.10) × 105, respectively. Neural pattern recognition was utilized for the classification of cell types, achieving successful rates of 69.0% (α-tubulin), 75.4% (Ras), 89.1% (c-Myc), 65.8% (β-tubulin), and 99.1% in combination, validating the capability of this platform of multiplex fluorescence detection to quantify various types of single-cell proteins, which could provide comprehensive evaluations on cell status.

Evaluation of the effects of multiple capture methods and immobilization drugs on mountain lion welfare

AbstractUsing a dataset of 591 capture events between 2001–2019 in California, USA, we examined the impact of capture methods and immobilization drugs on mountain lion (Puma concolor) welfare. The 3 methods used to capture mountain lions were cage traps, trained hounds, and cable restraints. The drugs used to immobilize mountain lions were either tiletamine/zolazepam (Telazol®), ketamine/medetomidine, or ketamine/xylazine. Mortality occurred in 1.4% of captures, with only one mortality out of 310 captures occurring since 2012. We used a logistic regression framework to compare morbidity and vital parameters of mountain lions among the different capture methods and immobilization drugs used. Vomiting (a risk factor for developing aspiration pneumonia) was the most common severe risk factor associated with cage trapping and was only seen with the use of ketamine/medetomidine or ketamine/xylazine. Morbidity scores were not well predicted by any of the variables we accounted for. Animals immobilized with Telazol® were more likely to experience abnormal heart and respiratory rates, as well as high body temperatures, than those immobilized with the other two combinations. Although there are risks associated with each of the capture methods and drug combinations commonly used in mountain lion captures in California, our analyses demonstrated they are all relatively safe when following appropriate animal welfare practices. Our analyses suggested that unaccounted for factors are equally or more important in explaining injury and physiological abnormality rates, and we urge that agencies train personnel in best practices and conservative decision‐making in order to assure that the welfare of the animal takes precedence over collaring. We suggest training on how to choose between several capture techniques, immobilization drugs and monitoring methods and how to reduce both detrimental effects to mountain lions and danger to humans.

Simultaneous multiple capture in the presence of evader's defenders

We consider a conflict-controlled process with the participation of three types of controlled objects: a group of pursuers, an evader, a group of defenders of the evader. Dynamic and inertial capabilities of all controlled objects are the same. The evader and the group of defenders act cooperatively. The group of pursuers is the other side of the conflict. If the positions of the pursuer and the defender of the evader coincide then both players die and cease to participate in the conflict-controlled process. In a conflict controlled process multiple capture of an evader occurs when a given number of pursuers catch the evader, and capture moments may not coincide. If (not necessarily least) capture moments coincide then nonstrict simultaneous multiple capture of the evader occurs. Finally, simultaneous multiple capture of the evader occurs when least capture moments appear identical. We obtain necessary and sufficient conditions for simultaneous multiple capture of the evader in terms of initial positions of the participants and other parameters of the conflict-controlled process.