A high-throughput multi-hop WSN for structural health monitoring

Two major challenges with existing multi-hop WSNs used for structural health monitoring (SHM) are how to increase the data transmission rate (DTR) for large amounts of sampling data and enlarge the data transmission range without degrading link quality. To handle these issues, this paper proposes a new design method of a multi-hop WSN with multi-radio sink node (M-RSN) and double-radio relay node (D-RRN) which can increase the data transfer ability at the sink and extend the monitoring distance without degrading wireless link quality. Additionally, a tight scheduled approach and multi-radio time synchronization method are designed for the stable implementation of the proposed network. To evaluate the effectiveness and robustness of the proposed network designing method, experiments in outdoor environment and for aircraft composite wing boxes monitoring are carried out. The evaluation results have shown the advantages of the proposed methods

A high-throughput multi-hop WSN for structural health monitoring

Two major challenges with existing multi-hop WSNs used for structural health monitoring (SHM) are how to increase the data transmission rate (DTR) for large amounts of sampling data and enlarge the data transmission range without degrading link quality. To handle these issues, this paper proposes a new design method of a multi-hop WSN with multi-radio sink node (M-RSN) and double-radio relay node (D-RRN) which can increase the data transfer ability at the sink and extend the monitoring distance without degrading wireless link quality. Additionally, a tight scheduled approach and multi-radio time synchronization method are designed for the stable implementation of the proposed network. To evaluate the effectiveness and robustness of the proposed network designing method, experiments in outdoor environment and for aircraft composite wing boxes monitoring are carried out. The evaluation results have shown the advantages of the proposed methods

An Expanded Gene Catalog of Mouse Gut Metagenomes

High-quality and comprehensive reference gene catalogs are essential for metagenomic research. The rather low diversity of samples used to construct existing catalogs of the mouse gut metagenome limits the numbers of identified genes in existing catalogs. We therefore established an expanded catalog of genes in the mouse gut metagenome (EMGC) containing >5.8 million genes by integrating 88 newly sequenced samples, 86 mouse gut-related bacterial genomes, and 3 existing gene catalogs. EMGC increases the number of nonredundant genes by more than 1 million genes compared to the so-far most extensive catalog. More than 60% of the genes in EMGC were assigned to Bacteria, with 54.20% being assigned to a phylum and 35.33% to a genus, while 30.39% were annotated at the KEGG orthology level. Nine hundred two metagenomic species (MGS) assigned to 122 taxa are identified based on the EMGC. The EMGC-based analysis of samples from groups of mice originating from different animal providers, housing laboratories, and genetic strains substantiated that diet is a major contributor to differences in composition and functional potential of the gut microbiota irrespective of differences in environment and genetic background. We envisage that EMGC will serve as a valuable reference data set for future metagenomic studies in mice.publishedVersio

Heterogeneous photocatalytic recycling of FeX2/FeX3 for efficient halogenation of C−H bonds using NaX

Environmental-friendly halogenation of C−H bonds using abundant, non-toxic halogen salts is in high demand in various chemical industries, yet the efficiency and selectivity of laboratory available protocols are far behind the conventional photolytic halogenation process which uses hazardous halogen sources. Here we report an FeX2 (X=Br, Cl) coupled semiconductor system for efficient, selective, and continuous photocatalytic halogenation using NaX as halogen source under mild conditions. Herein, FeX2 catalyzes the reduction of molecular oxygen and the consumption of generated oxygen radicals, thus boosting the generation of halogen radicals and elemental halogen for direct halogenation and indirect halogenation via the formation of FeX3. Recycling of FeX2 and FeX3 during the photocatalytic process enables the halogenation of a wide range of hydrocarbons in a continuous flow, rendering it a promising method for applications

Design of a Large-Scale Piezoelectric Transducer Network Layer and Its Reliability Verification for Space Structures

As an effective structural health monitoring (SHM) technology, the piezoelectric transducer (PZT) and guided wave-based monitoring methods have attracted growing interest in the space field. When facing the large-scale monitoring requirements of space structures, a lot of PZTs are needed and may cause problems regarding to additional weight of connection cables, placement efficiency and performance consistency. The PZT layer is a promising solution against these problems. However, the current PZT layers still face challenges from large-scale lightweight monitoring and the lack of reliability assessment under extreme space service conditions. In this paper, a large-scale PZT network layer (LPNL) design method is proposed to overcome these challenges, by adopting a large-scale lightweight PZT network design method and network splitting–recombination based integration strategy. The developed LPNL offers the advantages of being large size, lightweight, ultra-thin, flexible, customized in shape and highly reliable. A series of extreme environmental tests are performed to verify the reliability of the developed LPNL under space service environment, including extreme temperature conditions, vibration at different flying phases, landing impact, and flying overload. Results show that the developed LPNL can withstand these harsh environmental conditions and presents high reliability and functionality

Fatigue Crack Evaluation with the Guided Wave–Convolutional Neural Network Ensemble and Differential Wavelet Spectrogram

On-line fatigue crack evaluation is crucial for ensuring the structural safety and reducing the maintenance costs of safety-critical systems. Among structural health monitoring (SHM), guided wave (GW)-based SHM has been deemed as one of the most promising techniques. However, the traditional damage index-based method and machine learning methods require manual processing and selection of GW features, which depend highly on expert knowledge and are easily affected by complicated uncertainties. Therefore, this paper proposes a fatigue crack evaluation framework with the GW–convolutional neural network (CNN) ensemble and differential wavelet spectrogram. The differential time–frequency spectrogram between the baseline signal and the monitoring signal is processed as the CNN input with the complex Gaussian wavelet transform. Then, an ensemble of CNNs is trained to jointly determine the crack length. Real fatigue tests on complex lap joint structures were carried out to validate the proposed method, in which several structures were tested preliminarily for collecting the training dataset and a new structure was adopted for testing. The root mean square error of the training dataset is 1.4 mm. Besides, the root mean square error of the evaluated crack length in the testing lap joint structure was 1.7 mm, showing the effectiveness of the proposed method

On-Line Multi-Damage Scanning Spatial-Wavenumber Filter Based Imaging Method for Aircraft Composite Structure

Structural health monitoring (SHM) of aircraft composite structure is helpful to increase reliability and reduce maintenance costs. Due to the great effectiveness in distinguishing particular guided wave modes and identifying the propagation direction, the spatial-wavenumber filter technique has emerged as an interesting SHM topic. In this paper, a new scanning spatial-wavenumber filter (SSWF) based imaging method for multiple damages is proposed to conduct on-line monitoring of aircraft composite structures. Firstly, an on-line multi-damage SSWF is established, including the fundamental principle of SSWF for multiple damages based on a linear piezoelectric (PZT) sensor array, and a corresponding wavenumber-time imaging mechanism by using the multi-damage scattering signal. Secondly, through combining the on-line multi-damage SSWF and a PZT 2D cross-shaped array, an image-mapping method is proposed to conduct wavenumber synthesis and convert the two wavenumber-time images obtained by the PZT 2D cross-shaped array to an angle-distance image, from which the multiple damages can be directly recognized and located. In the experimental validation, both simulated multi-damage and real multi-damage introduced by repeated impacts are performed on a composite plate structure. The maximum localization error is less than 2 cm, which shows good performance of the multi-damage imaging method. Compared with the existing spatial-wavenumber filter based damage evaluation methods, the proposed method requires no more than the multi-damage scattering signal and can be performed without depending on any wavenumber modeling or measuring. Besides, this method locates multiple damages by imaging instead of the geometric method, which helps to improve the signal-to-noise ratio. Thus, it can be easily applied to on-line multi-damage monitoring of aircraft composite structures

Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures

Fatigue crack diagnosis (FCD) is of great significance for ensuring safe operation, prolonging service time and reducing maintenance cost in aircrafts and many other safety-critical systems. As a promising method, the guided wave (GW)-based structural health monitoring method has been widely investigated for FCD. However, reliable FCD still meets challenges, because uncertainties in real engineering applications usually cause serious change both to the crack propagation itself and GW monitoring signals. As one of deep learning methods, convolutional neural network (CNN) owns the ability of fusing a large amount of data, extracting high-level feature expressions related to classification, which provides a potential new technology to be applied in the GW-structural health monitoring method for crack evaluation. To address the influence of dispersion on reliable FCD, in this paper, a GW-CNN based FCD method is proposed. In this method, multiple damage indexes (DIs) from multiple GW exciting-acquisition channels are extracted. A CNN is designed and trained to further extract high-level features from the multiple DIs and implement feature fusion for crack evaluation. Fatigue tests on a typical kind of aircraft structure are performed to validate the proposed method. The results show that the proposed method can effectively reduce the influence of uncertainties on FCD, which is promising for real engineering applications

Impact Monitoring for Aircraft Smart Composite Skins Based on a Lightweight Sensor Network and Characteristic Digital Sequences

Due to the growing use of composite materials in aircraft structures, Aircraft Smart Composite Skins (ASCSs) which have the capability of impact monitoring for large-scale composite structures need to be developed. However, the impact of an aircraft composite structure is a random transient event that needs to be monitored on-line continuously. Therefore, the sensor network of an ASCS and the corresponding impact monitoring system which needs to be installed on the aircraft as an on-board device must meet the requirements of light weight, low power consumption and high reliability. To achieve this point, an Impact Region Monitor (IRM) based on piezoelectric sensors and guided wave has been proposed and developed. It converts the impact response signals output from piezoelectric sensors into Characteristic Digital Sequences (CDSs), and then uses a simple but efficient impact region localization algorithm to achieve impact monitoring with light weight and low power consumption. However, due to the large number of sensors of ASCS, the realization of lightweight sensor network is still a key problem to realize an applicable ASCS for on-line and continuous impact monitoring. In this paper, three kinds of lightweight piezoelectric sensor networks including continuous series sensor network, continuous parallel sensor network and continuous heterogeneous sensor network are proposed. They can greatly reduce the lead wires of the piezoelectric sensors of ASCS and they can also greatly reduce the monitoring channels of the IRM. Furthermore, the impact region localization methods, which are based on the CDSs and the lightweight sensor networks, are proposed as well so that the lightweight sensor networks can be applied to on-line and continuous impact monitoring of ASCS with a large number of piezoelectric sensors. The lightweight piezoelectric sensor networks and the corresponding impact region localization methods are validated on the composite wing box of an unmanned aerial vehicle. The accuracy rate of impact region localization is higher than 92%

Formation and Application of Starch–Polyphenol Complexes: Influencing Factors and Rapid Screening Based on Chemometrics

Understanding the nuanced interplay between plant polyphenols and starch could have significant implications. For example, it could lead to the development of tailor-made starches for specific applications, from bakinag and brewing to pharmaceuticals and bioplastics. In addition, this knowledge could contribute to the formulation of functional foods with lower glycemic indexes or improved nutrient delivery. Variations in the complexes can be attributed to differences in molecular weight, structure, and even the content of the polyphenols. In addition, the unique structural characteristics of starches, such as amylose/amylopectin ratio and crystalline density, also contribute to the observed effects. Processing conditions and methods will always alter the formation of complexes. As the type of starch/polyphenol can have a significant impact on the formation of the complex, the selection of suitable botanical sources of starch/polyphenols has become a focus. Spectroscopy coupled with chemometrics is a convenient and accurate method for rapidly identifying starches/polyphenols and screening for the desired botanical source. Understanding these relationships is crucial for optimizing starch-based systems in various applications, from food technology to pharmaceutical formulations