Composite materials are widely used in many fields due to their excellent properties. Quality defects in composite materials can lead to lower quality components, creating potential risk of accidents. Experimental and simulation methods are commonly used to predict the quality of composite materials. However, it is difficult to predict the quality of composite materials accurately due to the uncertain curing environment and incomplete feature space. To address this problem, a digital twin (DT) visual model of a composite material is first constructed. Then, a static autoclave DT virtual model is coupled with a variable composite material DT virtual model to construct a model of the curing process. Features are added to the proposed model by generating simulated data to enhance the quality prediction. An extreme learning machine (ELM) for quality prediction is trained with the generated data. Finally, the effectiveness of the proposed method is verified through result analysis.
An Industrial Internet platform is acknowledged to be a requisite promoter for smart manufacturing, enabling physical manufacturing resources to be virtualized and permitting resources to collaborate in the form of services. As a central function of the platform, manufacturing service collaboration optimization is dedicated to establishing high-quality service collaboration solutions for manufacturing tasks. Such optimization is inseparable from the functional and amount requirements of a task, which must be satisfied when orchestrating services. However, existing manufacturing service collaboration optimization methods mainly focus on horizontal collaboration among services for functional demands and rarely consider vertical collaboration to cover the needed amounts. To address this gap, this paper proposes a dual-dimensional service collaboration methodology that combines functional and amount collaboration. First, a multi-granularity manufacturing service modeling method is presented to describe services. On this basis, a dual-dimensional manufacturing service collaboration optimization (DMSCO) model is formulated. In the vertical dimension, multiple functionally equivalent services form a service cluster to fulfill a subtask; in the horizontal dimension, complementary service clusters collaborate for the entire task. Service selection and amount distribution to the selected services are critical issues in the model. To solve the problem, a multi-objective memetic algorithm with multiple local search operators is tailored. The algorithm embeds a competition mechanism to dynamically adjust the selection probabilities of the local search operators. The experimental results demonstrate the superiority of the algorithm in terms of convergence, solution quality, and comprehensive metrics, in comparison with commonly used algorithms.
The residual stress inside stock materials is a fundamental property related to the quality of manufactured parts in terms of geometric/dimensional stability and fatigue life. For large parts that must meet high-precision requirements, accurately measuring and predicting the residual stress field has been a major challenge. Existing technologies for measuring the residual stress field are either strain-based measurement methods or non-destructive methods with low efficiency and accuracy. This paper reports a new non-destructive method for inferencing the residual stress field based on deformation forces. In the proposed method, the residual stress field of a workpiece is inferred based on the characteristics of the deformation forces that reflect the overall effect of the unbalanced residual stress field after material removal operations. The relationship between deformation forces and the residual stress field is modeled based on the principle of virtual work, and the residual stress field inference problem is solved using an enforced regularization method. Theoretical verification is presented and actual experiment cases are tested, showing reliable accuracy and flexibility for large aviation structural parts. The underlying principle of the method provides an important reference for predicting and compensating workpiece deformation caused by residual stress using dynamic machining monitoring data in the context of digital and intelligent manufacturing.
This research focuses on the realization of rapid reconfiguration in a cloud manufacturing environment to enable flexible resource scheduling, fulfill the resource potential and respond to various changes. Therefore, this paper first proposes a new cloud and software-defined networking (SDN)-based manufacturing model named software-defined cloud manufacturing (SDCM), which transfers the control logic from automation hard resources to the software. This shift is of significance because the software can function as the “brain” of the manufacturing system and can be easily changed or updated to support fast system reconfiguration, operation, and evolution. Subsequently, edge computing is introduced to complement the cloud with computation and storage capabilities near the end things. Another key issue is to manage the critical network congestion caused by the transmission of a large amount of Internet of Things (IoT) data with different quality of service (QoS) values such as latency. Based on the virtualization and flexible networking ability of the SDCM, we formalize the time-sensitive data traffic control problem of a set of complex manufacturing tasks, considering subtask allocation and data routing path selection. To solve this optimization problem, an approach integrating the genetic algorithm (GA), Dijkstra's shortest path algorithm, and a queuing algorithm is proposed. Results of experiments show that the proposed method can efficiently prevent network congestion and reduce the total communication latency in the SDCM.
The topology optimization design of complex products can significantly improve material and power savings, and reduce inertial forces and mechanical vibrations effectively. In this study, a large-tonnage hydraulic press was chosen as a typically complex product to present the optimization method. We propose a new reliability topology optimization method based on the reliability-and-optimization decoupled model and teaching-learning-based optimization (TLBO) algorithm. The supports formed by the plate structure are considered as topology optimization objects, characterized by light weight and stability. The reliability optimization under certain uncertainties and structural topology optimization are processed collaboratively. First, the uncertain parameters in the optimization problem are modified into deterministic parameters using the finite difference method. Then, the complex nesting of the uncertainty reliability analysis and topology optimization are decoupled. Finally, the decoupled model is solved using the TLBO algorithm, which is characterized by few parameters and a fast solution. The TLBO algorithm is improved with an adaptive teaching factor for faster convergence rates in the initial stage and performing finer searches in the later stages. A numerical example of the hydraulic press base plate structure is presented to underline the effectiveness of the proposed method.
The soft sensing of key performance indicators (KPIs) plays an essential role in the decision-making of complex industrial processes. Many researchers have developed data-driven soft sensors using cutting-edge machine learning (ML) or deep learning (DL) models. Moreover, feature selection is a crucial issue because a raw industrial dataset is usually high-dimensional, and not all features are conducive to the development of soft sensors. A perfect feature-selection method should not rely on hyperparameters and subsequent ML or DL models. Rather, it should be able to automatically select a subset of features for soft sensor modeling, in which each feature has a unique causal effect on industrial KPIs. Therefore, this study proposes a causal model-inspired automatic feature-selection method for the soft sensing of industrial KPIs. First, inspired by the post-nonlinear causal model, we integrate it with information theory to quantify the causal effect between each feature and the KPIs in the raw industrial dataset. After that, a novel feature-selection method is proposed to automatically select the feature with a non-zero causal effect to construct the subset of features. Finally, the constructed subset is used to develop soft sensors for the KPIs by means of an AdaBoost ensemble strategy. Experiments on two practical industrial applications confirm the effectiveness of the proposed method. In the future, this method can also be applied to other industrial processes to help develop more advanced data-driven soft sensors.
In this paper, the novel design mode of human-aided design (HAD) is proposed to replace conventional computer-aided design (CAD). In HAD, computers can automatically complete the whole product design via a new isogeometric topology optimization (ITO), while humans just assist to slightly modify the design to meet requirements. An embedded domain ITO is presented to design complex models with irregular design domains, and editable geometric models of optimized results can be automatically generated based on layered ITO results. Three examples are tested to verify the proposed HAD mode, including a 3D cantilever beam with a regular design domain, an automotive part with an irregular design domain, and a Messerschmitt-Bölkow-Blohm (MBB) beam with a multiscale structure. The results demonstrate that the proposed HAD mode can automatically deliver high-quality optimized models; thus, it has great potential as a revolutionary technology to change the current design mode from CAD to HAD.
Terahertz (THz) technology is probably best known to the public as a powerful tool for imaging, since it has been applied in security and medical scanning, resulting in numerous impressive images that would be unobtainable using other technologies. With the roll-out of 5G mobile networks, research into 6G wireless communications is heating up. It is envisioned that THz technology will be used for 6G and future wireless communications. In this paper, we review how THz technology has been employed for imaging and wireless communications, identify state-of-the-art developments in the field, and then examine and compare common devices and issues in both applications. The possibility of integrating THz imaging/sensing and wireless communications is considered, and challenges and future perspectives are presented and discussed. It is shown that THz technology is indeed a key enabling technology for both imaging and wireless communications in the future.
This article presents a 39 GHz transceiver front-end chipset for 5G multi-input multi-output (MIMO) applications. Each chip includes two variable-gain frequency-conversion channels and can support two simultaneous independent beams, and the chips also integrate a local-oscillator chain and digital module for multi-chip extension and gain-state control. To improve the radio-frequency performance, several circuit-level improvement techniques are proposed for the key building blocks in the front-end system. Furthermore, an advanced low-temperature co-fired ceramic process is developed to package the 39 GHz dual-channel transceiver chipset, and it achieves low packaging loss and high isolation between the two transmitting (TX)/receiving (RX) channels. Both the chip-level and system-in-package (SIP)-level measurements are conducted to demonstrate the performance of the transceiver chipset. The measurement characteristics show that the TX SIP provides 11 dB maximum gain and 10 dBm saturated output power, while the RX SIP achieves 52 dB maximum gain, 5.4 dB noise figure, and 7.2 dBm output 1 dB compression point. Single-channel communication link testing of the transceiver exhibits an error vector magnitude (EVM) of 3.72% and a spectral efficiency of 3.25 bit·s−1·Hz−1 for 64-quadrature amplitude modulation (QAM) modulation and an EVM of 3.76% and spectral efficiency of 3.9 bit·s−1·Hz−1 for 256-QAM modulation over a 1 m distance. Based on the chipset, a 39 GHz multi-beam prototype is also developed to perform the MIMO operation for 5G millimetre wave applications. The over-the-air communication link for one- and two-stream transmission indicates that the multi-beam prototype can cover a 5–150 m distance with comparable throughput.
Lifestyle modification is an effective measure for diabetes prevention in people with prediabetes, but its associations with the long-term risks of cardiovascular disease (CVD), cancer, and mortality remain largely uncertain. We aimed to investigate the associations of combined healthy lifestyle factors with these health outcomes among participants with prediabetes. The study included 121 254 people with prediabetes from four prospective cohorts: the Dongfeng-Tongji (DFTJ) cohort and Kailuan study, both from China; the UK Biobank; and the US National Health and Nutrition Examination Survey (NHANES; for mortality analysis only). We documented a total of 18 333 incident diabetes, 10 829 incident CVD, 6926 incident cancer, and 9877 deaths during follow-up. Combined healthy lifestyle scores (scored from 0 to 5) were constructed based on never smoking or quitting smoking for ≥ 10 years, low-to-moderate alcohol drinking, optimal physical activity, healthy diet, and optimal waist circumference. First, Cox proportional-hazards regression models were used to quantify the associations of combined lifestyle score with health outcomes in each cohort; then, multivariable-adjusted hazard ratios (HRs) were pooled via a random-effects model of meta-analysis. Compared with participants with the least healthy lifestyle (a score of 0–1), participants with the healthiest lifestyle (a score of 4–5) had significantly reduced risks of all outcomes. The HRs (95% confidence interval (CI)) were 0.57 (0.48–0.69) for diabetes, 0.67 (0.62– 0.73) for CVD, 0.80 (0.73–0.88) for cancer, and 0.54 (0.42–0.70) for mortality. Significant associations were consistently found across subgroups of baseline demographic characteristics and metabolic health status. In conclusion, our pooled analyses of four cohorts from three countries reveal that greater adherence to a healthy lifestyle is associated with considerably lower risks of diabetes and its major complications among adults with prediabetes. These findings provide informative and compelling evidence for establishing clinical guidelines and public health policies.
Accurate structural and functional imaging is vital for the diagnosis and prognosis of urinary system diseases. Fluorescence bioimaging in the second near-infrared spectral region (NIR-II, 1000–1700 nm) has shown advantages of higher spatial resolution, deeper penetration, and finer signal-to-background ratio (SBR) compared to the conventional fluorescence imaging methods but limited to its clinical inapplicability. Herein, we first report in vivo NIR-II fluorescence imaging of the urinary system enabled by a clinically approved and renal excretable dye methylene blue (MB), which cannot only achieve clear invasive/non-invasive urography but also noninvasively detect renal function efficiently. These results demonstrate that MB assisted NIR-II fluorescence imaging holds a great promise for structural and functional imaging of the urinary system both clinically and preclinically.
The application of tumor antigen-based immunotherapy is hindered by the rarity of validated immunogenic peptides. In this study, we aimed to investigate the potential of circular RNAs (circRNAs) as a novel source of tumor antigen peptides in hepatobiliary tumor organoids. Using RNA-sequencing (RNA-seq) with an algorithm-based score tool, 3950 translated tumor-specific circRNAs were predicted to generate 18 971 antigen peptides in 27 organoids. In view of the antigen landscape, 11 amino acid length (mer) peptides and human leukocyte antigen (HLA)-A binding peptides harbored the highest immunogenicity-related scores. In three out of five analyzed organoids, 13 predicted antigen peptides were directly confirmed as HLA-A, -B, and -C (HLA-ABC) binding peptides with mass spectrometry (MS)-based immunopeptidomics. CircRNA-derived tumor-specific peptides presented by the HLA-ABC molecules stimulated cluster of differentiation 8 (CD8) T cells to exhibit increased CD107a interferon γ (IFNγ) co-expressions and IFNγ secretion in flow cytometry and enzyme-linked immunosorbent assay (ELISA). Cytotoxic T cell activity targeting the organoids, induced by the immunogenic circRNA-derived peptides, was verified in a killing assay. Notably, the antigen peptide YGFNEILKK from circTBC1D15 was not only recognized as an HLA-ABC-presented peptide of the organoids but also drastically reduced the tumor organoid survival rate. Our findings highlight a crucial subset for generating tumor antigens, which has implications for targeting tumor-specific circRNAs in cancers.
Plant pathogens continuously impair agricultural yields and food security. Therefore, the dynamic characterization of early pathogen progression is crucial for disease monitoring and presymptomatic diagnosis. Hyperspectral imaging (HSI) has great potential for tracking the dynamics of initial infected sites for presymptomatic diagnosis; however, no related studies have extracted fingerprint spectral signatures (FSSs) that can capture diseased lesions on leaves during the early infection stage in vivo or investigated the detection mechanism of HSI relating to the host biochemical responses. The FSSs denote unique and representative spectral signatures that characterize a specific plant disease. In this study, the FSSs of spot blotch on barley leaves inoculated with Bipolaris sorokiniana were discovered to characterize symptom development for presymptomatic diagnosis based on time-series HSI data analysis. The early spectral and biochemical responses of barley leaves to spot blotch progression were also investigated. The fullspectrum FSSs were physically interpretable and could capture the unique characteristics of chlorotic and necrotic tissues along with lesion progression, enabling the in situ visualization of the spatiotemporal dynamics of early plant–pathogen interactions at the pixel level. Presymptomatic diagnosis of spot blotch was achieved 24 h after inoculation—12 h earlier than the traditional polymerase chain reaction (PCR) assay or biochemical measurements. To uncover the mechanism of HSI presymptomatic diagnosis, quantitative relationships between the mean spectral responses of leaves and their biochemical indicators (chlorophylls, carotenoids, malondialdehyde (MDA), ascorbic acid (AsA), and reduced glutathione (GSH)) were developed, achieving determination coefficient of prediction set (Rp2) > 0.84 for regression models. The overall results demonstrated that, based on the association between HSI and in vivo planttrait alterations, the extracted FSSs successfully tracked the spatiotemporal dynamics of bipolaris spot blotch progression for presymptomatic diagnosis. Tests of this methodology on other plant diseases demonstrated its remarkable generalization potential for the early control of plant diseases.
In this work, we propose and realize a smart metasurface for programming electromagnetic (EM) manipulations based on human speech recognition. The smart metasurface platform is composed of a digital coding metasurface, a speech-recognition module, a single-chip computer, and a digital-to-analog converter (DAC) circuit, and can control EM waves according to pre-stored voice commands in a smart way. The constructed digital metasurface contains 6 × 6 super unit cells, each of which consists of 4 × 4 active elements with embedded varactor diodes. Together with the DAC and single-chip computer, the speech-recognition module can recognize voice commands and generate corresponding voltage sequences to control the metasurface. In addition, a genetic algorithm (GA) is adopted in the design of the metasurface for efficiently optimizing the phase distributions. To verify the performance of the smart metasurface platform, three typical functions are demonstrated: radar cross-section reduction, vortex beam generation, and beam splitting. The proposed strategy may offer a new avenue for controlling EM waves and establishing a link between EM and acoustic communications.
Early identification of abnormal regions is crucial in preventing the occurrence of underground geotechnical disasters. To meet the high-accuracy detection requirements of underground engineering, this paper proposes a tomography method for abnormal region identification in complex rock-mass structures that utilizes traveltime tomography combined with the damped least-squares method and Gaussian filtering. The proposed method overcomes the limitation of velocity difference in empty region detection and mitigates the impact from isolated velocity mutation in the iteration. Numerical and laboratory experiments were conducted to evaluate the identification accuracy and computational efficiency of forward modeling, including the shortest-path method (SPM), dynamic SPM (DSPM), and fast sweeping method (FSM). The results show that DSPM and FSM can clearly detect abnormal regions in numerical and laboratory experiments. Field experiments were conducted in the Shaanxi Zhènào mine and achieve the reconstruction of the underground roadway distribution. This paper not only realizes the application of abnormal region identification using traveltime tomography but also provides new insight into potential hazards detection in underground geotechnical engineering.
Long-term observations of the volume mixing ratio (VMR) profiles and total columns of key atmospheric constituents are significant for understanding climate change and the impact of the carbon budget in China. This study provides an overview of the first ground-based high-resolution Fourier-transform spectrometry (FTS) observation station in China, which is located in Hefei, Eastern China. The FTS observation station can observe the total columns and VMR profiles of more than 30 atmospheric constituents. Time series of some key atmospheric constituents observed at the Hefei station since 2014 have been released to the public. The major scientific achievements obtained to date at this station include spectral retrieval characterization and harmonization, investigation of the overall characteristics of key atmospheric constituents, emission estimates, satellite and chemical transport model (CTM) evaluations, and a summary of pollutant sources and transport patterns. An outlook is also presented of the envisaged plan for observations, scientific studies, and data usage at the Hefei station. China has explicitly proposed reaching a peak in its CO2 emissions by 2030 and realizing carbon neutrality by 2060. The Hefei station will provide scientific assistance to the Chinese Government for developing green economy policies and achieving carbon neutrality and the goals of the Paris Agreement.
Ultra-high performance concrete (UHPC) is a relatively new cementitious concrete composite with significant application potential in infrastructure construction because of its excellent mechanical strength and durability. The steel fiber–matrix interfacial bond is the main factor that governs other mechanical properties of UHPC, including tensile, flexural, and compressive strengths and failure mode (fracture behavior). This paper presents a comprehensive review on the research progress of fiber–matrix bond behaviors of UHPC by discussing and comparing a range of fiber pullout testing methods and analytical models. The parameters of the fiber–matrix bond, including the geometry and orientation of fibers, surface treatment, and composition and strength of the matrix, are identified and discussed in detail. Lastly, recommendations for future research related to UHPC strengthening methods and testing details are provided based on recent progress.