Electromagnetic riveting (EMR) has been widely used in aerospace field, but the riveting gun is heavy and its cost is high. In this paper, the reduction of cost and weight for rivet gun were ...investigated from the optimization of concentric-ring slave coil. For the lack of accuracy of the traditional genetic algorithm coupled with artificial neuron network (GA-ANN), an improved computational framework was proposed. In the framework, a genetic-drop sampling strategy enhanced the accuracy of GA-ANN and helped ANN to generalize to the optimal. The prototype was manufactured in a case study to verify the riveting quality of the optimized EMR gun. Besides, coils of different sizes proved the generalization ability. The proposed method has the potential to significantly reduce the prediction error by 62% and effectively decrease the number of numerical model calculations by 73%. Optimization for EMR gun led to a 51% reduction in weight, a 73% increase in material utilization and little effect on riveting quality. Meanwhile, the algorithm was still accurate on coils of different sizes. The proposed computational framework could improve efficiency in the design of EMR gun in industry.
We report here the fabrication of a Janus wire mesh by a combined process of laser structuring and fluorosilane/graphene oxide (GO) modification of the two sides of the mesh, respectively, toward its ...applications in efficient oil/water separation. Femtosecond laser processing has been employed to make different laser-induced periodic surface structures (LIPSS) on each side of the mesh. Surface modification with fluorosilane on one side and GO on the other side endows the two sides of the Janus mesh with distinct wettability. Thus, one side is superhydrophobic and superoleophilic in air, and the other side is superhydrophilic in air and superoleophobic under water. As a proof of concept, we demonstrated the separation of light/heavy oil and water mixtures using this Janus mesh. To realize an efficient separation, the intrusion pressure that is dominated by the wire mesh framework and the wettability should be taken into account. Our strategy may open up a new way to design and fabricate Janus structures with distinct wettability; and the resultant Janus mesh may find broad applications in the separation of oil contaminants from water.
Quantum memory networks as an intermediate stage in the development of a quantum internet1 will enable a number of significant applications2-5. To connect and entangle remote quantum memories, it is ...best to use photons. In previous experiments6-13, entanglement of two memory nodes has been achieved via photon interference. Going beyond the state of the art by entangling many quantum nodes at a distance is highly sought after. Here, we report the entanglement of three remote quantum memories via three-photon interference. We employ laser-cooled atomic ensembles and make use of a ring cavity to enhance the overall efficiency of our memory–photon entanglement. By interfering three single photons from three separate set-ups, we create entanglement of three memories and three photons. Then, by measuring the photons and applying feed-forward, we achieve heralded entanglement between the three memories. Our experiment may be employed as a building block to construct larger and complex quantum networks14,15.The entanglement of three remote quantum memories based on 87Rb atoms is created via three-photon interference by enhancing the memory–photon entanglement in ring cavities, demonstrating a genuine quantum network involving more than two quantum nodes.
Widespread soil contamination endangers public health and undermines global attempts to achieve the United Nations Sustainable Development Goals. Due to the lack of relevant studies and low precision ...of spaceborne spectroscopy, estimating soil heavy metal concentrations is challenging. In this study, we developed a coupled retrieval to qualify the heavy metal nickel (Ni) concentration in agricultural soil from spaceborne hyperspectral imagery. The retrieval couples spectral feature extraction from multi-scale discrete wavelet transform (DWT) and dimension reduction (DR), optimal band combination algorithm to five machine learning retrieval models using tree-based ensemble learning, neural network-based, and kernel-based. The comparison between the retrievals and Ni measurements shows that the DWT combined with t-distributed stochastic neighbor embedding (tSNE) coupled extreme gradient boosting (XGboost) retrieval model exhibited the best prediction for the validation dataset. Moreover, due to the integration of six statistical indicators of model performance and the fitted slope of the regression line, the retrieval framework can produce more robust and accurate predictions than those that rely on correlation coefficients. The demonstrated potential of spaceborne hyperspectral remote sensing to provide accurate quantitative measurements of soil heavy metal concentrations will serve as a reference for agricultural plot applications worldwide.
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•A coupled retrieval framework for soil pollutant elements using Zhuhai-1 VNIR spaceborne hyperspectral imagery was proposed.•Multi-scale DWT-spectral highlights soil heavy metal spectral features.•T-SNE outperforms PCA in exacting spectral features.•The XGBoost model can predict soil heavy metals effectively
Timely and accurate estimation of the area and distribution of crops is vital for food security. Optical remote sensing has been a key technique for acquiring crop area and conditions on regional to ...global scales, but great challenges arise due to frequent cloudy days in southern China. This makes optical remote sensing images usually unavailable. Synthetic aperture radar (SAR) could bridge this gap since it is less affected by clouds. The recent availability of Sentinel-1A (S1A) SAR imagery with a 12-day revisit period at a high spatial resolution of about 10 m makes it possible to fully utilize phenological information to improve early crop classification. In deep learning methods, one-dimensional convolutional neural networks (1D CNNs), long short-term memory recurrent neural networks (LSTM RNNs), and gated recurrent unit RNNs (GRU RNNs) have been shown to efficiently extract temporal features for classification tasks. However, due to the complexity of training, these three deep learning methods have been less used in early crop classification. In this work, we attempted to combine them with an incremental classification method to avoid the need for training optimal architectures and hyper-parameters for data from each time series. First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters. Then, starting at the first time point, we performed an incremental classification process to train each classifier using all of the previous data, and obtained a classification network with all parameter values (including the hyper-parameters) at each time point. Finally, test accuracies of each time point were assessed for each crop type to determine the optimal time series length. A case study was conducted in Suixi and Leizhou counties of Zhanjiang City, China. To verify the effectiveness of this method, we also implemented the classic random forest (RF) approach. The results were as follows: (i) 1D CNNs achieved the highest Kappa coefficient (0.942) of the four classifiers, and the highest value (0.934) in the GRU RNNs time series was attained earlier than with other classifiers; (ii) all three deep learning methods and the RF achieved F measures above 0.900 before the end of growth seasons of banana, eucalyptus, second-season paddy rice, and sugarcane; while, the 1D CNN classifier was the only one that could obtain an F-measure above 0.900 for pineapple before harvest. All results indicated the effectiveness of the solution combining the deep learning models with the incremental classification approach for early crop classification. This method is expected to provide new perspectives for early mapping of croplands in cloudy areas.
Two-beam-laser interference was used for the simultaneous reduction, patterning and nanostructuring of graphene oxide on flexible polyethylene terephthalate substrates for the production of a high ...performance humidity sensing device. Hierarchical graphene nanostructures were formed after laser interference treatment of graphene oxide, which holds great promise for gaseous molecular adsorption, and thereby significantly increases their sensing performance. By tuning the laser power, the content of oxygen functional groups, could be changed within a certain range, which contributes not only controllable conductivity but also tunable response/recovery time of the humidity sensor due to the interaction between water molecules and oxygen functional groups on the graphene oxide sheets. The laser interference processing of graphene oxide films is a mask-free, surfactant-free and large-area approach to the production of hierarchical graphene micro-nanostructures, and thus shows great potential for fabrication of future graphene-based microdevices.
The controlled self-assembly of cuprous iodide cluster-based supramolecular architectures with a tunable structure is still a big challenge to date. We adopt a conformation-adaptive self-assembly ...strategy to precisely construct two Cu
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RATIONALE:Targeting inflammation has been shown to provide clinical benefit in the field of cardiovascular diseases. Although manipulating regulatory T-cell function is an important goal of ...immunotherapy, the molecules that mediate their suppressive activity remain largely unknown. IL (interleukin)-35, an immunosuppressive cytokine mainly produced by regulatory T cells, is a novel member of the IL-12 family and is composed of an EBI3 (Epstein-Barr virus–induced gene 3) subunit and a p35 subunit. However, the role of IL-35 in infarct healing remains elusive.
OBJECTIVE:This study aimed to determine whether IL-35 signaling is involved in healing and cardiac remodeling after myocardial infarction (MI) and, if so, to elucidate the underlying molecular mechanisms.
METHODS AND RESULTS:IL-35 subunits (EBI3 and p35), which are mainly expressed in regulatory T cells, were upregulated in mice after MI. After IL-35 inhibition, mice showed impaired infarct healing and aggravated cardiac remodeling, as demonstrated by a significant increase in mortality because of cardiac rupture, decreased wall thickness, and worse cardiac function compared with wild-type MI mice. IL-35 inhibition also led to decreased expression of α-SMA (α-smooth muscle actin) and collagen I/III in the hearts of mice after MI. Pharmacological inhibition of IL-35 suppressed the accumulation of Ly6C and major histocompatibility complex II/C-C motif chemokine receptor type 2 (MHC II CCR2) macrophages in infarcted hearts. IL-35 activated transcription of CX3CR1 (C-X3-C motif chemokine receptor 1) and TGF (transforming growth factor) β1 in macrophages by inducing GP130 signaling, via IL12Rβ2 and phosphorylation of STAT1 (signal transducer and activator of transcription family) and STAT4 and subsequently promoted Ly6C macrophage survival and extracellular matrix deposition. Moreover, compared with control MI mice, IL-35–treated MI mice showed increased expression of α-SMA and collagen within scars, correlating with decreased left ventricular rupture rates.
CONCLUSIONS:IL-35 reduces cardiac rupture, improves wound healing, and attenuates cardiac remodeling after MI by promoting reparative CX3CR1Ly6C macrophage survival.
Novel renewable bisepoxide 2, 2′-diglycidyl ether-3, 3′-dimethoxy-5, 5′-diallydiphenylmethane (BEF-EP) and its hardener 3-methoxy-4-hydroxy-phenylbenzimidazole (VBZMI) were prepared from ...1-allyl-3-methoxy-4-hydroxybenzene (eugenol) and 2-methoxy-4-formylphenol (vanillin), respectively. The chemical structures of two monomers were confirmed by their 1H NMR spectra. Estrogenic activity test revealed that biobased bisphenol monomer 2,2′-dihydroxy-3,3′-dimethoxy-5,5′-diallydiphenylmethane (BEF) as the precursor of BEF-EP showed an extremely lower estrogenic activity than commercial bisphenols (BPA and BPF). This biobased, safe and green epoxy (BEF-EP) and commercial epoxy diglycidyl ether of bisphenol F (BPF-EP) were then cured with conventional and renewable hardener (benzimidazole BZMI and 3-methoxy-4-hydroxy-phenylbenzimidazole VBZMI), respectively, for comparison. The results showed the biobased epoxy thermoset (BEF-EP/BZMI) possessed excellent thermal stability (Td5% = 372 °C), almost reaching the properties of commercial epoxy thermoset (BPF-EP/BZMI, Td5% = 385 °C). Furthermore, BEF-EP/BZMI showed good hydrophobic properties, which exhibited a higher contact angle (79.53°) than BPF-EP/BZMI (75.16°). In addition, for the comparison between VBZMI and BZMI, the epoxy resins using VBZMI as the curing agent displayed higher performance than the epoxy resins using BZMI as the curing agent. Especially, BEF-EP/VBZMI showed a higher thermal stability (Td5% = 395 °C), a higher glass transition temperature (Tg = 97 °C) and a higher contact angle (94.07) than BEF-EP/BZMI. The results could be attributed to the existence of hydroxyl groups in the side chain of VBZIMI, which enhanced the crosslinking density and then improved the rigid of epoxy material. Herein, we believe the novel biobased epoxy resin (BEF-EP) and hardener (VBZMI) has wide application as the alternative of conventional petroleum-based epoxy resin and hardener.
•A novel renewable bisepoxide BEF-EP and its hardener VBZMI were prepared from eugenol and vanillin, respectively.•The precursor BEF showed extremely lower estrogenic activity than commercial bisphenols.•The biobased epoxy resin cured by VBZMI showed better performance than epoxy resin cured by traditional curing agent.
To establish a novel model using radiomics analysis of pre-treatment and post-treatment magnetic resonance (MR) images for prediction of progression-free survival in the patients with stage II-IVA ...nasopharyngeal carcinoma (NPC) in South China.
One hundred and twenty NPC patients who underwent chemoradiotherapy were enrolled (80 in the training cohort and 40 in the validation cohort). Acquiring data and screening features were performed successively. Totally 1133 radiomics features were extracted from the T2-weight images before and after treatment. Least absolute shrinkage and selection operator regression, recursive feature elimination algorithm, random forest, and minimum-redundancy maximum-relevancy (mRMR) method were used for feature selection. Nomogram discrimination and calibration were evaluated. Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analyses were applied to appraise the prognostic performance of nomograms. Survival curves were plotted using Kaplan-Meier method.
Integrating independent clinical predictors with pre-treatment and post-treatment radiomics signatures which were calculated in conformity with radiomics features, we established a clinical-and-radiomics nomogram by multivariable Cox regression. Nomogram consisting of 14 pre-treatment and 7 post-treatment selected features has been proved to yield a reliable predictive performance in both training and validation groups. The C-index of clinical-and-radiomics nomogram was 0.953 (all P < 0.05), which was higher than that of clinical (0.861) or radiomics nomograms alone (based on pre-treatment statistics: 0.942; based on post-treatment statistics: 0.944). Moreover, we received Rad-score of pre-treatment named RS1 and post-treatment named RS2 and all were used as independent predictors to divide patients into high-risk and low-risk groups. Kaplan-Meier analysis showed that lower RS1 (less than cutoff value, - 1.488) and RS2 (less than cutoff value, - 0.180) were easier to avoid disease progression (all P < 0.01). It showed clinical benefit with decision curve analysis.
MR-based radiomics measured the burden on primary tumor before treatment and the tumor regression after chemoradiotherapy, and was used to build a model to predict progression-free survival (PFS) in the stage II-IVA NPC patients. It can also help to distinguish high-risk patients from low-risk patients, thus guiding personalized treatment decisions effectively.
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