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Despite many technical challenges in the development of safe and environmentally friendly food packaging paper materials with excellent water and oil resistance using simple methods, producing paper-based functional materials using bio-based polymers is currently an important topic in the food packaging industry. In this study, novel water and oil-resistant coatings for the paper were developed through the combination of sodium alginate (SA), hydroxypropyl methylcellulose (HPMC), polyvinyl butyral (PVB), and hydrophobic silica nanoparticles (HSNPs). To impart oil-repellency to paper, SA and HPMC were first mixed uniformly and coated on the base paper, which was pre-treated with calcium chloride solution. A compact and tough coating layer was formed on paper due to the hydrogen bonding between SA and HPMC molecules, and the crosslinking between SA and Ca

This brief paper introduces a framework for supervised and semi-supervised learning by estimating a non-linear embedding that incorporates Spectral Graph Convolutions structure. The proposed algorithm exploits data-driven graphs in two ways. First, it integrates data smoothness over graphs. Second, its regression loss function jointly uses the data and its graph in the sense that the regressor model sees convolved data samples. The resulting framework can solve the problem of over-fitting on local neighborhood structures for image data having varied natures like outdoor scenes, faces and man-made objects. The proposed Graph Convolution based Semi-supervised Embedding (GCSE) not only provides a new perspective to non-linear embedding research but also induces the standpoint on Spectral Graph Convolutions methods. A series of experiments are conducted on four image datasets in order to compare the proposed method with some state-of-art semi-supervised methods. This evaluation demonstrates the effectiveness of the proposed embedding method.

Due to the structural constraints, mandatory volumetric examination is not implemented on small branch BOSS joints of nuclear power plant, which leads to internal defects cannot be detected thoroughly, causing potential risk to the safe and stable operation. Key factors affecting the welding quality of small branch BOSS joints are systematically analyzed in this paper. Besides, practical suggestions are provided on the operating level to improve the welding quality, including groove design, welding process, welder qualification and nondestructive examination. On this basis, further improvement suggestions are proposed to revise the codes and regulatory guides.

In this work, the as-prepared V2O5 nanobelts can sensitively quench the fluorescence of nitrogen-doped carbon dots (N-CDs) based on the inner filter effect (IFE). In the presence of ascorbic acid (AA), the fluorescence of N-CDs can recover through the redox reaction between V2O5 nanobelts and AA. Meanwhile, in the presence of both alkaline phosphatase (ALP) and ascorbyl-2-phosphate (AAP), the fluorescence of N-CDs can also restore since AAP can be hydrolyzed into AA by ALP. Under optimum conditions, the linear range for AA is from 0.01 to 2.5 μM with a detection limit of 3 nM and that for ALP is from 0.1 to 30 U/L with a detection limit of 0.04 U/L (S/N = 3). Particularly, the proposed probe could be successfully used to detect AA and ALP in human serum samples. Furthermore, N-CDs can be applied in fluorescence imaging of Human breast cancer cells with satisfactory results.

Changes of the morphology and pore structure of old corrugated container (OCC) fibers during an alkyl ketene dimer (AKD) sizing process were studied. The resulting samples were characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), contact angle, and BET surface area analysis. The length of fibers had obvious influence on the AKD sizing effect, and the length of fibers ranged from 100 to 200 meshes showed the best sizing performance. The surface roughness of 0.3% AKD sizing OCC fibers decreased from 27.949 nm to 12.811 nm. Compared with the control sample, the pore volume of fibers sized with 0.1% AKD decreased 4.3% when the average pore diameter was fixed at 2.4~3.0 nm. And when the usage of AKD increased to 0.3% and 0.5%, the pore volume decreased 1.4% and 6.3% accordingly. The decrease in the pore volume of AKD-sized fiber indicated the penetration and deposition of dispersed particles of AKD in the fiber lumens.

The application of carbon dots as a coreactant for Ru(bpy)32+ (where bpy is 2,2'-bipyridine) electrochemiluminescence (ECL) has been widely studied. However, the high cost of Ru(bpy)32+ and its derivatives has prohibited its widespread use in ECL biosensors. Herein, a novel anodic ECL system based on sulfur-doped graphitic carbon nitride nanosheets (S-g-C3N4 NSs) and nitrogen-doped carbon dots (N-CDs) is presented. In this ECL system, N-CDs serve as a new ECL coreactant that can significantly enhance the anodic ECL signal of S-g-C3N4 NSs (approximately 83 times) under optimal conditions. The possible ECL response mechanism of the S-g-C3N4 NSs/N-CDs system is proposed in detail on the basis of cyclic voltammograms, ECL-time curves, and ECL spectra. Furthermore, the ECL signal of the S-g-C3N4 NSs/N-CDs system was quenched by folic acid (FA), which was chosen as a model analyte to study the potential application of the new ECL system. The ECL intensity decreased linearly with the concentration of FA in the range from 0.05 to 200 μM. The detection limit for FA measurement is 16 nM (signal-to-noise ratio of 3). The proposed new ECL system has many advantages over traditional approaches, such as low toxicity and excellent biocompatibility. Especially, the proposed approach can detect FA in diluted human serum samples with satisfactory recoveries, indicating promising application for bioanalysis. Graphical abstract.

Ascorbic acid (AA) is one of the most important vitamins and serves many physiological activities in the body. In this work, we propose a simple colorimetric sensor for the rapid detection of AA through the redox reaction between CoOOH nanoflakes and ascorbic acid with the 2, 2′-azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) as a colorimetric signal indicator. CoOOH nanoflakes could catalytically oxidize colorless ABTS to produce green oxidized ABTS (ox ABTS) with an absorption band at 412 nm in UV–vis spectra. The presence of AA could induce the oxidation of ABTS, resulting in a significant color fading and decrease of the absorbance at 412 nm. Thus quantitative detection of AA is realized. Under the optimal experimental conditions, the linear range is obtained from 0.5 to 15 μM with a lower detection limit of 0.16 μM (S/N = 3). Particularly, this colorimetric assay is capable of detecting AA in different vitamins C tablet and fresh kiwi fruit samples. Hence, the fast response, high sensitivity and easy operation of the proposed colorimetric sensor make it suitable for further application in bioassay.

Graph-based embedding methods are very useful for reducing the dimension of high-dimensional data and for extracting their relevant features. In this paper, we introduce a novel nonlinear method called Flexible Discriminant graph-based Embedding with feature selection (FDEFS). The proposed algorithm aims to classify image sample data in supervised learning and semi-supervised learning settings. Specifically, our method incorporates the Manifold Smoothness, Margin Discriminant Embedding and the Sparse Regression for feature selection. The weights add l 2 , 1 -norm regularization for local linear approximation. The sparse regression implicitly performs feature selection on the original features of data matrix and of the linear transform. We also provide an effective solution method to optimize the objective function. We apply the algorithm on six public image datasets including scene, face and object datasets. These experiments demonstrate the effectiveness of the proposed embedding method. They also show that proposed the method compares favorably with many competing embedding methods.

In this study, in situ sulfur-doped carbon nitride nanosheets (S-g-C3N4 NSs) are used as the signal readout for the sensitive and selective sensing of L-cysteine (L-Cys) in human serum samples based on the competitive coordination chemistry of Cu2+ between L-Cys and S-g-C3N4 NSs. S-g-C3N4 NSs are prepared by using trithiocyanuric acid as a precursor for the first time, which exhibits stronger electrogenerated chemiluminescence (ECL) intensity compared with pristine graphitic carbon nitride nanosheets (g-C3N4 NSs). The ECL signals of the S-g-C3N4 NSs can be quenched by Cu2+ and the subsequent presence of L-Cys can recover the ECL signals of the S-g-C3N4 NSs. These changes are ascribed to the higher coordination ability between Cu2+ and L-Cys than that between Cu2+ and the S-g-C3N4 NSs. On the basis of this, the concentration of L-Cys can be quantitatively determined. Under optimized conditions, the ECL intensity recovery shows a linear relationship with the L-Cys concentration range from 30 nM to 0.2 mM with a lower detection limit of 5 nM (S/N = 3). The proposed method is highly sensitive and selective and is thus particularly useful for fast and simple clinical diagnosis of diseases, such as Alzheimer's disease and cardiovascular disease.

Despite recent advances in deep learning-based face frontalization methods, photo-realistic and illumination preserving frontal face synthesis is still challenging due to large pose and illumination discrepancy during training. We propose a novel Flow-based Feature Warping Model (FFWM) which can learn to synthesize photo-realistic and illumination preserving frontal images with illumination inconsistent supervision. Specifically, an Illumination Preserving Module (IPM) is proposed to learn illumination preserving image synthesis from illumination inconsistent image pairs. IPM includes two pathways which collaborate to ensure the synthesized frontal images are illumination preserving and with fine details. Moreover, a Warp Attention Module (WAM) is introduced to reduce the pose discrepancy in the feature level, and hence to synthesize frontal images more effectively and preserve more details of profile images. The attention mechanism in WAM helps reduce the artifacts caused by the displacements between the profile and the frontal images. Quantitative and qualitative experimental results show that our FFWM can synthesize photo-realistic and illumination preserving frontal images and performs favorably against the state-of-the-art results. Our code is available at https://github.com/csyxwei/FFWM.

The effect of fabrication process error on the nonlinearity of sandwich MEMS capacitive accelerometer is analyzed in the paper. Firstly, after the packaging of the accelerometer, the displacement offset and torsion angle of the sensitive proof mass are obtained by CV testing. The gravitational field rolling four-position testing method is used to calibrate the bias, the scale factor, and the second-order nonlinearity coefficient of the accelerometer under different compensation capacitance. Testing results show that the second-order nonlinearity coefficient has a linear relationship with the compensation capacitance. A physical model of the accelerometer is built to analyze the effect of fabrication process error on the nonlinearity of the accelerometer, justifying the experimental linear relationship. Using the relationship, the capacitance compensation method is optimized, thus improving the efficiency of compensation. In the optimal case, the second-order nonlinearity coefficient of the accelerometer can be reduced to the order of 10−4 only by three capacitance compensations.

A highly convergent one-pot synthesis of Hantzsch-type pyridines has been developed based on a three-component annulation of 1,3-dicarbonyl compounds, DMSO, and ammonium salt. A transition-metal-free oxidative methylenation reaction/Hantzsch pyridine synthesis cascade reaction was involved in this process. This intermolecular annulation reaction proceeds under mild reaction conditions, wherein DMSO serves as solvent, carbon source, and oxidant. A series of polysubstituted pyridines and methylene-bridged bis-1,3-dicarbonyl compounds were prepared in high yields.

Recently, several inductive and flexible nonlinear data projection methods for graph-based semi-supervised learning were proposed. These state-of-the art techniques have a good performance. However, they have not taken into account the relevance of the original features in their model estimation. In this paper, we propose a joint graph-based embedding and feature weighting for getting a flexible and inductive nonlinear data representation on manifolds. The proposed criterion explicitly estimates the feature weights together with the projected data and the linear transformation such that data smoothness and large margins are achieved in the projection space. Moreover, the paper introduces a kernel variant of the model in order to get an inductive nonlinear embedding that is close to a real nonlinear subspace for a good approximation of the embedded data. The proposed frameworks can be seamlessly used in semi-supervised and supervised settings. The resulting optimization problems can be solved efficiently. The proposed embedding methods are evaluated on six public scene and face datasets. Experiments on image classification, in a semi-supervised setting, show that our proposed methods can have a performance that is better than that of many state-of-the-art methods including linear and nonlinear methods.

The abnormal expression level of alkaline phosphatase (ALP) will lead to serious diseases. Therefore, a sensitive and rapid assay for ALP activity monitoring is of vital importance. In this work, a fluorescence turn-off approach for the detection of ALP is designed on the basis of nitrogen doped carbon dots (N-CDs), which were synthesized by one-step hydrothermal method and applied as signal readout. p-Nitrophenylphosphate (PNPP) can be hydrolyzed into p-nitrophenol (PNP) by ALP and their absorption peaks are different under alkaline conditions, so it was chosen as the ALP substrate. The absorption spectrum of PNP has good overlap with the excitation and emission spectra of N-CDs, thus the fluorescence of N-CDs can be effectively quenched by PNP via the inner filter effect (IFE). Consequently, quantitative detection of ALP is realized because the relative fluorescence intensity is linearly with the ALP activity in a wide range from 0.05 to 40 U L-1. The detection limit is 0.02 U L-1 (S/N = 3), which is much lower than the normal level of serum ALP in adults (about 40–190 U L-1). Moreover, the assay was successfully applied to evaluate ALP inhibitor efficiency and screen ALP inhibitors in drug discovery. It is also demonstrated that N-CDs possesses low cytotoxicity, excellent biocompatibility and photostability, and can be successfully applied in vivo fluorescence imaging, showing great potential in clinical applications.

Many learning problems can be cast into learning from data-driven graphs. This paper introduces a framework for supervised and semi-supervised learning by estimating a non-linear embedding that incorporates Spectral Graph Convolutions structure. The proposed algorithm exploits data-driven graphs in two ways. First, it integrates data smoothness over graphs. Second, the regression is solved by the joint use of the data and their graph in the sense that the regressor sees convolved data samples. The resulting framework can solve the problem of over-fitting on local neighborhood structures for image data having varied natures like outdoor scenes, faces and man-made objects. Our proposed approach not only provides a new perspective to non-linear embedding research but also induces the standpoint on Spectral Graph Convolutions methods. In order to evaluate the performance of the proposed method, a series of experiments are conducted on four image datasets in order to compare the proposed method with some state-of-art algorithms. This evaluation demonstrates the effectiveness of the proposed embedding method.

It's a major challenge to explore highly active and stable electrocatalysts for efficient hydrogen evolution reactions (HER) and oxygen evolution reactions (OER). In this work, we report a new hybrid electrocatalyst: iron-cobalt decorated nitrogen doped carbon/reduced graphene oxide-700 °C (Fe-Co-CN/rGO-700). The catalyst shows an excellent electrocatalytic performance toward HER and OER in alkaline solution, which has lower overpotentials of 0.215 V and 0.308 V at the current density 10 mA cm−2, respectively, and this catalyst has an outstanding durability (after 45 h). The excellent activity mainly benefit from the highly dispersed iron/cobalt species, sheet structure with high porosity, the improvement of electronic structures with nitrogen-doped carbon and high conductivity of rGO. Combined with the density function theory (DFT) calculations, the improvement of the HER and OER performance were also attributed to the balance of adsorption/desorption of the hydrogen and oxygen-containing intermediates, and the increased electronic states density near the Fermi level. The proposed work provides a novel and simple strategy to synthesize a porous carbon electrocatalyst material using metal-organic frameworks (MOFs) as precursor with high activity and strong stability for HER and OER.

An efficient synthesis of C4-functionalized quinolines through copper-catalyzed tandem annulation of alkynyl imines with diazo compounds is described. This transformation involves an in situ formation of allene and intramolecular electrocyclization, which features high efficiency, mild reaction conditions, easy operation, and broad functional-group tolerance. A wide variety of C4-functionalized quinolines were provided in up to 92% yield for 33 examples.

Offshore gas reservoirs are generally exploited without energy supplement, reservoir pressure depletion will cause rock stress changes, and water cut will also weaken rock cementation, which results in an increasing likelihood of serious sand production in the unconsolidated sandstone reservoir. This paper aims at offshore unconsolidated sandstone gas reservoirs, analyzes rock stresses of the borehole wall with impact of reservoir pressure depletion on in-situ stresses and water cut on rock strength. Combining with fully-polyaxial rock failure criterion Mogi-Coulomb, a new sanding critical drawdown pressure (CDP) calculating model and a numerical calculating method for the model are developed. Furthermore, an applicable sanding prediction method for pressure depletion and water cut offshore gas reservoir in well whole life cycle is proposed. The model was applied in DF gas reservoir in South China Sea, the chart of sanding CDP for well DF-A in the whole production life cycle was plotted. The research results indicate: Initially, the sanding CDP is relatively large, with formation pressure depletion, CDP is reduced step by step until depleted to the critical reservoir pressure, and sand will be produced no matter how small the drawdown is. Once water cut occurs, sanding CDP is furtherly decreased by 15%–20%. The research outcomes are consistent with actual oilfield production situation, which can provide some theoretical supports for sand production gas wells.

Graph-based embedding aims to reduce the dimension of high dimensional data and to extract relevant features for learning tasks. In this letter, we propose an Elastic graph-based embedding with deep architecture which deeply explores the structural information of the data. We introduce a flexible deep learning that can overcome the limitations and weaknesses of single-layer learning models. The proposed deep architecture incorporates the geometrical manifold structure of the data. The resulting framework can be used for semi-supervised and supervised settings. Besides, the resulting optimization problems can be solved efficiently. We apply the algorithm on five public image datasets including scene, face and object datasets. These experiments demonstrate the effectiveness of the proposed embedding method, and also show that the proposed method compares favorably with many competing state-of-the-art graph-based methods.

In the past years, various graph-based data embedding algorithms were proposed and used in machine learning and pattern recognition fields. This paper introduces a graph-based non-linear embedding learning algorithm for image classification and recognition. The proposed embedding method can be used for supervised and semi-supervised learning settings. The proposed criterion allows the simultaneous estimation of a linear and a non-linear embedding. It integrates manifold smoothness, Sparse Regression and Margin Discriminant Embedding. The deployed sparse regression implicitly performs feature selection on the original features of the data matrix and of the linear transform. The proposed method is applied to four image datasets: 8 Sports Event Categories dataset, Scene 15 dataset, ORL Face dataset and COIL-20 Object dataset. The experiments demonstrate the effectiveness of the proposed embedding method.

The emerging media network, which is represented by we-media, is in rapid development stage, and the hot spot in the society are often the most able to be discovered, shared and commented by we-media. Mining hot spot from we-media can help individuals to optimize their own investment behavior, help enterprises to adjust their production and investment strategies to meet market demand, and help government to monitor public opinions and seize the opportunity to guide the healthy development of public opinions. In this paper, we made some improvements to the basic K-Means algorithm according to the characteristics of hot spot discovery. The experimental results show that the purity and F value of the clustering result using our method improve slightly.

Selective and sensitive determination of nitrite is of great importance in practical application. In the present work, a novel nitrite sensing platform was built based on the fabrication of nitrogen-doped-carbon-coated hexagonal cobalt oxyhydroxide (CN@CoOOH) on reduced graphene oxide (RGO) using zeolitic imidazolate framework (ZIF)-67 as a precursor. The CN@CoOOH/RGO nanocomposite was confirmed by UV-visible spectroscopy, Fourier transform infrared spectroscopy, x-ray photoelectron spectrum, transmission electron microscopy, scanning electron microscopy, and x-ray diffraction. We applied the nanocomposite to detect nitrite selectively and sensitively through amperometry for the first time. The anodic current values increased with the addition of nitrite. Therefore, the concentrations of nitrite were quantitatively detected using a CN@CoOOH/RGO based sensor. A wider linear range of 0.1 to 7000 μM was obtained with a lower detection limit of 10 nM (S/N = 3). The proposed method was also applied to detect nitrite released from normal liver cells and human hepatoma cells.