Search

Purpose To investigate and compare the CT and MRI features of hepatic sclerosed hemangioma (HSH) and sclerosing cavernous hemangioma (SCH). Materials and methods Twelve HSH cases and 36 SCH cases were included, the imaging findings on CT (9 HSH and 34 SCH) and MRI (8 HSH and 10 SCH) were analyzed. Qualitative image analysis included the location, size, shape, capsular retraction, density, calcification, signal intensity on T1-weighted image (T1WI) and T2-weighted image (T2WI), presence of diffusion restriction, apparent diffusion coefficient (ADC) map, transient hepatic attenuation difference around the lesion, and the dynamic enhancement patterns. Results The presence of liver cirrhosis in patients with HSH (3/12) was higher than SCH (1/36) (P = 0.043). The morphology appearance before enhancement showed no significant difference between HSH and SCH. Moreover, SCH had a stronger trend of centripetal enhancement patterns of cavernous hemangiomas (83.3%) compared to HSH (25%) (P P P = 0.009, P = 0.002); however, there was no significant difference in ADC values between themselves (P = 0.613). Conclusion SCH showed the same trend of centripetal enhancement characteristics as typical hemangioma, while HSH exhibited atypical enhancement features due to complete sclerosis. Higher ADC values might contribute to the identification of atypical HSH and SCH from malignancies.

In general, there are a lot of uncertainties in uncertain information, natural language, and human knowledge. The conclusion can be better deduced by using an approximate reasoning method, while a fuzzy intelligent system can deal with uncertain data and rule evaluation information systems. In order to better explore diffusion and economic growth, this paper constructs a fuzzy intelligent system based on the DSGE model and uses this system to analyze diffusion and economic growth. In order to verify the feasibility of this system, we test the response time and accuracy of the system. In addition, we also use the system to simulate diffusion and economic growth. The results show that with the increase of the task amount, the gap between the actual response time and the expected response time of the fuzzy intelligent system based on the DSGE model increases. When the task quantity is 20, the expected response time is 2.31 and the actual response time is 2.24. When the task quantity is 40, the expected response time is 2.5 and the actual response time is 2.36. The larger the task quantity is, the faster the response time of a fuzzy intelligent system based on the DSEG model is. Therefore, the fuzzy intelligent system based on the DSEG model has good performance and can analyze diffusion and economic growth well.

Nowadays, the valid development and full utilization of renewable energy sources such as wind power and solar energy are considered to be one of the most significant approaches to get rid of the energy dilemma and realize environmental governance. However, although there are more and more renewable power sources connected to power systems, the renewable energy has not been fully used yet. In this context, an optimization model for the regional power system with the pump storage power, wind, hydro, and thermal power is proposed to enhance the economy, environmental protection, energy-saving and stability of the power system. Besides, the fuzzy chance constraint programming and the improved analytic hierarchy process are used to deal with the uncertainty of the wind power and the load demand and the multi-objectives function, respectively. The case study not only verify the effectiveness of the proposed model but also shows that better comprehensive performance of the power system can be achieved through the proposed model when considering the effect of the pump storage power.

Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning the link information of the marking-points, resulting in complex post-processing and erroneous detection. In this paper, we propose an attentional graph neural network based parking-slot detection method, which refers the marking-points in an around-view image as graph-structured data and utilize graph neural network to aggregate the neighboring information between marking-points. Without any manually designed post-processing, the proposed method is end-to-end trainable. Extensive experiments have been conducted on public benchmark dataset, where the proposed method achieves state-of-the-art accuracy. Code is publicly available at \url{https://github.com/Jiaolong/gcn-parking-slot}., Comment: Accepted by RAL

In this investigation, the regression analysis together with the desirability function approach was involved to optimize the resistance welding parameters for the high-strength steel HSLA 420. The Box-Behnken experimental design (BBD) was employed for the control factors of welding time, welding current, and electrode pressure. The values of nugget diameter, ultimate peak load, maximum displacement, and absorption energy were identified and processed using the approach of multiple-objective optimization based on ratio analysis (MOORA). The weight loss of the base metal before and after welding was measured and computed to estimate the welding expulsion magnitude. The calculated statistical parameters displayed that the welding current had the most momentous influence on the welding quality and welding expulsion. The welding joints free of expulsion with sufficient mechanical properties can be achieved at the welding current of 8.4 kA, electrode pressure of 0.5 MPa, and welding time of 13 cycles.

A couple of models were established to investigate the effects of welding parameters (voltage, wire feed speed, and welding speed) on the resultant mechanical properties of welding bead and welding heat input on the basis of a Box-Behnken experimental design (BBD). The three key mechanical parameters, that is, maximum displacement, peak load, and energy absorption, were processed via the technique in order of preference by similarity to ideal solution (TOPSIS) and Shannon entropy technique. After that, the correlations among the mechanical properties, welding heat input, and three technological variables in the welding process were established herein. Analysis of variance (ANOVA) implied that the heat input relied on voltage, welding speed, wire feed rate, and the interaction effects among these factors. These models act as the basis to achieve the multi-objective optimization problem by the desirability function approach. Results suggest that the welding settings favoring a robust trade-off between minimum welding heat input and maximum mechanical properties involve an intermediate value of wire feed speed, a high value of voltage, and welding speed. This welding parameter combination not only can produce an optimum welding bead with robust mechanical performances but also guarantees the goal of optimum welding heat utilization and production efficiency, in which the high level of welding speed is strongly recommended.

Pulmonary inflammatory myofibroblastic tumors (IMTs) are rarely reported in adult males. Given the low incidence of IMT and the lack of imaging references and pathological guidance, the misdiagnosis rate of IMT is high. In this article, we describe two cases of IMTs in the lungs. Both patients were adult males with lesions in the right lobe, a history of pulmonary tuberculosis, and a long period of refractory intermittent pulmonary inflammation. Our two male patients both experienced intermittent cough symptoms, but pulmonary IMTs were not suspected for a long time. Both patients were diagnosed with pulmonary tuberculosis before IMT was confirmed and treated with isoniazid (H), rifampin (R), pyrazinamide (Z), and ethambutol (E) (HRZE) or isoniazid (H), levofloxacin (L), pyrazinamide (Z), and ethambutol (E) (HLZE) for months. In Case 2, we observed multiple subpleural cord signs in the left lung, soft tissue mass shadows at the apex of the right upper lobe, a thickened interlobular interval, and scattered patches and nodules in the upper right lung. These features are novel in the identification of IMTs. Both of the pathological findings revealed a great deal of myofibroblasts, fibroblasts and collagen fibers in the lower right lung lesion, accompanied by a large number of plasma cells and foam cell infiltration, which were consistent with the features of IMT. The two patients displayed exceedingly different symptoms, computed tomography (CT) imaging features, and pathological results from those reported in traditional records. These findings provide novel references that will extend understandings of this rare disease.

The feedback vertex set (FVS) problem of network aims to find a vertex subset of smallest size that contains at least one vertex of each cycle of the network. The FVS problem has found wide applications, ranging from circuit design, network security and network control and observation etc. However, the majority of existing studies on FVS problem are confined to single networks. In this brief, under the consideration of one kind of control problem of multiplex network, we introduce the concept of FVS of multiplex network. The control problem of multiplex network is naturally mapped into the FVS problem of multiplex network. We then propose two simulated annealing (SA) algorithms to solve the directed FVS problem of multiplex network and undirected FVS problem of multiplex network respectively. The effectiveness of the SA algorithms is finally verified on both artificial multiplex networks and real-world multiplex networks.

Abstract Solar driven carbon dioxide (CO2) recycling into hydrocarbon fuels using semiconductor photocatalysts offers an ideal energy conversion pathway to solve both the energy crisis and environmental degradation problems. However, the ubiquitous presence of carbonaceous contaminants in photocatalytic CO2 reduction system and the inferior yields of hydrocarbon fuels raise serious concerns about the reliability of the reported experimental results. Here in this perspective, we focus on the accurate assessment of the CO2 reduction products, systemically discuss the possible sources of errors in the product quantification, elaborate the common mistakes spread in the analysis of reaction products obtained in 13CO2 labelling experiments, and further propose reliable protocols for reporting the results of these isotopic tracing experiments. Moreover, the challenges and cautions in the precise measurement of O2 evolution rate are also depicted, and the amplification of the concentration of O2 in photoreactors well above the limit of detection is still demonstrated to be the most effective solution to this troublesome issue. We hope the viewpoints raised in this paper will help to assessment the reliability of the reported data in future, and also benefit the beginners that intend to dive in the photocatalytic CO2 reduction area. Graphical abstract

Photovoltaic (PV) power generation has developed very rapidly worldwide in the recent years. There is a possibility that the PV power generation will switch from an auxiliary power supply, as of today, to a main power source in many power grids in the future. Naturally, dynamic studies on power grids with a high penetration of PV generators have become increasingly important, and thus have attracted major attentions from both the power industry and the academia. Consequently, dynamic modeling of PV generators has been investigated widely. However, among various proposed models, there is a confusion on the model applicability and a lack of the clarification on the required level of details on the modeling work, which severely limit the real industrial applications of the developed models. This paper reviews the state-of-the-art PV generator dynamic modeling work, with a focus on the modeling principles of PV generator for the power system dynamic studies. The paper presents the detailed modeling process for the recommended PV generator dynamic model, and clarifies the assumptions and simplifications made in the modeling process, thus raises the discussion on the model applicability. Studies that require further attentions on developing the dynamic models of PV generators for power system dynamic studies are identified and presented in the paper. However, this work does not intend to conclude the research work in this important field, instead, it aims to provoke more discussions on developing guidelines on building or selecting the appropriate models to fit into the purpose of the targeted dynamic studies.

The dismantling problem and minimum vertex cover (MVC) problem of network are two fundamental NP-hard problems where the former aims to find a minimal subset of nodes whose removal leaves the network broken in small components of sub-extensive size and the latter is also to find a minimal vertex set which contains at least one incident vertex of every edge. Both of them have a wide range of applications related to circuit design, communication, network security, transportation, epidemic control, molecular biology and economics. In this brief, we first propose a novel belief-propagation equation for the spin glass model of the MVC problem. A belief-propagation-guided decimation (BPD) algorithm is then presented which could construct approximate optimal vertex cover of network. In addition, based on the relationship between the network dismantling problem and the MVC problem, we propose a simple and fast algorithm formed by the MVC and a general node reinsertion strategy, for solving the network dismantling problem. The effectiveness of the proposed algorithms is finally verified by comparing with other well-known algorithms on a number of real world networks.

Deer species has both scientific research and economic value, and half of these species, however, are listed as endangered animals. For the conservation purpose, we designed a novel universal deer-specific PCR primer pair based on an evolutionarily conservative coding sequence (i.e., CEP295NL gene) across some deer species. This primer pair was successfully amplified and sequenced, showing around ~ 540 bp in cervids. Validation results showed that it can be utilized to develop a reliable and simple diagnostic tool for distinguishing other closely related species, as well as possibly interspecific identification amongst cervids.

In this investigation, the welding quality and the dynamic resistance signal in the welding process were correlated using different dimension reduction techniques and regression models. The 0.4-mm-thickness TC2 titanium alloy was used as the welding material, while the welding experiments were carried out by a small pneumatic high-frequency alternating current welding machine. The Rogowski coil and alligator type wire clips were respectively utilized to collect the welding current and voltage in the welding process. The mechanism of dynamic resistance signal changes was studied and explained. In order to extract the features related to the welding quality and reveal the efficiency of the automatic feature extraction method based on the dimension reduction techniques, several dynamic resistance signals with different welding process parameters were processed by the approaches of principal component analysis (PCA), isometric mapping (Isomap), and locally linear embedding (LLE). After that, the redundant information in the dynamic resistance signal which has little to do with the welding quality would be removed and the useful features could also be isolated. And then three regression models quantifying the extracted features and the nugget size were created and their performances were also compared with each other. The results implied that the regression model based on the LLE technique was more robust than those established on the basis of the features automatically extracted from the dynamic resistance signal using PCA and Isomap. Compared with the existing popular manual feature extraction methods in the previous research work, the method proposed in this investigation outperforms the other methods in terms of two aspects. It not only can minimize the prior assumptions about the certain shape of the dynamic resistance curve and remove the subjective factors caused by the manual extraction method, but can assess and monitor the welding quality with a good level of reliability.

Incorporating plasmonic nanostructures is a promising strategy to enhance both the optical and electrical characteristics of photovoltaic devices via more efficient harvesting of incident light. Herein, we report a facile fabrication scheme at low temperature for producing gold nanoparticles embedded in anatase TiO2 films, which can simultaneously improve the efficiency and stability of n-i-p planar heterojunction perovskite solar cells (PSCs). The PSCs based on rigid and flexible substrates with 0.2 wt % Au-TiO2/TiO2 dual electron transport layers (ETLs) achieved power conversion efficiencies up to 20.31 and 15.36%, superior to that of devices with TiO2 as a single ETL. Moreover, 0.2 wt % Au-TiO2/TiO2 devices demonstrated significant stability in light soaking, which is attributed to improved light absorption, low charge recombination loss, and enhanced carrier transport, and extraction with the plasmonic Au-TiO2/TiO2 dual ETL. The present work improves the practicability of high-performance and flexible PSCs by engineering the photogenerated carrier dynamics at the interface.

Due to the complicated coalbed methane (CBM) occurrence conditions and the diverse geological structures in China, the promotion and application of the coal and gas simultaneous extraction technology have been seriously restricted. In view of this, this paper chooses Qingdong Coal Mine protection layer mining and CBM extraction field practice as the research background. Firstly, based on the similar material simulation experiment that simulates coal mining, the dynamic changing pattern of a mining field’s overburdened strata and corresponding stress are obtained, the relationship between gas desorption and stress can then be clarified. Further, with the help of the fractal theory and box counting method, the fracture development characteristics of the overlying strata are quantitatively described on the basis of experimental images. Finally, by building a model for calculating the penetrability coefficient of coal seam based on fractal dimension of mining fissure and analyzing the relationship between fissure development and fractal dimension, the gas migration law and the fissure development areas of #7 and #8 overburden strata where CBM concentrates can be revealed and determined. According to the orientation of the area mentioned above, the location of the CBM pumping field in relation to the coal seam roof and the arrangement of CBM extraction boreholes can be optimized, which make CBM extraction efficient. Meanwhile, the risk of coal and gas outburst is significantly reduced when the CBM concentration is controlled within 0.2% to 0.6% outside the corner of the working face and 0.1% to 0.35% in return flow, which is lower than 0.8%, the threshold of CBM concentration.

Coal seam water injection is a widely used dust reduction technology in Chinese coal mines. During the process of coal seam water injection, a large amount of broken coal particles will accumulate in the pore, which will affect the flow characteristics of water. It's very important for improving the on-site coal seam water injection and dust reduction technology to study the influence of coal particles on the water migration law. In this paper, a spontaneous upward imbibition experiment was used to study the effect of coal particles stacked in front fractures on water migration in coal seam water infusion. Then, a mathematical model was established to express imbibition speed and stable imbibition height. The results show when the largest particle size is selected, theoretical calculation results of stable imbibition height is closest to the experimental data; coal particles with a size of 564–1589 μm have the greatest influence on the wetting phase of coal seam water infusion; the metamorphic grade and particle size of the samples mainly affect the initial stage of water migration, and the initial imbibition speed of each coal sample is quite different. However, with increasing imbibition height, the imbibition speed difference of each coal sample gradually decreases.

Organic-inorganic hybrid perovskite solar cells (PSCs) have achieved power conversion efficiency (PCE) from 3.8% to 25.2% in recent years. However, the charge transport ability of the transporting layers and the quality of crystalline films limit further development of PSCs. To fabricate highly efficient PSCs, effective charge transport to electrodes through transporting layers is a necessary requirement. Suitable interface engineering is very critical because it can not only benefit the charge extraction and transport, but also effectively passivate the traps on the surface and grain boundary of the perovskite. It has been shown that the passivation layer for the perovskite plays a vital role in the performance of PSCs. The close fit between the charge transport layer and the perovskite layer is required to reduce the interfacial recombination and accelerate the carrier extraction and collection. Besides, the appropriate passivation layer has a potential impact on the crystallization and the morphology of the perovskite film. To achieve high-performance devices, it is crucial to design an effective passivation layer with appropriate nanomaterials. In this work, we synthesized poly[(2-methoxy,5-octoxy)1,4-phenylenevinylene] (MOPPV) on few-layer graphene oxide (GO) by in situ polymerization at room temperature. MOPPV-GO can fully combine the advantages of the two materials. The novel nanocomposite MOPPV-GO is used as the ultra-thin passivation layer of PSCs, of which the structure is ITO/TiO2/(FAPbI3) x (MAPbCl3)1– x /MOPPV-GO/spiro-OMeTAD/Ag. Since the surface morphology of the perovskite layer would affect the performance of devices significantly, the perovskite films with and without the passivation layer by atomic force microscopy is found that the MOPPV-GO can facilitate the growth of perovskite crystal with larger grains and enhance the quality of perovskite films. The root mean square roughness values of the perovskite films with and without MOPPV-GO were evaluated to be 19.87 and 25.85 nm, justifying the improvement of the surface flatness of the perovskite film with the decoration of MOPPV-GO, which would enhance the contact and carrier transport between the hole transport layer and the perovskite layer. As a result, the PSCs with MOPPV-GO exhibited considerably low hysteresis indices (0.02) and showed a short circuit current density of 23.54 mA cm–1, an open circuit voltage of 1.09 V, and a fill factor of 76.46%, corresponding to a high power conversion efficiency of 19.73%. By contrast, the control PSCs without MOPPV-GO showed a high hysteresis index of 0.18, with a poor power conversion efficiency of 16.42%. Meanwhile, it is shown that the EQE curve of MOPPV-GO modified PSC is higher than that of unmodified devices, which is consistent with the experimental results of J-V curve. The steady-state power output of various devices in ambient conditions under continuous AM 1.5G illumination was tested, which shows excellent stability of the PSC with MOPPV-GO during the period of 1000 s, indicating the positive contribution of MOPPV-GO interlayer to the stability. This work could provide a new idea for the synthesis and application of graphene-based materials in solar cells.

In this study, a systematic research was conducted to compare the performances of the regression model and artificial neural network in predicting the nugget diameter of spot-welded joints by monitoring the dynamic power signature. The TC2 titanium alloy with a thickness of 0.4 mm was used as the welding material, and a high-frequency precision spot welder was used to join the titanium alloy sheets. The dynamic welding current curve was obtained using the Rogowski coil, while the voltage curve was detected via two leads clipped onto the upper and lower electrodes during the entire welding process. The variations in the welding power signal in the welding process were investigated, and the characteristics of the power signals for different welding currents and electrode forces were analyzed. The power signals of different types of welding joints varied significantly. Five characteristics were extracted from the power signal to describe the shape of the curve. The stepwise regression analysis and back propagation neural network were respectively used to classify the welding joints into three categories: bad welds, good welds, and welds with expulsion. The performances of the two established prediction models were compared, and their behavioral discrepancies were attributed to their own data-mapping capabilities. Keywords: Nugget size, Dynamic power, Quality assessment

Label correlations have always been one of the hotspots of multi-label learning. Using label correlations to complete the original label can enrich the information of the label matrix. At the same time, label-specific features give a thought that different labels have inherent characteristics that can be distinguished, and we can use label correlations to enhance the learning process of label-specific features among similar labels. At present, most of the algorithms combine label correlations and label-specific features to improve the multi-label learning effect, but do not consider the impact of label marking errors or defaults in data sets. In fact, the label completion method can further enrich the information of label matrix, and then the joint learning framework of joint label-specific features can effectively improve the robustness of the multi-label learning algorithm. Based on this, this paper proposes a multi-label learning algorithm for joint label completion and label-specific features, and constructs a new multi-label learning algorithm framework by means of joint label completion and label-specific features. Completion matrix and label-specific features are obtained by alternating iteration method, and the label matrix updating the optimization framework fully considers the label correlations. The algorithm in this paper has been demonstrated and trained on several benchmark multi-label data sets by extensive experiments, which verifies the effectiveness of the algorithm.

Summary Design of polymeric networks with unique structural motifs can permit dynamic features, yet most existing material systems exhibit limited operational states or irreversible responsiveness. Here, we use a hydrogen-bond topological network as the design principle to construct an ionic gel material based on cellulose, ionic liquid, and H2O (designated as Cel-IL dynamic gel). The prepared Cel-IL dynamic gels exhibit tunable properties of mechanical strength, ionic conductivity, viscoelasticity, and self-healing. With limited H2O, the Cel-IL dynamic gel exhibits a bramble-like Turing-pattern microstructure with excellent adhesion, rapid self-healing, and moderate ionic conductivity features. By increasing the H2O content to 32 wt %, the microstructure switched to a dense and compact Turing pattern network, giving the gel good stretchability, robust toughness, and a high ionic conductivity. With this material, we demonstrate a flexible, transparent, designable, and biocompatible ion sensor device, which exhibits great potential for use in electronic skins and intelligent devices.

Long time coherent integration technique is one of the most important methods to improve the radar detection ability of a weak maneuvering target. However, the integration performance may be greatly influenced by the range migration and Doppler migration effects caused by the complex motions of maneuvering targets when long time integration is applied. Besides, the frequency hopping characteristic of the transmitted signal will also result in Doppler migration which cannot be eliminated by the conventional coherent integration method. To deal with this problem, a novel coherent integration method based on phase compensation is proposed. By using the frequency hopping pattern, the Doppler migration is eliminated by adding an extra phase in the twiddle factor of Fourier Transform in this proposed method. Since the phase error is compensated, long time coherent integration can be achieved. The effectiveness of the proposed method is verified by the simulation results and the performance analysis.

In order to solve the problem when the target detection of passive bistatic radar is seriously affected by direct signal and multi-path, a clutter cancellation algorithm based on frequency domain analysis is proposed in this paper. By analyzing and processing the frequency domain correlation between the reference signal and the echo signal, the time delay and the amplitude of the direct signal and multipath in the echo signal can be estimated for clutter cancellation. This method can not only reduce the computation greatly, but also eliminate the influence of clutter echo residue on target detection. The effectiveness of the proposed algorithm is verified by simulation and real data processing.

Cooperation and competition widely exist in various kinds of network diffusions which however are usually studied separately. Recently, a novel network diffusion model, called multi-feature diffusion (MFD), attracts considerable attentions. The existing works usually assume that each feature diffuses independently and neglects the possible complex interplay between different features. In this paper, we introduce the cooperation and competition into the MFD and propose the \emph{c}ooperation and \emph{c}ompetition coupled diffusion of \emph{m}ulti-\emph{f}eature on multiplex network (CCMF). An unified framework and mathematical analytic theory regarding CCMF are then presented which are applicable and computationally efficient for any number of features and their own different sub-diffusion dynamics. In addition, an interesting finding is obtained in CCMF: compared with the high intensity competition, performing lower intensity competition under weak competition ability is more easier to result in positive effect. Due to the great importance of controlling network diffusion in many diverse contexts, we also propose an optimal allocation strategy of control resource for CCMF which first realizes the promotion and suppression of network diffusion simultaneously under one optimization framework and is also verified to be very efficient.

Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve the accuracy and robustness. However, due to the non-differentiable properties of image transformations, searching algorithms such as reinforcement learning or evolution strategy have to be applied, which are not computationally practical for large scale problems. In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained. To further improve the accuracy and robustness with adversarial examples, we propose a differentiable adversarial data augmentation method based on spatial transformer networks (STN). The combined adversarial and random transformations based method outperforms the state-of-the-art on multiple DA and DG benchmark datasets. Besides, the proposed method shows desirable robustness to corruption, which is also validated on commonly used datasets.

This paper presents an in-depth study and analysis of the prediction model of force resource recruitment demand using a convolutional neural network combined with a BP neural network algorithm. BP neural network technology is introduced to be applied to enterprise management talent assessment activities. Using BP neural network has strong parallel processing characteristics, as well as unique adaptive learning and feedback adjustment capabilities while combining the traditional enterprise talent assessment system, to build a business management talent assessment model based on BP neural network technology, to circumvent the possible influence of subjective factors in talent assessment, reduce assessment errors, and improve the accuracy and validity of the assessment. The first layer of convolutional layers may only extract some low-level features such as edges, lines, and corners, and more layers of the network can iteratively extract more complex features from low-level features. The constructed applicant reputation evaluation model based on multiplicative long- and short-term recurrent neural network and the hybrid project recommendation model based on conditional variational self-encoder were experimented on Freelancer’s dataset for effectiveness, respectively, and the experimental results showed that the proposed employer hiring decision model, reputation analysis model, and applicant project recommendation model have more reliable performance compared with the existing models. The research results achieve more efficient matching of labor supply and demand in the online labor market and provide technical support for the online labor market platform to realize personalized, intelligent, and accurate services for both employers and applicants.

In nature, stiffness-changing behavior is essential for living organisms, which, however, is challenging to achieve in synthetic materials. Here, a stiffness-changing smart material, through developing interchangeable supramolecular configurations inspired from the dermis of the sea cucumber, which shows extreme, switchable mechanical properties, is reported. In the hydrated state, the material, possessing a stretched, double-stranded supramolecular network, showcases a soft-gel behavior with a low stiffness and high pliability. Upon the stimulation of ethanol to transform into the coiled supramolecular configuration, it self-adjusts to a hard state with nearly 500-times enhanced stiffness from 0.51 to 243.6 MPa, outstanding load-bearing capability (over 35 000 times its own weight), and excellent puncture/impact resistance with a specific impact strength of ≈116 kJ m

With the forthcoming of the post-COVID-19 and the ageing era, the novel biomaterials and bioelectronic devices are attracting more and more attention and favor. Cellulose as one of the most globe-abundant natural macromolecules has multiple merits of biocompatibility, processability, carbon neutral feature and mechanical designability. Due to its progressive advancement of multi-scale design from macro to micro followed by new cognitions, cellulose shows a promising application prospect in developing bio-functional materials. In this review, we briefly discuss the role of cellulose from the "top-down" perspective of macro-scale fibers, micro-scale nanofibers, and molecular-scale macromolecular chains for the design of advanced cellulose-based functional materials. The focus then turns to the construction and development of emerging cellulose-based flexible bioelectronic devices including biosensors, biomimetic electronic skins, and biological detection devices. Finally, the dilemma and challenge of cellulose-based bioelectronic materials and their application prospects in basic biology and medical care have been prospected.

The way of information diffusion among individuals can be quite complicated, and it is not only limited to one type of communication, but also impacted by multiple channels. Meanwhile, it is easier for an agent to accept an idea once the proportion of their friends who take it goes beyond a specific threshold. Furthermore, in social networks, some higher-order structures, such as simplicial complexes and hypergraph, can describe more abundant and realistic phenomena. Therefore, based on the classical multiplex network model coupling the infectious disease with its relevant information, we propose a novel epidemic model, in which the lower layer represents the physical contact network depicting the epidemic dissemination, while the upper layer stands for the online social network picturing the diffusion of information. In particular, the upper layer is generated by random simplicial complexes, among which the herd-like threshold model is adopted to characterize the information diffusion, and the unaware–aware–unaware model is also considered simultaneously. Using the microscopic Markov chain approach, we analyze the epidemic threshold of the proposed epidemic model and further check the results with numerous Monte Carlo simulations. It is discovered that the threshold model based on the random simplicial complexes network may still cause abrupt transitions on the epidemic threshold. It is also found that simplicial complexes may greatly influence the epidemic size at a steady state.

In recent years, offshore wind power has been vigorously developed. High voltage AC transmission is one of the main grid connection technologies for offshore wind power. Submarine cable has more charging power causing the problem of overvoltage more prominent. To solve the problem of line overvoltage in the AC transmission system of the offshore wind power, this paper proposes a coordinated reactive power control strategy. First, the reactive power generated by wind turbine is considered to reduce the configuration capacity of the reactive power compensation devices. Second, the reactive power compensation is divided into two parts: the land side and the wind power collecting side. Then, with the objective function of minimizing the power loss and the voltage offset, the assignment of the optimal amount of reactive power on both sides is solved. Finally, simulation results prove that the line overvoltage suppression effect of dual-terminal coordinated control strategy proposed in this paper is both economical and effective.

Three-core submarine cables are mostly used in offshore wind power transmission recently. The capacitive effect of long-distance submarine cables may cause overvoltage along the line. At present, the analysis of the overvoltage in the offshore wind power transmission line is mainly based on the simplified equivalent model, but the influence of the detailed structure and characteristic parameters of the cables should not be neglected. Therefore, a detailed electromagnetic transient model of submarine cable is established in this paper. And it is used to analyze the overvoltage of the offshore wind power transmission line. Simulation results show that the detailed model of three-core submarine cable is more accurate than the simplified equivalent model. The detailed model will show a more accurate magnitude of overvoltage level which is higher than the simplified model. Besides, the importance of establishing an electromagnetic transient model is also verified in the three-phase closing overvoltage analysis.

Digital microscopes often require repeated manual focusing to obtain a clear image. Nevertheless, manual focusing easily results in artificial errors, making it difficult to evaluate the image definition, and the focusing process is slow and tedious. This paper presents an automatic focusing method based on Quantum Self-organizing Maps (QSOM) neural network and a new focusing evaluation function. The focusing evaluation function consists of Energy of Gradient (EOG) function and Discrete Wavelet Transform (DWT) function to better evaluates the sharpness of microscopic images at different focusing positions. The obtained data are used as training samples, and QSOM neural network is trained by focusing samples. The trained neural network can accurately predict the position achieve the focus position. Experimental results are given to verify the effectiveness of the proposed method.

As an important part of coastal wetlands, tidal flat wetlands provide various significant ecological functions. Due to offshore pollution and unreasonable utilization, tidal flats have been increasingly threatened and degraded. Therefore, it is necessary to protect and restore this important wetland by monitoring its distribution. Considering the multiple sizes of research objects, remote sensing images with high resolutions have unique resolution advantages to support the extraction of tidal flat wetlands for subsequent monitoring. The purpose of this study is to propose and evaluate a tidal flat wetland delineation and classification method from high-resolution images. First, remote sensing features and geographical buffers are used to establish a decision tree for initial classification. Next, a natural shoreline prediction algorithm is designed to refine the range of the tidal flat wetland. Then, a range and standard deviation descriptor is constructed to extract the rock marine shore, a category of tidal flat wetlands. A geographical analysis method is considered to distinguish the other two categories of tidal flat wetlands. Finally, a tidal correction strategy is introduced to regulate the borderline of tidal flat wetlands to conform to the actual situation. The performance of each step was evaluated, and the results of the proposed method were compared with existing available methods. The results show that the overall accuracy of the proposed method mostly exceeded 92% (all higher than 88%). Due to the integration and the performance superiority compared to existing available methods, the proposed method is applicable in practice and has already been applied during the construction project of Hengqin Island in China.

Five published Liquid Sublayer Dryout (LSD) models are assessed using the Groeneveld Look-up table 2006 (LUT-2006) and critical heat flux (CHF) experimental data in circular tube under both subcooled boiling conditions and saturated boiling conditions. The flow regime transition from inverted annular flow to dispersed flow at post-CHF is postulated as the upper limit of the LSD model application range in saturated boiling region. Lee and Mudawar’s LSD model modified by W.X. Liu shows good agreement when void fractions is lower than 0.7. The modified L&M’s LSD model is coupled into subchannel code, COBRA-TF, to predict DNB type CHF in rod bundles. In present study, the gap clearance or thermal equivalent diameter of rod bundles is adopted as characteristic length in the modified L&M’s LSD model. For rod bundles with simplified grid spacers, Karman velocity distribution equation is utilized to calculate the velocity distribution normal to pin wall. The rod bundle CHF predictions by COBRA-TF coupled with LUT-2006, Bowring’s CHF correlation and the modified L&M’s LSD model, are compared with the CHF experimental data in 2 × 2 rod bundles with non-uniform power profile. Based on reasonable predictability on both the input power and axial position at CHF, the deviations of CHF predictions by the modified L&M’s LSD model, which adopts gap clearance instead of thermal equivalent diameter as characteristic length, and Bowring’s CHF correlation are within ±20% as void fraction approaching to 0.7.

In multi-label learning, in order to improve the accuracy of classification, many scholars have considered the relationship between features and features, features and labels or labels and labels, but how to combine the correlation among them is rarely studied. Based on this, this paper proposes a multi-label learning algorithm with kernel extreme learning machine autoencoder. Firstly, the label space is reconstructed by using the non-equilibrium labels completion method in the label space. Then, the non-equilibrium labels space information is added to the input node of the kernel extreme learning machine autoencoder network, and the input features are output as the target. Finally, the kernel extreme learning machine is used for classification. Our method implements the information fusion between features and features, between labels and features, and between labels and labels. Compared with the traditional autoencoder network, the extreme learning machine autoencoder has no iterative process, which reduces the network training time and improves the classification accuracy. The experimental results of the proposed algorithm in the opening benchmark multi-label data sets show that the KELM-AE algorithm has some advantages over other comparative multi-label learning algorithms and the statistical hypothesis testing and stability analysis further illustrate the effectiveness of the proposed algorithm.

Efficient security patch distribution is of essential importance for updating anti-virus software to ensure effective and timely virus detection and cleanup. In this paper, we propose a mixed strategy of patch distribution to combine the advantages of the traditional centralized patch distribution strategy and decentralized patch distribution strategy. A novel network model that contains a central node and a multiplex network composed of patch dissemination network layer and virus propagation network layer is presented, and a competing spreading dynamical process on top of the network model that simulates the interplay between virus propagation and patch dissemination is developed. Such a new framework helps in effectively analyzing the impacts of patches distribution on virus propagation, and developing more realizable schemes for restraining virus propagation. Furthermore, considering the constraints of the capacity of the central node and the bandwidth of network links, an optimal allocation approach of patches is proposed, which could simultaneously optimize multiple dynamical parameters to effectively restrain the virus propagation with a given budget. Accepted version

Fabricating a high-performance photocatalyst to efficiently solve serious environmental problems is an urgent affair. Herein, a series of MoS2 /ZnO composites were successfully fabricated through a facile hydrothermal route using Na2 MoO4 , (NH2 )2 CS and urchin-like ZnO as precursors. According to the results of XRD and XPS, it was found that ZnS appeared in MoS2 /ZnO composite; meanwhile, the content was positively correlated with the weight of the precursor (NH2 )2 CS. It should be noted that the morphology and the metallic phase content of MoS2 grown in situ on the surface of ZnO were affected by the molar ratio of Na2 MoO4 and ZnO. Benefiting from the special dual II heterojunctions of MoS2 /ZnS/ZnO ternary composite, the material exhibited excellent charge separation and transfer performances. In the photocatalytic measurements, the MoS2 /ZnS/ZnO (Na2 MoO4 :ZnO 1:2 MZ2) composite not only exhibits excellent photocatalytic CrVI reduction activity of 42.3×10-3 min-1 , but also displays remarkable adsorption performance (nearly 32.1 %) for Cr2 O 2 - 7 . In addition, the ternary composite shows dominant photocatalytic CrVI reduction activities compared to other photocatalysts. This work provides a high-efficient MoS2 /ZnS/ZnO ternary photocatalyst for environmental treatment.