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Finding exact and analytic solutions of nonlinear system in high dimensions is a difficult but meaningful work. In this paper, by means of the symbolic package Maple, we investigate a Painleve integrable (3+1)-dimensional generalized Burgers (gBurgers) equation. Starting from the Cole-Hopf transformation with different seed solutions, abundant localized solutions are provided, including new variable separation solutions, lumps, lump-“multiple-soliton” solutions and other interaction solutions. Specifically, the lump-two soliton solution and lump-soliton-periodic solution are depicted by the three-dimensional plots and contour plots at different times, respectively. The methods and all the resulting solutions presented in this paper can be generalized to the Painleve integrable ( N + 1 )-dimensional Burgers equation.

Calculating accurately iron bridge saturation effects of the magnetic field, for Permanent Magnet Assisted Synchronous Reluctance Motors (PMASynRMs), remains to be a knotty problem. This paper presents an analytical modeling method to predict open-circuit magnetic field distributions and electromagnetic performances of PMASynRMs, considering iron bridge saturation effects. This analytical modeling method combines the magnetic equivalent circuit method, superposition principle, the solution of the governing Maxwell’s field equations and a complex relative permeance function. A quadruple-layer PMASynRM are remodeled into four surface-inserted permanent magnet synchronous motors (SPMSMs) which have different surface-inserted permanent magnets. Each layer of the interior permanent magnets of the PMASynRM is transformed into a new equivalent surface-inserted permanent magnet whose equivalent thickness needs to be defined by the magnetic equivalent circuit method due to iron bridge saturation effects. Based on superposition principle and the solution of the Laplace’s or Poisson’s field equation, the distribution characteristics of the radial and tangential magnetic flux density are obtained for PMASynRMs. The slotting effect is considered by a complex relative permeance function. To confirm the accuracy of the proposed analytical modeling method, the analytical solutions of magnetic flux density and cogging torque have been compared with Finite Element Method (FEM) simulation results. The root mean square errors of radial and tangential magnetic flux density between analytical and FEM results are 0.000553 and 0.0000916 respectively. The results indicate that the proposed analytical modeling method can consider iron bridge saturation effects effectively, which is suitable for performance predictions and parametric studies of PMASynRMs.

Effect of CXCL8 gene silencing-mediated JAK-STAT signaling pathway on epithelial-mesenchymal transition (EMT) of human cutaneous melanoma cells was explored. Eighty patients with cutaneous melanoma were enrolled in the study. Cells were transfected accordingly and divided into five groups: The blank group (human cutaneous melanoma cells), NC group (human cutaneous melanoma cells + blank vector plasmid transfection), CXCL8 siRNA group (human cutaneous melanoma cells + CXCL8 silent expression vector plasmid transfection), AG490 group (human cutaneous melanoma cells + JAK-STAT signal pathway inhibitor transfection), CXCL8 siRNA + AG490 group (human cutaneous melanoma cells + JAK-STAT signaling pathway inhibitor + CXCL8 silent expression vector plasmid transfection). The expression levels of CXCL8, JAK2, STAT3, epithelial cadherin (E-cadherin), neurotrophic cadherin (N-cadherin) and vimentin in tissues and cells were detected by RT-qPCR and western blot analysis. CCK-8 and flow cytometry were used to detect cell proliferation and apoptosis. Compared with adjacent normal tissues, the expression of E-cadherin in human cutaneous melanoma tissues was significantly decreased, whereas the expression of CXCL8, JAK2, STAT3, vimentin and N-cadherin was significantly increased (P

Wrinkling is one of the most common flaws of woven fabrics in domestic use and industrial applications. It is necessary to develop an objective evaluation method to quantify the smoothness appearance of fabrics effectively. Herein, a fabric multi-deformation tester (FMDT) was designed to evaluate the smoothness appearance of garment fabrics by one sequential mechanical test, overcoming the main difficulties of the existing visual measurement methods for wrinkling evaluation of fabrics with complex colors and patterns. The k-means clustering algorithm was used to objectively cluster the fabric samples based on the characteristic parameters, including the wrinkle-induced residual force ( F wr), hysteresis distance ( H fr), position deflection ( D fr) and stretching recovery slope ( S tr), from the testing curve and the thickness and weight of fabrics, and comparisons with subjective evaluation were also conducted. The results reveal that the k-means clustering is able to classify the smoothness appearance of fabrics using the selected characteristic parameters, showing a good consistency with the subjective clustering results. The feasibility of using the mechanical and deformation properties of textiles to characterize fabric smoothness appearance is proved, and the FMDT provides a potential method to analyze the wrinkling of fibrous materials in a convenient way.

Fiber-based artificial muscles with excellent actuation performance are gaining great attention as soft materials for flexible actuators; however, current advances in fiber-based artificial muscles generally suffer from high cost, harsh stimulation regimes, limiting deformations, chemical toxicity, or complex manufacturing processing, which hinder the widespread application of those artificial muscles in engineering and practical usage. Herein, a facile cross-scale processing strategy is presented to construct commercially available nontoxic viscose fibers into fast responsive and humidity-driven yarn artificial muscles with a recorded torsional stroke of 1752° cm-1 and a maximum rotation speed up to 2100 rpm, which are comparable to certain artificial muscles made from carbon-based composite materials. The underlying mechanism of such outstanding actuation performance that begins to form at a mesoscale is discussed by theoretical modeling and microstructure characterization. The as-prepared yarn artificial muscles are further scaled up to large-sized fabric muscles through topological weaving structures by integrating different textile technologies. These fabric muscles extend the simple motion of yarn muscles into higher-level diverse deformations without any composite system, complex synthetic processing, and component design, which enables the development of new fiber-based artificial muscles for versatile applications, such as smart textiles and intelligent systems.

Measurement methods based on the visual testing principle have been widely used for evaluating the wrinkle recovery property of fabrics; however, the cumbersome testing process and poor adaptability for various fabrics are the main shortcomings of these methods. Here, a facile mechanical testing method named the integrated shape retention evaluation system (ISRES) was developed, providing an alternative approach to assess the wrinkle recovery, as well as the compression recovery and elastic recovery of fabrics. The optimal system parameters of the ISRES in measuring the wrinkle recovery angle were determined using an orthogonal experiment design based on correlation analysis between the curve parameters from the force–displacement curves of the ISRES and the wrinkle recovery angles tested by a standard Shirley crease recovery tester. Moreover, the sensitivity of the ISRES for the differences of the wrinkle recovery of fabrics was analyzed. The results showed that the selected optimal system parameters were a good combination, and the ISRES with the optimal system parameters provided a feasible method to differentiate the wrinkle recovery of fabrics.

A novel type of exact rogue wave is found for the (1+1)-dimensional Ito equation, which is generated by the interaction solution between an algebraic localized soliton (named “lump”) and an exponentially localized twin soliton. In addition, the interaction solution among triangular periodic wave and twin soliton is also proposed. Three special interaction phenomenons are displayed by some visual figures, respectively.

Fabrics with good shape-retention properties are strongly expected to improve the aesthetic feeling, comfort and easy-care performance of clothing in daily life, and the efficient characterization of the wrinkle recovery property of fabrics is a necessary approach to facilitate the development of garments with good shape retention. Here, a double extraction method was presented to evaluate fabric wrinkling based on the wrinkling-induced residual force–displacement curves. The correlation analysis was used to determine applicable evaluation indices in order to cluster the wrinkle recovery property of fabrics based on a K-means clustering algorithm. Moreover, subjective judgements were conducted and compared with the objective K-means cluster method. The results show that there is good consistency between objective K-means clustering and subjective judgements, indicating that the indices featured from wrinkling-induced residual force–displacement curves can be used to evaluate the wrinkle recovery of fabrics. Therefore, the double extraction method is a starting point for the rapid identification of wrinkle recovery of fabrics by the mechanical performance of textiles.

The aim of this study was to investigate the therapeutic efficacy and safety of transplanting human umbilical cord blood-derived mesenchymal stem cells (hUCB-MSCs) in the treatment of cartilage injury. First, the articular cartilage defect model in rabbits was constructed. Then, the identified hUCB-MSCs and rabbit bone marrow stem cells (rBM-MSCs) were transplanted into the bone defect, respectively, and the cartilage repair effect was observed by hematoxylin-eosin (HE) staining and immunohistochemistry. Besides, the glycosaminoglycan (GAG) content and biomechanics of the restoration area were also evaluated. In our study, hUCB-MSCs and rBM-MSCs exhibited typical MSC characteristics, with positive expressions of CD73, CD105, and CD90 and negative for CD45, CD34, CD14, and HLA-DR. After the transplantation of hUCB-MSCs and rBM-MSCs, the overall quality of cartilage tissue was significantly improved, and the recipients did not show significant side effects in general. However, the expression of matrix metalloproteinase-13 (MMP-13) in the de novo tissues of the hUCB-MSCs and rBM-MSCs groups was both increased, indicating that the novel tissues may have some potential osteoarthritic changes. In conclusion, our results suggest the therapeutic effect of hUCB-MSCs transplantation in cartilage regeneration, providing a promising future in the clinical treatment of cartilage injury.

We study the effects of strain and oxygen vacancy on the electronic structure of the (SrMmO3)2/(LaAlO3)2.5 heterostructure based on the first-principles calculations of density functional theory. The results show that different strains change the occupation of the eg orbitals of Mn, which makes the system have different magnetic ground states and electronic properties. It is also found that the conductivity of the (SrMmO3)2/(LaAlO3)2.5 system increases with the increase of oxygen vacancies rate, which can be used for the new generation electronic devices.

V2O5-WO3/TiO2 as a commercial selective catalytic reduction (SCR) catalyst usually used at middle-high temperatures was modified by loading of MnOx for the purpose of enhancing its performance at lower temperatures. Manganese oxides were loaded onto V-W/Ti monolith by the methods of impregnation (I), precipitation (P), and in-situ growth (S), respectively. SCR activity of each modified catalyst was investigated at temperatures in the range of 100&ndash, 340 &deg, C. Catalysts were characterized by specific surface area and pore size determination (BET), X-ray diffraction (XRD), temperature programmed reduction (TPR), etc. Results show that the loading of MnOx remarkably enhanced the SCR activity at a temperature lower than 280 &deg, C. The catalyst prepared by the in-situ growth method was found to be most active for SCR.

Valley-filling markets were established for several provinces in China to accommodate high-level of renewable penetration. Demand side resource is potentially a type of more effective and efficient participant to the market. The paper presents a method to assess economic benefit of multi-type flexible demand side resource participating in valley-filling market. A generic demand side resource model considering power, energy and time-of-use is proposed to schedule the operations of different types of demand side resources. A chronological production cost simulation model considering demand side resource is presented to optimize the actual operation of demand side resources, thermal generators and renewable generation. In order to quantify the economic benefit of demand side resource, a set of indices reflecting benefits of renewable generation and valley-filling market is established. A case study base on an actual power system in North China is performed using the proposed methods. Numerical results show that demand side resource brings great economic benefit to renewable generation and valley-filling market.

Coordination development between electric vehicles (EVs) and charging infrastructures is the key of propelling the diffusion of electric vehicles, especially at the initial stage. This paper taking into account both the convenience of charging of EV owners and investment cost of charging network, proposes a particle swarm optimization(PSO) algorithm for planning the charging network of EVs, aiming to solve out the "chicken-egg" problem, a dilemma existing the initial period of EVs development, in which charging infrastructure investors lack investment enthusiasm, but meanwhile to trigger the purchase motivation of latent EVs users highly depends on charging convenience. The proposed method could not only support the EV charging network layout accounting for the cost of the investors but also consider the charging convenience of EVs owners. In addition, the simulation outcome also proves that our algorithm could quickly seek out the best layout scheme of charging infrastructures.

The comprehensive handle evaluation system for fabrics and yarns (CHES-FY) was developed to measure multiple mechanical properties, which are related to fabric handle. In this paper, the potential ...

With the rapid development of electric vehicles and charging facilities, the demand for high-power charging technology is becoming more and more clear. But the weight of charging cables increases dramatically with the increase of current, which is inconvenient to users. The existing automatic charging robot has a small supporting capacity and cannot be compatible with the high current demand of high-power liquid cold vehicle connector. At the same time, the user's charging time is random, and outdoor lighting conditions have a great influence on the visual recognition effect. Therefore, a high-power fully automatic charging device for complex light source environments is designed. The device uses the hybrid structure of joint bearing and linear bearing, as well as six degrees of freedom mechanical structure based on the complementary approach of parallel electric cylinder and flexible soft shaft to solve supporting capacity problem for the rough charging cable. At the same time, the concept of multi-sensor collaboration is introduced, based on the traditional image recognition technology, the application of ultrasonic technology, infrared technology and ring purple light development technology is used. X-Y plane positioning is realized by the minimum distance iteration algorithm, and the Z-axis positioning is achieved by the split wide-angle rangefinder based on ultrasonic-infrared ranging technology. Therefore, realizing the vehicle inlet in the day-time outdoor complex environment and night environment of 3D accurate positioning is achieved, which can help promote the intelligent and convenient development of the charging service system.

Ce(1.0)Mn/TiO2 catalysts with the same Ce/Mn molar ratio were prepared by impregnation method and an in situ deposition method. Compared with the Ce(1.0)Mn/TiO2-IP catalyst prepared by impregnation, the Ce(1.0)Mn/TiO2-SP catalyst prepared by in-situ deposition showed better catalytic performance in a wide temperature window (150–300 °C) under high gas hourly space velocities ranging from 10,500 to 27,000 h−1. And Ce(1.0)Mn/TiO2-SP catalyst by in situ deposition method has a better sulfur resistance than Ce(1.0)Mn/TiO2-IP catalyst. Various characterization methods were used to explore the difference of the catalysts prepared by the two methods. The Ce(1.0)Mn/TiO2-SP catalyst exhibited a higher surface area, better thermal stability, a higher concentration of acidic sites, and less agglomeration than the Ce(1.0)Mn/TiO2-IP catalyst. Moreover, the in-situ deposition method resulted in an enhanced electron mobility effect that originated from MnOx and CeOx, thereby enhancing the low-temperature DeNOx efficiency.

In this work, many kinds of resonant excitations to the N -solitary waves solution are constructed for a (1+1)-dimensional general high-order Burgers (HB) equation. And the complexiton solution, half periodic kink (HPK) and soliton molecules of the (1+1)-dimensional HB equation are found. In particular, for a fourth-order Burgers equation, the explicit expressions and forming conditions for the soliton molecule and HPK are revealed. The three-soliton molecule, two-soliton molecule and HPK molecule are also proposed and depicted. The soliton molecule-kink type and HPK molecule-kink type interaction solutions can exhibit abundant fusion and fission phenomena.

The main topic investigated in this paper is the effective measurement of the fabric friction property by the comprehensive handle evaluation system for fabrics and yarns (CHES-FY). The optimal system parameters of the CHES-FY were determined using an orthogonal experiment design based on the correlations between four curve parameters from the pulling-out force – the displacement curve of the CHES-FY and the friction coefficient tested by the Kawabata evaluation system for fabric surface tester. The repeatability and stability of the CHES-FY under settings of optimal system parameters were also analyzed. Moreover, the four curve parameters were used to cluster the smoothness/roughness handle of fabrics objectively by the k-means cluster method, and the clustering results of the k-means cluster method were compared with those of the subjective cluster method. Analysis of the results showed that the selected optimal system parameters were a good combination, and the CHES-FY with the optimal system parameters provided an effective and reliable evaluation to the friction coefficient and smoothness/roughness handle of fabrics.

To obtain robust, stable and well-defined image features, an excellent interest point detector is necessary. Interest points in images refer to some special points that are useful for subsequent processing tasks, such as edges, comers, textures, blobs, etc. Since the task is important in computer vision, more and more learning-based methods have been proposed. However, most of their methods are built upon hand-crafted methods, and the performance of hand-crafted detectors limits their further development. In this paper, we propose SeqNet, an unsupervised learning network for detecting interest points. We qualitatively and quantitatively show that our method performs better or on-par with baselines.

Facial expression is the main research content of acquiring human psychology and developing human-computer interaction. The expression is made up of deformation and movement of each facial muscle, especially the un-posed micro expression is difficult to study because of the small degree of deformation. How to find an intuitive and interpretable method to visualize the deformation of facial expressions is an important and neglected task. In this paper, we present a new method of facial expression deformation visualization based on pore-scale facial feature matching. We use PSIFT (Pore Scale-Invariant Feature Transform) to extract a large number of facial pore-scale keypoints. Then GMS (Grid-based Motion Statistics) and VFC (Vector Field Consensus) are used to get robust matching pairs of facial feature points, so that to obtain multi-frame pore-scale keypoints tracking trajectories with time. Based on Munsell Color System, we transform matching groups into displacement vector field, Gaussian filter kernel and grid-based principal direction vector are computed to visualize the amplitude and direction of facial muscle movement. Compared with other methods of displacement field visualization (such as Horn-Schunck Optical Flow, SIFT Flow and Line Integral Convolution), our method achieves the best visualization effect in the field of facial expressions deformation on BP4D database and the collected high resolution facial expression video, the research on this subject conducive to the further development of others, such as facial expression recognition, expression modeling, expression generation and so on.

Since skin disease is one of the most common human diseases, intelligent systems for classification of skin diseases have become a new line of research in deep learning, which is of great significance for both doctors and patients. Some skin-disease datasets have already been published, such as the SD-198 dataset, which contains 6584 clinical skin-disease images of 198 categories. However, because of the diversity of clinical dermatology, previous works have showed that the performance of deep visual features is not as good as or even worse than hand-crafted features for skin disease classification. In this paper, we propose an SD-198-P dataset, which includes additional high-level position information in the SD-198 dataset to guide the generation of better deep visual features. Our experiment shows that, after adding the position information, the performance of deep visual features is better than that of hand-crafted features. To the best of our knowledge, our method outperforms the current state-of-the-art clinical skin disease classification methods.