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In today’s era, the connection between science of technology and all aspects of social life is increasingly in-depth. Multimedia technology, particularly in the sphere of education and teaching, demonstrates its tremendous advantages, such as vast teaching resources and the ability to move between various teaching modalities at any moment. To a large extent, all of these have aided the successful execution of China’s education reform. Teachers and students were continually adjusting to the dispersion of conventional roles, resulting in an increase in instructional efficacy. How to improve the significance of multimedia in middle school curriculum development and achieve our academic objectives of "fostering individuals" has now become a contentious issue among academicians. Aiming at the current situation of the development of middle school education management in China, this paper adopts the method of combining questionnaire survey with comparative experiment, proving the advanced nature of multimedia teaching mode. Meanwhile, with the help of questionnaire analysis, the author also understands some middle school students’ real ideas about the application of multimedia technology to the classroom. The experimental results show that multimedia technology is very effective in middle school education management. The classroom quality has been effectively improved, and the enthusiasm of students in class has been improved by about 20%. Furthermore, such an improvement was obtained in only one month of testing, which is quite encouraging. Finally, the researcher provides a thorough review of the research, arguing that using multimedia technology should not only enhance the material’s effectiveness, but also prevent overwhelming the viewers. &nbsp

Gearboxes are critical transmission components in the drivetrain of wind turbine, which have a dominant failure rate and the highest downtime loss in all wind turbine subsystems. However, load variations of wind turbine gearbox are far from smooth and usually nondeterministic, which result in inconsistent data distributions. To solve the problem, a novel performance degradation assessment and prognosis method based on maximum mean discrepancy is proposed to test the difference between data distributions and extract the characteristics of multi-source working conditions data. Besides, the increase in sensors will bring more difficulties to establish prediction models in real-world scenarios due to different installation locations. In view of this, a transfer learning strategy called joint distribution adaptation is utilized to adapt data distribution between multi-sensor signals. Nevertheless, the presence of background noise of wind turbine signals restricts the applicability of these algorithms in practice. To further reduce the distribution difference, a novel criterion is proposed to evaluate and measure the data distribution difference between known and tested working conditions based on the witness function of maximum mean discrepancy. The application and superiority of proposed methodology are validated using a wind turbine gearbox life-cycle test data set. Meanwhile, model comparison and cross-verification are conducted between conventional and proposed prediction models. The results indicate that the proposed method has a better performance in performance degradation assessment for wind turbine gearbox.

Gearbox is one of critical transmission components in wind turbine (WT) having a high downtime rate among all subcomponents. Fault prognostics and health management (PHM) of WT gearbox is crucial to their high reliability operation. However, presence of background noise in WT signals restricts the applicability of existing PHM approaches in feature extraction. To solve this problem, a novel performance degradation assessment method based on deep belief network (DBN) and self-organizing map (SOM) is proposed to de-noise and merge multi-sensor vibration signals. Minimum quantization error (MQE) is defined as health indicator to detect incipient fault of WT gearbox. After health indicator construction, an improved particle filtering (PF) optimized by fruit fly optimization algorithm (FOA) is employed to predict the remaining use life (RUL) of WT gearbox. To take advantage of dynamic and random operation process of WT gearbox, Wiener-process-based degradation model is developed to improving RUL prediction efficiency. The effectiveness is validated by using simulated as well as experimental vibration signals obtained through a WT gearbox highly accelerated life test. The results illustrate that proposed method can evaluate performance degradation process and predict RUL of WT gearbox effectively.

During hematogenous metastasis, cancer cells escape from primary lesions and enter into the circulatory system, and only a few can colonize distant organs. However, the mechanism of cell survival and metastasis in the hematopoietic environment remains unclear. Angiorrhea is the character of pathological neovascularization in malignant tumors and commonly detected in osteosarcoma (OS), a bone tumor that prefers circulatory metastasis. In the present study, we focused on the notable role of serum albumin, the highest content in blood plasma, on OS progression. Our results indicated that serum albumin might act as a barrier against exogenous cancer cells during hematogenous metastasis. OS cells with high metastatic potential could gradually obtain strong viability through dedifferentiation under the effect of serum albumin in the angiorrhea region. Further exploration showed that serum albumin could increase the intracellular calcium concentration by activating the voltage-dependent calcium channel Cav2.1 in OS cells to affect the cytoskeleton, sequentially leading to dedifferentiation. Dedifferentiated OS cells with increased FAS apoptosis inhibitory molecule 2 (FAIM2) expression would gradually acquire survival ability, whereas knockdown of FAIM2 caused apoptosis in serum albumin. Moreover, FAIM2 overexpression rescued the viability of OS cells with low metastatic potential in serum albumin. In clinical specimens, OS cells showed markedly stronger positive staining of FAIM2 in the angiorrhea area. Taken together, our findings indicate that serum albumin in the angiorrhea region is a critical substance during pulmonary metastasis of OS cells. Angiorrhea is a nonnegligible prognostic element and FAIM2 might serve as a promising therapeutic target.

Gearbox is a critical transmission component in the drivetrain of wind turbine having a dominant failure rate and a highest downtime loss among all wind turbine subsystems. Ensemble empirical mode decomposition and principal component analysis have been extensively investigated for signal decomposition and fault feature extraction from the signals of a wind turbine gearbox. However, presence of background noise in wind turbine signals restricts the applicability of these algorithms in real scenarios. To solve this problem, a novel performance degradation assessment method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and kernel principal component analysis (KPCA) was proposed to de-noise and fuse vibration signals. A comparison is conducted between CEEMDAN-KPCA and other five cross combinations. After feature extraction, extreme learning machine (ELM) optimized by fruit fly of algorithm (FOA) is employed to predict the remaining use life (RUL) of wind turbine gearbox. To avoid the tedious hand-crafting hidden nodes, a dynamic adjustment strategy is presented for the improvement of search efficiency. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. The results illustrate that the CEEMDAN-KPCA gets better result in signal de-noising. Compared with different swarm intelligence algorithms, the RUL prediction rates of wind turbine gearbox are improved by using the FOA-ELM prediction model with multiple parameters.

Due to the low speed and heavy load conditions of slewing bearings, extracting of effective features for fault diagnosis and prediction is difficult but crucial. Moreover, challenges such as large data volumes, unlabeled and multi-source bring more difficulties for advanced prognosis and health management methods. To solve these problems, a novel method for performance degradation assessment of bearings based on raw signals is proposed. In this methodology, a combination of deep auto-encoder (DAE) algorithm and particle filter algorithm is utilized for feature extraction and remaining useful life (RUL) prediction. First, the raw vibration signal is employed to train parameters of a restricted Boltzmann machine to build the DAE model. Through encoding and decoding multi-source data, root mean square error of reconstruction error between the raw signal and reconstructed signal is employed to detect incipient faults of slewing bearings. Then, degradation trend model is established by particle filtering to predict RUL of bearings. The effectiveness of proposed method is validated using simulated and experimental vibration signals. Results illustrate that proposed method can evaluate the performance degradation process and RUL of slewing bearings.

Incipient fault detection of wind turbine large-size slewing bearings is crucial to their high availability and profitable operation. Many studies have been conducted using time-domain, frequency-domain analysis for fault feature extraction. However, they suffer from inherent disadvantages, such as spectrum leakage analysis under variable speed, which make them unsuitable for incipient fault detection. To overcome these shortcomings, a novel incipient weak fault feature extraction method is proposed based on circular domain analysis and piecewise aggregate approximation. A systematic methodology based on circular domain resampling approach is proposed to map the time series signals into angle domain, and eliminate the time attribute. Piecewise aggregate approximation with neighborhood correlation is utilized to reduce the amount of circular-domain signals, and detect the frequency variation of vibration signals when an incipient fault occurs. The application and superiority of the proposed methodology are validated using a wind turbine large-size bearing life-cycle test dataset. Meanwhile, a comparison is conducted between traditional fault features and various circular domain models. Results show that the proposed method has a better performance in detecting incipient faults for wind turbine large-size bearing.

This paper proposes a quadrature method based on multi-variate Bernstein polynomials. The method is used to solve multidimensional Volterra integral equations with weakly singular kernels. Firstly, we use multi-variate Bernstein polynomials to approximate the unknown function of an equation, then a discrete function equation can be obtained by substituting the approximate solution into the equation. Secondly, the discrete function system is transformed into an algebra equation system by using some discrete points. We can perform the integral operations without discrete kernel function, and the weakly singular integrals can be calculated directly by using quadrature method, so the method is easy to implement. Thirdly, we prove the existence and uniqueness of the solution of the approximate equation, as well as the error analysis of the proposed method. Six numerical examples are given to illustrate the efficiency of this method.

Slewing bearing is one of critical transmission in wind turbine and shield machine withstanding low-speed and heavy-load working condition. Fault recognition is crucial to their high reliability operation. Many studies have been conducted using traditional shallow networks for fault recognition. However, they suffer from inherent disadvantages, such as low learning ability under high-dimensional nonlinear features, which make them unsuitable for fault recognition of slewing bearing. To solve these shortcomings, a novel fault recognition method is proposed based on improved deep belief network (DBN) using sampling method of free energy in persistent contrastive divergence (FEPCD). A systematic methodology based on multi-domain feature extraction is proposed to describe the fault characteristic information. After that, improved DBN optimized by FEPCD is employed to capture the fault features and recognize the fault condition of slewing bearing. The application and superiority of proposed methodology are validated using a slewing bearing life-cycle test dataset. Meanwhile, a comparison is conducted between traditional sampling methods contrastive divergence (CD) and persistent contrastive divergence (PCD). The results illustrate that improved FEPCD gets better result in training sampling. Compared with other deep learning methods such as deep Boltzmann machine (DBM) and stacked auto-encoder (SAE), and shallow intelligent algorithms like back propagation (BP) neural network and support vector machine (SVM), the fault recognition accuracy of slewing bearing is improved by using the improved DBN with FEPCD.

BackgroundAs an emerging therapy with a promising efficacy, immunotherapy has been widely used in the treatment of solid tumors and hematologic malignancies. This clinical study compares the efficacy of tislelizumab, a domestic immune checkpoint inhibitor (ICI), to that of sorafenib when used as a first-line therapeutic option in hepatocellular carcinoma (HCC), and the concurrence of HCC and non-Hodgkin’s lymphoma (NHL) is rare, especially in the treatment of ICIs.Case presentationA 61-year-old patient presenting with primary HCC and indolent B-cell lymphoma had a partial clinical response to tislelizumab for his primary HCC. Besides, we described a phenomenon of pseudo-progression and delayed diagnosis of his lymphoma during a long course of treatment.ConclusionTislelizumab, an immunotherapeutic option with a favorable efficacy and toxicity, can be used to manage double primary tumors. However, studies should aim to elucidate the probable mechanisms of this therapy. Pseudo-progression and separation remission make the treatment of double primary tumors even more challenging, which calls for additional caution in patients undergoing immunotherapy to avoid misdiagnosis and, therefore, begin early appropriate interventions.

In this article two barycentric interpolation collocation methods are proposed for solving linear and nonlinear high-dimensional F r e d h o l m integral equations of the second kind. The approaches respectively utilize the modified weighted L a g r a n g e functions and the novel rational functions as the interpolation basis functions. They are effective schemes for evaluating the multidimensional undetermined function. Through the numerical strategies and some composite quadrature formulas, the linear and nonlinear F r e d h o l m integral equations are transformed into the corresponding linear and nonlinear algebraic equations. Further, we prove that the discrete collocation methods are equivalent to the N y s t r o m quadrature methods. Then the convergence analysis is established by the collectively compact theory. Moreover, the error estimation of the approximate solution and the exact solution are also provided. Numerical examples are presented to illustrate the capability and efficiency of the techniques by compared with the classic L a g r a n g e interpolation collocation method and other methodologies.

Sox2 (Sry-box2) is essential for a variety of stem cells and is also expressed in colorectal cancer (CRC). However, the underlying mechanism by which Sox2 enhances CRC progression remains unclear. In the present study, we show that elevated Sox2 expression is significantly correlated with poor clinical prognosis. CRC is phenotypically heterogeneous, and harbors several subtypes of cancer cells. Elevated Sox2 expression was always detected in rounded-shape cells, which co-located to poorly differentiated regions, the invasive frontier and metastatic lesions. Knockdown of Sox2 in CRC cells not only decreased the number of round-shaped cells, but also suppressed cell migration, invasion as well as attenuated colony forming capacity and tumorigenicity. By contrast, overexpression of Sox2 in CRC cells was associated with up-regulation of multidrug resistance genes and accelerated CRC progression. Moreover, Sox2 conferred activation of Rho-ROCK signaling, whereas inhibition of ROCK signaling decreased cell migration, invasion, colony formation and self-renewal of CRC. Our results reveal that CRC is phenotypically and functionally heterogeneous. Elevated Sox2 expression activates the Rho-ROCK pathway, which in turn changes cell morphology and promotes cell migration and progression.

This paper solves the two dimensional linear Fredholm integral equations of the second kind by combining the meshless barycentric Lagrange interpolation functions and the Gauss-Legendre quadrature formula. Inspired by this thought, we convert the equations into the associated algebraic equations. The results of the numerical examples are given to illustrate that the approximated method is feasible and efficient.

In this paper, we use mid-rectangular quadrature rules to solve multidimensional weakly singular integrals of product type (MWSIP) and construct corresponding multi-step and multi-parameter asymptotic expansions of errors. The convergence order can turn from O(hiαi+1) to O(h04), -1 ≤ αi ≤ 0, h0 = max1≤i≤s{hi} by using extrapolation and splitting extrapolation algorithm. Moreover, we address a posterior error estimate and formulate self-adaptive algorithm. Numerical results illustrate that the method is of high accuracy and efficiency for solving MWSIP.

In this paper, multidimensional weakly singular integrals are solved by using rectangular quadrature rules which base on quadrature rules of one dimensional weakly singular and multidimensional regular integrals with their Euler-Maclaurin asymptotic expansions of the errors. The presented method is suit for solving multidimensional and singular integrals by comparing with Gauss quadrature rule. The error asymptotic expansions show that the convergence order of the initial quadrature rules is , where . The order of accuracy can reach to by using extrapolation and splitting extrapolation, where h0 is the maximum mesh width. Some numerical examples are constructed to show the efficiency of the method.

By using the symbolic system of music, art, action and so on, the language symbol system of infants and young children in the prelinguistic stage can be activated, so that infants and young children can read picture books while learning oral language. This saves a lot of time for the learning and development of early writing language and provides the opportunity for infants and young children to integrate and apply oral language and early written language. The systematic training method include: the relationship between instruction and action, the cooperation between recognition pictures and language, the combination of literacy and meaning, and the guidance of reading and sentence breaking.

In this work, a mechanical quadrature method based on modified trapezoid formula is used for solving weakly singular Volterra integral equation with proportional delays. An improved Gronwall inequality is testified and adopted to prove the existence and uniqueness of the solution of the original equation. Then, we study the convergence and the error estimation of the mechanical quadrature method. Moreover, Richardson extrapolation based on the asymptotic expansion of error not only possesses a high accuracy but also has the posterior error estimate which can be used to design self-adaptive algorithm. Numerical experiments demonstrate the efficiency and applicability of the proposed method.

Cancer stem cells (CSCs) are believed to have a critical role in tumorigenesis, metastasis, therapeutic resistance or recurrence. Therefore, strategies designed to specifically target and eliminate CSCs have become one of the most promising and desirable ways for tumor treatment. Osteosarcoma stem cells (OSCs), the CSCs in osteosarcoma (OS), are critically associated with OS progression. Here, we show that single-walled carbon nanotubes (SWCNTs), including unmodified SWCNT (SWCNT-Raw) and SWCNT-COOH, have the ability to specifically inhibit the process of TGFβ1-induced OS cells dedifferentiation, prevent the stem cell phenotypes acquisition in OS cells and reduce the OSC viability under conditions which mimic the OS microenvironment. Concurrently, SWCNT treatment significantly down-regulates the expression of OSC markers in OS, and markedly reduces the tumor microvessel density and tumor growth. Furthermore, we found that SWCNT could suppress the TGFβ1-induced activation of TGFβ type I receptor and downstream signaling, which are key for the OSC formation and maintenance. Our results reveal an unexpected function of SWCNT in negative modulation of OSCs, and provide significant implications for the potential CSCs-targeted therapeutic applications of SWCNT.

Clouds have been known as the key components of atmospheric processes in climate, weather, and environment related issues. Owing to its highly complicated processes relating to atmospheric dynamic, macro and microphysical characteristics reveal distinct regional and seasonal features, thus observations at various typical sites are very important to quantitatively understand cloud characteristics with their functions. In this paper, we introduce combined ground-based instruments, i.e., a Mie Lidar, a Ka-band Doppler radar, an IR and a visible all sky imagers, and an automatic weather station, to continuously observe the clouds over Beijing. Synthetic analyses are made to derive the cloud base height, vertical structure, horizontal distribution, radiative effect, etc. About 1 year observation data are used to obtain the statistics of cloud characteristics in this area. A simple introduction of the surface-based remote sensing system and some preliminary results are given.

In this paper, we study model reduction by using rational orthogonal bases by consecutively selecting poles for the basis functions. An algorithm modified from adaptive Fourier decomposition (AFD) in Hardy space is presented. In this algorithm, the one-by-one selection of poles makes the numerical algorithm easy to implement, which also keeps the steady-state values equal. The approximating results are comparable to other classical methods.