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In this study, the inhibiting effect and underlying mechanism of plasma activated Liquid (PAL) on B16 melanoma cell are proved. The helium atmospheric-pressure plasma jet (APPJ) is used to generate the plasma-activated saline and medium (PAS and PAM). Then the PAS is injected subcutaneously to treat B16-tumor bearing mice in vivo and the PAM is used to treat cancer cells in vitro. The weight of tumor nodules, cryosections, cell viability, apoptosis, DNA damage, p53 pathway-related protein and the concentration of H 2 O 2 in PAM are detected. The in vivo results show that PAS can inhibit tumor growth and induce apoptosis. The in vitro results show the concentration of H 2 O 2 in PAM increases as APPJ irradiation time is longer, while the cell viability decreases as APPJ treatment time increases. The flow cytometry results indicate that, compared with the control group, all the PAM treatment groups show a higher apoptotic rate. The single cell gel assay and an 8-OHdG assay display that the PAM can induce DNA damage. And the western blotting assay indicate that the PAM increases the expressions of p53, p53-induced gene 8 protein, PIG3, cytoplasmic cytochrome c, cleaved-caspase-9/3. However, CAT can alleviate all these changes effectively. These outcomes suggest that plasma can exert an apoptotic effect on B16 cells mainly through activating the p53/PIGs/caspase pathway.

Web services classification and clustering rely on discriminative representations of service functional semantics. Most existing topic-based methods for Web service representation simply leverage word co-occurrence relations, which may cause sub-optimal results for some specific-tasks due to the sparse features in the short service descriptions. Despite sparse and discrete word statistical features, other information such as word-topic and word-tag relationships can be utilized to accurately represent Web services. In this paper, we propose a novel word embedding-based approach for representing Web services. First, we use Word2vec to generate word embeddings from service descriptions. Then, the word-topic and word-tag relationships are preserved for Web service representation generation via the topic-weighted and tag-weighted word embedding stages based on Gaussian Mixture Model and Bayesian rule, respectively. Experiments and validations on the real-world dataset demonstrate that our method outperforms the state of the arts by a wide margin on service classification and clustering.

Fast and accurate monocular depth estimation on mobile devices is a challenging task as one should always trade off the accuracy against the inference time. Most monocular depth methods adopt models with large computation overhead, which are not applicable on mobile devices. However, directly training a light-weight neural network to estimate depth can yield poor performance. To remedy this, we utilize knowledge distillation, transferring the knowledge and representation ability of a stronger teacher network to a light-weight student network. Experiments on Mobile AI 2021 (MAI2021) dataset demonstrate that our solution helps increase the fidelity of the output depth map and maintain fast inference speed. Specifically, with 94.7% less parameters than teacher network, the si-RMSE of student network only decrease by 10%. Moreover, our method ranks second in the MAI2021 Monocular Depth Estimation Challenge, with a si-RMSE of 0.2602, a RMSE of 3.25, and the inference time is 1197 ms tested on the Raspberry Pi 4.

Recently, mobile devices such as iPhone X start to be equipped with depth cameras, and more applications based on captured depth maps are emerging. Among many depth cameras on the market, Intel RealSense has the ability to capture depth information and is expected to be widely used in mobile devices and laptops. However, depth maps captured by RealSense always suffer from severe holes and noises, which make it hard to be used in real applications. In this paper, we propose a method to fill holes and remove noises in depth maps captured by RealSense. This method includes two parts: human depth prediction and human depth optimization. Firstly, we propose a two-stage stacked hourglass network to predict human part-segmentation and human depth simultaneously based on RGB image. Then we use GradientFMM method to optimize captured depth maps with the guidance of the above human depth prediction. The RGB image and depth maps mentioned above are captured by the same RealSense device. Furthermore, in order to show the effectiveness of the proposed method, we register and measure human 3D models based on optimized depth maps. The experimental results show that our method can restore depth maps for human using RealSense effectively.

In order to effectively reduce the influence of wind power uncertainty on the power grid and improve the safety of power system operation, it is necessary to carry out fine modeling of wind power forecasting error. Based on the actual wind farm operation data, this paper proposes a generalized Gaussian mixture model to describe the distribution characteristics of its forecasting errors, and uses an improved expectation maximization (EM) algorithm to solve the model parameters. The model can accurately describe the multi-peak and tailing in wind power forecasting errors and has good fitting effect. Finally, an example analysis is carried out based on the actual wind farm data, and compared with the commonly used normal distribution and t Location-Scale distribution models, which proves the effectiveness of the proposed model.

In response to the spirit of teacher certification, the major of electromechanical technology education promoted actively the secondary teacher certification of Vocational and technical. This paper discusses the reform ideas and contents under the guidance of teacher certification. The course system has been greatly improved in the aspects of construction mechanism, system framework, evaluation and so on. Finally, the certification spirit has implemented in the Special field building. The reform of course system will continuously summarize and improve.

Since the resistance of a power cable R is much larger than its reactance X, the conventional power flow calculation methods are not suitable. This paper proposes a power flow calculation approach for distribution network of pure power cables. Then, a new type of P-Q decomposition method is developed along with the derivation of the corresponding Jacobian Matrix. The proposed approach is compared with the existing P-Q decomposition method and the Gauss-Seidel method for solving the power flow of a revised IEEE14 node grid. The results show that the proposed approach has the best performance, while the existing P-Q decomposition method cannot converge, and the Gauss-Seidel method would take much more iterations to converge to the result.

Virtual simulation technology can reproduce the actual conditions and make the experimental project more engineering. The technology can make students free from the limitation of time and space and be able to practice anytime and anywhere. It not only greatly improves students' learning efficiency, but also promotes the connection between curriculum and practice teaching and strengthens the integration of theory teaching and practice teaching. At the same time, the automatic scoring function of virtual simulation system can help students get timely feedback, so as to find and solve problems in time, which plays a very important role in promoting the timely adjustment of curriculum arrangement and deepening of teaching reform. Based on idea of "student-centered", the course of electronic control of machine tool and PLC introduces the virtual simulation technology and carries out the course reform. This paper introduces the process of curriculum reform from the aspects of course reform ideas, concrete measures and evaluation reform. From the perspective of student feedback, course reform not only improves the subjective initiative of students, but also improves the teaching effect significantly.

With the explosive growth of Web APIs on the Internet, it is a challenge to recommend desirable Web APIs from multiple ecosystems to develop a Mashup. Most existing API service recommendation methods focus on functional semantic similarity, but underutilize the rich network relations which inherently reflect either positive or negative relevance between services. Moreover, in the recommendation process, they usually pay too much attention to the interactions between Mashups and APIs, but ignore the cooperation between APIs. In this paper, we propose a novel method named SRAWN (Structure Reinforcing and Attribute Weakening Network) based API recommendation approach for Mashup creation. Specifically, we first design a feature extractor layer to capture structure relationship and attribute information from an API relation network graph by introducing a GAT2VEC framework, and obtain representation vectors corresponding to each API. Then, a matching evolving layer is proposed to capture the matching evolving process between APIs. At this layer, APIs are chosen incrementally to composite a Mashup, and the embedding vectors of the Mashup's existing composition features are updated adaptively based on diverse candidate APIs, by introducing a Deep Interest Network. Comprehensive experiments on a real-world dataset show that SRAWN outperforms the other state-of-the-art solutions.

The time domain full waveform inversion (FWI) algorithm of ground penetrating radar (GPR) has high accuracy, but it needs a large amount of calculations, and it is easy to fall into the local optimal solution. In this paper, based on the full waveform inversion algorithm and genetic particle swarm optimization (GA_PSO) algorithm, the author proposes a full waveform inversion algorithm called genetic random linear weight particle swarm optimization algorithm guided by conjugate gradient in FWI. This algorithm avoids the calculation of the Hession matrix, and can greatly improve the efficiency of searching the global optimal solution. It can invert two parameters quickly. The paper first introduces particle swarm algorithm (PSO), genetic algorithm (GA) and full waveform inversion algorithm. In the next section, the paper introduces the first innovation point of this research-random linear weight particle swarm optimization based on genetic algorithm (GA_RLPSO). The algorithm changes its inertial weight according to genetic particle swarm algorithm, which brings it have a faster convergence rate. Then the paper introduces the second innovation point-GA_RLPSO with the guidance of conjugate gradient. In this paper, the gradient in-formatiom of full waveform inversion is added to the velocity weighting factor, which can greatly improve the speed and accuracy of the algorithm. Finally, the paper compares the inversion results of several different algorithms, which can prove that the proposed algorithm has the higher accuracy about model reconstruction.

Solar irradiance is one of the most crucial factors affecting the photovoltaic (PV) power output. Accurate solar irradiance forecasting can help predict future PV power generation for grid managers, thus to mitigate the adverse effect of random PV power on the power grid. However, research on real-time surface irradiance distribution in a certain region still not be presented in previous studies, which can bring further convenience to solar power forecasting and power grid dispatching work. In this paper, a surface irradiance distribution mapping model for PV power station based on ground-based sky images is proposed. First, we process raw sky image with distortion rectification and background message dislodged. Then after cloud motion displacement calculation by hybrid Fourier phase correlation (HFPC) theory, we extract the gray level and position information of pixels in sky images to explore the mapping relationship between sky image and solar irradiance. Subsequently, an adaptive sky image-irradiance mapping model is built, trained, and updated according to real-time sky images and solar irradiance. Simulation results show that the proposed method in relation to forecast can achieve real-time surface irradiance distribution mapping at quite high accuracy.

This paper introduces the full waveform inversion algorithm of ground penetrating radar, which can make full use of the travel time, phase, amplitude and other echo signal information carried by echo signals, thus its imaging resolution is much higher than the other algorithms. The full waveform inversion algorithm has a large amount of computation, so the conjugate gradient method is used to simplify the calculation of gradient, which can avoid to calculate Hessian matrix directly and can greatly improve the efficiency of the algorithm. This paper first introduces the background and significance of the full waveform inversion about ground penetrating radar. Then it introduces the principle of the full waveform inversion algorithm. Finally, the effectiveness about FWI (full waveform inversion) is verified by numerical simulation. In the paper, time-domain finite difference method is used to calculate the wave field about electromagnetic waves propagating in underground media. Based on the adjoint matrix method, the iterative step length and the gradient for each point in the underground media are calculated, and the initial model can be updated by the gradient matrix and the step length until the terminal condition is reached. Numerical simulation experiments can prove that full waveform inversion algorithm has a higher resolution than direct time-domain imaging method. At the end, the development trends of GPR are put forward.

This paper aims at the problem of vehicle detection in UAV aerial videos and tries to investigate the performance of different kinds of object detection algorithms under such a condition. We chose ViBe, HOG+SVM, Faster R-CNN and YOLOv3 as the typical detection algorithms. After applying them on UAV aerial videos with different height and traffic scenes and testing their precision and recall, we make a theoretical analysis of their performance. Our experiments showed that Faster R-CNN is the best method. Meanwhile, ViBe, with the advantages of less calculation and relatively low hardware requirements, is a good choice for a simple scene where the vehicles keep moving all the time.

The randomness and fluctuation of photovoltaic (PV) power brings new challenges to power system operation. Accurate PV power forecasting is critical to system dispatch. This paper applies long short-term memory network (LSTM) to forecast short-term PV power. First, Pearson correlation analysis is applied to identify features affecting PV power. The features with high correlation coefficient are selected as LSTM inputs. Short-term LSTM PV power forecasting models are then established according to different seasons and weather types. Case study is performed using PV power and numerical weather prediction (NWP) of a practical PV station in northwest China. The results obtained indicate that the forecasting models can effectively improve the forecast accuracy of short-term PV power.

With the increasing popularity of SOA (Service Oriented Architecture), a large body of innovative applications emerge on the Internet with mashup (e.g., composition of multiple Web APIs is a representative). Recommending suitable Web APIs to develop Mashup applications has received much attention from both research and industry communities. Prior efforts have shown the importance of incorporating multi-dimensional features extracted from a service repository into their recommendation models. Despite their effectiveness, they are insufficient by simply modelling all these features with the same importance degree, neglecting the fact that not all features are equally useful and predictive. Some useless features may even introduce noises and adversely degrade the performance. In this paper, we propose a novel service recommendation method, which tackles this challenge by discriminating the importance of each feature from data via Attentional Factorization Machine. It endows our model with better performance and a certain level of explainability. In this model, we first extract the valuable features implied in the raw dataset and subsequently transform them to the input format of Attentional Factorization Machine. Then, multi-dimensional information, such as functional similarity, tags, popularity of Web APIs, are modeled by Attentional Factorization Machine to predict the ratings between mashups and services. Comprehensive experiments on a real-world dataset indicate that the proposed approach significantly improves the quality of the recommendation results while compared with up-to-date ones.

The service-oriented architecture makes it possible for developers to create value-added Mashup applications by composing multiple available Web services. Due to the overwhelming number of Web services online, it is often hard and time-consuming for developers to find their desired ones from the entire service repository. In the past, various approaches aim at recommending Web services for automatic Mashup creation have been proposed, i.e., TFIDF, collaborative filtering and topic model-based methods, which rely on the original service descriptions given by service providers. However, most traditional methods fail to capture the function-related features of services since words contained in service descriptions usually correspond to different intent aspects (e.g., functional and non-functional related). To tackle this problem, we propose a tag attention-based recurrent neural networks model for Web service recommendation. The model consists of two Siamese bidirectional Long Short-Term Memory (LSTM) networks, which jointly learn two embeddings representing the functional features of Web services and the functional requirements of Mashups. In addition, by considering the tags of services as functional context information, the model can learn to assign attention scores to different words in service descriptions according to their intent importance, thus words used to reveal the functional properties of Web service will be given special attention. We compare our approach with the state-of-the-art methods (e.g., RTM, Word2vec, etc.) on a real-world dataset crawled from ProgrammableWeb, and the experimental results demonstrate the effectiveness of the proposed model.

Clustering Web services can promote the quality of services discovery and management within a service repository. Traditional clustering methods primarily focus on using the semantic distance between service features, i.e., latent topics learned from WSDL documents, to measure the service content similarity between Web services. Few works exploited the structural information generated during the usage of Web services, i.e., the service compositing and tagging behaviors. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex service relationship network. The rich network relations inherently reflect either positive or negative categorical relevance between services, which can be strong supplement of service semantics in characterizing the functional affinities between services. In this paper, we propose to utilize the services relationship network for augmented services clustering algorithm design. We first learn semantic information from service descriptions based on the widely used Doc2Vec model. Then, we propose a revised K-means algorithm for service clustering that benefits simultaneously from service semantics and network relations, where the service relations are previously preserved in a set of low-dimensional vectors achieved based on a recently proposed network embedding technique. Experiments on a real-world dataset demonstrated that the proposed clustering approach yields an improvement of 6.89% than the state-of-the-art.

The rapid growth in both the number and diversity of Web services raises new requirement of clustering techniques to facilitate the service discovery, service repository management etc. Existing clustering methods of Web services primarily focus on using the semantic distances between service features, e.g., topic vectors, mined from WSDL documents. However, these quality topic vectors are hard to be obtained due to the lack of abundant textual information in Web service description documents. In practice, prior knowledge from human's trajectory of utilizing Web services could be helpful in improving the accuracy of Web services clustering. With an analysis in the dataset of Web services and Mashups from ProgrammableWeb, we observe that Web services Mashuped together are highly likely to belong to different clusters and Web services being annotated with identical tags tend to be within the same cluster. Based on these observations, this paper proposes an efficient clustering approach for Web services. The approach firstly uses a probabilistic topic model to elicit the latent topic vectors from Web service description documents. It then performs clustering based on the K-means++ algorithm by incorporating parameters representing above mentioned prior knowledge. A comprehensive evaluation is conducted to validate the performance of our proposed approach based on a ground truth dataset crawled from ProgrammableWeb. Experimental comparisons of the approaches with and without these prior knowledge considerations show that our approach has a significant improvement on the clustering accuracy.

In this paper, we introduced a behavioral decision and execution method to simulate crowded passengers in a waiting hall. The method, as well as its simulation framework, is designed under the special purpose of passenger safety investigation. It supports the simulation of both regular crowded passenger behaviors and emergency passenger behavior. Situations under different time tables and density control measure could easily be conducted and simulated for safety purposes.

In video compression, motion estimation based on block matching technology can effectively eliminate the large amount of time redundancy among video sequences, and is crucial for improving the compression efficiency. The full search algorithm can be used to obtain the optimal solution, but its implementation requires a large number of operations. So many fast search algorithms are proposed, like three-step method, new three-step method and four-step method. These algorithms can greatly reduce the computational complexity under certain conditions. This paper introduced a new three step method, based on the algorithm description, analysis and research of the search template and the performance of the algorithm, using VerilogHDL hardware description language to complete the design, and completed the function simulation imitation use software tools. The results show that the algorithm is superior to the three — step method in all aspects, and has strong adaptability.

In this paper, we design a Linux-based embedded DVR monitoring system. Firstly, the kernel driver of receiving chip is designed for Hi3520D to control its receiving mode, so that the video data captured by the camera is transferred to data receiver which carries on the AD conversion and the data preprocessing via BT.656 interface. Then the processed data is transmitted to the master chip Hi3520D. Finally, we use output interface and network transmission to complete playback operation. In order to implement the parallel operation of the multifunction module, we have achieved following functions, such as video data acquisition, flow configuration of video encoding and decoding, realization of thread mode. The video data is transmitted via RTSP\RTP protocol to attain the goal of network video surveillance.

In this paper, spacecraft attitude control is investigated by fuzzy control and quantization. The spacecraft attitude control model is transformed into a T-S fuzzy system with appropriate fuzzy rules. Moreover, a “zoom” strategy is applied to deal with the influence of quantization. A fuzzy controller is designed to guarantee the stability of the fuzzy system with quantization. A sufficient condition is obtained for stability of the fuzzy system. Lastly, a example of spacecraft attitude control is given to illustrate the advantages and effectiveness of the proposed results.

In this paper, the leader-following consensus problem of a class of fractional-order nonlinear multi-agent systems is considered. By using the algebraic graph theory, Lyapunov theory and LMI method of fractional-order systems, the control gain matrix is designed to achieve leader-following consensus under fixed and switching topologies respectively. Furthermore, the convergence rate of the controlled multi-agent systems is studied based on the sense of Mittag-Leffler stability of fractional-order systems. Simulations indicate the effectiveness of the theoretical results.

Due to the rapid growth in both the number and diversity of Web services on the web, it becomes increasingly difficult for us to find the desired and appropriate Web services nowadays. Clustering Web services according to their functionalities becomes an efficient way to facilitate the Web services discovery as well as the services management. Existing methods for Web services clustering mostly focus on utilizing directly key features from WSDL documents, e.g., input/output parameters and keywords from description text. Probabilistic topic model Latent Dirichlet Allocation (LDA) is also adopted, which extracts latent topic features of WSDL documents to represent Web services, to improve the accuracy of Web services clustering. However, the power of the basic LDA model for clustering is limited to some extent. Some auxiliary features can be exploited to enhance the ability of LDA. Since the word vectors obtained by Word2vec is with higher quality than those obtained by LDA model, we propose, in this paper, an augmented LDA model (named WE-LDA) which leverages the high-quality word vectors to improve the performance of Web services clustering. In WE-LDA, the word vectors obtained by Word2vec are clustered into word clusters by K-means++ algorithm and these word clusters are incorporated to semi-supervise the LDA training process, which can elicit better distributed representations of Web services. A comprehensive experiment is conducted to validate the performance of the proposed method based on a ground truth dataset crawled from ProgrammableWeb. Compared with the state-of-the-art, our approach has an average improvement of 5.3% of the clustering accuracy with various metrics.

Atmospheric pressure cold plasma that contains numerous act ive components including electrons, ions, free radicals, reacti ve molecules and photons has been extensively researched as a promising anticancer treatment technology in recent years1. Melanoma is the most aggressive form of skin cancer with a high prevalence, a median overall survival of less than 2 year s, and resistance to current modalities of cancer therapy2. Plas ma can inactivate melanoma caner cells effectively, but the m echanism of the interaction between plasma and cells is not y et fully understood and needs to be investigated further. In this work, an improved atmospheric pressure plasma jet (A PPJ) with three electrodes in helium was utilized for treatmen t of murine melanoma caner cells cultured in vitro. After bein g exposed to APPJ with different time durations, the cell viab ility, transmission electron microscopy (TEM) images, intrac ellular superoxide dismutase (SOD), malondialdehyde (MD A), reactive oxygen species (ROS) and cell cycle distribution of melanoma cells were measured, respectively. It was show ed that, compared with the control cells, the cell viability and SOD activity decreased, the degree of cell damage, MDA an d ROS content increased in a time-dependent manner with 10, 30, and 60 s of plasma treatment. Furthermore, the APPJ ma y reduced cell viability of melanoma cells via inducing G1/S and G2/M cell cycle arrest. It was concluded that oxidative st ress in melanoma cells was mainly induced by the reactive ox ygen and nitrogen species (ROS and RNS) in the plasma jet, which were detected by optical emission spectroscopy.

In view of the problem that the unfixed CRC calculation and the compatibility with gigabit Ethernet are the two questions during design of ten gigabit Ethernet MAC controller. This paper solves the above two questions according to the proposed solution. CRC calculation module can be designed to be a fast calculation speed and low gate delay by using a new type CRC method which contains the preprocessing operation before calculating CRC. Also, by using ping-pong FIFO in the XGMII interface, the improved XGMII interface can be compatible with the gigabit Ethernet speed. After solving the above two problems, the ten gigabit Ethernet MAC controller is realized. The experiment result shows the correctness and feasibility of the project after the timing simulation and FPGA verification.

In this paper, we present a UART-USB interface converter design based on FPGA, which implements asynchronous serial communication protocol and USB protocols conversion. Design is implied by Hardware Description Language using modular design, it can be integrated into different SoC system. After Modelsim functional simulation and FPGA simulation with two computers, we verify the feasibility of the design.

In this paper, a general design scheme of universal video monitoring System is introduced based on the Hi3520D hardware platform and the Embedded Linux Operating System, describing the design and implementation of the overall structure and the major function module. The embedded, as the video monitor terminal, implements the function of video data gathering, coding, decoding and transmission, and successfully transmits the video data through the network. The PC is the video monitoring control terminal which achieves the receiving and showing of the video data. This System is easy to use and shows high reliability. The real-time and fluent video monitoring picture is obtained through the System testing.

Based on the research of ARMv4 architecture, a kind of 32bit embedded microprocessor is designed and implemented. Firstly, the ARMv4 instruction set is divided into different types according to its characteristics, which is conducive to the design of a simple structure of the decoding circuit. Secondly, an improved pipeline architecture is proposed to improve the efficiency of the instruction execution, aiming at the problem of clock period pause in current traditional pipeline. Finally, considering the delay of the long multiplication and long addition operation, the design of the independent single cycle multiplier module and the fast adder module is designed to improve the operation speed. The entire design uses Verilog to carry on the structure level description, through the Modalism simulation verifies the validity of the design function, and carries on the hardware realization in the FPGA development board. The experimental results show that the embedded microprocessor can execute ARM instruction correctly, and the performance is improved compared with the traditional microprocessor in the same instruction set.

In the field of cloth animation, efficient and vivid simulation with deformation details especially small wrinkles has always been challenging. Animation with adaptive meshes is a typical way to improve the quality and efficiency simultaneously. Existing works mostly focus on how to model the adaptive mesh and work out the numerical solution while rarely highlight the problem of predicting deformation trend of cloths. In real as well as virtual scenes, cloth movements are usually driven by human body. Intuitively there is some strong relationship between human motion and cloth deformation. Therefore, it is significant to learn the relationship between them to support the research on mesh resolution adaption. In this paper, we describe and evaluate a new machine learning approach to predict deformation distribution. Firstly, we extract human posture feature from motion data and cloth deformation distribution feature from high resolution cloth animation examples. Then we build and train a neural network to predict deformation distribution on cloth based on human motion. The predicted result has high reliability and could be used in further dynamic adaption of cloth mesh in animation.

The commercial embedded microprocessor requires to be authorized, which increases the cost of the chip. The free embedded microprocessor soft-core is of poor portability, which causes difficulty on system design. In order to solve the above problems, this paper introduces an improved IP core base on ARMv4 instruction set. With an independent architecture, the IP core possesses good portability and compatibility. Firstly, hardware multiplier is used to solve the problem of the delay of multiplier, which can raise the speed of arithmetic. Secondly, taking the place of the three-stage instruction pipeline, a five-stage instruction pipeline is used to solve the problem of low efficiency. Finally, the whole core is implemented on FPGA, the utilization of resource is increased with resource sharing technique. The experiment results show that the ARMv4 instruction set can run correctly on the embedded microprocessor described in this paper. Compared with the similar ARMv4 microprocessor, it not only contains the advantage of high performance and open interface, but also is easy to design. The embedded microprocessor can be used in both the SOC system and the FPGA embedded system.

Studies discovered that Low temperature plasma (LTP) could promote wound healing. However, the detailed mechanisms in the cellular and molecular levels are still not fully understood. In this study, an atmospheric pressure discharge plasma jet in flowing argon by means of dielectric barrier discharge (DBD) was used to treat murine fibroblasts cultured in vitro, in order to explore the molecular mechanism of cold plasma on cell viability and collagen synthesis. Experimental results showed that, compared with the control cells, the treatment of fibroblasts with 15 s of plasma led to a significant increase of cell viability and collagen synthesis, while the treatment of 25 s plasma resulted in a remarkable decrease. Exploration of related mechanisms suggested that cold plasma could up-regulate CyclinD1 gene expression and down-regulate p27 gene expression at a low dose, while it could down-regulate CyclinD1 expression and up-regulate p27 expression at a higher dose, thus altering the cell cycle progression, and then affecting cell viability and collagen synthesis of fibroblasts.

Mashups are prevalent Service-Oriented Architecture (SOA) based applications consisting of multiple Web Application Programming Interfaces (APIs) and content. Tags have been extensively used to organize and index mashup services. However, people favor manual tags creation in the past. This approach demands user intervention, which is extremely time-consuming and probes to errors. In this paper we propose a novel Mashup-API-Tag model for automatic mashup tag recommendation. The model simultaneously incorporates the composition relationships between mashups and APIs as well as the annotation relationships between APIs and tags to discover the latent topics. Then the semantic similarity between Web APIs and mashups can be acquired. Subsequently, tags of chosen APIs are recommended to a mashup where the mashup and the APIs are most similar. In addition, we develop a tag filtering algorithm to select the most relevant tags for recommendation. The experimental results on a real world dataset prove that our approach outperforms other methods, including frequency-based methods and the methods that only consider the composition relationships and the annotation relationships separately.