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Background: Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) technique could not only quantify blood-retinal barrier (BRB) breakdown leading to macular edema associated with diabetes, but also provide a two-dimensional imaging method that is not interfered by refracting media. Objective: The current study was aimed to evaluate the macular change in the patients with diabetic retinopathy using DCE-MRI technique. Methods: Twenty patients with Diabetic Retinopathy (DR) and 20 Normal Controls (NC) were included. The fast spoiled gradient echo sequence was used to perform dynamic contrast T1WI enhancement on 3.0T MR system. The macular region, optic papila and nasal retina were performed with quantitative DCE-MRI evaluation using Omni-Kinetics software. Results: The maximal concentration, the area under the concentration-time curve (AUCconcentration-time) and maximal slope of macular region were significantly higher in DR [0.270(0.03,1.20)mmol/ 100ml, 2.71(0.04,9.91) mmol*min and 0.38(0.06,3.18) mmol/min, respectively] than that [0.169(0.03,0.72) mmol/1.25(0.13,10.41) mmol*min and 0.245(0.06,1.34) mmol/min] in NC (U value = 515.00 and P value = 0.080, U value = 433.00 and P value = 0.000, and U value = 563.00 and P value = 0.023, respectively). The receiver operating characteristic curve (ROC) analysis demonstrated that the area under AUCconcentration-time was 0.729±0.058 with the cut-off value 1.479 mmol*min (sensitivity 80.00% and specificity 62.50%) for macular region. Conclusion: The quantitative DCE-MRI technique could be used to evaluate the maculopathy associated with diabetic retinopathy.

Objective Preeclampsia is a hypertensive disorder of pregnancy marked by an excessive inflammatory response. The anti-inflammatory effect of pyridostigmine (PYR) was previously reported; however, its role in hypertensive pregnancies remains unclear. We hypothesized that PYR could attenuate increased blood pressure and other pathological features in preeclampsia models. Methods The expression of tumour necrosis factor (TNF)-α was evaluated in normal and preeclampsia pregnant women. PYR (20 mg/kg) was administered daily to reduced uterine perfusion pressure (RUPP) and TNF-α (150 ng/day) infused rats from gestation day 14 to GD19. In a cell culture experiment, the effect of acetylcholine (ACh) on TNF-α-stimulated primary human umbilical endothelial cells (HUVEC) was assessed. Results Preeclampsia women had higher placental TNF-α expression than normal pregnant women. Mean arterial pressure (MAP) in the RUPP group was higher than in the Sham group. PYR inhibited serum and placental acetylcholinesterase activity in rats, and reduced MAP, placental oxidative stress, apoptosis and inflammation in the RUPP group but not in the Sham group. In addition, PYR significantly attenuated the TNF-α-induced increase in MAP, placental oxidative stress and apoptosis. Moreover, TNF-α decreased cell viability and increased the number of TUNEL-positive nuclei of HUVEC, which could largely be abolished by ACh treatment. Conclusion Collectively, PYR ameliorated hypertension and other preeclampsia-like symptoms in rat models of preeclampsia not only by inhibiting the synthesis of TNF-α but also by acting against TNF-α-induced detrimental effects directly, which is worthy of further investigation and may be used as a potential agent for preeclampsia management.

To investigate the biomarker(s) that could affect the decision for immediate or delayed delivery in severe preeclampsia.Data on serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), creatinine, blood urea nitrogen (BUN), uric acid (UA) and platelet counts from 134 cases were collected and analysed.Higher UA levels were seen in case with immediate delivery. Higher stillbirth was seen in cases with delayed delivery.UA levels could be a potential management biomarker for immediate or delayed delivery in severe preeclampsia. However, the higher risk of stillbirth must be considered in delayed delivery.

Azobenzene compounds have been widely used in many fields and through their response to light they can be used to regulate the properties of ordered structures. In this paper, sub-micrometer colloidal SiO2 spheres are prepared and azobenzene groups are grafted on the surface of SiO2 microspheres. The SiO2 microspheres grafted with azobenzene groups could self-assemble to form photosensitive photonic crystals (PCs), whose photonic bandgaps red-shifted as irradiated by UV light and showed good reversibility.

Introduction Placental abruption is a serious pregnancy complication that causes maternal and neonatal mortality and morbidity. Whether maternal and neonatal outcomes differ between patients who concurrently presented with preeclampsia and those who did not, have not been fully investigated. Methods A total number of 158 patients with placental abruption were included. Of them, 66 concurrently had preeclampsia. Maternal and neonatal characteristics including delivery weeks, time of onset and birthweight as well as the grade of placental abruption were collected and analysed. Results The time at diagnosis of placental abruption in patients who concurrently presented preeclampsia was significantly earlier than that in patients who did not. The number of patients with grade III placental abruption was significantly higher in patients who concurrently presented with preeclampsia, compared to patients who did not. The odds ratio of an increase in grade III placental abruption in patients who concurrently presented preeclampsia was 5.27 (95%CL: 2.346, 12.41), compared to patients who did not. The numbers of infants who were born pre-term and the numbers of stillbirth/neonatal deaths as well as the number of fetal distress were significantly higher in patients who concurrently presented with preeclampsia, compared to patients who did not. The fetal birthweight was significantly lower in patients who concurrently presented with preeclampsia compared to patients who did not. Discussion Our study demonstrates that women with preeclampsia experiencing placental abruption had worse maternal, fetal and neonatal outcomes, compared to women experiencing placental abruption alone.

Sol–gel derived ormosil gel glass can act as matrix for different functional molecules or nanomaterials due to its many advantages. Gold and silver colloidal nanoparticles have great application potential in nonlinear optical field due to their ultrafast optical response. In this letter, the nanocomposites of gold nanorods and ormosil gel glass were prepared by a solvent-assisted dispersing process and characterized by UV–vis spectra, TEM, XRD and optical limiting measurement. The results showed that these gold/ormosil nanocomposites could preserve optical limiting property of gold nanorods and had enhanced thermostability due to thermostable silica matrix. Such nanocomposite material of metal-doped gel glass matrix could be considered as a promising candidate for optical limiter against tunable laser.

Today the only effective “treatment” for preeclampsia is to deliver at the optimal time for both maternal and foetal well-being. Studies reported that severe preeclampsia can benefit from the expectant management including mild preeclampsia between 34 and 37 weeks. However it is unclear whether mild preeclampsia before 34 weeks also benefits from the expectant management. Data on 274 women with mild preeclampsia before 37 weeks of gestation were retrospectively collected and analysed. Blood pressure and proteinuria at time of onset were not clinically associated with delivery time. For women who developed preeclampsia before 34 weeks, the median latency from onset to delivery or from onset to admission to hospital or from admission to hospital to delivery was 27 or 21 or 3 days, respectively. There were four women (2%) who delivered within 48 h after onset, 28 (14%) FGR and 14 (7%) stillbirths. The median birth-weight was 2240 g. For women who developed preeclampsia between 34 and 37 weeks, the median latency from onset to delivery or from onset to admission to hospital or from admission to hospital to delivery was 11 or 7 or 2 days, respectively. There were seven women (10%) who delivered within 48 h after onset and eight (12%) FGR. The median birth-weight was 2880 g. Our study demonstrates that mild preeclampsia before 37 weeks has benefits from expectant or outpatient management with a median prolongation of over 11 days dependent on the time of onset, but it increases the risk for stillbirths before 34 weeks.

With the advent of the Internet era, deep learning technology is developing rapidly in a wide range of application scenarios. As one of the main research methods of deep learning, convolutional neural network is widely used in the field of image recognition [5]. This paper hopes to build a modern intelligent tea image recognition system by combining in-depth learning technology, so as to help inherit traditional Chinese culture. The main work of this paper is as follows:(1)Making a sample data set containing about 49000 pictures;(2)Constructing convolution neural network;(3)The correct rate of training image recognition model is 96%;(4)Realize tea picture recognition;(5)Build the web page.Under the framework of tensorflow, the program of this subject realizes the tea picture recognition function by training convolution neural network [5][6], and builds it in the beautiful web page, so that the tea culture can be presented and spread in a more modern form, which is conducive to the inheritance and development of Chinese traditional culture.

Vulnerability is one of the main causes of network intrusion. An effective way to mitigate security threats is to find and repair vulnerabilities as soon as possible. Traditional vulnerability detection methods are limited by expert knowledge. Existing deep learning-based methods neglect the connection between semantic graphs and cannot effectively deal with the structure information. Graph neural network brings new insight into vulnerability detection. However, benign nodes on the graph account for a large proportion, resulting in vulnerability information could be disturbed by them. To address the limitations of existing vulnerability detection approaches, in this paper, we propose ACGVD, a vulnerability detection method by constructing a graph network with attention. We first combine multiple semantic graphs together to form a more comprehensive graph. We then adopt the Graph neural network instead of the sequence-based model to automatically analyze the comprehensive graph. In order to solve the problem that the vulnerability information could be covered up, we add a double-level attention mechanism to the graph model. We also add a novel classification layer to extract the high-level features of the code. To make the experiment more realistic, the model is trained over the latest published real-world dataset. The experiment results demonstrate that compared with state-of-the-art methods, our model ACGVD achieves 5.01%, 13.89%, and 8.27% improvement in accuracy, recall and F1-score, respectively.

Web 3D was initially realized by installing plug-ins on the browser, and WebGL can render interactive 3D graphics on web pages without using plug-ins. Three.js is the open-source framework of WebGL, a small amount of JavaScript code can create complex 3D scenes. This article uses Three.js to realize 3D data visualization, including 3D histograms, pie charts, maps, earth, and force-oriented graphs, and uses them for a visualized display of film data.

This application, Color Dust, extracts and filters the color of image pixels online through Canvas, then performs clustering analysis and statistics based on the K-Means algorithm. With the help of front-end libraries such as D3.js, Vue.js, and Vuetify, the data visualization of image color is realized. The color extracted by the K-Means algorithm is the average of several tones that frequently appear in the image, and it is a good reference for harmonizing colors. After the extraction is completed, various methods of data visualization are used to display the information about colors in the picture in real time with D3.js and Vue’s responsiveness, and the color of the website can be changed according to different images in real time. In addition, this project encapsulated the algorithm steps of extraction process into npm packages, which can be quickly transplanted to Node.js or front-end of website.

Objective This study aimed to investigate the optimal strategy for myomectomy for removing giant uterine fibroids when necessary in women undergoing cesarean section. Methods This study was retrospective in design, and assessed outcomes in 26 patients who underwent myomectomy using a “base purse-string suture” during cesarean section. The operative duration, blood loss, uterine involution, and duration of postpartum lochia were analyzed. Results This suture was associated with a mean operative duration of 11.17 ± 5.36 minutes and the mean estimated blood loss was 11.15 ± 6.05 mL. The mean postpartum duration of lochia was 34.92 ± 7.55 days and there were no cases of postpartum hemorrhage. Uterine size returned to normal within 6 weeks of delivery, without any apparent defects or abnormalities in the uterine wall as shown by an ultrasonic examination. Conclusion This novel implementation of a base purse-string suture during cesarean myomectomy for removal of giant fibroids is a simple, safe, and effective intervention that should be considered for implementation in appropriate patients.

To date, data on postpartum management of preeclampsia including the optimal time for discharge are limited. The aims of this study were to investigate the appropriate time for discharge after delivery, and factors that could affect the time for discharge. Data on 210 preeclamptic women including the severity or time of onset and days in the ward after delivery were collected and analysed. In total, 167 (73%) patients were followed up for at least 16 months and none of them developed any complications after delivery. The mean days in the ward after delivery in preeclamptic women with vaginal delivery or with caesarean section was 3.4 or 5.8 days, respectively. After adjusting for delivery modes and parity, women with severe or early onset preeclampsia or preeclamptic women complicated with IUGR were in the ward longer than women with mild or late onset of preeclampsia or preeclamptic women without IUGR. In addition, women with severe preeclampsia or with IUGR delayed the time for blood pressure to return to normal range. Our descriptive data reported that preeclamptic women stayed in the ward for 4-6 days after delivery, dependent on the delivery modes. Preeclamptic women with severe or early onset form or complicated with IUGR delayed the improvement of clinical symptoms after delivery. We further found that women with severe preeclampsia, and preeclampsia complicated with IUGR delayed blood pressure returning to normal range. These pre-discharge checklists may help obstetricians and midwives decide when to discharge.

Photonic crystals (PCs) are periodic dielectric structures with photonic bandgaps and they can be used to control and manipulate photons effectively. Novel photonic crystal materials with tunable bandgaps can be prepared by changing the refractive index of the dielectric or lattice parameters under external stimuli, while using temperature to adjust the photonic band gap is a simple and convenient method. In this paper, silica PCs having different pseudo-gaps in the range of 450–750 nm were prepared with colloidal SiO2 spheres of different sizes. Thermo-sensitive PNIPAM hydrogel was then infiltrated into the PCs to obtain PNIPAM-PCs, whose pseudo-gap blue-shifted when the temperature was changed from 24 to 34 °C and exhibited good reversibility. The PCs with tunable bandgaps are significant for the development of integrated photonic devices, sensors, and in detection and other technologies.

Sewage irrigation has been widespread in the water shortage area of eastern China and inevitably tends to result in heavy metal accumulation in soils. A total of 148 surface soil samples from five land-use types were collected in Longkou, a typical sewage irrigation area of China, and As, Cd, Cu, Pb, and Zn concentrations were determined. The Nemerow index method and improved fuzzy comprehensive evaluation method were used to examine the pollution status of heavy metals. The potential ecological risk was evaluated by the Hakanson model by adjusting the assessment threshold, and its spatial distribution was interpolated using geostatistical techniques. As, Cd, Cu, Pb, and Zn accumulated in different amounts in the five land-use types. Urban industrial land and mining land were moderately polluted, irrigated land was slightly polluted, orchards were minimally polluted, and bare land was at a safe level of pollution. Cd exhibited high percentages of strong and severe levels of potential ecological risks. For Cd, irrigated land, orchard, and bare land mainly presented moderate risks, whereas urban industrial land and mining land mainly presented high risks. The comprehensive ecological risk of the five heavy metals was at a severe level for all tested land-use classes except for bare land.

The research of complex network is a hot spot in current scientific research, such as the Internet network, neural network and social network. Main contents of this paper are about visualization and structural analysis of the political network of the Northern Song Dynasty in China, to show and dig objective information about history of this period. Firstly, we use community-structure to measure complexity of relationship network, besides, we fit plots to find rules of degree distribution and its scale-free property, finally, we use small-world model to verify its small-world phenomenon.

Objectives Pre-eclampsia is a specific disease during gestation without a clear etiology. The lack of effective forecasting and prevention methods threatens the safety of maternal and infant lives. Study design We prospectively evaluated 356 pregnancies in their first trimester at the First Affiliated Hospital of Xi'an Jiaotong University and followed up the whole gestation. Main outcome measures Pre-eclampsia correlation parameters were subjected to dimensionality reduction analysis using the least absolute shrinkage and selection operator (LASSO) method. Results Of the 356 pregnancies, 25 developed pre-eclampsia during late gestation. Sonographic parameters of the placenta, uterine artery, and umbilical artery were calculated using LASSO regression analysis. Five factors (vascularization and blood flow index of the placenta, peak systolic velocity and peak systolic to end-diastolic artery ratio of the left uterine artery, and pulse index of the umbilical artery) were applied in a final nomogram model. The fitting curve was closely correlated with the actual situation, with a C-index of 0.877. Conclusions The nomogram described here could be used to predict the risk of pre-eclampsia in pregnant women and provide strong support for early intervention.

With the rapid development of film and television industry, it is necessary to find suitable stand-ins for actors, select appropriate actors for different periods of roles, and calculate the acting time of actors. The system detects the actor's face, extracts feature points, calculates face feature vectors, and then obtains the actor's identity information. Based on Dlib, this paper designed and implemented a film actor face recognition system, and designed experiments to verify the effectiveness of the face recognition system. The system has a high recognition rate for relatively clear faces, but the face recognition effect with occlusion needs to be improved.

This paper presents the modeling, optimization, and validation of a hollow plunger type solenoid for high speed On/Off valve. In the preliminary design, an accurate equivalent magnetic circuit model of the proposed solenoid is carried out, and the magnetic circuit is arranged in Kirchhoffs voltage law matrix form for the convenience of computer calculation. An iterative method is applied to obtain accurate permeability of the nonlinear magnetic material. To optimize the design parameters, a multiobjective optimization process is developed, and the multiobjective particle swarm optimization method is used to obtain the Pareto front of the desired objectives. An approved solution in Pareto front is selected by analytic hierarchy process and validated by the finite element analysis (FEA) method. A prototype based on the final optimized design is manufactured and tested. The experiment results verified the validation proposed design and optimization process.

Objective Enamel structure reflects ameloblast function. By studying the structure of the superficial enamel, information about ameloblast function toward the end of the secretory stage may be obtained. Design The superficial enamel in midcoronal areas of acid-etched facio-lingual sections from human third molars was studied in the scanning electron microscope (SEM). Results A great variation was observed in occurrence of prism-free enamel. Prism-free enamel dominated in 40% (mandibular) and 47% (maxillary) of observed areas and had a mean thickness of about 30 μm. Striations in the prism-free enamel had an interstriae distance of about 3.3–3.8 μm. The angle between prisms and enamel surface was about 60°, between prisms and Retzius lines about 45° and between Retzius lines and enamel surface about 15°. The distances between regularly occurring Retzius lines and between striations in the prism-free enamel both tended to decrease toward the enamel surface. Prisms could change direction as they approached the enamel surface, mostly in cervical direction. Where Retzius lines curved and converged occlusally, prisms tended to deviate in an occlusal direction. Conclusions Judged from the incremental lines and occurrence of prism-free enamel, ameloblasts slow down and tend to lose their Tomes’ process as they approach the end of secretion. The crystals of prism-free enamel belong to the same system as the interprism crystals of prismatic enamel. A method, based on the disposition of fine incremental lines, is suggested for evaluation of ameloblast dynamics in the last stage of enamel secretion.

A new type of semi-submersible drilling platform is designed. The numerical simulation software is used to analyze the heave response of the new platform in the frequency domain and time domain, and the semi-submersible drilling platforms with double-floating four-column structure and heave plate structure are compared with the new platform. This paper introduces the design principles of the new platform and the theoretical basis, mathematical model and boundary conditions during correlation analysis. The numerical simulation results show that the maximum and mean values of the heave response of the new platform are significantly reduced in the frequency domain analysis compared with the double-floating four-column and the heave-plate structure platform; and the new platform has a significant increase in the natural heaving period of the new platform, which can effectively prevent the occurrence of resonance. In the mooring time domain coupling analysis, the surge, sway and roll response of the new platform is small, and the heave response is greatly reduced. In the spectral analysis, the new platform has a smaller peak response and better wave frequency characteristics. The new platform has excellent anti-heave performance, reasonable structure and feasibility, and can provide reference for the design and selection of new generation semi-submersible drilling platform. Key words: drilling platform, semi-submersible drilling platform, heave prevention, frequency domain analysis, time domain analysis

With the sustained development of social networks, increasing attention has been paid to social recommender systems. Current studies usually focus on indirect factors such as the similarity between users, but multiple direct interactions, such as mentions, reposts, and comments, are seldom considered. This paper addresses direct connections between users in social recommender systems. We analyze direct interactions to investigate the connection strength between users, and then, user preferences and item characteristics can be better described. Based on the analysis of social influence between users and users’ influence over the whole social network, we propose a recommendation method with social influence, which makes full use of information among users in social networks and introduces the mechanisms of macroscopic and microscopic influences. Direct interactions between users are incorporated into a matrix factorization objective function. Real-world microblog data are applied to verify our model, and the results show that the proposed recommendation method outperforms other state-of-the-art recommendation algorithms.

Traditional text classification methods are based on statistics and feature selection. It does not perform well in processing large - scale corpus. In recent years, with the rapid development of deep learning and artificial neural networks, many scholars use them to solve text classification problems and achieve good results. Common text classification neural network models include textCNN, LSTM, and C-LSTM. Using a specific model can obtain more accurate features but ignore the context information. This paper proposes a C-LSTM with word embedding model to deal with this problem. Experiments show that the model proposed in this paper has great advantages in Chinese news text classification.

Nowadays, The network is developing rapidly and spreading all over the world, so a lot of data was generated, and we also felt the power of public opinion analysis. It can be said that every good or poorly-spoken movie will produce a wave of consensus for a period of time after release, and people are paying more and more attention to it. Our requirements for works and actors are becoming more and more strict. Therefore, whenever a good movie appears, the directors of various countries will remake, because there is already a mass foundation. This article mainly introduces the remake comparison between Chinese and foreign films and conducts a series of data visualization analysis, sorting, and comparison. In this paper, the data is visualized through analysis, and it is presented through three-dimensional word cloud, Nightingale rose diagram and force-Directed graph, so it is more convenient for observation. For the specific analysis of each film, this article has made a knowledge graph, which can be used to specifically view movies or characters.

In the thousands of years Chinese history, countless important figures have emerged. Some of their life experience, works and research results are data that can be processed by programs and visualized. China Biographical Database (CBDB) is a relational database on Chinese historical figures started by Harvard University. This paper using the above database, taking the literati of Song Dynasty as an example, constructed and visualized a social network based on the social relations and correspondence between the characters. PageRank algorithm is used to evaluate the importance of people in the social network.

Nowadays, complex networks are widely used in various fields. As a hosting platform for open source and proprietary software projects, GitHub is used by many Internet companies and programmers. With the popularity of the Internet and the rapid development of the Internet, the number of users has exploded, and GitHub is not only a code hosting platform, but also a social networking platform for Internet practitioners to obtain information and knowledge, and even make friends. The sheer volume of users and the number of code repositories have also made it increasingly difficult for users to find code libraries that match their interests and users who share their interests. In order to effectively solve the above problems, based on the theory of ternary closure relations in the theory of social relations, according to the HITS algorithm, the repositories that may be interested and the like-minded friends are respectively recommended to the users. Another aspect of the system is data visualization, where users can intuitively get recommendations.

Using the complex network theory to analyze the character social network in literatures provides a perspective in literary analysis. This paper proves that the character social networks of Harry Potter and The Lord of the Rings are small-world and scale-free and not only preferential attachment but also some specific plots affect the development of The Lord of the Rings trilogy. That means the social networks from two literatures obey the complex network theories that the social networks in real word follow. Hence, these above properties make the two fictional novels’ networks more like networks in the real world and make people accept them well.

Entity relationship extraction is an important research topic in the field of NLP, which is to extract the relationship between two entities from natural language texts. Research shows that deep neural networks are feasible in the task of entity relation extraction and are superior to traditional relation extraction methods. In this paper, we compare the performance of the BLSTM model and the Bidirectional LSTM model with Attention under the entity relationship extraction task. The comparison includes the different performance of the model after adding additional NLP features and modifying embedding size. Compared with the BLSTM model, the Bidirectional LSTM model with Attention can achieve similar accuracy under the condition of simpler and less preprocessing. In addition, the larger size of dimension of word embedding and additional features acquired by NLP tools can improve the accuracy of relationship extraction. This article provides a reference standard for engineers to select the optimal model under different conditions.

With the development of social networks, we are more interested in every aspect of the relationship. We conducted a visualization analysis of the papers published on two journals of Communication University of China in five years. In addition, we combine the academic social network of three art universities in Beijing based on CNKI and use complex network-related knowledge to analyze the collaboration of these universities. In this paper, we apply the knowledge of visualization tools and complex networks to analyze the authors who have high productivity, the keywords of these papers and the academic partnerships of the three universities. In experiments, each college is regarded as a social network model. From the perspective of academic social networks and collaborative networks, we extract some features of each of the three art universities and make some conclusions.

With the development of information technology, the application began to show two levels of differentiation. The one, based on high-performance processors, graphics cards, and hardware with high read/write speed hard drives, such as tens of gigabytes of games. The other one, a lightweight program similar to WeChat mini program, based on the mobile terminal, can be used without downloading and installing, and can realize the function of the original ecological APP. The WeChat mini program can also be used in the field of education. This paper introduces the development framework of WeChat mini program and a language education platform based on WeChat mini program. This language education platform provides students with language teaching resources, such as text reading audio and video, text analysis, and extended reading. The system framework of the language education platform and the implementation of key technologies are highlighted.

Technology will affect the learning experience in many ways in the next few years, Internet of Things (IOT) continues to confirm an important position in the development of the environment and the society of information and communication technology. Through the Internet support of things, institutions provide a richer learning experience, improve operational efficiency, real-time student performance. By obtaining practical insight, improve learning outcomes. This research aims to maximize how the benefits and higher education are to find the possibility of the Internet of things to reduce the risks associated with it. There is a need to make further efforts to release the full potential of IoT systems and technology. Therefore, proposes research on the impact of the Internet of Things in higher education, especially in universities. The Internet of Things stands out in many areas and, at any level, significantly changes the way colleges work and improves student learning. This has great potential for universities and other educational institutions. If prepared early, a wide range of leaders, staff, and students will ensure successful implementation. The development of the Internet of Things can lead to the development of local universities. Scholars, researchers, and students show the discovery and development of IoT systems, devices, applications, and services in unique locations. In the following year, will be provided by many scientific research institutes and companies to provide some evidence of the future of the Internet of Things in higher education. The Internet of Things, on the other hand, also poses a significant challenge to higher education.

With the deepening of convolutional neural networks, training a delicate network may be more difficult. This article explores which training strategies on a limited set of training data will make the network perform better, and demonstrated a terse hierarchy training strategy. We only need to do a small-scale data processing, add a priori class label for each piece based on the original dataset, the network will learn functions to classify the priori class and final class simultaneously and end to end. The knowledge learned by hierarchy A network part could guide the classification task at the hierarchy B network part. Experiments on CIFAR-10 show that network trained by this strategy will perform better and robust. Finally, the classification accuracy on the test set also has a certain improvement compared with the ordinary training strategy.

With the rapid development of neural network and face recognition, the Convolution Neural Networks(CNN) is more and more widely applied. Considering the shortcomings of traditional clustering methods, spectral clustering and self-organizing feature mapping neural network (SOM), a variety of new clustering methods based on deep learning are constantly created. In this paper, firstly a convolutional neural network is used in automatically feature extraction. After extracting features with CNN, S-SOM, an integrated algorithm of SOM and spectral clustering, is proposed to cluster face image data. Finally, to verify the effectiveness of the method, an experiment is employed. Meanwhile, we also analyze the effect of some related parameters of S-SOM on face clustering for further research.

The design of flight control actuation system is facing major challenge due to the development of more electrical aircraft. The task is to find the combinations of power sources, actuators and computers, which becomes more complex because of the new power sources and actuator types of more electrical aircraft. It is impossible to determine optimal architecture by traditional trial-and-error method within acceptable time. Therefore, the need for new methodology for actuation system architecture design emerges. This study proposes an intelligent design method which has steps of design space exploration of actuation system architectures by constraint satisfaction problem (CSP) method, safety assessment process to exclude unsafety solution, multi-objectives optimization to get Pareto optimal front and comprehensive decision for final architecture via analytic hierarchy process. And the design method is implemented in python and a software platform is developed. Furthermore, within the paper a case study for A350 flight control actuation system is presented to testify the application of this methodology. Compared to the traditional hydraulic architecture, the optimal architecture is more competitive in weight, power and cost. At the same time, the optimal architecture is found in less than 30 minutes among 1075 candidates, which greatly reduces the design cycle. This method deals with the problem in the design of flight control actuation system.

This paper describes the importance of film and TV production projects management, the management solutions of TV network projects are provided by using contemporary computer technology. The shooting notice management is a sub module of the big data intelligent drama script system. It focuses on realizing automation and intelligent shooting notice management through improving the traditional way of writing and announcing shooting notices by using Excel spreadsheets. Be able to automatically supplement the other information of the field according to the completed field and avoid repeatedly finding the fill. Automatically query and fill out various personnel information. Determining the repeat of scene and the completion level of filming. Automatically judging expiration shooting notices. Archiving shooting notices for subsequent inspection. The announcement and management of the shooting notice is greatly facilitated.

In recent years, the network dramas have been more and more popular, and many people have become the screenwriter and director. How to create a better script has become a hot topic. This paper introduces the play script management system and discusses the realistic significance of character’s biography and relationships for both screenwriters and directors. The system uses the online writing mode of the script to draw graphs of relationships and helps screenwriter and director better grasp the relationships among the characters and the plots of the story by visualization. Finally, the graph of relationships can not only help screenwriters create play scripts conveniently but also help directors understand dramas well.

Assessing the importance of a node in a network is often very important. It can show the core of a network and provide guidance to decision makers. This paper combines the relevant characteristics of social networks, and proposes to use related smoothing weight factors to balance the clustering coefficient of nodes in the network, and to measure the importance of nodes as an important index. Experiments show that the importance of the nodes evaluated by this method is closer to the real situation. The importance of all nodes in the entire network is more consistent with the power law distribution.

With the continuous development of Internet technology, there is a growing desire for greater breakthroughs in the field of artificial intelligence to promote social development and progress. However, artificial intelligence has developed slowly in the past due to the complexity of its theory and the constraints of the related software and hardware conditions. As a branch of the field of AI, deep learning can learn the essential features of data and promote the development of AI. Convolution neural network is a typical multi-layer supervised learning neural network, which is widely used in various fields, especially image processing and speech recognition. This paper first introduces the research significance of convolution neural network, and then introduces its structure. The following paper studies and analyzes the architecture of LeNet-5, and improves it. Finally, the Keras framework is used to carry out the experiment on the improved network structure.

With the exponential growth of network information resource, distributed Web Crawler was introduced for fetching massive web pages. However, when many websites designed a series of Anti-crawlers tactics to disturb Web Crawlers, it is our first imperative to deal with these strategies. We first elaborated on Web-Crawlers and Anti-Crawlers strategies. At the same time, this paper mainly proposed some effective measures to guarantee efficient web-crawling when faced with Anti-crawler tactics. Then we designed a experiment to analyze possible influencing factors. Finally, the future direction about distributed Web Crawels which runs on Hadoop platform was proposed by this paper.

The current script system management mainly by human to complete the production process time-consuming and labor-intensive. This article analyzes the present situation of script writing, expounds the importance of standardizing script, and studies the layered modeling method of SAAS software. Using the system point of view the division of functional modules, the design of the system workflow, the use of big data for script writing, and in Python3.5 + Flask0.11 + MySQL5.6 environment to achieve a full range of film and television drama management system. The system has finished writing from the script writer to improve the process of shooting shots. Practice has proved that the use of SAAS software model management system is reasonable, the cloud online writing script can greatly improve the efficiency of script writing.

As the Chinese film industry flourishes, it is of great significance to assess the influence of film and film participants. Based on the theory of complex networks, this paper studies the ranking of influence in the film-participant heterogeneous network. Participants may have multiple identities such as directors, screenwriters, and actors. Referring to the PageRank algorithm of the page ranking algorithm and combining the features of the film industry, a new ranking algorithm, MovieRank, is proposed. The core three rules are as follows: (1) If the movie rank is high, the ranking of the participating players is also high; and if the participants have a high ranking. It also has a high ranking in participating movies; (2) the rankings of films and participating players are influenced by their social attributes; (3) the movie contributes more to their high-position participants, and the participants contribute more to the movie that they play an important role in it. Experimenting with Chinese movie information as experimental data, it is found that the new algorithm MovieRank actually performs better than the original PageRank algorithm. At the same time, through the analysis of the experimental results, it is found that the cooperation between actors from Hong Kong and Taiwanese is very close in the Chinese movie network, and that the directors and screenwriters have higher stability and less change than the actors.

Most NLP tasks are based on large, well-organized corpus in general domain, while limited work has been done in specific domain due to the lack of qualified corpus and evaluation dataset. However domain-specific applications are widely needed nowadays. In this paper, we propose a fast and inexpensive, model-assisted method to train a high-quality distributional model from scattered, unconstructed web pages, which can capture knowledge from a specific domain. This approach does not require pre-organized corpus and much human help, and hence works on the specific domain which can’t afford the cost of artificially constructed corpus and complex training. We use Word2vec to assist in creating term set and evaluation dataset of embroidery domain. Next, we train a distributional model on filtered search results of term set, and conduct a task-specific tuning via two simple but practical evaluation metrics, word pairs similarity and in-domain terms’ coverage. Furthermore, our much-smaller models outperform the word embedding model trained on a large, general corpus in our task. In this work, we demonstrate the effectiveness of our method and hope it can serve as a reference for researchers who extract high-quality knowledge in specific domains.

Software complexity brings software developers and learners a series of challenges to face. Automatically analyzing large-scale software systems with complex network provides a new insight into software analysis, design, evolution, reuse, and iterative developing. Nowadays, extracting network models derived from software systems and making it easily comprehensible remains challengeable for software engineers. This paper focus on Python software. We propose a series of algorithms to extract python software networks, and a concept of visual information entropy to visualize network to an optimal statue by D3.js. Then we analyze python software networks in different perspectives by Pajek. A series experiments illustrate that software network can disclose the internal hidden associations to facilitate programmer and learner to understand the software complex structure and business logic through the simplified complexity. Finally we create a synthetic software tool integrated by above three functions, which can assist programmers to understand software macro structure and the hidden backbone associations.

with the continuous development of Internet technology, information overload is becoming more and more serious. It's getting harder to get useful information from the network. Although the search engine can help users find information they need from the vast amounts of information in a certain extent, but cannot completely solve the problem of information overload, when users cannot accurately describe the information they need, you need to recommend system to help users find valuable information for users. So recommender systems are becoming more and more important. The movie recommender system implemented in this paper is based on the traditional user-based collaborative filtering algorithm, and the user project scoring matrix is pre filled. At the same time, database technology of this system uses graph database which is good at dealing with complex relations. In data visualization, the degree of recommendation of a movie is expressed by the size of the node and the thickness of the edge, so as to improve the user experience.

With the continuous enrichment of film and television, the script management system came into being. At the same time, the arrival of the Web2.0 era has made people become stricter about the usability and interactive experience of the website. Well designed and well featured web sites can increase user experience and increase user retention, while poorly designed and slow acting sites make users feel bad and churn out a large number of users. Therefore, the front-end design and interaction of web applications is becoming more important. The first chapter introduces the background and significance of the development of art play library system, the second chapter introduces the three basic front-end technologies and the common framework, the third chapter introduces the overall structure of the system and the specific design and implementation of front end, the fourth chapter summarizes.

With the rapid development of the Internet, people have a higher demand for the performance experience of the Web site. To improve page utilization and user interaction experience, web page capacity becomes bigger and bigger and use a lot of JavaScript code and its framework technology in web pages which makes web pages more expressive and richer.[1] However, it also affects the resolution efficiency of the Web page in the browser, and directly delays the loading time of the Web page. To improve the user experience, web optimization becomes extremely necessary. Web site optimization is divided into front-end optimization and back-end optimization of two ways. We found that only 10%∼20% of the time was used to get HTML documents from the Web server to the front-end browsers and the 80%∼90% of the time was used in response to the front end[2]. Front-end optimization requires less time and resources, and modifying a small number of programs can quickly improve the speed of your site[3]. This article focuses on the front-end, from five aspects of the reduction of HTTP requests, page element optimization, Client caching optimization, DNS optimization and compression technology to propose some optimization methods to achieve the performance of the Web site to improve without changing the database and background code[4].

Tariff recovery risks of electrical users are always big problems for electric power company, and the user portrait technology can realize tariff recovery risks assessment and defense by analyzing user power consumption, payment and arrears data from electricity information acquisition system and marketing system. This paper studies the multi-source data fusion and clean technology to handle power consumption, payment and arrears data. On this basis, the electrical user label system is established, the scene design of tariff recovery risks assessment is realized, and the C4.5 algorithm is used to evaluate the risks of tariff recovery. The analysis results of test case show that the model and algorithm proposed in this paper have high availability and accuracy, and can provide the references for electric power company to reduce the risk of tariff recovery.

Introduction Preeclampsia is a pregnancy specific disorder and currently the only effective treatment is delivery of placenta. To control maternal and fetal complications, immediate delivery is well recommended, although studies have suggested that expectant management is possible for both severe and mild preeclampsia. A recent study also suggested that the most trigger for delivery in preeclampsia was difficult to control blood pressure. The indications for delivery within or after 48hours were largely dependent on obstetricians’ experiences. Objectives In this study we investigated whether there is any biomarker(s) or clinical parameter(s) that may affect the decision for either immediate delivery or expectant management. Methods Data from157 women with preeclampsia were collected from a referral university teaching hospital and data included clinical parameters and laboratory biomarkers. A retrospective analysis was performed. Results There were 122 cases (77%) who delivered within 48 hours because of either maternal or fetal conditions. Of 157 cases, 110 cases (70%) were early onset preeclampsia. In early onset preeclampsia, 79 cases (72%) delivered within 48 hours. There was no difference in clinical parameters between cases who delivered within 48 hours and who did not. There were no differences in ALT or ASL or ALP or Uric acid or Blood urea nitogen or creatinine or cystatin C or platelet counts between cases who delivered within 48 hours and who did not. In late onset preeclampsia (n = 47), 43 cases (91%) delivered within 48 hours. There were also no differences in both clinical parameters and liver or renal biomarkers between cases who delivered within 48 hours and who did not. Similar findings were also observed in severe and mild preeclampsia. Conclusions Our study demonstrated that there is currently no biomarker(s) for predicting immediate delivery or expectant management. These results were regardless of the severity or the time of onset of preeclampsia.

As a kind of new optical limiting materials, gold nanoparticles have optical limiting property owing to their optical nonlinearities induced by surface plasmon resonance (SPR). Gold nanorods (GNRs) possess transversal SPR absorption and tunable longitudinal SPR absorption in the visible and near-infrared region, so they can be used as potential optical limiting materials against tunable laser pulses. In this letter, GNRs were prepared using seed-mediated growth method and surface-modified by silica coating to obtain good dispersion in polydimethylsiloxane prepolymers. Then the silicone rubber films doped with GNRs were prepared after vulcanization, whose optical limiting property and optical nonlinearity were investigated. The silicone rubber samples doped with more GNRs were found to exhibit better optical limiting performance.