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PurposeCreativity is vital for the innovation and survival of organizations. The present study aimed to explore the impact of entrepreneurial leadership on team creativity through team psychological safety and knowledge sharing. Social learning theory (SLT) was used to explore the relationships in this study.Design/methodology/approachData were collected from the manufacturing sector of Pakistan, and a sample composed of 70 team leaders and 378 team members was used.FindingsThe results showed the positive influence of entrepreneurial leadership on team creativity. Furthermore, team psychological safety and knowledge sharing mediated the relationship between entrepreneurial leadership and team creativity. Finally, team psychological safety and knowledge sharing sequentially mediated the relationship between entrepreneurial leadership and team creativity.Research limitations/implicationsA small sample size and cross-sectional research design may hinder the generalizability of the findings. The findings suggest that leaders should practice entrepreneurial leadership principles to develop team creativity. Organizations should provide training and development programmes for their leaders and employees to learn the importance of entrepreneurial behaviors and how to explore and exploit entrepreneurial opportunities.Originality/valueThis study extends the leadership and creativity literature by exploring the role of entrepreneurial leadership in developing team creativity. Furthermore, this study was conducted in established organizations to explore entrepreneurial leadership's influence on team creativity. In contrast, earlier scholars recognized entrepreneurial leadership as a leadership style of entrepreneurs and thus examined its implications in new ventures or small and medium enterprises.

This article investigates the attitude tracking control problem for a rigid spacecraft without angular velocity feedback, in which external disturbances, parametric uncertainties, and input saturation are considered. Initially, an angular velocity observer is developed incorporated with adaptive technique, which could tackle the unmeasurable angular velocity and system uncertainties simultaneously. By introducing adaptive updating law into the proposed observer, the synchronized uncertainties are handled such that robustness of the observer is enhanced, even in the presence of external disturbances. Further, for solving the input constraints problem, command filter and backstepping method are utilized; thus, a bounded attitude tracking control law is derived. Finally, the attitude tracking performance is evaluated by numerical examples.

To solve the problem of lacking geometric and topological information for conventional 3D point clouds registration algorithm, this paper proposed a novel 3D point clouds registration algorithm based on improved extended Gaussian image. The proposed registration algorithm first estimates a normal vector and curvature of every point by least square method. Then, according to the normal vector to calculate extended Gaussian image (EGI) and complex extended Gaussian image (CEGI). By using the calculated EGI/CEGI and spherical harmonic function, a correlated function is constructed to calculate 3D rotation space to obtain initial positions coarse registration result. At last, by using the Fourier transform to estimate translation vector and coarse registration, the iterative closest point algorithm is used to obtain the fine registration results. Experiments on three groups of different 3D point clouds are performed to validate the proposed registration algorithm. Experimental results have shown that the proposed algorithm has good performances on registration of different forms of 3D point clouds. The robustness and efficiency of our proposed algorithm can effectively solve the problem that it is difficult to find the target or the homonymic feature points in the registration process of 3D point clouds.

According to the Common GNSS Generic Time Transfer Standard Version2E (CGGTTS_V2E) developed by the GNSS (Global Navigation Satellite System) Working Group of the International Consultative Committee for Time and Frequency (CCTF), the data processing software is developed by using the pseudorange signal measured by the GNSS receiver, which is used to generate the CGGTTS files in the standard format, and its reliability is verified. The results show that, compared with the CGGTTS files generated by sbf2cggtts software, the offset between the GNSS system time and local time scale calculated by the same GPS and BDS satellite observations in the same epoch is identical, and the difference with the absolute value of the difference less than 0.5 ns accounts for 96% and 94% of the total, respectively. Taking Chinese standard time UTC(NTSC) (Coordinated Universal Time (National Time Service Center)) as the reference time scale, the data processing software is used to process the observations of the B1I and B3I dual-frequency ionospheric combination of BeiDou-2 and BeiDou-3 satellites, and generate the CGGTTS files in the standard format, and the performance of BeiDou system time is evaluated by analyzing the parameter of offset between the GNSS system time and local time scale. The results show that, compared with BeiDou-2, the internal precision of BeiDou-3 system time is increased by about 28%, and the frequency stability of medium and long-term is obviously better than BeiDou-2 after one day.

PurposeEntrepreneurial leadership has been developed from the existing leadership and entrepreneurship literature as a new leadership theory to meet the dynamic changes in the 21st century. Accordingly, the main purpose of this study is to investigate the effect of entrepreneurial leadership on employee creativity and the mediating influence of psychological empowerment and psychological safety, with the lens of social learning theory.Design/methodology/approachData were collected from the manufacturing sector of Pakistan with a sample size of 280 dyads of managers and employees. A two-step analytical strategy was adopted through which the measurement model was validated by using confirmatory factor analysis and structural equation modeling to estimate the structural model to fit the data. The bootstrapping method was applied for testing the mediation analysis.FindingsThe current study found that entrepreneurial leadership positively related to employee creativity. Moreover, it was found that psychological empowerment and psychological safety mediated the relationships between entrepreneurial leadership and employee creativity.Research limitations/implicationsThe findings of the current study provide guidance to managers and businessmen on how to develop the creative skills of employees by adopting entrepreneurial leadership behaviors. Besides, this study enriches the literature by exploring the performance mechanism of entrepreneurial leadership to develop employee creativity.Originality/valueThe current study is considered the first to examine the role of entrepreneurial leaders in stimulating employee creativity through psychological safety and psychological empowerment. As such, it enriches the creativity literature by exploring the employee creativity mechanism through entrepreneurial leadership and validating the importance of psychological factors in the development of creativity compared to previous studies, which have focused primarily on the concept or scale development of entrepreneurial leadership. Furthermore, it provides several theoretical and managerial implications along with future opportunities.

The internal consumption and production structure of the service industry that now dominates the Chinese economy needs to be optimized. Introducing supply-side structural reform to improve the in-d...

In this paper, we report that the far field diffraction pattern of a specimen which is consisting of an array of spherical particles enables the determination of the size and refractive index of each scattering sphere. The far field 2D angular resolved diffraction pattern, of which the diffraction angle reaches tens of degrees, is measured by CCD camera. On one hand, the diffraction pattern distorted obviously at high angles is corrected for curvature. The amplitude and phase images of the specimen are reconstructed by difference map iterative algorithm. The size of each scattering microsphere is calculated according to its pixel numbers and the image resolution. On the other hand, the diffraction pattern without curvature correction is used to reconstruct the amplitude and phase images of the specimen by HIO algorithm. The amplitude and phase images of each sphere are isolated. Then the angular resolved scattering signals of the individual spheres are quantitatively indicated by Fourier transform. Finally, the refractive index of each microsphere is obtained by fitting the experimental results using Mie theory.

Selective laser melting at various laser inclination angles was used to prepare Hastelloy X alloy specimens. The morphology, fracture, tensile strength, stress, and strain of Hastelloy X alloy specimens were studied using optical microscopy, scanning electron microscopy, and a tensile tester. The temperature field of the manufacturing process was analyzed based on finite element analysis, and the internal relationship between the temperature field and the process was constructed in terms of cooling speed. The results show that the temperature field is a dynamic process with a high cooling rate; the average cooling rate reaches 3.23 × 106 °C × s−1. The greater the inclination angle, the greater the thermal gradient, resulting in higher cooling rates. Due to the cross-influence of grain refinement at high cooling rates and residual stress, the tensile strength and yield strength of Hastelloy X alloy showed first increasing and then decreasing trends with respect to inclination angle. However, at an inclination angle of 30°, the voids and crack defects of Hastelloy X alloy fractures were reduced, and the tensile strength and yield strength reached 881.38 and 701.60 MPa, respectively. At this angle, the mechanical properties were excellent and met the requirements of the aviation industry.

Renewable energy is the development direction of power system of China. To further boost the development of China’s power industry, the digital transformation of the power is underway. This study examined the panorama of digital transformation of power system of China. Through analyzing the effect of digital transformation on renewable energy utilization, three business applications were identified: short-term prediction of renewable energy output, renewable energy equipment maintenance, and intelligent power-grid maintenance. The implementation process and effect of the three business applications are analyzed by using the data covering 2012–2019 from three aspects: the short-term prediction accuracy of renewable energy output, the reduction of the fault restoration mean time for renewable energy equipment to the industry average, the reduction ratio of the mean time of power grid fault restoration to the industry average, and the rate of renewable energy utilization. The results indicated that digital transformation boosted the efficiency of renewable energy utilization. Based on the findings, the following suggestions are proposed for improving the digital transformation of the power industry and thereby promoting renewable energy utilization: 1) the first is to emphasize the scale efficiency of digital application in the early stage of digitization and renewable energy development, and emphasize the technical performance of a digital application in the middle and later stages of development; 2) the second is to enhance data accuracy, such as meteorological data, historical data for renewable energy power stations, power equipment status data, and strengthen the application of algorithms and computational models; 3) the third suggestion is to improve the overall digitalization rate of the power industry.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

To reduce shaking of a vibration screed system (VSS) and improve the paving performance of an asphalt paver (AP), the root-mean-square (RMS) acceleration responses at points on the front and rear screed floors are analyzed via an experimental method. A 3D nonlinear dynamic model of the VSS is also built to evaluate the influence of the dynamic parameters of the VSS on the compression efficiency, paving quality, and working stability of the AP based on the objective functions of the vertical, pitching, and rolling RMS values at the centre of gravity of the screed. The angular deviations, $alpha$ and $gamma$, of the tamper are then controlled to improve the paving performance. The research results show that the excitation frequency, $f_{t}$, and both angular deviations, $alpha$ and $gamma$, of the tamper strongly affect the paving performance. The compression efficiency is quickly enhanced, while both paving quality and working stability are significantly reduced with increasing the excitation frequency $f_{t}$ and reducing the angular deviations. $alpha$ and $gamma$. and vice versa. Additionally, the screed shaking and paving performance of the AP are remarkably improved by control of the angular deviations, $alpha$ and $gamma$, under different working conditions.

For multi-color yarn-dyed fabrics which are cross-woven by yarns with different colors, the different colors cannot be directly measured by a traditional spectrophotometer because it can only obtain the average color of solid-color sample in the limited aperture. In this paper, a novel method for color segmentation and extraction for multi-color yarn-woven fabrics based on a Hyperspectral Imaging System (HIS) was proposed. First, the multi-color yarn-woven fabric images were acquired with the HIS. Then a space transformation based on Fréchet distance was used to transform the pre-processed hyperspectral fabric images into gray images, and then an improved watershed algorithm was used to segment the transformed gray images into different color regions. Finally, to solve the problems of over-segmentation with the improved watershed algorithm, an improved k-means clustering algorithm was adopted to merge the over-segmented color regions. The experimental results on four multi-color yarn-woven fabrics showed that the color segmentation accuracy of the proposed method outperformed the ordinary k-means, Fuzzy C-means (FCM), and Density peak cluster (DPC) algorithms on evaluation indexes of compactness (CP) and separation (SP), and the execution efficiency was improved by at least 55%. Furthermore, the color difference between the proposed method and the spectrophotometric measurements ranged from 0.60 to 0.88 CMC (2:1) (Color Measurement Committee) units, which almost satisfied the accuracy of color measurement.

The optimization of park green space landscape pattern is of great significance to the construction of an ecological civilized city. Through a case study of the national garden city – Mianyang City, Sichuan Province – and with the help of analysis softwares such as ENVI and ArcMap, the public green space landscape pattern in the central urban area has been quantitatively analyzed in terms of Normalized Vegetation Difference Index (NVDI) and landscape pattern index, and corresponding optimization strategies have been proposed. It is found out that: 1. NVDI in Fucheng District presents a growing trend from the perspective of spatial distribution characteristics, although the balance of NVDI increased significantly. 2. Urban park green space is highly fragmented under city scale, and the park green space landscape is featured by small patches and medium-sized and large scale coexisting. 3. Spatial distribution of park green space landscape types in Fucheng District is badly imbalanced and landscape structures are unreasonable 4. Calculation and analysis of landscape pattern index show the small patches in Fucheng District are highly spatially heterogeneous and fragmented with a large patch shape index. This paper aims to provide reference for the planning and construction of an urban park green space landscape.

Aiming at the method of indoor calibration of a transmission visibility meter has not been established; a new calibration method by measuring the luminous transmittance of attenuator piece was established. Based on the high homogeneity stability lighting and the multi-spectral transmittance analysis technique, a calibration system of the transmission visibility meter by measuring the luminous transmittance of the attenuator piece was designed. The experiment was designed by measuring the luminous transmittance with the range of 0.2–99.2% of a series of calibrated attenuators and the visibility in the range of 40 m to 20 km, then the luminous transmittance errors, the meteorological optical range (MOR) errors and their uncertainty were obtained. The result showed that the luminous transmission error was within ±0.18%, which satisfied the calibration requirement for transmission visibility meter transmittance; the MOR calibration error met the accuracy requirements of the International Civil Aviation Organisation for visibility measurement; this work will have guiding significance in establishing a calibration procedure for the transmission visibility meter.

Wave energy exploitation is getting more attention from researchers Many types of devices that can convert wave energy into electricity have been designed. At present, engineers and scientists are researching how to make a simple and efficient wave energy capture device. This paper explored the rim-driven generator. In a rim-driven wave energy generator, the rotor blade could harvest kinetic energy of wave motions. The blade is inside of the generator and the gap between the stator and the rotor is water-filled. This structure could improve the compactness and reliability of the wave energy generator. The paper studied the influence of the arrangement of the blade and the structure of the generator on the performance of the generator. Analytical models were designed to obtain generator’s performance. The result could optimize the design of generator and improve the energy conversion efficiency.

With the development of remote sensing, a large amount of high-spatial-resolution (HSR) images is available, which makes refined land-cover mapping possible. However, the details of ground objects in HSR images are complex, especially in edges, therefore brings new challenges in land-cover classification. Existing deep learning method views it as a semantic segmentation task based on the fully convolutional networks (FCN), taking no account of complex details recognition. In this paper, we tackle this problem by proposing a point representation network (PointNet) for HSR land-cover classification Specifically, the uncertain point selection is designed for finding the most uncertain details at the end of ResNet encoder. According to these points, the coarse and fine features in the encoder are fused, followed by a multilayer perceptron (MLP). Different from convolutional sampling, the MLP focuses on the recognition of uncertain points, which modifies the network optimization during training. During the prediction, the coarse features are successively upsampled with the points refining, improving the performance on land-cover details recognition. Experimental results on a Nanjing land-cover dataset demonstrate that the PointNet outperforms the state-of-the-art methods.

In this paper, a computer-aided diagnosis (CAD) method based on self-supervised learning was proposed for helicobacter pylori (HP) infection classification. The proposed method is composed of an encoder and a prediction head. The encoder can be trained by using self-supervised learning and contrastive loss. After obtaining the trained encoder, the prediction head can be trained by using the small medical image dataset. To evaluate the performance of the proposed method, some medical images are collected for testing. According to experimental results, the F1-score rates of the CAD system based on VGGNet-16 are 0.89 and 0.9 for HP+ and HP- images, respectively. The results show that the proposed method composed of VGGNet-16 and a multi-layer neural network can distinguish HP+ images from HP- images well. Compared with ResNet-50 and InceptionV3, VGGNet-16 can achieve a better classification performance. The experimental results show that VGG-16 can extract useful features from endoscopic images for HP infection classification via self-supervised contrastive learning.

OCD (Optical Critical Dimension) is a sufficiently accurate and reliable metrology used in semiconductor industry. Understanding how to setup a proper library will not only help to give the measurements that is needed, it will also prevent unnecessary downtime to the wafer fab factory. In this paper, a multiple layer stack, Poly-ONO-Poly, was seen performing according to the usual baseline until there was a downward shift seen on an OCD parameter SWA (Side Wall Angle). We took some readings off the suspected processes, ETCH trim time and tool’s dependency and LITHO’s resist thickness correlation to try to understand more on their effects on SWA. However, there is little correlation seen from them to the effects on SWA. Simulations were then done to show the spectra’s relationship to the changes in the parameters: SWA, CD, HEIGHT. SWA was seen to have the least sensitivity to raw spectra versus CD and HEIGHT. This led to the suspicion that there was some changes in the spectra that caused the change in SWA, and thus leading us off the ETCH and LITHO into diffusion that could have changed the stack during ONO formation. It was thus found that ONO thickness has shifted. Once the thickness was tuned back to its original intent, SWA followed suit to recover to its measured units. This shows us that there’s a need to fully understand the library/spectra involved when using OCD.

Background: Desmoplastic fibroma (DF) is a rare, benign tumor. The most common sites are the long tubular bones and mandible. Although generally considered a benign tumor, it can invade surrounding tissues and has a high rate of local recurrence after incomplete surgical excision. However, there is currently no stantard treatment. Here we present a novel case of DF in the left 6th rib in a 35-year-old man.Case presentation: The man presented to our center with left chest pain and swelling. Enhanced computed tomography (CT) showed a 4.5×2.0 cm mass on the left 6th rib with pathological fracture. Wide resection was performed. Open biopsy revealed that the tumor destroyed medullary cavity and partially broke through the bone cortex. Pathologically, the tumor was composed of spindle-shaped cells arranging in a woven pattern on a backgroung of abundant collagenous fiber. No β-catenin were detected. Based on the pathological and radiological findings, a final diagnosis of DF was made. No postoperative adjuvant treatments were administerded. Fortunately, there was no evidence of recurrence 22 months after surgery. Conclusions: DF originated from rib is a kind of extremly rare benign tumor but locally aggressive and show unique biologically features. Wide resection or total resection can effectively reduce the risk of local recurrence when compared with curettage. Rarity of the tumor favors documentation in literature.

The imbalance may lead to an adverse effect over the airspace network for scheduled air traffic system, which is a major concern to air traffic controller. This paper focuses on the problems related to coordinating departure flight within the airspace network to meet the constraints so as to produce regular traffic flow. Particularly, we propose a method to generate regular flights on the basis of max-plus model. Firstly, the system model is built. Then, a regular schedule is obtained based on the optimization algorithm. In this way, the control method can be derived for the air transportation network by incorporating system parameters and traffic flow data, The air traffic flow control policy has the potential to be applied to large-scale airspace networks in real-time operations.

Background: The protective role of green tea against cancer is still unknown.Objectives: To investigate the association between green tea consumption and esophageal cancer risk through meta-analysi...

The cloud radio access network (C-RAN) with mobile edge computing (MEC) structure which consists of a baseband unit (BBU) pool integrating with an MEC server and several remote radio heads (RRHs) beside the mobile terminals can help users with computational resource-intensive tasks and bring extra profits to network operator at the same time. This paper presents a novel task-aware C-RAN with MEC structure and formulates a profit maximization problem by jointly optimizing offloading strategy, radio and computational resources allocation under the constraints of offloading latency, fronthaul capacity along with limited bandwidth and computational resource. To solve this NP-hard optimization problem in a distributed and efficient way, we propose a spectrum efficiency (SE)-based joint optimization for offloading and resource allocation (SJOORA) scheme which decomposes the original problem into two sub-problems. A SE-based offloading strategy is proposed with confirmed resource allocation, and on the other hand, bandwidth and computational resource allocation problem is solved by using a Lagrangian multiplier method with predetermined offloading strategy. Finally, by solving these two sub-problems iteratively, a suboptimal solution is obtained for the original problem. The simulation results show that the proposed SJOORA scheme can effectively increase the profit of network operator with relative lower complexity.

INTRODUCTION The aim of this study was to explore the prevalence and predictors of cesarean scar defect (CSD) at 6 weeks postpartum in Shanghai, China. MATERIAL AND METHODS Women scheduled to receive a cesarean section (CS) were recruited from a university hospital. Surgery-related factors, pregnancy complications, routine examinations, perioperative medications, and physical signs were collected and transvaginal ultrasonography was performed to assess the presence of a CSD at 6 weeks postpartum. Multivariate logistic regression was applied to identify the predictors for CSD. RESULTS A CSD was detected in 223 of 514 women (43.4%, 95% CI 39.1%-47.7%) by transvaginal ultrasonography. Of women with normal temperature (T

Particleboards are widely used in the artificial board market, which can be constructed from a variety of raw materials and require small amounts of energy to be produced. In the particleboard production process, forming machines play an important role as the key equipment for achieving continuous production. In recent years, airflow forming machines have received increasing attention in particleboard production lines because of their strong separation ability and low price. However, the internal flow field is complex and difficult to control, which affects the surface quality and strength of the particleboard. The most pressing technical difficulty is controlling the flow field characteristics of the airflow paver. At present, the research on this subject is conducted primarily through repeated experiments, which entail long research periods and high processing costs. To reduce human and financial costs, in this study, Computational Fluid Dynamics (CFD) is employed to investigate the flow field and the gas-solid two-phase flow field coupled with particle movement of an airflow forming machine. The accuracy of the calculation model is verified by comparing characteristic point velocities obtained from experimental analysis and a simulation. The simulation results show that in practical production, the frequency of a negative pressure fan should be greater than 27 Hz. It is necessary to set the shoulder properly, and the slab smoothness can be improved by moving the shoulder back on the premise of meeting the strength requirements of the box. The distance between the shoulders of the box body should be less than 2570 mm, and particles with uniform diameter should be added to the paving box to reduce the turbulence effect, improve the quality of particle forming and provide actual particleboard production with a solid theoretical foundation.

Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

Achieving excellent quality printing of graphene oxide is highly demanded in superior performance device fabrication. Laser-induced forward transfer (LIFT) is a promising method for printing low viscosity liquids, which alleviates the restrictions regarding the rheological properties and the size of the particles suspended in the printing ink. However, the investigation of GO printing is very limited, and this procedure still suffers from low quality and reproducibility. Herein, an effective approach of printing reproducible and well-defined GO droplets through LIFT was presented. Laser ablation was used to create a few microns deep cavity on the microscope glass, which led to the superhydrophilicity of the ablated surface and helped in the formation of a uniform GO film. Subsequently, droplet printing using LIFT method with the modified donor substrate and various pulse energies was investigated. Droplets with a relatively circular shape and limited debris were successfully printed. The droplet diameter grew linearly from 40 to 165 μm with the increasing pulse energy from 4 to 11 μJ. Finally, LIFT of GO with the cleaned donor substrate was studied, and the repetitive printing results showed the feasibility of reusing the modified donor substrate.

This study investigates the relationship between both-hand input area and time delay before effective inputs, the relationship between button size and hit accuracy, as well as the influence of button size on users’ perception of the ease of button touching despite the size of the touchable area. The lower-left-hand area with x coordinates between 28 mm to 36 mm is the optimal input area for left the hand and the lower-right-hand area with x coordinates between 30 mm to 40 mm is the most ideal input area for the right hand. The input area with a time delay of 400 ms on the right is slightly larger than the one on the left. When the button with a diameter of 9 mm is placed in the lower-right-hand corner with a x coordinate between 20 mm–50 mm, we can expect a hit rate of 90%. Users’ perception of how easy a button is to touch is mainly related to the button size instead of the size of the touchable area. These findings provide a reference for designing buttons in landscape-orientation MMO mobile games and quantitative suggestions for the sizes of touch input area and button in landscape-orientation MMO mobile games. Based on these insights, game producers and designers can create better user experience for MMO mobile games’ battle interface.

Conventional seismic reservoir prediction usually faces problems such as low signal-to-noise ratio, low resolution, and long research period. It is difficult to efficiently implement fine reservoir prediction, especially for thin reservoir. Therefore, machine learning technology is applied to high-precision reservoir prediction, with high-dimensional, non-linear characterization of reservoir characteristics through multiple seismic attributes. Based on seismic, well logging, geology and other datas of M oilfield in South China Sea, they are collected and preprocessed firstly. The preferred sesmic attributes and logging datas are encoded and structured as label datas. Taking the geological model as the unified output carrier, different types of datas are outputed based on the same dimension, so as to obtain a large number of machine learning samples. The samples are divided into training sets and validation sets for cross validation training. The comparison and error analysis of various machine learning algorithms are carried out. The optimal algorithm is used to establish the reservoir prediction model and the blind wells are used for verification. The results show that the ensemble algorithm has an advantage over the single machine learning algorithm in reservoir prediction, and the XGBoost model has the best prediction accuracy and stability among the several mainstream machine learning algorithms. Its reservoir prediction rate with the wells is more than 85%, and the vertical resolution reaches 2–3 m. The accuracy of Random Forest is lower than XGBoost model, but higher than Decision Tree and Support Vector Machine. Compared with conventional reservoir prediction methods, the machine learning method is faster and more efficient in calculation, better coincidence with the actual drilling data, and less dependent on the experience of researchers. The machine learning method improves the accuracy of thin reservoir prediction in M oilfield and provides a direction for oilfield exploration in later stage, which offers a new technical means for reservoir prediction in other oilfields.

Water is our essential resource, urban water supply, and drainage is the lifeblood of the city, its design and construction directly affect the operation and development of the city. With the development of urbanization, the existing problems in the design of municipal water supply and drainage systems are becoming more and more prominent. The application of the concept of sponge city greatly improves the design quality of municipal road water supply and drainage systems and lays the foundation for the sustainable development of the city.

Exopolysaccharides (EPSs) possess many bioactivities such as immune regulation, antioxidant, anti-tumor and modulation of intestinal microbial balance but their direct effect on inflammatory bowel disease (IBD) response has not been studied. The purpose of this study was to evaluate the anti-inflammatory effect of EPS produced by L. plantarum YW11 administered at different dosages in IBD mouse model induced with 5% dextran sulphate sodium (DSS). The DSS-induced colitis, accompanied by body weight loss, reduction of colon coefficient and histological colon injury was considerably ameliorated in mice fed the EPS (10 mg/kg). The middle dose of the EPS (25 mg/kg) could effectively recover the intestinal microbial diversity and increase the abundance of Roseburia, Ruminococcus and Blautia with increased content of butyric acid. Moreover, EPS also reduced the production of pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, IFN-γ, IL-12 and IL-18) and enhanced the anti-inflammatory cytokine IL-10. This study showed that EPS might help in modulation of gut microbiota and improve the immunity of the host to reduce the risk of IBD symptoms.

Traditional mobile communication network plan depends on simple data analysis and manual judgment, which is easily influenced by the personal experience and ability. To fully use the data and avoid manual error, intelligent plan becomes the trend. The paper proposes an intelligent mobile communication network plan method with insight big data. With call record, personal position and measurement report data, machine learning method is adopted to design the network. From the collected data, it can be summarized into 7 dimensional insight big data, including service coverage rate, communication capacity, business income, complaint hot topics, user distribution and occupancy rate. With the final target, mobile sensitive algorithm, LSTM based call traffic prediction and suppressed traffic recognition, accurate site planning, XGBoost based 5G user profile and other methods are adopted. It has been successfully applied in the 5G communication network plan of Shandong Unicom. 2466 5G stations have been established and the efficiency has been increased significantly.

With its lightweight structure and acceptable accuracy loss, MobileNet-SSD has been widely used in embedded system. The defect of multi-scale detection structure in the original SSD algorithm makes it insufficient in detecting small targets. In this paper, SKBlock structure is used to adaptively expand the receptive field of SSD shallow feature map to improve its detection ability for small targets. Experiments show that it can effectively improve the target detection ability, and does not increase the computational burden.

The operating environment of offshore wind power is harsh, and the random fluctuation of wind power is large, which makes power devices easy to fail. Fault tolerant control is an effective means to improve the reliability and availability of wind power converters. Taking an open circuit fault of a parallel converter as an example, this paper analyzes the fault operation mechanism of a parallel wind power converter in detail. On this basis, a fault-tolerant control strategy based on negative-sequence current compensation is proposed, and a non-faulty converter module is used to compensate the negative-sequence current of the faulty converter module. When the system power is less than or equal to 0.5 pu, the maximum output power of the converter is realized under the condition of ensuring the current balance of the grid-connected side; when the system power is greater than 0.5pu, the negative sequence of the grid-side is controlled to minimum under the condition of outputting the maximum power. Finally, the correctness and feasibility of the control strategy theory are verified by simulation.

Energy feedback system (EFS) has been widely used in urban rail, whereas its duty classes are rarely considered. To calculate the load process of EFS accurately, an algorithm taking duty class into consideration is proposed and verified. Two indexes are proposed to evaluate the energy-saving performance effect and economic benefit, including daily feedback energy and feedback ratio, and an optimal capacity design scheme of EFSs based on the proposed indexes is presented. A subway case is studied, and the optimized scheme is found. The daily feedback energy is 11152.92 kWh, and the system-level feedback ratio is 5.70, which is greater than the highest one of pre-selected schemes by 0.60.

An IoT (Internet of Things) system is a multi-machine system composed of a master controller and multiple slave controllers. In some automatic control systems such as petroleum and electric power, the main control uses UART (Universal Asynchronous Receiver/Transmitter) to communicate with the slave controller. However, the communication rate of the UART interface is limited and the delay time is long, which affects the system’s response to external events. In order to improve the response speed of the system to external events, this paper designs a multi-machine system based on SPI (Serial Peripheral Interface) interface. First, we use the SPI interface of the microcontroller to build a communication network. The circuit structure of the system is simple, and the hardware resource consumption is low. Then, for the problem that the SPI interface cannot detect communication errors and there is no response mechanism, we designed a communication protocol to make up for these defects from the software level. Finally, we use STM32F429IGT6 as the master controller and STM32F103C8T6 as the slave controller to build the experimental platform. The experimental results show that the system is safe and reliable, and can meet the requirements of real-time applications.

Objective: To compare the effect of a hysteroscopic niche resection with a Levenorgestrel-releasing intrauterine device (LNG-IUD) on postmenstrual spotting in women with a symptomatic niche in the uterine cesarean scar. Design: Single center, prospective cohort study. Setting: A hospital affiliated to a medical college in China. Population: Women diagnosed with a niche by MRI scan and postmenstrual spotting of at least two days. Method: Women were allocated to two groups based on the shared medical decision-making approach, and were followed up for 1 year after treatment. Main outcome measures: The primary outcome was effectiveness in reducing postmenstrual spotting. Secondary outcomes were menstrual characteristics, direct medical costs, complications and side effects. Results: Effectiveness of LNG-IUD was significantly higher than a hysteroscopic niche resection during the first year, based on linear mixed models (P=0.009), and the effectiveness increased overtime within 1 year. Postmenstrual spotting reduced statistically in both groups. More slight side effects, although not statistically significant, were reported in LNG-LUD group at obviously lower direct medical costs. Conclusion：LNG-IUD is more effective in the treatment of postmenstrual spotting from the 6th month onwards than a hysteroscopic niche resection in women with a symptomatic niche at lower direct costs. Key Words：Niche, uterine cesarean scar, hysteroscopy niche resection, LNG-IUD, postmenstrual spotting, amenorrhea Tweetable abstract: LNG-IUD is more effective in the treatment of postmenstrual spotting related to a niche than hysteroscopic niche resection.

The emergence of distributed real-time database provides a convenient and unified data storage method for ship monitoring and control software. Based on the distributed real-time database developed by ourselves, aiming at the application demand of ship monitoring and control system to distributed real-time database, combining the self defined protocol with the distributed real-time database, this paper proposes a data synchronization method of the distributed real-time database. It effectively solves the problem of data synchronization of distributed real-time database, and constructs a fully peer-to-peer distributed real-time database.

In recent years, radio frequency identification (RFID) technology has attracted wide attention because of its characteristics of fast reading information and high storage capacity. Aiming at the problems of short recognition distance and low recognition rate, this paper designs a simple radio frequency card reader with C51 as the control chip. The radio frequency chip U2270B is combined with the Manchester coding to modulate and demodulate the signal, so that the RFID card reader can realizes contactless recognition. After experiments, the designed RFID reader can increase the distance to more than 90mm, which is more than 8.13% of the current distance.

GIFT is a new lightweight block cipher with smaller area and higher efficiency, which is very suitable for the Internet of Things (IoT) devices with constrained resources. The power analysis attack is an efficient method to extract the key from the cryptographic equipment. However, it is not easy to reveal the key by means of power analysis attack, when the cipher is implemented by hardware. In this article, we present the method of power analysis attack against GIFT. Firstly, we implemented GIFT on FPGA using the SAKURA-G board. Then, we explored the impact of point of interest (POI) on power analysis attack. We proposed the method of power analysis attack against the diffusion layer of GIFT. The experimental results show that the result of power analysis attack is affected by POI, and the key can be recovered when POI is registered. We can reveal the key using correlation power analysis, when targeting the diffusion layer of GIFT.

In order to solve the problem of low positioning accuracy and danger to the workers in traditional testing method of the Aerial Blast Point, we decided to build a model of locating the Point. By using the peak overpressure information of incident shock wave and Newton’s Method, we can solve the problem. Finally, positioning accuracy of the model can be verified through experiments. And it is proved to be feasible and practical to achieve high positioning accuracy in the new testing method of the Aerial Blast Point.

In this paper, combined with depth information and rich texture information in RGB video sequences, a multi-stream behavior recognition algorithm is proposed. The algorithm uses DenseNet as the main network to obtain color texture information, optical flow information and depth information, and uses them as input to the information flow network. Then use LSTMs for feature fusion and behavior classification. Through experiments, the recognition rate on the UTD-MHAD data set of the public action recognition library is 93.88%, which is significantly better than similar algorithms.