bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of 3D force perception system of surgical robots based on Fiber Bragg Grating
Authors: Wang Haodong , Wang Weidong , Du Zhijiang , Gao Yongzhuo
Published: February 2021

Surgical robots have been widely researched due to their features of accurate positioning, no jitter, high precision, and low error rate under certain tasks. However, it is generally relatively slow to the feedback generated by force, deformation, or sudden and arbitrary impact during operation. Besides, the feedback is always provided through a vision that cannot meet the actual operation requirements for doctors. The customized design and integration of force perception functions on different robots have become one of the research hotspots of surgical robotics-worldwide recently. A force perception sensor for surgical robots based on Fiber Bragg grating (FBG) is proposed in this paper. It can be used to measure three-dimensional force. The experimental parameters are utilized to calibrate the model through the least square method. A four DoFs experimental platform is constructed. The system errors of the sensor involved are evaluated. The effectiveness of the proposed algorithm can be proved by the experimental results
Design and application of accelerated life test of grating ruler based on step stress loading
Authors: Wang Jili , Li Qingyu , Zhu Xiaocui , Gao Cheng , Li Yi , Li Guofa , He Jialong
Published: February 2021

Combined with the actual project, a grating ruler accelerated life test device is designed, which can simulate the actual loads, including temperature, humidity and speed stress. An accelerated life test scheme based on stepped stress loading is proposed, and 6 grating rulers are tested based on time-censored test method. An accelerated life model based on Weibull distribution used for evaluating the lifetime is established. Related experimental techniques also can be typical application cases for innovative practical teaching.
Binocular intelligent following robot based on YOLO-LITE
Authors: Zheng Yuanyuan , Ge Jun
Published: February 2021

In order to solve the problem that the deep neural network model is large in scale, the calculation time is too long, and the real-time performance is severely limited when combined with embedded devices, so studied the intelligent follower robot system based on YOLO-LITE algorithm combined with Raspberry Pi 3B+. The system mainly includes camera processing, target detection and other modules. Obtained the internal and external parameters of the camera through calibration, and according to these parameters to correct the binocular camera. Recognized and located the target in each frame of image, calculated the distance from the camera to the target and the center location error, and driven the car to move. The experimental results show that the following car has excellent real-time performance, the average detection frame rate can reach 20Fps, and the average detection accuracy can reach more than 80%.
A model reference adaptive variable impedance control method for robot
Authors: Sun Xinchao , Zhao Lianyu , Liu Zhenzhong
Published: February 2021

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.
Parameter estimation and signal detection algorithm based on adaptive capture in non-cooperative communication
Authors: He Zilong , Sun Peng , Gong Kexian , Jiang Hua
Published: February 2021

Aiming at the problem that the frequency offset in the non-cooperative communication system causes the received signal spectrum to shift, which exceeds the passband of the matched filter and affects the subsequent demodulation, a parameter estimation and signal detection algorithm based on adaptive capture is proposed by this paper, which is more convenient for hardware implementation and consumes less resources. The algorithm is divided into three parts. Firstly, use the correlation value between the signal and the preamble sequence as the basis for frequency capture. Secondly, the frequency is accurately estimated based on the interpolation algorithm. Finally, the phase-locked loop structure is used to track the frequency according to the characteristics of the frequency gradually changing and the signal frequency offset is eliminated in the Digital Down Converter stage. It provides necessary conditions for accurate signal detection and phase estimation. The simulation results show that the algorithm has high estimation accuracy, wide esti-mation range and low complexity. It can also achieve better estimation accuracy and detection performance under low signal-to-noise ratio.
Wireless secure communication involving UAV: an overview of physical layer security
Authors: Li Jiawei , Cheng Ruixia , Zhu Junwen , Tian Yu , Zhang Yiwen
Published: February 2021

Unmanned aerial vehicle (UAV) is a flight device with power and energy based on computer program control, which has the characteristics of small size, light weight, high maneuverability and low cost. With its characteristics, UAV can play an important role in military and civil fields. However, due to the broadcast nature of wireless communication and inherent air-to-ground line-of-sight channel, UAV wireless communication system is more vulnerable to security threats. On the basis of traditional encryption technology, the secrecy capacity of the UAV communication system can be improved by introducing physical layer security. This article aims to study the security of the physical layer in the UAV communication system, and summarizes the latest research results on the safety communication involving UAVs on the physical layer, such as trajectory optimization, power allocation, user scheduling and cooperative UAVs. Further, some potential research directions and challenges in physical layer security of UAV system are discussed.
Application and development trend of unmanned aerial vehicle navigation technology
Authors: Bei Haohan , Liu Yubo , Li Wenhao , Huang Ying
Published: February 2021

UAV (Unmanned Aerial Vehicle) equipment is novel because of its remote automatic control characteristics, so navigation technology is particularly critical. It is necessary to study and apply UAV technology. This paper first analyzes the UAV navigation technology, then the different types of UAV navigation system, and finally looks forward to the development direction of UAV navigation system, hoping that through this study, we can have a general understanding of the application and development trend of UAV navigation technology, and hope that the relevant research can be further enriched and improved to guide practical application.
Analysis and research on wireless sensor networks
Authors: Zhang Huanan
Published: February 2021

With the development of sensor, computer and communication, the researcher attaches more and more attentions to the research of the wireless sensor networks because of its bright future in applications. This paper introduces the structure, characteristics of wireless sensor networks. Then, some key questions of the research field are discussed based on the basic principles. The end of paper discusses the application fields of wireless sensor networks and comes to a conclusion.
Research on distributed real-time synchronous motion control based on time-sensitive networking
Authors: Han Li , Yang Hong , Zhuo Lan , Zhang Chi
Published: February 2021

As a set of protocol standards defining time sensitive mechanism of Ethernet data transmission, Time sensitive network (TSN) can support transmission of isochronous data and non-periodic data in the same network . Distributed synchronous control plays an important role in large precision manufacturing scene. In this paper, a distributed real-time synchronous motion control scheme of two motors based on TSN is proposed, and the experiment shows that the two motors moving at high speed can still maintain phase synchronization under the scheduling mechanism of IEEE Std 802.1Qbv even if there is a large traffic load in the network.
Design and implementation of intelligent audio-video interaction and control platform based on distributed architecture
Authors: Huo Ruicai , Huang Wei , Liu Chao
Published: February 2021

The traditional audio-video interaction mainly adopts matrix centralized processing technology with limited transmission distance and expansion. In application mode, the function of remote signal sharing and multi-screen linkage cannot be realized. Aiming at the above problems, an audio-video interactive platform based on distributed architecture is designed in the background of multi-type audio-video network information interconnection. This platform takes the distributed architecture as the core and combines the audio-video codec technology to build the distributed display control system, which can realize the arbitrary display and control of signals from many places and scenes, and satisfy the rapid sharing and forwarding and integrated call of audio-video information through the permission setting.
Research on online teaching of college teachers under the background of education informatization
Authors: Dong Xiangli , Chen Xiaoyu
Published: February 2021

With the rapid development of information technology, educational informatization is the popularization and penetration of information technology. At present, online teaching has been implemented all over the world, which is a practical reform of educational informatization. This paper discusses the teaching concept, platform selection, study situation analysis, resource preparation, curriculum implementation, summary and reflection and other aspects of the curriculum under the background of educational informatization, so as to do a good job in the organization and management of online teaching under the background of educational informatization, and lay a foundation for the innovative practice of information-based leading educational concept and education mode.
Long-term recurrent convolutional network violent Behaviour recognition with attention mechanism
Authors: Liang Qiming , Li Yong , Yang Kaikai , Wang Xipeng , Li Zhi
Published: February 2021

Violent behavior recognition is an important direction of behavior recognition research. For traditional violent behavior recognition algorithms, there is too much background information when processing video information, which will cause greater interference in feature extraction, so the recognition accuracy is not high. Improved on the basis of effective recurrent convolutional network, a long-term recurrent convolutional network with attention mechanism is proposed. In the video preprocessing stage, a variety of attention mechanisms are introduced. In the feature extraction stage, the lightweight end-to-side neural network architecture GhostNet and convLSTM are selected to build a long-term recurrent convolutional network. The global average pooling and fully connected layer are used in the classification process. The combined approach realizes the classification of behaviours. The final results show that in the Hockey dataset, the algorithm in this paper has increased by 0.4% compared to LRCN, in the RWF-2000 dataset with more samples, it has increased by 10.5% compared to LRCN, and has increased by 1.75% compared to I3D, indicating that the algorithm in this paper can effectively suppress the background information. Interference, improve the performance of the algorithm.
Design and implementation of a distributed data acquisition function architecture based on DOA/Handle technology
Authors: Li Hongfei , Kong Yong , Wang Xueli
Published: February 2021

With the development of new generation Internet technology, Digital Object Architecture (DOA)/Handle system plays an important role in industrial system. The development and technical characteristics of DOA/Handle technology is studied in this paper. Then function architecture of distributed data acquisition system based on DOA/Handle technology is proposed. Through the detailed design of the functions and characteristics of the architecture, the core functions such as distributed data acquisition, distributed data management, data security sharing, and data security governance are realized in the industrial Internet environment. At the end of the paper, the typical scenarios of this architecture are summarized, and a prospect of the application prospect of DOA/Handle technology is introduced.
Study on internet of things architecture of substation online monitoring equipment
Authors: Zhang Hejun , Ge Dong , Yang Ning , Jia Pengfei , Li Lihua , Yang Yang
Published: February 2021

This paper introduces the structure of the existing on-line monitoring system, and analyzes the problems of poor compatibility and reliability of IED in different manufacturers. Then it gives two ways to transform the on-line monitoring system of substation equipment to the Internet of things, which is to retain IED and to remove IED. On this basis, this paper introduces the application scenarios of the on-line monitoring system of substation equipment connected to the Internet of things, including accurate perception of equipment status, accurate evaluation of equipment status and intelligent research and judgment of equipment status.
Research on complex integrated energy planning and operation optimization
Authors: Wen Zhou , Meng Liang , Yang Zhengfu , Liu Zhibin , Liu Yajing
Published: February 2021

The safe and stable operation of the energy system is the top priority of the world in the industrial field. The energy integrated system optimizes the system through multiple channels and cascade utilization of energy, which greatly improves the utilization rate of energy and provides a convenient way for the development of low-carbon society. In this paper, the composite integrated energy system model is constructed through the analysis of the structure of the integrated energy system. On this basis, a coordinated optimal scheduling method for integrated energy system with multiple energy stations is proposed, which takes the system operation economy as the goal. The case simulation results show that the coordinated operation of energy stations can greatly improve the stability and economy of the hybrid integrated energy system.
Research on pedestrian detection algorithm in driverless urban traffic environment
Authors: Liu Xinchao , Yan Ying , Gan Haiyun
Published: February 2021

Pedestrian detection in urban traffic environment is an important field of driverless vehicle research. Due to the variability of traffic flow, target detection algorithm cannot extract complete feature information, which brings great challenges to driverless pedestrian detection. Target detection algorithm YOLOv4 has excellent detection performance in object detection, but it is not perfect in identifying semi-blocked pedestrians. In this paper, the Spatial Pyramid Pooling was added in front of the third yolo detection head module of YOLOv4 to optimize the extraction of deep network features. Then, on the basis of optimizing the network, pruning strategy was adopted to simplify the target detection algorithm, which was called TidyYOLOv4.TidyYOLOv4 and YOLOv4 (network set input image size is 864×864) were compared on the self-made human head data set. Total BFLOPS decreased by 95.04% and Inference time decreased by 82.82%. The above experimental results show that the optimized TidyYOLOv4 algorithm is more suitable for driverless pedestrian detection in urban traffic environment.
Gesture recognition system based on CNN-IndRNN and OpenBCI
Authors: Wu Na , JIN Hao , Pei Xiachuan , Dong Shurong , Luo Jikui , Yan Ruijian , Feng Gang
Published: February 2021

Surface electromyography (sEMG), as a key technology of non-invasive muscle computer interface, is an important method of human-computer interaction. We proposed a CNN-IndRNN (Convolutional Neural Network-Independent Recurrent Neural Network) hybrid algorithm to analyse sEMG signals and classify hand gestures. Ninapro’s dataset of 10 volunteers was used to develop the model, and by using only one time-domain feature (root mean square of sEMG), an average accuracy of 87.43% on 18 gestures is achieved. The proposed algorithm obtains a state-of-the-art classification performance with a significantly reduced model. In order to verify the robustness of the CNN-IndRNN model, a compact real¬time recognition system was constructed. The system was based on open-source hardware (OpenBCI) and a custom Python-based software. Results show that the 10-subject rock-paper-scissors gesture recognition accuracy reaches 99.1%.
Cross domain meta-network for sketch face recognition
Authors: Shao Yuying , Cao Lin , Chen Changwu , Du Kangning
Published: February 2021

Because of the large modal difference between sketch image and optical image, and the problem that traditional deep learning methods are easy to overfit in the case of a small amount of training data, the Cross Domain Meta-Network for sketch face recognition method is proposed. This method first designs a meta-learning training strategy to solve the small sample problem, and then proposes entropy average loss and cross domain adaptive loss to reduce the modal difference between the sketch domain and the optical domain. The experimental results on UoM-SGFS and PRIP-VSGC sketch face data sets show that this method and other sketch face recognition methods.
Research on text summarization classification based on crowdfunding projects
Authors: Zhou Gang
Published: February 2021

In recent years, artificial intelligence technologies represented by deep learning and natural language processing have made huge breakthroughs and have begun to emerge in the field of crowdfunding project analysis. Natural language processing technology enables machines to understand and analyze the text of crowdfunding projects, and classify them based on the summary description of the project, which can help companies and individuals improve the project pass rate, so it has received widespread attention. However, most of the current researches are mostly applied to topic modeling of project texts. Few studies have proposed effective solutions for classification prediction based on abstracts of crowdfunding projects. Therefore, this paper proposes a sequence-enhanced capsule network model for this problem. Specifically, based on the work of the capsule network, we propose to connect BiGRU and CapsNet in order to achieve the effect of considering both the sequence semantic information and spatial location information of the text. We apply the proposed method to the kickstarter-NLP dataset, and the experimental results prove that our model has a good classification effect in this case.
Word embedding and text classification based on deep learning methods
Authors: Li Saihan , Gong Bing
Published: February 2021

Traditional manual text classification method has been unable to cope with the current huge amount of data volume. The improvement of deep learning technology also accelerates the technology of text classification. Based on this background, we presented different word embedding methods such as word2vec, doc2vec, tfidf and embedding layer. After word embedding, we demonstrated 8 deep learning models to classify the news text automatically and compare the accuracy of all the models, the model ‘2 layer GRU model with pretrained word2vec embeddings’ model got the highest accuracy. Automatic text classification can help people summary the text accurately and quickly from the mass of text information. No matter in the academic or in the industry area, it is a topic worth discussing.
Knowledge discovery in Chinese herbal medicine: a machine learning perspective
Authors: Liang Nan , Liang Qing , Ji Fenglei
Published: February 2021

Traditional Chinese Medicine (TCM) has attracted more and more attention due to its remarkable effects on treating diseases, and Chinese herbal medicine (CHM) is an important partition of TCM, rich in natural active ingredients. Researchers are trying multiple analytical methods to dig out more valuable information about CHM and reveal the principle of TCM. Machine learning is playing an important role in the studies. Knowledge discovery of CHM using machine learning mainly includes quality control of CHM, network pharmacology in CHM, and medical prescriptions composed by CHM, aiming to understand TCM better, provide more efficiency methods in the production of CHM and find novel treatment of disease not curable nowadays. In this paper, we summarized the basic idea of frequently used classification and clustering machine learning algorithms, introduced pre-processing algorithms commonly used to simplify and accelerate machine learning procedure, presented current status of machine learning algorithms’ applications in knowledge discovery of CHM, discussed challenges and future trends of machine learning’s application in CHM. It is believed that the paper provides a valuable insight for the starters trying to apply machine learning in the study of CHM and catch up the recent status of related researches.
Research on forage hyperspectral image recognition based on F-SVD and XGBoost
Authors: Zhao Xuanhe , Pan Xin , Ma Yubao , Yan Weihong
Published: February 2021

Aiming at the high time complexity and poor accuracy of traditional SVD in hyperspectral recognition. we proposed F-SVD, which introduces the latent factors(F) into the SVD decomposition strategy and uses the correlation between the latent variable and the original variable to improve the singular matrix. Firstly, we used F-SVD to reduce the dimension of visible-near infrared hyperspectral image, and consequently designed a forage recognition model based on XGBoost. When the test set sets 40%, the OA of F-SVD-XGBoost is 91.67%, which takes 0.601s. Compared with the traditional FA-XGBoost and SVD-XGBoost, OA increases 1.98% and 1.67%, and the time consumption decreases 1.369s and 0.522s, respectively. The results show that our model not only effectively extracts the essential features of forage hyperspectral and improves the accuracy of classification, but also has a faster processing speed, so that can efficiently and quickly realize the identification of forage hyperspectral images.
Scene classification of remote sensing image based on compound pruning
Authors: Jiang Fengbing , Li Fang , Yang Guoliang
Published: February 2021

Convolution neural network for remote sensing image scene classification consumes a lot of time and storage space to train, test and save the model. In this paper, firstly, elastic variables are defined for convolution layer filter, and combined with filter elasticity and batch normalization scaling factor, a compound pruning method of convolution neural network is proposed. Only the superparameter of pruning rate needs to be adjusted during training. in the process of training, the performance of the model can be improved by means of transfer learning. In this paper, algorithm tests are carried out on NWPU-RESISC45 remote sensing image data to verify the effectiveness of the proposed method. According to the experimental results, the proposed method can not only effectively reduce the number of model parameters and computation, but also ensure the accuracy of the algorithm in remote sensing image classification.
A minimum cross-entropy multi-thresholds segmentation algorithm based on improved WOA
Authors: Zhu Zhenkun , Sun Yuan
Published: February 2021

Minimum cross-entropy is widely used in image segmentation for its effectiveness. However, when the algorithm is applied to multi-threshold segmentation, there are some problems such as large amount of calculation, time-consuming and poor practicability due to exhaustive search for the optimal threshold. Therefore, in this paper, a hybrid whale optimization algorithm (IWOA) which incorporates whale optimization algorithm (WOA) and invasive weed optimization (IWO) is proposed and the minimum cross-entropy is used as the fitness function of optimization algorithm to select the optimal threshold. It is established that IWOA algorithm is able to select the optimal threshold in more accuracy and segment high quality image than other algorithms.
Risk prediction of early diabetes mellitus based on combination model
Authors: Tang Haoxin , Zhang Yi , Xiang Baolin , Liu Mingkun , Hu Junming , Liu Cheng
Published: February 2021

Aiming at the current low pre-diabetes detection rate, this paper proposes a PSO-SVM model to assist doctors in identifying the risk of patients with pre-diabetes. The paper uses the Support Vector Machine as the verification algorithm, takes the radial basis kernel as the kernel function, uses the adaptive Particle Swarm Optimization algorithm to optimize the penalty factor and kernel parameters of the Support Vector Machine, and establishes a PSO-SVM model, finally compares the model with Neural Network, Logistic Regression, and Naive Bayes model, and use Sensitivity, Specificity indicators and ROC curve to evaluate model performance. Empirical analysis proves that the combined model proposed in this paper can effectively identify the risk of patients with prediabetes.
A self-sovereign identity management scheme using smart contracts
Authors: Niu Jianlin , Ren Zhiyu
Published: February 2021

The existing self-sovereign identity management schemes have some problems, such as weak availability and security risks. To solve these problems, we proposed a cross-domain self-sovereign identity management scheme using smart contracts. This scheme takes into account the entire lifecycle of identity, especially including the cross-domain use and recovery. To preserve the privacy data of users on the blockchain, we proposed a data storage method of anchoring on blockchain. Finally, we implemented this scheme using the Solidity programming language for smart contract. This scheme has been experimentally verified to be capable of maintaining the expenditure of resources under control and having good usability. Compared with other self-sovereign identity management schemes, this scheme has better performance in terms of controllability, security and portability.
Design of web vulnerability scanner based on go language
Authors: Chen Jingxia , Chen Xiuling , Yu Bo
Published: February 2021

The vulnerability scanner designed in this paper completed the collection of information and scanning of vulnerability, including six parts: input assets, asset collection, vulnerability profile, plug-in upload, single case detection and report display. The framework of vue realized the front end that included six pages, and the framework of gin realized the back end. The interface completed the separation of the front and back end. The database using MySQL designed seven tables. This scanner can avoid tedious and repetitive work, it can realize automatic scanning and testing of network vulnerabilities.
Research on software credibility algorithm based on deep convolutional sparse coding
Authors: Xu Zhaosheng
Published: February 2021

Based on the author's research time, this paper studies the software credibility algorithm based on deep convolutional sparse coding. Firstly, it summarizes the convolutional sparse coding and trust classification system, and then constructs the algorithm from two aspects: factor processing based on deep convolution neural network and trust classification based on sparse representation.
Research on recycling channel selection of closed-loop supply chain considering consumer preference and fairness concern
Authors: Sun Jiayi , Chen Wei , Yang Shuangfei , Yan Yingluo
Published: February 2021

This paper studied the influence of consumer preference and fairness concern on recycling and pricing decisions of closed-loop supply chain (CLSC) under different recycling channels. The results show that consumers' fairness concern cannot affect the manufacturer's decisions and profit; consumers' fairness concern is benefit to consumers, and it can improve the recycle rate. The channel selection strategy is depend on consumers' fairness attitude.
Innovation network characteristics and equipment manufacturing industry upgrading: based on the mediating effect of innovation ability
Authors: Li Yue , Guo Bobo , Sun Xiaoyan , Li Xiaoli
Published: February 2021

In order to strengthen the guiding and supporting role of the innovation-driven development strategy, and to promote the structural optimization and efficiency improvement of the equipment manufacturing industry, this paper analyzes the structure, function and characteristics of the regional equipment manufacturing industry innovation network, and proposes a conceptual model and theoretical hypothesis that the innovation network based on the mediating effect of innovation capability affects the upgrade effect of the regional equipment manufacturing industry. Through a questionnaire survey of 219 enterprises in my country’s equipment manufacturing industry and structural equation model verification, it measures the action path and industrial upgrading law of innovation network influencing regional equipment manufacturing industry upgrading.
Research on incentive mechanism of benefit distribution in collaborative innovation of industry alliance
Authors: Li Yue , Huang Jiepeng , Guo Hang , Wang Zhuo
Published: February 2021

In order to improve the performance of alliance collaborative innovation and stimulate members' willingness and behavior to participate in collaborative innovation, this paper puts forward the incentive mechanism of benefit distribution. This paper divides the needs of members to participate in collaborative innovation into two stages: "risk avoidance-return on investment". Firstly, an effective benefit distribution model is established by using Logistic function. Then, by building a game model, we can get the best effort. The results show that building the benefit distribution model of alliance collaborative innovation according to the different needs of alliance members can fully stimulate members to participate in collaborative innovation and improve the performance of alliance collaborative innovation.
Technical standards opportunity identification of Chinese manufacturing industry
Authors: Wang Xueyuan , Sun Meixia
Published: February 2021

Based on Standard Literature Library of CNKI, opportunities for technical standards development are identified. The initial keywords of technical standard are determined based on LDA model, whose similar keywords are further confirmed by Word2Vec, and then combining LDA model with Word2Vec the representative keywords of technical standard of manufacturing industry are determined. According to confirmed representative keywords, the core technical standards of manufacturing industry are identified by using the center degree and “M-core” method. The dimensions of the frequency square matrix are reduced by using the PCA module of Python, and the scatter plot is brawn, the technology gaps are identified by combing the consideration on the core technical standards, and then the future development opportunities are confirmed based on the map of patent technology effect matrix.
Case analysis on indirect economic benefits of industrial building construction
Authors: Qi Yuan , Kang Ning
Published: February 2021

In this paper, the economic benefits of prefabricated buildings which are not directly reflected in the economic returns of investors are called indirect economic benefits. Based on the literature mining of the indirect economic relationship of a large number of prefabricated buildings, this paper constructs an analysis framework of indirect environment and social and economic benefits. Through BIM modeling software, the three prefabricated building models are modified into traditional building models. The indirect economic benefits of the project are calculated by using the index system. The functional relationship between the indirect economic benefits of prefabricated buildings and the assembly rate is established by using the SSPS statistical data processing software, which more intuitively shows the law of the indirect economic benefits of prefabricated buildings with the assembly rate It shows the impact of prefabricated building on environment and society, which is of great significance for the harmony between prefabricated building and society and environment, and the healthy and sustainable development of construction industry.
A new alternative Hotelling’s T2 control chart using MEWMA to monitor minimal mean deviation
Authors: Wang Kunyun , Li Qianqian , Li Guangdong
Published: February 2021

Hotelling T2 control chart not only reflects the correla-tions between different quality characteristics but also has good efficiency on monitoring multivariate quality characteristics in production process. A new alternative control chart was constructed after the original products data are processed by using multivariate exponentially weighted moving average for cumulating failure effects because T2 control chart is ineffective on detecting minimal mean deviations. Exemplified by bivariate quality characteristics, we compared the monitoring effects of Hotelling T2 control chart and new control chart which is called as T2MEWMA control chart. Paper showed the improved T2MEWMA control chart has smaller average run length than Hotelling T2 control chart on monitoring minimal mean deviation and that also studied the relationships between T2MEWMA control chart’s forgetting factor, sample sizes N and type II error. It indicated the smaller forgetting factor is more sensitive to minimal mean value deviation and that average run length tended to become bigger gradually along with increase of sample sizes N when production process is out of control.
Identification of abuse of market power by power generation enterprises
Authors: Du Fengqing , Liu Zhe , Tao Hongyuan , Hua Huichun , Zhang Lifeng
Published: February 2021

At present, the reform of the power market is progressing steadily. To ensure the efficient and healthy operation of the power market, there is an urgent need to strengthen the credit supervision of the electricity market entities. Identifying violations of power generation companies' abuse of market power is a key task in the credit supervision of power market entities. Traditional power generation companies' abuse of market power identification mainly relies on expert decision-making. However, with the increase in market transaction volume, expert decision-making cannot meet the needs of work, and an intelligent identification method suitable for computer analysis must be proposed. This paper first proposes a quantitative definition of abuse of market power, and then takes into account the specific data characteristics of the electricity market, and proposes a method of identifying violations of power generation companies based on improved cost-sensitive support vector machines. Finally, the power market simulation experiment data set labeled by the definition method is used for training and testing. The test results show that the abuse of market power by power generation companies can be quickly and accurately identified, which verifies the effectiveness of the proposed method.
Value perception impact and countermeasures analysis of new energy vehicle purchase behavior based on consumer level user review big data mining
Authors: Lv Yihang , Liu Qin
Published: February 2021

The development of new energy vehicles is inseparable from the drive of consumers. Therefore, to explore the influencing factors of purchase behavior from the consumer's personal level is helpful for businesses to adopt corresponding sales strategies and the government to adopt relevant policies. Based on the individual level of consumers, this paper constructs a new energy vehicle purchase behavior prediction model from the review text, and explores the predictive effect of consumer personal factors on the purchase behavior of new energy vehicles. First of all, this paper proposes a quantitative method of consumer individual level factors, which combines word-of-mouth reviews with statistics. In this method, word2vec is used to train word vectors in word-of-mouth corpus to mine initial keywords, and core keywords are selected through statistical correlation analysis. Secondly, based on the core keywords of consumers' personal level, the gbdt model is constructed to predict the purchase behavior of new energy vehicles. The results show that the probability of correctly predicting consumers' purchase behavior is more than 72%.
Distribution of male and female procedural and surgical specialists in Australia
Authors: Turtle Elizabeth , Vnuk Anna , Isaac Vivian
Published: February 2021

Objective This study examined the distribution of the sexes across Australian medical procedural specialties in 2017 and investigated the proportion of currently registered female specialists based on their graduation date from 1969 to 2008. Methods A cross-sectional analysis of current Australian procedural and surgical specialists registered with the Australian Health Practitioner Registration Agency as of January 2017 was undertaken. Participants included 4851 surgical specialists (594 female, 4257 male) and 14 948 specialists in specialties with high levels of procedural clinical work (4418 female, 10 530 male). The number of male and female specialists across each procedural specialty and the medical school graduation date of current female specialists were analysed. Results In 2017, female fellows represented only one in 10 surgeons and three in 10 procedural specialists. All surgical specialties are underrepresented by female specialists. Cardiology is least represented by female practitioners (one in 10), followed by intensive care and ophthalmology (two in 10). General surgery, otolaryngology and urology saw more female specialists with graduation dates between 1983 and 2003 compared with the other surgical specialties. Conclusion The number of female practitioners registered as specialists is increasing, but they continue to be underrepresented at specialist level across many procedural and surgical specialties. What is known about the topic? Although the number of female students entering medical school now outnumbers that of males, female practitioners remain underrepresented at the specialist level. What does this paper add? Surgery continues to be underrepresented by female specialists, but general surgery, otolaryngology and urology have shown increases in females reaching specialist level. All procedural specialties have shown increasing numbers of female practitioners reaching the specialist level. What are the implications for practitioners? All surgical specialties and nearly all procedural specialties need to adopt evidence-based practices to make their training programs both appealing and sustainable to female trainees in order to work towards achieving gender parity.
Producing health information in consultation with health workers and the hepatitis B-affected communities is worthwhile
Authors: Bennett Gabrielle , Richmond Jacqueline , Thompson Alexander J.
Published: February 2021

Globally, approximately 257 million people are living with chronic hepatitis B. Many people are undiagnosed, have low health literacy and experience barriers to engaging in care. In Australia, there is a lack of culturally and linguistically appropriate resources to support people living with the infection to increase their understanding and build their capacity. This innovative practice paper describes the process of developing a culturally and linguistically appropriate resource using the principles of participatory action research. The hepatitis B story was designed to facilitate discussion between healthcare workers and consumers, and to increase the knowledge and understanding of both. Consultation with consumers and a broad range of health services contributed to the quality of and demand for the resource. A case study tells the story of ‘Thuy’. This case study demonstrates the practical application of the resource and describes the positive affect its use had on Thuy and her family. Increasing our understandings of how people experience chronic hepatitis B is crucial to improving health information, testing and engagement in care. Producing health information with consumers is a worthwhile process to increase consumers’ health literacy and improve service delivery.
Lowland tapir exposure to pesticides and metals in the Brazilian Cerrado
Authors: Medici Emília P. , Fernandes-Santos Renata Carolina , Testa-José Caroline , Godinho Antonio Francisco , Brand Anne-Fleur
Published: February 2021

Abstract Context The Cerrado is a Global Biodiversity Hotspot as well as Brazil’s main frontier for large-scale agriculture and livestock production, making it one of the most threatened biomes in the country. Brazil is one of the biggest consumers of pesticides in the world and allows the use of chemicals that are banned in many other countries due to their adverse health effects in a wide range of species, including humans. Aims This study aimed to assess pesticide and metal exposure of the lowland tapir – a threatened, large herbivorous mammal – to support future studies of the role of these chemicals in tapir health, survivorship, and population viability. Methods Foot pad, proboscis, stomach contents, liver, bone, and nail samples were obtained from tapir carcasses found along highways (n = 87). (i) Atomic absorption spectrophotometry (AAS) was used to detect metals in bone, nail and liver tissue; (ii) gas chromatography – nitrogen phosphorous detector (GC-NPD) to detect organophosphates in liver and skin; and (iii) high performance liquid chromatography – ultraviolet (HPLC-UV) to detect pyrethroids and carbamates in stomach contents. Key results Two carbamates (aldicarb and carbaryl), three organophosphates (diazinon, malathion, and mevinphos), two pyrethroids (deltamethrin and permethrin), and two toxic metals (cadmium and lead) were detected in different tapir tissue samples, some at concentrations high enough to cause adverse health effects. In 90% of roadkill tapirs that were subjected to a full post-mortem examination (n = 25), macroscopic alterations of liver and/or kidney tissue were observed. Conclusions This study provides the first report to date of the detection of pesticides and metals in lowland tapirs. Implications Some of the reported pesticide concentrations exceed environmental safety thresholds. Consequently, results from this study raise concerns over potential adverse health effects in tapirs that could lead to population level impacts, thus requiring further investigation.
How quoll-ified are northern and spotted-tailed quoll detection dogs?
Authors: Jamieson La Toya J. , Hancock Amanda L. , Baxter Greg S. , Murray Peter J.
Published: February 2021

Abstract Context Wildlife detection dogs have been used globally in environmental monitoring. However, their effectiveness in the Australian context has been only minimally researched. Increased understanding of detection dog accuracy and efficacy is required for their inclusion in survey guidelines used by proponents of referred actions potentially impacting Australia’s threatened mammals. Evaluation of new methods is also important for advancing population monitoring, particularly for threatened species. Aims To determine the efficacy of wildlife detection dogs as a survey tool for low-density, cryptic species, using northern (Dasyurus hallucatus) and spotted-tailed (D. maculatus) quolls as subjects. We compared detection dogs, human search effort and camera trapping results, in simulated accuracy and efficacy trials, and field surveys. Methods Two wildlife detection dogs’ scores for sensitivity (ability to identify a target species scat) and specificity (ability to distinguish target from non-target species scats) were calculated during accuracy trials. The dogs were tested using 288 samples, of which 32 were targets, where northern and spotted-tailed quoll scat were the targets in separate trials. Survey efficacy was determined following completing 12 simulated surveys (6 per target species) involving a single, randomly placed scat sample in a 1–1.5 ha search area. During the northern quoll simulated surveys the dogs’ survey efficacy was compared with that of a human surveyor. The dogs also undertook field surveys for both northern and spotted-tailed quolls, in conjunction with camera trapping for comparison. Key results During accuracy trials the dogs had an average sensitivity and specificity respectively of 100% and 98.4% for northern quoll, and 100% and 98% for spotted-tailed quoll. Their average search time in efficacy trials for northern quoll was 11.07 min (significantly faster than the human surveyor), and 2.98 min for spotted-tailed quoll in the 1–1.5 ha search areas. During field surveys, northern quoll scats were detected at sites where camera trapping failed to determine their presence. No spotted-tailed quoll scat was detected by the dogs during field surveys. Conclusions Trained and experienced detection dogs can work very accurately and efficiently, which is vital to their field success. Detection dogs are therefore capable of detecting evidence of species presence where alternative methods may be unsuccessful. Implications Our study supports the future use of highly trained detection dogs for wildlife surveys and monitoring in Australia. Our results demonstrate that detection dogs can be highly accurate and are a beneficial stand-alone or complimentary method.
“Abeg na! we write so our comments can be posted!”
Authors: Unuabonah Foluke Olayinka , Oyebola Folajimi , Gut Ulrike
Published: February 2021

This paper examines three borrowed pragmatic markers from Nigerian Pidgin into Nigerian English,abeg, sefandna, with a view to exploring their meanings, frequencies, spelling adaptability, syntactic positions, collocational patterns and discourse-pragmatic functions in Nigerian English. The data which were extracted from the International Corpus of English-Nigeria and the Nigerian component of the corpus of Global Web-based English were analysed quantitatively and qualitatively, using the theory of pragmatic borrowing. The results indicate that the three pragmatic markers differ distinctly in their frequency across text types, syntactic position, the range of pragmatic meanings, the number of spelling variants and their collocations:abegis used as a mitigation marker which can also function as an emphasis marker,sefis an emphasis marker but has additive and dismissive functions, whilenais used purely as an emphasis pragmatic marker. The study shows the influence of Nigerian Pidgin on Nigerian English.
Child language acquisition and sociolinguistic variation
Authors: Smith Jennifer
Published: February 2021

The majority of studies conducted on first language acquisition concentrates on the acquisition of standard varieties, where deterministic, or invariant, forms are the focus of research. At the same time, research in sociolinguistics has shown that language is full of variable forms which are governed by a series of interacting social and linguistic constraints on variant choice. Such variation is interesting from the perspective of acquisition because of the apparent challenge it presents to a young child: learning a language is alreadyhighly complexbut what happens when variation is added to the mix? The initial chapters in this volume focus on the analysis of variation in the speech of young children in a number of different languages worldwide and across different contexts of use in both production and perception. In doing so, they provide an excellent contribution to questions surrounding the acquisition of vernacular norms in the initial stages of acquisition and in later life.