bluuebunny/crossref_metadata_2025 huggingface dataset:
Modified particle swarm optimization algorithm and its application in neural network
Authors: Xu Qiulin
Published: November 2020

Intelligent optimization algorithm solves the optimization problem by simulating some natural phenomena and provides a new method for optimization theory. As a swarm based stochastic optimization algorithm, particle swarm optimization (PSO) is widely used in various optimization problems due to its simple model and fast convergence speed. However, particle swarm optimization algorithm still has some problems to be solved, such as premature phenomenon and balancing global exploration and local exploitation capabilities. In order to solve these problems, a nonlinear inertial weights and adaptive position updating strategy based on chaos is proposed. The algorithm can effectively avoid premature convergence of particle swarm, and has better convergence and faster convergence rate. In this paper, the modified particle swarm optimization algorithm(MPSO) is used as a learning algorithm for neural network optimization, and the MPSO is used to optimize the connection weights and thresholds of the neural network. In this paper, three sets of standard classification data sets are used for testing. Experimental results and statistical analysis verify the good performance of the MSPO.
Research and Implementation of Machine Programming Based on Agile Thinking
Authors: Zhou Jinlian , Chang Huili , Xie Tao
Published: November 2020

With the rapid development of information, communication, network and computer, the field, depth and breadth of computer applications are also rapidly expanding. The traditional production methods, development techniques and development tools of software development are no longer suitable for the system software development with larger and larger scale and more complex structure. Facing the fast changing demand, how to develop software quickly and quickly has become an urgent problem. This paper takes the agile development methodology as the support point and the core idea, puts forward to adopt the agile development thought as the core, take IDE as the tool, take machine programming as the core technology, carries on the research to the agile thinking machine programming technology, carries on the development in Visual Studio, finally, realizes carries on the programming with the machine.
An Improved Grey-Wolf Optimization Algorithm Based on Circle Map
Authors: Wang Yongliang , Wang Ting , Dong Shuxian , Yao Chen
Published: November 2020

To overcome the shortcomings of grey wolf optimization algorithm (GWO) such as being easy to fall into the local optimum, and the slow convergence rate in the later stage, an adaptive weighted grey-wolf optimization algorithm based on the Circle map is proposed. Firstly, in this algorithm, Circle chaotic map, which enhances the diversity of the initialization population, is introduced into the initialization of population, therefore, the search space can be searched more thoroughly; Secondly, trigonometric function and the beta distribution are introduced in the convergence factor ‘a’ and the population position update formula, which improve the convergence speed in the later period of the algorithm; Finally, the simulation experiments on the four common test functions on CEC2017 show that under the same experimental conditions, the improved grey wolf optimization algorithm improves the solution accuracy and convergence speed significantly, and its performance is obviously better than other smart optimization algorithms and other improved GWO algorithms.
Evaluation of the screening and diagnosis model for the lung adenocarcinoma protein function module based on WGCNA and machine learning
Authors: Liang Jiankun , Zhai Fei , Min Junting , Xiang Rongwu , Xiao Shen , Liang Luhua
Published: November 2020

This paper aims to identify lung adenocarcinoma biomarkers through WGCNA and machine learning and construct a clinical diagnosis model for lung adenocarcinoma. We used the lung cancer protein expression data from the CPTAC database to conduct differentiation analysis, built a WGCNA network of lung adenocarcinoma samples and a WGCNA network of lung tumor samples and normal samples, and assessed the overlapped module of these two networks using machine learning. GO and KEGG abundance analysis was conducted to find proteins related to lung tumor, a correlation network for proteins in the overlapped module was created to mine the target protein biomarkers, and machine learning was used to create and analyze a screening model. Therewere2317 differentially expressed proteins obtained from 213 lung tumor samples from the CPTAC database; the lung tumor network and the lung tumor para-carcinoma tissue joint network had two overlapping modules; through PPI network optimization, we mined 11 protein biomarkers related to lung adenocarcinoma. Through verification based on data outside TCGA, we found that the lung adenocarcinoma diagnosis model constructed based on the 11 protein biomarkers had high accuracy and stability, showing clinical and biological significance.
Transfer learning for the design of a digital twins-based automatic relay production line
Authors: Yang Rui , Mo Qinglong , Huang Zijuan , Zhang Yu
Published: November 2020

At present, the world is experiencing a new round of scientific and technological revolution and industrial transformation. It has entered a historic intersection with the transformation and upgrading of the manufacturing industry, bringing new opportunities to the industry. Digital twins are considered as an effective way to realize the interaction and integration of the manufacturing physical world and the information world, attracting great attention from the relevant academic and business sectors at home and abroad. Specifically, they use virtual models and simulation technology to design industrial production lines and predict the future operation of equipment, which contributes to the efficient operation of intelligent production processes. Nonetheless, the key learning problem of accurate modeling should be solved, so as to adapt to the random and dynamic changes in industrial equipment design. Transfer learning, an innovative learning paradigm in machine learning, is developed to solve challenging learning problems with only a few or no labelled samples in the target field.
The hybridized Harris hawk optimization and slime mould algorithm
Authors: Zhao Juan , Gao Zheng-Ming
Published: November 2020

Both the Harris hawk optimization (HHO) algorithm proposed in 2016 and the slime moud algorithm proposed recently had complicated disciplines for individuals to update their positions. And both of them were proved to be capable of finding the best solutions for either benchmark functions or real engineering problems. In this paper, we further hybridized the SM and HHO algorithms and allowed the individuals in swarms to take more ways to update their positions. Simulation experiments were carried out and the better performance in either accuracy or convergence rate verified the capability of the hybridization.
Nondestructive detection of yellow peach quality parameters based on 3D-CNN and hyperspectral images
Authors: Xu Shipu , Liu Yong , Hu Wenwen , Wu Yingjing , Liu Sijia , Wang Yunsheng , Liu Chang
Published: November 2020

In recent years, efficient and accurate detection of fruit quality has become an important means of non-destructive testing of fruit quality. In order to solve the problems of cost, efficiency and accuracy in the non-destructive testing of yellow peach quality. This paper proposes a new method for simultaneous detection of yellow peach hyperspectral multiple quality parameters. This method uses the method of extracting and recombining wavelengths at equal intervals in the full-band spectrum instead of the characteristic wavelength selection method. It can make the hyperspectral image contain all-band spectral information. This method uses 3D-CNN to replace the original regression modelling method, which improves the accuracy of model prediction. This method uses the shared network convolutional layer method to perform multi-task learning on the sugar content and hardness of yellow peaches, and finally realizes the simultaneous detection of multiple quality parameters of yellow peaches.
Research on the Vocabulary Relevancy Algorithm of the Improved Search Engine
Authors: Wang Taoyu , Shi Xianjun , Chen Qijie
Published: November 2020

Aiming at the limitation of the existing search engine search algorithm and the low reliability of the derived vocabulary relevancy algorithm, a vocabulary relevancy algorithm based on search engine and vocabulary relevancy algorithm is proposed under the background of making full use of the knowledge base of Hownet. Firstly, the main defects of search engine algorithm are considered in two aspects, meaningless text and redundancy. Then, the two problems are solved by using “Hownet” and DSC algorithm. Finally, the search engine-based vocabulary relevancy algorithm is improved by integrating multiple factors such as weight reduction and noise reduction. Experimental results show that compared with the pure improvement method based on the search engine, the method of spearman coefficient and Pearson coefficient were improved, as well as reduce meaningless language segment on the calculation of correlation, verified the “Hownet”, and DSC algorithm into the search engine algorithm can effectively improve the computing performance of relevant vocabulary.
Research on Vehicle Detection Technology of Blind Spots at Night Based on CAdaBoost Algorithm
Authors: Cao Yong , Wang Botao
Published: November 2020

Aiming at the problem of vehicle video detection in blind areas at night, a method of vehicle detection in blind areas at night based on CAdaBoost algorithm is proposed. Since night vehicles and daytime vehicles have different illumination, visibility, vehicle edge characteristics, vehicle contours, etc., the test image is first grayed out and ROI is determined. Then, because the features of the car head and wheels at night are more obvious than other features, a parallel method is used to train multiple weak classifiers offline at the same time, mainly to train the Haar-like features of the car head and wheels of the test image. In the meantime, in order to improve the accuracy of detection, the weighted parameter is introduced to determine the role of the weak classifier in the final strong classifier. Then, the offline trained model is used to match the Haar-like features of the test vehicle’s car head and wheels respectively, and finally realize the real-time detection of vehicles in the blind area. The results show that: for vehicle detection in blind spots at night, the algorithm has a high detection accuracy and low detection time.
A Novel Data Traceability Model Based on Blockchain and Digital Watermarking in Edge Computing
Authors: Liu Caiyun , Chen Xuehong , Li Jun , Yang Shuaifeng , Sun Yan
Published: November 2020

Most of the current data traceability models are centralized models, which cannot meet the traceability requirements of distributed edge nodes. Blockchain is a decentralized trust management mechanism based on the Internet. Its features that are difficult to tamper with and traceable provide a new solution to secure data traceability. However, many edge nodes have limited computing and storage resources, and cannot store large amounts of blockchain information. In order to solve the above problems, we propose a novel distributed traceability model based on digital watermarking and blockchain in edge computing. The model improves the security of the traditional traceability model by dividing the internal and external areas and electing the master nodes, reduces the storage capacity of resource-constrained nodes and solves the problem of poor scalability of the blockchain. The model can be applied to widely distributed edge node traceability scenarios, and can also trace the data in the case of watermark failure.
Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model
Authors: Wei Wei , Wang Zhuo , Fu Changlong , Damaševičius Robertas , Scherer Rafal , Wožniak Marcin
Published: November 2020

Nowadays, data plays a unique role in various fields. Based on the background of the era of big data, this paper collects user evaluations of certain commodities and labels the evaluations into positive emotions and negative emotions. We propose an intelligent recommendation algorithm based on Naive Bayes algorithm, which determines the user’s preference for a product according to the user’s opinion on a product, then uses the collaborative filtering algorithm to recommend other similar products according to the similarity between the product and other products, so as to realize the recommendation of other similar products. This can not only help users choose more goods in the environment, but also maximize the value of goods. In this paper, bayesian classifier was used for prediction, and the accuracy reached about 97%.
Cross-Media Retrieval Based on Canonical Correlation Analysisand Decision Tree
Authors: Duan Hongye , Wang Meng , Zou Peiqing
Published: November 2020

The retrieval of approximate data from massive multimedia data is the key of computer science research. With the explosive growth of data scale, data retrieval needs to face the overlapping massive multimodal data called “dimension disaster”, and the traditional retrieval methods appeared to be inefficient. The cross-media retrieval with canonical correlation analysis (CCA) is a kind of way to map different media features to the largest correlation isomorphism subspaceand then compare the similarity between cross-media data in this subspace. However CCA is a linear model and it is difficult to map the lower data to the higher semantic. This paper proposed a cross-media retrieval method based on CCA and Decision Tree to solve the problem. CCA was uesd to depict the correlation between image and text feature, Decision Tree was uesd toapproach to feedback and repeatedly adjust the correlation. Experiments on the Wikipedia text-image datasetsverified that the Tree-CM model can mine the complex correlation between cross-media data and has better performance than other state-of-the-art models.
QTPC: A Q-Learning Transmission Power Control Mechanism for Edge-Cloud Wireless Body Area Networks
Authors: Su Haoru , Yuan Xiaoming , Sun Enchang , Si Pengbo , Chen Huamin
Published: November 2020

Wireless body area networks collect biological signals from human body and sensors connect wirelessly for various nonmedical and medical applications. Energy efficiency is one of the most essential problems in wireless body area networks since the limited battery capacity. In this paper, a Q-learning transmission power control (QTPC) mechanism for edge-cloud wireless body area networks is proposed. Simulation results show that the proposed scheme improves the network performance in the metrics of energy efficiency and system throughput.
Individual identification of communication radiation sources based on Inception and LSTM network
Authors: Chen Yiming , Zhu Lei , Yu Lu , Yao Yanyan
Published: November 2020

The difference in fingerprint characteristics of communication radiation sources is small, and it is difficult to extract characteristics for recognition using traditional machine learning algorithms, so deep learning methods are considered. LSTM is an improved recurrent neural network that is good at processing long-term sequence data. The Inception module can obtain features of different scales on the same layer. This article combines the inception structure and the LSTM network to identify 5 USRPs. The data set used in the experiment was collected by USRP and Lab VIEW. Two sets of data were collected based on the obstacles between the sending end and the receiving end, which are closer to the real environment than the data set that only uses software simulation. Compared with other network structures, this method has a higher recognition rate.
Improved whale optimization algorithm based on the tent chaotic mapping and nonlinear convergence factor
Authors: Dong Shuxian , Bu Chunguang , Wang Yongliang
Published: November 2020

To overcome the shortcomings of whale optimization algorithm (WOA) such as the slow convergence rate, and low convergence accuracy, and being easy to fall into the local optimum, an improved whale optimization algorithm based on the tent chaotic mapping and nonlinear convergence factor (TNWOA)is proposed. Firstly, In this algorithm, tent chaotic mapping, which enhances the diversity of the initialization population, is introduced into the initialization of population, therefore, the search space can be searched more thoroughly; Secondly, trigonometric function and the beta distribution are introduced in the convergence factor ‘a’, which balance the global search ability as well as local optimization ability and speed up the convergence speed of the algorithm. Simulation experiments on the four kinds of common test functions on CEC2017 show that under the same experimental conditions, the improved whale optimization algorithm improves the solution accuracy and convergence speed significantly, and its performance is obviously better than other smart optimization algorithms and other improved WOA algorithms.
Discussion on the application of Lagrange mean value theorem
Authors: Jiang Yinshan
Published: November 2020

The Lagrange mean value theorem has been widely used in the following aspects;

( 1 )Prove equation; ( 2 )Proof inequality; ( 3 ) Study the properties of derivatives and functions;

(4) Prove the conclusion of the mean value theorem; (5) Determine the existence and uniqueness of the roots of the equation; (6 ) Use the mean value theorem to find the limit.
Research on Top-k Association Rules Mining Algorithm Based on Clustering
Authors: Wu Lei , Wang Zhe
Published: November 2020

Until now, the association rule mining algorithm is still one of the core issues in the field of big data research. At present, there are many research related to association rule mining algorithms, mainly focusing on how to find frequent item sets and how to tailor rules, and based on this direction, there have been many classic algorithms, such as Apriori and FP-growth algorithms. However, most of the above algorithms process and analyze the entire data set, so that although related algorithms can be used to obtain the results, the results are not meticulous. To solve this problem, this paper proposes an algorithm, which first uses the k-means algorithm to perform cluster analysis on the data set to generate a specific number of clusters. Then, in different clusters, the association rules in each cluster are found by the improved Top-k algorithm combined with the correlation coefficient. By integrating clustering and the improved Top-k algorithm, the data set can be analyzed directionally to improve the accuracy and efficiency of the whole algorithm. And the final experiment shows that compared with the original algorithm, the running time is shortened by 14%.
Research on the oriented teaching method of computer network foundation course
Authors: Gan Weimin , Li Yuli
Published: November 2020

In recent development of modern education reform, it is imperative to accelerate high-level education structure adjustment, and the trend from local undergraduate universities transformed to the ones which accord application and technology -type development being the general consensus in the industry. These universities took the demanding of application technology as orientation,definited the subject objection and optimized the teaching method, focused on the combination of theory & practice teaching methods.This article is mainly about the computer network technology application application oriented design concept in teaching.Convey useful simplification of theory to the students and they can learn in order to practise.
Improved PULSE: (Photo Upsampling via Latent Space Exploration of Generative Models) Using Anti-spoofing System
Authors: Wang Yubo , Wang Aaron , Chen Weilan , Jiang Yuting , Wang Shaoan , Jiang Yuzhe , Wang Yuzheng
Published: November 2020

Image super-resolution is typically explained as converting a low-resolution (LR) image into a high resolution (HR) output. However, previous approaches could be blurry and unrealistic, especially for regions with many details. This paper took advantage of Latent Space Exploration from PULSE and a facial liveliness detection algorithm to receive a more accurate and rational outcome. (Related code can be found on https://github.com/cisgroupb/PULSE with Anti Spoofing-Detection)
Semantic Segmentation of Urban Street Scene Based on Convolutional Neural Network
Authors: Li Tingting , Jiang Chunshan , Bian Zhenqi , Wang Mingchang , Niu Xuefeng
Published: November 2020

Artificial Intelligence (AI) has developed rapidly in recent years, making computer vision a hot spot in recent years. As the core technology of computer vision, semantic segmentation has important theoretical significance and practical application value in the field of computer vision. The research purpose of this technique is to classify each pixel in the image and correctly predict the object category of the pixel, that is, to realize the transformation from pixel to object category. Traditional semantic segmentation algorithms cannot meet the requirements of complex image segmentation, and the requirements of complex image segmentation mechanism are also increasing. Therefore, it is of great significance and application prospect to study semantic segmentation algorithm of image. This paper first introduces the current research status in the field of semantic segmentation at home and abroad, tests and analyzes the CamVid data set with three mainstream algorithms, and finally forecasts the semantic segmentation of images.
Research on the Application of Artificial Intelligence Natural Language Processing Technology in Japanese Teaching
Authors: Jing Yao
Published: November 2020

In the context of artificial intelligence, natural language processing technology has matured and it is a key technology for foreign language teaching and research direction. The application of artificial intelligence natural language processing technology to Japanese teaching is essentially a language processing technology that combines computer science and artificial intelligence. Based on this, this article will analyze the application of artificial intelligence natural language processing technology in Japanese teaching, hoping to have a certain reference significance for Japanese teachers’ educational technology research.
Prediction Method of Enterprise Return on Net Assets Based on Improved Random Forest Algorithm
Authors: Cai Yongsong , Yin Qi , Su Qian , Huang Xinyu , Zhang Yin , Liu Ting
Published: November 2020

In order to accurately predict the change trend of enterprise assets, this paper proposes a prediction method of enterprise return on net assets based on improved random forest algorithm. Firstly, this paper analyzes the research background and significance of the enterprise net income rate prediction, and on this basis, it considers to use the random forest algorithm to achieve the prediction. In view of the shortcomings of the algorithm, the paper introduces the simulated annealing algorithm to improve the random forest algorithm and optimizes the prediction performance of the algorithm in the direction of feature selection, parameter optimization and weight setting, finally establishes the prediction model of enterprise return on net assets. In this paper, the proposed algorithm is compared with other algorithms and results show that the method proposed in this paper has a good prediction effect on the enterprise return on net assets, which is helpful for the capital market to evaluate the asset value and profitability of enterprises.
Analysis of the Impact of Big Data Technology on News Ecology
Authors: Yang Zhang
Published: November 2020

With the rapid rise of big data technology, big data has become an important resource of news media. Big data constantly changes the production of news and the operation of media companies, leading to profound changes in the news ecology. Based on the theory of media technology, this paper studies the problems of news production, news dissemination, news occupation and the evolution of news sources, and analyzes the impact of big data technology on news media, news content, news environment and other elements of news ecology.
Coping strategies of financial work in the era of artificial intelligence
Authors: Chao Fu
Published: November 2020

The emergence of artificial intelligence has accelerated the transformation of financial personnel’s work function from bookkeeping accounting type to operation and management type. Financial personnel have ushered in important development opportunities, but also will face more severe challenges. Through the research on the financial work reform caused by artificial intelligence and the coping strategies of financial personnel, this paper believes that financial personnel should clearly understand the development trend of artificial intelligence, actively meet the challenges, comprehensively improve the ability of financial digitization and lean through learning, establish macro thinking, become the leader of industry and finance integration, and realize their own career transformation and upgrading.
Research on Blended Learning Implementation
Authors: Chuanzhen Song
Published: November 2020

With the advancement of information technology and the gradual advancement of “internet+”, blended learning has become a hot topic in current higher education discussions and research. This paper analyzes the connotation of blended learning and the shortcomings of traditional teaching, it is believed that traditional classroom teaching mode and pure online teaching mode can no longer meet the needs of talents training in higher education institutions in the era of “internet+”. Blended learning is the direction of teaching reform in colleges and universities, and it is an important practice trend of higher education informatization. The implementation of blended learning is not simply a combination of online teaching and classroom teaching, but a systematic project, which needs to be determined from the teaching objectives, teaching link design, online platform construction, classroom teaching guidance, teaching effect evaluation, etc. Starting from the pre-class knowledge learning, in-depth discussion in the class, after-class consolidation and expansion, curriculum assessment and evaluation, teaching effect reflection and other links, we truly achieve the result of “teaching is methodical and learning is effective” to help students learn independently and efficiently to improve teaching effectiveness and students’ practical innovation ability.
Information Security and Adoptable Solutions in the Implementation of Industry 4.0 Strategy for the Fourth-generation Industrial Revolution
Authors: Cheng Xiaolei , Zhang Pinghua
Published: November 2020

Industry 4.0 is recognized as the fourth-generation industrial revolution, which can drive the development of the world’s smart manufacturing industry. The information security in the implementation of Industry 4.0 strategy was analyzed in this article. The specific measures to ensure the information security of Industry 4.0 were proposed from the perspectives of industrial control system security strategy, security measures, security awareness, data security protection, data security responsibility, and encryption technology. Furthermore, the distributed control system and IT network security communication platform construction are applied to solve the information security problem of Industry 4.0. The adoptable solutions will provide an important reference to maintain the information security of Industry 4.0.
Study of nanomechanical properties of thin films using in-situ “Push-to-Pull” method in the column of transmission electron microscope
Authors: Kiryukhantsev-Korneev Ph V , Loginov P A , Orekhov A S , Levashov E A
Published: November 2020

The purpose of this study was to develop a method for determining the tensile strength of ion-plasma coatings during their nanodeformation in the column of a transmission electron microscope. The advanced oxidation-resistant coating in the Mo-Zr-Si-B system was selected as the object for mechanical testing. Coatings were deposited on alumina substrates by magnetron sputtering with hetero-phase cathode in system MoSi2-MoB-ZrB2 in argon environment. Coating’s characterization and testing were performed using XRD, SEM, EDS, GDOES, TEM, and nanoindentation. Nanomechanical study in TEM column was conducted in binding scheme using Push-to-Pull device and Picoindenter module on lamella prepared by FIB. Problems related to nanomechanical measurements and interpretation of the obtained dependences are discussed. Recommendations for the method of lamella preparing and parameters for testing were developed.
The strength increase by nano-scale particles precipitated during magnesium solid solution decomposition in magnesium alloys with the rare-earth metals of the yttrium group
Authors: Rokhlin L L , Dobatkina T V , Tabachkova N Yu , Tarytina I E , Lukyanova E A
Published: November 2020

Magnesium-base alloys attract a great attention as light structural materials, where economy of weight is of great importance. According to the last investigations, the highest strength properties of these alloys, can be obtained, when they contain some of the rare-earth metals. All of them belong to the yttrium group. Existence of the rare-earth metals in magnesium alloys enable to form in their structures small hard particles, which increase significantly strength of the alloys. The hard particles appear after the special heat treatments and should have the certain measurements and amounts. In this article the main results of some investigations are described, where the best conditions for the heat treatments of the magnesium alloys with high strength were established. The highest strength in the alloys was observed, when the hard particles were nano-scale in them with certain disposition in the structure.
Flutter suppression of wind turbine blade based on nonlinear block backstepping control
Authors: Yi Chenghong , Wang Yingbo , Sun Haipeng , Sun Changle , Zhang Xiaolin , Li Shanyao , Liu Tingrui
Published: November 2020

Aiming at solving the nonlinear flutter problem of aeroelastic system of wind turbine blades, a controller combing block backstepping control and variable pitch motion was designed. Based on the typical blade-section model of the spring-mass-damper and the second-order model of the pitch actuator, nonlinear aeroelastic equation of the system under steady aerodynamic was given. Considering the multivariable, strong coupling factors and the unitary pitch control of the wind turbine blade aeroelastic system, a state space equation for a single-input/multiple-output nonlinear model was deduced. Based on the principle of block control, a nonlinear system with block control standard was obtained, which was composed of two subsystems. In order to suppress blade flutter and protect the blade, backstepping control was used to control blade pitch motion. The nonlinear block backstepping controller was designed by constructing Lyapunov function, and time response which can track the dynamic error of system was demonstrated. The simulation results showed that the designed controller can converge the divergent chattering trend to zero, and make the system globally stable.
Topological Optimization Design of the Gantry Steel Beam of the Simplified Freight Ropeway
Authors: Yi Wang , Enze Cai , Gang Li , Wenchuan Jing , Chaoming He
Published: November 2020

Simplified freight ropeway are mostly built on high mountains and ridges inaccessible by roads, where the gantry of the ropeway needs to be handled manually. The traditional gantry with a single-piece weight of 63.9kg is hard to be handled manually. Based on the existing gantry structure, the single-piece lightest combined gantry construction is analyzed, and finite element analysis is conducted in this paper. After analyzing and calculating the steel beam of the combined gantry, it is determined that the weight of a single steel pipe and that of the steel bushing are 17.7kg and 30.5kg, respectively, which can meet the requirement in manual handling operation in steep terrain. On the premise of considering the cost and weight of the gantry, the H-shaped steel of different materials is determined to be selected for strength and stiffness analysis, and the H-shaped steel beam of Q345 material of 200mm×100mm×5.5mm×8mm is selected as the gantry for topology optimization design and finite element topology iterative calculation in this paper. The stress is distributed uniformly after optimization, and the mass of the steel beam of the H-shaped gantry is reduced from 63.9kg to 49.1kg, which meets the maximum single-lift load of 50kg for an adult man specified in GB12330-90 “the Limit on Carrying-load for Physical Work”.
Research on the problems and improvement measures of Beijing public rental housing households
Authors: Liu Hongjie , Ma Jing , Su Yu
Published: November 2020

Through the investigation of some public rental housing projects in Beijing, the family structure, rent, income and satisfaction of the public rental housing households can be revealed. Through the statistical analysis, we found the following problems: some public rental housing family structure does not match with the type of occupancy; the rental income ratio of part of the public rental housing is unreasonable; some of the households has low satisfaction; the income of part of the public rental housing exceeds the prescribed upper limit. In view of the above problems, this paper proposes solutions: strengthen the planning and design of public rental housing; improve the rent subsidy mode of public rental housing; strengthen the construction of supporting facilities for public rental housing; promote the relevant legislative work of public rental housing.
Attenuation Characteristics of Truck-induced Ground Vibrations Due to Different Truck Weights and Speeds
Authors: Xu Zhaogang , Lou Yu , Chen Liu
Published: November 2020

Knowledge of the attenuation characteristics of truck-induced ground vibrations has gained importance for micro-vibration mitigation of high-tech facilities. This study conducted an in-situ experimental study on the attenuation characteristics of truck-induced ground vibrations. The ground vibrations were generated by the passage of a truck on a low-cost road with different weights and speeds. Eight low frequency, one component, 941B vibration sensors were fastened on the ground surface to record the vertical velocity time histories of ground vibrations at eight measurement points with different distances. The vibration level was evaluated in terms of the vertical maximum displacement obtained by integrating the measured velocity time histories. The vibration level represented by the vertical maximum displacement decayed monotonically but nonlinearly with distance from the road. Ground vibrations at the measurement points that were close to the road decayed more rapidly than that at the measurement points far away. In general, the maximum displacement of ground vibrations induced by the 30-ton truck at a measurement point was smaller than that of ground vibrations induced by the 18-ton truck with the same speed.
Activist Mothers in Need of Public Housing and the Welfare Services: Negotiating Othering, Mothering and Resistance
Authors: Shwartz-Ziv Tamar , Blit-Cohen Edith , Ajzenstadt Mimi
Published: November 2020

Recent decades have witnessed an increasing trend towards the development of critical approaches to the study of poverty, which call upon social workers to perceive people living in poverty as active agents who resist their condition. Relatively few studies, however, have examined how women living in poverty, who embody collective-political agency, experience the encounter with the welfare services, and how their manner of organisation is shaped by this encounter. This study examines this issue based on fifteen semi-structured interviews with mothers in need of public housing in Israel who are activists in protest groups against the public housing policy. The findings reveal that the encounter with welfare services generated feelings of othering due to the meanings attributed to their homelessness and to the nature of their mothering. Conversely, activism was perceived as a practice for challenging, redesigning and reducing power relations with the welfare agencies. The findings also show that the encounter shaped the participants’ manners of activist mobilisation. The study contributes to the research literature on social work with women who live in poverty, by highlighting the important role of women’s activism within the encounter.
Compartmentalized evolution of hepatitis B virus contributes differently to the prognosis of hepatocellular carcinoma
Authors: Yin Jianhua , Chen Xi , Li Nan , Han Xuewen , Liu Wenbin , Pu Rui , Wu Ting , Ding Yibo , Zhang Hongwei , Zhao Jun , Han Xue , Wang Hongyang , Cheng Shuqun , Cao Guangwen
Published: November 2020

Serum hepatitis B virus (HBV) mutations can predict hepatocellular carcinoma (HCC) occurrence. We aimed to clarify if HBV evolves synchronously in the sera, adjacent liver and tumors and predict HCC prognosis equally. A total of 203 HBV-positive HCC patients with radical hepatectomy in Shanghai, China, during 2011–15 were enrolled in this prospective study. Quasispecies complexity (QC) in HBV core promoter region was assessed using clone-based sequencing. We performed RNA sequencing on tumors and paired adjacent tissues of another 15 HCC patients and analyzed it with three public data sets containing 127 samples. HBV QC was positively correlated to APOBEC3s’ expression level (r = 0.28, P < 0.001), higher in the adjacent tissues than in the tumors (P = 6.50e-3), and higher in early tumors than in advanced tumors (P = 0.039). The evolutionary distance between the sera-derived HBV strains and the tumor-derived ones was significantly longer than that between the sera-derived ones and the adjacent tissue-derived ones (P < 0.001). Multivariate Cox regression analyses indicated that high HBV QC in the sera predicted an unfavorable overall survival (P = 0.002) and recurrence-free survival (RFS; P = 0.004) in HCC, whereas, in the tumors, it predicted a favorable RFS (P < 0.001). APOBECs-related HBV mutations, including G1764A, were more frequent in the sera than in the adjacent tissues. High-frequent A1762T/G1764A in the sera predicted an unfavorable RFS (P < 0.001), whereas, in the tumors, it predicted a favorable RFS (P = 0.035). In conclusion, HBV evolves more advanced in the sera than in the tumors. HBV QC and A1762T/G1764A in the sera and tumors have contrary prognostic effects in HCC.
Comparative effectiveness of bisoprolol and carvedilol among patients receiving maintenance hemodialysis
Authors: Wu Ping-Hsun , Lin Yi-Ting , Liu Jia-Sin , Tsai Yi-Chun , Kuo Mei-Chuan , Chiu Yi-Wen , Hwang Shang-Jyh , Carrero Juan-Jesus
Published: November 2020

Background

Despite widespread use, there is no trial evidence to inform β-blocker’s (BB) relative safety and efficacy among patients undergoing hemodialysis (HD). We herein compare health outcomes associated with carvedilol or bisoprolol use, the most commonly prescribed BBs in these patients.

Methods

We created a cohort study of 9305 HD patients who initiated bisoprolol and 11 171 HD patients who initiated carvedilol treatment between 2004 and 2011. We compared the risk of all-cause mortality and major adverse cardiovascular events (MACEs) between carvedilol and bisoprolol users during a 2-year follow-up.

Results

Bisoprolol initiators were younger, had shorter dialysis vintage, were women, had common comorbidities of hypertension and hyperlipidemia and were receiving statins and antiplatelets, but they had less heart failure and digoxin prescriptions than carvedilol initiators. During our observations, 1555 deaths and 5167 MACEs were recorded. In the multivariable-adjusted Cox model, bisoprolol initiation was associated with a lower all-cause mortality {hazard ratio [HR] 0.66 [95% confidence interval (CI) 0.60–0.73]} compared with carvedilol initiation. After accounting for the competing risk of death, bisoprolol use (versus carvedilol) was associated with a lower risk of MACEs [HR 0.85 (95% CI 0.80–0.91)] and attributed to a lower risk of heart failure [HR 0.83 (95% CI 0.77–0.91)] and ischemic stroke [HR 0.84 (95% CI 0.72–0.97)], but not to differences in the risk of acute myocardial infarction [HR 1.03 (95% CI 0.93–1.15)]. Results were confirmed in propensity score matching analyses, stratified analyses and analyses that considered prescribed dosages or censored patients discontinuing or switching BBs.

Conclusions

Relative to carvedilol, bisoprolol initiation by HD patients was associated with a lower 2-year risk of death and MACEs, mainly attributed to lower heart failure and ischemic stroke risk.
Association Between Brain Volumes and Patterns of Physical Activity in Community-Dwelling Older Adults
Authors: Wanigatunga Amal A , Wang Hang , An Yang , Simonsick Eleanor M , Tian Qu , Davatzikos Christos , Urbanek Jacek K , Zipunnikov Vadim , Spira Adam P , Ferrucci Luigi , Resnick Susan M , Schrack Jennifer A
Published: November 2020

Background

Larger brain volumes are often associated with more free-living physical activity (PA) in cognitively normal older adults. Yet, whether greater brain volumes are associated with more favorable (less fragmented) PA patterns, and whether this association is stronger than with total PA, remains unknown.

Methods

Brain magnetic resonance imaging and wrist-worn accelerometer data were collected in 301 participants (mean age = 77 [SD = 7] years, 59% women) enrolled in the Baltimore Longitudinal Study of Aging. Linear regression models were fit to examine whether brain volumes (cc) were cross-sectionally associated with: (a) total daily PA minutes and (b) activity fragmentation (mean number of PA bouts / total PA minutes × 100). Sensitivity analyses were conducted by adjusting for counterpart PA variables (eg, fragmentation covariate included in the PA minutes model).

Results

Greater white matter volumes in the parietal and temporal lobes were associated with higher daily PA minutes (2.6 [SE = 1.0] and 3.8 [0.9] min/day, respectively; p < .009 for both) after adjusting for demographics, behavioral factors, medical conditions, gait speed, apolipoprotein E e4 status, and intracranial volume. Greater temporal white matter volume was associated with lower fragmentation (−0.16% [0.05], p = .003). In sensitivity analyses, observed associations between brain volumes and daily PA minutes remained significant while associations with fragmentation no longer remained significant.

Conclusions

Our results suggest white matter brain structure in cognitively normal older adults is associated with the total amount of PA and, to a lesser extent, the PA accumulation patterns. More work is needed to elucidate the longitudinal relationship between brain structure and function and PA patterns with aging.
Age at Immigration and Depression: The Mediating Role of Contemporary Relationships With Adult Children Among Older Immigrants
Authors: Jang Heejung , Pilkauskas Natasha V , Tang Fenyan
Published: November 2020

Objectives

For the growing population of older immigrants in the United States, both age at immigration and familial relationships are important factors affecting psychological well-being. This study explores how age at immigration and contemporary relationships with adult children combine to explain older immigrants’ depressive symptoms.

Method

This study uses 2014 Health and Retirement Study data from a sample of 759 immigrants aged 65 and older who have at least one adult child aged 21 or older. A series of ordinary least squares regressions and mediational analyses were conducted.

Results

Findings indicate that structural solidarity significantly mediates the association between age at immigration and depressive symptoms. Specifically, immigrating in later life was associated with a lower level of depressive symptoms through its relationship with structural solidarity. In addition, giving monetary support to children and providing care for grandchildren may alleviate depressive symptoms for older immigrants.

Discussion

This study suggests that relationships with adult children may differ with age at immigration. The types of support that older immigrants provide to their adult children may be crucial because such support may instill a sense of obligation and reciprocity that may be beneficial to the psychological well-being of older immigrants.
A reference guide to rapidly implementing an institutional dashboard for resource allocation and oversight during COVID-19 pandemic surge
Authors: Jawa Randeep S , Tharakan Mathew A , Tsai Chaowei , Garcia Victor L , Vosswinkel James A , Rutigliano Daniel N , Rubano Jerry A ,   Stony Brook Medicine Enterprise Analytics Team
Published: November 2020

Objectives

We develop a dashboard that leverages electronic health record (EHR) data to monitor intensive care unit patient status and ventilator utilization in the setting of the COVID-19 pandemic.

Materials and methods

Data visualization software is used to display information from critical care data mart that extracts information from the EHR. A multidisciplinary collaborative led the development.

Results

The dashboard displays institution-level ventilator utilization details, as well as patient-level details such as ventilator settings, organ-system specific parameters, laboratory values, and infusions.

Discussion

Components of the dashboard were selected to facilitate the determination of resources and simultaneous assessment of multiple patients. Abnormal values are color coded. An overall illness assessment score is tracked daily to capture illness severity over time.

Conclusion

This reference guide shares the architecture and sample reusable code to implement a robust, flexible, and scalable dashboard for monitoring ventilator utilization and illness severity in intensive care unit ventilated patients.