bluuebunny/crossref_metadata_2025_split huggingface dataset:
3D printing-assisted fabrication of a patient-specific antibacterial radial head prosthesis with high periprosthetic bone preservation
Authors: Maydanshahi Mohammad Reza , Nazarian Ara , Eygendaal Denise , Ebrahimzadeh Mohammad Hossein , Kachooei Amir Reza , Mousavi Shaegh Seyed Ali 
Published: February 2021

Abstract
We present a novel fabrication and surgical approach for anatomical reconstruction of a fractured radial head using a patient-specific radial head prosthesis (RHP) made of polymethylmethacrylate (PMMA) bone cement. To this end, the use of PMMA bone cement for prosthesis fabrication was initially investigated using computational modeling and experimental methods. The RHP was fabricated through casting of PMMA bone cement in a silicone mold in the operating room before implantation. To enhance the precision of bony preparation for replacement of the radial head, a patient-specific surgical guide for accurate resection of the radial neck with the desired length was developed. Post-surgical clinical examinations revealed biomechanical restoration of elbow function, owing to the use of the patient-specific RHP and surgical guide. Importantly, follow-up radiographs after a mean follow-up of 18 months revealed bone preservation at the bone–prosthesis interface without any signs of erosion of the capitellum. Taken together, our method demonstrated the safety and efficacy of the PMMA RHP in restoring elbow biomechanics. This also provides a very safe and cost-effective method for making various patient-specific prostheses with localized antibacterial delivery and close mechanical properties to native bone for improved periprosthetic bone regeneration. The clinical trial of this study was registered at Mashhad University of Medical Sciences under the trial registration number 970493.
An LSTM-based neural network method of particulate pollution forecast in China
Authors: Chen Yarong , Cui Shuhang , Chen Panyi , Yuan Qiangqiang , Kang Ping , Zhu Liye 
Published: February 2021

Abstract
Particulate pollution has become more than an environmental problem in rapidly developing economies. Large-scale, long-term and high concentration of particulate pollution occurs much more frequently, which not only affects human health but also economic production. As PM10 is one of the main pollutants, the prediction of its concentration is of great significance. In this study, we present a PM10 forecast model based on the long short-term memory (LSTM) neural network method and evaluate its performance of predicting PM10 daily concentrations at five representative cities (Beijing, Taiyuan, Shanghai, Nanjing and Guangzhou) in China. Our model shows excellent adaptability for various regions in China. The predicted PM10 concentrations have good correlations with observations (R = 0.81–0.91). We also achieve great predication accuracy (70%–80%) on predicting the next-day changing trend and the model has the best performance for heavy pollution situation (PM10 > 100 μg m−3). In addition, the comparison of LSTM-based method and other statistical/machine learning methods indicates that our model is not only robust to different pollution intensities and geographic locations, but also with great potential on pollution forecast with temporal-correlated feature.
How environmental values influence trust and beliefs about societal oversight and need for regulation of the Australian cattle industry
Authors: Witt G B , Althor G , Colvin R M , Witt K J , Gillespie N , McCrea R , Lacey J , Faulkner T 
Published: February 2021

Abstract
Livestock grazing covers half of Australia and vast areas of global terrestrial ecosystems. The sustainability of the beef cattle industries are being scrutinised amid ongoing environmental concerns. In response, industry discourse has identified public trust as critical to avoiding reactive environmental regulation. However, public perceptions of the cattle industry’s sustainability performance and trust are largely unknown and speculative. We present the first model of public attitudes toward the Australian cattle industry (n = 2913). Our results reveal that societal perceptions of the industry’s environmental performance strongly predict trust in the industry. However, trust only weakly predicts a perceived right for societal oversight and has only an indirect relationship on need for environmental regulation. Environmental values influence perceptions of industry performance and the perceived right for societal oversight. We conclude that effective industry governance must be values literate and recognise that strong environmental performance is critical for public trust. Public trust is high but does not translate to support for a relaxed regulatory environment.
Development of an intelligent ergonomic model for Hazards predictions in Domestic Tasks
Authors: Adeyemi H O 
Published: February 2021

Abstract
Behavioural pattern is linked to many home accidents among occupants which have caused many injuries to life and properties. This study considered the categories and prevalence of risks among women in domestic tasks. A model was developed to predict the safety level of the subjects using Artificial Neural Network (ANN) function. With the use of a structured questionnaire, the study was conducted among 169 unmarried and 171 married women and the data was analysis with SPSS software. The ANN function was developed using the participants’ conducts in performing house chore duties, as the input. The “level of safety” was the output of the system. To test for its functionality, the ANN system was applied to predict safety in the performance of regular domestic duties among some women in the study area. The prediction accuracy of the system met with the objectives of the study. Among the widely reported, 40% of the participants suffered from ‘Cuts injury’ and while 35.6% complained of ‘contact with hot objects’. The developed ANN system is useful in homes where women are deeply engaged in domestic work.
Reliability of a capacitated system with parallel subsystems considering performance sharing mechanism
Authors: Ma Ye , Wu Di , Gao Kaiye , Li Yingchun , Peng Rui 
Published: February 2021

Abstract
Performance sharing mechanism is widely applied in energy systems, computing systems and other types of industrial systems to increase system reliability. In reality, some systems may allow certain extent of performance deficiency instead of requiring every subsystem in the system satisfy its demand. Moreover, the subsystems in systems may combine with amounts of components, say that, a series of generators should be simultaneously used to provide the necessary energy for a given area. In this paper, we consider a system with performance sharing mechanism where the system remains functional as long as the summed weighted deficiency for subsystems after performance sharing is smaller than a predesigned reliable threshold. The amount of shared performance is further assumed to restricted by the bandwidth capacity of the system. We further consider the case where the surplus can be redistributed to maximize system reliability. Universal generating function technique is employed to model the reliability of the system and numerical examples are provided to illustrate the applications.
An information aggregation method for hierarchical system reliability analysis with insufficient reliability information based on Bayesian Melding Method
Authors: Xu Yingchun , Yao Wen , Chen Xiaoqian , Liu Jianfeng , Wang Sheng , Chen Xing , Zhou Tao 
Published: February 2021

Abstract
In practical engineering, the reliability analysis of the hierarchical system structure is a hot topic, which has made great progress in recent years. From former experiments, there exists multi-source reliability information for the complex system. In the literature, the Bayesian Melding Method is a useful tool to merge the subsystem and system information based on the deterministic structure. However, when the system reliability information is insufficient, how to integrate the limited information for the system reliability analysis remains a problem to be solved. In this paper, an information aggregation method is developed for the hierarchical system to make full use of the limited reliability information based on the traditional Bayesian Melding Method. Three main steps are involved in the proposed method, i.e. distribution assumption, structure reconstruction, and reliability inference, based on which the existed reliability information can be fully merged and partial missing reliability information can be acquired through the system structure. Finally, the benefit illustration and effectiveness verification of the proposed method are given in the numerical example.
Research on the construction of supportability index of aviation equipment system of systems
Authors: Ding Gang , Cui Li-Jie , Wang Li-Yong , Hu Rong , Qian Guang 
Published: February 2021

Abstract
According to the characteristics of the air force operation under the condition of informationization, the concept, connotation and characteristics of the supportability of the AESos(Aviation equipment system of systems) based on task are defined. Based on the supportability logical control relation of the AESos, the capability requirements of the system to different levels are defined, and the requirement of the system support capability is described in particular. Secondly, following the idea of model-capability-index, and on the basis of constructing the supportability index system of AESos, the paper puts forward three system-level indexes to evaluate the supportability of the AESos, which are the intact rate of the AESos, the availability of the AESos and the mission continuity probability of the AESos.Finally, the definition and calculation formula of the three system-level indexes are given, which can lay a foundation for modeling and analyzing the supportability of the AESos. It provides support for the evaluation, control, overall planning and optimization of maintenance support capability of modern AESos.
Research on Backup Strategy of BeiDou Global Navigation System Constellation
Authors: Qinghua Wang , Haitao Zhao , Lei Chen , Zhaozhao Gao , Xiaofang Zhao 
Published: February 2021

Abstract
BeiDou Global Navigation System (BDS-3) will be fully completed by 2020, and provide global users with high-quality services including positioning, navigation and timing, global short messages communication (GSMC), international search and rescue(SAR) etc. Compared with the regional navigation system, the global navigation system serves a wider area and more users, and the consequences are more serious in the event of a service outage. It is necessary to study the backup strategy of the space segment as early as possible in the early stage of system operation. Firstly, the paper analyses the effects of three kinds of satellite in different positions on the system performance, then analyses the probability distribution of multiple satellites failure, and then puts forward the satellite backup strategy. From the perspectives of improving GSMC coverage and increasing the available time of SAR, combined with the remaining life and configuration of the satellites in orbit, the orbit positions of redundancy satellites are suggested. The analysis results show that if one or two satellites are backed up in three MEO orbital planes, the robustness of the system will be improved effectively, and the availability of the system will be guaranteed even if any satellite fails. Furthermore, if four satellites equipped with SAR and GSMC load are backed up in proper phase on the first and third orbit planes, the availability of GSMC service will reach 100% in the global scope, and the availability of SAR service will be significantly improved.
Comprehensive Evaluation for Design for Design Scheme of Metro Auxiliary Power supply System Based on FAHP
Authors: Gong Q , Chen B Z , Zhi P P , Li Y H 
Published: February 2021

Abstract
Considering the main factors that affect the design scheme of power supply system, the fuzzy analytic hierarchy process (FAHP) is used to comprehensively evaluate the design scheme of metro auxiliary power supply system. Firstly, the evaluation model of the auxiliary power supply system design scheme is established, and the factors influencing the system evaluation are divided into two levels, and the factor evaluation grade is the third level. Secondly, analytic hierarchy process (AHP) is used to determine the weight of each factor, and the method of triangular fuzzy number is used to establish a comprehensive evaluation matrix. Finally, the three-level fuzzy comprehensive evaluation is carried out based on the principle of fuzzy transformation, and the comprehensive evaluation results of each design scheme are calculated. The research results show that the comprehensive evaluation score of the parallel centralized power supply mode is the highest, and the design scheme is the best, which is consistent with the actual application. It shows that the method can directly reflect the advantages and disadvantages of each design scheme, and provide some reference for the selection of the design scheme of the auxiliary power supply system of the metro.
Time-dependent reliability moment-independent global sensitivity analysis of planar motion mechanisms
Authors: Guo Q , Liu Y S , Chen B Q 
Published: February 2021

Abstract
The planar motion mechanisms are widely applied in aerospace field to realize the scheduled output. A moment-independent global sensitivity analysis method (MIGSA) is developed for time-dependent reliability of planar motion mechanisms. The defined sensitivity index can indicate the influence of the uncertainty of the input variables in the entire uncertain region on time-dependent reliability of the planar motion mechanisms. An active learning Kriging (ALK) model is established to solve time-dependent failure probability as well as sensitivity index efficiently. A representative rack-and-pinion steering linkage example is investigated for validating its significance of the time-dependent reliability MIGSA method in this paper, and the efficiency of ALK method for approximating the time-dependent reliability MIGSA index.
Reliability analysis of mixer structure under mixing simulation
Authors: Cheng Hai-Ying , Luo Zhun , Yong Liang 
Published: February 2021

Abstract
When the forced two-horizontal shaft mixer is used to mix concrete, the mixing drum and the mixing components are subjected to random loads of the mixture. Structural static analysis usually ignores the randomness of the load, resulting in a large calculation error. Based on the EDEM software, this paper simulates the mixing process of the mixture, obtains the random load data of the mixture on the mixing drum and the mixing parts, and describes the variation law of the random load with a probability distribution, establishes the stress-strength interference reliability model of the mixer structure, and Monte Carlo simulation method is used to solve the structural reliability of the mixer. The results show that the load distribution of the mixing arm is the same as the mixing blade, the load distribution follows the gamma distribution, and the mixing shaft also follows the gamma distribution; the load distribution of the mixing drum follows the normal distribution. After calculation, the above load probability distribution and its intensity distribution do not interfere, and the structural reliability of the mixer meets the requirements, which can ensure the safe operation of the mixer. This method solves the reliability calculation error caused by random loads.
Modelling and Failure Analysis of SiC MOSFET under the Effects of Parasitic Parameters
Authors: Ye Xuerong , Wang Haonan , Lai Yaokang , Zhai Guofu , Cao Yufeng 
Published: February 2021

Abstract
Recent years, SiC MOSFET has been gradually utilized in high power devices. However, it has become one of the highest failure rate devices in circuit systems. Its failure relates to the degradation which caused by the hot carrier injection(HCI) effect. Because the parasitic parameters make the oscillations in switching processes more intense, the switching time and voltage overshoot increases, and thus HCI effect exacerbates. So, it is necessary to analyse the influence of parasitic parameters on the failure of SiC MOSFET. In this paper, an improved SiC MOSFET model with all key parasitic parameters is built to set up an accurate test circuit. The parasitic parameters were divided into four parts: body diode, parasitic resistance, parasitic capacitance and parasitic inductance. According to the simulation, it concluded that only the parasitic inductance has a great impact on the HCI effect and the impacts are different according to the different locations of parasitic inductance. The influence of the parasitic inductance on the load circuit and near the source is much greater than other parts. Their increase greatly increases switching time and voltage overshoot, and thus exacerbates HCI effect and accelerates the degradation of SiC MOSFET.
Using data mining and root cause analysis method for failure analysis in electronic components
Authors: Wang Hongjian , Zhao Zhenbo , Dai Zongbei , Peng Zeya , Li Shaoping 
Published: February 2021

Abstract
The electronic components are the core of all electronic products, the reliability and maintainability directly affect manufacture products quality. And as products become more complex and more demanding. We need more reliable components. In order to solve this problem, we need to analysis how and why those components failed. Failure analysis cases are important data for analysis, data mining and improving reliability. China Electronic Product Reliability and Environment Testing Research Institute (CEPREI) is the earliest research organization engaged in reliability research and failure analysis in China, and has accumulated rich failure case data. By combining data mining methods such as Bayesian network, correlation analysis, we can perform statistical analysis and root cause analysis to assist failure analysis and improve product reliability. This paper will apply the failure analysis case of the electromagnetic relay, and explores and analyses related regular pattern.
Reliability Engineering Design Method based on Generalized Margin Design theory
Authors: Peng Wensheng , Li Yonghong 
Published: February 2021

Abstract
Traditional reliability indexes in engineering are statistic parameters of failure time or failure rates, these reliability parameters are not related to products’ design parameters directly. In fact, reliability design in engineering process is margin design, for instance, the safety factors approach, the derating design approach, and even the selection of the high quality components carried out by enhancing the margin of resisting the sense stress of failures. This paper proposes a reliability engineering design method based on the general margin theory, and the quantification relationships between the statistic reliability parameters and general margin parameters are established. The general margin proposed in this paper contains the stress margin and function margin. The margin design rules and requirements are defined according to the products(or concepts design) ‘s loads in the predicted lifecycle; and then the stress margin parameters that satisfying the reliability could be determined based on the stress-strength interference model, and these margin parameters could support the products concepts selection; if the stress margin parameters could not satisfy the requirements, then the function margin parameters are analyzed to guide the key function redundancy design until the reliability of the products are meet. This method could improve the suitability of reliability theory in engineering field. This paper demonstrates the availability of this design method with a case study.
A Fault Diagnosis Algorithm for Wind Turbine Blades Based on BP Neural Network
Authors: Bi Jun-Xi , Fan Wen-Ze , Wang Ying , Ren Jun , Li Hai-Bin 
Published: February 2021

Abstract
As one of the most critical wind power generation components, wind turbine blades play a key role in generating wind power. Aiming at the problem that the wind turbine blades are subjected to multiple loads in combination, the crack problem is easy to occur. Through the analysis of the macroscopic expansion mechanism and microscopic damage mechanism of short cracks and main cracks, the hidden relationship between crack appearance and damage nature is deeply explored. A fault diagnosis algorithm for wind turbine blades established on the basis of the BP neural network is raised. On the multi-discriminator fusion network structure, BP neural network algorithm is used to train the multi-feature sample data including wind turbine blades, so that the network parameters tend to convergence and gradually approach the real tag. The experimental analysis shows that the algorithm effectively diagnoses and evaluates the damage degree of the blade structure, and has a high recall rate and accuracy, which proves the effectiveness and robustness of the algorithm.
Remaining Life Prediction for Aircraft Turbine Engines Based on LSTM-RNN - HMM – APPROACH
Authors: Bi Jun-Xi , Fan Wen-Ze , Wang Shou-Bin 
Published: February 2021

Abstract
Prognostics and Health Management (PHM) of the aircraft gas turbine engine is essential in the safety of the aircraft. In this paper, engine remaining useful life (RUL) was predicted with a novel architecture based on a hybrid recurrent neural network. This hybrid model trains HMM firstly and then gives a small LSTM to get distributions of HMM states. These HMM states are further trained to fill in gaps in HMM. Subsequently, a jointly trained hybrid model is constructed, which can enhance stability and accuracy of prediction significantly.
Reliability oriented multi-AGVs online scheduling and path planning problem of automated sorting warehouse system
Authors: Yu N N , Li T K , Wang B L , Yuan S P , Wang Y 
Published: February 2021

Abstract
The automated sorting warehouses are operated by multiple AGVs simultaneously, which can quickly sort a large number of packages. How to solve the problems of multi-AGVs scheduling, path planning and conflict resolution is the key to the reliable and rapid operation of the warehouse system. In order to improve the reliability and sorting efficiency of the system, the objective of this paper is to minimize the maximum handling completion time. Firstly, a multi-AGVs path planning algorithm based on Grid Blocking Degree (GBD) is proposed. This algorithm can plan a conflict-free handling path for each handling task of each AGV, and update the path dynamically according to the real-time traffic congestion. Furthermore, aiming at the comprehensive optimization of scheduling and path planning, a scheduling algorithm based on priority rules is proposed to determine the handling task for AGVs. The data experiments compare the difference of the solution effect of different priority rules, and verify the effectiveness of the algorithm proposed in this paper.
A Method for Thermal Resistance Test of Reverse-Conducting IGBT (RC-IGBT)
Authors: Chen Y , Zhang P , Hu X F , Huang H Z , Lai P , He Z Y , Chen Y Q 
Published: February 2021

Abstract
The RC-IGBT is integrated IGBT and fast recovery diode (FRD) on the same chip. RC-IGBT has smaller size, higher power density, lower cost, and higher reliability. However, due to the snap-back effect, the traditional test circuit and method cannot accurately measure the thermal resistance of RC-IGBT. In this paper, a thermal resistance test circuit based on series compensation of two devices is proposed, and the voltage drop Vec of the body diode is taken as the temperature sensitive parameter of RC-IGBT. The thermal response curve is obtained basing on the change of Vec with temperature. By comparing the traditional method with the new method, it can be seen that the new method can greatly improve the test accuracy.