Removal of sulfur element from high-sulfur coal by superconducting HGMS technology

Shuai-shuai Han,Su-qin Li,Rui-ming Yang,Chang-qiao Yang,Yi Xing 한국초전도.저온공학회 2019 한국초전도저온공학회논문지 Vol.21 No.2

Coal is the most abundant fossil fuel on Earth and is used in a wide range of applications. The direct combustion of high-sulfur coal produces a large amount of sulfur dioxide, which is a toxic and corrosive gas. A new superconducting high gradient magnetic separation (HGMS) technology was studied to remove sulfur from high sulfur coal. The magnetic separation concentrate was obtained under the optimum parameters, such as a particle size of -200 mesh, a magnetic field strength of 2.0 T, a slurry concentration of 15 g/L, and a slurry flow rate of 600 ml/min. The removal rate of sulfur is up to 59.9%. The method uses a magnetic field to remove sulfur-containing magnetic material from a pulverized coal solution. It is simple process with, high efficiency, and is a new way.

A Stiffness Modeling Approach for Multi-Leaf Spring Mechanism Supporting Coupling Error Analysis of Nano- Stages

Shuai-Shuai Lu,Peng Yan 한국정밀공학회 2017 International Journal of Precision Engineering and Vol.18 No.6

In the present paper, we take the multi-leaf spring mechanism as a particular element to develop a modeling approach for the stiffness by considering the coupling effects of the cross axis force. With this, a more accurate model of the multi-leaf spring based nano-stage is derived by incorporating the effect of cross axis input force, which can better predict the coupling error. The new analytical model indicates that the total axial stiffness is an inverse-quadratic function of the cross axis input load instead of a constant value, which can be used to analyze the cross axis coupling error of multi-leaf spring based nano-stages. The proposed analysis method is then verified by FEA and experiments, where excellent results are demonstrated in predicting the cross axis coupling effects of nano-stages such as stiffness variation and coupling error. The proposed approach offers a new look into the design and manipulation of multileaf spring based nano-stages.

Removal of sulfur element from high-sulfur coal by superconducting HGMS technology

Han, Shuai-shuai,Li, Su-qin,Yang, Rui-ming,Yang, Chang-qiao,Xing, Yi The Korea Institute of Applied Superconductivity a 2019 한국초전도저온공학회논문지 Vol.21 No.2

Coal is the most abundant fossil fuel on Earth and is used in a wide range of applications. The direct combustion of high-sulfur coal produces a large amount of sulfur dioxide, which is a toxic and corrosive gas. A new superconducting high gradient magnetic separation (HGMS) technology was studied to remove sulfur from high sulfur coal. The magnetic separation concentrate was obtained under the optimum parameters, such as a particle size of -200 mesh, a magnetic field strength of 2.0 T, a slurry concentration of 15 g/L, and a slurry flow rate of 600 ml/min. The removal rate of sulfur is up to 59.9%. The method uses a magnetic field to remove sulfur-containing magnetic material from a pulverized coal solution. It is simple process with, high efficiency, and is a new way.

Construction of magnetic nanochains to achieve magnetic energy coupling in scaffold

Cijun Shuai,Xuan Chen,Chongxian He,Guowen Qian,Yang Shuai,SHUPING PENG,Youwen Deng,Wenjing Yang 한국생체재료학회 2022 생체재료학회지 Vol.26 No.3

Background: Fe3O4 nanoparticles are highly desired for constructing endogenous magnetic microenvironment in scaffold to accelerate bone regeneration due to their superior magnetism. However, their random arrangement easily leads to mutual consumption of magnetic poles, thereby weakening the magnetic stimulation effect. Methods: In this study, magnetic nanochains are synthesized by magnetic-field-guided interface co-assembly of Fe3O4 nanoparticles. In detail, multiple Fe3O4 nanoparticles are aligned along the direction of magnetic force lines and are connected in series to form nanochain structures under an external magnetic field. Subsequently, the nanochain structures are covered and fixed by depositing a thin layer of silica (SiO2), and consequently forming linear magnetic nanochains (Fe3O4@SiO2). The Fe3O4@SiO2 nanochains are then incorporated into poly l-lactic acid (PLLA) scaffold prepared by selective laser sintering technology. Results: The results show that the Fe3O4@SiO2 nanochains with unique core–shell structure are successfully constructed. Meanwhile, the orderly assembly of nanoparticles in the Fe3O4@SiO2 nanochains enable to form magnetic energy coupling and obtain a highly magnetic micro-field. The in vitro tests indicate that the PLLA/Fe3O4@SiO2 scaffolds exhibit superior capacity in enhancing cell activity, improving osteogenesis-related gene expressions, and inducing cell mineralization compared with PLLA and PLLA/Fe3O4 scaffolds. Conclusion: In short, the Fe3O4@SiO2 nanochains endow scaffolds with good magnetism and cytocompatibility, which have great potential in accelerating bone repair

Risk Factors for Work-Related Musculoskeletal Disorders Among Intensive Care Unit Nurses in China: A Structural Equation Model Approach

Shuai Yang,Li Li,Liqian Wang,Jiaqi Zeng,Yinglan Li 한국간호과학회 2020 Asian Nursing Research Vol.14 No.4

Purpose: This study explored the direct and indirect effects of risk factors of work-related musculoskeletaldisorders (WRMDs) in nurses working in intensive care units (ICUs). Methods: A cross-sectional study design was used. ICU nurses from 28 tertiary hospitals in the Hunanand Guangdong provinces participated in a survey conducted via a self-reported online questionnaire. Astructural equation model was used to fit the data and to evaluate associations among WRMDs and riskfactors. Results: Valid questionnaire samples were submitted by 984 ICU nurses. The prevalence of WRMDswithin the previous year among ICU nurses was 96.8%. A valid structural equation model was constructed,and a good fit was shown: Chi-square value/degrees of freedom ¼ 2.248; comparative fitindex ¼ .931; normal fit index ¼ .905; goodness-of-fit index ¼ .978; adjusted goodness-of-fit index ¼.966; and root mean square error of approximation ¼ .036. All regression coefficients for direct effectreached significant levels (critical ratio > 1.96 and p < .05). In the structural equation model, theoccurrence of WRMDs was directly affected by the following: physical factors, risk perception, and jobstress. Physical factors and a safe environment indirectly affected WRMDs through risk perception andjob stress. The strongest correlations with WRMDs were physical factors. Conclusion: The model provided a new perspective for understanding the associations among physicalfactors, workplace safety environment, risk perception, job stress, and WRMDs. To improve the practicesetting of the ICU, efforts should be made to help prevent WRMDs from physical, psychosocial, andenvironmental factors.

Notify boiling water by using TMP36 sensor

Shuai Lau 한국인공지능학회 2016 인공지능연구 (KJAI) Vol.4 No.1

These days, citizens have a lot of ways to get access to IT. In the past, they tended to neglect IT that was thought to be difficult. But, currently, everyone can manufacture and get access not only software but also hardware when he has an idea. Arduino is used. Rinnai had recently released new product named Smart Sensor Range. Safe consumer who gave priority to the safety made new trend gave attention to fire prevention and smart sensor range. The ones who buy gas range prefer safety to economic advantage and/or fire power. The safety system does not always prevent fire accident. This study makes design and produces alarm that perceives temperature of pot when boiling. Not only temperature sensor but also alarm sensor was used to make alarm of boiling water and to give convenient living life. The arduino can be used at practical life to make products for various kinds of people. The invention can give convenience to housewives at kitchen, children and many persons making use of gas range. Another function can be added to develop. This arduino can develop a lot of products by using the study and other designs.

A study on Mobile e-commerce in China

Shuai SU 한국유통과학회 2018 KODISA ICBE (International Conference on Business Vol.2018 No.-

The rapid development of Internet makes electronic commerce become a new favorite business mode, and the popularity of mobile network and mobile innovation leads to a new round of business innovation.This study to investigate Mobile service quality factors influencing on customer satisfaction and repurchase intention for the research purpose. This study collected data by a survey method for an empirical. A total of 340 replies were used for data analysis. While 340 replies were collected from Chinese users of Mobile e-commerce. Our results support previous researches to results of SPSS analysis all Of the 7 hypotheses, 4 hypotheses are adopted and 3 are deleted. After an empirical analysis of this study, the academic implications are as follows. As there have not been many academic studies related to mobile commerce quality, this study can be a meaningful framework when quality factors associated with mobile commerce are being analyzed. This paper as the mobile commerce industry is very sensitive to environmental changes, academic research that can be used to cope with these changes is continuously required. Further studies should be carried out to overcome limitations that have not been analyzed by this study.

Visual Tracking with Online Incremental Deep Learning and Particle Filter

Shuai Cheng,Yonggang Cao,Junxi Sun,Guangwen Liu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12

To solve the problem of tracking the trajectory of a moving object and learning a deep compact image representation in the complex environment, a novel robust incremental deep learning tracker is presented under the particle filter framework. The incremental deep classification neural network was composed of stacked denoising autoencoder, incremental feature learning and support vector machine to achieve the feature-extracting and classification of particle set. Deep learning is successfully taken to express the image representations obtained effectively. Unsupervised feature learning is used to learn generic image features and transfer learning transforms knowledge from offline training to the online tracking process. The incremental feature learning was consisted of adding features and merging features to online learn compact feature set. Linear support vector machine increases the discretion for target with similar appearance and is further tuned to adapt to appearance changes of the moving object. Compared with the state-of-the-art trackers in the complex environment, the results of experiments on variant challenging image sequences show that incremental deep learning tracker solves the problem of existent trackers more efficiently, it has better robust and more accurate, especially for occlusions, background clutter, illumination changes and appearance changes.

Perceived Risks, Mitigating Factors and Travel Intentions of Potential Tourists in a High-Risk Environment: Case Studies on the 2021 Tokyo Olympics and 2022 Beijing Winter Olympics

( Shuai Zhang ),( Xinqi Zhang ),( Yu Cheng ),( Naiqiao Ning ),( Yinsheng Lin ),( Liang Chen ) 아시아스포츠융합과학회 2021 Asia Pacific Journal of Applied Sport Sciences Vol.2 No.3

PURPOSE This research establishes a reasonable research model to investigate the relationship between the perceived risks, mitigating factors, and travel intentions of potential visitors to the Olympic Games under the COVID-19 epidemic environment. And examine the mediating role of mitigating factors between perceived risk and travel intention. METHOD This study conducted two online surveys with Chinese athletes as the survey subject, divided into two samples for the Tokyo Olympics and the Beijing Olympics. The data analysis process is divided into two stages. In the first stage, confirmatory factor analysis (CFA) is used to test the reliability and validity of the scale. In the second stage, structural equation modeling (SEM) analysis is used to test the proposed hypothesis. Finally, the data test results of the two samples are compared. RESULT After the CFA test, the data fit well with the model, and the model had good combination reliability and convergence validity. On this basis, a structural equation model was established, and the mediation validity of the model was tested. CONCLUSION The results show that risk perception has a significant effect on mitigating factors, and mitigating factors have very significant positive effects on behavioral intentions. Mitigating factors mediate between risk perception and behavioral intentions.

Rolling element-bearing feature extraction based on combined wavelets and quantum-behaved particle swarm optimization

Shuai Zhang,Yongxiang Zhang,Jieping Zhu 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.2

A new approach is proposed to quickly determine the optimal band-pass filter for the resonant demodulation of rolling element bearings. The combined wavelet filter is joined with quantum-behaved particle swarm optimization for optimal filtering. The correlated kurtosisis used as an evaluation index to identify the optimal filtered signal. The proposed approach possesses good band-pass filteringproperties and improves optimal filtering speed and effect to converge rapidly to an optimal solution. Simulation and experiment resultsshow that this approach can generate fault features that are superior to those produced with the classic algorithm. It also shortens the timespent on the same filtering effect in comparison.