The Work Experience of Newly Recruited Male Nurses during COVID-19: A Qualitative Study

Huiyue Zhou,Xin Wang,Ruofei Du,Xiang Cheng,Kexin Zheng,Shiqi Dong,Justin Henri,Changying Chen,Tao Wang 한국간호과학회 2021 Asian Nursing Research Vol.15 No.3

Purpose: This study was to investigate the work experience of newly recruited male nurses during the COVID-19 pandemic. Methods: With a phenomenological approach, this qualitative study was adopted semistructured interviews by phone or video calls. A total of 9 male nurses newly recruited for the COVID-19 wards in Chinese hospitals were interviewed for this study. And Colaizzi's method was applied for evaluation in the data analysis. Results: Based on our findings, three themes were extracted. First, the newly recruited male nurses showed negative emotions at the beginning of COVID-19 epidemic, which was caused by changes in working conditions and content, but also prompted the nurses to change the way of coping with the crisis. Second, they gradually mastered the working skills and psychological training to cope with COVID- 19 and developed a positive attitude toward life and a high sense of professional responsibility. Finally, we learned about their needs to respond to public health emergencies such as the COVID-19 pandemic. Conclusion: COVID-19 is a disaster for all of humanity. The newly recruited male nurses are an important force in emergency rescue. Although they suffered from short-term negative emotions, they quickly adapted to the crisis. In order to better prepare for future emergencies, the disaster response capacity of newly recruited male nurses needs to be further improved. In addition, newly recruited male nurses have a strong demand for timely and personalized career development guidance.

Generalized Association Rules Mining with Multi-Branches· Full-Paths and Its Application to Traffic Volume Prediction

Huiyu Zhou,Shingo Mabu,Manoj Kanta Mainali,Xianneng Li,Kaoru Shimada,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

Time Related Association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, a Generalized Class Association Rule Mining is proposed using Genetic Network Programming (GNP) in order to find time related sequential rules more efficiently. GNP has been applied to generate the candidates of the time related association rules as a tool. For fully utilizing the potential ability of GNP structure, the mechanism of Generalized GNP with Multi-Branches· Full-Paths mechanism is proposed for class association data mining. The aim of this algorithm is to better handle association rule extraction from the databases with high efficiency in a variety of time-related applications, especially in the traffic volume prediction problems. The algorithm capable of finding the important time related association rules is described and experimental results are presented using a traffic prediction problem.

SLQE : An Improved Link Quality Estimation based on Four-bit LQE

An Zhou,Baowei Wang,Xingming Sun,Xingang You,Huiyu Sun,Tao Li 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.1

Link quality estimation (LQE) is an effective basic building block in wireless sensor networks (WSNs) and higher cross layer design of network protocol. Some researchers have investigated the statistical properties of the link quality estimators independently from higher-layer protocols, and their impact on the Collection Tree Routing Protocol (CTP). Then they set up a dedicated LQE, independent of the protocol interface, which has in total of four bits information: one from the physical layer, one from the link layer, and two from the network layer. Four-bit has been found to be a good estimator; however its performance heavily depends on the tuning of its parameters. But we found that Four-bit couldn’t be working effectively in responding to the burst situation after repeated experiments. So we redesigned the link estimation method, called Stable Link Quality Estimation (SLQE), which combines active probing with passive snooping to make estimation more stable. We have found that the new design can cope with the emergency. Moreover it also enhances the robustness of the network, and saves the overall energy consumption of the network.

Genetic Network Programming with Estimation of Distribution Algorithms and its Application to Association Rule Mining for Traffic Prediction

Xianeng Li,Shingo Mabu,Huiyu Zhou,Kaoru Shimada,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

In this paper, a novel evolutionary paradigm combining Genetic Network Programming(GNP) and Estimation of Distribution Algorithms(EDAs) is proposed and used to find important association rules in time-related applications, especially intraffic prediction. GNP is one of the evolutionary optimization algorithms, which uses directed-graph struc-tures. EDAs is a novel algorithm, where the new population of individuals is produced from aprobabilistic distribution estimated from the selected individuals from the previous genration. This model replaces random cross over and mutation to generate off spring. In stead of generating the can didate association rules using conventional GNP, the proposed method can obtainalarge number of important association rules more effectively. The purpose of this paper is to compare the proposed method with conventional GNP intraffic prediction systems interms of the number of rules obtained.

Time Related Association Rules Mining for Traffic Prediction based on Genetic Network Programming combined with Estimation of Distribution Algorihms

Yang Wang,Shingo Mabu,Huiyu Zhou,Xianneng Li,Kaoru Shimada,Bofeng Zhang,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

In this paper, a method of time-related classas sociation rule mining is proposed based on Genetic Network Programming(GNP) combined with Estimation of Distribution Algorithms(EDAs). The reare two important points in this paper: The first important point is to combine GNP with Estimation of Distribution Algorithms which are a novel evolution strategy. The second important point is that three kinds of probability models have been put for ward for generating new individuals. The aim of this paper is to extract more interesting association rules and to improve the traffic prediction accuracy by combining Genetic Network Proramming with Estimation of Distribution Algorithms. We applied the proposed data mining algorithm to traffic system sin order to predict the traffic volume in future. The simulation results show that our proposed method is effective compared with the conventional method based on GNP.

Energy-efficiency Optimization Schemes Based on SWIPT in Distributed Antenna Systems

( Weiye Xu ),( Junya Chu ),( Xiangbin Yu ),( Huiyu Zhou ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.2

In this paper, we intend to study the energy efficiency (EE) optimization for a simultaneous wireless information and power transfer (SWIPT)-based distributed antenna system (DAS). Firstly, a DAS-SWIPT model is formulated, whose goal is to maximize the EE of the system. Next, we propose an optimal resource allocation method by means of the Karush-Kuhn-Tucker condition as well as an ergodic method. Considering the complexity of the ergodic method, a suboptimal scheme with lower complexity is proposed by using an antenna selection scheme. Numerical results illustrate that our suboptimal method is able to achieve satisfactory performance of EE similar to an optimal one while reducing the calculation complexity.

Deeper SSD: Simultaneous Up-sampling and Down-sampling for Drone Detection

( Han Sun ),( Wen Geng ),( Jiaquan Shen ),( Ningzhong Liu ),( Dong Liang ),( Huiyu Zhou ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.12

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone’s detection and recognition. These proposed methods can also detect small and large objects simultaneously.

A facile, green synthesis of biomass carbon dots coupled with molecularly imprinted polymers for highly selective detection of oxytetracycline

Haochi Liu,Lan Ding,Ligang Chen,Yanhua Chen,Tianyu Zhou,Huiyu Li,Yuan Xu,Li Zhao,Ning Huang 한국공업화학회 2019 Journal of Industrial and Engineering Chemistry Vol.69 No.-

Biomass carbon dots (CDs) prepared by sweet potato peels were superior fluorophores with low toxicity and excellent photostability. A novel designed fluorescence probe for specific recognition and sensitive detection of oxytetracycline (OTC) was fabricated with CDs and molecularly imprinted polymer (MIP). The quenching of CDs happened when rebinding with OTC due to electron-transfer-induced fluorescence quenching mechanism. The fluorescence probe was successfully applied in honey with the recoveries ranging from 90.2% to 97.3%. The detection limit of OTC was 15.3 ng mL−1. This work provides promising perspectives that the development of fluorescent MIP will be valuable for rapid analysis in complex samples.

Effect of rehabilitation on the somatosensory evoked potentials and gait performance of hemiparetic stroke patients

Yoon, Hyun S.,Cha, Young J.,Sohn, Min K.,You, Joshua (Sung) H.,Gó,mez, Carlos,Schwarzacher, Severin P.,Zhou, Huiyu IOS Press 2018 Technology and health care Vol.26 No.1

<P><B>BACKGROUND:</B></P><P>Gait performance of stroke patients is affected by impaired sensory ability. The purpose of the present study was to determine the relationship between somatosensory-evoked potential (SSEP) parameters and gait performance in hemiparetic stroke patients.</P><P><B>METHODS:</B></P><P> A convenience sample of 17 hemiparetic stroke patients (mean age 60.11 [FORMULA OMISSION] 8.83 years; 10 women; right hemiplegia: 10, left hemiplegia: 7) were recruited for the present study. The Electro Synergy system (Viasys Healthcare; San Diego, CA, USA) was used for SSEP evaluation. The 17 patients were assigned to two groups according to their SSEP results as follows: 8 patients to the normal response group and 9 patients to the abnormal group. All the participants underwent the same rehabilitation exercise programs during 4 weeks, followed by clinical evaluation. A mixed-design analysis of a variance model was used to test for differences in timed up-and-go (TUG) test and 10-meter walking test (10MWT) scores between the two independent groups while the participants were subjected to repeated measures (pretest and posttest). </P><P><B>RESULTS:</B></P><P> Analysis of variance revealed the main time effect ([FORMULA OMISSION] 0.05) and group by time interaction effect ([FORMULA OMISSION] 0.05). The post hoc test result confirmed that the normal sensory group showed greater improvement in TUG test and 10MWT scores than the abnormal sensory group ([FORMULA OMISSION] 0.05). The TUG test and 10MWT scores in the posttest were greater in the normal sensory group than in the abnormal sensory group.</P><P><B>CONCLUSIONS:</B></P><P>The present study demonstrated the importance of the clinical contribution of the baseline sensory function of individuals with hemiparetic stroke to their gait performance and recovery after stroke rehabilitation. As anticipated, the individuals who had intact or spared sensory function showed greater improvements in gait speed and performance measures than those who had impaired sensory function.</P>