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1.
BMC Ophthalmol ; 22(1):474, 2022.
Article in English | PubMed | ID: covidwho-2153541

ABSTRACT

PURPOSE: To investigate mental health and self-management in glaucoma patients during the COVID-19 pandemic in China and to describe the correlation between anxiety, depression, glaucoma, and self-management. METHODS: This cross-sectional study included glaucoma patients who enrolled in the case management platform and completed an online survey. The survey included the Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9), and Glaucoma Self-Management Questionnaire (GSMQ). RESULTS: Among 109 glaucoma patients enrolled in this study, the proportions of patients suffering from depression and anxiety during the COVID-19 pandemic were 26.6% and 20.2%, respectively. A statistical association was found between depression and self-management behaviour in these glaucoma patients (r = -0.247, P = 0.010). The self-management scores in patients less than 35 years were lower than those in patients aged 35-60 years (P = 0.046). The scores of body function promotion in men were lower than those in women (P = 0.048). Patients with primary school education and below had lower scores in the medical management of disease than those with either middle school education (P = 0.032) or community college education or higher (P = 0.022). CONCLUSION: A high proportion of anxiety and depression was found in glaucoma patients during the COVID-19 pandemic. Better self-management behaviour was associated with stronger mental health regulation. It is important to help glaucoma patients improve their self-management behaviours, especially for young men with low educational levels.

2.
J Dairy Sci ; 2022 Nov 01.
Article in English | MEDLINE | ID: covidwho-2099063

ABSTRACT

Bovine respiratory disease complex (BRDC) involves multiple pathogens, shows diverse lung lesions, and is a major concern in calves. Pathogens from 160 lung samples of dead cattle from 81 cattle farms in northeast China from 2016 to 2021 were collected to characterize the molecular epidemiology and risk factors of BRDC and to assess the major pathogens involved in bovine suppurative or caseous necrotizing pneumonia. The BRDC was diagnosed by autopsy, pathogen isolation, PCR, or reverse transcription-PCR detection, and gene sequencing. More than 18 species of pathogens, including 491 strains of respiratory pathogens, were detected. The positivity rate of bacteria in the 160 lung samples was 31.77%, including Trueperella pyogenes (9.37%), Pasteurella multocida (8.35%), Histophilus somni (4.48%), Mannheimia haemolytica (2.44%), and other bacteria (7.13%). The positivity rate of Mycoplasma spp. was 38.9%, including M. bovis (7.74%), M. dispar (11.61%), M. bovirhinis (7.94%), M. alkalescens (6.11%), M. arginini (0.81%), and undetermined species (4.68%). Six species of viruses were detected with a positivity rate of 29.33%, including bovine herpesvirus-1 (BoHV-1; 13.25%), bovine respiratory syncytial virus (BRSV; 5.50%), bovine viral diarrhea virus (BVDV; 4.89%), bovine parainfluenza virus type-3 (BPIV-3; 4.28%), bovine parainfluenza virus type-5 (1.22%), and bovine coronavirus (2.24%). Mixed infections among bacteria (73.75%), viruses (50%), and M. bovis (23.75%) were the major features of BRDC in these cattle herds. The risk analysis for multi-pathogen co-infection indicated that BoHV-1 and H. somni; BVDV and M. bovis, P. multocida, T. pyogenes, or Mann. haemolytica; BPIV-3 and M. bovis; BRSV and M. bovis, P. multocida, or T. pyogenes; P. multocida and T. pyogenes; and M. bovis and T. pyogenes or H. somni showed co-infection trends. A survey on molecular epidemiology indicated that the occurrence rate of currently prevalent pathogens in BRDC was 46.15% (6/13) for BoHV-1.2b and 53.85% (7/13) for BoHV-1.2c, 53.3% (8/15) for BVDV-1b and 46.7% (7/15) for BVDV-1d, 29.41% (5/17) for BPIV-3a and 70.59% (12/17) for BPIV-3c, 100% (2/2) for BRSV gene subgroup IX, 91.67% (33/36) for P. multocida serotype A, and 8.33% (3/36) for P. multocida serotype D. Our research discovered new subgenotypes for BoHV-1.2c, BRSV gene subgroup IX, and P. multocida serotype D in China's cattle herds. In the BRDC cases, bovine suppurative or caseous necrotizing pneumonia was highly related to BVDV [odds ratio (OR) = 4.18; 95% confidence interval (95% CI): 1.6-10.7], M. bovis (OR = 2.35; 95% CI: 1.1-4.9), H. somni (OR = 8.2; 95% CI: 2.6-25.5) and T. pyogenes (OR = 13.92; 95% CI: 5.8-33.3). The risk factor analysis found that dairy calves <3 mo and beef calves >3 mo (OR = 5.39; 95% CI: 2.7-10.7) were more susceptible to BRDC. Beef cattle were more susceptible to bovine suppurative or caseous necrotizing pneumonia than dairy cattle (OR = 2.32; 95% CI: 1.2-4.4). These epidemiological data and the new pathogen subgenotypes will be helpful in formulating strategies of control and prevention, developing new vaccines, improving clinical differential diagnosis by necropsy, predicting the most likely pathogen, and justifying antimicrobial use.

3.
Applied Soft Computing ; 126, 2022.
Article in English | Web of Science | ID: covidwho-2085937

ABSTRACT

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research. (c) 2022 Elsevier B.V. All rights reserved.

4.
10th IEEE International Conference on Healthcare Informatics, ICHI 2022 ; : 664-668, 2022.
Article in English | Scopus | ID: covidwho-2063259

ABSTRACT

Previous studies have documented an association of D-dimer levels with COVID-19 severity. Elevated D-dimer is reported to be associated with patient demographics, comorbidities, lab results, and overall higher incidence of critical illness. However, due to small sample sizes, limited availability of data on essential covariates, and lack of standardization of the admission laboratory protocol, the role of D-dimer in the progression of COVID-19 remains uncertain and needs further investigation using data from larger cohorts. The objectives of this study were to study the factors predicting elevated D-dimer level and to characterize the risk factors that predict D-dimer elevation over the course of inpatient admission. We used statistical modeling, applying machine learning methods to maximally leverage all the available clinical and care variables without being limited by the assumptions of traditional regression analysis methods. Our sample consisted of 1005 COVID-19 inpatients admitted to a large US hospital from March 2020 to July 2020, using detailed data on various clinical and biochemical laboratory test results at admission and throughout the course of hospital stay. Analytic methods used in this study included a) descriptive statistics at baseline using chi-square tests to compare patients with normal and elevated D-dimer at baseline, b) adjusted multivariable regression modeling, and c) evaluation of importance of each feature using two decision-tree-based supervised machine learning algorithms, random forest and XGBoost methods. Results show that machine learning methods could identify 20 important features that predict D-dimer some of which could be used to prevent the processes that lead to D-dimer elevation. Our study suggests that continual laboratory monitoring of D-dimer levels from the time of detection of COVID-19 infection, and monitoring of selected risk factors out of the panel of identified risk factors may enable clinicians to triage patients into risk levels, initiate appropriate therapeutic strategies, and tailor care management to each patient in order to minimize the morbidity and mortality of COVID-19. © 2022 IEEE.

5.
22nd COTA International Conference of Transportation Professionals, CICTP 2022 ; : 899-908, 2022.
Article in English | Scopus | ID: covidwho-2062367

ABSTRACT

Ridesplitting, as an emerging shared mobility, has gradually become one of the important travel modes for urban residents. With the spread of COVID-19, ridesplitting has been affected due to restrictions such as social distance and home office. However, few studies have analyzed the impact of COVID-19 on ridesplitting demand. This paper selects four periods before and after the pandemic as the research objects from the ridesplitting data in Ningxia of China, and compares the changes in ridesplitting demand in the four periods. On this basis, geographically and temporally weighted regression (GTWR) model has been used to explore the impact of COVID-19 on spatiotemporal factors affecting ridesplitting demand. The results show that the impact of some factors on ridesplitting demand has changed in different periods. In addition, we visualize the spatiotemporal coefficients of the model to deeply analyze the changing trends of factors affecting ridesplitting demand under the pandemic. © ASCE.

6.
Investigative Ophthalmology and Visual Science ; 63(7):4368-A0305, 2022.
Article in English | EMBASE | ID: covidwho-2057601

ABSTRACT

Purpose : Although the ICL is more invasive than laser-assisted in situ keratomileusis (LASIK), it is indicated for patients with very high myopia, commonly over -7D. ICL is associated with certain risks including cataract and glaucoma which may develop years after surgery requiring additional procedures. In this study, we examined the outcome and safety profile of ICL vs. LASIK at 1 week, 1 month, and 1 year postoperatively. Methods : In this retrospective study, we examined records from a single surgeon (KK) as well as 2 patients with post ICL complications requiring ICL removal. An important aim of this study was to use the 1 year follow up data since this is one of the standard ICL follow up visits. We hypothesized that the FDA approved ICL (2005) would have a comparable target refractive outcome and safety profile when compared to LASIK. Results : There were a total of 45 ICL eyes and 65 LASIK eyes. Preoperatively, ICL patients had a significantly higher manifest refraction spherical equivalent (MRSE) and cycloplegic refraction spherical equivalent (CRSE) than LASIK patients (p<0.05). For patients who received the ICL implants, the average MRSE at 1-week, 1-month, 1-year post-op was -0.37D±(0.13), -0.29D±(0.09), -0.53D±(0.15);and -1.60D±(0.16), -0.36D±(0.15), -0.36D±(0.07) for patients who received LASIK. The differences in post-op MRSE between ICL and LASIK were not statistically significant (p>0.05). The only significant differences were 1 month LogMAR best corrected visual acuity and 1 year LogMAR distance uncorrected visual acuity (p<0.05), in which LASIK had better visual acuity. Common postoperative findings in both groups were refractive target deviations and punctate keratitis. Reoperation rates in the ICL and LASIK groups were 21.4% and 10.8% respectively, which was not statistically significant (p>0.05). 42.6% of ICL patients underwent the procedure during the COVID-19 pandemic compared to 26.2% of LASIK. Conclusions : Our results demonstrate that ICL is safe and effective for patients with high myopia. Although ICL patients had a significantly higher preoperative MRSE compared to the LASIK group, the ICL patients were able to achieve similar refractive targets. There were no cases of glaucoma or cataract at 1 year in the ICL group. In conclusion, ICL surgery is as safe and effective as LASIK surgery in correcting patients with high myopia, regardless of pre-operative refractive error.

7.
International Conference on Transportation and Development 2022, ICTD 2022 ; 6:134-142, 2022.
Article in English | Scopus | ID: covidwho-2050653

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has caused a reduction in business and routine activity and resulted in less motor fuel consumption. Thus, the gas tax revenue is reduced, which is the major funding resource supporting the rehabilitation and maintenance of transportation infrastructure systems. The focus of this study is to evaluate the impact of the COVID-19 pandemic on transportation infrastructure funds in the United States through analyzing the motor fuel consumption data. Machine learning models were developed by integrating COVID-19 scenarios, fuel consumptions, and demographic data. The best model achieves an R2-score of more than 95% and captures the fluctuations of fuel consumption during the pandemic. Using the developed model, we project future motor gas consumption for each state. For some states, the gas tax revenues are going to be 10%-15% lower than the pre-pandemic level for at least one or two years. © 2022 International Conference on Transportation and Development

8.
Asia-Pacific Journal of Clinical Oncology ; 18:60, 2022.
Article in English | EMBASE | ID: covidwho-2032339

ABSTRACT

Objectives: In order to provide useful reference information for researchers in the field of pharmacology and toxicology, this paper studies the current research hot spots in this field, as well as the correlation closeness between research topics. Methods: This paper studies on the hot papers of pharmacology and toxicology field based on ESI (Essential Scientific Indicators) database, and the time span of the data is from January 1, 2010 to December 31, 2020. The data about these 110 hot papers are analyzed by the authors from the aspects of published time, country/territory, institution, journal, citation, and so on. The methods of multi-dimension analysis, cluster analysis, Vosviewer visualization are used to analyze these papers. Results: The results shows that United States is in the first place in the ranking of published papers, England is in the second place, and China is in the third place. The research hotspots are COVID-19, anxiety, depression, and mental health. Conclusions: The cluster of hot papers show the correlativity of the topic in the pharmacology and toxicology field. This research provides researchers in the field of pharmacology and toxicology with the current international hot research direction, and helps China researchers to improve their research in the field.

9.
Digital Innovation for Healthcare in COVID-19 Pandemic: Strategies and Solutions ; : 189-199, 2022.
Article in English | Scopus | ID: covidwho-2027780

ABSTRACT

Coronavirus disease 2019 (COVID-19) has become a global pandemic that significantly challenged healthcare systems worldwide, with over 4 million deaths among 18.6 million identified cases as of June 2021. Understanding the current COVID-19 cases and determining clinical solutions is of paramount importance. In this chapter, we describe an exploratory study of identifying risk factors associated with COVID-19 inpatient care. Based on a set of COVID-19 inpatient medical health records in a US hospital system, we used both unsupervised and supervised machine learning methods to explore risk factors associated with hospitalized COVID-19 patients. We found that the most important features related to the COVID-19 disease include (1) influenza vaccines, (2) pneumococcal vaccines, and (3) weight-related variables (i.e., weight, height, and BMI). As such, we provide a use case that machine learning methods are valuable for predicting COVID-19 inpatient risk factors, and the results are promising to guide further research in this area. © 2022 Elsevier Inc. All rights reserved.

10.
IEEE Frontiers in Education Conference (FIE) ; 2021.
Article in English | Web of Science | ID: covidwho-1978339

ABSTRACT

Work in Progress: This Innovative Practice Work in Progress Paper presents how a cross-regional online and offline mixed teaching practice has been carried out by coordinating multiple local universities' laboratory resources. Owing to the COVID-19 epidemic, students could not go back to the campus but stay home all over the country. To work with an electronic system design and implementation project in the Electronic Technology Projects course, students in each team need a public physical workplace equipped with the necessary tools and instruments for circuit debugging and implementation. By utilizing local universities' laboratory resources near their homes, students of the same group could have face-to-face discussions and get offline support from local university laboratory teachers. Each team could also communicate online with course teachers on the technical scheme, detailed design, and fault debugging. While online education can share virtual teaching resources, cross-regional online and offline fusion education can further realize the sharing of entity teaching resources. Twenty-three students have fulfilled their projects in eight local universities under online and offline guidance. Such a teaching attempt has also promoted in-depth cooperation between teachers and students across universities.

11.
45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022 ; : 1984-1989, 2022.
Article in English | Scopus | ID: covidwho-1973880

ABSTRACT

Concept drift in stream data has been well studied in machine learning applications. In the field of recommender systems, this issue is also widely observed, as known as temporal dynamics in user behavior. Furthermore, in the context of COVID-19 pandemic related contingencies, people shift their behavior patterns extremely and tend to imitate others' opinions. The changes in user behavior may not be always rational. Thus, irrational behavior may impair the knowledge learned by the algorithm. It can cause herd effects and aggravate the popularity bias in recommender systems due to the irrational behavior of users. However, related research usually pays attention to the concept drift of individuals and overlooks the synergistic effect among users in the same social group. We conduct a study on user behavior to detect the collaborative concept drifts among users. Also, we empirically study the increase of experience of individuals can weaken herding effects. Our results suggest the CF models are highly impacted by the herd behavior and our findings could provide useful implications for the design of future recommender algorithms. © 2022 ACM.

12.
IEEE Internet of Things Journal ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-1961406

ABSTRACT

Distributed Spatial Cloaking () enables users to enjoy precise Location-Based Service (LBS) with location privacy-preserving. An incentive mechanism is necessary to encourage users to cooperate. However, due to the inappropriate design of incentive mechanisms, the existing works cause low user benefits and fail to encourage users, ruining the expected incentive effect. Moreover, introducing a third party to manage users’information also causes the existing works to disclose users’privacy and be unpractical. To address these issues, we propose a utility-awaRe incEntive mechanism based diStributed spATial cloaking (RESAT). By the idea of utility theory and optimization theory, RESAT devises basic and extended incentive mechanisms. The two mechanisms for assuming that all users are honest and that malicious users provide unreasonable locations. RESAT proposes an incentive mechanism-based cloaking cooperation without a third party, incorporating the developed mechanisms based on the blind signature. Theoretical analysis indicates that RESAT achieves incentive compatibility and is secure. Extensive experiments on the real dataset show that compared with the existing works, RESAT enables 1 time more users to cooperate at best while eliminating the malicious behaviors that provide unreasonable locations. The required construction time delay is limited. IEEE

13.
Wireless Communications & Mobile Computing ; 2022:12, 2022.
Article in English | English Web of Science | ID: covidwho-1883334

ABSTRACT

In order to solve the error prevention problem of secondary equipment in intelligent substations, this paper designs the Substation Secondary Equipment- (SSE-) oriented error risk Prevention, Control, and Management (P&C&M) system. Firstly, the basic principle of SSE error prevention is reviewed. The SSE model is expanded based on the existing microcomputer error prevention system's Substation Primary Equipment (SPE). Thereupon, the SSE status acquisition device is designed, and the overall architecture is implemented for SSE error prevention. Secondly, edge-node cooperation is analyzed along with the specific architecture of the edge gateway. Finally, the wireless communication network is designed based on the edge gateway. The delay and flow of different data streams are compared, and the error proof verification mechanism is introduced into SSE. The numerical results corroborate that when the Sampled Value (SV) traffic exceeds 32 Mbps, the maximum delay exceeds the specified delay (3 MS). The average flow of the Manufacturing Message Specification (MMS) message is 90 kbps, which can meet the requirements of the intelligent substation. The delay of star networking is higher than that of ring networking. Meanwhile, the proposed network analyzer has a measured flow closer to the calculated flow of SSE. In the 60-hour accuracy statistics, the proposed SSE-oriented error P&C&M system reaches an accuracy as high as 84%. Therefore, the proposed SSE-oriented error P&C&M has strong feasibility. The outcome provides a reference for the intelligent development of error prevention of secondary equipment in intelligent substations.

14.
21st IEEE International Conference on Data Mining (IEEE ICDM) ; : 976-981, 2021.
Article in English | Web of Science | ID: covidwho-1806912

ABSTRACT

Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved either by using ensemble learning models which ignore temporal patterns and correlation within features or by defining a fixed-size window to select specific parts of the data sets. On the other hand, many studies have shown major improvement to handle the irregularity of time-series, yet none of these studies has been applied to multi-source data. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. PIETS has the following characteristics: (1) irregularity encoders for multi-source samples that can leverage all available information and accelerate the convergence of the model;(2) parallelised neural networks to enable flexibility and avoid information over-whelming;and (3) attention mechanism that highlights different information and gives high importance to the most related data. Through extensive experiments on real-world data sets related to COVID-19, we show that the proposed architecture is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.

15.
Progress in Biochemistry and Biophysics ; 49(2):349-358, 2022.
Article in Chinese | Web of Science | ID: covidwho-1754036

ABSTRACT

Exosome is one of the extracellular vesicles, which plays an important role in intercellular communication and material transportation. Its content includes proteins, lipids, RNAs and other substances from host cells, and has an important influence on the physiological state of recipient cells. Flaviviridae including hepatitis C virus and Coronaviridae including SARS-CoV-2 are pathogens causing a variety of human infectious diseases. Understanding the interaction between virus and host is of great significance for screening therapeutic cellular targets and developing exosome-based vaccines. Accumulating studies have shown that exosomal protein and RNA play inhibitory roles for viruses. Moreover, Flaviviridae and Coronaviridae could hijack exosomemediated cellular communication to harm the hosts and promote virus spread. In current review, we summarized the recent progress on the interaction between Flaviviridae/Coronaviridae and exosome, shedding the mechanistic insights into Flaviviridae/Coronaviridae induced exosome.

16.
IEEE Transactions on Information Forensics and Security ; 2022.
Article in English | Scopus | ID: covidwho-1701899

ABSTRACT

Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users (e.g., avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users’location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design a LEFS algorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further design LSRE that allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the well-known differential privacy based solutions. IEEE

17.
Urban Book Series ; : 83-93, 2021.
Article in English | Scopus | ID: covidwho-1627125

ABSTRACT

Using an analysis of the spread of COVID-19, this chapter concludes by emphasizing the significance of strengthening risk management, improving resilience, and enhancing urban governance in mega-cities within the context of rapid urbanization in China. In facing the sudden impact of an epidemic, it is necessary to execute population mobility control, including isolation and lockdown approaches, though these measurements also have their limitations by hampering the normal functioning of social and economic life. The author suggests implementing scientific and rational population mobility management to optimize epidemic prevention and urban functioning, including (1) adopting dynamic adjustments based on timely multi-level risk assessments;(2) standardizing mobility management according to the rule of law;and (3) utilizing big data and technological innovations better to implement accurate tracing, tracking, and epidemic prevention. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

18.
8th International Conference on Bioinformatics Research and Applications, ICBRA 2021 ; : 70-78, 2021.
Article in English | Scopus | ID: covidwho-1599550

ABSTRACT

The research project was conducted to probe into the vaccine's impact on the cataphoresis of COVID-19. The data involved in the project was based on official statistics from different states of the United States. The project intended to ascertain the correlations between the number of positive cases and the number of fully vaccinated populations. Also, the project includes identifying correlations between other variables like the links between the number of fully vaccinated people and the change in time. Moreover, the research project briefly studied pandemic prevention policies and outcomes in the state Connecticut. As a result of analysis, it indicated that virus spread increasingly slowed down when the fully vaccinated population reached a critical proportion with the rise in the vaccinated population. However, the necessary proportion varied from state to state. For state Connecticut, first-dose vaccination of the governor Lamont may encourage the local public to vaccinate, leading to a surge in the number of people vaccinated after Lamont's action. Therefore, it is simply inferred that vaccines play an important role in fighting against coronavirus and that the action of leaders is speculated to be influential for the public's attitude toward vaccines. © 2021 ACM.

19.
3rd International Conference on Computer Science and Technologies in Education, CSTE 2021 ; : 14-18, 2021.
Article in English | Scopus | ID: covidwho-1447811

ABSTRACT

Since the outbreak of novel coronavirus related epidemic on February 2020, China's Ministry of Education has decreed all of school to suspension their curriculums and produced the slogan: stop courses dose not means stop teaching and studying. And then, colleges and universities were developing online teaching. Until the epidemic in China were alleviated on September, the offline teaching model was gradually restored across the country, and the online and offline hybrid teaching model was widely used. Based on the traditional online and offline teaching mode, this paper proposes a new form of mixed teaching mode based on "Flipped Classroom + SPOC + MOOC", and systematically studies the integration of the new form of online and offline mixed teaching mode. This model has been applied in the Python programming course. This paper sorts out the problems that appeared in the implementation of the new mixed teaching model, proposes corresponding solutions, and evaluates the implementation process and results of this model. © 2021 IEEE.

20.
6th IEEE International Conference on Computer and Communication Systems, ICCCS 2021 ; : 435-439, 2021.
Article in English | Scopus | ID: covidwho-1379522

ABSTRACT

In order to prevent people from touching the floor buttons when riding in the elevator in response to the COVID-19 pandemic, we propose a design method of elevator auxiliary control system based on speech recognition. The hardware design part of the system includes voice module, photoelectric sensor module and main control circuit module. On the basis of hardware design, the process from recognizing the voice commands of passengers in the elevator car to transmitting the recognition results to the elevator control main system is realized through programming. With the goal of improving the accuracy of speech recognition, the differential feature parameter algorithm based on the Mel-frequency cepstral coefficients (MFCC) is used. Then, our experiments show that least mean square (LMS) adaptive filtering is an effective method for the noise reduction of the voice signal when processing passengers' commands. © 2021 IEEE.

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