Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

Efficacy of Online Learning and Influence on Skin and Eyes Among College Students

To evaluate the efficacy of online learning and explore the impact of long-term use of electronic products on facial skin as well as eyes.A cross-sectional survey was conducted to 180 sophomores in Xi′an Jiaotong University by cluster random sampling from September to October 2021.The questionnaire covering study condition,skin lesion and Ocular Surface Disease Index.χ_(2) test was used to compare the facial skin condition among different groups,and spearman correlation test was used to test the correlation of rank data.During online education,students′learning pressure is reduced,their autonomy is improved,and the learning efficiency is reduced.There were differences in the incidence of facial itching and papules among different groups.Duration of use of electronic products was positively correlated with the facial itching,with an r value of 0.231(P<0.05);the proportion of pigmentation in non-blue light protection groups(12.8%)was higher than that in blue light protection groups(1.7%),the difference was statistically significant(χ_(2)=8.384,P<0.05).The prevalence of dry eye among college students is 66.7%,and the proportion of moderate to severe dry eye is 34.5%.The study autonomy has been improved during online teaching.Long-term use of electronic products and no blue light protection have an impact on facial skin.Students should enhance the knowledge of skin-care and eye-care and develop better habits.

Air combat target maneuver trajectory prediction based on robust regularized Volterra series and adaptive ensemble online transfer learning

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.

QoS-Aware Congestion Control with Online Learning

In emerging applications such as industrial control and autonomous driving,end-to-end deterministic quality of service(QoS)transmission guarantee has become an urgent problem to be solved.Internet congestion control algorithms are essential to the performance of applications.However,existing congestion control schemes follow the best-effort principle of data transmission without the perception of application QoS requirements.To enable data delivery within application QoS constraints,we leverage an online learning mechanism to design Crimson,a novel congestion control algorithm in which each sender continuously observes the gap between current performance and pre-defined QoS.Crimson can change rates adaptively that satisfy application QoS requirements as a result.Across many emulation environments and real-world experiments,our proposed scheme can efficiently balance the different trade-offs between throughput,delay and loss rate.Crimson also achieves consistent performance over a wide range of QoS constraints under diverse network scenarios.

Surviving the shift: College student satisfaction with emergency online learning during COVID-19 pandemic

BACKGROUND The coronavirus disease 2019(COVID-19)epidemic disrupted education systems by forcing systems to shift to emergency online leaning.Online learning satisfaction affects academic achievement.Many factors affect online learning satisfaction.However there is little study focused on personal characteristics,mental status,and coping style when college students participated in emergency online courses.regression analyses were performed to identify factors that affected online learning satisfaction.RESULTS Descriptive findings indicated that 62.9%(994/1580)of students were satisfied with online learning.Factors that had significant positive effects on online learning satisfaction were online learning at scheduled times,strong exercise intensity,good health,regular schedule,focusing on the epidemic less than one hour a day,and maintaining emotional stability.Positive coping styles were protective factors of online learning satisfaction.Risk factors for poor satisfaction were depression,neurasthenia,and negative coping style.CONCLUSION College students with different personal characteristics,mental status,and coping style exhibited different degrees of online learning satisfaction.Our findings provide reference for educators,psychologists,and school adminis-trators to conduct health education intervention of college students during emergency online learning.

A Study of Multimodal Intelligent Adaptive Learning System and Its Pattern of Promoting Learners’Online Learning Engagement

As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.

Achieve Intended Learning Outcomes and Improving Digital Literacy Skills for Practical-Based Subjects Using Online Teaching via Propagation of OER Materials

As professors are subjected to teaching their classes online due to the recent COVID-19, our local Hong Kong students find it difficult to consult their teachers, and ultimately would fail to achieve the intended learning outcomes, especially for practical-based subjects. In this research, students having online classes of a practical-based fabric design subject were encouraged to self-study from Open Educational Resource (OER) materials for a further and better understanding of their subject. Additionally, online materials were developed to improve students’ understanding via skill of digital literacy. Their learning progress was evaluated and compared to the face-to-face version. The majority of students found online classes combined with self-studying OER materials, potentially be a substitute for face-to-face classes. Most of the students further opined different OER videos assisted them without any face-to-face instructions in practical works, to develop new fabric samples from the inspiration. Analysis of test results, and comparison of students’ final grades with different learning modes, supported these phenomena.

Influence of Online Learning Attitude in the Blended Teaching Model on the Final Grades

The combination of online teaching and traditional offline teaching can maximize the advantages of both.Based on the blended teaching of English Reading course,39 students were selected as the research subjects to study the relationship between their online learning attitudes and their grades in the final examination.Judged from the number of times for each student to download teaching resources,the number of assignments submitted online,and the quality of the submitted assignments,each student’s attitude toward online learning was examined comprehensively,and a correlation analysis was conducted through SPSS Statistics 21.0 to explore the influence of online learning attitude on English reading performance.Through data collection and analysis of the online learning attitudes over a 16-week period,a significant positive correlation was found between the online learning attitudes and the English reading grades,indicating that the online learning attitude in the blended learning model plays a crucial role in improving the English reading skill,and students should maintain a positive attitude toward online teaching in blended learning.

An Empirical Study of the Influence of Online Learning Behaviors on Learning Outcomes in Blended Teaching Mode—Taking College English Listening as an Example

Blended teaching, which integrates the advantages of online and offline teaching, has become the main direction of higher education teaching reform. In the era of education big data, research on the online learners’ behavior based on data mining has attracted more and more attention from higher education researchers. However, in the field of foreign language teaching, research on the relationship between online learning behaviors and learning outcomes in the blended teaching mode is still at an early stage. Taking the course College English Listening in Zhejiang Yuexiu University (ZYU) as an example, this study conducted a comprehensive data analysis of online learning behaviors of 152 students of ZYU to explore the influence of online learning behaviors on learning outcomes in the blended teaching mode by utilizing Microsoft Excel and SPSS.20 statistic software. The result shows that the number of course login, the quantity and the quality of forum replies, the number of note submission, the quality of the notes, the average score of vocabulary tests, the number of the times of taking listening tests and the average score of listening tests are all significantly and positively correlated with students’ learning outcomes, while the study does not find a correlation between students’ learning outcomes and the number of the times of taking vocabulary tests, the total length of online learning and the length of video viewing. Based on the study results, implications are put forward to give reference for the teaching design and the management of the foreign language blended courses.

A Study on the Factors Influencing Learning Burnout Among College Students in an Online Teaching Environment

With the advancement of economic globalization,mobile networks and media technology are developing rapidly.Media information is manufactured and spread at any time,and people can better understand global information with the help of the media.In the Internet era,college students,overwhelmed by complex information and a lack of information discernment,are susceptible to indulging in the curated online world presented by others.At the same time,negative information such as false,violent,and pornographic also spread rapidly in various media.These phenomena impact students’media literacy and affect their mental health,thereby leading to learning burnout.This study analyzes the current situation of learning burnout among university students,explores the effective path of improving online education of college students,and provides a theoretical basis for reducing the burnout of college students and assisting students in developing a positive mentality.

The Effectiveness of Self-regulated Learning Strategies on Chinese College Students' English Learning

The purpose of this paper is to argue the effectiveness of self-regulated learning in English education in Chinese college classroom instruction. A study is given to show whether the introduction of self-regulated learning can help improve Chinese college students' English learning, and help them perform better in the National English test-CET-4 (College English Test Level-4,).

Strategies for developing Online Learning

This paper examines the strategies of developing online learning in Chinese universities.Top-down strategies include policy,funding,Senior initiative and task-based management,etc,in which funding generally plays the most important role followed by Senior initiative and task-based management.Bottom-up strategies,especially staff training and contest are often seen as essential to successfully improve online learning.

Delivering forestry courses online:experiences,lessons learned,and future of forestry online education in the Asia Pacifi c

Innovation in forestry education is needed to address changing contexts of the positionality of forests.This is particularly signifi cant in the Asia–Pacifi c region,where deforestation and degradation are high.However,the accessibility of high-quality forestry education to address changing regional and global contexts is lacking.A series of innovative sustainable forest management(SFM)open education resource(OER)courses were developed and implemented to improve the accessibility of SFM education to enhance teaching quality,curriculum,and research capacity of universities in the Asia-Pacifi c Region.To evaluate the SFM-OER program in terms of student experiences,this study investigated student achievement,perceived success of the pedagogical approach and instructional design,and perceived eff ectiveness of the learning activities in promoting active and transformative learning through the assessment of a 1,191-course feedback survey between 2018 and 2020,including the global pandemic.This study revealed that the program attracted diverse student demographics,including a higher proportion of female students majoring in forestry,ecology,and other environmental studies.Their primary motivation to participate in the courses was to gain international experience,followed by the fl exibility of online learning,mandatory course requirements,and earning course credits.Students were satisfi ed with the Canvas learning management system.Most students spent less than 5 to 10 h of their weekly time in the course and agreed or strongly agreed that the workloads were manageable.Students refl ected positively on various learning activities and assignments,such as watching lecture videos,taking quizzes,reading and summarizing,having discussions,and peer review writing.However,they did not clearly prefer specifi c learning activities,signifying the importance of using diverse learning activities to satisfy diverse individual learning styles in online settings.This analysis contributes to the further development of student-centered pedagogical development for online learning and provides insight into the ways forward for online higher forestry education,while repurposing existing OER courses in a post-Covid-19 era.

Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components.Shunt active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents.However,CCVSI with traditional controllers have a limited transient and steady state performance.In this paper,we propose an adaptive dynamic programming(ADP) controller with online learning capability to improve transient response and harmonics.The proposed controller works alongside existing proportional integral(PI) controllers to efficiently track the reference currents in the d-q domain.It can generate adaptive control actions to compensate the PI controller.The proposed system was simulated under different nonlinear(three-phase full wave rectifier) load conditions.The performance of the proposed approach was compared with the traditional approach.We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison.The online learning based ADP controller not only reduced average total harmonic distortion by 18.41%,but also outperformed traditional PI controllers during transients.

Topic Sentiment Analysis in Online Learning Community from College Students

Purpose:Opinion mining and sentiment analysis in Online Learning Community can truly reflect the students’learning situation,which provides the necessary theoretical basis for following revision of teaching plans.To improve the accuracy of topic-sentiment analysis,a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approach:We aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularitylevels.The proposed method comprised data preprocessing,topic detection,sentiment analysis,and visualization.Findings:The proposed model can effectively perceive students’sentiment tendencies on different topics,which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitations:The model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach,without considering the intensity of students’sentiments and their evolutionary trends.Practical implications:The implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint,which can be utilized as a reference for enhancing the accuracy of learning content recommendation,and evaluating the quality of their services.Originality/value:The topic-sentiment analysis model can clarify the hierarchical dependencies between different topics,which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.

Quality Models for Open, Flexible, and Online Learning

This article is based on research conducted for the European CommissionEducation & Training 2020 working group on digital and online learning(ET2020 WG-DOL) specifically regarding policy challenges, such as thefollowing: 1) Targeted policy guidance on innovative and open learningenvironments under outcome;2) Proposal for a quality assurance modelfor open and innovative learning environments, its impact on specificassessment frameworks and its implication for EU recognition and transparencyinstruments. The article aims to define quality in open, flexible,and online learning, particularly in open education, open educationalresources (OER), and massive open online courses (MOOC). Hence,quality domains, characteristics, and criteria are outlined and discussed,as well as how they contribute to quality and personal learning so thatlearners can orchestrate and take responsibility for their own learningpathways. An additional goal is to identify the major stakeholders directlyinvolved in open online education and to describe their visions, communalities,and conflicts regarding quality in open, flexible, and online learning.The article also focuses on quality in periods of crisis, such as duringthe pandemic in 2020. Finally, the article discusses the rationale and needfor a model of quality in open, flexible, and online learning based on threemajor criteria for quality: excellence, impact, and implementation fromthe learner’s perspective.

Social Presence and Implications for Designing Online Learning Communities

With the rapid global proliferation of the Internet and telecommunication networks, online education, one form of distance education is currently becoming the fastest growing trend of domestic and international education. A major challenge to designing online learning is the development of appropriate means to facilitate the social environment that is critical for higher order learning in many disciplines. In many online learning designs, the majority of resources are channeled to web interface design and technology, while little or no resources are devoted to facilitating the teaching and learning process, the negotiation of meaning and the validation of knowledge among peers and instructors that depends on a conducive socio-cultural environment and adequate learner support.

A Model of Teaching and Learning Online in Asian Context

Online teaching and learning practices in Asian universities are different from that in western universities. Western universities give emphasis on student-student interaction for learning. Online courses offered by most Asian universities are a kind of mixed mode that comprised simultaneous face-to-face tutorials and online interaction facility. In this situation most students use the online facility to collect resources and to contact their teachers. The quantity of student-student interaction was sporadic in many courses. So research is needed to improve the situation and create an environment for students where they can learn what peer group interaction is and practice it. This paper has presented a model of teaching and learning online for Asian universities. Possible barriers in teaching and learning situations in Asia and students' abilities have been considered to develop the model.

Study of Adaptive Learning for Online Education

Although the World Wide Web is now accessible almost everywhere, on - line instruction is not catching on so rapidly. In large part this is because courses must be assembled manually and cannot be adapted easily to individual student needs. The article points out that with the development of ww\v and computer technology, adaptive learning is necessary and possible for online education. Construction of adaptive program is described and some teaching strategies for adaptive learning is proposed.